From 9fb821cca8cfe8c88eed0f6a7ea1f9ed9b645d4f Mon Sep 17 00:00:00 2001
From: vishalh21 <vishalh21@iitk.ac.in>
Date: Thu, 16 Jun 2022 22:55:54 +0530
Subject: [PATCH 01/14] Assignment 1

---
 211175_Vishal_1/SnT FaC assignment 1.ipynb | 1910 ++++++++++++++++++++
 1 file changed, 1910 insertions(+)
 create mode 100644 211175_Vishal_1/SnT FaC assignment 1.ipynb

diff --git a/211175_Vishal_1/SnT FaC assignment 1.ipynb b/211175_Vishal_1/SnT FaC assignment 1.ipynb
new file mode 100644
index 0000000..3583f91
--- /dev/null
+++ b/211175_Vishal_1/SnT FaC assignment 1.ipynb	
@@ -0,0 +1,1910 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "3769614b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np \n",
+    "import pandas as pd\n",
+    "import matplotlib as mpl\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from math import sqrt\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "6c553b28",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "missing_value=np.nan\n",
+    "df=pd.read_csv('Dataset.csv',na_values=missing_value)\n",
+    "df.drop('CUST_ID',axis=1,inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "7658d5f9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(8950, 17)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "ee407e2e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>BALANCE</th>\n",
+       "      <th>BALANCE_FREQUENCY</th>\n",
+       "      <th>PURCHASES</th>\n",
+       "      <th>ONEOFF_PURCHASES</th>\n",
+       "      <th>INSTALLMENTS_PURCHASES</th>\n",
+       "      <th>CASH_ADVANCE</th>\n",
+       "      <th>PURCHASES_FREQUENCY</th>\n",
+       "      <th>ONEOFF_PURCHASES_FREQUENCY</th>\n",
+       "      <th>PURCHASES_INSTALLMENTS_FREQUENCY</th>\n",
+       "      <th>CASH_ADVANCE_FREQUENCY</th>\n",
+       "      <th>CASH_ADVANCE_TRX</th>\n",
+       "      <th>PURCHASES_TRX</th>\n",
+       "      <th>CREDIT_LIMIT</th>\n",
+       "      <th>PAYMENTS</th>\n",
+       "      <th>MINIMUM_PAYMENTS</th>\n",
+       "      <th>PRC_FULL_PAYMENT</th>\n",
+       "      <th>TENURE</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>40.900749</td>\n",
+       "      <td>0.818182</td>\n",
+       "      <td>95.40</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>95.4</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.166667</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.083333</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1000.0</td>\n",
+       "      <td>201.802084</td>\n",
+       "      <td>139.509787</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3202.467416</td>\n",
+       "      <td>0.909091</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6442.945483</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.250000</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7000.0</td>\n",
+       "      <td>4103.032597</td>\n",
+       "      <td>1072.340217</td>\n",
+       "      <td>0.222222</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2495.148862</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>773.17</td>\n",
+       "      <td>773.17</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>12</td>\n",
+       "      <td>7500.0</td>\n",
+       "      <td>622.066742</td>\n",
+       "      <td>627.284787</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1666.670542</td>\n",
+       "      <td>0.636364</td>\n",
+       "      <td>1499.00</td>\n",
+       "      <td>1499.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>205.788017</td>\n",
+       "      <td>0.083333</td>\n",
+       "      <td>0.083333</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.083333</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7500.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>817.714335</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>16.00</td>\n",
+       "      <td>16.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.083333</td>\n",
+       "      <td>0.083333</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1200.0</td>\n",
+       "      <td>678.334763</td>\n",
+       "      <td>244.791237</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       BALANCE  BALANCE_FREQUENCY  PURCHASES  ONEOFF_PURCHASES  \\\n",
+       "0    40.900749           0.818182      95.40              0.00   \n",
+       "1  3202.467416           0.909091       0.00              0.00   \n",
+       "2  2495.148862           1.000000     773.17            773.17   \n",
+       "3  1666.670542           0.636364    1499.00           1499.00   \n",
+       "4   817.714335           1.000000      16.00             16.00   \n",
+       "\n",
+       "   INSTALLMENTS_PURCHASES  CASH_ADVANCE  PURCHASES_FREQUENCY  \\\n",
+       "0                    95.4      0.000000             0.166667   \n",
+       "1                     0.0   6442.945483             0.000000   \n",
+       "2                     0.0      0.000000             1.000000   \n",
+       "3                     0.0    205.788017             0.083333   \n",
+       "4                     0.0      0.000000             0.083333   \n",
+       "\n",
+       "   ONEOFF_PURCHASES_FREQUENCY  PURCHASES_INSTALLMENTS_FREQUENCY  \\\n",
+       "0                    0.000000                          0.083333   \n",
+       "1                    0.000000                          0.000000   \n",
+       "2                    1.000000                          0.000000   \n",
+       "3                    0.083333                          0.000000   \n",
+       "4                    0.083333                          0.000000   \n",
+       "\n",
+       "   CASH_ADVANCE_FREQUENCY  CASH_ADVANCE_TRX  PURCHASES_TRX  CREDIT_LIMIT  \\\n",
+       "0                0.000000                 0              2        1000.0   \n",
+       "1                0.250000                 4              0        7000.0   \n",
+       "2                0.000000                 0             12        7500.0   \n",
+       "3                0.083333                 1              1        7500.0   \n",
+       "4                0.000000                 0              1        1200.0   \n",
+       "\n",
+       "      PAYMENTS  MINIMUM_PAYMENTS  PRC_FULL_PAYMENT  TENURE  \n",
+       "0   201.802084        139.509787          0.000000      12  \n",
+       "1  4103.032597       1072.340217          0.222222      12  \n",
+       "2   622.066742        627.284787          0.000000      12  \n",
+       "3     0.000000               NaN          0.000000      12  \n",
+       "4   678.334763        244.791237          0.000000      12  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "7c589707",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "BALANCE                               0\n",
+       "BALANCE_FREQUENCY                     0\n",
+       "PURCHASES                             0\n",
+       "ONEOFF_PURCHASES                      0\n",
+       "INSTALLMENTS_PURCHASES                0\n",
+       "CASH_ADVANCE                          0\n",
+       "PURCHASES_FREQUENCY                   0\n",
+       "ONEOFF_PURCHASES_FREQUENCY            0\n",
+       "PURCHASES_INSTALLMENTS_FREQUENCY      0\n",
+       "CASH_ADVANCE_FREQUENCY                0\n",
+       "CASH_ADVANCE_TRX                      0\n",
+       "PURCHASES_TRX                         0\n",
+       "CREDIT_LIMIT                          1\n",
+       "PAYMENTS                              0\n",
+       "MINIMUM_PAYMENTS                    313\n",
+       "PRC_FULL_PAYMENT                      0\n",
+       "TENURE                                0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "3e611957",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "BALANCE                             0\n",
+       "BALANCE_FREQUENCY                   0\n",
+       "PURCHASES                           0\n",
+       "ONEOFF_PURCHASES                    0\n",
+       "INSTALLMENTS_PURCHASES              0\n",
+       "CASH_ADVANCE                        0\n",
+       "PURCHASES_FREQUENCY                 0\n",
+       "ONEOFF_PURCHASES_FREQUENCY          0\n",
+       "PURCHASES_INSTALLMENTS_FREQUENCY    0\n",
+       "CASH_ADVANCE_FREQUENCY              0\n",
+       "CASH_ADVANCE_TRX                    0\n",
+       "PURCHASES_TRX                       0\n",
+       "CREDIT_LIMIT                        0\n",
+       "PAYMENTS                            0\n",
+       "MINIMUM_PAYMENTS                    0\n",
+       "PRC_FULL_PAYMENT                    0\n",
+       "TENURE                              0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"CREDIT_LIMIT\"].fillna(df[\"CREDIT_LIMIT\"].mean(),inplace=True)\n",
+    "df[\"MINIMUM_PAYMENTS\"].fillna(df[\"MINIMUM_PAYMENTS\"].mean(),inplace=True)\n",
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c0e562d2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
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+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>BALANCE</th>\n",
+       "      <th>BALANCE_FREQUENCY</th>\n",
+       "      <th>PURCHASES</th>\n",
+       "      <th>ONEOFF_PURCHASES</th>\n",
+       "      <th>INSTALLMENTS_PURCHASES</th>\n",
+       "      <th>CASH_ADVANCE</th>\n",
+       "      <th>PURCHASES_FREQUENCY</th>\n",
+       "      <th>ONEOFF_PURCHASES_FREQUENCY</th>\n",
+       "      <th>PURCHASES_INSTALLMENTS_FREQUENCY</th>\n",
+       "      <th>CASH_ADVANCE_FREQUENCY</th>\n",
+       "      <th>CASH_ADVANCE_TRX</th>\n",
+       "      <th>PURCHASES_TRX</th>\n",
+       "      <th>CREDIT_LIMIT</th>\n",
+       "      <th>PAYMENTS</th>\n",
+       "      <th>MINIMUM_PAYMENTS</th>\n",
+       "      <th>PRC_FULL_PAYMENT</th>\n",
+       "      <th>TENURE</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
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+       "    <tr>\n",
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+       "      <td>0.909091</td>\n",
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+       "      <td>1072.340217</td>\n",
+       "      <td>0.222222</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2495.148862</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>773.17</td>\n",
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+       "      <td>622.066742</td>\n",
+       "      <td>627.284787</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1666.670542</td>\n",
+       "      <td>0.636364</td>\n",
+       "      <td>1499.00</td>\n",
+       "      <td>1499.00</td>\n",
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+       "      <td>205.788017</td>\n",
+       "      <td>0.083333</td>\n",
+       "      <td>0.083333</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.083333</td>\n",
+       "      <td>1</td>\n",
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+       "      <td>0.000000</td>\n",
+       "      <td>864.206542</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>817.714335</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>16.00</td>\n",
+       "      <td>16.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.083333</td>\n",
+       "      <td>0.083333</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1200.0</td>\n",
+       "      <td>678.334763</td>\n",
+       "      <td>244.791237</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       BALANCE  BALANCE_FREQUENCY  PURCHASES  ONEOFF_PURCHASES  \\\n",
+       "0    40.900749           0.818182      95.40              0.00   \n",
+       "1  3202.467416           0.909091       0.00              0.00   \n",
+       "2  2495.148862           1.000000     773.17            773.17   \n",
+       "3  1666.670542           0.636364    1499.00           1499.00   \n",
+       "4   817.714335           1.000000      16.00             16.00   \n",
+       "\n",
+       "   INSTALLMENTS_PURCHASES  CASH_ADVANCE  PURCHASES_FREQUENCY  \\\n",
+       "0                    95.4      0.000000             0.166667   \n",
+       "1                     0.0   6442.945483             0.000000   \n",
+       "2                     0.0      0.000000             1.000000   \n",
+       "3                     0.0    205.788017             0.083333   \n",
+       "4                     0.0      0.000000             0.083333   \n",
+       "\n",
+       "   ONEOFF_PURCHASES_FREQUENCY  PURCHASES_INSTALLMENTS_FREQUENCY  \\\n",
+       "0                    0.000000                          0.083333   \n",
+       "1                    0.000000                          0.000000   \n",
+       "2                    1.000000                          0.000000   \n",
+       "3                    0.083333                          0.000000   \n",
+       "4                    0.083333                          0.000000   \n",
+       "\n",
+       "   CASH_ADVANCE_FREQUENCY  CASH_ADVANCE_TRX  PURCHASES_TRX  CREDIT_LIMIT  \\\n",
+       "0                0.000000                 0              2        1000.0   \n",
+       "1                0.250000                 4              0        7000.0   \n",
+       "2                0.000000                 0             12        7500.0   \n",
+       "3                0.083333                 1              1        7500.0   \n",
+       "4                0.000000                 0              1        1200.0   \n",
+       "\n",
+       "      PAYMENTS  MINIMUM_PAYMENTS  PRC_FULL_PAYMENT  TENURE  \n",
+       "0   201.802084        139.509787          0.000000      12  \n",
+       "1  4103.032597       1072.340217          0.222222      12  \n",
+       "2   622.066742        627.284787          0.000000      12  \n",
+       "3     0.000000        864.206542          0.000000      12  \n",
+       "4   678.334763        244.791237          0.000000      12  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ce4036bf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEGCAYAAACUzrmNAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAX00lEQVR4nO3df5RfdX3n8efLIL8MKGkGhARMpKAiu0WNLNXqWmlL1G7RVm3YHmHPuqZroccfrVsoe7b8k67drdZ1F3FROYBHRVrlgEWtiKzWc1AMbBQCAolBjIkkSFVcXNyM7/3jewcuw3fmfpPMd77fSZ6Pc75n7vf9vfd+33Mnmdfcez/fe1NVSJI0m6eMugFJ0vgzLCRJnQwLSVInw0KS1MmwkCR1OmDUDQzL0qVLa8WKFaNuQ5IWlFtvvfXBqpqYXt9nw2LFihWsX79+1G1I0oKS5Lv96h6GkiR1MiwkSZ0MC0lSJ8NCktTJsJAkdTIsJEmdDAtJUifDQpLUaZ/9UN6empycZPPmzY89P/7441m0aNEIO5Kk0TMsptm8eTNvufh6Fi89hp8+uI0PnfsaTjzxxFG3JUkjZVj0sXjpMRx21HGjbkOSxobnLCRJnYYWFkmOTXJTkruSbEzytqZ+UZLvJ9nQPF7dWuaCJJuS3J3kjFb9RUlub157f5IMq29J0pMN8zDULuBPquq2JIcBtya5oXntb6rqr9szJzkJWAM8HzgG+GKSE6tqErgEWAt8DfgssBr43BB7lyS1DG3Poqq2V9VtzfTDwF3AslkWORO4qqoeraotwCbg1CRHA4dX1c1VVcCVwGuH1bck6cnm5ZxFkhXAC4CvN6XzknwryWVJjmhqy4DvtRbb2tSWNdPT6/3eZ22S9UnW79y5cy6/BUnarw09LJIsBj4FvL2qfkLvkNLxwCnAduA9U7P2WbxmqT+5WHVpVa2qqlUTE0+60ZMkaQ8NNSySPJVeUHysqj4NUFUPVNVkVf0C+BBwajP7VuDY1uLLgW1NfXmfuiRpngxzNFSAjwB3VdV7W/WjW7O9Drijmb4OWJPkoCQrgROAW6pqO/BwktOadZ4NXDusviVJTzbM0VAvBd4E3J5kQ1P7c+CsJKfQO5R0H/CHAFW1McnVwJ30RlKd24yEAngrcDlwCL1RUI6EkqR5NLSwqKqv0v98w2dnWWYdsK5PfT1w8tx1J0naHX6CW5LUybCQJHUyLCRJnQwLSVInw0KS1MmwkCR1MiwkSZ0MC0lSJ8NCktTJsJAkdTIsJEmdDAtJUifDQpLUybCQJHUyLCRJnQwLSVInw0KS1MmwkCR1MiwkSZ0MC0lSJ8NCktTJsJAkdTIsJEmdDAtJUifDQpLUybCQJHUyLCRJnQwLSVInw0KS1MmwkCR1MiwkSZ2GFhZJjk1yU5K7kmxM8ramviTJDUnubb4e0VrmgiSbktyd5IxW/UVJbm9ee3+SDKtvSdKTDXPPYhfwJ1X1POA04NwkJwHnAzdW1QnAjc1zmtfWAM8HVgMfSLKoWdclwFrghOaxeoh9S5KmGVpYVNX2qrqtmX4YuAtYBpwJXNHMdgXw2mb6TOCqqnq0qrYAm4BTkxwNHF5VN1dVAVe2lpEkzYN5OWeRZAXwAuDrwFFVtR16gQIc2cy2DPhea7GtTW1ZMz293u991iZZn2T9zp075/R7kKT92dDDIsli4FPA26vqJ7PN2qdWs9SfXKy6tKpWVdWqiYmJ3W9WktTXUMMiyVPpBcXHqurTTfmB5tASzdcdTX0rcGxr8eXAtqa+vE9dkjRPhjkaKsBHgLuq6r2tl64DzmmmzwGubdXXJDkoyUp6J7JvaQ5VPZzktGadZ7eWkSTNgwOGuO6XAm8Cbk+yoan9OfBu4OokbwbuB94AUFUbk1wN3ElvJNW5VTXZLPdW4HLgEOBzzUOSNE+GFhZV9VX6n28AOH2GZdYB6/rU1wMnz113kqTd4Se4JUmdDAtJUifDQpLUybCQJHUyLCRJnQwLSVInw0KS1MmwkCR1MiwkSZ0MC0lSJ8NCktTJsJAkdTIsJEmdDAtJUifDQpLUybCQJHUyLCRJnQwLSVInw0KS1MmwkCR1MiwkSZ0MC0lSJ8NCktTJsJAkdTIsJEmdDAtJUifDQpLUybCQJHUyLCRJnQwLSVInw0KS1GloYZHksiQ7ktzRql2U5PtJNjSPV7deuyDJpiR3JzmjVX9Rktub196fJMPqWZLU3zD3LC4HVvep/01VndI8PguQ5CRgDfD8ZpkPJFnUzH8JsBY4oXn0W6ckaYiGFhZV9RXgoQFnPxO4qqoeraotwCbg1CRHA4dX1c1VVcCVwGuH0rAkaUYDhUWSlw5SG9B5Sb7VHKY6oqktA77XmmdrU1vWTE+vz9Tn2iTrk6zfuXPnHrYnSZpu0D2L/z5grcslwPHAKcB24D1Nvd95iJql3ldVXVpVq6pq1cTExB60J0nq54DZXkzyq8BLgIkk72y9dDiwqP9SM6uqB1rr/hDw983TrcCxrVmXA9ua+vI+dUnSPOraszgQWEwvVA5rPX4CvH5336w5BzHldcDUSKnrgDVJDkqykt6J7FuqajvwcJLTmlFQZwPX7u77SpL2zqx7FlX1ZeDLSS6vqu/uzoqTfAJ4BbA0yVbgL4BXJDmF3qGk+4A/bN5nY5KrgTuBXcC5VTXZrOqt9EZWHQJ8rnlIkubRrGHRclCSS4EV7WWq6pUzLVBVZ/Upf2SW+dcB6/rU1wMnD9inJGkIBg2LvwU+CHwYmOyYV5K0jxk0LHZV1SVD7USSNLYGHTr7mSR/lOToJEumHkPtTJI0Ngbdszin+fquVq2AZ89tO5KkcTRQWFTVymE3IkkaXwOFRZKz+9Wr6sq5bUeSNI4GPQz14tb0wcDpwG30LuwnSdrHDXoY6o/bz5M8HfjoUDqSJI2dPb1E+SP0LskhSdoPDHrO4jM8frXXRcDzgKuH1ZQkabwMes7ir1vTu4DvVtXWmWaWJO1bBjoM1VxQ8Nv0rjh7BPDzYTYlSRovg94p743ALcAbgDcCX0+y25colyQtTIMehroQeHFV7QBIMgF8Efi7YTUmSRofg46GespUUDR+uBvLSpIWuEH3LD6f5B+ATzTPfx/47HBakiSNm657cP8ycFRVvSvJ7wK/BgS4GfjYPPQnSRoDXYeS3gc8DFBVn66qd1bVO+jtVbxvuK1JksZFV1isqKpvTS82tzpdMZSOJEljpyssDp7ltUPmshFJ0vjqCotvJHnL9GKSNwO3DqclSdK46RoN9XbgmiR/wOPhsAo4EHjdEPuSJI2RWcOiqh4AXpLk14GTm/L1VfWloXcmSRobg97P4ibgpiH3IkkaU34KW5LUybCQJHUyLCRJnQwLSVInw0KS1MmwkCR1MiwkSZ2GFhZJLkuyI8kdrdqSJDckubf5ekTrtQuSbEpyd5IzWvUXJbm9ee39STKsniVJ/Q1zz+JyYPW02vnAjVV1AnBj85wkJwFrgOc3y3wgyaJmmUuAtcAJzWP6OiVJQza0sKiqrwAPTSufCVzRTF8BvLZVv6qqHq2qLcAm4NQkRwOHV9XNVVXAla1lJEnzZL7PWRxVVdsBmq9HNvVlwPda821tasua6en1vpKsTbI+yfqdO3fOaeOStD8blxPc/c5D1Cz1vqrq0qpaVVWrJiYm5qw5SdrfzXdYPNAcWqL5uqOpbwWObc23HNjW1Jf3qUuS5tF8h8V1wDnN9DnAta36miQHJVlJ70T2Lc2hqoeTnNaMgjq7tYwkaZ4MdInyPZHkE8ArgKVJtgJ/AbwbuLq50979wBsAqmpjkquBO4FdwLlVNdms6q30RlYdAnyueUiS5tHQwqKqzprhpdNnmH8dsK5PfT2P33hJkjQC43KCW5I0xgwLSVInw0KS1MmwkCR1MiwkSZ0MC0lSJ8NCktTJsJAkdTIsJEmdDAtJUifDQpLUybCQJHUyLCRJnQwLSVInw0KS1MmwkCR1MiwkSZ2Gdqe8fUH94hds2bLlsefHH388ixYtGmFHkjQahsUs/s9DP+Cia+5nybIf8dMHt/Ghc1/DiSeeOOq2JGneGRYdDl3yTA476rhRtyFJI+U5C0lSJ8NCktTJsJAkdTIsJEmdDAtJUifDQpLUybCQJHUyLCRJnQwLSVInw0KS1MmwkCR1GklYJLkvye1JNiRZ39SWJLkhyb3N1yNa81+QZFOSu5OcMYqeJWl/Nso9i1+vqlOqalXz/Hzgxqo6AbixeU6Sk4A1wPOB1cAHksz7dcKnLld+zz33cM899zA5OTnfLUjSyIzTVWfPBF7RTF8B/C/gz5r6VVX1KLAlySbgVODm+WxukMuVT05Osnnz5seee/8LSfuKUYVFAV9IUsD/rKpLgaOqajtAVW1PcmQz7zLga61ltza1edd1ufLNmzfzlouvZ/HSY7z/haR9yqjC4qVVta0JhBuSfHuWedOnVn1nTNYCawGOO24096BYvPQY738haZ8zknMWVbWt+boDuIbeYaUHkhwN0Hzd0cy+FTi2tfhyYNsM6720qlZV1aqJiYlhtS9J+515D4skT0ty2NQ08FvAHcB1wDnNbOcA1zbT1wFrkhyUZCVwAnDL/HYtSfu3URyGOgq4JsnU+3+8qj6f5BvA1UneDNwPvAGgqjYmuRq4E9gFnFtVDkWSpHk072FRVd8BfqVP/YfA6TMssw5YN+TW9kh7BNSWLVuo5mzK1FDbKY6MkrSQjdPQ2QWpPQJqx70bOHz5c4HBhtpK0kJhWMyBqRFQP33wiefdu4baStJC4bWhJEmdDAtJUicPQ80DT3ZLWugMiz3Q/uXfHgE1E092S1roDIs90P7l3x4BNRtPdktayDxnsYemfvkfesSR3TNL0gJnWEiSOhkWkqROnrOYZ46MkrQQGRbzzJFRkhYiw2IEpk6Ot/cy3MOQNM48ZzFCvb2MDbzl4uufcO9uSRo37lmM2KFLnsnBhxw86jYkaVbuWUiSOrlnMQYcISVp3BkWY8ARUpLGnWExJrx2lKRx5jkLSVIn9yzG2OTk5BOG1HouQ9KoGBZjbPPmzbzl4utZvPQYHt6xlQt/+2RWrlzJ5OQkwGPBYYhIGjbDYsxMv7HS037pGA476jh++uA2Lrpmw2P30Djg0GewZNkKT4hLmheGxZiZ7cZKUyfBf/rgNg542pInXTIE3MuQNByGxRhqh0IXh91Kmg+GxT6g34UJwb0MSXPHsNiHtPcy2ifEweCQtHcMi31M+xDW1Alxg0PS3jIs9mFdwdEegutwXEmzMSz2E/2Coz0Etz09SKAMEi5+qFDadxgW+6F+Q3CnT3cFyiDhsmXLFv7y+jtZPDHzhwqnpqe4ZyONJ8NCfQ0SKIOEy+HLnztQAO165Ed7/EFD92Ck4VswYZFkNfDfgEXAh6vq3SNuSS0zhcsg8xzwtCXsOujAvh80HOQw2O7swQw6DYaO1LYgwiLJIuBi4DeBrcA3klxXVXeOtjMNw/RPsXcdBtudPZhBp+cqdHYnmIDd2kNq71HNFHS7O88g76v904IIC+BUYFNVfQcgyVXAmcBQwmLqL+JH/mkHBzz6cx4+5OChTu965Efz8j7jPP2kbXDoM3brZ/bIQz/Y42X7+dmPH+Q/fOTzPP3IZTx0/90sOnjx0KYf+dFO1r3plQBc+NEvcegzJh6rTQ137mfLli2Pzd9vfStXrtzteQZ5X423YV3BIVU1lBXPpSSvB1ZX1b9rnr8J+BdVdd60+dYCa5unzwHu3sO3XAo8uIfLzgf72zv2t3fsb++Me3/PqqqJ6cWFsmeRPrUnpVxVXQpcutdvlqyvqlV7u55hsb+9Y397x/72zrj3N5OFcqe8rcCxrefLge6r7EmS5sRCCYtvACckWZnkQGANcN2Ie5Kk/caCOAxVVbuSnAf8A72hs5dV1cYhvuVeH8oaMvvbO/a3d+xv74x7f30tiBPckqTRWiiHoSRJI2RYSJI6GRYtSVYnuTvJpiTnz+P7HpvkpiR3JdmY5G1N/aIk30+yoXm8urXMBU2fdyc5o1V/UZLbm9fen6TfsOM96fG+Zr0bkqxvakuS3JDk3ubrEaPoL8lzWttoQ5KfJHn7KLdfksuS7EhyR6s2Z9sryUFJPtnUv55kxRz091+TfDvJt5Jck+QZTX1Fkp+1tuMHR9TfnP08h9TfJ1u93Zdkw6i231BUlY/eeZtFwGbg2cCBwDeBk+bpvY8GXthMHwbcA5wEXAT8aZ/5T2r6OwhY2fS9qHntFuBX6X025XPAq+aox/uApdNq/wU4v5k+H/irUfU37ef4A+BZo9x+wMuBFwJ3DGN7AX8EfLCZXgN8cg76+y3ggGb6r1r9rWjPN20989nfnP08h9HftNffA/ynUW2/YTzcs3jcY5cUqaqfA1OXFBm6qtpeVbc10w8DdwHLZlnkTOCqqnq0qrYAm4BTkxwNHF5VN1fvX9mVwGuH2PqZwBXN9BWt9xplf6cDm6vqux19D7W/qvoK8FCf952r7dVe198Bp+/OXlC//qrqC1W1q3n6NXqfZ5rRfPc3i7HYflOa9bwR+MRs6xhmf8NgWDxuGfC91vOtzP4Leyia3c0XAF9vSuc1hwUuax22mKnXZc309PpcKOALSW5N77IqAEdV1XboBR5w5Aj7m7KGJ/4nHZftB3O7vR5bpvkF/2Pgl+aw139L7y/dKSuT/O8kX07yslYP893fXP08h7n9XgY8UFX3tmrjsv32mGHxuIEuKTLUBpLFwKeAt1fVT4BLgOOBU4Dt9HZtYeZeh/k9vLSqXgi8Cjg3yctnmXcU/ZHeBzZ/B/jbpjRO2282e9LP0HpNciGwC/hYU9oOHFdVLwDeCXw8yeEj6G8uf57D/FmfxRP/YBmX7bdXDIvHjfSSIkmeSi8oPlZVnwaoqgeqarKqfgF8iN6hstl63coTDx3M2fdQVduarzuAa5peHmh2pad2qXeMqr/Gq4DbquqBptex2X6Nudxejy2T5ADg6Qx+2GZGSc4Bfhv4g+bQCM3hnR8207fSOydw4nz3N8c/z2FtvwOA3wU+2ep7LLbf3jIsHjeyS4o0xyI/AtxVVe9t1Y9uzfY6YGrkxXXAmmbExErgBOCW5tDGw0lOa9Z5NnDtHPT3tCSHTU3TOxF6R9PHOc1s57Tea177a3nCX3Tjsv1a5nJ7tdf1euBLU7/c91R6Nxj7M+B3quqRVn0ivXvKkOTZTX/fGUF/c/nznPP+Gr8BfLuqHju8NC7bb6+N+gz7OD2AV9MbibQZuHAe3/fX6O1ifgvY0DxeDXwUuL2pXwcc3VrmwqbPu2mN2AFW0ftPtBn4HzSf0t/L/p5Nb7TJN4GNU9uG3jHUG4F7m69LRtFfs95DgR8CT2/VRrb96IXWduD/0fsr8c1zub2Ag+kdbttEb0TNs+egv030jpNP/RucGo3ze83P/ZvAbcC/GlF/c/bzHEZ/Tf1y4N9Pm3fet98wHl7uQ5LUycNQkqROhoUkqZNhIUnqZFhIkjoZFpKkToaFNIMkk81VQr+Z5LYkL5n2+juS/N8kT2/VXpHk72dZ5zeTfGJa7fL0rqZ6UPN8aZL7Wq+fmOSz6V2B9K4kVyc5qnmvH+eJV9z9jTnbAFKLYSHN7GdVdUpV/QpwAfCfp71+Fr0Pc75ukJUleR69/3Mvbz7c2DZJ73pM05c5GLgeuKSqfrmqnkfvshcTzSz/2PQ49fjioN+ctDsMC2kwhwP/NPUkyfHAYuA/0guNQfxreh8s+wK9a1i1vQ94R3Nph+nL3FxVn5kqVNVNVXUH0jya/g9T0uMOSe8GNgfTu+fIK1uvTV1a5B+B5yQ5snrXzZrN7wO/CTwHOI8nXmzufuCrwJuAz7TqJwO3zrLOlzU9Tvm9qtrc0Ye029yzkGY2dRjqucBq4MrmGj7Qu3bYVdW7qN2ngTfMtqIkLwZ2Vu8+GzcCL2xdYnvKXwLvYvf+X04/DGVQaCjcs5AGUFU3J1kKTCR5Jr2Lwd3QZMeBwHeAi2dZxVnAc1snrg+nd82gD7feY1Ozl/DG1nIbgX85R9+GtMfcs5AGkOS59G7Z+kN6v/gvqqoVzeMYYFmSZ82w7FPo7Xn886ll6N0Jrd+5jnXAn7aefxx4SZLXtNa3Osk/m4vvSxqUYSHN7JCpIan07k9wTlVN0jsEdc20ea9p6tC7BebWqQe9y35/v6q+35r/K8BJ0y67TVVtpHdl0qnnP6N3f4k/TnJvkjuBf8Pj98J42bShs6+fg+9behKvOitJ6uSehSSpk2EhSepkWEiSOhkWkqROhoUkqZNhIUnqZFhIkjr9fxU2lzD1GK6pAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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98Ja1l+mZ/vMf/5D3nr2SHZ/+yBfdj763nK23254dn/6Mhx/vsMevk62y3ufoX0/XOga9B8uXL+dvz72IJ+6829DruOPay3jvN3+63uf92Y/u4O1HvWKtMOx/Xze2jkG19Ncx6Hn7ddUxjNlaxzBtXevofc9guPdgtteh0RnVru1UbfhX5DRIcgRwaFX9t/bxHwIHVtWf9s13HHBc+/BZwA2P8il3Bn70KJcdl2mvcdrrg+mvcdrrg+mvcdrrg+mr8Veqam5/46ayZTHoJ9g6KVdVZwJnPuYnS5ZW1aLHup5RmvYap70+mP4ap70+mP4ap70+2DRqhE3nehargN17Hs8Hbp9QLZK0xdlUwuJbwMIkeySZAywGLpxwTZK0xdgkdkNV1Zokfwp8kebQ2U9U1YoRPuVj3pU1BtNe47TXB9Nf47TXB9Nf47TXB5tGjZtGB7ckabI2ld1QkqQJMiwkSZ226LBIcmiSG5KsTPLOAdOT5MPt9GuSPHfK6nttW9c1Sb6RZL9x1jdMjT3zPS/Jg+05M1NVX5KXJLkqyYokXx1nfcPUmGSHJJ9NcnVb4x+Pub5PJLkrycAzACf9ORmyxol+Vrrq65lvIp+ToVTVFnmj6Sj/HrAnMAe4Gti7b55XAZ+nOc/jIODyKavvhcCO7f1XjrO+YWvsme/LwOeAI6apPuApNCMBPKN9vMu0vYfAu4D3t/fnAj8G5oyxxhcDzwWWr2f6xD4nG1HjpD8rG6yv5//C2D8nw9625C2Lh4cQqarVwMwQIr0OA86pxjeBpyTZrX9Fk6qvqr5RVf/ePvwmzfkn4zTMewjwBuDTwF3jLI7h6vsD4PyquhWgqqaxxgKenCTAk2jCYs24Cqyqr7XPuT6T/JwA3TVO+rMyxHsIk/ucDGVLDot5wA96Hq9q2zZ2nlHZ2Oc+lubX3Th11phkHvAa4KNjrGvGMO/hrwE7JrkkyZVJjh5bdY1havwHYC+aE1GvBd5YVQ+Np7yhTPJz8mhM4rOyQRP+nAxlkzjPYkSGGUJkqGFGRmTo507yUpoPwG+OtKIBTz2grb/GU4F3VNWDzQ/jsRqmvscBBwAHA9sBlyX5ZlV9d9TFtYap8RDgKuBlwDOBi5JcWlU/GXFtw5rk52SjTPCz0uVUJvc5GcqWHBbDDCEyyWFGhnruJL8BfAx4ZVXdM6baZgxT4yLgvPYDsDPwqiRrqupfp6S+VcCPqupnwM+SfA3YDxhXWAxT4x8DJ1ezY3tlkpuBZwNXjKfETpvEcDwT/qx0meTnZDiT7jSZ1I0mKG8C9uCRjsV9+ub5bdbuuLtiyup7BrASeOG0vod985/FeDu4h3kP9wIubud9ArAc2HfKajwDeHd7f1fgNmDnMf9bL2D9nccT+5xsRI0T/ax01dc331g/J8Pettgti1rPECJJ/kc7/aM0RyW8iuY/2c9pfuFNU33/G3gqcHr7i2RNjXH0yiFrnJhh6quq65N8AbgGeIjmKowbd5GIEdcI/DVwVpJrab6Q31FVYxvSOsm5wEuAnZOsAv4S2Kanvol9Tjaixol+Voaob+o53IckqdOWfDSUJGlIhoUkqZNhIUnqZFhIkjoZFpKkToaFJKmTYaFZl+RpSc5L8r0k1yX5XJJfa6e9Ockvk+zQM/8TknwyybVJlif5epIntdN+2rfuP0ryD0PUcHV7bHtv21lJbm6nfTfJOe2YPLRjQx3SN/+bkpze3p+b5IEk/71vnluSfLrn8RFJzup5/MokS5Ncn+Q7SU5p29+d5LY0Q6PP3J6yntfykiT39sz3pQHruC7JUQNe68wy3+iZdng7VPd32vf7iJ5plyRZ1PN4wcyw2gPquCrJy9tpleTvepZ7a5J39zw+un2uFW2tb03yviTv75nnV5LctL73QZNlWGhWpTnj6TPAJVX1zKram2aI7V3bWY4CvkUzaNqMNwJ3VtWvV9W+NGP3PPAYatiL5v/2i5M8sW/y26pqP+BZwDLgK0nmAOcCi/vmXdy2AxxJM1rpUaxrUZJ9BtSxL80ggK+rqr2AfWnO1p7xwap6Ts/tPzbwsi7tme/l/eugGfn1/yTZpu+1zizzwram/YBTgMOq6tnAfwben+SADTz3+up4TlV9qW2/H/jdJDsPeB9eCbwJ+K2q2odmqO57aU42PKz99wL4EPC/Ot4HTYhhodn2UuCB3rNSq+qqqro0yTNphtj+n6z9pbsbzRAWM/PfUFX3P4Ya/gD4R+D/Ab8zaIZqfBD4Ic31DT4FvDrJttD8ogaeDny9XeQo4M+B+TNbIz1OoQnEfm8HTqqq77TPuaaqTn8Mr2u9qupGmrOnd+yY9a3A+6rq5na5m4H30by2x2INcCbw5gHTTgTeWlW3t8/5y6r6v1X1C+AtNGdVvxJ4clV98jHWoRExLDTb9gWuXM+0o2h+qV8KPCvJLm37J4B3JLksyXuTLOxZZrve3R7AXw1Rw+8D/9w+16AtgV7fBp5dzcByVwCHtu2LgX+uqkqyO/C0qroCWNKuv9cS4LlJfrWvfUPvBcCbe17bVzrq/E898/5F/8Q0V6e7sda+HscHepaZ+RLeZ0BNS4G9O55/UB1XtT8AZpwGvDY9uxhb630fqupzNNd5OAd4/ZA1aAK22LGhNBGLgddU1UNJzqfZtXNaVV2VZE/gt4CXA99K8oKquh74RbubBWj6LGhG6BwoyfOAu6vq+2nG4PlEkh3rkQvfrLNIz/2ZXVEXtH//a0/dS9r75wEfB/6+Z7kHgQ/Q/ILemOskfLCqThly3kur6tUD2t+c5E9orrR3aN+0t1XVp/rawoaH4h80/k9v2/rqoKp+kuQc4M+AXwyaZz1OA7arqhs2YhmNmVsWmm0raK4PsZY0w0MvpLkWwy00X8AP/+qvqp9W1flV9Xrgn2gGpns0jgKe3T7H94Dtgf+ygfn3B65v7/8rcHD7K327qvp2zzr/qF3nhcB+fVs/0Oz2ejHN6KYzBr4Xs+yDVfUsmq2dc5I8vmP+Fawbts+l2boAuIe1d2XtBGzMoIWn0vQ59fYVdb0PD7U3TTHDQrPty8C27a9d4OFf+x+iGWZ7QXt7OjCvPQLmRUl2bOedQ7NL5Psb+8RJtqLZWvmNmeeh6fhdZ1dUGn9G01/yBWgCC7iEZrfYue18zwKeWFXzetb5N/R1hlfVA8AHaTpyZ3wAeFceORJsqyRv2djXNYyqOp/mC/+YjllPAU5s+2Rm+mbe1NYKzet/XXugAu36unaR9dbxY5qtsGN7mv8G+NskT2ufc9v2vdcmxLDQrKpmGOPXAK9Ic+jsCuDdNMMzf6Zv9s/QfOk+E/hqmiG4l9F86X2ajfdi4Laquq2n7WvA3nnkmtAfSHI1zcWNnge8tJprX884l+biR+e1j48aUPenGdwX8nF6du1W1TU0X8TnJrme5loZvdem7u2zuGrmC/wx+CvgLW1owtp9FlclmVNVVwHvAD6b5Ls078PxPbuAzgTuA65u36cn0QTMjP4+iyNY19/RXMAHeLhf4jTgS+3/hytxF/gmxyHKpS1YkpOBA4FD+kJTWothIUnq5KagNknt4aNH9jX/S1WdNIl6ZkOaM8jf39d8c1W9ZtD80ji5ZSFJ6mQHtySpk2EhSepkWEiSOhkWkqRO/x/di6uOBG36eQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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PtLmkyjQHxOjuEa67Y5AZ/QP0zOgrr0Nc9sV7y6OKa/9qA7f97r8EYOmX7mdsdIzajD5uv+rXyrCIiDJAZs+eXV7DaA6ViCifsm5ex3CRFNPpnHVE1ID/A5wPbAUeBS7NzKcnWn/RokU5ODh4WN81NDTE0pX/a8Jle3a9Tk9tBj212pQsm2j56MhrZWjs2fU6uWeUGf0DjI68BrDfsp5a4/JQc4B89uK3cv3qDdRmzNorVAYGBqjX61y+Yj23XPZO/mDVD4ieGXuFC7wRMBMFCzSOVsbbh4eHWzpyOdCQH+PXUODww6l5H811t7o/hyNRt4iIDZm5aMJl0ywUzgb+NDMvKOaXAWTmn020/pGGwke/cM+Ey0Z3jZRjGE3FsqO939FdI9Rm9rFr5yv0zj6OnlpP2Tb++bklZ3L96sfK+fHtxj97emfQO2s2Kz7+bq78ygNEbSYrPv5url71A1ZdfT4Av33Td8r2K7/yAGOjjd+lW3//PObOnTthP+v1OktvuY9VV5+/17WSer1e7u9rn7jgsK6jNO+jue5W9zde2y1Lz2FgYMBrOZrWjuT381gKhQ8BizPzd4v5y4Bfycxrm9a5EriymH0LsPkIvvJE4OUj2P5YY387m/3tbEezv/80M+dMtGC6XVOY6Jh9r9TKzJXAyqPyZRGDB0rLTmR/O5v97WxT1d/p9pzCVuDUpvlTgJfaVIskdZ3pFgqPAgsjYkFEzASWAGvbXJMkdY1pdfooM/dExLXAPTRuSb0tMzdW+JVH5TTUMcT+djb729mmpL/T6kKzJKm9ptvpI0lSGxkKkqRSV4ZCRCyOiM0RsSUibmx3PYciIm6LiKGIeKqp7YSIuC8ini0+j29atqzo5+aIuKCp/V9ExJPFss9H8QhvRPRFxDeK9ocjYv6UdrBJRJwaEQ9GxKaI2BgR1xXtndrfWRHxSET8qOjvvy/aO7K/4yKiFhGPR8R3i/mO7W9E/LSo84mIGCzapld/M7OrfmhcwP4JcBowE/gRcHq76zqE+n8VOBN4qqnts8CNxfSNwH8qpk8v+tcHLCj6XSuWPQKcTePZkO8Bv1G0Xw18uZheAnyjjX2dB5xZTL+JxhAop3dwfwM4rpieATwMvKtT+9vU7xuAvwK+28m/z0UNPwVO3KdtWvW3rb8MbfqfcjZwT9P8MmBZu+s6xD7MZ+9Q2AzMK6bnAZsn6huNu7rOLtZ5pqn9UmBF8zrFdC+NJyij3X0u6rmLxrhYHd9foB94DPiVTu4vjWeR1gHv5Y1Q6OT+/pT9Q2Fa9bcbTx+dDLzYNL+1aDuWnZSZ2wCKz/GBhw7U15OL6X3b99omM/cA/wj8k8oqb1FxGPwOGv967tj+FqdSngCGgPsys6P7C9wE/BEw1tTWyf1N4N6I2BCNIXtgmvV3Wj2nMEUOOpRGBzlQXyf7bzDt/vtExHHAXwOfzMxX4sAjmB7z/c3MUeDtEfHzwLcj4m2TrH5M9zciPgAMZeaGiPj1VjaZoO2Y6W/h3Mx8KSLmAvdFxDOTrNuW/nbjkUInDqWxPSLmARSfQ0X7gfq6tZjet32vbSKiF/g54B8qq/wgImIGjUC4IzO/VTR3bH/HZebPgO8Di+nc/p4L/GZE/BRYDbw3Ir5G5/aXzHyp+BwCvg2cxTTrbzeGQicOpbEWWFpML6Vx7n28fUlxR8ICYCHwSHGI+mpEvKu4a+Fj+2wzvq8PAQ9kcYJyqhW1fRXYlJl/0bSoU/s7pzhCICJmA+8DnqFD+5uZyzLzlMycT+PP4QOZ+VE6tL8RMRARbxqfBt4PPMV062+7Lri08we4kMadLD8BPt3ueg6x9q8D24DdNP5VcAWNc4brgGeLzxOa1v900c/NFHcoFO2Lil/InwBf5I2n22cBdwJbaNzhcFob+/puGoe+PwaeKH4u7OD+/jLweNHfp4B/V7R3ZH/36fuv88aF5o7sL407Hn9U/Gwc/7tnuvXXYS4kSaVuPH0kSToAQ0GSVDIUJEklQ0GSVDIUJEklQ0FdKSJGi5Eqn4qIOyOiv2jvjYiXI+LPivn3R8QPm0ahrBXbnRMRfxoRGRG/2LTf64u2RcV886iYT0TE54v22yPibyOir5g/sVj3l5rW/YeIeK6Yvj8ieooRMZ8q9vlocf+6dNQYCupWr2Xm2zPzbcAu4PeL9vfTuCf8koiIzLwXeJ7G8yAAnwAezcwfFPNP0njwatyHgKf3+a73FN/19sz8w6b2UeDjzStm5pPj69J4EOlTxfz7gI8AvwD8cmb+EvBB4GeH2X9pQoaCBP8bGP/X/qXAzcALNIatBrgeWBYRZwDXAn/ctO3/AC4CiIjTaAxAtqPF770JuL4YjqAV84BtmTkGkJlbM/P/tbit1BJDQV2t+Av5N4Ani6ElzgO+S+PJ8UuhHLnyJuCHwH/IzOaxZF4BXiwGrrsU+MYEX/Ng0ymh65vaXwAeAi5rsdw1wL8u9vNfIuIdrfZTapWhoG41uxiiepDGX85fBT4APJiZwzQG4ftgRNSK9f8rjRec3D7BvlbTOIV0MY1BzvbVfProc/ss+4/Ap2jhz2JmbgXeQmOc/TFgXUScd7DtpEPRjUNnS1BcU2huiIhLgXOLUTuhMSbNe4D7M3MsIg40Jsx3gP8MDObkQ3vvJzO3FOF0SYvrj9B409b3ImI7jSBa1/IXSgdhKEhARLyZxgB8pxZ/8RIRv0PjlND9k22bma9FxB/TGGTxcCwH/qaFGs8E/i4b4/H30BhA78eH+Z3ShAwFqeG3aAwzPNLUdhfw2Yjo26d9P5m5epLFD0bEaDH948z82D7bboyIx2i8e3syc4GvjN/GSmMUzC8eZBvpkDhKqiSp5IVmSVLJUJAklQwFSVLJUJAklQwFSVLJUJAklQwFSVLp/wP6nT+boooJfgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for col in df:\n",
+    "    plt.figure()\n",
+    "    sns.histplot(df[col])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "8aab7983",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for col in df:\n",
+    "    df[col]=np.sqrt(df[col])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "ab99bfbc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "BALANCE                             0.829498\n",
+       "BALANCE_FREQUENCY                  -2.819495\n",
+       "PURCHASES                           1.730752\n",
+       "ONEOFF_PURCHASES                    2.129460\n",
+       "INSTALLMENTS_PURCHASES              1.546939\n",
+       "CASH_ADVANCE                        1.486159\n",
+       "PURCHASES_FREQUENCY                -0.421872\n",
+       "ONEOFF_PURCHASES_FREQUENCY          0.724607\n",
+       "PURCHASES_INSTALLMENTS_FREQUENCY    0.130409\n",
+       "CASH_ADVANCE_FREQUENCY              0.706976\n",
+       "CASH_ADVANCE_TRX                    1.417779\n",
+       "PURCHASES_TRX                       1.185757\n",
+       "CREDIT_LIMIT                        0.680933\n",
+       "PAYMENTS                            1.951535\n",
+       "MINIMUM_PAYMENTS                    3.805340\n",
+       "PRC_FULL_PAYMENT                    1.297280\n",
+       "TENURE                             -3.064332\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.skew(axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "6bfd5aea",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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TktcOmZ4kb++nX5PkiQtR52xG6MPz+9qvSfL5JIctRJ0trX4MzPekJHf3595MnVH6keTpSa5KsjHJpyddY8sIf1MPS/KRJFf3ffidhaizJcn7ktyR5Kvbmb4YPt+tPkzu811VS/JGt6P8G8CjgN2Bq4GDZ8zzHOBjdOd5HAF8caHr3oE+HAns3d9/9rT1YdR+DMz3SeCjwPELXfcO/j4eTjfywIH94/0Wuu4d6MPrgDf195cB3wd2X+jah/TlacATga9uZ/pUf75H7MPEPt9Lec3ip0OIVNW/AduGEBl0DHBedb4APDzJAZMudBbNPlTV56vq//UPv0B3jsq0GeV3AfD7wIeAOyZZ3ByM0o/fAi6sqpsBqmra+jJKHwp4aLrxNR5CFxZbJ1tmW1V9hq627Zn2z3ezD5P8fC/lsFgO3DLweHPfNtd5FtJc6zuF7pvUtGn2I8ly4DjgXROsa65G+X38ArB3ksuSXJnkpIlVN5pR+vBXwOPoToy9FnhZVd0zmfLm1bR/vudqrJ/vRXGexZiMMoTISMOMLKCR60vyDLo/pl8ea0U7ZpR+vBV4TVXdPcWD3I3Sj92Aw4GjgD2By5N8oaq+Pu7iRjRKH44GrgKeCTwauCTJZ6vqh2Oubb5N++d7ZJP4fC/lsBhlCJFpH2ZkpPqS/CLwHuDZVfW9CdU2F6P0Yw1wQR8U+wLPSbK1qv5uIhWOZtS/qe9W1T8D/5zkM8BhwLSExSh9+B3gjOo2lG9KchPwWOCKyZQ4b6b98z2SSX2+l/JmqFGGELkYOKk/auII4AdVddukC51Fsw9JDgQuBF44Rd9eZ2r2o6oOqqpVVbUK+CDwkikLChjtb+oi4FeS7JbkZ4BfAq6fcJ2zGaUPN9OtGZFkf7oRnr850Srnx7R/vpsm+flesmsWtZ0hRJL81376u+iOunkOsAn4F7pvVFNjxD78D+DfAWf138q31pSNuDliP6beKP2oquuTfBy4BriH7qqPQw+LXAgj/i7+J3BOkmvpNuW8pqqmbsjvJOcDTwf2TbIZ+BPgQbA4Pt8wUh8m9vl2uA9JUtNS3gwlSRqRYSFJajIsJElNhoUkqcmwkCQ1GRaSpCbDQvMuySOSXJDkG0muS/LRJL/QT3tFkn9N8rCB+X8myfuTXJvkq0k+l+Qh/bQfzXju307yVyPUcHV/jPpg2zlJbuqnfT3Jef2YU/RjNR09Y/6XJzmrv78syU+SvGjGPN9K8qGBx8cnOWfg8bOTbEhyfZKvJTmzb399ku+kG6p82+3h2+nL05P8YGC+Twx5juuSnDikr9uW+fzAtGPTDWn9tf79Pn5g2mVJ1gw8XpV+eOwhdVyV5Fn9tEry5wPLvSrJ6wcen9S/1sa+1lcleWOSNw3M83NJvrm990ELy7DQvEp3ZtCHgcuq6tFVdTDdkNb797OcSHeW8HEDi70MuL2q/n1VHUo3xs1PHkANj6P7235akp+dMfnVVXUY3VnHXwE+1Z+pfD7d2cqD1vXtACfQjep5Ive3JskhQ+o4lG7QvRdU1eOAQ7nvmc5vqarHD9z+aZZufXZgvmfNfA66EVTfneRBM/q6bZkj+5oOA84EjqmqxwL/EXhTksNnee3t1fH4qvpE334X8BtJ9h3yPjwbeDnwa1V1CN2Q2z+gO7nvmP73BfA24L833gctEMNC8+0ZwE8Gz7quqquq6rNJHk03pPV/477/dA8AvjMw/w1VddcDqOG3gL8G/i/w68Nm6Ielfgvwj3TXAfgg8Nwke0D3jRp4JPC5fpETgT8EVmxbGxlwJl0gzvRHwOlV9bX+NbdW1VkPoF/bVVU30p2FvHdj1lcBb6yqm/rlbgLeSNe3B2IrcDbwiiHTTgNeVVW39q/5r1X1f6rqx8Ar6c4+fjbw0Kp6/wOsQ2NiWGi+HQpcuZ1pJ9J9U/8s8Jgk+/Xt7wNek+TyJP8ryeqBZfYc3OwB/OkINTwP+ED/WsPWBAZ9GXhsPwDbFcDavn0d8IGqqiQrgUdU1RXA+v75B60Hnpjk52e0z/ZeALxioG+fatT5KwPz/vHMiemu8nbjjOtjvHlgmW3/hA8ZUtMG4ODG6w+r46r+C8A27wCen4FNjL3tvg9V9VG66zWcB7xkxBq0AJbs2FBaEOuA46rqniQX0m3aeUdVXZXkUcCvAc8CvpTkKVV1PfDjfjML0O2zoBuBdqgkTwK2VNW3042l874kew9cIOZ+iwzc37Yp6qL+538eqHt9f/8C4L3AXwwsdzfwZrpv0HO5nsBbqurMEef9bFU9d0j7K5L8Lt2V7dbOmPbqqvrgjLYw+1D8w8b/GWzbXh1U1Q+TnAf8AfDjYfNsxzuAPavqhjksowlzzULzbSPd9RruI90wyqvprn3wLbp/wD/91l9VP6qqC6vqJcDf0A3wtiNOBB7bv8Y3gL2A/zTL/E/g3lFf/w44qv+WvmdVfXngOX+7f86LgcNmrP1At9nracCBA21D34t59paqegzd2s55SR7cmH8j9w/bJ9KtXQB8j/tuytoHmMsggW+l2+c0uK+o9T7c0980xQwLzbdPAnv033aBn37bfxvw+m3DjFfVI4Hl/REwT02ydz/v7nSbRL491xdOsgvd2sovDgxnfgxDNkWl8wd0+0s+Dl1gAZfRbRY7v5/vMcDPVtXygef8M2bsDK+qnwBvoduRu82bgdfl3iPBdknyyrn2axRVdSHdP/yTG7OeCZzW75PZtm/m5X2t0PX/Bf2BCvTP19pENljH9+nWwk4ZaP4z4H8neUT/mnv0770WEcNC86q/IM5xwK+mO3R2I/B6umGWPzxj9g/T/dN9NPDpdENef4Xun96HmLunAd+pqu8MtH0GODj3Xlv5zUmuprvY0JOAZ1R3reltzqe7GNEF/eMTh9T9IYbvC3kvA5t2q+oaun/E5ye5HvgqXThtM7jP4qpt/8AfgD8FXtmHJtx3n8VVSXavqquA1wAfSfJ1uvfhxQObgM4G7gSu7t+nh9AFzDYz91kcz/39Od0FqoCf7pd4B/CJ/u/hStwEvug4RLm0hCU5g+4CTEfPCE3pPgwLSVKTq4JalPrDR0+Y0fy3VXX6QtQzH9KdQf6mGc03VdVxw+aXJsk1C0lSkzu4JUlNhoUkqcmwkCQ1GRaSpKb/DwB0UZV3eokYAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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G1rXldcC1Q/U1SQ5PchKDC9k3t6GqB5Oc2e6CWju0jyRpTBb1deAkHwJeABybZAL4feASYFOS84F7gfMAqur2JJuAO4A9wIVV9XA71AUM7qw6AriuPSRJY9RbWFTVq/ex6ux9bL8B2DBFfStw6gw2TZK0n+bKBW5J0hxmWEiSOhkWkqROhoUkqZNhIUnqZFhorJYsO5EkIz2WLDtxtpsrqent1llpKjsndvCq935hpG0//Iazem6NpFF5ZiFJ6mRYSJI6GRaSpE6GhSSpk2EhSepkWEjSHDcXbjn31llJmuPmwi3nnllIkjoZFpKkToaFJKmTYSFJ6mRYSJI6GRaSpE6GhRaUuXC/ujQf+T0LLShz4X71JctOZOfEjpG2ffLSZXxzx729tEPaH4aFNGZzIbCk/eUwlHSQmO0httl+f/XLMwvpIDHbZyyz/f7z0f4MSc42w0Jz1yGLSDLSpo7t92c+faDNhetB+/vnNUrAzoVwnTdhkWQV8C7gUOB9VXXJLDdJffvxntn9n6phBYx+xjAXbgiA0T58Yf/aOxfaMNvmRVgkORR4N/BiYAL4UpLNVXXH7LZMc8Z+fLCPbLbDCvrpV5/H7UFvw1v7+Wcw6/8WZtm8CAvgDGB7VX0dIMnVwGrAsNDAiB/s8+5Dva/Amu0gnAthNdt/BvNMqmq229ApySuBVVX1+vb6tcAvVNVvTNpuPbC+vXwacNdjfMtjgW89xn3nK/u8MCy0Pi+0/sKB9/lfVNXiycX5cmYx1X9B9kq5qroCuOKA3yzZWlUrD/Q484l9XhgWWp8XWn+hvz7Pl+9ZTADLhl4vBXbOUlskacGZL2HxJWBFkpOSHAasATbPcpskacGYF8NQVbUnyW8An2Bw6+wHqur2Ht/ygIey5iH7vDAstD4vtP5CT32eFxe4JUmza74MQ0mSZpFhIUnqtKDDIsmqJHcl2Z7koinWJ8mlbf1XkzxrNto5U0bo72taP7+a5AtJnjEb7ZxJXX0e2u7ZSR5u3+mZ10bpc5IXJLk1ye1JPjPuNs60Ef5tPynJx5J8pfX5dbPRzpmS5ANJdiW5bR/rZ/6zq6oW5IPBhfJ/AJ4CHAZ8BTh50jYvBa5j8D2PM4GbZrvdPff3LODotvxL87m/o/Z5aLtPAn8DvHK22z2Gv+ejGMx+cGJ7fdxst3sMfX478J/b8mLg28Bhs932A+jz84FnAbftY/2Mf3Yt5DOL/z+FSFX9CHhkCpFhq4GrauCLwFFJThh3Q2dIZ3+r6gtV9X/ayy8y+D7LfDbK3zHAG4G/BHaNs3E9GaXP/xb4aFXdC1BV873fo/S5gCdmMMfIExiExZ7xNnPmVNVnGfRhX2b8s2shh8USYHgayYlW299t5ov97cv5DP5nMp919jnJEuAVwHvG2K4+jfL3/HPA0Uk+neSWJGvH1rp+jNLnPwWezuDLvNuAN1XVj8fTvFkx459d8+J7Fj0ZZQqRkaYZmSdG7kuSFzIIi1/stUX9G6XP7wTeVlUPz/rEdjNjlD4vAk4HzgaOAG5M8sWq+l99N64no/T5HOBW4EXAzwI3JPlcVX2v57bNlhn/7FrIYTHKFCIH0zQjI/Ulyb8C3gf8UlU9MKa29WWUPq8Erm5BcSzw0iR7quqvxtLCmTfqv+tvVdUPgB8k+SzwDGC+hsUofX4dcEkNBvS3J7kH+Hng5vE0cexm/LNrIQ9DjTKFyGZgbbuz4Ezgu1V137gbOkM6+5vkROCjwGvn8f8yh3X2uapOqqrlVbUc+Ajw6/M4KGC0f9fXAs9LsijJTwG/ANw55nbOpFH6fC+DMymSHM9gVuqvj7WV4zXjn10L9syi9jGFSJJfa+vfw+DumJcC24F/YvC/k3lpxP7+R+BngMva/7T31DyesXPEPh9URulzVd2Z5Hrgq8CPGfzy5JS3YM4HI/49/yFwZZJtDIZo3lZV83bq8iQfAl4AHJtkAvh94HHQ32eX031Ikjot5GEoSdKIDAtJUifDQpLUybCQJHUyLCRJnQwLSVInw0LzSptG/NYktyX5i/alssn1jyU5amiftyT5Wlv3lenmQmrzJd3VjnVrklcmWT55Kugk70jylrZ85eSpzafaZ5r3vDLJPe39/j7Jc4bWvSvJN5MckuTxrR//cmj97yR5T3u/SvKHQ+uOTfLPSf50qM3fHOrbrUmOymC68kryy0P7frzVr2nbbU/y3aH9zhqlbzp4GBaab35YVadV1anAj4Bfm6L+beBCgPbFrBcDZ7R1z2fqeXOGvaYd67Sq+kg/3djLW6vqNOAi4L0ASQ5hMMnhDuD5VfV/gTcz+NJkMpgE8Q3Axe0YXwdeNnTM84DJv1X/x0N9O62qvtPqE8DvTm5UVb2itev1wOeG9vvCgXZY84thofnsc8BTp6jfyKMzbL6dwRQe3wOoqu9W1cYxte+x+CyP9umFwG3A5cCrAarqeuA+YC3wx8A7hqaV/yFwZ5JHvnX/KmDTiO/7FeC7SV58wD3QQcmw0LyUZBGDH2jaNql+KIM5gDYneSLwxKr6h/08/AeHhlt+ZmZaPLJf5tE+vRr4EHAN8LIkj2v1NwMbgMVV9eeT9r8aWJNkKfAwe08e95tDffvUpHX/Cfi9memGDjaGheabI5LcCmxlMDnc+yfVHwCOAW5gMNz0WOazGR6GemCaY8zkXDn/pbV/PXB+mxDvpcBftbOim4CXAFTVTga/7Hf5FMe5nsGw26uBD0+xfngY6oXDK6rqcwBJnjczXdLBZMFOJKh564dtDH3KepInAR8HLqyqS5P8IMlTqupAZhh9ADh6Uu0Y4J4DOOZkbx2+PpLk5cCTgG1tUsefYjAh3F+3TX7cHj+hqn6U5Bbgt4FTGJyp7I8NDK5dzNtfkVM/PLPQQaWqvgv8B+Atbdjmj4B3JzkSIMmRSdbv5zG/D9yX5JEpro8BVgGfn9HG/6RXA68fmj79JOAlj9z91eG/MZhVdb9/j6Sq/pZBMD5jf/fVwc2w0EGnqr7M4ILtGgZDNZ8CvtRuZf0Mg/+h76+1wO+1oaJPAn8w6VrIe5NMtMeNrfa0odpEkvNGeaMWCOfw6FkE7YeKPs8IZwpVdfs0F/GHr1ncmmT5FNtsYP7//rpmmFOUS5I6eWYhSerkBW4tSEmuYXAdYNjbquoTPb7nu4HnTiq/q6r+rK/3lGaKw1CSpE4OQ0mSOhkWkqROhoUkqZNhIUnq9P8ANQyVR5JywlQAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for col in df:\n",
+    "    plt.figure()\n",
+    "    sns.histplot(df[col])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "1fb22bc6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Correlation between different fearures')"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1224x1224 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "correlation = df.corr()\n",
+    "plt.figure(figsize=(17,17))\n",
+    "sns.heatmap(correlation, vmax=1, square=True,annot=True,cmap='cubehelix')\n",
+    "\n",
+    "plt.title('Correlation between different fearures')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "82883ccc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler\n",
+    "df_std = StandardScaler().fit_transform(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "e150e382",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Cumulative explained variance')"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.decomposition import PCA\n",
+    "pca = PCA().fit(df_std)\n",
+    "plt.plot(np.cumsum(pca.explained_variance_ratio_))\n",
+    "plt.xlim(0,17,1)\n",
+    "plt.xlabel('Number of components')\n",
+    "plt.ylabel('Cumulative explained variance')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "6a3a942c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.decomposition import PCA \n",
+    "sklearn_pca = PCA(n_components=7)\n",
+    "Y_sklearn = sklearn_pca.fit_transform(df_std)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "f4e6187f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[-0.88512191 -2.48301693  0.23090242 ...  0.04336989 -0.38202562\n",
+      "  -0.35720617]\n",
+      " [-3.00034342  2.01508943 -0.16533426 ...  1.67093843 -0.28801526\n",
+      "   0.94274938]\n",
+      " [ 1.19172624  0.38517395 -1.92678896 ... -0.55010278 -0.23006842\n",
+      "   0.52287556]\n",
+      " ...\n",
+      " [ 0.10596162 -3.06675754  1.18931984 ... -2.96585047  1.26333337\n",
+      "   1.97973232]\n",
+      " [-2.84716017 -2.51797947 -0.29519488 ... -2.99036079  0.69668999\n",
+      "   1.77427724]\n",
+      " [-0.16460436 -0.5243077  -1.64424995 ... -4.69253162  1.53231934\n",
+      "   0.09281539]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(Y_sklearn)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "44f2459b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(8950, 7)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Y_sklearn.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "bb8b8a5b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df=pd.DataFrame(Y_sklearn,columns=['PC1','PC2','PC3','PC4','PC5','PC6','PC7'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "6bb4406e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PC1</th>\n",
+       "      <th>PC2</th>\n",
+       "      <th>PC3</th>\n",
+       "      <th>PC4</th>\n",
+       "      <th>PC5</th>\n",
+       "      <th>PC6</th>\n",
+       "      <th>PC7</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-0.885122</td>\n",
+       "      <td>-2.483017</td>\n",
+       "      <td>0.230902</td>\n",
+       "      <td>0.807129</td>\n",
+       "      <td>0.043370</td>\n",
+       "      <td>-0.382026</td>\n",
+       "      <td>-0.357206</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-3.000343</td>\n",
+       "      <td>2.015089</td>\n",
+       "      <td>-0.165334</td>\n",
+       "      <td>-1.087171</td>\n",
+       "      <td>1.670938</td>\n",
+       "      <td>-0.288015</td>\n",
+       "      <td>0.942749</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.191726</td>\n",
+       "      <td>0.385174</td>\n",
+       "      <td>-1.926789</td>\n",
+       "      <td>1.859338</td>\n",
+       "      <td>-0.550103</td>\n",
+       "      <td>-0.230068</td>\n",
+       "      <td>0.522876</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-0.794805</td>\n",
+       "      <td>0.218433</td>\n",
+       "      <td>-1.661542</td>\n",
+       "      <td>1.195618</td>\n",
+       "      <td>0.058950</td>\n",
+       "      <td>0.798510</td>\n",
+       "      <td>-0.086756</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-1.265058</td>\n",
+       "      <td>-1.593317</td>\n",
+       "      <td>-0.689436</td>\n",
+       "      <td>1.339644</td>\n",
+       "      <td>-0.114019</td>\n",
+       "      <td>-0.837737</td>\n",
+       "      <td>0.231600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8945</th>\n",
+       "      <td>0.668484</td>\n",
+       "      <td>-2.871696</td>\n",
+       "      <td>1.452469</td>\n",
+       "      <td>-2.236975</td>\n",
+       "      <td>-2.854943</td>\n",
+       "      <td>0.766981</td>\n",
+       "      <td>2.343375</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8946</th>\n",
+       "      <td>0.262604</td>\n",
+       "      <td>-2.240280</td>\n",
+       "      <td>1.844972</td>\n",
+       "      <td>-0.568118</td>\n",
+       "      <td>-3.339266</td>\n",
+       "      <td>1.706628</td>\n",
+       "      <td>1.774529</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8947</th>\n",
+       "      <td>0.105962</td>\n",
+       "      <td>-3.066758</td>\n",
+       "      <td>1.189320</td>\n",
+       "      <td>-1.775107</td>\n",
+       "      <td>-2.965850</td>\n",
+       "      <td>1.263333</td>\n",
+       "      <td>1.979732</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8948</th>\n",
+       "      <td>-2.847160</td>\n",
+       "      <td>-2.517979</td>\n",
+       "      <td>-0.295195</td>\n",
+       "      <td>-2.148352</td>\n",
+       "      <td>-2.990361</td>\n",
+       "      <td>0.696690</td>\n",
+       "      <td>1.774277</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8949</th>\n",
+       "      <td>-0.164604</td>\n",
+       "      <td>-0.524308</td>\n",
+       "      <td>-1.644250</td>\n",
+       "      <td>-1.315554</td>\n",
+       "      <td>-4.692532</td>\n",
+       "      <td>1.532319</td>\n",
+       "      <td>0.092815</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>8950 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           PC1       PC2       PC3       PC4       PC5       PC6       PC7\n",
+       "0    -0.885122 -2.483017  0.230902  0.807129  0.043370 -0.382026 -0.357206\n",
+       "1    -3.000343  2.015089 -0.165334 -1.087171  1.670938 -0.288015  0.942749\n",
+       "2     1.191726  0.385174 -1.926789  1.859338 -0.550103 -0.230068  0.522876\n",
+       "3    -0.794805  0.218433 -1.661542  1.195618  0.058950  0.798510 -0.086756\n",
+       "4    -1.265058 -1.593317 -0.689436  1.339644 -0.114019 -0.837737  0.231600\n",
+       "...        ...       ...       ...       ...       ...       ...       ...\n",
+       "8945  0.668484 -2.871696  1.452469 -2.236975 -2.854943  0.766981  2.343375\n",
+       "8946  0.262604 -2.240280  1.844972 -0.568118 -3.339266  1.706628  1.774529\n",
+       "8947  0.105962 -3.066758  1.189320 -1.775107 -2.965850  1.263333  1.979732\n",
+       "8948 -2.847160 -2.517979 -0.295195 -2.148352 -2.990361  0.696690  1.774277\n",
+       "8949 -0.164604 -0.524308 -1.644250 -1.315554 -4.692532  1.532319  0.092815\n",
+       "\n",
+       "[8950 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "e3c952eb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "wcss = [] \n",
+    "m = range(1,11)\n",
+    "for i in m: \n",
+    "    kmeans = KMeans(n_clusters = i, init = 'k-means++', random_state = 42)\n",
+    "    kmeans.fit(df) \n",
+    "    wcss.append(kmeans.inertia_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "356a0afe",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(m,wcss,'bx-')\n",
+    "plt.plot(range(1, 11), wcss)\n",
+    "plt.xlabel('PC')\n",
+    "plt.ylabel('Inertia') \n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "db9726c6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "X = df[[\"PC1\", \"PC2\",\"PC3\",\"PC4\",\"PC5\",\"PC6\",\"PC7\"]]\n",
+    "plt.scatter(X[\"PC1\"], X[\"PC2\"], c=\"m\")\n",
+    "plt.xlabel(\"PC1\")\n",
+    "plt.ylabel(\"PC2\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "aac290b7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "K=3\n",
+    "\n",
+    "Centroids = (X.sample(n=K))\n",
+    "plt.scatter(X[\"PC1\"], X[\"PC2\"], c=\"y\")\n",
+    "plt.scatter(Centroids[\"PC1\"], Centroids[\"PC2\"], c=\"red\")\n",
+    "plt.xlabel(\"PC1\")\n",
+    "plt.ylabel(\"PC2\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "dc503960",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PC1</th>\n",
+       "      <th>PC2</th>\n",
+       "      <th>PC3</th>\n",
+       "      <th>PC4</th>\n",
+       "      <th>PC5</th>\n",
+       "      <th>PC6</th>\n",
+       "      <th>PC7</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>7399</th>\n",
+       "      <td>0.603924</td>\n",
+       "      <td>-2.836092</td>\n",
+       "      <td>0.567445</td>\n",
+       "      <td>-1.301385</td>\n",
+       "      <td>1.141140</td>\n",
+       "      <td>-0.246224</td>\n",
+       "      <td>0.104236</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7573</th>\n",
+       "      <td>-1.089214</td>\n",
+       "      <td>-2.436550</td>\n",
+       "      <td>-1.705584</td>\n",
+       "      <td>0.399184</td>\n",
+       "      <td>0.913203</td>\n",
+       "      <td>1.305055</td>\n",
+       "      <td>-0.857026</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1042</th>\n",
+       "      <td>-1.260052</td>\n",
+       "      <td>1.162674</td>\n",
+       "      <td>1.187329</td>\n",
+       "      <td>1.260699</td>\n",
+       "      <td>0.428170</td>\n",
+       "      <td>0.327633</td>\n",
+       "      <td>0.966585</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           PC1       PC2       PC3       PC4       PC5       PC6       PC7\n",
+       "7399  0.603924 -2.836092  0.567445 -1.301385  1.141140 -0.246224  0.104236\n",
+       "7573 -1.089214 -2.436550 -1.705584  0.399184  0.913203  1.305055 -0.857026\n",
+       "1042 -1.260052  1.162674  1.187329  1.260699  0.428170  0.327633  0.966585"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Centroids"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "a23db718",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.783716560094868\n",
+      "0.2777828784681925\n",
+      "0.12630699466929887\n",
+      "0.15419909005545476\n",
+      "0.1920261263912584\n",
+      "0.25113517413302006\n",
+      "0.36608646705129794\n",
+      "0.3510967441454697\n",
+      "0.363597742343025\n",
+      "0.2655010926877184\n",
+      "0.16723936650169546\n",
+      "0.10233341134743755\n",
+      "0.09420165033060114\n",
+      "0.05713076777382271\n",
+      "0.03746854274397527\n",
+      "0.024473834526929996\n",
+      "0.022228298448357765\n",
+      "0.016623241606870228\n",
+      "0.006266589186782384\n",
+      "0.005518544633962164\n",
+      "0.0059137446813048306\n",
+      "0.005043910213152325\n",
+      "0.005754658626990507\n",
+      "0.0020179893491221665\n",
+      "0.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "diff = 1\n",
+    "j=0\n",
+    "\n",
+    "while(diff!=0):\n",
+    "    XD=X\n",
+    "    i=1\n",
+    "    for index1, row_c in Centroids.iterrows():\n",
+    "        ED=[]\n",
+    "        for index2, row_d in XD.iterrows():\n",
+    "            d1 = (row_c[\"PC1\"]-row_d[\"PC1\"])**2\n",
+    "            d2 = (row_c[\"PC2\"]-row_d[\"PC2\"])**2\n",
+    "            d3 = (row_c[\"PC3\"]-row_d[\"PC3\"])**2\n",
+    "            d4 = (row_c[\"PC4\"]-row_d[\"PC4\"])**2\n",
+    "            d5 = (row_c[\"PC5\"]-row_d[\"PC5\"])**2\n",
+    "            d6 = (row_c[\"PC6\"]-row_d[\"PC6\"])**2\n",
+    "            d7 = (row_c[\"PC7\"]-row_d[\"PC7\"])**2\n",
+    "            d = sqrt(d1+d2+d3+d4+d5+d6+d7)\n",
+    "            ED.append(d)\n",
+    "        X[i] = ED\n",
+    "        i = i+1\n",
+    "    \n",
+    "    C = []\n",
+    "    for index, row in X.iterrows():\n",
+    "        min_dist=row[1]\n",
+    "        pos=1\n",
+    "        for i in range(K):\n",
+    "            if row[i+1] < min_dist:\n",
+    "                min_dist = row[i+1]\n",
+    "                pos = i+1\n",
+    "        C.append(pos)\n",
+    "    X[\"Cluster\"]=C\n",
+    "    Centroids_new = X.groupby([\"Cluster\"]).mean()[[\"PC1\", \"PC2\",\"PC3\",\"PC4\",\"PC5\",\"PC6\",\"PC7\"]]\n",
+    "    if j == 0:\n",
+    "        diff = 1\n",
+    "        j = j+1\n",
+    "    else:\n",
+    "        diff = (Centroids_new['PC1'] - Centroids['PC1']).sum() + (Centroids_new['PC2'] - Centroids['PC2']).sum() + (Centroids_new['PC3'] - Centroids['PC3']).sum() + (Centroids_new['PC4'] - Centroids['PC4']).sum() +(Centroids_new['PC5'] - Centroids['PC5']).sum()+ (Centroids_new['PC6'] - Centroids['PC6']).sum()+(Centroids_new['PC7'] - Centroids['PC7']).sum()\n",
+    "        print(diff.sum())\n",
+    "    Centroids = X.groupby([\"Cluster\"]).mean()[[\"PC1\",\"PC2\",\"PC3\",\"PC4\",\"PC5\",\"PC6\",\"PC7\"]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "db9b4083",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PC1</th>\n",
+       "      <th>PC2</th>\n",
+       "      <th>PC3</th>\n",
+       "      <th>PC4</th>\n",
+       "      <th>PC5</th>\n",
+       "      <th>PC6</th>\n",
+       "      <th>PC7</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>Cluster</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-0.885122</td>\n",
+       "      <td>-2.483017</td>\n",
+       "      <td>0.230902</td>\n",
+       "      <td>0.807129</td>\n",
+       "      <td>0.043370</td>\n",
+       "      <td>-0.382026</td>\n",
+       "      <td>-0.357206</td>\n",
+       "      <td>5.717627</td>\n",
+       "      <td>1.987937</td>\n",
+       "      <td>3.911431</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-3.000343</td>\n",
+       "      <td>2.015089</td>\n",
+       "      <td>-0.165334</td>\n",
+       "      <td>-1.087171</td>\n",
+       "      <td>1.670938</td>\n",
+       "      <td>-0.288015</td>\n",
+       "      <td>0.942749</td>\n",
+       "      <td>6.413887</td>\n",
+       "      <td>5.502300</td>\n",
+       "      <td>2.453188</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.191726</td>\n",
+       "      <td>0.385174</td>\n",
+       "      <td>-1.926789</td>\n",
+       "      <td>1.859338</td>\n",
+       "      <td>-0.550103</td>\n",
+       "      <td>-0.230068</td>\n",
+       "      <td>0.522876</td>\n",
+       "      <td>3.366650</td>\n",
+       "      <td>3.467866</td>\n",
+       "      <td>4.701523</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-0.794805</td>\n",
+       "      <td>0.218433</td>\n",
+       "      <td>-1.661542</td>\n",
+       "      <td>1.195618</td>\n",
+       "      <td>0.058950</td>\n",
+       "      <td>0.798510</td>\n",
+       "      <td>-0.086756</td>\n",
+       "      <td>4.447815</td>\n",
+       "      <td>3.141441</td>\n",
+       "      <td>2.955205</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-1.265058</td>\n",
+       "      <td>-1.593317</td>\n",
+       "      <td>-0.689436</td>\n",
+       "      <td>1.339644</td>\n",
+       "      <td>-0.114019</td>\n",
+       "      <td>-0.837737</td>\n",
+       "      <td>0.231600</td>\n",
+       "      <td>5.556942</td>\n",
+       "      <td>2.555326</td>\n",
+       "      <td>3.316609</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8945</th>\n",
+       "      <td>0.668484</td>\n",
+       "      <td>-2.871696</td>\n",
+       "      <td>1.452469</td>\n",
+       "      <td>-2.236975</td>\n",
+       "      <td>-2.854943</td>\n",
+       "      <td>0.766981</td>\n",
+       "      <td>2.343375</td>\n",
+       "      <td>6.891655</td>\n",
+       "      <td>4.841445</td>\n",
+       "      <td>6.715344</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8946</th>\n",
+       "      <td>0.262604</td>\n",
+       "      <td>-2.240280</td>\n",
+       "      <td>1.844972</td>\n",
+       "      <td>-0.568118</td>\n",
+       "      <td>-3.339266</td>\n",
+       "      <td>1.706628</td>\n",
+       "      <td>1.774529</td>\n",
+       "      <td>6.604518</td>\n",
+       "      <td>4.608522</td>\n",
+       "      <td>6.223211</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8947</th>\n",
+       "      <td>0.105962</td>\n",
+       "      <td>-3.066758</td>\n",
+       "      <td>1.189320</td>\n",
+       "      <td>-1.775107</td>\n",
+       "      <td>-2.965850</td>\n",
+       "      <td>1.263333</td>\n",
+       "      <td>1.979732</td>\n",
+       "      <td>7.025480</td>\n",
+       "      <td>4.658972</td>\n",
+       "      <td>6.416311</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8948</th>\n",
+       "      <td>-2.847160</td>\n",
+       "      <td>-2.517979</td>\n",
+       "      <td>-0.295195</td>\n",
+       "      <td>-2.148352</td>\n",
+       "      <td>-2.990361</td>\n",
+       "      <td>0.696690</td>\n",
+       "      <td>1.774277</td>\n",
+       "      <td>8.220182</td>\n",
+       "      <td>5.554943</td>\n",
+       "      <td>5.340378</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8949</th>\n",
+       "      <td>-0.164604</td>\n",
+       "      <td>-0.524308</td>\n",
+       "      <td>-1.644250</td>\n",
+       "      <td>-1.315554</td>\n",
+       "      <td>-4.692532</td>\n",
+       "      <td>1.532319</td>\n",
+       "      <td>0.092815</td>\n",
+       "      <td>6.404796</td>\n",
+       "      <td>5.613511</td>\n",
+       "      <td>6.016475</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>8950 rows × 11 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           PC1       PC2       PC3       PC4       PC5       PC6       PC7  \\\n",
+       "0    -0.885122 -2.483017  0.230902  0.807129  0.043370 -0.382026 -0.357206   \n",
+       "1    -3.000343  2.015089 -0.165334 -1.087171  1.670938 -0.288015  0.942749   \n",
+       "2     1.191726  0.385174 -1.926789  1.859338 -0.550103 -0.230068  0.522876   \n",
+       "3    -0.794805  0.218433 -1.661542  1.195618  0.058950  0.798510 -0.086756   \n",
+       "4    -1.265058 -1.593317 -0.689436  1.339644 -0.114019 -0.837737  0.231600   \n",
+       "...        ...       ...       ...       ...       ...       ...       ...   \n",
+       "8945  0.668484 -2.871696  1.452469 -2.236975 -2.854943  0.766981  2.343375   \n",
+       "8946  0.262604 -2.240280  1.844972 -0.568118 -3.339266  1.706628  1.774529   \n",
+       "8947  0.105962 -3.066758  1.189320 -1.775107 -2.965850  1.263333  1.979732   \n",
+       "8948 -2.847160 -2.517979 -0.295195 -2.148352 -2.990361  0.696690  1.774277   \n",
+       "8949 -0.164604 -0.524308 -1.644250 -1.315554 -4.692532  1.532319  0.092815   \n",
+       "\n",
+       "             1         2         3  Cluster  \n",
+       "0     5.717627  1.987937  3.911431        2  \n",
+       "1     6.413887  5.502300  2.453188        3  \n",
+       "2     3.366650  3.467866  4.701523        1  \n",
+       "3     4.447815  3.141441  2.955205        3  \n",
+       "4     5.556942  2.555326  3.316609        2  \n",
+       "...        ...       ...       ...      ...  \n",
+       "8945  6.891655  4.841445  6.715344        2  \n",
+       "8946  6.604518  4.608522  6.223211        2  \n",
+       "8947  7.025480  4.658972  6.416311        2  \n",
+       "8948  8.220182  5.554943  5.340378        3  \n",
+       "8949  6.404796  5.613511  6.016475        2  \n",
+       "\n",
+       "[8950 rows x 11 columns]"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "1bba3fc5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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O2grZN21v22+i65X6SMS+oyH7RmXutALv08jRXba5+3v4gevu7Zt0/TrA3e4nX6bmk6pj/7I3ZRp8SOOCw722sDz7uOBrry2sl9kdvN6wYe0x/mzpfuccFBEwrUtvOs3/StRvM+zuoqE3AEkxgDVKTtcrxLiWdf+wfP6onnmW0Uhw8fao7xzW8/fLXfjfvxV4H3rMU6pcEv8gxrUg/fb8XbQ06aHdLik/qJxG2UYprLRB5KzURUEm/a9kjUtVzcAVydAagG7F3tR0M23TtjFV3ez6xgRaK/DdWxmOC2Y57UhzPx6KfxijWndeej9+/36a7ybatCn7sb2mh/q98iSjEpfT368i62dk4Zpe146ok4urKIbSAKTN/Gn1IVQWV1EZLfg/3c85EvdLmBsQ1/wHstXqTQFH9f7Sunr8B39uLnm/7sJ3adw1ceeL+jw4kS6rUUyryOrS048izb3LcpyNAAbcALQiTtTq2q+flMm80i3zasGefKHXWtG+3EB5tpER1W3bsgVj08Yems21vv3umjxZJ5aFuYdgbUXVokYA/SjCKowQ4u5d1mytOhm+PBhKA5A286fVh1BVcv+U3h5T58o/rnX33oMPfpbYQ1zwNiobKi57KGlGcFExgF5dIVVSoFH3Lk22lmsD5pKhNACtiBO1uvbrJwZQtRFAqW1FnSv5rK2XeQtjY73X9wle01c8aRRxEb76XkcAZbtQ4r5jFvfYMLl4khhKA5A2BuDv29LowmlltzKuH5WqmrqtqDcKyNkVlLYGUJVblJspLlZRtMLqtSdfZhA1ScYsI7dhCvImMZQGQDX7pKvgca4CvM2Crz+iKTN90rafam5GwHd1uFDaYS6jXlucm6nZrPaqat2UOQJIulaexfyGiaE1AP1StCHo7oWP6KqRahV43bxdV80n+y8L0Wxmm1mbd8t63aRFUbK4K9IUn3NFmTGAXtxjRc2PGCTMAPRBUQYgakUvvwZZreILfaSFBpXgtm39n6eMNjoaL2uWtNSgkkta+Uu1mIBm0jnLCqL2OtoY9iBvEj0bgM7avS8J2X5e0rF5N1cjgCK+TJyPf7Rz7V4MT99+/V5bxTOC8mxpM4nSLAGZ5jpr/h9jeuO9KsEqZflUSZZBoicDALwTeAS4F7gf+MXAZ/fEHVtEc2EAWv0KHdJEk0tHzKXYp18Zcj1nBgNQ1wBvt2JNcvH4rqC4GcBJrbvnW0RcoWoTpaw3nz+9GoB7gbM7r18LfAe4uPP+W3HHFtFcGIC8FWVDo10/fhvVEiZy5d1SuoB6TanMq4X5i9Mo5+CEMF8pJY0A0irvLIYnqyFJs1SnlUoYfKIMwAjxjKrqowCq+g3gTcCMiPweoAnHpkJERkXkWyJyRx7ny5uJnM+3HTiWsM80cDDn6xZOSoFFYGUln0uOJP33hvDRj8LTT8PmzZ4srRZccQU0GtHHjI/DE0/A0pKnGpeWYHoaLrgg+rhGA3btWrtt167460QRvOaZZ2Y79siR5H0mIv7Jo7Ybg0PSI/SkiLzEf9MxBm8ELgJekZMMVwEP5HSu3NkF9PDMRnJryv160G3uOAbsTLfr8eP5XHLTJtiwobdjVeGppzzFv7gIu3fD7KxnDESg2fSabyBOPx1OnFh7juVl75jt2719AEZHvb+tlvdZuw3z8zA56RmrmZm1+2dledn7221E0hqVoCyTk9578AxZGFHbyyBKViNnwoYFfgN+HnhpyPYxoB13bJoGvBDYB7wZuCNpf1dZQNuSBBvWVtBEsKQWXPylnzbqR9u7yJKKGixslyVg208Z7M2bV687Ouq5taLk9V1AvcjiMgZggeB8occYwEuBN4Rs/xVCMoOyNuCvgNfgjSpCDQCeR2QBWJiYmCj0JkUxmvRFhrk9FK48imq95NjHtbxWJAtTUnGKNc+FcPzJc1FrECTJUrUYQNUM0iDQqwG4g5B0T2AKuD3u2KQGvBXY3XkdaQCCzdUIoOcvOQwtJvhbRK1/v0eb10IyIyP5zgAOKqkkxZqX4Qkalaw1dOJKVIcp3DIydKpmkAaBXg3AgZjP9scdm9SA/wA8DCwCPwKWgdi0e1cGwEYAMa3k/P+0tfxdNl9Bxs2MDpaTzvO+RJF1NJJ2DgKszuDOCxsB5E+vBuDBXj7L2qo+Anh+kmDD3I6GP6xlKNm8FpPJs2Ut/5BX2YukdM8kJZ+mZx83WsnTR28xgPyJMgBJySbfFJHLuzeKyPuAuxOOHQiuxJsJZ0RwOnBJtkNOO63/yy4twbFjq5k3VUDEU1fdRMl44kS6NM00PPFEfKZMu+1lJjWbq9s2boSvftXLsrnsMm/brbd6mVHt9vpzHIxJ9V1e9rKc8sCX1c/KCmZVGfkiGvYf638o8lzg08BxVhX+FDAOvENVf1S4hAGmpqZ0YWGhzEuyAThV6hVriAJLeKmgex3LUkGiDEPetFqe8o5ift6bS+Cnk4bRaKxXtvPznnJfWoq/fp5zPIx8EZG7VXWqe3vsCEBVf6yqvwz8CZ6vfhH4E1V9fdnKvyzmgUm8GzOJKf9UCN7NuonMo4FBQiR8e1kTqpaW4vPmZ2bilT+s78n7RiNJ+UP2SWpBLO/fEWF+Ib8BzwKuBj4MvB/YELd/0a3oGEA/K4NZ67SH1Lkf3mWL8l27KnGdpXaR34IB5SxZSmnKToRhPv/ioccYwB48l89+4C3AhwqzRBVgBi8VyeiDIS8f0D2jeOPGVf+6C/wSEvPz6Uciwf3i/P7dPP54Ntl8wkYmecYUjGiSDMA5qnqpqt4I/DbwqyXI5Iza1d+pIjkFNeuIiOc7X1z0gqlPP+0FeVXzC/b2gq9M09YiCpaAyOK+6tXVFWVkshgfozeSDMAzFVBU9WTBsjhnyDuv+dAE/ptrIdzQaKz6sK+6Ktnf3u91snDw4PrsmqjspDvvXH0dZjTGx2FsbL1M3cXvfJL8+1aMziFhfiG/4cVAn+i0J4GTgddPxB1bRLMYQE3aKS29PtCwNJHe1kwO88+nnXEbNkcguK3ZjF7JLI1/32IAxYMtCZmO7kXkd2gNa/NXoT2kmZWUtXSt1eq9hERQQUcFprPMuE1S3mln9doiMMViBqBPiloacmBbDmsEWwtvfkG8XovJRRWPg3SriAWVdVS5C1/BW12fahBlAGpVdt4lbWpWo981QxwMDuL72/NkYmKtPx+yXWN52VsYp3uNA/DWPoibcRucF6AKpyImyvgBXPPvVxvTaRl4k2sBjNqxuOjNju11EZhuRkZWg61+xpGqt7hNHoYmKZUzzWQy8BbsgfAgclzA2CgXMwAZeNC1AHWimbzLoCOymvlywQVe9ky/rKx4GUbBjJr5edizxzME/RLVM/czedLMCAZvxbUrr7S6PlUnthZQ1XBRCyjICOSzEPIwsAi82LUQ1aKImkCNhjfZLI95BmF1gCBdDaEwRkfh5MAnj9eDnmoBGWtrA9nNSoniLSVkrKGIvtbycrLybzbTuaD8CWPdefpp3T7dRMUHjOpgOi2Gebz1KJfwdJr9P6dEgMsZ6sJwVaHRgOuvTz8LeGkJLr0Uzjpr1RD0OiO3SqW6jXDMAEQwD2zHagP1zGnADa6FGA42bQpX7s3mqksnay/+yBGvhpEfxwgjKeg8PZ3+eoYbzACEMA+8F+vx900Pi8UY0WzeHN6rPn4ctm9fG2idm4PDh1f9+b304n2XVZgrp9GAN7853AiIwI4dsHt39msa5eLMAIjIi0Tk70TkARG5X0SuciVLN1fhrYBj9IkA17oWYjAQ8VZSC1PGJ0549Xv8InTguXE2bPCOm5zsr1a/z8jI2kyeBx8Mj2tMTJjyrwvOsoBE5GzgbFW9R0ROx1tx7O2q+vdRx5SVBZTzvJ3hZgUwX3DhiHjKPypbJ68MpOA5RkbCz2krg1WPymUBqeqjqnpP5/WTwAPAC1zJYxSIuYEKZ2Ii3s9fRD/PZvnWn0rEAERkEvgF4Oshn02LyIKILBw6dKgUeWwOU46MYG6gElhaSj9Jq1eaXQ+GzfKtP84NgIhsBv4auFpVn+j+XFVnVXVKVae2bt1aikzXA2OJexmpsR5h7Rkb89JJg9gs3/rj1ACIyBie8p9X1dtcyhKkDdyMua5zQ6i0G+gS5nmISU4xwkNMcgm2InmQVgtuvjlcsfv1iFZWvL+m/OvFBlcXFhEBPg48oKp/7kqOKNqAw6VcBwsBPvwwHPjfsL9aGuIS5rmJaTZ1ZnxMssRNeAnse6mWrC5otTzFbgwmLkcAb8DTsW8WkXs77YKkg8rEPBc58uznw4XTcG61etfXMvOM8vfZxDLXMjwrkjcaXt6++fOHD5dZQP9HvYW3zlPVV3XanclHlscuIMXseSMNRw/C+DJsq5ZinSB8hlTU9rqQds1g32+/e7f584cRZy6gOuD/78/g1QMyeuTkz2DfTu/1lmop1oNMMBny6x6s+fgvTR6+yFr3TrttCn/YcJ4FVHXaeJWNc1rPY3jwV/87dgg++x44sNfbfrRainUnuzjWNc47RoOdDL7vI4/ZwUa9MQOQEnMHZUQElg/Dh56zqvxPjsHYU3DNCFw9WYl4wF7aXM4si7RYQVikxeXMWgDYGApsQZgMzAOXOrt6DVGFf9/pYyigIzAS8E0osNyEL1xfueygYcBKNgwPlSsFUUdMRfXAKzsTAIS1yt/ftulIJbODhgEr2WCYAchA2Srqkvl5Hpqc5NTICA9NTnJJ91JNVUcEtqWoA1HB7KBBx1I8DbAsoNT4awSUxSXz89w0Pc2mTnWvyaUlbuqssLG3TqkaW1J2MyuWHTTItFqe8q/Tv5FRDDYCSMkM5a4RcO3MzDPK32fT8jLXztSsp3w0pWKvWHbQoOLP7DXlb4CNAFJT9jyAiYglnKK2V5JTJ1bz/+NQ4LsXeJlBWw56xmBfxz+xbWbtNgsW94y5fYxuzACkZARvbZOyODgxwWRIfd+DdYjcqcLxJ+GOK1ZTQGP3B1770dWVeM5YgosvXZs1dMaSFywGMwIp2bzZK+F88KAX8DW3j9GNuYBSUna23M5duzjWVZzlWKPBzqp34Y4fg9va8B+3pFf+I6xfhi0sa8iCxZk4dswqdRrxmAGoKHvbbS6fnWWx1WJFhMVWi8tnZ6sdAF45Bbdfnk7x+2Rdf9OCxampw2DRcIu5gFLSBI6UfM297Xa1FX6Qkz9bW/KhKILB4nPnhz5G4K/SdaTrn9P8/UYabASQkuuBcddCVBFV+MliOcpfgWcd9kpJ/Luz4KL3erEB0U7c4DJ4y5XFylAhxse9VboOH4a5OavkaWTHSkFk4ErgBjw9ZHT4ySJc/2LXUqyiwDd2wOd3u5akUEZG4JOfNCVvpMNKQeTAnZjyX8PxY+nSPMtE8DKKrpHKFJwrAlP+Rh64XhP4fBH5rog8KCIfdClLGiz8GEA1e8C3LKTTzljy3EQDZgSaTVP+Rj44MwAiMgp8BHgLcA5wiYic40qeNFhSRYCjS9VU/t1sOA7nX+VaitxoNDy/v2HkgcsRwGuBB1X1H1T1OPAp4CKH8iRiawJ0UK2e6yeORtn5W8VhwV0jT1ymgb4A+MfA+4eBX3IkSyr85247cMqlIC5RhW98pB69/yDXjMByZwmsxuO1TBvdts2Uv5EvLg1A2BSgdTFWEZkGpgEmHM5smccrCHcQOBP4f5Q/O9g5p07CZ95dP+UvAOqtPeBTs9IS55wDX/qSaymMQcOlC+hh4EWB9y8EHuneSVVnVXVKVae2bt1amnBB5vEs0BKehTpC9gmsA8HISP2Ufxw1KC0xOurl+N9/v2tJjEHEpQH4JvAyEXmxiIwD7wI+51CeSGaA5a5tQ+kCSlvauU5sWapEltCGDTA2tnZbowF79pjbxygOZwZAVU8Cvwv8T+AB4C9UtZL9nAFUe9mpYs5/Hghw0Xu8mcWOFqtvNuGWW+Dmm202r1EuTmsBqeqdePOrKs0E5a8H4BxV+NmTcNpmr+e/b+dguX+CbDgBGzrxgZJjA2NjXlqnr+hN4RtlYqUgUuDHALrdQAOLrsA3dsMX/o1rSdxxrAnXHS7lUv4qXYZRFFYKog/awCzQci1IWcgIvPytrqVwS+NIaa6gOi3yZgwWZgBS0gYWgTmGZDJY2sXcBxWhtAwhq9tvuMLWA8iI76KdYcDjAoOY8ZOVLUteUbmVURg5BUdbPU0ee9az4GMf815PT8NywJdodfsNl5gB6IF2p40woNVBBzXjJyv+ZI/RTtKvX1wOUhuBDRs85R8M7s7M2Dq9RjWwIHAfTDIgo4BTJ+CnP4FGc/AzfvIgY4DYgryGa6KCwDYC6INd1Dw7SBWOPwl3XGEKPwsZi8tZkNeoKmYA+sAfub+bmtYFWj4MH3qOaynqyTUjqQvKWZDXqCqWBZQDtVT+x4/BFwanTn6pCKvrEF84/Uy66LZtXlA3iAV5jSpjBqBPql1KLAR/EfeqruZVNzoF5Xbs8Kp1zs5aOQejPlgQuE9qlQl06gR8ZrsTxX/JfXDtPpg4Cge3wM5tsPe80sXoDyW0DKwgrFxTy3GgMSRYELggalEnSBVO/hQ+9z5nyv+m22HTCe/95FHvPdTQCIQwMeyT5ozaYi6gPqncMpHdIzpd8VbwurbhzOVz7b5V5e+z6YS3vVYsN+H42l9bTjbYtc2c/EY9MQPQJ36doFHXgoAX2P3GRzwfv654f2+71HlRt4mj2bZXkXFpMPa318Pts/CTFqggR1tc8fxZ2ueak9+oJ+YCygH/8b8Mh/EA1dXAbsWqeB7c4rl9wrZXlebGJgCPP/04E1smvF7+S9reLN7r2zaL1xgIzADkRBv4KnADjozA0aXKZvXs3LY2BgBwbMzbXiU2jW3iqZ1PRe9wril8Y7AwF1CO7AaucHFh1UrX7tl7Hlx+ISxu8eZMLG7x3lctAHzjhTe6FsEwSsXJCEBErgMuBI4DPwDeo6o/cSFL3jhZ3uz4U5Xt/fvsPa96Cj9Ic2PTfPnG0OFqBHAX8EpVPQ/4HvCHjuTIHSdlX8Y3rdvk+7CNZBpjDa5/y/WuxTCM0nFiAFT1i51F4QG+BrzQhRxF4CQjvKt2f2tLi8MfOExry9CsYZaZ5sYmgtDa0mL2QsvkMYaTKgSB3wv8j6gPRWQar+gmEzWoqrWLkrOBQmr3Lx1dYn7/PLu27WL69mmWT9S2XmkhbBrbxOEPlLPer2FUmcJGACLyJRE5ENIuCuwzA5zEW3c9FFWdVdUpVZ3aunVrUeLmRpsSlX8w9bOL6dunAdj+89vLkqYWjMqoBXsNo0NhIwBV/fW4z0VkO/BWYJvWqSBRClqUWB4iIvi7fGKZqz5/FU+ffLosSSpPa0uLXdt2mbvHMDq4ygI6H/gD4NdUdeD8E7uA9wAnknbsl+V4N8aRp7MtXDKI7Jjawe7f2u1aDMOoJK6ygD4MnA7cJSL3isgNjuQohDZwM1BoHo6q1fNPgSl/w4jGyQhAVV/q4rpl4i8cDwWsHazq1fxJyP1vbmwO9SjAsqAMIx6bCVwCuc4NWDkFt7UT6/00NzZ55yvemeeVa0VjzKp0GkYSZgBKILfk1ZM/hU9fltjzb4w1eOcr3snH7vlYXleuBaPi1WS13H7DSEcV5gEMPBcAH83jRD97MlH5+5kuM/tmOLFSeBjaKRtkA7e84xZT9IbRI2YASiC3+kCN+LDy3MVzzyjDy267LK+rVg5L5zSMfDAXUAlkigGowqmT4Z8djT7T5vHNaxTioC5TODYyZsrfMHLCDEAJpFbFuuIFeD/zbq/EQ5CQkg8+46Pj3PDWtZm0gxoAPbFygpl9M67FMIyBwAxACWRaN/jAXq/dfvnapR27Sj4IAnjukE9c9Il1PeJB7iEfjBkJGYaRHosBlICvimdImA8QVGy+IQghceUq4Mq/uTKLiLViUN1bhlE2NgIoiTawiFcobo6QEUGMi6ebpOqe8/vn+ehCLnlHlcPy+w0jP8wAOKANzOIVjUOV0ScehtsvRw58KtXxST3gQfWRW36/YeSLGQBHPDMiEOHkz70Q/Rf/nVsvvpXWltYzC5XsmNpBY2ztWCFND3jQfOTjo+PMXTzH4tWLpvwNI0csBlAh2ue21ym4N0y8gZl9Mxw8epCJLROpUiAntkywdLS0gtS5MsII7596P3d+/85M39kwjOxInUrxT01N6cLCgmsxKs/8/nkuve1S12JkZtPYJm688EZT9oaRMyJyt6pOdW83F9AA0j63zTlnneNajNQ0NzaZu3iOp3Y+ZcrfMErEDMCAcv+/vp/TRk9zLUYsgrBjageHP3DYFL9hOMAMwADz8Ys+vi6IXBVaW1rcevGttmCLYTjEaRBYRH4fuA7Yqqrx6xsamfF71cEg8uHlwxw7cSzhyGJpbWmxePWiUxkMw3A4AhCRFwG/Qc7rpRhraZ/bZvHqRVauWWHx6kVuvPBGxkbGYo8RhLmL5zKNHkZllB1TOxJX4bKJXIZRHVy6gP4L8AG8ybFGSbTPbXPz22+OVdQTWyZon9tm9sLZZ/bzF1tpbmyuMyCNsQZ73rGH3b+1m8WrFyPPPSqjNpHLMCqEEwMgIm8Dfqiq306x77SILIjIwqFDh0qQbvDxRwVhvfxgD93fT69RTv7RSfQa5fAHDj9jQPwJa91Kfde2XaHn3fOOPab8DaNCFDYPQES+BDwv5KMZYCfwm6p6VEQWgak0MQCbB5A/8/vnM080c3lewzCyEzUPoPSJYCJyLrAP8CuavRB4BHitqv4o7lgzAIZhGNmJMgClZwGp6n7gOf77LCMAwzAMIz9sHoBhGMaQ4rwYnKpOupbBMAxjGLERgGEYxpBiBsAwDGNIqVU5aBE5RMKyuj1wFlD1ALTJmA8mY37UQU6TcZWWqm7t3lgrA1AEIrIQlh5VJUzGfDAZ86MOcpqMyZgLyDAMY0gxA2AYhjGkmAGAWdcCpMBkzAeTMT/qIKfJmMDQxwAMwzCGFRsBGIZhDClmAAzDMIaUoTMAIvLHIvJDEbm30y6I2O98EfmuiDwoIh8sWcbrROQ7InKfiHxaRM6I2G9RRPZ3vkcpZVKT7ot4/NfO5/eJyKvLkCtw/ReJyN+JyAMicr+IXBWyzxtF5Gjgf+CPypSxI0Psb1eB+/jywP25V0SeEJGru/Zxch9F5BMi8piIHAhsO1NE7hKR73f+Pjvi2FKe6wgZq/dcq+pQNeCPgd9P2GcU+AHwT4Bx4NvAOSXK+JvAhs7rPwP+LGK/ReCsEuVKvC/ABcDnAQFeB3y95N/3bODVndenA98LkfGNwB0u/v/S/nau72PI7/4jvMlEzu8j8KvAq4EDgW3/Cfhg5/UHw56ZMp/rCBkr91wP3QggJa8FHlTVf1DV48CngIvKuriqflFVT3befg1vzYQqkOa+XAR8Uj2+BpwhImeXJaCqPqqq93RePwk8ALygrOvniNP72MU24Aeqmvcs/J5Q1S8Dj3dtvgjY03m9B3h7yKGlPddhMlbxuR5WA/C7nWHYJyKGii8A/jHw/mHcKZH34vUEw1DgiyJyt4hMlyBLmvtSmXsnIpPALwBfD/n49SLybRH5vIi8olzJgOTfrjL3EXgXsDfiM9f30ee5qvooeJ0AAmuOBKjSPa3Ec+28HHQRJCxH+VHgT/Fu8p8C/xnvx1hzipBjc82XjZNRVT/b2WcGOAnMR5zmDar6iIg8B7hLRL7T6XkURZr7Uvi9S4OIbAb+GrhaVZ/o+vgePHfGU50Y0GeAl5UsYtJvV5X7OA68DfjDkI+rcB+zUJV7WpnneiANgKr+epr9ROQm4I6Qjx4GXhR47y9bmRtJMorIduCtwDbtOAZDzvFI5+9jIvJpvCFukQYgzX0p/N4lISJjeMp/XlVv6/48aBBU9U4R2S0iZ2mJq9Kl+O2c38cObwHuUdUfd39QhfsY4McicraqPtpxlT0Wso/ze1q153roXEBdftR3AAdCdvsm8DIReXGnB/Qu4HNlyAdepgLwB8DbVHU5Yp9NInK6/xovwBT2XfIkzX35HPDuThbL64Cj/tC8DEREgI8DD6jqn0fs87zOfojIa/GegyMlypjmt3N6HwNcQoT7x/V97OJzwPbO6+3AZ0P2see6mzIizVVqwK3AfuA+vB//7M725wN3Bva7AC+D5Ad4bpkyZXwQz1d5b6fd0C0jXibDtzvt/rJkDLsvwBXAFZ3XAnyk8/l+vPWey7x3/wxvWH9f4P5d0CXj73bu2bfxgnG/XLKMob9dle5jR4YGnkLfEtjm/D7iGaRHgRN4vfr3AU1gH/D9zt8zO/s6ea4jZKzcc22lIAzDMIaUoXMBGYZhGB5mAAzDMIYUMwCGYRhDihkAwzCMIcUMgGEYxpBiBsAwUiAipzrVGQ+IyF+KSKOz/Xki8ikR+YGI/L2I3Cki/7Tz2RdE5CciEjbZ0DCcYwbAMNLxtKq+SlVfCRwHruhMgvo08L9U9SWqeg6wE3hu55jrgMvciGsYyZgBMIzsfAV4KfAm4ISq3uB/oKr3qupXOq/3AU+6EdEwkjEDYBgZEJENePVx9gOvBO52K5Fh9I4ZAMNIx0YRuRdYAA7i1RsyjFozkNVADaMAnlbVVwU3iMj9wG+7Eccw+sdGAIbRO38LnCYil/sbROQXReTXHMpkGKkxA2AYPaJeJcV3AL/RSQO9H2/N6UcAROQrwF8C20TkYRH5586ENYwQrBqoYRjGkGIjAMMwjCHFDIBhGMaQYgbAMAxjSDEDYBiGMaSYATAMwxhSzAAYhmEMKWYADMMwhpT/DxPqcsrpKJyPAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "color=['blue','green','cyan']\n",
+    "for k in range(K):\n",
+    "    data=X[X[\"Cluster\"]==k+1]\n",
+    "    plt.scatter(data[\"PC1\"],data[\"PC2\"],c=color[k])\n",
+    "plt.scatter(Centroids[\"PC1\"],Centroids[\"PC2\"],c='red')\n",
+    "plt.xlabel('PC1')\n",
+    "plt.ylabel('PC2')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1bd70e58",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 64f1eca9a11a6779d8cdf921bd6a4ad6d437ac12 Mon Sep 17 00:00:00 2001
From: vishalh21 <vishalh21@iitk.ac.in>
Date: Thu, 16 Jun 2022 22:58:58 +0530
Subject: [PATCH 02/14] Assignment 1

---
 .../211175_Vishal_1}/SnT FaC assignment 1.ipynb                   | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename {211175_Vishal_1 => Assignment 1/211175_Vishal_1}/SnT FaC assignment 1.ipynb (100%)

diff --git a/211175_Vishal_1/SnT FaC assignment 1.ipynb b/Assignment 1/211175_Vishal_1/SnT FaC assignment 1.ipynb
similarity index 100%
rename from 211175_Vishal_1/SnT FaC assignment 1.ipynb
rename to Assignment 1/211175_Vishal_1/SnT FaC assignment 1.ipynb

From b57db364917e735cce076c6daf5f08b90b81d1db Mon Sep 17 00:00:00 2001
From: vishalh21 <vishalh21@iitk.ac.in>
Date: Thu, 16 Jun 2022 23:23:00 +0530
Subject: [PATCH 03/14] Assignment 1

---
 .../SnT FaC assignment 1.ipynb => 211175_Vishal_1.ipynb}          | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename Assignment 1/{211175_Vishal_1/SnT FaC assignment 1.ipynb => 211175_Vishal_1.ipynb} (100%)

diff --git a/Assignment 1/211175_Vishal_1/SnT FaC assignment 1.ipynb b/Assignment 1/211175_Vishal_1.ipynb
similarity index 100%
rename from Assignment 1/211175_Vishal_1/SnT FaC assignment 1.ipynb
rename to Assignment 1/211175_Vishal_1.ipynb

From e966fc3044572b8908ade9a5cc9a3d5a79605da2 Mon Sep 17 00:00:00 2001
From: vishalh21 <106445212+vishalh21@users.noreply.github.com>
Date: Thu, 23 Jun 2022 20:03:22 +0530
Subject: [PATCH 04/14] Add files via upload

---
 Assignment 1/211175_Vishal_1.ipynb | 1021 +++++++++++++++++-----------
 1 file changed, 616 insertions(+), 405 deletions(-)

diff --git a/Assignment 1/211175_Vishal_1.ipynb b/Assignment 1/211175_Vishal_1.ipynb
index 3583f91..68a4ecd 100644
--- a/Assignment 1/211175_Vishal_1.ipynb	
+++ b/Assignment 1/211175_Vishal_1.ipynb	
@@ -524,14 +524,72 @@
   {
    "cell_type": "code",
    "execution_count": 8,
+   "id": "71282e13",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 8950 entries, 0 to 8949\n",
+      "Data columns (total 17 columns):\n",
+      " #   Column                            Non-Null Count  Dtype  \n",
+      "---  ------                            --------------  -----  \n",
+      " 0   BALANCE                           8950 non-null   float64\n",
+      " 1   BALANCE_FREQUENCY                 8950 non-null   float64\n",
+      " 2   PURCHASES                         8950 non-null   float64\n",
+      " 3   ONEOFF_PURCHASES                  8950 non-null   float64\n",
+      " 4   INSTALLMENTS_PURCHASES            8950 non-null   float64\n",
+      " 5   CASH_ADVANCE                      8950 non-null   float64\n",
+      " 6   PURCHASES_FREQUENCY               8950 non-null   float64\n",
+      " 7   ONEOFF_PURCHASES_FREQUENCY        8950 non-null   float64\n",
+      " 8   PURCHASES_INSTALLMENTS_FREQUENCY  8950 non-null   float64\n",
+      " 9   CASH_ADVANCE_FREQUENCY            8950 non-null   float64\n",
+      " 10  CASH_ADVANCE_TRX                  8950 non-null   int64  \n",
+      " 11  PURCHASES_TRX                     8950 non-null   int64  \n",
+      " 12  CREDIT_LIMIT                      8950 non-null   float64\n",
+      " 13  PAYMENTS                          8950 non-null   float64\n",
+      " 14  MINIMUM_PAYMENTS                  8950 non-null   float64\n",
+      " 15  PRC_FULL_PAYMENT                  8950 non-null   float64\n",
+      " 16  TENURE                            8950 non-null   int64  \n",
+      "dtypes: float64(14), int64(3)\n",
+      "memory usage: 1.2 MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
    "id": "ce4036bf",
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\himma\\AppData\\Local\\Temp\\ipykernel_17708\\868331368.py:2: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  plt.figure()\n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 432x288 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -541,9 +599,18 @@
     },
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 432x288 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -553,9 +620,18 @@
     },
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 432x288 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -565,9 +641,18 @@
     },
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 432x288 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -577,9 +662,18 @@
     },
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 432x288 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -589,9 +683,18 @@
     },
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 432x288 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -601,9 +704,18 @@
     },
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 432x288 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -613,9 +725,18 @@
     },
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 432x288 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -625,9 +746,18 @@
     },
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 432x288 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -637,9 +767,18 @@
     },
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 432x288 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -649,9 +788,18 @@
     },
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 432x288 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -661,9 +809,18 @@
     },
     {
      "data": {
-      "image/png": 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s0nNVlFdISZpJDItttOHxR3njx2/kqScfJ3N23WJ+b5jMnz+fJFNcoSTtOIbFdpiz25489cSsMTu7u2Eya/ZsLnvDS0liaEiatuyz6KM5u+3JLvP2YP369fZhSJrWDIsdYKJ7L7rz7MOQNJ0ZFjvARGNH9d6X0Xt5rZfVSppO7LPYQbr3Xox1lNGdt379et70iZsB7MeQNK14ZLGDjXeHdzdEdpm3xxb9GA5zLmnYGRZ9MNYd3mOdqur2Y4w1zLkBImmYGBZTaPSpqu69GCMjI8yZt8fT+jTWrVvHa87/J+69916qatO87lAiBomkqWSfxQB0T1XN3nX3TTf2MXsu999/P3/5uR9QVZz3B88hCX9y4XV88s/+CIBlF1zFR097if0ekqbctDmySHJckruTrEpyzqDr2V7do4w5u+3JnJ6n8T1jl3kENp2ymrXr7k87+oDOsCJzdt2d+++/f9PRx8MPP8zDDz/8tKON8U5leVWWpK01LY4skswC/h54ObAa+FaSq6vqzsFWtmP19nV0p3uHFWH23E1XWo11dLLhySf52PLfY/78+UCnL+QNF32Vj572kk1t3VNg/+OKb1NVfOikF7L33ntvOjLphsfoz6PbkrBgwQKSbAqfXvPnz99Ua/fIZ/QQKN0aAfbee2/Wr1//tHaPmKThMS3CAjgCWFVVPwRIcgVwAtCXsHjysUd5xi6PdH4BP/7o5vZfPL1taz9v6zrdAQofH1nLGz96L3s/85BOeNAJk+78DU/8gte990p2W7CQp375OI8/+ghz5u22RRuwaRuve++VAFvMH/15dNucXXfjkrP+K/Pnz2dkZIRTzv8Uc/fah6d++TgbN27kA69/MX/+qVsANoVVN7y6bcCmz+951eG89eP/xkVnHg/AaR/+HBedefym8JA0OQsWLOjLdjMdTkMkeRVwXFX99+bzycBvVdWZo5ZbDixvPv46cPc2fuW+wE+2cd2pNF3qhOlT63SpE6ZPrdOlTpg+tfazzmdV1cLRjdPlyGKscxFbpFxVXQhcuN1flqysqqXbu51+my51wvSpdbrUCdOn1ulSJ0yfWgdR53Tp4F4NHNTz+UDggQHVIkk7nekSFt8CliQ5JMkuwDLg6gHXJEk7jWlxGqqqNiQ5E/gCMAu4uKru6ONXbveprCkyXeqE6VPrdKkTpk+t06VOmD61Tnmd06KDW5I0WNPlNJQkaYAMC0lSK8Oix7APKZLk3iS3JbklycqmbZ8k1yT5fvPenztyJq7r4iRrk9ze0zZuXUnObfbx3UmOHYJa35Xkx81+vSXJ8YOuNclBSb6c5K4kdyQ5q2kfuv06Qa1DtV+T7JrkxiTfaep8d9M+jPt0vFoHt0+7I5ru7C86Hec/AH4V2AX4DnDooOsaVeO9wL6j2t4LnNNMnwO8ZwB1vRh4IXB7W13Aoc2+nQsc0uzzWQOu9V3A28dYdmC1AgcAL2ym9wS+19QzdPt1glqHar/SuV9rj2Z6DvBN4Mgh3afj1TqwfeqRxWabhhSpql8C3SFFht0JwKXN9KXAiVNdQFVdDzw8qnm8uk4ArqiqJ6rqHmAVnX0/JcapdTwDq7Wq1lTVzc30I8BdwCKGcL9OUOt4BlJrdfy8+TineRXDuU/Hq3U8fa/VsNhsEfCjns+rmfgf/CAU8MUkNzVDmwDsX1VroPOfFthvYNU93Xh1Det+PjPJrc1pqu5piKGoNcli4AV0/roc6v06qlYYsv2aZFaSW4C1wDVVNbT7dJxaYUD71LDYbFJDigzYUVX1QuAVwBlJXjzogrbBMO7nFcCzgcOBNcAFTfvAa02yB/Bp4M1V9bOJFh2jbdC1Dt1+raqNVXU4nVEgjkhy2ASLD3SfjlPrwPapYbHZ0A8pUlUPNO9rgc/SOcx8MMkBAM372sFV+DTj1TV0+7mqHmz+Yz4FfIzNh+8DrTXJHDq/fC+vqs80zUO5X8eqdVj3a1PbCPAV4DiGdJ929dY6yH1qWGw21EOKJNk9yZ7daeD3gdvp1Hhqs9ipwFWDqXAL49V1NbAsydwkhwBLgBsHUN8m3V8UjVfS2a8wwFqTBLgIuKuq3tcza+j263i1Dtt+TbIwyfxmeh7wMuC7DOc+HbPWge7TqejZny4v4Hg6V3L8AHjHoOsZVduv0rna4TvAHd36gF8BrgW+37zvM4DaPkHnkPhJOn/hnDZRXcA7mn18N/CKIaj148BtwK3Nf7oDBl0r8Lt0TiPcCtzSvI4fxv06Qa1DtV+B3wC+3dRzO/BXTfsw7tPxah3YPnW4D0lSK09DSZJaGRaSpFaGhSSplWEhSWplWEiSWhkWkqRWhoVmlCQbm6Gbb0/yySS7JVmcniHJm+XeleTtzfQlSe5p1vtOkmN6ljsiyfXNsM/fTfIPzTZfn+TDo7b5lSRLez6/IEmNHi46yTuaYadvbb7zt3rWv7tn+OlPNe2/3sy7JZ1hwMd8pGaSY3vW/XnPti5LcnSS9Um+3fwc5/es99YkF/V8fm2Sf9mW/a+Za1o8g1vaCo9VZzwdklwOvAH4zIRrdJxdVZ9K8lI6zzdekmR/4JPAsqq6oblT+b/RGYZ7Mk4Cvt68f6Gp6beBP6QzpPcTSfalMyR+12urauWo7XwQeH9VXdVs4/ljfVlVfaHne75CZyjr7nNPjga+VlV/2NwR/O0kn62qf2u2vzLJUXRu+Pxr4Jgtv0E7M8NCM9nX6NwJuzVuYPNonWcAl1bVDdAZNhro/rU/4UaaYHkV8HLga0l2rarH6Tz74SdV9USzzZ9MoqYD6NxtTrPObVvzA41WVY81o5kuaj5vSPJG4CN0hoi4uKp+uD3foZnH01CakZLMpjM679b+Yj0O+Kdm+jDgpgmWfU3PaZ9bgKU9844C7qmqH9AZBK77RLMvAgcl+V6SjyR5yahtXt6zzb9t2t4PXJfk80ne0h0zaFulM6z1EuD6bltV/Tud51C8jM7DgKSnMSw008xrfnGvBO6nM8DdeGPa9Lb/bZIfAv8H+J+T/K5/rKrDu6/mO7tOovMALZr3kwCq80CbFwHLgYeAf0zy+p71XtuzzbObdf438Fw6p8SOBr6RZO4ka+z1X5LcCvwH8P+q6j+6M9IZXnwpnYfsLNyGbWuGMyw00zzW88v2TdV56uFPgdHPJt8H6D0FdDbwHOAv2fzUtDvo/GLfKklm0enb+Ksk9wIfAl6RZtTg6gwx/ZWqeidwZrPshKrqgaq6uKpOADbQOerZWl+rqt8Ang+cnuTwnnnvphOU59E5kpGexrDQjNf8Nb+me5VTkn3onG76+qjlngI+ADyjuYLpw8Cp3auVmnVfl+Q/tXzly4DvVNVBVbW4qp5F51kPJzZXNi3pWfZw4L6JNpbkuHSeF0Hz3b8C/Ljt5x5PVX0P+F/AnzfbfD7wB8B76HTuPyvJy7d1+5qZ7ODWzuIU4O+TdJ8s9u6mP+FpqqqS/DXwZ1V1TJJlwPlJ9gOeonOev+3qqpPoPJyq16eB04E7gQ81/Q4b6DwreXnPcpcneayZ/klVvYzOs0s+kOTxpv3s3lNI2+ijwNubZx+sAN7SdMDTdHZfluTw5shMcohySVI7T0NJklp5Gkqahpo+lfeMar6nql45iHo083kaSpLUytNQkqRWhoUkqZVhIUlqZVhIklr9f1Jqr2C3yQzQAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 432x288 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -673,9 +830,18 @@
     },
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 432x288 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -685,9 +851,18 @@
     },
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 432x288 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -697,9 +872,18 @@
     },
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 432x288 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -709,9 +893,18 @@
     },
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 432x288 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -721,9 +914,18 @@
     },
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 432x288 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -735,12 +937,12 @@
    "source": [
     "for col in df:\n",
     "    plt.figure()\n",
-    "    sns.histplot(df[col])"
+    "    sns.displot(df[col])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "id": "8aab7983",
    "metadata": {},
    "outputs": [],
@@ -751,7 +953,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "id": "ab99bfbc",
    "metadata": {},
    "outputs": [
@@ -778,7 +980,7 @@
        "dtype: float64"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -789,15 +991,200 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "id": "6bfd5aea",
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\himma\\AppData\\Local\\Temp\\ipykernel_17708\\868331368.py:2: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  plt.figure()\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 432x288 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 432x288 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 432x288 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 432x288 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 432x288 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 432x288 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 432x288 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 432x288 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 432x288 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
     {
      "data": {
-      "image/png": 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\n",
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smOUYA75G0pY0RDE+1VqBPaU2XanWmqZ71yuStERSh6SO7u7uWvfbzKymhjuAlwFnAjOBfcCNqV5pXDf6qVcUEcsjoj0i2ltaWobYVTOz+hrWAI6I/RFxJCKOAp8FZqWXuoCJpaZtwN5Ub6tQNzNresMawGlMt8fFQM8ZEmuBBZLGSppCcbBtU0TsAw5Kmp3OflgE3D2cfTYzq5fj67VgSV8CzgNOk9QFfBw4T9JMimGEJ4ErASJiu6Q1wA7gMHB1RBxJi7qK4oyKccC69DAza3p1C+CIWFih/Pl+2i8FllaodwAzatg1M7OG4CvhzMwycQCbmWXiADYzy8QBbGaWiQPYzCwTB7CZWSYOYDOzTBzAZmaZOIDNzDJxAJuZZeIANjPLxAFsZpaJA9jMLBMHsJlZJg5gM7NMHMBmZpk4gM3MMnEAm5ll4gA2M8vEAWxmlokD2MwsEwewmVkmDmAzs0wcwGZmmTiAzcwycQCbmWXiADYzy8QBbGaWSd0CWNLtkg5I2laqnSppvaQn0s/xpdeul9QpaZekOaX62ZK2ptdukaR69dnMbDjVcw94BTC3V+06YENETAU2pOdImgYsAKaneW6TNCbNswxYAkxNj97LNDNrSnUL4Ih4CPhpr/I8YGWaXgnML9VXR8ShiNgNdAKzJE0ATo6IjRERwKrSPGZmTW24x4DPiIh9AOnn6aneCuwptetKtdY03btekaQlkjokdXR3d9e042ZmtdYoB+EqjetGP/WKImJ5RLRHRHtLS0vNOmdmVg/DHcD707AC6eeBVO8CJpbatQF7U72tQt3MrOkNdwCvBRan6cXA3aX6AkljJU2hONi2KQ1THJQ0O539sKg0j5lZUzu+XguW9CXgPOA0SV3Ax4FPAmskXQE8BVwKEBHbJa0BdgCHgasj4kha1FUUZ1SMA9alh5lZ06tbAEfEwj5eurCP9kuBpRXqHcCMGnbNzKwhNMpBODOzUccBbGaWiQPYzCwTB7CZWSYOYDOzTBzAZmaZOIDNzDJxAJuZZeIANjPLxAFsZpaJA9jMLBMHsJlZJg5gM7NMHMBmZpk4gM3MMnEAm5ll4gA2M8vEAWxmlokD2MwsEwewmVkmDmAzs0wcwGZmmTiAzawuWidOQtKQH60TJ+XelLo5PncHzGxk2tu1h8s+870hL+fOK8+pQW8ak/eAzcwycQCbmWXiADYzy8QBbGaWSZYAlvSkpK2SNkvqSLVTJa2X9ET6Ob7U/npJnZJ2SZqTo89mZrWWcw/4/IiYGRHt6fl1wIaImApsSM+RNA1YAEwH5gK3SRqTo8NmZrXUSEMQ84CVaXolML9UXx0RhyJiN9AJzBr+7pmZ1VauAA7gXyU9KmlJqp0REfsA0s/TU70V2FOatyvVzMyaWq4LMc6NiL2STgfWS3q8n7aqUIuKDYswXwIwadLIvXrGzEaGLHvAEbE3/TwAfI1iSGG/pAkA6eeB1LwLmFiavQ3Y28dyl0dEe0S0t7S01Kv7ZmY1MewBLOk/SDqpZxp4G7ANWAssTs0WA3en6bXAAkljJU0BpgKbhrfXZma1l2MI4gzga5J61v/FiLhf0g+ANZKuAJ4CLgWIiO2S1gA7gMPA1RFxJEO/zcxqatgDOCJ+DLyhQv1Z4MI+5lkKLK1z18zMhlUjnYZmNmr51o2jk29HadYAfOvG0cl7wGZmmXgP2Mwa23HHkw7ajzgOYDNrbEcPj9jhGQ9BmJll4gA2M8vEAWxNx6ds9SONl/q9aQ4eA7am41O2+jGCx0tHIu8BW1VqsdfpvSqzF/MesFWlFnud3qtqIiP41K9G4gA2G4LWiZPY27Vn4IbNpgZDGf6FOzAHsI1eNdrL85irHSsHsI1e3suzzHwQzswsEwewmVkmDmAzs0wcwGZmmTiAzcwycQCbmWXiADYzy8QBbGaWiQPYzCwTB7CZWSYOYDOzTBzAZmaZOIDNzDJxAJuZZeIANjPLpGkCWNJcSbskdUq6Lnd/zMyGqikCWNIY4J+BtwPTgIWSpuXtlZnZ0DTLN2LMAjoj4scAklYD84AdWXtlg+MvejR7EUVE7j4MSNIlwNyI+M/p+eXAmyPiml7tlgBL0tPXArsGuarTgGeG2N1G421qDt6m5nCs2/RMRMztXWyWPeBKu00v+c0REcuB5ce8EqkjItqPdf5G5G1qDt6m5lDrbWqKMWCgC5hYet4G7M3UFzOzmmiWAP4BMFXSFEkvAxYAazP3ycxsSJpiCCIiDku6BvgmMAa4PSK212FVxzx80cC8Tc3B29QcarpNTXEQzsxsJGqWIQgzsxHHAWxmlsmoDOCBLmtW4Zb0+hZJb8zRz8GoYpvek7Zli6TvSXpDjn4ORrWXn0t6k6Qj6XzxhlbNNkk6T9JmSdslfWe4+zhYVXz2fkPSPZIeS9v0pzn6WS1Jt0s6IGlbH6/XLh8iYlQ9KA7i/Qh4FfAy4DFgWq82FwHrKM4/ng08krvfNdimc4DxafrtI2GbSu2+DdwHXJK73zX4dzqF4grPSen56bn7XYNt+gjwd2m6Bfgp8LLcfe9nm/4T8EZgWx+v1ywfRuMe8L9f1hwRvwZ6LmsumwesisL3gVMkTRjujg7CgNsUEd+LiP+bnn6f4lzqRlbNvxPA+4CvAgeGs3PHqJpt+mPgroh4CiAiGn27qtmmAE5ScR36iRQBfHh4u1m9iHiIoo99qVk+jMYAbgX2lJ53pdpg2zSSwfb3Corf4I1swG2S1ApcDHx6GPs1FNX8O70GGC/pQUmPSlo0bL07NtVs063A6yguntoKvD8ijg5P9+qiZvnQFOcB11g1lzVXdelzA6m6v5LOpwjg36trj4aumm26GfhwRBxpkpv8VLNNxwNnAxcC44CNkr4fEf9W784do2q2aQ6wGbgAOBNYL+m7EfFcnftWLzXLh9EYwNVc1txslz5X1V9Jrwc+B7w9Ip4dpr4dq2q2qR1YncL3NOAiSYcj4uvD0sPBq/az90xE/AL4haSHgDcAjRrA1WzTnwKfjGIAtVPSbuC3gU3D08Waq10+5B7wzjDAfjzwY2AKLxw0mN6rzTt48SD7ptz9rsE2TQI6gXNy97dW29Sr/Qoa/yBcNf9OrwM2pLavALYBM3L3fYjbtAy4IU2fATwNnJa77wNs12T6PghXs3wYdXvA0cdlzZL+PL3+aYoj6hdRBNYvKX6DN6wqt+mvgd8Ebkt7jIejge9UVeU2NZVqtikidkq6H9gCHAU+FxEVT4dqBFX+O/03YIWkrRSh9eGIaNjbVEr6EnAecJqkLuDjwAlQ+3zwpchmZpmMxrMgzMwaggPYzCwTB7CZWSYOYDOzTBzAZmaZOIDNzDJxADeIdDvFzZK2SfqypFdImtz7lniSbpD0l2l6haTdab7HJF1YajdL0kPpNoGPS/pcWuZ7Jd3aa5kPSmovPT9LUkia06vdR9PtBLekdb65NP+uVNss6Sup/tr02mZJOyX1+XUu6RaM96bp90o6mq7c63l9m6TJafrPJG1N/dgmaZ6kf07r2SHpV6W+XJLmuVvSxr7ey1715yvUbkjvyatLtQ+mWnt6/mTqV8+6byn9Oz0taWx6flpq+zultj8t/Vt+S9JxKm55uC0t8weSpvTz/vVe9znp89PzXuyQtErSCaX3++el9pslvTW91pberyck/VjSraW+9/v5Gex7UFrGayTdp+IWjzslrZH0+5L+TdK4UrtvSFrQ1/vQbEbdhRgN7FcRMRNA0h3AnwN3VTHftRHxFRX3eFhO8eWlZwBfBhZExEZJAt4FnFRlXxYCD6ef30x9+l3gncAbI+KQpNMornzq8Z6I6Oi1nFuAmyLi7rSM36ly/VBc7vlR4LJyUVJbqr8xIn4u6USgpbSOycC9Pe9lqp1CcXvB5yVNiYjdg+hH2VaKL4T92/T8EopbR5ad38dFBkeAP6O4KgyAiNgKzEx9XJH63fPLayHwSuD1EXE0bfcvBujfi9ad3osfRcRMSWOA9cC7gTtSk+9GxDvLC0iflbuAZRExL823HPh74P0DrL9iP0pe8h6kdb4c+AbwoYi4J9XOB55Nffko8DFJ84ETImJ1lf1oeN4DbkzfBV49YKsX28gLd2S6GlgZERsBovCViNg/0ELSf8BLgPcCb0v/OQAmUNyj4FBa5jMRMdD17xMogpQ0z9ZBbM+9wHRJr+1VPx04CDyflvl8FYH6LuAeilslDmXv6eukWy1KehXwc6C7ynlvBj4oqdqdngnAvkh3DYuIrnjhdqKDFhFHKO69MNBduy4A/l9EfKE03weBRemX3VDcTOX34I+BjT3hm9b7QLoC8G+ASyXNBD5J8dkeMRzADSZ9ON9Osbc1GHMpAgJgBvBoP20vK//pSXFTmx7nArsj4kfAgxSXXAL8KzAx/Ul4m6Tf77XMO0rL/IdUuwn4tqR16c/1UwaxPUcp9ro+0qv+GLAf2C3pC5L+oIplLQS+lB4LB9GH3p4D9kiakZZzZ4U2D5Tehw+W6k9R/FVxeZXrWgP8QVrOjZLOqmKennU/0vuF9Iv0zcD9pfJbeg1BnAlMp9dnJ4q7lj1J9TsFg30P+vy8RsQvgb8EHgJWR8QTVfahKXgIonGMS2EIxR7w5yn2giopXz/+D5L+nmLPcHaV67ozIq7peSLpwdJrCyn2FEk/L6e4Qfjzks4G3gKcD9wp6bqIWJHavmQIIiK+IOmbFL8c5gFXSnpDz150Fb4IfLQ89hnFrSfnAm+iuGXjTZLOjogbKi0gDce8Gng4IkLSYUkzhnB/hZ696Dlp/b3vA9DXn98A/x1YS/Hndr8ioivt/V+QHhskXRoRG/qZrdK6z0yfq6nAVyJiS+m1voYgKt2foOcWjH3du6Bcr8l78O8LjrhH0s+A26qdp1l4D7hx/CoiZqbH+6L4doFngfG92p0KlD/c11IEzMeAlam2neKesoOSxvveBfx1OkDyT8DbJZ0ERfhFxIMR8XHgmtS2XxGxNyJuj4h5FN+CMKPa/kTEYeBG4MO96hERmyLif1CEYX/9uIziPdydtmkyQxuGuIfil9JTMcj72UZEJ8V9cd9dZftDEbEuIq6lCK75g+sqkMaAKT4jsyX94QDtt/Piv4iQdDLFXcx2Ud1nsk99vAfVfF6PpseI4gBuYBHxPLBP6ewGSadS7E0+3KvdUeB/AsepOHPhVmCx0lkKad4/kfRbA6zyrcBjETExIiZHxH+k+Lqf+SrOaJhaajsT+El/C1PxZY09R91/i+JubE8PtN29rEj9aknLeaVe/CWIA/VjITA3bc9kiv/oxxzAEfEril8IS49xEUsp/qTul6Q3Snplmj4OeD0DvN/9iYh9wHXA9QM03QC8QumbONIv5RuBW9O2/wA4t+ezpOLsh7G8+BsiBtL7PfgicI6kd/QU0mdnMAdtm5IDuPEtojgCvJniyyc/kcZnXySK29r9LfBX6WDbAuBTKk4P20kxdDDQHttC4Gu9al+lOEhyIrBSxelMW4BpwA2lduUx4G+l2tuAbZIeozib4tqI+D/Vbnjarl9TnE1xeiqdkLbr8fSeXEYfR+fTWQCTKL4Dr2d5u4HnSr+cPiapq+eRaq8o1yR9qFefVkfED/vocnn8c1WF7dkO9DVv2enAPSpOQ9xC8dfDrf3PMqCvU2zbW9Lz3mPAl6TP0cXAJZKeoNjjPRoRS1P/91O83/el9/9mYGG8+CuGBvUepGB/J/A+Fae+7aA4CNzo34c3ZL4dpZn1SdI5FAcv/ygi+juwa8fAAWxmlonPgrBhlcao/65XeXdEXJyjP80mnWI2tlf58kGeY20NwnvAZmaZ+CCcmVkmDmAzs0wcwGZmmTiAzcwy+f/ALcv42DeloAAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 360x360 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -807,21 +1194,18 @@
     },
     {
      "data": {
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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 432x288 with 0 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 360x360 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -831,21 +1215,18 @@
     },
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 432x288 with 0 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 360x360 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -855,21 +1236,18 @@
     },
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 432x288 with 0 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 360x360 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -879,21 +1257,18 @@
     },
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 432x288 with 0 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 360x360 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -903,21 +1278,18 @@
     },
     {
      "data": {
-      "image/png": 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TktcOmZ4kb++nX5PkiQtR52xG6MPz+9qvSfL5JIctRJ0trX4MzPekJHf3595MnVH6keTpSa5KsjHJpyddY8sIf1MPS/KRJFf3ffidhaizJcn7ktyR5Kvbmb4YPt+tPkzu811VS/JGt6P8G8CjgN2Bq4GDZ8zzHOBjdOd5HAF8caHr3oE+HAns3d9/9rT1YdR+DMz3SeCjwPELXfcO/j4eTjfywIH94/0Wuu4d6MPrgDf195cB3wd2X+jah/TlacATga9uZ/pUf75H7MPEPt9Lec3ip0OIVNW/AduGEBl0DHBedb4APDzJAZMudBbNPlTV56vq//UPv0B3jsq0GeV3AfD7wIeAOyZZ3ByM0o/fAi6sqpsBqmra+jJKHwp4aLrxNR5CFxZbJ1tmW1V9hq627Zn2z3ezD5P8fC/lsFgO3DLweHPfNtd5FtJc6zuF7pvUtGn2I8ly4DjgXROsa65G+X38ArB3ksuSXJnkpIlVN5pR+vBXwOPoToy9FnhZVd0zmfLm1bR/vudqrJ/vRXGexZiMMoTISMOMLKCR60vyDLo/pl8ea0U7ZpR+vBV4TVXdPcWD3I3Sj92Aw4GjgD2By5N8oaq+Pu7iRjRKH44GrgKeCTwauCTJZ6vqh2Oubb5N++d7ZJP4fC/lsBhlCJFpH2ZkpPqS/CLwHuDZVfW9CdU2F6P0Yw1wQR8U+wLPSbK1qv5uIhWOZtS/qe9W1T8D/5zkM8BhwLSExSh9+B3gjOo2lG9KchPwWOCKyZQ4b6b98z2SSX2+l/JmqFGGELkYOKk/auII4AdVddukC51Fsw9JDgQuBF44Rd9eZ2r2o6oOqqpVVbUK+CDwkikLChjtb+oi4FeS7JbkZ4BfAq6fcJ2zGaUPN9OtGZFkf7oRnr850Srnx7R/vpsm+flesmsWtZ0hRJL81376u+iOunkOsAn4F7pvVFNjxD78D+DfAWf138q31pSNuDliP6beKP2oquuTfBy4BriH7qqPQw+LXAgj/i7+J3BOkmvpNuW8pqqmbsjvJOcDTwf2TbIZ+BPgQbA4Pt8wUh8m9vl2uA9JUtNS3gwlSRqRYSFJajIsJElNhoUkqcmwkCQ1GRaSpCbDQvMuySOSXJDkG0muS/LRJL/QT3tFkn9N8rCB+X8myfuTXJvkq0k+l+Qh/bQfzXju307yVyPUcHV/jPpg2zlJbuqnfT3Jef2YU/RjNR09Y/6XJzmrv78syU+SvGjGPN9K8qGBx8cnOWfg8bOTbEhyfZKvJTmzb399ku+kG6p82+3h2+nL05P8YGC+Twx5juuSnDikr9uW+fzAtGPTDWn9tf79Pn5g2mVJ1gw8XpV+eOwhdVyV5Fn9tEry5wPLvSrJ6wcen9S/1sa+1lcleWOSNw3M83NJvrm990ELy7DQvEp3ZtCHgcuq6tFVdTDdkNb797OcSHeW8HEDi70MuL2q/n1VHUo3xs1PHkANj6P7235akp+dMfnVVXUY3VnHXwE+1Z+pfD7d2cqD1vXtACfQjep5Ive3JskhQ+o4lG7QvRdU1eOAQ7nvmc5vqarHD9z+aZZufXZgvmfNfA66EVTfneRBM/q6bZkj+5oOA84EjqmqxwL/EXhTksNnee3t1fH4qvpE334X8BtJ9h3yPjwbeDnwa1V1CN2Q2z+gO7nvmP73BfA24L833gctEMNC8+0ZwE8Gz7quqquq6rNJHk03pPV/477/dA8AvjMw/w1VddcDqOG3gL8G/i/w68Nm6Ielfgvwj3TXAfgg8Nwke0D3jRp4JPC5fpETgT8EVmxbGxlwJl0gzvRHwOlV9bX+NbdW1VkPoF/bVVU30p2FvHdj1lcBb6yqm/rlbgLeSNe3B2IrcDbwiiHTTgNeVVW39q/5r1X1f6rqx8Ar6c4+fjbw0Kp6/wOsQ2NiWGi+HQpcuZ1pJ9J9U/8s8Jgk+/Xt7wNek+TyJP8ryeqBZfYc3OwB/OkINTwP+ED/WsPWBAZ9GXhsPwDbFcDavn0d8IGqqiQrgUdU1RXA+v75B60Hnpjk52e0z/ZeALxioG+fatT5KwPz/vHMiemu8nbjjOtjvHlgmW3/hA8ZUtMG4ODG6w+r46r+C8A27wCen4FNjL3tvg9V9VG66zWcB7xkxBq0AJbs2FBaEOuA46rqniQX0m3aeUdVXZXkUcCvAc8CvpTkKVV1PfDjfjML0O2zoBuBdqgkTwK2VNW3042l874kew9cIOZ+iwzc37Yp6qL+538eqHt9f/8C4L3AXwwsdzfwZrpv0HO5nsBbqurMEef9bFU9d0j7K5L8Lt2V7dbOmPbqqvrgjLYw+1D8w8b/GWzbXh1U1Q+TnAf8AfDjYfNsxzuAPavqhjksowlzzULzbSPd9RruI90wyqvprn3wLbp/wD/91l9VP6qqC6vqJcDf0A3wtiNOBB7bv8Y3gL2A/zTL/E/g3lFf/w44qv+WvmdVfXngOX+7f86LgcNmrP1At9nracCBA21D34t59paqegzd2s55SR7cmH8j9w/bJ9KtXQB8j/tuytoHmMsggW+l2+c0uK+o9T7c0980xQwLzbdPAnv033aBn37bfxvw+m3DjFfVI4Hl/REwT02ydz/v7nSbRL491xdOsgvd2sovDgxnfgxDNkWl8wd0+0s+Dl1gAZfRbRY7v5/vMcDPVtXygef8M2bsDK+qnwBvoduRu82bgdfl3iPBdknyyrn2axRVdSHdP/yTG7OeCZzW75PZtm/m5X2t0PX/Bf2BCvTP19pENljH9+nWwk4ZaP4z4H8neUT/mnv0770WEcNC86q/IM5xwK+mO3R2I/B6umGWPzxj9g/T/dN9NPDpdENef4Xun96HmLunAd+pqu8MtH0GODj3Xlv5zUmuprvY0JOAZ1R3reltzqe7GNEF/eMTh9T9IYbvC3kvA5t2q+oaun/E5ye5HvgqXThtM7jP4qpt/8AfgD8FXtmHJtx3n8VVSXavqquA1wAfSfJ1uvfhxQObgM4G7gSu7t+nh9AFzDYz91kcz/39Od0FqoCf7pd4B/CJ/u/hStwEvug4RLm0hCU5g+4CTEfPCE3pPgwLSVKTq4JalPrDR0+Y0fy3VXX6QtQzH9KdQf6mGc03VdVxw+aXJsk1C0lSkzu4JUlNhoUkqcmwkCQ1GRaSpKb/DwB0UZV3eokYAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 432x288 with 0 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 360x360 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -927,21 +1299,18 @@
     },
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 432x288 with 0 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 360x360 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -951,21 +1320,18 @@
     },
     {
      "data": {
-      "image/png": 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CmZllHApmZpZxKJiZWcahYGZmGYeCmZllPCSmnaFarVKpVLJ5j55q1jn8m27A6GG0BwYGuO0bh+hauMyjp5p1GIeCAaOH0a7fbnTe4p6yyzKzKeZjCpapD6Pt242adS6HgpmZZRwKZmaWcSiYmVnGoWBmZhmHgpmZZRwKZmaW8XUKdk581bPZzOTfYjsnlUqFzTv20tW91Fc9m80gDoVx5P8aHhgYIKLkgqaZru6lvurZbIZxKIwj/9dwfeiHS8ouqkQOSbOZz6Ewgfpfw6eGny27lFI0GyjPIWk2MzkUbFzNBsrr1JA0m+kcCjah+kB5zYIgvzdR57ORzNpTYb+1kr4IvA8YioirUtulwJeBHuBp4OaIeDE9tw3YBIwAH4uIbxdVm02u/N4E4LORzNpYkRev3QWsG9O2FdgXEauBfWkeSVcAG4Ar0zp3SJpVYG02yep7E/MW99DVvbTscszsHBUWChHxA+CFMc3rgV1pehdwY659d0ScjoingApwbVG1mZlZY1M9zMWiiDgBkB4XpvZlwLHccoOp7QySNkvql9Q/PDxcaLFmZp1muox9pAZtDc+Cj4idEdEbEb3d3b5DmJnZZJrqUDgpaQlAehxK7YPAitxyy4HjU1ybmVnHm+pQ2ANsTNMbgfty7RskzZG0ElgNPDzFtZmZdbwiT0n9EvBu4DJJg8CngduAPkmbgKPATQARcVBSH3AIqAJbImKkqNrMzKyxwkIhIj7Y5Km1TZbfDmwvqh4zM5uYLzm1Qvm+C2btxb+dVijfd8GsvTgUbNKNHVn1LZf5vgtm7cKhYJOu0ciqHmLbrD1Ml4vXbIapj4U0d74vMDRrJ95TsCkzdohtH3Q2m378G2lTJt+t5IPOZtOTQ8GmVL1bycymJ4eCTSu+rsGsXP5ts9Llg2BgYIDbvnGIroXL3MVkVgKHgpUuf4Fb/RRWdzGZlcOhYKVodoHbqeFnS67MrLM5FMYY25URDW/1Y+fLF7iZTU8OhTEadWX4y6oY9TORvHdgNn04FBro6nZXxnTQ7GI3n6FkVhz/Jtm01exiN4+8alYch4JNa80udqvvzZnZ5HIoWFsYe7ZSsxMA8l1L1WoVIOtacjeT2cT8G2JtodnZSmOPO+Qvfht64gCz5l7CguUr3c1k1iKHgrWNRmcr5cMCGHXx26nhZ5nVtYB5i3s8QqtZi/xbYW0vf9yh2RljHqHVrDUOBesYjQ5a+/RWs9H86beOM/agdf0YxM9OHmPbDVdx+eWXA42vixh78Dq/nNlM4E8yrZ/ZYjNDo4PW9WMQn7n3wLjXReQPXgPuirIZx6GAx+HpRM2G2Ki3jzdgX/3gNfgWozbz+NObeBwey2v1D4X8cs26n/LO5xjG2HXPd30HmDUy7T4RktYBnwVmAV+IiNtKLsk6VKt/KOSXq3c/5QMifxzibI9h5Nvz3VhASyGUl1//XAKsldew9jet/nclzQJ2AP8UGAQekbQnIg6VW5lZaxoFRP44RLNjGPkv6WbBke/GAloKobGBlO8GO5sAA1p6jSKm6yYKzVauZm91b6nZzyriKvlWw3eq9vSmVSgA1wKViBgAkLQbWA8UEgqnho8D8MqLw8w6/RovX3Rh4dN+vZnz2hO+3tzGR6Zeef7EGcu8+tLz3HrnN7lk0TJefOZxupatpqtJu8Qbr91k/Qsu7Go4XV+/1XXrdZzNa0z2NMCrLw7zHz+0NgvHP/6rfVw0v7tpe379VtYdq9nPavZzz0f+tca+12bLvfriMHd/+g8KOcFBMY1OtZH0AWBdRPyrNP8h4J9ExEdyy2wGNqfZtwGPn8dLXgb85DzWn0m8LUbz9niDt8VoM2F7/KOI6G70xHTbU1CDtlGpFRE7gZ2T8mJSf0T0TsbPanfeFqN5e7zB22K0mb49Lii7gDEGgRW5+eXA8ZJqMTPrONMtFB4BVktaKenNwAZgT8k1mZl1jGnVfRQRVUkfAb5N7ZTUL0bEwQJfclK6oWYIb4vRvD3e4G0x2ozeHtPqQLOZmZVrunUfmZlZiRwKZmaW6chQkLRO0uOSKpK2ll1PGSQ9LekxSQck9ae2SyXdL+nJ9Di/7DqLIOmLkoYk/TjX1vS9S9qWPiuPS7q+nKqL02R7/AdJz6bPxwFJv5V7bsZuD0krJD0g6bCkg5I+nto75vPRcaGQG0rjN4ErgA9KuqLcqkrznoi4OnfO9VZgX0SsBval+ZnoLmDdmLaG7z19NjYAV6Z17kifoZnkLs7cHgB/lj4fV0fEN6AjtkcV+MOI+AXgncCW9J475vPRcaFAbiiNiHgNqA+lYbXtsCtN7wJuLK+U4kTED4AXxjQ3e+/rgd0RcToingIq1D5DM0aT7dHMjN4eEXEiIh5N0z8DDgPL6KDPRyeGwjLgWG5+MLV1mgC+I2l/GjoEYFFEnIDaLwewsLTqpl6z997Jn5ePSPpR6l6qd5d0zPaQ1ANcAzxEB30+OjEUJhxKo0NcFxHvoNaNtkXSr5Vd0DTVqZ+XzwH/GLgaOAH8SWrviO0hqQv4W+ATEfHyeIs2aGvr7dGJoeChNICIOJ4eh4B7qO3ynpS0BCA9DpVX4ZRr9t478vMSEScjYiQiXgc+zxtdIjN+e0h6E7VAuDsivpqaO+bz0Ymh0PFDaUh6i6SL69PAe4EfU9sOG9NiG4H7yqmwFM3e+x5gg6Q5klYCq4GHS6hvStW/AJP3U/t8wAzfHpIE3Akcjog/zT3VMZ+PaTXMxVQoYSiN6WgRcE/t889s4K8j4luSHgH6JG0CjgI3lVhjYSR9CXg3cJmkQeDTwG00eO8RcVBSH7V7elSBLRExUkrhBWmyPd4t6WpqXSFPA/8aOmJ7XAd8CHhM0oHU9kd00OfDw1yYmVmmE7uPzMysCYeCmZllHApmZpZxKJiZWcahYGZmGYeCdSRJI2n0zx9L+htJc1P7bEk/kfRf0vx7JT2Yzl9H0qy03rvSSKIhaVXu596S2nrTfH402gOS/jy135VGIZ2T5i9Ly/5ibtkXJD2Vpr8r6QJJf55qfkzSI+nceLNJ41CwTvVqGv3zKuA14N+k9vcCjwM3S1JEfAd4BtiUnv8o8EhE/J80/xi1CyDrPkDtnPW8+mi0V0fEx3LtI8C/zC8YEY/Vl6V2YdSn0vxvAL8DLAV+KSJ+kdpFZT89x/dv1pBDwQz+Hqj/tf9B4LPULlB6Z2q7Bdgm6UrgI8CtuXXvJY2yK+ly4CVguMXXvR24RVKrF5EuAU6koSeIiMGIeLHFdc1a4lCwjpa+kH+T2hWsFwFrga8DX6IWEPVRMW8HHgT+U0Tkh5l+GTgm6aq0/JcbvMwDuS6hW3LtR4F/oHYFbSv6gH+Wfs6fSLqm1fdp1iqHgnWqi9IwBv3UvpzvBN4HPBARr1AbEO39uRum7ABmRcRdDX7WbmpdSDdSG1xwrHz30Z+Nee4/A5+ihd/FiBgE3gZsA14H9klaO9F6Zmej48Y+MkteTf32GUkfBK6T9HRqWgC8B/huRLwuqdmYMF8D/hvQHxEvp2PSLYmISgqnm1tc/jTwTeCbkk5SC6J9Lb+g2QQcCmaApHnArwIr0hcvkn6fWpfQd8dbNyJelXQr8MQ5vvx2YG8LNb4DeC4ijku6APgl4Efn+JpmDTkUzGp+G/hePRCS+4D/KmnOmPYzRMTucZ5+QFJ95MwfRcSHx6x7UNKjwDsmqHEh8Pn6aazUhmj+iwnWMTsrHiXVzMwyPtBsZmYZh4KZmWUcCmZmlnEomJlZxqFgZmYZh4KZmWUcCmZmlvn/tNm5idE5oI0AAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 432x288 with 0 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 360x360 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -975,21 +1341,18 @@
     },
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 432x288 with 0 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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nOG5JWhQTC+CqOlRVX2j7TwIPAGuBLcCu1mwXcEXb3wLcWlVPVdXDwAHgkiRrgNOr6u6qKuCWoT6SNLUWZQ04ybnAq4B7gLOr6hAMQho4qzVbCzw61G221da2/bl1SZpqEw/gJC8Cfhd4V1V9fb6mI2o1T33Uz9qeZCbJzJEjR058sJK0iCYawEmezyB8P1hVH2nlx9qyAu31cKvPAuuHuq8DDrb6uhH1Z6iqnVW1sao2rl69euEmIkkTMMm7IAJ8AHigqn596K09wLa2vw24bai+NclpSc5j8GHbvW2Z4skkm9o5rxrqI0lTa+UEz30p8FZgX5L7Wu2XgfcAu5NcDTwCXAlQVfuT7AbuZ3AHxbVVdaz1uwa4GVgF3N42SZpqEwvgqvqfjF6/BbjsWfrsAHaMqM8AFy7c6CSpP78JJ0mdGMCS1IkBLEmdGMCS1IkBLEmdGMCS1IkBLEmdGMCS1IkBLEmdGMCS1IkBLEmdGMCS1IkBLEmdGMCS1IkBLEmdGMCS1IkBLEmdGMCS1IkBLEmdGMCS1IkBLEmdGMCS1IkBLEmdGMCS1IkBLEmdGMCS1IkBLEmdGMCS1IkBLEmdGMCS1MnEAjjJbyU5nORLQ7Uzk9yZ5KH2esbQe9cnOZDkwSSXD9UvTrKvvXdDkkxqzJK0mCZ5BXwzsHlO7Tpgb1VtAPa2Y5KcD2wFLmh9bkyyovW5CdgObGjb3HNK0lSaWABX1R8AfzanvAXY1fZ3AVcM1W+tqqeq6mHgAHBJkjXA6VV1d1UVcMtQH0maaou9Bnx2VR0CaK9ntfpa4NGhdrOttrbtz62PlGR7kpkkM0eOHFnQgUvSQlsqH8KNWteteeojVdXOqtpYVRtXr169YIOTpElY7AB+rC0r0F4Pt/ossH6o3TrgYKuvG1GXpKm32AG8B9jW9rcBtw3VtyY5Lcl5DD5su7ctUzyZZFO7++GqoT6SNNVWTurEST4EvA54WZJZ4F8C7wF2J7kaeAS4EqCq9ifZDdwPHAWurapj7VTXMLijYhVwe9skaepNLICr6i3P8tZlz9J+B7BjRH0GuHABhyZJS8JS+RBOkk45BrAkdWIAS1InBrAkdWIAS1InBrAkdWIAS1InBrAkdWIAS1InBrAkdWIAS1InBrAkdWIAS1InBrAkdWIAS1InBrAkdWIAS1InBrAkdWIAS1InBrCkiVi7/hySnPS2dv05vacyMRP7o5ySTm0HZx/lzb/5mZM+z2+//bULMJqlyStgSerEAJakTgxgSerEAJaWET/4mi5+CDdk7fpzODj76Emf5/vXredPHn1k2YxloSzHOS01fvA1XQzgIUvpX96lNJaFshzn5C8VnQwDWDoJy/GXihaPa8CS1IkBLEmdTE0AJ9mc5MEkB5Jc13s8knSypiKAk6wA/iPwBuB84C1Jzu87Kkk6OVMRwMAlwIGq+kpVfRu4FdjSeUySdFJSVb3HcFxJ3gRsrqp/2I7fCvxIVf38nHbbge3t8ELgS4s60Ml6GfB470EssOU2J+ez9PWa0+NVtXlucVpuQ8uI2jN+c1TVTmAnQJKZqto46YEtluU2H1h+c3I+S99Sm9O0LEHMAuuHjtcBBzuNRZIWxLQE8OeADUnOS/ICYCuwp/OYJOmkTMUSRFUdTfLzwO8DK4Dfqqr9x+m2c/IjW1TLbT6w/ObkfJa+JTWnqfgQTpKWo2lZgpCkZccAlqROpjqAk6xP8qkkDyTZn+Sd87R9TZJj7Z7iJWnc+SR5XZL7WptPL/Y4xzXOfJJ8X5KPJfmj1uZtPcY6riQvTHLv0Hh/ZUSbJLmhfW3+i0le3WOs4xhzPj/T5vHFJJ9J8soeYx3HOPMZats/E6pqajdgDfDqtv9i4H8B549otwL4JPAJ4E29x30y8wFeAtwPnNOOz+o97pOczy8Dv9r2VwN/Bryg99jnmVOAF7X95wP3AJvmtHkjcHtruwm4p/e4T3I+rwXOaPtvmPb5tPeWRCZM9RVwVR2qqi+0/SeBB4C1I5q+A/hd4PAiDu+EjTmfvwd8pKoeae2W7JzGnE8BL04S4EUMAvjoog70BNTAN9rh89s295PsLcAtre1ngZckWbOY4xzXOPOpqs9U1f9ph59lcB/+kjTmPx9YIpkw1QE8LMm5wKsY/MYbrq8Ffgr4jQ7Des6ebT7ADwFnJLkryeeTXLXog3sO5pnP+4BXMPhizT7gnVX1F4s7uhOTZEWS+xj8x3tnVc2d01pg+M9kzDL6wmBJGGM+w65mcHW/ZB1vPkspE5ZFACd5EYPfZu+qqq/Pefu9wLur6tiiD+w5Os58VgIXAz8BXA788yQ/tMhDPCHHmc/lwH3A9wMXAe9LcvqiDvAEVdWxqrqIwZXgJUkunNNkrK/OLxVjzAeAJK9nEMDvXsThnbAx5vNelkgmTMUXMeaT5PkM/uP+YFV9ZESTjcCtg//D5WXAG5McrarfW7xRjm+M+cwyeLDHN4FvJvkD4JUM1leXnDHm8zbgPTVYmDuQ5GHgh4F7F3GYz0lVfS3JXcBmvvvBT1P51fl55kOSvwa8H3hDVT3RYXgnbJ75LJlMmOor4LZu+AHggar69VFtquq8qjq3qs4FPgz83BIO3+POB7gN+BtJVib5HuBHGKytLjljzucR4LLW/mzg5cBXFmeEJy7J6iQvafurgB8Hvjyn2R7gqnY3xCbg/1bVocUd6XjGmU+Sc4CPAG+tqiX5i/5p48xnKWXCtF8BXwq8FdjX1nxg8Kn6OQBV1X2N5wQddz5V9UCSO4AvAn8BvL+qlupjN8f55/NvgJuT7GPwv+7vrqql/AjENcCuDP5IwPOA3VX18ST/CP5yTp9gcCfEAeBbDK7yl6px5vMvgJcCN7arxqO1hJ4oNsc481ky/CqyJHUy1UsQkjTNDGBJ6sQAlqRODGBJ6sQAlqRODGAtG0le2p4Sd1+SP03yJ0PHNbR/X5LrWp+7kswMnWNju3mfJP8gyfvm/Iy7kmxs+3+cZF97Stink/zAULtjo36eNGza7wOW/lL7htZFAEn+FfCNqvq1dvyN9vXUUc5K8oaqei7POHh9VT3eHnv4z4CfbfU/n+fnSYBXwBLAv2MQnifjbpbwA3e0NBnAOlWsmrMk8Oah9+4GnmoPm3muNgO/N+bPkwCXIHTqON6SwL9lcBU8/KSvZ/ua6HD9U+0ZFof57qtolyB0XF4BS0BVfRJ4IYO/YPG0J4Az5jQ9Exh+VsXrgR8A9gP/epJj1PJjAEvfsQP4paHjzwGXJvkrMLhDAjiN737YOlX158C7GDwB7czFGaqWA5cgdKpYNfRENoA7quq7bg2rqk8kOTJ0/FgGf0j0E0meB3wDeMuov9hRVYeSfAi4lsET3o778ySfhiZJnbgEIUmdGMCS1IkBLEmdGMCS1IkBLEmdGMCS1IkBLEmd/H9jysOMOgv1MAAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 360x360 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -1001,12 +1364,12 @@
    "source": [
     "for col in df:\n",
     "    plt.figure()\n",
-    "    sns.histplot(df[col])"
+    "    sns.displot(df[col])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "id": "1fb22bc6",
    "metadata": {},
    "outputs": [
@@ -1016,13 +1379,13 @@
        "Text(0.5, 1.0, 'Correlation between different fearures')"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1224x1224 with 2 Axes>"
       ]
@@ -1036,14 +1399,15 @@
    "source": [
     "correlation = df.corr()\n",
     "plt.figure(figsize=(17,17))\n",
-    "sns.heatmap(correlation, vmax=1, square=True,annot=True,cmap='cubehelix')\n",
+    "sns.heatmap(correlation, vmax=1, square=True,annot=True,cmap=\"YlGnBu\")\n",
     "\n",
-    "plt.title('Correlation between different fearures')"
+    "plt.title('Correlation between different fearures')\n",
+    "#Through heatmap we can see correlation between different attributes through different shades of colour."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "id": "82883ccc",
    "metadata": {},
    "outputs": [],
@@ -1054,7 +1418,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "id": "e150e382",
    "metadata": {},
    "outputs": [
@@ -1064,13 +1428,13 @@
        "Text(0, 0.5, 'Cumulative explained variance')"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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z755DVkYaz151lJKASCcU5dZQpbtXmhlmlh72Nh4ZYbsJwEp3XwVgZo8BpwPvxazjQE8Luqf2AEqB2t37CLInKqrr+PFTi3lm4XpOOnAgvz9rDFkZaYkOS0QSIEoiKAqLzj0D/MvMNgHrI2w3CCiM3Q8wscE6fwKeC/fXEzjL3esb7sjMphH0ciYvLy/CoaU5azeWc9lD81nxSRk/OGUEV04+QIPIi3RiUVoNnRG+/YWZzQR6AS9G2HdjZ5aG/RC+QPDc4QRgf4JE86a7lzWIYQYwAyA/P199GfbCzIJirnv0XcyMey8cz+SRAxIdkogkWIvPCMzsNjM7EsDdX3f359y9OsK+i4DYXkg5fP5K4iLCjmruvhJYDYyKFrrsjvp65/ZXP+Di++YyKDuTv199tJKAiADRHhYvAH5qZivN7Ldmlh9x33OB4WY2NHwAfDbBbaBYa4ETAcKRz0YCqyLuXyKqqq3j6kcXcMu/3ucrY/bjqSuOJK+vSkeLSCDKraH7gfvNrA/wdeBmM8tz9+EtbFdrZlcDLxE0H73H3ZeZ2eXh8unAr4D7zGwJwa2kH7n7hr37SBKrsqaOyx+az6yCEn5y6iguPWaYSkeLyC4idSgLHUBw22YIu7b8aZK7v0Aw4H3svOkx79cDp+xGDLIbtlfVcsn985i9eiP/+7VDmDpBD9pF5POidCi7Gfga8CHwOPArd98c57hkL5VV1nDxvXNZsHYTv//mGM4Ym5PokESknYpyRbAaOEK3bJLH5vJqzr/nHd5bX8afzhnHqYeok5iINC3KM4LpLa0j7ceGbVWcd/ccVpVs5y/fOpwTDxyY6JBEpJ3bnWcE0s59WlbJOXfNZt3mCv56YT7HDO+f6JBEJAkoEXQQRZvKOffuOWzYWsX9F03QmMIiElmTiSBsLtokdy9t/XBkT6zZsJ1z757D1soaHrpkImPzshMdkogkkeauCOYTlIQwgjLRm8L3vQk6gg2Nd3DSspXFWznnrjnU1NXzyKWTOHiQRhMTkd3TZM9idx/q7sMIOoR92d37uXtf4DTgqbYKUJr23voyzvrLbOodHr/sCCUBEdkjUUpMjA87hgHg7v8EjotfSBLFosLNTL1rNl1Tu/DEZZMYMVAjiorInonysHiDmf2UYCQxB84DNsY1KmnW3DWlXHTvXLK7p/HIJZPI7aO6QSKy56JcEUwF+gNPhz/9w3mSAP9euYHz//oOA3qm88RlRygJiMhei9KhrBS4zsx6uPu2NohJmjCzoJjLH5zPkL7deeiSifTvmZ7okESkA4gyHsGRZvYeYaE5MxtjZn+Oe2SyixeXfsK0B+YxfGAPHp02SUlARFpNlFtDfyAYSWwjgLsvAo6NZ1Cyq+cWreeqRxZw8KBePHzJJPp075rokESkA4mSCHD3wgaz6uIQizTiiXmFXPfYu+QPzubBb0+kVzcNMC8irStKq6HCcKhKD0cauxZYHt+wBODB/6zhZ88u45jh/ZjxrXy6dU1JdEgi0gFFuSK4HLgKGEQwDvFh4bTE0YOzP+Jnzy7jpAMHcvcFSgIiEj9RWg1tAM5tg1gk9OLST/j5s0s56cCB3HneONJSIt3BExHZI1FGKOsPXEowROXO9d394viF1XnNXVPKtY+9y2G5vbl96lglARGJuyjPCJ4F3gReQQ+J42pl8VYuuX8eOb278dcLxut2kIi0iSiJINPdfxT3SDq5T8squeCeuaSldOH+iyeoiaiItJko9x3+YWan7snOzWyKmRWY2Uozu6GR5deb2cLwZ6mZ1bU0DkJHVFZZwwX3vMPm8mruu2i8ykaISJuKkgiuI0gGFWZWZmZbzayspY3MLAW4A/giMBqYamajY9dx99+6+2HufhjwY+D1zjbgTXVtPVc8NJ+Vxdu487zDVUpaRNpclFZDe1rfeAKw0t1XAZjZY8DphKUqGjEVeHQPj5WU6uudHz65iLdXbuSWb4zh2BEaY1hE2l5zQ1WOcvcVZjauseXuvqCFfQ8CYnskFwETmzhWJjAFuLqJ5dOAaQB5eXktHDZ53PzSCp5ZuJ7rvzCSrx+ek+hwRKSTau6K4HsEJ99bGlnmwAkt7Nua2K4xXwbebuq2kLvPAGYA5OfnN7WPpHLv26v5y+ur+NakwVw5ef9EhyMinViTicDdp4Wvx+/hvouA3JjpHGB9E+ueTSe6LfTCko/573+8xymjB/KLrxyEWWM5U0SkbURpPoqZHUzwwDdjxzx3f6CFzeYCw81sKLCO4GR/TiP77kUw9OV5EWNOau+sLuU7jy9kXF42f5w6lpQuSgIiklhRehbfCEwmSAQvELQCegtoNhG4e62ZXQ28BKQA97j7MjO7PFw+PVz1DOBld9++px8iWXzw6VYuuX8uOdnduPv8fDLS1GFMRBLP3Ju/5W5mS4AxwLvuPsbMBgJ3u/uX2yLAhvLz833evHmJOPRe+WRLJV/789vU1DtPXXGk+gqISJsys/nunt/Ysij9CCrcvR6oNbMsoBgY1poBdnRllTVceO87lFXWqsOYiLQ7UZ4RzDOz3sBdwHxgG/BOPIPqSKpq67jsgaDD2H0XTeCg/dRhTETalygdyq4M3043sxeBLHdfHN+wOob6eucHf1vMf1Zt5A9njeHo4f0SHZKIyOc016Gs0Y5kO5ZF6FDW6f3vP5fz90Xr+dGUUZwxVh3GRKR9au6KoLGOZDtE6VDWqf31rdXc9eZqLjhiMJcfp0cqItJ+NdehbE87knV6/1i8nl8//x5TDtqHn39ZHcZEpH2L0o8gA7gSOJrgSuBNYLq7V8Y5tqQ0e9VGvvf4IvIHZ3Pr2Yepw5iItHtRWg09AGwFbg+npwIPAt+IV1DJquCTrVz6wDzy+mZylzqMiUiSiJIIRrr7mJjpmWa2KF4BJavirZVceO87dEtL4b6LxtM7UyOMiUhyiNKh7F0zm7RjwswmAm/HL6TkU1fvXPvou2wqr+bei8aTk60OYyKSPKJcEUwEzjezteF0HrA8LD3h7n5o3KJLEre+8j6zV5Xyu2+MUYcxEUk6URLBlLhHkcRmFRRz+2sr+WZ+DmdqcBkRSUJREsFwd38ldoaZXeDu98cppqSxfnMF3318IaP26ckvv3JwosMREdkjUZ4R/NzM7jSz7mY20Mz+TjCiWKdWU1fP1Y8soLq2nj+fO45uXdVCSESSU5REcBzwIbCQYByCR9z9zHgGlQxu/ucKFqzdzM1nHsqw/j0SHY6IyB6LkgiyCR4YfwhUAYOtk3eVfXHpJ9z9VlA+4rRD90t0OCIieyVKIpgN/NPdpwDjgf3oxM1H124s5/onF3FoTi9+8qUDEx2OiMhei/Kw+CR3Xwvg7hXAtWZ2bHzDap8qa+q48pH5GHDHOeNIT9VzARFJflGuCDaY2c/M7C4AMxsOZMU3rPbp18+/x9J1ZdzyzcM0ypiIdBhREsG9BM8Gjgini4Bfxy2idurZhet4aPZaLjt2GCePHpjocEREWk2URLC/u/8GqIGdt4ciPSw2sylmVmBmK83shibWmWxmC81smZm9HjnyNrSyeBs/fmoJ44dk84MvjEx0OCIirSrKM4JqM+tGUIIaM9uf4AqhWWaWAtwBnExwFTHXzJ5z9/di1ukN/BmY4u5rzWzA7n+E+KqoruPKh+eTkZbC7VPHkZYSJXeKiCSPKGe1G4EXgVwzexh4FfhhhO0mACvdfZW7VwOPAac3WOcc4KmYh9HFkSNvIz97dikfFG/j1rMOY59eGYkOR0Sk1UUZvP5fZrYAmERwS+g6d98QYd+DgMKY6SKC/gixRgBpZjYL6Anc5u4PNNyRmU0DpgHk5eVFOHTreGJuIU/OL+LaE4dz7Ij+bXZcEZG2FOXWEO6+EXh+N/fd2HMEb+T4hwMnAt2A/5jZbHd/v8HxZwAzAPLz8xvuIy6Wf1zGz55dylEH9OW6E4e3xSFFRBIiUiLYQ0VAbsx0DrC+kXU2uPt2YLuZvQGMAd4ngbZW1nDlwwvo1S2NW88aq+EmRaRDi+eTz7nAcDMbamZdgbOB5xqs8yxwjJmlmlkmwa2j5XGMqUXuzg1PLWFtaTm3Tx1L/57piQxHRCTuIl0RmNnRBOWo7zWz/kAPd1/d3DbuXmtmVwMvASnAPe6+zMwuD5dPd/flZvYisBioB+5296V784H21oOzP+L5xR/zoymjmDisbyJDERFpE+be/C13M7sRyCcYu3iEme0H/M3dj2qLABvKz8/3efPmxWXfiwo3c+b0f3Ps8P7cdX4+XXRLSEQ6CDOb7+75jS2LcmvoDOArwHYAd19P0MKnQ9lSXsNVjyxgQM8MbvnmGCUBEek0oiSCag8uG3Z0KOse35Danrvz/b8t5NOySv50zlh6Z3ZNdEgiIm0mSiJ4wsz+AvQ2s0uBV4C74htW27rrzVW8sryYn5x6IGPzshMdjohIm4rSoex3ZnYyUAaMBH7u7v+Ke2RtZO6aUm5+sYBTD9mHC48ckuhwRETaXIuJwMy+S/BwuMOc/HfYuK2Kqx9ZQG52N276+qF08oHXRKSTinJrKAt4yczeNLOrzKzD1GC+4aklbCqv4Y5zx5GVkZbocEREEqLFRODuv3T3g4CrCIapfN3MXol7ZHG2aXs1ry7/lEuOHspB+/VKdDgiIgmzOz2Li4FPgI1AuysXvbve+KCEekeDzIhIp9diIjCzK8LqoK8C/YBL3f3QeAcWb7MKSujTvSuH5vROdCgiIgkVpcTEYOA77r4wzrG0mbp65/X3S5g8or8KyolIp9dkIjCzLHcvA34TTveJXe7upXGOLW4WFW2mdHs1k0cl/R0uEZG91twVwSPAacB8gl7FsV+dHRgWx7jiataKYroYHDu8X6JDERFJuCYTgbufFr4Obbtw2sbMghLG5WWrlISICNEeFr8aZV6yKN5ayZJ1Wzhet4VERIDmnxFkAJlAPzPL5rNbQ1kE/QmS0usFJQBMHqkxiEVEoPlnBJcB3yE46c/ns0RQBtwR37DiZ1ZBCQOz0hm9b1aiQxERaReae0ZwG3CbmV3j7re3YUxxU1NXzxsflPClQ/ZVXSERkVCU6qO3m9nBwGggI2b+A/EMLB7mf7SJrZW1TB6p5wMiIjtEqT56IzCZIBG8AHwReAtIukQws6CYtBTjqAM0FrGIyA5Rag2dCZwIfOLuFwFjgPS4RhUns1aUMH5IH3qq0qiIyE5REkGFu9cDtWaWRVB8Luk6k63bXEHBp1s5XreFRER2ESURzDOz3gTDU84HFgDvRNm5mU0xswIzW2lmNzSyfLKZbTGzheHPz3cn+N0xq6AYgONHqdmoiEisKA+LrwzfTjezF4Esd1/c0nZmlkLQzPRkoAiYa2bPuft7DVZ9c0cv5niauaKEnOxu7N+/R7wPJSKSVJrrUDauuWXuvqCFfU8AVrr7qnCbx4DTgYaJIO6qaut4e+UGvpGfo2ajIiINNHdFcEszyxw4oYV9DwIKY6aLgImNrHeEmS0C1gM/cPdlDVcws2nANIC8vLwWDvt5c1aVUlFTp+cDIiKNaK5D2fF7ue/Gvnp7g+kFwGB332ZmpwLPAMMbiWUGMAMgPz+/4T5aNLOgmPTULkwapmajIiINRelHcH5j8yN0KCsCcmOmcwi+9cfuoyzm/Qtm9mcz6+fuG1qKa3fMKijhiP370q1rSmvuVkSkQ4gyQtn4mPcZBH0KFtByh7K5wHAzGwqsA84Gzoldwcz2AT51dzezCQStmDZGjD2S1Ru2s3rDdi48ckhr7lZEpMOI0mromthpM+sFPBhhu1ozuxp4CUgB7nH3ZWZ2ebh8OkFntSvMrBaoAM52992+9dOcnc1G9XxARKRRUa4IGiqnkfv4jXH3FwjKUsTOmx7z/k/An/YghshmFpQwrH938vpmxvMwIiJJK8ozgr/z2UPeLgQ1h56IZ1Ctpby6ltmrNnL+pMGJDkVEpN2KckXwu5j3tcBH7l4Up3ha1b9XbqS6tl6jkYmINCPKM4LXAcI6Q6nh+z7uXhrn2PbazIJiundNIX9IdqJDERFpt6LcGpoG/IrgYW49Qf8Ap50XnnN3ZhWUcNQB/UhPVbNREZGmRLk1dD1wUGu37Y+3D4q3sW5zBVefcECiQxERadeiVB/9kKClUFKZuSJoNqpB6kVEmhfliuDHwL/NbA5QtWOmu18bt6hawcyCYkbt05N9e3VLdCgiIu1alETwF+A1YAnBM4J2r6yyhnlrNjHt2Hb9GENEpF2Ikghq3f17cY+kFb31wQZq613NRkVEIojyjGCmmU0zs33NrM+On7hHthdmrigmKyOVsbm9Ex2KiEi7F+WKYEehuB/HzGu3zUfr651Z75dw7Ij+pKZEyXMiIp1blA5lQ9sikNby3sdllGytUpE5EZGI4jkeQULsaDZ6nJqNiohEEs/xCBJiZkExY3J60a9HeqJDERFJCnEbjyARSrdX827hZq47MVKVbBERIVqroYYij0fQ1t54vwR3DUIjIrI7OtR4BDMLiunbvSuHDOqV6FBERJJGhxmPoK7eef39Ek4YNYAuXSzR4YiIJI0mE4GZHQAM3DEeQcz8Y8ws3d0/jHt0u2Fh4WY2l9fotpCIyG5q7hnBrcDWRuZXhMvalVkFxXQxOHa4mo2KiOyO5hLBEHdf3HCmu88DhkTZuZlNMbMCM1tpZjc0s954M6szszOj7LcxMwuKOXxwNr0y0/Z0FyIinVJziSCjmWUt1nY2sxTgDuCLBA+Yp5rZ6CbWuxl4qaV9NqW4rJKl68pUZE5EZA80lwjmmtmlDWea2beB+RH2PQFY6e6r3L0aeAw4vZH1rgH+DyiOsM9GzSooAdRsVERkTzTXaug7wNNmdi6fnfjzga7AGRH2PQgojJkuAibGrmBmg8J9ncCuPZhpsN40YBpAXl7e55bPLChmn6wMRu3TM0JYIiISq8lE4O6fAkea2fHAweHs5939tYj7bqwNpzeYvhX4kbvXmTXd5NPdZwAzAPLz83fZR01dPW9+sIEvj9mX5vYhIiKNi1JiYiYwcw/2XQTkxkznAOsbrJMPPBaewPsBp5pZrbs/E/Ug89ZsYltVLZN1W0hEZI9E6VC2p+YCw81sKLAOOJvPxjYAdi1xbWb3Af/YnSQAQbPRtBTjqAP67XXAIiKdUdwSgbvXmtnVBK2BUoB73H2ZmV0eLp/eGseZWVDMhKF96JEez5wmItJxxfXs6e4vAC80mNdoAnD3C3d3/0Wbynn/0218Mz+35ZVFRKRRST2W485mo+o/ICKyx5I6EcxcUUxen0yG9eue6FBERJJW0iaCypo63v5wA8eP7K9moyIieyFpE8Gc1aVU1tQzWbeFRET2StImgpkriklP7cIRw/omOhQRkaSWtIlgVkExR+7fl4y0lESHIiKS1JIyEazesJ01G8s5QbeFRET2WlImgpkrgkKlKishIrL3kjMRFBRzwIAe5PbJTHQoIiJJL+kSQb07c1aVcvxIDUkpItIaki4RbKuqpbquXoPQiIi0kqRLBFsra+mRnkr+kD6JDkVEpENIykRw9AH96JqadKGLiLRLSXc2ramr5/hRej4gItJaki4RgJqNioi0pqRLBL0z0xiYlZHoMEREOoykSwS52eo7ICLSmpIuEYiISOtSIhAR6eSUCEREOjklAhGRTi6uicDMpphZgZmtNLMbGll+upktNrOFZjbPzI6OZzwiIvJ5qfHasZmlAHcAJwNFwFwze87d34tZ7VXgOXd3MzsUeAIYFa+YRETk8+J5RTABWOnuq9y9GngMOD12BXff5u4eTnYHHBERaVPxTASDgMKY6aJw3i7M7AwzWwE8D1zc2I7MbFp462heSUlJXIIVEems4nZrCLBG5n3uG7+7Pw08bWbHAr8CTmpknRnADAAz22pmBa0ca2vqB2xIdBBNUGx7rj3H155jg/YdX2eKbXBTC+KZCIqA3JjpHGB9Uyu7+xtmtr+Z9XP35j58gbvnt1aQrc3M5rXX+BTbnmvP8bXn2KB9x6fYAvG8NTQXGG5mQ82sK3A28FzsCmZ2gJlZ+H4c0BXYGMeYRESkgbhdEbh7rZldDbwEpAD3uPsyM7s8XD4d+DpwvpnVABXAWTEPj0VEpA3E89YQ7v4C8EKDedNj3t8M3Lybu53RCqHFU3uOT7HtufYcX3uODdp3fIoNMH0BFxHp3FRiQkSkk1MiEBHp5JIqEbRUuyhRzCzXzGaa2XIzW2Zm1yU6pobMLMXM3jWzfyQ6lobMrLeZPWlmK8Lf4RGJjmkHM/tu+G+61MweNbOEDo9nZveYWbGZLY2Z18fM/mVmH4Sv2e0ott+G/66LzexpM+udiNiaii9m2Q/MzM2sX3uKzcyuCc95y8zsN/E6ftIkgpjaRV8ERgNTzWx0YqPaqRb4vrsfCEwCrmpHse1wHbA80UE04TbgRXcfBYyhncRpZoOAa4F8dz+YoPXb2YmNivuAKQ3m3QC86u7DCep3JepL0n18PrZ/AQe7+6HA+8CP2zqoGPfx+fgws1yCmmhr2zqgGPfRIDYzO56gLM+h7n4Q8Lt4HTxpEgERahclirt/7O4LwvdbCU5knyunkShmlgN8Cbg70bE0ZGZZwLHAXwHcvdrdNyc0qF2lAt3MLBXIpJlOkW3B3d8AShvMPh24P3x/P/DVtoxph8Zic/eX3b02nJxN0LE0IZr43QH8AfghCax11kRsVwA3uXtVuE5xvI6fTIkgUu2iRDOzIcBYYE6CQ4l1K8F/9PoEx9GYYUAJcG946+puM+ue6KAA3H0dwbewtcDHwBZ3fzmxUTVqoLt/DMGXEmBAguNpysXAPxMdRCwz+wqwzt0XJTqWRowAjjGzOWb2upmNj9eBkikRRKpdlEhm1gP4P+A77l6W6HgAzOw0oNjd5yc6liakAuOAO919LLCdxN3a2EV4r/10YCiwH9DdzM5LbFTJycz+i+AW6sOJjmUHM8sE/gv4eaJjaUIqkE1wu/l64IkdlRhaWzIlgt2qXdTWzCyNIAk87O5PJTqeGEcBXzGzNQS3004ws4cSG9IuioAid99xBfUkQWJoD04CVrt7ibvXAE8BRyY4psZ8amb7AoSvcbuFsCfM7ALgNODcdlY5YH+CJL8o/PvIARaY2T4JjeozRcBTHniH4Io+Lg+zkykRtFi7KFHCLP1XYLm7/z7R8cRy9x+7e467DyH4nb3m7u3mW627fwIUmtnIcNaJwHvNbNKW1gKTzCwz/Dc+kXbyILuB54ALwvcXAM8mMJZdmNkU4EfAV9y9PNHxxHL3Je4+wN2HhH8fRcC48P9ke/AMcAKAmY0gqMUWl0qpSZMIwgdOO2oXLQeecPdliY1qp6OAbxF8214Y/pya6KCSyDXAw2a2GDgM+H+JDScQXqU8CSwAlhD8vSS0JIGZPQr8BxhpZkVm9m3gJuBkM/uAoPXLTe0otj8BPYF/hX8X05vdSdvH1y40Eds9wLCwSeljwAXxuqJSiQkRkU4uaa4IREQkPpQIREQ6OSUCEZFOTolARKSTUyIQEenklAikTYSVHW+Jmf6Bmf2ilfZ9n5md2Rr7auE43wiro86M97ESzcx+kugYpO0oEUhbqQK+lqgyv00Jq9pG9W3gSnc/Pl7xtCNKBJ2IEoG0lVqCzljfbbig4Td6M9sWvk4Oi209YWbvm9lNZnaumb1jZkvMbP+Y3ZxkZm+G650Wbp8S1sOfG9bDvyxmvzPN7BGCjmIN45ka7n+pmd0czvs5cDQw3cx+28g2Pwy3WWRmN4XzDjOz2fZZLf7scP4sM/uDmb0RXmGMN7OnLBhP4NfhOkMsqON/f7j9k2FtHMzsxLBA3xIL6tinh/PXmNkvzWxBuGxUOL97uN7ccLvTw/kXhsd9MTz2b8L5NxFUXF1oZg+H2z8ffralZnbWbvy7SzJwd/3oJ+4/wDYgC1gD9AJ+APwiXHYfcGbsuuHrZGAzsC+QDqwDfhkuuw64NWb7Fwm+2AwnKBWQAUwDfhqukw7MI6gtM5mguN3QRuLcj6C0RH+Col+vAV8Nl80iGJug4TZfBP4NZIbTfcLXxcBx4fv/jol3FnBzzOdYH/MZi4C+wBCCoopHhevdE/7OMgiq8I4I5z9AUOSQ8Hd7Tfj+SuDu8P3/A84L3/cmGBegO3AhsCr898gAPgJyY/8NwvdfB+6Kme6V6P9P+mndH10RSJvxoCLrAwSDvUQ114PxHqqAD4EdZaCXEJwsd3jC3evd/QOCk9so4BTgfDNbSFAWvC9BogB4x91XN3K88cAsDwrN7aiWeWwLMZ4E3OthLR13LzWzXkBvd389XOf+BvvZUSdrCbAs5jOu4rPiioXu/nb4/iGCK5KRBIXw3m9ivzsKHs7ns9/PKcAN4e9hFsFJPy9c9qq7b3H3SoIaT4Mb+XxLCK64bjazY9x9Swu/D0kyqYkOQDqdWwlq99wbM6+W8DZlWNyta8yyqpj39THT9ez6/7dhrRQnKF1+jbu/FLvAzCYTXBE0Zk/K/Fojx29J7Odo+Bl3fK6mPlOU/dbF7MeAr7t7QeyKZjaxwbFjt/nsoO7vm9nhwKnA/5rZy+7+3y3EIUlEVwTSpty9FHiC4MHrDmuAw8P3pwNpe7Drb5hZl/C5wTCggKBA4RUWlAjHzEZYy4PezAGOM7N+4YPkqcDrLWzzMnBxzD38PuG35k1mdky4zrci7KehPPts/OapwFvACmCImR2wG/t9CbgmTLKY2dgIx66J+b3tB5S7+0MEA/W0lzLh0kp0RSCJcAtBJdkd7gKeNbN3CMbcberbenMKCE6IA4HL3b3SzO4muD2yIDwJltDCMI7u/rGZ/RiYSfBN+gV3b7ass7u/aGaHAfPMrBp4gaDVzQUED5czCW75XLSbn2k5cIGZ/QX4gGDwnkozuwj4mwXDZ84FWqro+SuCK7HF4e9hDcH4AM2ZEa6/gOB23m/NrB6oIRhCUToQVR8VaYcsGPL0H+5+cKJjkY5Pt4ZERDo5XRGIiHRyuiIQEenklAhERDo5JQIRkU5OiUBEpJNTIhAR6eT+P7KYJJt8GZzbAAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1084,27 +1448,29 @@
    "source": [
     "from sklearn.decomposition import PCA\n",
     "pca = PCA().fit(df_std)\n",
-    "plt.plot(np.cumsum(pca.explained_variance_ratio_))\n",
+    "k=range(1,17)\n",
+    "plt.plot(np.cumsum(pca.explained_variance_ratio_),color=\"red\")\n",
     "plt.xlim(0,17,1)\n",
     "plt.xlabel('Number of components')\n",
-    "plt.ylabel('Cumulative explained variance')"
+    "plt.ylabel('Cumulative explained variance')\n",
+    "#Through Scikit learn we are plotting graph for no. of components over our data after data cleaning."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "id": "6a3a942c",
    "metadata": {},
    "outputs": [],
    "source": [
     "from sklearn.decomposition import PCA \n",
-    "sklearn_pca = PCA(n_components=7)\n",
+    "sklearn_pca = PCA(n_components=7) #90% of variance is covered around 7 components from the above plot.\n",
     "Y_sklearn = sklearn_pca.fit_transform(df_std)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "id": "f4e6187f",
    "metadata": {},
    "outputs": [
@@ -1134,7 +1500,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "id": "44f2459b",
    "metadata": {},
    "outputs": [
@@ -1144,7 +1510,7 @@
        "(8950, 7)"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1155,7 +1521,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "id": "bb8b8a5b",
    "metadata": {},
    "outputs": [],
@@ -1165,7 +1531,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "id": "6bb4406e",
    "metadata": {},
    "outputs": [
@@ -1250,100 +1616,31 @@
        "      <td>-0.837737</td>\n",
        "      <td>0.231600</td>\n",
        "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8945</th>\n",
-       "      <td>0.668484</td>\n",
-       "      <td>-2.871696</td>\n",
-       "      <td>1.452469</td>\n",
-       "      <td>-2.236975</td>\n",
-       "      <td>-2.854943</td>\n",
-       "      <td>0.766981</td>\n",
-       "      <td>2.343375</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8946</th>\n",
-       "      <td>0.262604</td>\n",
-       "      <td>-2.240280</td>\n",
-       "      <td>1.844972</td>\n",
-       "      <td>-0.568118</td>\n",
-       "      <td>-3.339266</td>\n",
-       "      <td>1.706628</td>\n",
-       "      <td>1.774529</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8947</th>\n",
-       "      <td>0.105962</td>\n",
-       "      <td>-3.066758</td>\n",
-       "      <td>1.189320</td>\n",
-       "      <td>-1.775107</td>\n",
-       "      <td>-2.965850</td>\n",
-       "      <td>1.263333</td>\n",
-       "      <td>1.979732</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8948</th>\n",
-       "      <td>-2.847160</td>\n",
-       "      <td>-2.517979</td>\n",
-       "      <td>-0.295195</td>\n",
-       "      <td>-2.148352</td>\n",
-       "      <td>-2.990361</td>\n",
-       "      <td>0.696690</td>\n",
-       "      <td>1.774277</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8949</th>\n",
-       "      <td>-0.164604</td>\n",
-       "      <td>-0.524308</td>\n",
-       "      <td>-1.644250</td>\n",
-       "      <td>-1.315554</td>\n",
-       "      <td>-4.692532</td>\n",
-       "      <td>1.532319</td>\n",
-       "      <td>0.092815</td>\n",
-       "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>8950 rows × 7 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "           PC1       PC2       PC3       PC4       PC5       PC6       PC7\n",
-       "0    -0.885122 -2.483017  0.230902  0.807129  0.043370 -0.382026 -0.357206\n",
-       "1    -3.000343  2.015089 -0.165334 -1.087171  1.670938 -0.288015  0.942749\n",
-       "2     1.191726  0.385174 -1.926789  1.859338 -0.550103 -0.230068  0.522876\n",
-       "3    -0.794805  0.218433 -1.661542  1.195618  0.058950  0.798510 -0.086756\n",
-       "4    -1.265058 -1.593317 -0.689436  1.339644 -0.114019 -0.837737  0.231600\n",
-       "...        ...       ...       ...       ...       ...       ...       ...\n",
-       "8945  0.668484 -2.871696  1.452469 -2.236975 -2.854943  0.766981  2.343375\n",
-       "8946  0.262604 -2.240280  1.844972 -0.568118 -3.339266  1.706628  1.774529\n",
-       "8947  0.105962 -3.066758  1.189320 -1.775107 -2.965850  1.263333  1.979732\n",
-       "8948 -2.847160 -2.517979 -0.295195 -2.148352 -2.990361  0.696690  1.774277\n",
-       "8949 -0.164604 -0.524308 -1.644250 -1.315554 -4.692532  1.532319  0.092815\n",
-       "\n",
-       "[8950 rows x 7 columns]"
+       "        PC1       PC2       PC3       PC4       PC5       PC6       PC7\n",
+       "0 -0.885122 -2.483017  0.230902  0.807129  0.043370 -0.382026 -0.357206\n",
+       "1 -3.000343  2.015089 -0.165334 -1.087171  1.670938 -0.288015  0.942749\n",
+       "2  1.191726  0.385174 -1.926789  1.859338 -0.550103 -0.230068  0.522876\n",
+       "3 -0.794805  0.218433 -1.661542  1.195618  0.058950  0.798510 -0.086756\n",
+       "4 -1.265058 -1.593317 -0.689436  1.339644 -0.114019 -0.837737  0.231600"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df"
+    "df.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "id": "e3c952eb",
    "metadata": {},
    "outputs": [],
@@ -1359,13 +1656,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "id": "356a0afe",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1377,22 +1674,23 @@
     }
    ],
    "source": [
+    "#elbow method for finding optimal no. of K\n",
     "plt.plot(m,wcss,'bx-')\n",
-    "plt.plot(range(1, 11), wcss)\n",
-    "plt.xlabel('PC')\n",
+    "plt.plot(range(1, 11), wcss,color=\"red\")\n",
+    "plt.xlabel('No. of Principle components')\n",
     "plt.ylabel('Inertia') \n",
     "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "id": "db9726c6",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1405,7 +1703,7 @@
    ],
    "source": [
     "X = df[[\"PC1\", \"PC2\",\"PC3\",\"PC4\",\"PC5\",\"PC6\",\"PC7\"]]\n",
-    "plt.scatter(X[\"PC1\"], X[\"PC2\"], c=\"m\")\n",
+    "plt.scatter(X[\"PC1\"], X[\"PC2\"], c=\"r\")\n",
     "plt.xlabel(\"PC1\")\n",
     "plt.ylabel(\"PC2\")\n",
     "plt.show()"
@@ -1413,13 +1711,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "id": "aac290b7",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1431,11 +1729,11 @@
     }
    ],
    "source": [
-    "K=3\n",
+    "K=3 #through elbow method\n",
     "\n",
     "Centroids = (X.sample(n=K))\n",
-    "plt.scatter(X[\"PC1\"], X[\"PC2\"], c=\"y\")\n",
-    "plt.scatter(Centroids[\"PC1\"], Centroids[\"PC2\"], c=\"red\")\n",
+    "plt.scatter(X[\"PC1\"], X[\"PC2\"], c=\"cyan\")\n",
+    "plt.scatter(Centroids[\"PC1\"], Centroids[\"PC2\"], c=\"r\") #getting three centroids as K=3\n",
     "plt.xlabel(\"PC1\")\n",
     "plt.ylabel(\"PC2\")\n",
     "plt.show()"
@@ -1443,7 +1741,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "id": "dc503960",
    "metadata": {},
    "outputs": [
@@ -1479,34 +1777,34 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>7399</th>\n",
-       "      <td>0.603924</td>\n",
-       "      <td>-2.836092</td>\n",
-       "      <td>0.567445</td>\n",
-       "      <td>-1.301385</td>\n",
-       "      <td>1.141140</td>\n",
-       "      <td>-0.246224</td>\n",
-       "      <td>0.104236</td>\n",
+       "      <th>8741</th>\n",
+       "      <td>1.969331</td>\n",
+       "      <td>-2.098843</td>\n",
+       "      <td>1.774436</td>\n",
+       "      <td>-1.199984</td>\n",
+       "      <td>0.603137</td>\n",
+       "      <td>-1.134845</td>\n",
+       "      <td>0.374469</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>7573</th>\n",
-       "      <td>-1.089214</td>\n",
-       "      <td>-2.436550</td>\n",
-       "      <td>-1.705584</td>\n",
-       "      <td>0.399184</td>\n",
-       "      <td>0.913203</td>\n",
-       "      <td>1.305055</td>\n",
-       "      <td>-0.857026</td>\n",
+       "      <th>6506</th>\n",
+       "      <td>-3.015030</td>\n",
+       "      <td>-2.606008</td>\n",
+       "      <td>-0.736925</td>\n",
+       "      <td>-0.288787</td>\n",
+       "      <td>-0.099649</td>\n",
+       "      <td>2.730445</td>\n",
+       "      <td>-1.405308</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1042</th>\n",
-       "      <td>-1.260052</td>\n",
-       "      <td>1.162674</td>\n",
-       "      <td>1.187329</td>\n",
-       "      <td>1.260699</td>\n",
-       "      <td>0.428170</td>\n",
-       "      <td>0.327633</td>\n",
-       "      <td>0.966585</td>\n",
+       "      <th>286</th>\n",
+       "      <td>-2.716373</td>\n",
+       "      <td>-0.016864</td>\n",
+       "      <td>-0.088214</td>\n",
+       "      <td>0.869378</td>\n",
+       "      <td>0.431633</td>\n",
+       "      <td>-0.521458</td>\n",
+       "      <td>0.443901</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1514,12 +1812,12 @@
       ],
       "text/plain": [
        "           PC1       PC2       PC3       PC4       PC5       PC6       PC7\n",
-       "7399  0.603924 -2.836092  0.567445 -1.301385  1.141140 -0.246224  0.104236\n",
-       "7573 -1.089214 -2.436550 -1.705584  0.399184  0.913203  1.305055 -0.857026\n",
-       "1042 -1.260052  1.162674  1.187329  1.260699  0.428170  0.327633  0.966585"
+       "8741  1.969331 -2.098843  1.774436 -1.199984  0.603137 -1.134845  0.374469\n",
+       "6506 -3.015030 -2.606008 -0.736925 -0.288787 -0.099649  2.730445 -1.405308\n",
+       "286  -2.716373 -0.016864 -0.088214  0.869378  0.431633 -0.521458  0.443901"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1528,90 +1826,10 @@
     "Centroids"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "a23db718",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.783716560094868\n",
-      "0.2777828784681925\n",
-      "0.12630699466929887\n",
-      "0.15419909005545476\n",
-      "0.1920261263912584\n",
-      "0.25113517413302006\n",
-      "0.36608646705129794\n",
-      "0.3510967441454697\n",
-      "0.363597742343025\n",
-      "0.2655010926877184\n",
-      "0.16723936650169546\n",
-      "0.10233341134743755\n",
-      "0.09420165033060114\n",
-      "0.05713076777382271\n",
-      "0.03746854274397527\n",
-      "0.024473834526929996\n",
-      "0.022228298448357765\n",
-      "0.016623241606870228\n",
-      "0.006266589186782384\n",
-      "0.005518544633962164\n",
-      "0.0059137446813048306\n",
-      "0.005043910213152325\n",
-      "0.005754658626990507\n",
-      "0.0020179893491221665\n",
-      "0.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "diff = 1\n",
-    "j=0\n",
-    "\n",
-    "while(diff!=0):\n",
-    "    XD=X\n",
-    "    i=1\n",
-    "    for index1, row_c in Centroids.iterrows():\n",
-    "        ED=[]\n",
-    "        for index2, row_d in XD.iterrows():\n",
-    "            d1 = (row_c[\"PC1\"]-row_d[\"PC1\"])**2\n",
-    "            d2 = (row_c[\"PC2\"]-row_d[\"PC2\"])**2\n",
-    "            d3 = (row_c[\"PC3\"]-row_d[\"PC3\"])**2\n",
-    "            d4 = (row_c[\"PC4\"]-row_d[\"PC4\"])**2\n",
-    "            d5 = (row_c[\"PC5\"]-row_d[\"PC5\"])**2\n",
-    "            d6 = (row_c[\"PC6\"]-row_d[\"PC6\"])**2\n",
-    "            d7 = (row_c[\"PC7\"]-row_d[\"PC7\"])**2\n",
-    "            d = sqrt(d1+d2+d3+d4+d5+d6+d7)\n",
-    "            ED.append(d)\n",
-    "        X[i] = ED\n",
-    "        i = i+1\n",
-    "    \n",
-    "    C = []\n",
-    "    for index, row in X.iterrows():\n",
-    "        min_dist=row[1]\n",
-    "        pos=1\n",
-    "        for i in range(K):\n",
-    "            if row[i+1] < min_dist:\n",
-    "                min_dist = row[i+1]\n",
-    "                pos = i+1\n",
-    "        C.append(pos)\n",
-    "    X[\"Cluster\"]=C\n",
-    "    Centroids_new = X.groupby([\"Cluster\"]).mean()[[\"PC1\", \"PC2\",\"PC3\",\"PC4\",\"PC5\",\"PC6\",\"PC7\"]]\n",
-    "    if j == 0:\n",
-    "        diff = 1\n",
-    "        j = j+1\n",
-    "    else:\n",
-    "        diff = (Centroids_new['PC1'] - Centroids['PC1']).sum() + (Centroids_new['PC2'] - Centroids['PC2']).sum() + (Centroids_new['PC3'] - Centroids['PC3']).sum() + (Centroids_new['PC4'] - Centroids['PC4']).sum() +(Centroids_new['PC5'] - Centroids['PC5']).sum()+ (Centroids_new['PC6'] - Centroids['PC6']).sum()+(Centroids_new['PC7'] - Centroids['PC7']).sum()\n",
-    "        print(diff.sum())\n",
-    "    Centroids = X.groupby([\"Cluster\"]).mean()[[\"PC1\",\"PC2\",\"PC3\",\"PC4\",\"PC5\",\"PC6\",\"PC7\"]]"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 26,
-   "id": "db9b4083",
+   "id": "f25df742",
    "metadata": {},
    "outputs": [
     {
@@ -1642,10 +1860,6 @@
        "      <th>PC5</th>\n",
        "      <th>PC6</th>\n",
        "      <th>PC7</th>\n",
-       "      <th>1</th>\n",
-       "      <th>2</th>\n",
-       "      <th>3</th>\n",
-       "      <th>Cluster</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -1658,10 +1872,6 @@
        "      <td>0.043370</td>\n",
        "      <td>-0.382026</td>\n",
        "      <td>-0.357206</td>\n",
-       "      <td>5.717627</td>\n",
-       "      <td>1.987937</td>\n",
-       "      <td>3.911431</td>\n",
-       "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -1672,10 +1882,6 @@
        "      <td>1.670938</td>\n",
        "      <td>-0.288015</td>\n",
        "      <td>0.942749</td>\n",
-       "      <td>6.413887</td>\n",
-       "      <td>5.502300</td>\n",
-       "      <td>2.453188</td>\n",
-       "      <td>3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
@@ -1686,10 +1892,6 @@
        "      <td>-0.550103</td>\n",
        "      <td>-0.230068</td>\n",
        "      <td>0.522876</td>\n",
-       "      <td>3.366650</td>\n",
-       "      <td>3.467866</td>\n",
-       "      <td>4.701523</td>\n",
-       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
@@ -1700,10 +1902,6 @@
        "      <td>0.058950</td>\n",
        "      <td>0.798510</td>\n",
        "      <td>-0.086756</td>\n",
-       "      <td>4.447815</td>\n",
-       "      <td>3.141441</td>\n",
-       "      <td>2.955205</td>\n",
-       "      <td>3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
@@ -1714,10 +1912,6 @@
        "      <td>-0.114019</td>\n",
        "      <td>-0.837737</td>\n",
        "      <td>0.231600</td>\n",
-       "      <td>5.556942</td>\n",
-       "      <td>2.555326</td>\n",
-       "      <td>3.316609</td>\n",
-       "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -1728,10 +1922,6 @@
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8945</th>\n",
@@ -1742,10 +1932,6 @@
        "      <td>-2.854943</td>\n",
        "      <td>0.766981</td>\n",
        "      <td>2.343375</td>\n",
-       "      <td>6.891655</td>\n",
-       "      <td>4.841445</td>\n",
-       "      <td>6.715344</td>\n",
-       "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8946</th>\n",
@@ -1756,10 +1942,6 @@
        "      <td>-3.339266</td>\n",
        "      <td>1.706628</td>\n",
        "      <td>1.774529</td>\n",
-       "      <td>6.604518</td>\n",
-       "      <td>4.608522</td>\n",
-       "      <td>6.223211</td>\n",
-       "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8947</th>\n",
@@ -1770,10 +1952,6 @@
        "      <td>-2.965850</td>\n",
        "      <td>1.263333</td>\n",
        "      <td>1.979732</td>\n",
-       "      <td>7.025480</td>\n",
-       "      <td>4.658972</td>\n",
-       "      <td>6.416311</td>\n",
-       "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8948</th>\n",
@@ -1784,10 +1962,6 @@
        "      <td>-2.990361</td>\n",
        "      <td>0.696690</td>\n",
        "      <td>1.774277</td>\n",
-       "      <td>8.220182</td>\n",
-       "      <td>5.554943</td>\n",
-       "      <td>5.340378</td>\n",
-       "      <td>3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8949</th>\n",
@@ -1798,44 +1972,27 @@
        "      <td>-4.692532</td>\n",
        "      <td>1.532319</td>\n",
        "      <td>0.092815</td>\n",
-       "      <td>6.404796</td>\n",
-       "      <td>5.613511</td>\n",
-       "      <td>6.016475</td>\n",
-       "      <td>2</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>8950 rows × 11 columns</p>\n",
+       "<p>8950 rows × 7 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "           PC1       PC2       PC3       PC4       PC5       PC6       PC7  \\\n",
-       "0    -0.885122 -2.483017  0.230902  0.807129  0.043370 -0.382026 -0.357206   \n",
-       "1    -3.000343  2.015089 -0.165334 -1.087171  1.670938 -0.288015  0.942749   \n",
-       "2     1.191726  0.385174 -1.926789  1.859338 -0.550103 -0.230068  0.522876   \n",
-       "3    -0.794805  0.218433 -1.661542  1.195618  0.058950  0.798510 -0.086756   \n",
-       "4    -1.265058 -1.593317 -0.689436  1.339644 -0.114019 -0.837737  0.231600   \n",
-       "...        ...       ...       ...       ...       ...       ...       ...   \n",
-       "8945  0.668484 -2.871696  1.452469 -2.236975 -2.854943  0.766981  2.343375   \n",
-       "8946  0.262604 -2.240280  1.844972 -0.568118 -3.339266  1.706628  1.774529   \n",
-       "8947  0.105962 -3.066758  1.189320 -1.775107 -2.965850  1.263333  1.979732   \n",
-       "8948 -2.847160 -2.517979 -0.295195 -2.148352 -2.990361  0.696690  1.774277   \n",
-       "8949 -0.164604 -0.524308 -1.644250 -1.315554 -4.692532  1.532319  0.092815   \n",
-       "\n",
-       "             1         2         3  Cluster  \n",
-       "0     5.717627  1.987937  3.911431        2  \n",
-       "1     6.413887  5.502300  2.453188        3  \n",
-       "2     3.366650  3.467866  4.701523        1  \n",
-       "3     4.447815  3.141441  2.955205        3  \n",
-       "4     5.556942  2.555326  3.316609        2  \n",
-       "...        ...       ...       ...      ...  \n",
-       "8945  6.891655  4.841445  6.715344        2  \n",
-       "8946  6.604518  4.608522  6.223211        2  \n",
-       "8947  7.025480  4.658972  6.416311        2  \n",
-       "8948  8.220182  5.554943  5.340378        3  \n",
-       "8949  6.404796  5.613511  6.016475        2  \n",
+       "           PC1       PC2       PC3       PC4       PC5       PC6       PC7\n",
+       "0    -0.885122 -2.483017  0.230902  0.807129  0.043370 -0.382026 -0.357206\n",
+       "1    -3.000343  2.015089 -0.165334 -1.087171  1.670938 -0.288015  0.942749\n",
+       "2     1.191726  0.385174 -1.926789  1.859338 -0.550103 -0.230068  0.522876\n",
+       "3    -0.794805  0.218433 -1.661542  1.195618  0.058950  0.798510 -0.086756\n",
+       "4    -1.265058 -1.593317 -0.689436  1.339644 -0.114019 -0.837737  0.231600\n",
+       "...        ...       ...       ...       ...       ...       ...       ...\n",
+       "8945  0.668484 -2.871696  1.452469 -2.236975 -2.854943  0.766981  2.343375\n",
+       "8946  0.262604 -2.240280  1.844972 -0.568118 -3.339266  1.706628  1.774529\n",
+       "8947  0.105962 -3.066758  1.189320 -1.775107 -2.965850  1.263333  1.979732\n",
+       "8948 -2.847160 -2.517979 -0.295195 -2.148352 -2.990361  0.696690  1.774277\n",
+       "8949 -0.164604 -0.524308 -1.644250 -1.315554 -4.692532  1.532319  0.092815\n",
        "\n",
-       "[8950 rows x 11 columns]"
+       "[8950 rows x 7 columns]"
       ]
      },
      "execution_count": 26,
@@ -1850,12 +2007,22 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "id": "1bba3fc5",
+   "id": "a23db718",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x228d4fec940>"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1867,23 +2034,67 @@
     }
    ],
    "source": [
-    "color=['blue','green','cyan']\n",
-    "for k in range(K):\n",
-    "    data=X[X[\"Cluster\"]==k+1]\n",
-    "    plt.scatter(data[\"PC1\"],data[\"PC2\"],c=color[k])\n",
-    "plt.scatter(Centroids[\"PC1\"],Centroids[\"PC2\"],c='red')\n",
-    "plt.xlabel('PC1')\n",
-    "plt.ylabel('PC2')\n",
-    "plt.show()"
+    "from sklearn.cluster import KMeans\n",
+    "\n",
+    "kmeans = KMeans(n_clusters=3)\n",
+    "kmeans.fit(X)\n",
+    "plt.scatter(X[\"PC1\"],X[\"PC2\"], label='True Position') \n",
+    "plt.scatter(Centroids[\"PC1\"],Centroids[\"PC2\"],c='black')#applying Kmeans to our data through Scikit learn"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "1bd70e58",
+   "execution_count": 28,
+   "id": "b6aec8f9",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1 3 2 ... 1 3 1]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(kmeans.labels_+1) #Representing which customer to which cluster"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "c1579aa8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'PC2')"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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9V35jodF0lN5tAPbuhR//WClKt1v9NTYqQ7BvX/jxXq9KmlqxQr0mmWhqgqeeCperoQF+9asun76WJtZyMEihevBRTSNbDIXbEXxIDlPZ5jHpuFjIaOYzgkHkcTcLWchoZjKUCxlj6sRx46WEQwA00mJyhHKltFbsh6nkIKeD3psbLwc5HVHGepp5ja38muX8jvdYxd42DVkkZjAkbJZhQ5BHOgPJDdoeqWKpD0laHKqZevCylwr2UN5pI69Jbnq3C+jFF80Tnbxete873zm7bd06uPxyZSCEUOGS//qXKqGQDNTVRa7bc+pUl09/hEoc2MKUnNtQEh31QZ+iNuK+FBx8j8VhfvoMUphr1P8ppzZiJVC/f7yILE5QE7a/gIygcx/kNB4TBefByyFOhy2ytuDhYVZRT3PAd/8hezhKFTdwdp2tigb2UmGUd+5Lqkld/1zSuYVZvMxmqmkCJMUUcJXhxmrNbIZxKMRlJBDkkxG09hGKD8leyjlNPQVkMoLCdquo7ucUz7M+6BxXMZlx9G/zdZrYspsTvMsuKmkghzQuYDQTY9gdrncbgLb84q33NTbC4sVQVRV8zK23wtSpMDz6qpRxo08flcx1+HDwdiHgvPO6fPp0UkzdIQKVYdtRVmEywzLIwMU+KhhBYcTF2gIycWKnJURxO7AxgQEALGY8T7E2qJmMAxsXc07I9VJwYA8b5Tqwk25SinozR2jCHbRw68HHPioopxYfPl5mM+XUIhDYsfEG27ie6YykMOx8g8nn61xAPS04sZESoQHMCApZwChWUoodGz4keaTzOWaaHg9qpvJPVlNnLDU7sJFNKrczL+KsoRE3z1ISdj9eZBMDySO7E593ZzjMGbZzHIFgAgPCZkQ9nd2c5N9sDHx/K2ngNbbgRTKFQTG5Ru92AV19NdhMboHNpvb5ef1185mC2x2bWjlSQm1t18ouCKEWe12us8bL4YCsLPjFLzouj78PgsEQ8kjHFaaOHZ0sptaWz/oMDTzPBl5mc0QfvA3B1UzFiT3gQ3dhpw+Zgdo+xfThC8xlBIVkkcpwCvg8sxkRooTPob+pmRHGvlAOU2nqEhEI1nOIR/iYk9QiUSNnt7FG8TzracFj+n4EgkxSIip/P/MYwbf5DJ9lOl9kHndyfpsK+U22U0UDLXjxIWnByxkaeJsdEV+zi+Om2yWwjaNtyhcr3mY7T7KWtRxkDQd4nNWspDQh104WVrAzrBOeGx/vsytm1+jdBmDECKU0U1OV4nS51OOf/zx4VF9ZGdkARHKvbN6smsp897vKfRSJZ56BQYMgPx9yc9WaRGcMwa9/DV/+slL6drsyYpdfrur6jx0b/XmefFLNJHJy1L246SZwuxEIPs8c+hgjbxcOUnCwlMkUkdXmKcup5SnW8Qve5g+8zwbKKCCrTSeEGy9bOdpm39/hFHAXCziXkUxhMJcziS8zPygqaSC53Mws7mURtzCbwSYx86k4uZnZZJKCo5VzpIBMTlMfdnwfMiIWpNvMEdN8BVAGZR8Vkd90lKTiZBgF9CW7zeOk0evAFyKPD8mOCEoeMIJMw9+DFx9NEQxYLDlONRsoC1mT8fEJ+0w/D6vw4mMlpfyGd/lf3uElNlFL7NYGz9Bguj00Eqwr9G4XEKj690uXKp+/EGrkPywks/TCC80LpmVmwqWXhm9/8EH47/9WYZhSwp//DHfdpRqttOatt1SHsQbjg66rUxFIbrcyQtGyZg38v/8XvgD8wQcwsAP+wldeUfL4S2D7fPD00/DJJ1BaSp4rnbtYwCkjEr0f2e1W5jxDPY/yccBV04yHt9nBOfQ3dbu0RqLcDveyyNR/DipSZgGjo3+PERhMHosZxytsCai+Y1TzOJ8yl+Fs4QjVNJFLGnMZjh0RJLkNQQYu6k2azrQmknFING1JMZxC3mN32HYndlMXVqwp5WTYyBeUzHs4SZ8ONAKKJ89SYqwfKVm3cpT9nOLrLGh3JhcNOaRRaWIEMjpRDj0SvXsG4Gf4cLjvPtXzN1T5g5opfPWrqjian4wMVSrhkkuCj923D/7rv5RS93qVEm1ogD/9KbzL1k9+clb5+2loUKGbHek69o9/mEcleb2q0Uu0/OhH4V3QAA4dgsceCzwtIJOB5Eb1JfyIvaahmNs4xlImtft6Lz42cbjd4zpCE27eZzd/5gMe5WO2cwwfPpabTrm9fMQeqmhEIqmkgeXsYD4jKCADOwI7gmL6cB3T21SsPiQjKIjpe2kLgWAkhWEzLQGMpiji64rIYgqDg/IKnNgZTRGDyYuPsK1wYDdd+7EZ+5KBcmqDlD8oA9WMOxCG3FUuZAzOkN+YEzsLQnJYuoKeAUTLb38LF12kYuobGuCWW+Bzn1Pulta89pr561ta4OWXYcqUs9vMQk1BzRpOn4YBA6KTrbbWfIYiZXCTl/bYvz/yvscfh698JfpzGRyl0lQp2rHhwIaznVmAD0kZlcyJsF8iOcQZamhkILntlnduwcPfWUUNTYFp9Em2cIgz1EcIGw2V342PTRzhbhZSTzOOVgu3fcnmqEk5CzuCJZwTl3DNtriUCfyDj2nBQwteXNhJwcmSkIXwUC7hHEZTxGaO4EMykYGMoW+nMqgPU8m77OQENWSRyvmMZFIbi5jj6c8HlIa5oSQwln4dvn48OEG1aSSVGx+HqWJWDK5xDgPw4mMFu6mliQxcLGA004ldWXxtAKJFCFUd87LL2j7O6TSPLrLZ1BpDayZPVjkFobhcqvlMtFx7rTI8ocre7YYLLoj+PMOHw9at5vuy2/Y3RyKfDE6Z+G09+KIq52xHBGXDtqaGRh7j04DbxYdkDP24mikRwxw3cZi6Vsof1Ch/A2VGZE106y9VxtQ8tGH9Z5nOMtZyhnoEAg9eBpLHUiZS0M5aSTzIJo27WcgOjlNBLUVkMd5wv7WFmj0UMbKNmUI0HKWKJ1kTMPJnqOd1ttGIO2IRvzzSuYQJvMk2hGFyfEiuZDKZJlFZfppws5MTNNJCMQUMiGO58DzSTQc2DmwUxrDHxCQGMYlB+JDthu52Bm0AYs011wTnD/ix2+Gznw3e9uCDsHp1sBsoPR1++lO1mNuRa/7jH/Dxx8oI2GxqAfd//1eFh0bLH/5gbjBcLrjzzujP04pzGckBTuEOCcUcQ18c2PG1s5hlayPK6P/YGHDN+CnlJOs5xMwIXb72UREkix8ndoZTwF7KTfeHkh2Spesni1S+ynmcpIZamhlATpiRSDRO7EyOUdhgR3mPXaYuwJWUMoOhEd2IUxnMaIrYSwUCGEVf0trwq5dxhqdYi0S5De3sYTRFXMPUuNR9GkQeeaRzirqgmYoNwVQGx/x68VD+6rya2NKvn1LGqalqnSA9XT1+6CG1ltCa2bNh+XKYN08dO2oU/PWvqn5PR7Db4Y03lJvmppuUsv7oI1UaoiMsWKBcXH63lhDKEH3tayqiqBMMIo9rmUY2qdgROLAxmUFcyWS2cjTij9NmjPxvYXZYSQSAOpo5TnVYmGjrTGAzskkzvaJEMpthzGCo4ZqykYKDc+gfVmNHQCDXwAyBoB85jKIoZspfGuGk8S5NEWtORkj48+KjIYLLzU8GKUw2RsBtKX8fkudYHygJ4g+9LaWc7W1EO3UFgeBW5jCSImwIbAj6ks0XmNupvBir0DOAeHDTTSpx7NVX1ULs5Zcrw2DGvHlq5N4eGzbAP/+punZde606Z+scBrtdzQS62sDly19WBeXeeAPOnIGFC7uc6DaavoyiiCbcuHAERn21rapatsYGnMfINqN7PG1UCA1NDmvNTIrZzJGg6woEWaQymDyGkM9CxtCAOxBtUcIh3mR7QPlKVGG2NJzMY0SEK8UGiWQNB/iIvTThJoMULmRszBKB4k0e6aaKXiDaVOod4RhVpt8jN142UtamsY5EJQ3s4gQA4+hHLulhx6Tj4kZmGEbHF5PIn0SjDUC8KCiA22+Pzbl+9zv44Q9VpI/PBy+8oFw1L78MBw6oUfvRo7BkCVx/ffhaQ0dJSQlOhIsB6gcfLNdQ8inhYJjCtmNv1/ecQxoZuIzyCa1fKxjfRrmCIrK4lqm8whbjZyspJIsbmB6YjTiwk91q1K9cCgJPUMlm5caYypBOK7IqGjjEGdJwMoJCU3fIGg7wPqUBN0odzbzJNpzYOKcTii3RLGAUz7MhyA3kxG7MtGIT0dPWrKgz86VP2MdKSo3zCt5nN59hXCDBMBQ1Q0yO6KSOog1AazwelfW7fbuqpb90qVrU9fPhhyreftcumDABfvYzmDs3vjJVVMD3vx8cFlpXBytXwn/8x9m8Abdb5TL8+teqYF16+IglVlTRwBaO0oSbERQynIJO+VlHUkg/sjlOdcDv7vfFt5f2LxBcxRSeYh0+fHiROLGTgYvhFPAJ+8gghXH0CytXPYZ+3EcRFdSRgsN0dNeaSHHpdmwcpbLDC6USyXJ2UsIhY5FT4DAS7Vond0lkxDDalZRaagC8+PiAPWzgEC14KaYPFzM+LAprJEVcwSSWs4N6WnBgYxbFXMCYoOPceKmjmSyjLIcZdTRRTRP5ZAQZ3YHkmvrIndg7PFM6TR0rKW31eSsT8i47GU1Ru9+V7oalBkAIkQv8A5iAutNflFKutkSYU6eUO+bECbWQmpGhsnNXr1YROT//Ofznf56Nkz95Uu177TXlJokXK1aoEX1oXkBdnVrkbR23X1+vjNNf/6pyGuLATo7zAhsDCU3rOMQw+nAjMzu8UCUQ3MJs1lPGZo4gEExjsOki2t5Ar+A6MkjhPEYwg2LuYgEbKKOSBoaSTynlPMd6vEbdm7fYzueZExYRYsPWbiatn0h+fIk0rdDpxcdOjlNKORm4mMYQCltFAO2hnPWUBRmVFuBp1nEPFwaMqQcfTbhNr11NY9BzHz52cIJtHMOJjakMYXgccw5eYCN7KG/VmKaCI3zMXSwgM8QHPoEBnEN/WvDgDMq1VvfwXXaxjoOB9z2fEZzHyFb3wcvLbGYXJwMFCaczlMWMQyCwYeM6pvEsJUgkHnw4sTOMPkzoYOG0nSaZ00pO2MVJ5nTz9qOhWD0D+B3wlpTyOiGECyw0r/feCwcPqpE0qNj6xkblxtm9WyVDhVbbbGhQinbjxvjJlZ5uHlYqhHn1z8ZGlb0bBwPgxsu/2Rj0A/EaRdC2c6xTVQod2JnNsDb7+u6lnGcpCRidOpp5h13U08IFjGGhMZrcSFlQWWe/a+k5SoIUqxl1NLGBw1RQyyDymMygQPbxLIrZzYmg6CABZJISFsrqwcu/WE0FdbiNdYr1lLGUSeSTyfvs4iCnTTOCm3BzjOrA7MeBjQxSwnohA0GhsRLJ05QENZcppTzQ/SzWVNIQpPz9uPGxjkNMZwgfsId9VJCGk7kMZyIDTX3kH7KHEg4Gnetj9pGGi5lG9Nfb7GS30QvBH767gUPkkcYs43sznAK+yUK2c4wGWhhGAUPIj3EEUPdagI8Gy6KAhBDZwPnAIwBSyhYpZZVV8vDCC2eVvx+PB95+21z5+9m2LXYyfPKJWtwdMwa+8AXVqWzxYnMDYLeHJ6H5yTCPm4+aHTuUK+yIymiUSDZymD/zQcTR0ZqQJiuxooZGnmml/P148fEhe4OyLjdw2DSprBE35W2Unz5BNX/kA1axl+0c5z128ydWUmOMsgeRx2LG4zQ6kzmxk08GNzM7TMFsMoyIXw6JGsm/whb+xSfs41QbtYJE4HUevFTTyAJGh0UhObAFKfY9lId1FnPjZQ0HAvkKsaScWtP1Ci8+DnGGh1nFJg5TQxMnqeV1tpmWlpBIo2NbePb1x+wF/J3aDpsam9DGPhmkMIthXMAYhtKnU8p/HP1MZ7KC5ElCiyVWzgCGAxXAP4UQk4H1wD1SyqCsISHEHcAdAEOGxC4DLoxICr69fR1J2GpNfb1abPXH+7/8ssosbjSm9vv2KaP06afK/fS1rwW/XghVsM1fb8hPRoaqOxSJjRvVjGbCBPXXmupqZYA2bFBrH83NcOONvPzIN9lpO9lmxm5jBFdFV3mDbaZGx89rbKGYPuSQFrFAlqDtsdsrbAmq0qm6i3lZzi6uZSoA0xnKRAZyjGrScFJEFgJBDY2Bmv+jKWIbx03zCLxtvguFRDKQHFaxl1XsDbS5HE4Bp6ijhib6kMEixgXV5InUsEUgOMBppsZ4Yt2HDNN7bTcS35qDlsvPGqN5DA8KBPAhaY5QXM6fle02FurNiNTwpyv0IZMFjOID9gSua0NwIWN7nP8frDUADmAacLeUco0Q4nfA/cCPWx8kpXwYeBhgxowZ8ZuDXX01PPecGvX7sduVcjUrswDKPXP//R27znvvqTj9AweU8r/9drVw+/Wvn1X+oMJH6+vVAnBNeFMT3G51TFGRckVJqWT/whdUJBDAli0q/+DAAbW+sXy5Gt0LoZR7fr4yLHfdBYWFKgR07dqgdYVTm1axw3sNHrOy2a0YHEWt9moaOcwZ0kmhmD7trhlIJHvbqZ4pgRIOUcYZTpo0fwFw4aBvSBZuAy3U0EgGKaazA4lyPYWep5iziXUfs5eV7MGGQCLx4ovYH7mtL65ALShfwSQ2czRs4fcAp5nH8Ihhsak4sSHCFKUXb8Ty012hgEwGk0cZlUGGwN+jwNw42DhJbdD9s2Mjj3TTgmf+CrMu7BGLoplVdo0F8xnJWPoHhYHmR8hG7+5YaQCOAEeklGuM5/+HMgDW8NBDarR99OjZBVefT5VAqDapXe90KuX8jW9Ef43Nm+GKK85m/no8qp/A0aMq2icUKZVbyBPhR1xdrcpZnzmjitgtWnS2mN1rr8ENN6j34vWqCKZQQ3bypIpk+t3vVBlokw5ph6cOQ7S0gDNycouAsKiOCmo5Qz2FZJFHOu+wg/WUBZR+Cg5uZa5pmQeJRBgKrb3EJ4lkHQdD2rMoHNiwIbiOaUELiq+ylR0cx4ENTxvJVY42PKTHqOZD9hh+6bNEGtGaKWhQo/TZFDOdIfQhk9+xwjTq51MOcD6jTN0akxnEGg6YlH2G99jNAHJjXsTtBmbwFjvYylF8+OhHDpcxkU/Zz0lqwt6pF59p34KLGc//sSHIxePExmLGA+r+XMoEnqUkcIxARfhcxLiYvqfW9CGD+XHO8UgGLDMAUsoTQojDQogxUsrdwCJoo0tFbC6qYulTU8P96oWFSpl+/vPhxzudajbQ1KRG/YWFylhESu7ys2ED7Nyp6vFPn65cOaFVOxsb1TpDpO5kBQXq2rtMmkC43ap/gL8HwNtvKwPg8wWXmYbIsxivV/U7uPxyU1dX5olKhDeya8WBneuYFpget+DhGUo4zJlAQ5QUHHjwBvm+W/DyDOu4iwUBpXaQ07zFdqOTlvrxRzPl8xqmIlS2URRxGRMDkTqNuHmatRwxirV5WymUUOwIprZRdGtLSDJZW9gRzGcEqzkQEg+vylxcZCg7gLoIbo0WPEZkU/i6TwGZXM5Eo4FOMG68vM5W7uT8iPJ58bGGA5RQhhcvY+nPAkZF7EEMaja0lElcwUR8yMCawByGs5tgd6EdwQByTUfRo+nLzcxiJaWcpp6+ZHEBY4LCgEdQyO3M42P2cYo6BpLLKIp4hx2coIYcUjmf0Yyhb0R5NeZYHQV0N7DMiADaD8Qoc8qEZcvge99To97sbJVYdd99wYr3Zz8LL4fc3KwMxvXXqxH31VfDZz7TdrJVXZ3qE7B+vcrW9flg2jTVUtJMEaemqsSud94JdgP5XUxpaco9E1o6GoKPv/xyOH5cuXzq6qK5K2eJsM4x4t1NuLyEqSUHNq5mCqPpG7Qg+BbbKeNM0Gg00qi4hiZOUUchWRynmqdZG/Cf+/3f0RBpYTqb1IASO0M9j/Cx6VqF2VXScHFeGyNA85YpkeXbzFEWMSYoxnwaQ8OidPqSxTGTbmk5pLWZODWJQbzONtO1gApq8eCN+Pr/YwP7qAjItZ5D7OEkX2NB2AJ0KKrl5dnf0AByuJopvM7WQBey4RRwNVMinmOo0bmtLfqTw3VMA1Tm72N8GnivDbTwAhu4hAlMiUMdnp6MpQZASrkJWnXRjhcvvgh33HFWgVZWqiQqjyfYhx/aT9dPUxM88YR6/NprSqnbbEqpP/lkeI2f735XNWlpbUzWrlUlFRyOcJdOUxP85S+qiNwLLyjj4nar57ffrozU7t0q7t/rPftnJufq1SqKqCvtJf3Y7diyc7itYTLP5h2nkgYEghQcXM0UBpPHpxzgNHVkk0oRWWzhaNSqUXA2VPND9kRVhC2USFdyYWdQK7fHG0YFymipo5kP2MtnGIsXH814SMMZmK2Mox9bOdrmwnhrGRtpIYtUvsNF1NNCGk5ThbyY8SxjTVjxvItbzRIi4cJhKo/N+GdGObVByh+Uwaqnha0cZVonSg+PpR+j6Us1jaTiiHkJ7BWmBeZ8vMsuJjMoLsXfeipWzwASw49/bN545ec/V7MC/wLnpEmqiFpb+HxnR/Fr18L8+SpMNKVVstDjj4fPJFpaVGRPWpoanftH3Onpyl3Tv7+apZw6pdYERoxQHcdArTX8/vfqsZkB8eNf3C0shPPPV9nCoaGtkfIHzJg0Cd5+m/zCQr7GaCppwI2XM9TxNjvaDK2MBlU0LZtm3BwzqaHfEWwQZD48+NjBcYrIooBMDnC6w+f8hH0042YrR/EicWFnEWOZZiRZjaUfuzgRlRFw4+U09diwkUkKtTTjwhfW6WwI+dzKXN5nN+XUkk8GCxjFsCiSumYylFXsDVLmDmxMZGDEBfdjVJkqTDdeDnGmUwYA1JpHXoyjZk5Rx3J2RvwsW/DQQEtY4p4bL5s5wl7KySKVmRS328a0t9A7DMChCNUhGxqUMvbXuv/FL5R7x8zVYoa/29dLL6nF3RUrlHJubDQ/3u2GkhK4+WYVjQMqFPO++9RjKeH991XryIoKlQNw4YWqm5hZx69QvF5lkACeegouvlhFAnm96txjx6ptjz6q3rfDoYyf2x3umsrIUAvjJ06oxeRVq8jLyqLsrht5+f9dQrOz86Msm+E2uJLJrOMg77G7Aw6VSAhSsQd61vpQ/XBLKWcgOZ2uormRwwHZGvHxNjtIxcl4+nMVkznEYDZzJJCo5M9EDcWJnSKy2EcFr7KFBlqMEM9CrmJy0Cg5lzT6kMEp6mighdPUUWzEtfvwUUczabjC3DPnMoLT1LOT49iNjNli+rTZ/CUnQnVUB7aIfRisoJpGHuHjiO5EP6EGtRkPj7CKapoCiXmbOcKVTO4WtZTijZDRjgaTgBkzZsiSkpLOvFD540MpLFQKDpTi3bBBjdQfeUT50aPB4VChl88+q577jYIZfrfR+vVnR+F2u5o9pKaqaJ7WI3T/4m607SEnTFBtJ+12JcfUqWoR2j8L8C9mgzIodru6xve/D7/6lbq2x6PkvOQSKC9XUUitjIM7zcXOq+fx0jKTngdRMJwCishiGkOooYlnKYlqBN0e/oiftiqBxopCMvkaC4K2SSRHqaKaRt5mR1j2rgMbtzE3yHcNyhj2J4cvoQx3M27+zIfU0xwwPE7sjKc/A8jhfXYHcgqmMYTFjAtz79TQSAV15JPR7ihcIvkjK6miIchEurDzdS5ImtLGb7OddRyKOFAwW1AHWMVePmRPmFFOwcF3uChmvXWTHSHEeillmLu9588AKivVAuuWLcHukPR01ZClsVGNsnfsOBsh1JEF1JQUtQ4QjZKWUs0AWuP1KoPhNxqtDbLHEzl6x4yDB1Wj+csuUwvKe/cGv2d/0bjW1/Z6VbvL5cuVS0sI5YL6059MDZmzsYVxL3zMiiO3UTuoY7Vm/IXARhvRGsvZGRPlDxjx54kZzNQQPhsTCAaRxyDyIma9vsl2PCHv14eknFrKjW5dmzhCIy1Bis6Nl60cZTvHghTZRsoQiLD1gWzSIjasAVVyogk32aRhQ3Abc3mBjRyhEhDkkMZVTE4a5Q9whKqIyt+OzXRBHVTtqkgN5k9Q027RwZ5OzzYAK1bAlVeefS6EGt0OG6a6bt10kxr9btly1sXS0eiZoqJAyYQ2iRTm2R4dMQB1dfDmm8oAfPJJ9K6s2lqVQyCl+v/99yO7sQz6bdrfYQPgwce/2cgw+uBFcryLfv/WnE3Gah87NoaSzzHDMdBRQv3H5dSyixPYEAwil1oTA+E1FL2ZChMoF0cRWRzglKnC8iHDFKAbH+s5xCLGRFVauRk3L7GZvVRgQy0aX8oExtGfLzCXRtx48JJJStItpBaSyTGTBkAANzEz4hpJpBr90ghP7u303DvQ1KSao4T2yU1NhT/+UfnCQUX3RONfN8PlUr5//wJtWyTC1WazKf/+s89Cbm7HXuu/B8uXtyuro8nNkm/+jUPnT6Alu2MLff5OTbFEGIr3FPXU09zuPCCHNIZTwH5OtXtulSwWnO06kFzKOMNg8viQPXzMPrz4AqWdI7W5dGAzXSNowRtompJPRsSksUg04SEzCgPwPBs4ZBSi8wJuWniRTWSTxkByjRLL4QrTjZdtHOUIVRSQyWQGtZkjEA/mMpztHDedMT7HeoaSTxmVuIxeA/MYjs2YcR6lKuh1ArXOUhDD3r3dlZ7rAFu50nx7fb3qrOWnIyPsUBwOFZ6ZLPh8auR+6pSKOOoMbnfkKCMDAWQdPc3MP77WuWvEGInkEGeoa6X8bRHGsKpG/EBWUtrueZ3YmMYQ+hpV6v2FjEs4xDLW8jc+CkTd+JPeItX8cRg+6khx9a+zlTqaTPvk2hARX+fCQUYUyriKBso4E+Ym8+DjE/ZHfF09zfyJlbzFDjZymPfZzR94v8sRYB2lkCw+y3TTfc14KKWcJtzU0MSH7OElNgMwhr7Mohi70eLTX1riRmYmUvykpecagLbi4FsruBtvDA7h7Ahut3IzJSNxnnE4WjyM/3cUrSwTROi7jVREYjB5nKY+YuG41rjxUUUDVzCJDFx48eEx/tx4KafWdN3B3/vYX0pCZUyr/gNzGBbRvbKN4+STwY3MIJtUHNiwY2MI+XyW6WFGwImdzzA2KndNDU0RFzyrqDfdDvAuu6ijuVWVUpUT8bKhYONBLU18QCnPs55P2BfI38gkBVcUMx0PPnZxgiojb2URY/kmC1nKJG5iFnezMOYhqt2VnusCWrjQ3AhkZASXe/jpT5Xbo6xM+dBdrvAY/kiExtj3MrwOo1heZ9c3TPDHq3c1LDTSq6Nx+7RmD+XspaJD8khgIaPZzBFOU48PSZOhNFUV0XD5/IoVYBgF3MOFVNOIC0fA3XKbkR9wnGpySON8RgUW1NujiKyIFTyHtirQFspuTpq+95PU0Iwn5n70E9TwL1bjMwztHsr5hP18mflkkBL1Qr+/+Jy/REkWqYxro1Vob6XnGoD0dOXfv/lm5RppaVHbli5Vfns/OTmqSNsrr6gw0MGDVQx9SUn0i6jxwG6PTTavGR0xchGQQP7uo6SXV9NQkA32rk8mndiMxjCCVUY9+FDsgSJxiaEjJSn8+JBkkUoVjWHRPCeoieiaGtFqIVMgwsoP9yeHm5jVIVn8pBqNWT5tVY9IoFxIcxke8XX2NmYXHe0AFw2vhZTm9uDDSwvL2cn1TA+s3bQ3g/MhyW0jEkqj6LkuIFB1e0pLVSvHH/xAFUtbtuxs5q8fh0MtGP/Xf8FXv6rWD15+WWXoZmZGbrwSL5YsURFKXW3uHomWli6/JwGkV9dz5+RvMP2vbyA8XTNWDmyczygWMoYDbYzSI9fu7BqxVGVFRm2jSCGuoU3kbQjGUBT3kMQLGM3lTKQvWWSRyiQGcQfntRnuOYXBYVVRbQiGU9BunaCO4sHLcZM6SBLYZ5QFv4apjKAQOzZc2HFhD3Nt2RH0JSvqlp+9md6RCNYV3G4YP17F1JvRkdIKoaSnh88yHA7YulVlJw8e3LVF6gThcTl4cdl32HnduZ16vUAplWIKmEA/XmZrbAWMgtBSEu0fHzlSx9+MfANlUbss0o0RejEFePDSjxxT98pp6nnPaCuZjot5DGcKg7sctimRnKEeYZRwaF0+exlrjQJ1qkx3JqncxlwyI/RK7iw+fPwPb5nes3ScfIfFgef1NNNAC3mkc5xqXmWrIT+MoR+XMzEsK7g303sTwbrKf/93ZOUPnVf+Tqe5i8njUUXpcnK6hfIH2Lt4KruXzu7Qa1orUIka2e+jIjDSSzQdudMCwUQGss2oERSKGy8HOW0o0ei+Hw24WcFuYDcpOPDiYyGjmduqImkVDfyDVbQYHbcacfMWOzhDA6Mo4n12c4o6+pDJBYwOar7SFkeo5N9sDJSnyCGN65lOEVk4sHMrczhGNSepIZd0hnWy3WJ72LAF6iu1vq8Oo8l9azJICdT8GUw+d7GAJtzYscV8ZtKTadcFJITIFkKE1cUVQkyKj0hJRHOzqsAZa/z1dyLx8593mwXmLZ9bwHMv/xifM/qxhKODce7Jgj+y5xqmUElDm6P709SzlEm4jD7CHSk50IwHDz5WGo3V/XzMPtwhV1XNYvbzBJ9yiDPU00IZZ1jGWvZwst1rNdDCk6yhmkajFaaP09TzGKtbrRUIBpIbKIIXzySxy5hIX7JxGu4dBzaG0ocFjGr3tak4tfLvIG3+aoUQnwUeAsqFEE7gNinlOmP3v8Ao0N1T8dcJihV2u3LrnDzZdqZtXZ1aA0gEXXBh1RVk88pj94avqUTAjqAPGZTTwWzrGGFHdLpcxABymMwgRlFEDmltxs77GUM/xtKPw1SymSNs4WiHrunGy3Osp4gsZjMsrM+CH7NFcS8+XmQT3+PiNq+xLUL5bi8+SjmZ8IJpqTj5EvM5RjWV1FNEtq7cGUfa++X+EJgupZyCatbyhBDiGmNfcuWKx4O+Me4wNHCg+munzAJLlqjCcImgk8q/IT+L3+9/BJ8j+hFXDumWKX8/jpDYlUgJY6GvGUAO7xlJUA/xHn3IiNgysvUiqQM7wyhgJsWdGp268XKUKl5lS8TIl0ifYBMeTpgsqramhibT0hNefKYlLRKBf8YxgYFa+ceZ9gyAXUp5HEBKuRZYCDwghPgm0To320EIYRdCbBRCJEdaaWtSU+Hee1UN/1gwahRs3972MRkZqgx0DGPr48E7v/oSngyT1pptYNbYu7P4k6Q6gtcowyBRC7WZpHAuI5huZN/6s339mbcpOHBiZzgFbOYozYbvvZYmdnKCTFLCjIDDaHS+lMlgHPsRe9hAGQPIabPPcFu48VJNI44QcxVq0ELZ28qFVEMjWzhCqVG6GlRinNkZJPFruh4NEkkNjXFpaq85S3uO21ohxAgp5T4AKeVxIcQFwEvQRpHxjnEPsBOSNGbrgQdU0/RYsGJF+wqzoQHuuksVaEtiBq3eydZbFyI7MKqNZQCnHVuXlIMPH/eyKDD+P4+RHKOaLFLpZ4RxnqCGUk6a1i7y4qOaRqYzlFPUkYqDAeQykNxA7f7DnOFJ1gbq/zixk46LHNI4SmWH+5/ZsTGe/uzkOM2Gf34AOaTgZI+JjCrOX30+K9jFpxww4voFDgS3MIcUnKafiyqWZo0/fTvHeIvtAYM7jn5cwSTt348D7RmArxHi6pFS1gohlgCf7erFhRCDgMuAB4Fvd/V8ceGZZzpeIbQt2nO5SKmqkyY5Ux5bQX3/PD746S0Jva4NQQYpNOHukjnxIinhEDMYShlnOEENGbg4yCleYXNUtW4kqi6QC3vAjz6X4QijMukLbArKBXAblYJGUsRxqiMWjYtEi9HZqvX7Pk4Nw+ljGm9kQzCe/uylnLUcxIsvIE0L8DRrmcRA0/toQ7CPUxQk2AVTxhleYUvQfdvFCTx4+WwCusf2Ntqbj9aDaa75HODTGFz/IeB7tBGFJ4S4QwhRIoQoqaiwIETwlVcSf81ugKPFw5zfvpzQUFU7gmuYylJiE4D2Jtv5He/xNOt4hx28wCbeZVeHC521GNEze6ngfaMXQBWN1BPeI8KLjwOcYhz9OzWiDVXWbrzs5zQXMhY7Aie2wN81TCWTVNZTZpqU1oyHJjymLiAbNlwm40MfPnZzgk/Yxz4qYp6W5490ao0qCVFBnUVrEj2Z9mYAD6EWgkNpNPZdYbIvKoQQlwPlUsr1hlvJFCnlw8DDoBLBOnu9TpOTk/BLdhec9U3YWzx4UxNVGlhQQCY5pEVVzC0azJq7dBYPPtZTxmyGUUtTRNXYjIeJDKQfWbzLri6rUB8+BPAdFrPf8PkPpzCQSBbJVSYQDCafzRwxjQQaR7+g57U08Sif0EBLoL1iCk5uZiYDyeviu1BURihM58BGDc1kJlGTmp5AezOAYillmD9CSlkCFHfx2vOBpUKIg8AzwIVCiCe7eM7Yc++9VkuQtNT1y8Obkri68F58PMVaSjjYZgEzK2nGw+95n2WG799sxaeBFp5mLWs4GJNr2rCRjosUHIyjP+PoH5RFPIEBprMNH5Jx9GMpk3AESiuoksnXMz0sk/ZVtlBj5AuAmo004eZRPqGM2EStDTHcWaF48VGQRD2KewrtGYC2zG2XQmOklD+QUg6SUhYDNwLvSSkT61BuDynhZz+zWoqkJaO8mpwjHauuKeha/HANTaxgNwc4lbRxyF6jXLQ/Pt9M+UrUe4nFlNaNN6y+UGsmMSiQXOVHAOPpj0QykDy+yDyWMomrmcJ9XMRICsPe035OmcorgTfYFoN3ohrbu0JinZzYOZeRpi4pTddozwCsE0J8JXSjEOJLgEmX9R7G88/DCy9YLUXSUtc/n4xjpzucSxArP153yCUWwOAE9J19gU00R2hvacfGbcxhGoMDilWiom1+yTv8hQ/4J6t5l11kkmJqsFoCAbTmqN4IXXfL5ZLOVziXcfQnAxdFZHE5EzmPkV0+tyac9kzqt4AXhRA3c1bhzwBcwNWxEkJKuRJYGavzdYmPPoL/+R84cEAlY3WjYnmJxGe38c+PfkHtwD4dygXoyXfTji1MCapOYfFH4uMVttCHTEZTxKAQn7wPycaQCKKzCWBqaxWNPMEa7uHCwIziKFW8xlYq2lkY9+dPdIVqGgO9ka/r4UUGkoU2DYCU8iQwTwixEJhgbH5dSvle3CWzgmeeUSWgrewD0E3Ye/E0mnIzkR3IBO7pmI2AndgZTREHOR3Xa3uQ7ESVLlnFXgrJ5FxGMIq+pOKM2IcgFB8+tnGUmRRTTSNP8CktEcpat6azCW6gZhf/xwYOcjpgROcwjIWMSbrm9D2N9moBpQJ3AiOBrcAjUsqemZrn9cLdd2vlHyU1QwrxObXyD8WJPajhigcv77ObDFzU07UmPB2hgjpeYjN2bFzBJPqRHVUBPg++QGTUOiN3IBq8SMqp7VQN/lfYwgFOB1puAqzhIPlkMIXBHT6fJnraM9uPoVw+W4FLgF/FXSKrOHZMK/8O0L9kL8LXkx06neNixtOXrIAfXaJ6CydS+fuRKIX+KltwGs3Q2xtP21DlIYCIPY/NXycCLS07QjMedrcqTeHHjZfVURTc03SN9gzAeCnlLVLKvwHXAecnQCZryM2NXwvGHsjAkj0MXrUdR0N4slNvZSA51NPCPEZEbEpvBRLYwXE+x0xySAuUWTbDB/QhE4BB5EXt2pFI+hM5Z8aLjzqawhR9M+6IRqnBAqPZ22hvETgQViCl9IgkL1DWJbKy4NprVdRPk844jIabLv0JWz93PmvvWcrJqSM61BOgJ3KcGo5Sjc0oBREvUnHQ1IHRthcfLXjII527WchhKvmYfab1gxzYOMAp+pDBdIawhgNGQOvZ/er9EeTqKiSLtRxgKkMCTexBGYZV7ONj9uHDhw0b5zGCeYxAIMgilVSc1IVkTQtgWKseyZr40J55nyyEqDH+aoFJ/sdCiJpECJhQ/v53uPRSVQU0O1tVAc3PVxU6NWHYpGTyUx/w5dn3cdfYOynYUWa1SJbgHxb51WQ8m9bbEcxvo4l7JD5hP7/jPVZSSj+yyYrQztGGCBSQyyCFL3MuY+mHy6ieOp8RfItFLGYcxfTBjg0bNo5SxQfs4Y+8HxQxtIaDrGIvLUaTmxY8fMheSowkOIHgMibibKWKbAhScLKQMR1+n5qOoXsCm1FertYERo1SxuDdd+G662JbFK6H4QOa8rP43aFHcWfGqHy2xpTpDKGcWo5Q2WFD48BGLulU04A7QtTSt1lESjv9dKto4FW2cJAzYeZuMHnczjwAfsVyU1dOJil8m88Enh+jio/ZTyX1DCWfuQwnu2u5pppW6J7AHaGoSP35ufhiVaI5Hu0hewg2wN7iZvzzq9h8+0VWi5MU+JvNdLYLmRl+Bb6AUTzBGs7Q0KEELA8+zlAXUaIrmNim8j9FHc+znkoaTBvJgOoxrOoTiYh+/NBCeQPI5Xod+59wOh+829u4+26rJUh6XPVN5BzuWGmInkwmKdzPEu5lUZeTpPwIYDKDyCSVOzmfW5hFBq4Ond2HeUKeCzvpEVxDoEJa/8VqKqiLqPz9UkqUeyc/Qv0e/0Kzxlq0AYiWN9+0WoKkx5Pq4tjM9pt39xZScXKESjJwkRWjKpZTGIwLe2AEPZQ+nMvImCRMeZH0iaCwJZJP2B9VEx6J5E+sxIOXixlv2jVtMeO6LK+m62gXULT89rdWS5DUSMDe5MZZrXMp/JyijmWsJYtUhlPARg53+ZzrOMR6DiEQZJPGEs5hBbuiSvJqCwe2QMP7UCpp4Ak+pZamqN1ZVTSyin1cwGhuZCYr2c1p6ulDJgsZrSN8kgRtANpCSnj7bfjnP2G/TkppCwEIKbnm87/m3SOnKPn6ZXjTIrsTegM+JD4klTTEtB+ycr6o8z7HeqPNY2QEAhuizbUCLz4OcYYPKOVcRgb6LUskT7OOaho7bGI2cZgLGM1wChiuFX5Sol1AbXHPPSr657nnoFknPEWD3eNl4Y+fYMFPn9KF9BKAr1X5BDMEMImB3MD0NtchJCrx6iP28ndWBQzWKeo6pfyBmK17aOKHNgCReO45+NOfoN68Q5EmMq4mN3h9oEtFxJ227rAdQTouLmA0L7MlKjeRz6jp82c+4BP2UU9Lp9cXZna5Z5Qm3mgXkBmvvQY33ZTQfrc9CQnsWToHbHoEmAjScCARePAikbjxkU86UxnMNIZwmnrcHazT48XHu+wCzBv42BFkkBKxpWY/spnDsI6+FU2C0QYglLo6VRJC1wXqNAcvmMiJqcM71CdA0zb+lo0NJk1fmvFyMzPxGKP3o1RxjCrWcJBdnOjygmvovEEVlkul1qTpvUAlql3KxC5dU5MYLDMAQojBwONAP9S61sNSyt9ZJU+AX/wCWnQRqq5wcOEk3Om9ewE41nja8PUL4BT1FJLFSkoDNXoA6mjmZIy6dQ0lHxcORlFEHzJ41qQpoERVEdV0D6ycAXiA+6SUG4QQWcB6IcRyKeUOC2WCf//b0sv3BFy1jQiZPNUwewP5ZPAaW4OUv5+2k7aiZzB5XMhYQJVuiLQCYddLi90Gyz4pKeVxKeUG43EtsBMYaJU8AdLTrZagWyOBxtx0pF03i0kUbnx8QCmniW+tqtbNXvqTQ4rJ+NGJnWkMiascmtiRFKZaCFEMTAXWmOy7QwhRIoQoqaioiL8wd96pqoBqOoW0CRoLI9eF18SHsk4UhusIqTgYS7/Ac4HgRmaSiiPQX8CBjXPoz3j6x1ESTSyxvBqoECIT+AB4UEr5QlvHJqQaqM8Ht90Gy5bpKKBO0JSbwa+PP4E31XU2D0AvBndrcknjduaSZZIl7MZLKSdpoIVi+lBIlgUSatojKauBCiGcwL+BZe0p/4Rhs8Hjj6u1AN0iskP4bIJnXvoRNrcHr10gW1ywIxv6NiOG6CY73ZEJDOAapkbc78TOOQxIoESaWGKZC0io9mKPADullL+xSo6I5OVZLUG3w+aTzPjzG3hdTuRfRkLfxfCZ2TDmAuQFc5Cn264xr7EOs6xdJ3bdlL2HY+UawHzg88CFQohNxt+lFsoTzD336AXhDuIV8NIT38a7sj/8YCzUO6DGCU12WJ0L1+t678lECg4c2DiPkVzCOYF2j6CU/3j6M4w+FkupiSeWuYCklKswTzJMDr79bdi3Dx57TPcIjhIhBDP/8BobXn2AloaQr1aLHVbnIY+kIgbp+2k1NmARYzmH/qQZPXyH0oetHMWNlzH0Ywh5MSkzrUlekiIKKCmx2+Gvf4VDh6BAVzKMBuGTLPzRE9z70RT6sTH8AJcPyl3h2zUJR6JG+WmtGrgXkMlCxrCY8QwlXyv/XoA2AO1RVAQ//KF2B0WBAFxNLaT6arie6whLFPIJyPYgV+Yjj+tMYSuRwFGqrBZDYzHaAETDPffAV7+qIoQ07eKz2xCFDeSl7jy7Mc0Do+pg4vlw1QwYvhB52ySkR48yrWIv5VaLoLEYrdGiwWaD3/wGBlqfqJzMSGDV/dfzizPP8cdDf6fqRBncvweymmFJBezMUgvC1cbC8HP94b9HWC12r6WrXcQ03R9tADqCzhCOSAvpvHvxvXzwwE24s1ORaQJyvPDAPrjpOLxWpJR+axod8MdiS+Tt7dixMUHH7/d6tAGIlqeegtJSq6VIKvzjx2YyOcJcVj+6GG9mcKy/yPTCj/aCO8JXrUZXJE80Luz0IYPzGGW1KBqL0b++aDhwAG691Wopkg4fduop4g3+RClXwIC3zQ/s14J5xK+EeZXxFFETgh3BVUxhNEXY9Piv16O/AdHwn/+pG8SYYMdLKjXUMhCJA1kaIVJqV0b4NocPsrzwUOTq37Lchfy/fsh3++jF4hhgx8Z0hjKWflr5awA9A4iOl16yWoKkReBjACUcYxbcNx75zAZExtkierLBBveND3mVhJlVsGwTYlhj8J4mAduy4N/94KHh4DTOle5FLl+LmKibjURLHunU0xxw1fUjmwsZY6lMmuRCG4BoqKqyWoKkxYeDKn/z79f6wlXTkQ+WwsgGNfJ/YAysDEmkc/ng1qOwOwN501TYnw43H4X/3anmpDUO+P0wtWjsXziudcCSWcjDKxB68NouDmzMpph+5HCaeorIYgA5OrlLE4Q2AJoOITnrzfdip5F89nHx2b3vFsK7RW2fpMUGn2TDt8dDox2EhO/tQzjUOeTfhkJDaEMZAbWqnATz9bpBNAgEQ8hnCPlWi6JJUvRYKhqGDrVagqTCix0PLg5zLo+yColfWQuiLu/0xBCl/AEm10B6qzWWKqf5eQRQF2wYLG5nYSmti7eZMaZVAxeNxgxtAKLhb3/TTU0MBCCx8zzP8hgrqWVQJ8/S6n76RLC+v+sgpLvDX+a2hUUN9eaPRQK3ModB5AbuqB2BAxtLGE82qRZLqEl2tAsoGi6+GB56SJWE0HCCKZxhdOxOuDUzuGzQ0nLY+hEsngX7MsHugxQf/H47IktHY4Gq3z+dIQwhny8ynzPUs5uT2BCMpR85Jt27NJpQtAGIlrIycDrBbTIy7WX0oZQzjIzdCa85CY6zFkAIkMMaYfUncNcEKHDDHWWIqTWxu2Y35womMYmzpUnyyWAuwy2USNMd0QYgWt59Vyt/A4GPYbzHPpbE5oT3HECkB/dfFgIodMPzwWWlpezdbh+AmQxlcqdcbxpNMJauAQghlgghdgsh9goh7rdSlnYpLrZagqTCSQz7JeeZG1bTBV5v71741T14NbHEyp7AduBPwCXAeOBzQojQjKHk4bvfjXtPgGVAMepDKTaeW42ZrrXTwkEWxu4iL/VFNtmQPpAf5iOf7Y88FMGHLUWvngH0I5vB6H7Vmthg5QxgFrBXSrlfStkCPANcaaE8bTN/vooGys2Ny+mXAXcAh1BK95DxPNFGwK/wfdhoIY3jTKWZTGObHQ8pvMVDNMVSCf16BJRkw8gL4PKZ8JWJMGYBLJmJbLAh623IGjuy1g6lGciSHOSFs5E5i5ETz0OecvaKWcFAcrmF2TqZSxMzrFwDGAgcbvX8CDDbIlnapr4efvYzeOIJSE2Fc86B7dtjeokHIMyp0mBsvzmmV4qMBMqYB9ipZDifci/lTGAUbzCOf9NMBhv4CuVMie2Fq5ywYC5Iof78vFMEQy+Ea0+o7OBX+sJlJ1XGsb/n8DYnjLoAnt4IS07FVq4kIgUHtzEXu47c1sQQKw1AhPKQIQcJcQdqMMyQIUPiLVM4Ph8sXAhbt55tDl9ZqXoDNDa2/doOUNbB7fFAAJmU80f2BG0v5QpV7TOe+CIotlMp8LdWiXjP9yfsq1Plgs9OQ55ajnD1rKmAQCV8fZlztfLXxBwrv1FHgMGtng8CjoUeJKV8WEo5Q0o5o7CwMGHCBXj3Xdi586zyB2hujqnyB4hk2hJt8mwkeZy9tAXPEvx4BRw7m/gkJWpNYWcasrFtl0myuo9ScXIeo7ibC+mDSUVVjaaLWGkA1gGjhBDDhBAu4EbgFQvlMWfDhpgrezMeBEKXmNON7YnCTSpbuCWBV4whjXbYn6oUvxeoscNPR8Ks8yB3CfI3Q9tU9FYaASd2RlKIAxsu7IGGLXdxPhcwmkxSrBNO06OxzAUkpfQIIb4BvA3YgUellLF1rMeC4mLl7qmri+tl/H7+B1BunyEo5R9P/7+HFAQe7Hhxk0oF41hFckfjRkQKuGw2fOMg4pe7IMeL/P5+uKocZs+HHdnQZIO04HwDKYH/GgFXnUROqEt4hFEGLr7K+WSSwhnqOUY12aQymDy92KuJO5Y6FaWUb0gpR0spR0gpEznYjZ6rrlLhn7b436qbgYOAz/g/nsq/mXS28jl2cTUeHNQwkH/xAZ6weUg3oskOfyhGnlFtKUW6D0bWw+IKeGwQvNgXWX+2mJxsFnDCpUpRjKy3ROQMUgIj/HwymMAAhpCvlb8mIehVpfZITYXVq2H2bHD0nMRpF41M5V+M5C3qGMBGbseLC7UOn6RO8WiwSdVQBqMZjU+ot+MR8Pmp8P0xyLJUFVK6JQtumwR/2Y5Ik5bkF5yhnjNYY3w0mp6j0eLJ8OHwySdw4gSMHKnCQrs5wlDyKdRhp4UsTuDrCb5mjw1SPchLZ8K7RiMam9HFQPjginLIcyOyvMgpNfD6evBJy0pM2LDRhCfxF9Zo0DOAjtGvH/zoR5DRsyIyHLRwDs9ZLUYMkDCtCm6dCu8UqPLRbhs029S+607A/MpARVHhBOGQCFf8lb+L0AY3CgH0JSu+F9doIqANQEf5/vfht7+FgoL2j00S6uiDRODGFdG548OZUJnihk3C7gzwtv5qG9XybzyGyExsmKsNwZVM5h4uJJc0HMZPzh/ffykTdHy/xjL0N6+jCAFf+Qo8/jhkJf/ITQI2fPiQPMlyDrIAb8ho1E0qG/miNQLGFAGr+xCxK1m9Hekz3+VHyrM5BLHAjo0CMknDxVc5j4WMYRh9mMQgbmceE1uVdNZoEo02AJ3lM59R4aFJjgCOMos/UUoZc3mRJ6hhEM1k4SaVFjI4ymw+4odWixp//jHYpNdwCM02uGI6/K7tvIFoceOllJMApOBkLsP5PHO4ksn0J6frF9BouoBeBO4sTiesWKG6hR0LS2C2nCayeZqXyaGMXVyL28gkrWUwf2API3mLHMo4wVSOMJeoe/l2Z1YWwC+GIS+vgGwPDGhSGSgVThjUpJrV/34YvN4PptWAW0AXS0vYEbj0z0yTpOhvZleYMAHuvht+8AOrJQlD4CWTCkpZiickukfiZA+X0yuUfij/NQYeHK3mvrktUGtXbr2vHYLlhbDdcOs9PRDu39chA6D6JYduE5xD/1hJr9HEFO0C6gp1dfCf/2m1FKakUM9I3qSZXKSpne+Fyt+PFKp20OkUaHFAsx0eGn5W+QPszYD7xiEbbcgIUZoBF5EPxtOfzzIdp1HKwYUdBzYuZyK53Tm5TtOj0TOArvDWW0mbHObBSR39jGdGHLymY/y1GF7sB2+sUy4hE+TuNNIePIdrHy9CIPg2n2EfFUgkIygktadEV2l6JMmpvboLLS1JU0oyVMX7cLKRLxnPtPLvNCdTYIx5HSghQGb4kK/3DdzhFByM1y4fTTdBu4C6wuLF4EmOLE5p5Pb6sNNENi/wFJWMsFqsbo6EOZVgA1nlUFVGQxB9Wxh9WeIl02higZ4BdIWCApUUdu+9wf0CLOB9fsIpxuOkge18Fh+p7b9I0w4CPs1HjFoI8yqRz2wIP2JfBov+2wLRNJoYoA1AV7nzTliwAK64Avbts0SEJrL4lO/iwYH6SLXLp6vYUsDuUL0FCvulMuOXDt6y2fBwNkPM5rZxzcDxZCd/PqBGY4o2ALFg3DjYvFnNCBI8E5BAKZcZoZ7ao9dV+s+AK/8JRePhdCk40yFnCEAhOcxgJaWcpp4CMljoHMMwZ/cpCaLRhKINQKzIyIB162DixIRf+gN+hFb+XUTAXduhcNzZTQVjgw8ZQSEjsKAtqUYTJ7TWiCV790JKYksq19GXuoR3Du55jL48WPlrNL0BSwyAEOKXQohdQogtQogXhRC5VsgRc7ZsUaGhCUCiWjq+wDJadDnhLuFIg6WPWC2FRpN4rJoBLAcmSCknAaVA8tVS6AwjRya0V8AulnKQRcEbBcz6JqTmJ0wMy9nCMn5LMT/Bxm8pZgvL2n2NzQEp2ZCaCze+DBnas6PphVhiAKSU70gZSLD/FBhkhRwx55prIDMzIZfykEIpl4fvEOBthqm3g6MXRIJuYRmvcgfVHAIk1RziVe5o0wik5MEt78CNr8B3ymHERYmTV6NJJpJhDeCLwJuRdgoh7hBClAghSioqKhIoVidITVU5AXFGAieZgM9sDd8Hmx+DY+tgwCyS4xOOIyt4ADcNQdvcNLCCB8xfIGD2N2HYQiheAHZdqUHTi4mbehBCvCuE2Gbyd2WrYx4APBB5uCalfFhKOUNKOaOwsBvM0xOQGazaCG5jB9ebi9AEx0rgnOuh6Jy4i2Mp1ZRFvd3mhOxBMOeeeEul0XQP4hYGKqX8TFv7hRBfAC4HFkmZJAV1YsH8+WomEOd8ABs+XDTQHKGpiLsB3vsxeBrjKobl5DDEcP+EbwfIHgx5w6G5FkZfBnO+BWl5CRZSo0lSLMkDEEIsAb4PLJBSNrR3fLfi/PPh3HPhgw/A7e7SqSLV8PRi50MeoJnsNl/fXAPEqLVhsrKIB3mVO4LcQE7SWcSDFE2Er22xUDiNJsmxykP8RyALWC6E2CSE+KtFcsQeIeD11+FXv1INY/r3Ny0ZvQwoRn0AQwn3gbXgpIHwLFMJvMkf+IgHaKvkg83ROxaBJ3EzV/AwOQwFBDkM5QoeZrLjZi74idXSaTTJjehO3pcZM2bIkpISq8XoGH/+M9x3X5BLaBlwBwQtXaYDDwM3G8/dpPAkb3ITV2CnhXLOoZYB7ORaNnMb7dnu/jPgxGaQXZuEdFucGfCdk+BKXFSuRpO0CCHWSylnhG7XpSDiTXGx6h/cygA8AIT6vRqM7X4DYMdNE7n8inIkNnw4kPgbmrdd7M3ugnNugOPdzFZ2GRs4jETs657Vyl+jaQ9tAOLN4sWQnQ21tYFN5nErZ7dLBD7suEnHQxpRV/cUyu0z5154/0ddEbp7IOxw81sqpPN0Kex9Syn9cddAuq7RptG0izYA8cZuh/TgnrBDwCRuRW334MRNJi2kU8loolb+Njj3fphwI5StSppGZXGjaAJ8dTPYDE9Y4Thdy0ej6SjaAMSbTZvg2LGgTQ9ivgbwILCBr/ARPyCTcjpS179wLCx6UD0+9AFqtbgHklMMl/8FRlys1ts1Gk3n6eF5oklARUVYFNDNqAVfFbei/n8YGMwPeZM/UcdATjCtQ5eZcdfZx2OW9lzl2FAOGX177vvTaBKJNgDxZtYsaG4O23wzcBAVpn8QuAEXR5ll7O2AdhOQOxxmfPXsppwhMOGWzgqc3HiaYNM/rZZCo+kZaAMQb3Jz4T/+I2wdIBQPqUSr+G0uSM2DtHyYcSd8db2K+/fTUgcH3+28yMmM9EFztdVSaDQ9A70GkAjuvx+mTIHf/141jTl4MCxL2IaXA6Glnc0QMOEGuPpx8921x+Hh6VB3vMtSJyWuTBh3ndVSaDQ9Az0DSBRLlsAbb8Du3XDLLZCejrTZ8IhU3KTxAk/gJorAdQkVOyPvXv5dqCuPndjJhCsThl6gavpoNJquo2cAiUYIePRR+NrXEG++iSMri4pJN5D1wgAc/1Q+bqSK5/eY1JOzOWHQnMinL30V8MZLeGsQNig8Bxb+DMZcoZ5rNJquow2AVcycqf6AQuCyRTDpFlj3R6g7CWOvUqUctj2lKnsCIMCZBvO+E/m0th5Q396ZoZraSKmymgfNhpvf7B21jTSaRKINQBIxeK768yN9Kr7/04egqQqGnAeLfw25QyOfY8rtsPpX8ZY0fhQvglvegH3vQNVBGDADBs7WYZ8aTTzQxeB6GO4G+EW+GkF3GwRkFMFlf4Ox2sWj0cScSMXg9E+th+FMh2ufsVqK6Bk0D659Cu47BuOu1Mpfo0kk+ufWAxl3FZyXxMXghBPsqXDdM/Clj1X9Iq34NZrEo392PZSFP4WRl6gFVT92l3Xy+HGkwsyvwbePqJLVGo3GOiw1AEKI7wghpBBCF++NMcIGn3sVrnwUxl4NE2+G655TLqL2sDkha2Anrmlv/xhnBiz+JaT36fj5NRpNbLHMAAghBgMXEbk8vqaL2Oxwzmfhhhfgmidh7JVwyzuQXkSbn3z2ILj8r8pYROWaEao655KHVB2iSK9xZcGtK5JjJqLRaKydAfwW+B49tnBxcjJkPnznOHxlDUy+LbiGECilf8FPYPTlcPtHyk3TfzqccyNc8Qhc8zQ4WhkGmxNSsmDJ72DWN+DLa8GeEn5dewp8dQP0mxzvd6jRaKLFkjwAIcRS4KiUcrNoJ8BbCHEHqnw+Q4YMSYB0PR9hU/H1Vz6q8gw++h9wNypFfsF/wuRb1XH9p6kInVAKx8KqX8Dp3TB4vkpM8+cmZPaFqx6Dl76gZiBSgvTCJX+A/JGJe48ajaZ94pYHIIR4F+hnsusB4IfAYilltRDiIDBDSnmqvXPqPID44PNCSy2kZMcuGqfxDJS+rpT/qEtVnL9Go7GGhDeFl1J+JoIgE4FhgH/0PwjYIISYJaU8ES95NJGx2SE1N7bnTMuHyZ+P7Tk1Gk1sSbgLSEq5FQiMBzsyA9BoNBpN7NB5ABqNRtNLsbwYnJSy2GoZNBqNpjeiZwAajUbTS9EGQKPRaHop3aoctBCiAjgU49MWAMm+AK1ljA1axtjRHeTUMp5lqJSyMHRjtzIA8UAIUWIWH5tMaBljg5YxdnQHObWM7aNdQBqNRtNL0QZAo9FoeinaAMDDVgsQBVrG2KBljB3dQU4tYzv0+jUAjUaj6a3oGYBGo9H0UrQB0Gg0ml5KrzMAQoifCCGOCiE2GX+XRjhuiRBitxBirxDi/gTL+EshxC4hxBYhxItCiNwIxx0UQmw13kdC6mS3d1+E4vfG/i1CiGmJkKvV9QcLId4XQuwUQmwXQtxjcswFQojqVt+B/5dIGQ0Z2vzskuA+jml1fzYJIWqEEN8KOcaS+yiEeFQIUS6E2NZqW74QYrkQYo/xf16E1ybkdx1BxuT7XUspe9Uf8BPgO+0cYwf2AcMBF7AZGJ9AGRcDDuPxL4BfRDjuIFCQQLnavS/ApcCbgADmAGsS/Pn2B6YZj7OAUhMZLwBes+L7F+1nZ/V9NPncT6CSiSy/j8D5wDRgW6tt/wvcbzy+3+w3k8jfdQQZk+533etmAFEyC9grpdwvpWwBngGuTNTFpZTvSCk9xtNPUT0TkoFo7suVwONS8SmQK4TonygBpZTHpZQbjMe1wE6gEy3uLcfS+xjCImCflDLWWfidQkr5IXAmZPOVwGPG48eAq0xemrDftZmMyfi77q0G4BvGNOzRCFPFgcDhVs+PYJ0S+SJqJGiGBN4RQqw3WmfGm2juS9LcOyFEMTAVWGOye64QYrMQ4k0hxDmJlQxo/7NLmvsI3Ag8HWGf1ffRT18p5XFQgwBa9RxpRTLd06T4XVteDjoetNOO8i/Az1A3+WfAr1EfRtApTF4b03jZtmSUUr5sHPMA4AGWRTjNfCnlMSFEEbBcCLHLGHnEi2juS9zvXTQIITKBfwPfklLWhOzegHJn1BlrQC8BoxIsYnufXbLcRxewFPiBye5kuI8dIVnuadL8rnukAZAR2lGGIoT4O/Caya4jwOBWzwcBx2IgWoD2ZBRCfAG4HFgkDcegyTmOGf+XCyFeRE1x42kAorkvcb937SGEcKKU/zIp5Quh+1sbBCnlG0KIPwshCmQCu9JF8dlZfh8NLgE2SClPhu5IhvvYipNCiP5SyuOGq6zc5BjL72my/a57nQsoxI96NbDN5LB1wCghxDBjBHQj8Eoi5AMVqQB8H1gqpWyIcEyGECLL/xi1wGT2XmJJNPflFeBWI4plDlDtn5onAiGEAB4BdkopfxPhmH7GcQghZqF+B6cTKGM0n52l97EVnyOC+8fq+xjCK8AXjMdfAF42OUb/rkNJxEpzMv0BTwBbgS2oD7+/sX0A8Ear4y5FRZDsQ7llEinjXpSvcpPx99dQGVGRDJuNv+2JktHsvgB3AncajwXwJ2P/VlS/50Teu3NR0/otre7fpSEyfsO4Z5tRi3HzEiyj6WeXTPfRkCEdpdBzWm2z/D6iDNJxwI0a1X8J6AOsAPYY/+cbx1ryu44gY9L9rnUpCI1Go+ml9DoXkEaj0WgU2gBoNBpNL0UbAI1Go+mlaAOg0Wg0vRRtADQajaaXog2ARhMFQgivUZ1xmxDieSFEurG9nxDiGSHEPiHEDiHEG0KI0ca+t4QQVUIIs2RDjcZytAHQaKKjUUo5RUo5AWgB7jSSoF4EVkopR0gpxwM/BPoar/kl8HlrxNVo2kcbAI2m43wEjAQWAm4p5V/9O6SUm6SUHxmPVwC11oio0bSPNgAaTQcQQjhQ9XG2AhOA9dZKpNF0Hm0ANJroSBNCbAJKgDJUvSGNplvTI6uBajRxoFFKOaX1BiHEduA6a8TRaLqOngFoNJ3nPSBFCPEV/wYhxEwhxAILZdJookYbAI2mk0hVSfFq4CIjDHQ7quf0MQAhxEfA88AiIcQRIcTFlgmr0Zigq4FqNBpNL0XPADQajaaXog2ARqPR9FK0AdBoNJpeijYAGo1G00vRBkCj0Wh6KdoAaDQaTS9FGwCNRqPppfx/byXhDgfSQLEAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(X[\"PC1\"], X[\"PC2\"], c=kmeans.labels_+1, cmap='rainbow')\n",
+    "plt.scatter(Centroids[\"PC1\"],Centroids[\"PC2\"],c='black')\n",
+    "plt.xlabel('PC1')\n",
+    "plt.ylabel('PC2')"
+   ]
   }
  ],
  "metadata": {

From 4c09a6d93464d5a3bcb191f1df1cd38f6edd54bd Mon Sep 17 00:00:00 2001
From: vishalh21 <106445212+vishalh21@users.noreply.github.com>
Date: Tue, 12 Jul 2022 17:09:20 +0530
Subject: [PATCH 05/14] Add files via upload

---
 Assignment 1/211175_Vishal_1.ipynb | 72 +++++++++++++++---------------
 1 file changed, 35 insertions(+), 37 deletions(-)

diff --git a/Assignment 1/211175_Vishal_1.ipynb b/Assignment 1/211175_Vishal_1.ipynb
index 68a4ecd..2eb5851 100644
--- a/Assignment 1/211175_Vishal_1.ipynb	
+++ b/Assignment 1/211175_Vishal_1.ipynb	
@@ -572,7 +572,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\himma\\AppData\\Local\\Temp\\ipykernel_17708\\868331368.py:2: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "C:\\Users\\himma\\AppData\\Local\\Temp\\ipykernel_2304\\868331368.py:2: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
       "  plt.figure()\n"
      ]
     },
@@ -999,7 +999,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\himma\\AppData\\Local\\Temp\\ipykernel_17708\\868331368.py:2: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "C:\\Users\\himma\\AppData\\Local\\Temp\\ipykernel_2304\\868331368.py:2: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
       "  plt.figure()\n"
      ]
     },
@@ -1717,7 +1717,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1777,34 +1777,34 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>8741</th>\n",
-       "      <td>1.969331</td>\n",
-       "      <td>-2.098843</td>\n",
-       "      <td>1.774436</td>\n",
-       "      <td>-1.199984</td>\n",
-       "      <td>0.603137</td>\n",
-       "      <td>-1.134845</td>\n",
-       "      <td>0.374469</td>\n",
+       "      <th>3277</th>\n",
+       "      <td>1.013192</td>\n",
+       "      <td>3.354367</td>\n",
+       "      <td>1.319917</td>\n",
+       "      <td>-0.014576</td>\n",
+       "      <td>0.554097</td>\n",
+       "      <td>0.582816</td>\n",
+       "      <td>0.092109</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>6506</th>\n",
-       "      <td>-3.015030</td>\n",
-       "      <td>-2.606008</td>\n",
-       "      <td>-0.736925</td>\n",
-       "      <td>-0.288787</td>\n",
-       "      <td>-0.099649</td>\n",
-       "      <td>2.730445</td>\n",
-       "      <td>-1.405308</td>\n",
+       "      <th>2019</th>\n",
+       "      <td>-3.111458</td>\n",
+       "      <td>0.924265</td>\n",
+       "      <td>-0.075973</td>\n",
+       "      <td>0.372736</td>\n",
+       "      <td>0.634577</td>\n",
+       "      <td>-0.313942</td>\n",
+       "      <td>0.531160</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>286</th>\n",
-       "      <td>-2.716373</td>\n",
-       "      <td>-0.016864</td>\n",
-       "      <td>-0.088214</td>\n",
-       "      <td>0.869378</td>\n",
-       "      <td>0.431633</td>\n",
-       "      <td>-0.521458</td>\n",
-       "      <td>0.443901</td>\n",
+       "      <th>7074</th>\n",
+       "      <td>0.490194</td>\n",
+       "      <td>-0.365434</td>\n",
+       "      <td>-1.724350</td>\n",
+       "      <td>0.641266</td>\n",
+       "      <td>0.418541</td>\n",
+       "      <td>-0.681641</td>\n",
+       "      <td>0.896270</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1812,9 +1812,9 @@
       ],
       "text/plain": [
        "           PC1       PC2       PC3       PC4       PC5       PC6       PC7\n",
-       "8741  1.969331 -2.098843  1.774436 -1.199984  0.603137 -1.134845  0.374469\n",
-       "6506 -3.015030 -2.606008 -0.736925 -0.288787 -0.099649  2.730445 -1.405308\n",
-       "286  -2.716373 -0.016864 -0.088214  0.869378  0.431633 -0.521458  0.443901"
+       "3277  1.013192  3.354367  1.319917 -0.014576  0.554097  0.582816  0.092109\n",
+       "2019 -3.111458  0.924265 -0.075973  0.372736  0.634577 -0.313942  0.531160\n",
+       "7074  0.490194 -0.365434 -1.724350  0.641266  0.418541 -0.681641  0.896270"
       ]
      },
      "execution_count": 25,
@@ -2013,7 +2013,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x228d4fec940>"
+       "<matplotlib.collections.PathCollection at 0x241ed2d9f70>"
       ]
      },
      "execution_count": 27,
@@ -2022,7 +2022,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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NV9sEaDCQI2Pj+Mx397bs7/Zpq0faenyhrqUA11x4dkuIys2zjBJKsPvQ7fuZmOz1OqfbPmE96CDeb9CwFo17OIwkBKnqHlUdUtULVXWtqr5h4rhuBBX5+9XhUACvvTmFOx9+rm0Cjtf5+nrdQxALey3fpAm3pIpmnIkipmKjZauEmy9Z7Nq2+9ataKkA6DWAeDn1znaGHXxsSh5rxjkNn309ghbFqswqRg8d9d0nTIy2MqPYeWAiUCJSJ4lOsx7fL8hkr6kEmLAJV1HopqSdOMldin6nul2bWQWmakqRKAtGnDanp6UwU9kq4e7rltU/5+VhuMXRm7Ef2CgTt27YD+DQuWe2tG300FFXDzAMTmMYdfCZ0faTvlEGiW21qozNi0rY1yTsnMqRyanA3qfd5rDeZhRvPg7FS9wx5m5K2omT3BnyoHHHsA+n183jZUQnpyq4b90Kz4c0SNIE0L4qn9v3sEqC+XPnYHKqEiT0jIG+ckO7nCGQux7Zh2MnO1OxOFUafoqTQMipErx2zNx+O1m7ciDSIGHLG51zCc6B2+6PZo27F27SQlOZpzZRJgbzlABj913YCWqGYdzJnSEP6qmEfTiBU9pZ543SI+6TXl5hABu3G87N+2gX17W/h3MNyvlz59QnJZ3by1YPpme1IdHH+fD7qV+iIIDnJGhUKjMKkcbl1uxJ3M2P7Y88SHgtKtE8wDVP0v727WnMOEsBl8RVWhhk0tdNz29yYjAvCTB+OQU2XhmveS88FheiHbxOR2VoaEhHR0cjfdbtJihbpXrooPlBPHZyuiWD0Yu+kPs3e8N+nnKQdSCdxxm+abnn5KEXZauEGy8eaAghXHp+P3YemPBVdUTl5S3X1H9eveUpIyGgJBEAv3R8h2ZGxsZda7oPf2S5pzfpVK543as3XjzgOlnaafy53bORFdrdK15t9vpckAqbRUFEdqvqUPP23HjkzQZ6ntXTIteLqpYAqg9oZWY2sBEHWo1iZUbr53TzAINSmdG2qc1u2FpxW5XTXIPbpBEvNema03p9t5U4ZaunPucRlCCyxOY3lsqs1u9DN9pN+nrp+cPGhf08+qyHHvzuFb/a9XkKHSVNLgy5m4F2y5CLqpYA4KkNTgvnzRnG07UNxPjkVCAdfVRmVBsSkbzKDISlbJVw2pyeQIOwAHjpnqsxMjaO230WtgCqbzleISebMAlGXn9f4Eio8lNOeR03CO1CDFkz3M149V1f2fL1rPMSOkqDXNQjD1rLuZOReTJDRhyoZvLZ0sioxB00c04emjDiPQJcNLgAbaYf6izqK9eNWjvmz51Tl9EtrNU8uWP7nrr01E0G59UM29O1elr3OFZbccjez40gMks/OqltnoW6935997mRfZ7tS6Ieel7JhSEP+krVycjcO9df2500tt49b3HnTphV4OmXjra8GVkud6lzrdAgb2GTUxVsuHIp7l23Aicqs5icqjTolu96pHUOQoEWY+5cgej0ea0vtHZYDPA2PF56/qAGKWqIISuabb++27brsGf7ktC155VchFaCvlKFlRw6Od6hBI/Eh5uzf3J6pm04pZkN39uLaVXXzFQvFFWDMT451SKH9HqLsw2qX8zaTc8f1CBFDTEkrdn2i+N79V1QdRFpJBeGPKimtvnBCSOxiysM0ZzgEgduafNZoK9s4TcnKq7yzU4JMSddJ8p1sCff3GLSC2p692acBtXL8HRikKIWn0pysrBdHD9MohsnM9uTi9BKmFeqtSsH8PTGy/DLLddg7PNXYPgjywNV9IuDvrIV6/l7ANy6ahDz5mQrLARUDcu1y8+OxYi3wyoFDLIHwK+apEjruWydeZxEDTGYXA+zHe3i+G5hJ785CeJPLjxyILoH40z0CJMcFJYeALNNv29as6xe+CqO884CxpUpC3st/GZquqN0/b6yhU1rlmHTo/sNtiw4tpY/KF76etuw+FWsbCGhgSvK85BkGdl23r9b2KlZLhtn+4pGbgx5p6xdOYDNj8VjWHpdpHezAEYPHY2cVh4U03bDhATz2NvT+N7o4dgGzXaEOW9JBKvetRBPv9RaWEuBeoG0oP1Sma3KMjc/tt+1LLGTONLN/Y6ZpM48SBzfbTDqZO6gm8ldZmdUgmiNo+AXAy+J4KV7rsaKzU+ENmpZjXsXCTuD0K/mB1B9w2i3tFuQ8zSn7ntlYQLRjG2WMjuz1JYi4ZXZmYsYuQmCaGzDIvX/uDOjipGxcRw7OR362EGNuLlocHhcpMC5oDmu3O6N6c2pSkNMOixuGm+vGPLmx/Zjw4N7GyR4Gx7cG0gi2Im+3DSUCiZL14RWTIc3ylYJAvVNhCmJVNO8o0gsApKW0271CKZTfGW4ddVgSzw1SC0Z55J6toFrp6BYVKseaRuhKHVlxiencN7Gx9suTOIWwqnMKDY/tr+tEcxaCjulgslhzCMXkZKIjInID0wd0ySmZ75vvHigbTbjzZcsLq50ymDoJ8pNeP+uwzhRmcH8uaW6x3fLqkHfRTuskuCtE9MtCSeXnt/v+Tm3ybYgi4O44TzngpBKpiAx+iRVKSRbmPTIPwXgBQC/Y/CYxugkWciNR54Nlg3X47O8Wp4x9ZbRa/WgMquYjXA8BXDs5AxuXTWIL62txpadk2ULyhZEUJ90PPb2dMtchV3E6uZLFterRNqFuJwFnJonEe0qk1Eyb6cqM5hnuS9MEuT+9JrQvPT8ftxfW0TDyaXn94duoylYPzwZjEx2isg5AL4N4M8AfFpVr/XbP43JTgC45a9+4qpOIOlglQSnn9Z5sTJ7UrmZMJJTOyzjVn3Pb+Ku3USpH/PnlnD85Ay09h1uvmQxfrD3Ndf29pUt7Ln7ikhtSavMKyc8zRN3Gdv7APwpgDN8GrAewHoAGBwcNHTacOz6RWxLiZKQCIDhm5bjDgNKIntSGUBkw+q1epB9TK9JxE7e9JwrM82o4qHd47jx4gFs/4dXWmqg2wuJ+LUlazFyLuOWHB3HyEXkWgCvq+puv/1UdWttgeah/v50XvWKGOJIgjjEKX29Vj1V2wSf3r6nrvbolGalh5+BdKozTJx354EJDH9keYPaY/gjpxYZ8WtLmBh5ElUQszawFBkTk52rAawRkZcBfAfAZSJyv4HjGqfd8mzEnTiGP7tokqmsvVmYi9sDVc/cNnQ9HveNAvUyw6a+hz042GUmnt54Wcui4m7Y8ecgVRXdqiDevn0PVmx+wqhB5+RrcnRsyFX1TlU9R1WXAPgYgKdU9daOWxYDZ52eTs0V0ooCWLLxcQzvOIhetzq1KSNA3dD5vck51xM1QV+v/z3qZ6yDare9Sv9OTlWMlrVl/fDk6Bod+edG9uHXvz2ZdjMKxdyS4GSHXrAdCvFa5DoNvPToJQ8FUmVWja0u9daJ6YYl9JpxW1R8ntWD0UNHG9QhzatnOfELbZiMYedl6bkiYNSQq+rfA/h7k8c0xQPPvJJ2EwpHp0bcSVaMOOAdSppNYI7FXhO0nbF7e/pUDsMbxysNskOv1eVtKWC7b2Eyhs2koGQorEferF/lRCcJipdHHqaGdic0Z4EGDY04afas3aSAXoRNVnJC3Xg6ZC84aQC3yRxCgqKAZ2x3YZsYtsk22JOQS5qUJUE9Zud+YRYmj6oJyMpSct1IIQ15mJuWEDeck4bOxZrTerFzGsWgqg/nfmHCJVEXIs9S0a5uo5CGnDpV0gmCUytNNS/WnFaNdaAxCSlIrRdnan4YyV9UeSB14+lRyBh5UrFMUkzmWT31GPXxk9Oxvd2VrRJOTM+E8vJtnTlwSg3iVc9n54GJ+s9uGahWSQBtXMvUTx7YLv4ddVFo0jmF9MijVqcjBACmKrP1GK8pWWEzgmoFzbChGltn7kwa8lLTOD1hN4358E3LG7JI+8oW5lnVEFJztmeQ+Dd14+lR2BWCmr2HS8/vj1ytjpA4sNP6o9yTzuJeK7/whOuAE6ZYVrsCV1412JvPQdVKvMRdNCtzeOlX41ryjZCwHJmcwr3rVkQqumV7xKOHjuKtE60rUFklaesJO42uW3jGKWEMGv+mbjwdChla8WPtyoHcLlFG0iPqMm9+2CsPOYtuhTnHVGUG9+867Lpm7Py5c3wNanOoxCvPwjbUrJuSbbrOkAPA+991ZtpNIDnDjkebqHIIVB8822O2490vb7kGt6waNDJgvNlGXRNUots7txrzZvw72xQ2tOLHy//MODkJjgB1Fcul5/dj+09f6bjS4iyq9VLu2L6nHksGgId2jxupNunlKdvhlKBx+WMnZ/C5kX31FZgY/84mhZ3s9OO8jY+ntmgxIW6UrRLmWT1GVDJeq/CESdN34rUCE0merpvsbKbdxA4haTJVmWlrYBf2WuidO6etN+3MpnQa86gZz3xWsk9XxMiDTuwQklXKVgl3X7cscI6EXadl5ReeCF2jpRkuyJJ9Cm/IR8bG8Znv7mXtFZJ5eq0eVyO9sNeqh0rCetVvHK/gjlrhLa+VjtqZ6ZsvWRz4fCQdCh1aGRkbx4YH99IDJ5li/twSpiozLTXYKzOKde87BzsPTHhOKEbxqu3TuD0HZauEiwYX4P+9dLRl3kgA3LJqsD7RSbJLx5OdIrIYwN8AeCeqk/FbVfVrfp9JarLTK+ONkDTpK1uexbfsTEmnusRemWigr4xjb093XLhLagXX7YHCS8USJjOUJEOck53TAD6jqs+KyBkAdovIk6r6jwaO3RE04iSL+BniI5NTLeoS25M2VV5CFXh5yzX13+/wyHRm1cL8YGLx5ddU9dnaz78F8AIAiksJicCivnLi9fSZtZl/jE52isgSACsBPOPyt/UiMioioxMTEy2fjYO+DpasIiQNxienYi/s1vxcMGsz/xgz5CJyOoCHANyuqr9p/ruqblXVIVUd6u/vbz1ADGxaswwWC6sQUsfqEWxas6xhm1uJW7eEIpJdjKhWRMRC1YhvU9WHTRzTBPaN+JnvUrlCyIBPWj2rFuabjj1yEREA3wTwgqp+tfMmmWXtygHPwvuEdAu2AoXGupiYCK2sBvDvAFwmIntq/zJVmIGTNqQbKFsl3LpqkPHuLqTj0Iqq/l+EK6OcOG7rFRKSB0QQaDk4Z9hk6NwzWaWwyyh0ZqeNc7FaLvVG8kQQIy5AQ+IO493dR+FrrdjYxftNLQxASFZYQJlt19M1htwmaPU4QvICixOSrgitOLFfObkAMykKkyxF0fV0nUcOgPFDUiioyiJdacjtQvuE5B1KCwnQhaEVu0Y5IXnHL1OTdBddZ8iHdxzseAV0QtKGtcKJk64LrVBHTvIOwymkma4z5JRqkbwxf26JlQmJL10XWmH9LJI3jp+cwf4vMIxCvOk6j5yQvEF5IWlH1xnyhb1MZybZo69sua5oxXg4CULXGfK7r1sGq8RAOckOVqm6as+eu6/AfetWMB5OQtN1MfK1Kwcweugotu06DIbLSdr0CDB80/K6sWblQhKFrvPIAWDngQkacZIJvvrRFTTcpGOMGHIRuUpEDorIz0Vko4ljxskRaslJBljYa9GIEyOYWLOzBODrAD4M4D0AbhaR93R63DihCoCkTdkq4e7rlrXfkZAAmPDI3wfg56r6C1U9CeA7AK43cNzYYE1ykjacxCQmMWHIBwC84vj91dq2zLJ25QDuueEClJjmSVJg9b88k0acGMWEasXNGrbMJYrIegDrAWBwcNDAaaMxMjZeX5i2r9fCm8crmE2tNaTbePc75mPbH74/7WaQgmHCkL8KYLHj93MAHGneSVW3AtgKAENDQ6mIRkbGxnHnw/swVZkBALxxvIIegcuwQ4hZSiL4ykeX0xMnsWAitPJTAO8WkfNEZC6AjwF41MBxjTO842DdiNvM0ogTg5R6BFZP40tq2SrRiJNY6dgjV9VpEfkkgB0ASgC+par7O25ZDFB2SOJkYa9VV6LY4btFXPyBJICRzE5V/SGAH5o4Vpws6iuzHjmJBatHcPd1yxoyNAlJiq7K7KTskMRFZVYxvONg2s0gXUpX1VqxvaThHQfpmRPjMHRH0qKrPHKgasyf3ngZ7lu3gt45MQozhkladJVH7oTeOQnLaXN68OUbLwSABhkrwLrhJF261pADp0qGnrfxcUrJiS+lHsGXb7ywYRKTyhSSFbrakNtQzULaMVObzGTdcJJFui5G7gbVLCQInMwkWYUeOU7Fy+/47h4oYyzEA05mkqxCj9wBjTgBqtUJm9/QOJlJsgwNeQ0mcxAAuHXVILb94ftxzw0XcBFkkhsYWqnB+CcRAF9aewEATmaSfEGPvAbjn4T3AMkrNOQ1qFzpbgRgDJzkFhryGlz+rTuwSq31wgXALasGGUohuYUxcgd1GeL2Pcz0LAh9ZQsiwOTxSj0DE2BWJikWNORNrF05gNFDR7Ft12Ea85xStnrwwhc/7LsPDTcpEgytuPCltRfgllXpLRBNOuOeGy5MuwmEJEpHhlxEhkXkgIg8JyKPiEifoXalzs4DE2k3gURgYa9Fb5t0HZ165E8CeK+qXgjgZwDu7LxJ2SAruvKFvVbaTcgNZatUXzOTkG6iI0Ouqk+o6nTt110Azum8SdkgC5rigb4yxj5/BQYy0JassrDXYvYl6XpMTnZ+HMB2rz+KyHoA6wFgcDD78ecNVy5NXb0yPjmFkbFxbLhyactCBgTotXow9vkr0m4GIanT1iMXkb8Tkedd/l3v2OcuANMAtnkdR1W3quqQqg719/ebaX2MrF05kAnVyp0P7wMA3HgxPU0nPQL8V05qEgIggEeuqh/0+7uI3AbgWgCXqxarfuBABhacmKrMYPNj+3GiMptqO7LEALXfhDTQUWhFRK4C8FkA/0ZVj5tpUnbYcOVSbPjeXlRm0x2f3jheSfX8WeDWVYP1glaEkEY6Va38JYAzADwpIntE5BsG2pQZ1q4cwPBHlqOvTOVI2tCIE+JNRx65qv4rUw3JKs5ypqu3PJVKqKWvbGFyqnu9cqp2CPGHmZ0hSENb3le2cO3ysxM/b1bgyjyEtIeGPARJa8vLVgnXLj8b2//hlUTPmzZ2BUpqwwkJBotmheDS8/tx/67DiZzLVmYM7ziY+mRr3JQE+MpHV9BgExIRGvIQJFV/5b51KxpK6hYVyggJMQNDKyFIIkY+f26pwbBloVRAHFg9QiNOiCFoyEMQt1G1SoI/+4NGmV1RJ/oqs4rhHQfTbgYhhYCGPARxrOtpLzo20FfG8E3LWzzUInusWakwSUjeYYw8BLZRHd5x0IiePMhKNp8b2dfxebJKUcNGhCQNDXlInAlCI2PjHVUlbFc/ZWRsPDGVTNJQH06IOWjIO6DZQy+JYEYVAgSqnNjOIy1qDJlqFULMQkPeIU4P3WZkbLxhlfZLz+/HQ7vHGzz3IB5p0WLIVklc5wEIIZ1BQx4DbsZ96NwzG4x7EI90UQbK6EalB8C/XTWInQcmQn1nQkh4aMgTws24t2PDlUtxew4TgspWD+654UIabUISgoY8w6xdOYCv73wRL75+LO2mBGJhr4W7r1tGA05IwlBHnnGe/PQHMLck7XdMEUF14Yexz19BI05ICtCQ54A/v2m58UQkUwz0lXHvuhVc+IGQFDESWhGRPwEwDKBfVf/JxDHJKZwyR3vi8OixtzGV8jqeA31lPL3xslTbQAgxYMhFZDGADwEoZuZKRmieLB0ZG2+7nqgAuHfdilBJSyUR3HzJYuw8MOGrmGFCDyHZwYRHfi+APwXwfQPHIgEJUi5gUV/ZM2lpYa+Ft05MNwwEZavUsJCD19J2JREu+EBIhhDV6IsWiMgaAJer6qdE5GUAQ16hFRFZD2A9AAwODl586NChyOcljbiVCmg2yl6f89O2Rz0uISQeRGS3qg61bG9nyEXk7wC80+VPdwH4zwCuUNU32xlyJ0NDQzo6Ohqo4SQY7Yxy1o5LCAlPZEPuc8ALAPwIwPHapnMAHAHwPlX9ld9nacgJISQ8XoY8coxcVfcBeIfjBC8joEdOCCHEHNSRE0JIzjGWoq+qS0wdixBCSHDokRNCSM6hISeEkJzTkY488klFJgCYFpKfBSDrE61soxnYRnPkoZ1s4ynOVdX+5o2pGPI4EJFRN1lOlmAbzcA2miMP7WQb28PQCiGE5BwackIIyTlFMuRb025AANhGM7CN5shDO9nGNhQmRk4IId1KkTxyQgjpSmjICSEk5+TWkIvIJhEZF5E9tX9Xe+x3lYgcFJGfi8jGhNs4LCIHROQ5EXlERPo89ntZRPbVvkciZSHb9YtU+W+1vz8nIhcl0S7H+ReLyE4ReUFE9ovIp1z2+YCIvOm4Bz6fZBtrbfC9dhnox6WO/tkjIr8Rkdub9kmlH0XkWyLyuog879h2pog8KSIv1v6/0OOziTzXHm3M3nOtqrn8B2ATgD9ps08JwEsA3gVgLoC9AN6TYBuvADCn9vOXAXzZY7+XAZyVYLva9guAqwH8Laorxq0C8EzC1/dsABfVfj4DwM9c2vgBAD9I4/4Leu3S7keX6/4rVJNKUu9HAL8P4CIAzzu2/TmAjbWfN7o9M0k+1x5tzNxznVuPPCDvA/BzVf2Fqp4E8B0A1yd1clV9QlWna7/uQrVmexYI0i/XA/gbrbILQJ+InJ1UA1X1NVV9tvbzbwG8ACCPK1qk2o9NXA7gJVXNxPJcqvpjAEebNl8P4Nu1n78NYK3LRxN7rt3amMXnOu+G/JO115tvebyCDQB4xfH7q0jPGHwcVc/MDQXwhIjsri2JFzdB+iUzfSciSwCsBPCMy5/fLyJ7ReRvRWRZsi0D0P7aZaYfAXwMwAMef0u7H21+V1VfA6qDORxrHjjIUp9m4rk2VsY2DtosM/c/AHwR1c76IoCvoNqpDYdw+axRvaVfG1X1+7V97gIwDWCbx2FWq+oREXkHgCdF5EDNE4iLIP0Se98FQUROB/AQgNtV9TdNf34W1TDBW7U5khEA7064ie2uXVb6cS6ANQDudPlzFvoxDFnp08w815k25Kr6wSD7ichfAfiBy59eBbDY8bu9HJ0x2rVRRG4DcC2qi1S73myqeqT2/9dF5BFUXx3jNORB+iX2vmuHiFioGvFtqvpw89+dhl1Vfygi/11EztIEV6kKcO1S78caHwbwrKr+uvkPWehHB78WkbNV9bVaCOp1l31S79OsPde5Da00xRn/AMDzLrv9FMC7ReS8mkfyMQCPJtE+oDqzDuCzANao6nGPfeaLyBn2z6hOpLh9F5ME6ZdHAfz7mupiFYA37VfeJBARAfBNAC+o6lc99nlnbT+IyPtQvZ//OcE2Brl2qfajg5vhEVZJux+beBTAbbWfbwPwfZd9+Fw3k8SMahz/APxvAPsAPIfqRTy7tn0RgB869rsaVcXDS6iGO5Js489RjeXtqf37RnMbUZ1531v7tz+pNrr1C4A/AvBHtZ8FwNdrf9+H6nqsSfbdv0b1dfk5R/9d3dTGT9b6bC+qk06/l3AbXa9dlvqx1oZeVA3zAse21PsR1YHlNQAVVL3sTwD4F6gu6v5i7f9n1vZN5bn2aGPmnmum6BNCSM7JbWiFEEJIFRpyQgjJOTTkhBCSc2jICSEk59CQE0JIzqEhJ4SQnENDTgghOef/A6TfdiduTV84AAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2035,11 +2035,10 @@
    ],
    "source": [
     "from sklearn.cluster import KMeans\n",
-    "\n",
     "kmeans = KMeans(n_clusters=3)\n",
     "kmeans.fit(X)\n",
     "plt.scatter(X[\"PC1\"],X[\"PC2\"], label='True Position') \n",
-    "plt.scatter(Centroids[\"PC1\"],Centroids[\"PC2\"],c='black')#applying Kmeans to our data through Scikit learn"
+    "#applying Kmeans to our data through Scikit learn"
    ]
   },
   {
@@ -2052,7 +2051,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1 3 2 ... 1 3 1]\n"
+      "[1 2 3 ... 1 2 1]\n"
      ]
     }
    ],
@@ -2078,7 +2077,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2091,7 +2090,6 @@
    ],
    "source": [
     "plt.scatter(X[\"PC1\"], X[\"PC2\"], c=kmeans.labels_+1, cmap='rainbow')\n",
-    "plt.scatter(Centroids[\"PC1\"],Centroids[\"PC2\"],c='black')\n",
     "plt.xlabel('PC1')\n",
     "plt.ylabel('PC2')"
    ]

From 4cdea745b81b024686cef0f57b994a86122dc012 Mon Sep 17 00:00:00 2001
From: vishalh21 <106445212+vishalh21@users.noreply.github.com>
Date: Tue, 12 Jul 2022 23:46:46 +0530
Subject: [PATCH 06/14] Create Assignment 2

---
 Assignment 2 | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 Assignment 2

diff --git a/Assignment 2 b/Assignment 2
new file mode 100644
index 0000000..8b13789
--- /dev/null
+++ b/Assignment 2	
@@ -0,0 +1 @@
+

From 47f88653a7a5bcd592d2f2105d811dfeb0569923 Mon Sep 17 00:00:00 2001
From: vishalh21 <106445212+vishalh21@users.noreply.github.com>
Date: Tue, 12 Jul 2022 23:48:03 +0530
Subject: [PATCH 07/14] Delete Assignment 2

---
 Assignment 2 | 1 -
 1 file changed, 1 deletion(-)
 delete mode 100644 Assignment 2

diff --git a/Assignment 2 b/Assignment 2
deleted file mode 100644
index 8b13789..0000000
--- a/Assignment 2	
+++ /dev/null
@@ -1 +0,0 @@
-

From 8f31756c16e9c236a4f00fa7fea902c69f105201 Mon Sep 17 00:00:00 2001
From: vishalh21 <106445212+vishalh21@users.noreply.github.com>
Date: Tue, 12 Jul 2022 23:48:33 +0530
Subject: [PATCH 08/14] Add files via upload

---
 211175_Vishal_2.ipynb | 3366 +++++++++++++++++++++++++++++++++++++++++
 1 file changed, 3366 insertions(+)
 create mode 100644 211175_Vishal_2.ipynb

diff --git a/211175_Vishal_2.ipynb b/211175_Vishal_2.ipynb
new file mode 100644
index 0000000..9e5056b
--- /dev/null
+++ b/211175_Vishal_2.ipynb
@@ -0,0 +1,3366 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "0d5f58b8",
+   "metadata": {},
+   "source": [
+    "## Environment Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "c0d00887",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import pandas, numpy, seaborn, pyplot\n",
+    "import numpy as np \n",
+    "import pandas as pd\n",
+    "import matplotlib as mpl\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from math import sqrt\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "8718c756",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import train_test_split, RepeatedStratifiedKFold, cross_val_score, LogisticRegression, roc_curve, roc_auc_score,\n",
+    "# confusion_matrix, precision_recall_curve, auc, f_classif, Pipeline, BaseEstimator, TransformerMixin, chi2_contingency\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.model_selection import RepeatedStratifiedKFold\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn import metrics\n",
+    "from sklearn.metrics import roc_curve\n",
+    "from sklearn.metrics import roc_auc_score\n",
+    "from sklearn.metrics import confusion_matrix\n",
+    "from sklearn.metrics import precision_recall_curve\n",
+    "from sklearn.feature_selection import f_classif\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.base import BaseEstimator\n",
+    "from sklearn.base import TransformerMixin\n",
+    "from scipy.stats import chi2_contingency"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "452fe749",
+   "metadata": {},
+   "source": [
+    "## Import Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "8dfd3c5f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load the dataset\n",
+    "df=pd.read_csv('credit_risk_dataset.csv',low_memory=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "88db398b",
+   "metadata": {},
+   "source": [
+    "## Data Exploration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0a185963",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>member_id</th>\n",
+       "      <th>loan_amnt</th>\n",
+       "      <th>funded_amnt</th>\n",
+       "      <th>funded_amnt_inv</th>\n",
+       "      <th>term</th>\n",
+       "      <th>int_rate</th>\n",
+       "      <th>installment</th>\n",
+       "      <th>grade</th>\n",
+       "      <th>sub_grade</th>\n",
+       "      <th>...</th>\n",
+       "      <th>total_bal_il</th>\n",
+       "      <th>il_util</th>\n",
+       "      <th>open_rv_12m</th>\n",
+       "      <th>open_rv_24m</th>\n",
+       "      <th>max_bal_bc</th>\n",
+       "      <th>all_util</th>\n",
+       "      <th>total_rev_hi_lim</th>\n",
+       "      <th>inq_fi</th>\n",
+       "      <th>total_cu_tl</th>\n",
+       "      <th>inq_last_12m</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1077501</td>\n",
+       "      <td>1296599</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>4975.0</td>\n",
+       "      <td>36 months</td>\n",
+       "      <td>10.65</td>\n",
+       "      <td>162.87</td>\n",
+       "      <td>B</td>\n",
+       "      <td>B2</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1077430</td>\n",
+       "      <td>1314167</td>\n",
+       "      <td>2500</td>\n",
+       "      <td>2500</td>\n",
+       "      <td>2500.0</td>\n",
+       "      <td>60 months</td>\n",
+       "      <td>15.27</td>\n",
+       "      <td>59.83</td>\n",
+       "      <td>C</td>\n",
+       "      <td>C4</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1077175</td>\n",
+       "      <td>1313524</td>\n",
+       "      <td>2400</td>\n",
+       "      <td>2400</td>\n",
+       "      <td>2400.0</td>\n",
+       "      <td>36 months</td>\n",
+       "      <td>15.96</td>\n",
+       "      <td>84.33</td>\n",
+       "      <td>C</td>\n",
+       "      <td>C5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1076863</td>\n",
+       "      <td>1277178</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>10000.0</td>\n",
+       "      <td>36 months</td>\n",
+       "      <td>13.49</td>\n",
+       "      <td>339.31</td>\n",
+       "      <td>C</td>\n",
+       "      <td>C1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1075358</td>\n",
+       "      <td>1311748</td>\n",
+       "      <td>3000</td>\n",
+       "      <td>3000</td>\n",
+       "      <td>3000.0</td>\n",
+       "      <td>60 months</td>\n",
+       "      <td>12.69</td>\n",
+       "      <td>67.79</td>\n",
+       "      <td>B</td>\n",
+       "      <td>B5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 74 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        id  member_id  loan_amnt  funded_amnt  funded_amnt_inv        term  \\\n",
+       "0  1077501    1296599       5000         5000           4975.0   36 months   \n",
+       "1  1077430    1314167       2500         2500           2500.0   60 months   \n",
+       "2  1077175    1313524       2400         2400           2400.0   36 months   \n",
+       "3  1076863    1277178      10000        10000          10000.0   36 months   \n",
+       "4  1075358    1311748       3000         3000           3000.0   60 months   \n",
+       "\n",
+       "   int_rate  installment grade sub_grade  ... total_bal_il il_util  \\\n",
+       "0     10.65       162.87     B        B2  ...          NaN     NaN   \n",
+       "1     15.27        59.83     C        C4  ...          NaN     NaN   \n",
+       "2     15.96        84.33     C        C5  ...          NaN     NaN   \n",
+       "3     13.49       339.31     C        C1  ...          NaN     NaN   \n",
+       "4     12.69        67.79     B        B5  ...          NaN     NaN   \n",
+       "\n",
+       "  open_rv_12m  open_rv_24m max_bal_bc all_util total_rev_hi_lim inq_fi  \\\n",
+       "0         NaN          NaN        NaN      NaN              NaN    NaN   \n",
+       "1         NaN          NaN        NaN      NaN              NaN    NaN   \n",
+       "2         NaN          NaN        NaN      NaN              NaN    NaN   \n",
+       "3         NaN          NaN        NaN      NaN              NaN    NaN   \n",
+       "4         NaN          NaN        NaN      NaN              NaN    NaN   \n",
+       "\n",
+       "  total_cu_tl inq_last_12m  \n",
+       "0         NaN          NaN  \n",
+       "1         NaN          NaN  \n",
+       "2         NaN          NaN  \n",
+       "3         NaN          NaN  \n",
+       "4         NaN          NaN  \n",
+       "\n",
+       "[5 rows x 74 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# explore the dataset(use the data dictionary)\n",
+    "# look at the columns, which data type is present in a column, range of values in each column, their mean, etc.\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "de2273c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(466285, 74)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "1c1516bf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 466285 entries, 0 to 466284\n",
+      "Data columns (total 74 columns):\n",
+      " #   Column                       Non-Null Count   Dtype  \n",
+      "---  ------                       --------------   -----  \n",
+      " 0   id                           466285 non-null  int64  \n",
+      " 1   member_id                    466285 non-null  int64  \n",
+      " 2   loan_amnt                    466285 non-null  int64  \n",
+      " 3   funded_amnt                  466285 non-null  int64  \n",
+      " 4   funded_amnt_inv              466285 non-null  float64\n",
+      " 5   term                         466285 non-null  object \n",
+      " 6   int_rate                     466285 non-null  float64\n",
+      " 7   installment                  466285 non-null  float64\n",
+      " 8   grade                        466285 non-null  object \n",
+      " 9   sub_grade                    466285 non-null  object \n",
+      " 10  emp_title                    438697 non-null  object \n",
+      " 11  emp_length                   445277 non-null  object \n",
+      " 12  home_ownership               466285 non-null  object \n",
+      " 13  annual_inc                   466281 non-null  float64\n",
+      " 14  verification_status          466285 non-null  object \n",
+      " 15  issue_d                      466285 non-null  object \n",
+      " 16  loan_status                  466285 non-null  object \n",
+      " 17  pymnt_plan                   466285 non-null  object \n",
+      " 18  url                          466285 non-null  object \n",
+      " 19  desc                         125983 non-null  object \n",
+      " 20  purpose                      466285 non-null  object \n",
+      " 21  title                        466265 non-null  object \n",
+      " 22  zip_code                     466285 non-null  object \n",
+      " 23  addr_state                   466285 non-null  object \n",
+      " 24  dti                          466285 non-null  float64\n",
+      " 25  delinq_2yrs                  466256 non-null  float64\n",
+      " 26  earliest_cr_line             466256 non-null  object \n",
+      " 27  inq_last_6mths               466256 non-null  float64\n",
+      " 28  mths_since_last_delinq       215934 non-null  float64\n",
+      " 29  mths_since_last_record       62638 non-null   float64\n",
+      " 30  open_acc                     466256 non-null  float64\n",
+      " 31  pub_rec                      466256 non-null  float64\n",
+      " 32  revol_bal                    466285 non-null  int64  \n",
+      " 33  revol_util                   465945 non-null  float64\n",
+      " 34  total_acc                    466256 non-null  float64\n",
+      " 35  initial_list_status          466285 non-null  object \n",
+      " 36  out_prncp                    466285 non-null  float64\n",
+      " 37  out_prncp_inv                466285 non-null  float64\n",
+      " 38  total_pymnt                  466285 non-null  float64\n",
+      " 39  total_pymnt_inv              466285 non-null  float64\n",
+      " 40  total_rec_prncp              466285 non-null  float64\n",
+      " 41  total_rec_int                466285 non-null  float64\n",
+      " 42  total_rec_late_fee           466285 non-null  float64\n",
+      " 43  recoveries                   466285 non-null  float64\n",
+      " 44  collection_recovery_fee      466285 non-null  float64\n",
+      " 45  last_pymnt_d                 465909 non-null  object \n",
+      " 46  last_pymnt_amnt              466285 non-null  float64\n",
+      " 47  next_pymnt_d                 239071 non-null  object \n",
+      " 48  last_credit_pull_d           466243 non-null  object \n",
+      " 49  collections_12_mths_ex_med   466140 non-null  float64\n",
+      " 50  mths_since_last_major_derog  98974 non-null   float64\n",
+      " 51  policy_code                  466285 non-null  int64  \n",
+      " 52  application_type             466285 non-null  object \n",
+      " 53  annual_inc_joint             0 non-null       float64\n",
+      " 54  dti_joint                    0 non-null       float64\n",
+      " 55  verification_status_joint    0 non-null       float64\n",
+      " 56  acc_now_delinq               466256 non-null  float64\n",
+      " 57  tot_coll_amt                 396009 non-null  float64\n",
+      " 58  tot_cur_bal                  396009 non-null  float64\n",
+      " 59  open_acc_6m                  0 non-null       float64\n",
+      " 60  open_il_6m                   0 non-null       float64\n",
+      " 61  open_il_12m                  0 non-null       float64\n",
+      " 62  open_il_24m                  0 non-null       float64\n",
+      " 63  mths_since_rcnt_il           0 non-null       float64\n",
+      " 64  total_bal_il                 0 non-null       float64\n",
+      " 65  il_util                      0 non-null       float64\n",
+      " 66  open_rv_12m                  0 non-null       float64\n",
+      " 67  open_rv_24m                  0 non-null       float64\n",
+      " 68  max_bal_bc                   0 non-null       float64\n",
+      " 69  all_util                     0 non-null       float64\n",
+      " 70  total_rev_hi_lim             396009 non-null  float64\n",
+      " 71  inq_fi                       0 non-null       float64\n",
+      " 72  total_cu_tl                  0 non-null       float64\n",
+      " 73  inq_last_12m                 0 non-null       float64\n",
+      "dtypes: float64(46), int64(6), object(22)\n",
+      "memory usage: 263.3+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "88a46339",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>member_id</th>\n",
+       "      <th>loan_amnt</th>\n",
+       "      <th>funded_amnt</th>\n",
+       "      <th>funded_amnt_inv</th>\n",
+       "      <th>int_rate</th>\n",
+       "      <th>installment</th>\n",
+       "      <th>annual_inc</th>\n",
+       "      <th>dti</th>\n",
+       "      <th>delinq_2yrs</th>\n",
+       "      <th>...</th>\n",
+       "      <th>total_bal_il</th>\n",
+       "      <th>il_util</th>\n",
+       "      <th>open_rv_12m</th>\n",
+       "      <th>open_rv_24m</th>\n",
+       "      <th>max_bal_bc</th>\n",
+       "      <th>all_util</th>\n",
+       "      <th>total_rev_hi_lim</th>\n",
+       "      <th>inq_fi</th>\n",
+       "      <th>total_cu_tl</th>\n",
+       "      <th>inq_last_12m</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>4.662850e+05</td>\n",
+       "      <td>4.662850e+05</td>\n",
+       "      <td>466285.000000</td>\n",
+       "      <td>466285.000000</td>\n",
+       "      <td>466285.000000</td>\n",
+       "      <td>466285.000000</td>\n",
+       "      <td>466285.000000</td>\n",
+       "      <td>4.662810e+05</td>\n",
+       "      <td>466285.000000</td>\n",
+       "      <td>466256.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.960090e+05</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>1.307973e+07</td>\n",
+       "      <td>1.459766e+07</td>\n",
+       "      <td>14317.277577</td>\n",
+       "      <td>14291.801044</td>\n",
+       "      <td>14222.329888</td>\n",
+       "      <td>13.829236</td>\n",
+       "      <td>432.061201</td>\n",
+       "      <td>7.327738e+04</td>\n",
+       "      <td>17.218758</td>\n",
+       "      <td>0.284678</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.037909e+04</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>1.089371e+07</td>\n",
+       "      <td>1.168237e+07</td>\n",
+       "      <td>8286.509164</td>\n",
+       "      <td>8274.371300</td>\n",
+       "      <td>8297.637788</td>\n",
+       "      <td>4.357587</td>\n",
+       "      <td>243.485550</td>\n",
+       "      <td>5.496357e+04</td>\n",
+       "      <td>7.851121</td>\n",
+       "      <td>0.797365</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.724713e+04</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>5.473400e+04</td>\n",
+       "      <td>7.047300e+04</td>\n",
+       "      <td>500.000000</td>\n",
+       "      <td>500.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.420000</td>\n",
+       "      <td>15.670000</td>\n",
+       "      <td>1.896000e+03</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>3.639987e+06</td>\n",
+       "      <td>4.379705e+06</td>\n",
+       "      <td>8000.000000</td>\n",
+       "      <td>8000.000000</td>\n",
+       "      <td>8000.000000</td>\n",
+       "      <td>10.990000</td>\n",
+       "      <td>256.690000</td>\n",
+       "      <td>4.500000e+04</td>\n",
+       "      <td>11.360000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.350000e+04</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>1.010790e+07</td>\n",
+       "      <td>1.194108e+07</td>\n",
+       "      <td>12000.000000</td>\n",
+       "      <td>12000.000000</td>\n",
+       "      <td>12000.000000</td>\n",
+       "      <td>13.660000</td>\n",
+       "      <td>379.890000</td>\n",
+       "      <td>6.300000e+04</td>\n",
+       "      <td>16.870000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.280000e+04</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>2.073121e+07</td>\n",
+       "      <td>2.300154e+07</td>\n",
+       "      <td>20000.000000</td>\n",
+       "      <td>20000.000000</td>\n",
+       "      <td>19950.000000</td>\n",
+       "      <td>16.490000</td>\n",
+       "      <td>566.580000</td>\n",
+       "      <td>8.896000e+04</td>\n",
+       "      <td>22.780000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.790000e+04</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>3.809811e+07</td>\n",
+       "      <td>4.086083e+07</td>\n",
+       "      <td>35000.000000</td>\n",
+       "      <td>35000.000000</td>\n",
+       "      <td>35000.000000</td>\n",
+       "      <td>26.060000</td>\n",
+       "      <td>1409.990000</td>\n",
+       "      <td>7.500000e+06</td>\n",
+       "      <td>39.990000</td>\n",
+       "      <td>29.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9.999999e+06</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>8 rows × 52 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 id     member_id      loan_amnt    funded_amnt  \\\n",
+       "count  4.662850e+05  4.662850e+05  466285.000000  466285.000000   \n",
+       "mean   1.307973e+07  1.459766e+07   14317.277577   14291.801044   \n",
+       "std    1.089371e+07  1.168237e+07    8286.509164    8274.371300   \n",
+       "min    5.473400e+04  7.047300e+04     500.000000     500.000000   \n",
+       "25%    3.639987e+06  4.379705e+06    8000.000000    8000.000000   \n",
+       "50%    1.010790e+07  1.194108e+07   12000.000000   12000.000000   \n",
+       "75%    2.073121e+07  2.300154e+07   20000.000000   20000.000000   \n",
+       "max    3.809811e+07  4.086083e+07   35000.000000   35000.000000   \n",
+       "\n",
+       "       funded_amnt_inv       int_rate    installment    annual_inc  \\\n",
+       "count    466285.000000  466285.000000  466285.000000  4.662810e+05   \n",
+       "mean      14222.329888      13.829236     432.061201  7.327738e+04   \n",
+       "std        8297.637788       4.357587     243.485550  5.496357e+04   \n",
+       "min           0.000000       5.420000      15.670000  1.896000e+03   \n",
+       "25%        8000.000000      10.990000     256.690000  4.500000e+04   \n",
+       "50%       12000.000000      13.660000     379.890000  6.300000e+04   \n",
+       "75%       19950.000000      16.490000     566.580000  8.896000e+04   \n",
+       "max       35000.000000      26.060000    1409.990000  7.500000e+06   \n",
+       "\n",
+       "                 dti    delinq_2yrs  ...  total_bal_il  il_util  open_rv_12m  \\\n",
+       "count  466285.000000  466256.000000  ...           0.0      0.0          0.0   \n",
+       "mean       17.218758       0.284678  ...           NaN      NaN          NaN   \n",
+       "std         7.851121       0.797365  ...           NaN      NaN          NaN   \n",
+       "min         0.000000       0.000000  ...           NaN      NaN          NaN   \n",
+       "25%        11.360000       0.000000  ...           NaN      NaN          NaN   \n",
+       "50%        16.870000       0.000000  ...           NaN      NaN          NaN   \n",
+       "75%        22.780000       0.000000  ...           NaN      NaN          NaN   \n",
+       "max        39.990000      29.000000  ...           NaN      NaN          NaN   \n",
+       "\n",
+       "       open_rv_24m  max_bal_bc  all_util  total_rev_hi_lim  inq_fi  \\\n",
+       "count          0.0         0.0       0.0      3.960090e+05     0.0   \n",
+       "mean           NaN         NaN       NaN      3.037909e+04     NaN   \n",
+       "std            NaN         NaN       NaN      3.724713e+04     NaN   \n",
+       "min            NaN         NaN       NaN      0.000000e+00     NaN   \n",
+       "25%            NaN         NaN       NaN      1.350000e+04     NaN   \n",
+       "50%            NaN         NaN       NaN      2.280000e+04     NaN   \n",
+       "75%            NaN         NaN       NaN      3.790000e+04     NaN   \n",
+       "max            NaN         NaN       NaN      9.999999e+06     NaN   \n",
+       "\n",
+       "       total_cu_tl  inq_last_12m  \n",
+       "count          0.0           0.0  \n",
+       "mean           NaN           NaN  \n",
+       "std            NaN           NaN  \n",
+       "min            NaN           NaN  \n",
+       "25%            NaN           NaN  \n",
+       "50%            NaN           NaN  \n",
+       "75%            NaN           NaN  \n",
+       "max            NaN           NaN  \n",
+       "\n",
+       "[8 rows x 52 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "cc753927",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['id', 'member_id', 'loan_amnt', 'funded_amnt', 'funded_amnt_inv',\n",
+       "       'term', 'int_rate', 'installment', 'grade', 'sub_grade', 'emp_title',\n",
+       "       'emp_length', 'home_ownership', 'annual_inc', 'verification_status',\n",
+       "       'issue_d', 'loan_status', 'pymnt_plan', 'url', 'desc', 'purpose',\n",
+       "       'title', 'zip_code', 'addr_state', 'dti', 'delinq_2yrs',\n",
+       "       'earliest_cr_line', 'inq_last_6mths', 'mths_since_last_delinq',\n",
+       "       'open_acc', 'pub_rec', 'revol_bal', 'revol_util', 'total_acc',\n",
+       "       'initial_list_status', 'out_prncp', 'out_prncp_inv', 'total_pymnt',\n",
+       "       'total_pymnt_inv', 'total_rec_prncp', 'total_rec_int',\n",
+       "       'total_rec_late_fee', 'recoveries', 'collection_recovery_fee',\n",
+       "       'last_pymnt_d', 'last_pymnt_amnt', 'next_pymnt_d', 'last_credit_pull_d',\n",
+       "       'collections_12_mths_ex_med', 'mths_since_last_major_derog',\n",
+       "       'policy_code', 'application_type', 'acc_now_delinq', 'tot_coll_amt',\n",
+       "       'tot_cur_bal', 'total_rev_hi_lim'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# get a list of columns that have more than 80% null values\n",
+    "df.columns[df.isnull().mean() < 0.8]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "bb70a075",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>member_id</th>\n",
+       "      <th>loan_amnt</th>\n",
+       "      <th>funded_amnt</th>\n",
+       "      <th>funded_amnt_inv</th>\n",
+       "      <th>term</th>\n",
+       "      <th>int_rate</th>\n",
+       "      <th>installment</th>\n",
+       "      <th>grade</th>\n",
+       "      <th>sub_grade</th>\n",
+       "      <th>...</th>\n",
+       "      <th>next_pymnt_d</th>\n",
+       "      <th>last_credit_pull_d</th>\n",
+       "      <th>collections_12_mths_ex_med</th>\n",
+       "      <th>mths_since_last_major_derog</th>\n",
+       "      <th>policy_code</th>\n",
+       "      <th>application_type</th>\n",
+       "      <th>acc_now_delinq</th>\n",
+       "      <th>tot_coll_amt</th>\n",
+       "      <th>tot_cur_bal</th>\n",
+       "      <th>total_rev_hi_lim</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1077501</td>\n",
+       "      <td>1296599</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>4975.0</td>\n",
+       "      <td>36 months</td>\n",
+       "      <td>10.65</td>\n",
+       "      <td>162.87</td>\n",
+       "      <td>B</td>\n",
+       "      <td>B2</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Jan-16</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>INDIVIDUAL</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1077430</td>\n",
+       "      <td>1314167</td>\n",
+       "      <td>2500</td>\n",
+       "      <td>2500</td>\n",
+       "      <td>2500.0</td>\n",
+       "      <td>60 months</td>\n",
+       "      <td>15.27</td>\n",
+       "      <td>59.83</td>\n",
+       "      <td>C</td>\n",
+       "      <td>C4</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Sep-13</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>INDIVIDUAL</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1077175</td>\n",
+       "      <td>1313524</td>\n",
+       "      <td>2400</td>\n",
+       "      <td>2400</td>\n",
+       "      <td>2400.0</td>\n",
+       "      <td>36 months</td>\n",
+       "      <td>15.96</td>\n",
+       "      <td>84.33</td>\n",
+       "      <td>C</td>\n",
+       "      <td>C5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Jan-16</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>INDIVIDUAL</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1076863</td>\n",
+       "      <td>1277178</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>10000.0</td>\n",
+       "      <td>36 months</td>\n",
+       "      <td>13.49</td>\n",
+       "      <td>339.31</td>\n",
+       "      <td>C</td>\n",
+       "      <td>C1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Jan-15</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>INDIVIDUAL</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1075358</td>\n",
+       "      <td>1311748</td>\n",
+       "      <td>3000</td>\n",
+       "      <td>3000</td>\n",
+       "      <td>3000.0</td>\n",
+       "      <td>60 months</td>\n",
+       "      <td>12.69</td>\n",
+       "      <td>67.79</td>\n",
+       "      <td>B</td>\n",
+       "      <td>B5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Feb-16</td>\n",
+       "      <td>Jan-16</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>INDIVIDUAL</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 56 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        id  member_id  loan_amnt  funded_amnt  funded_amnt_inv        term  \\\n",
+       "0  1077501    1296599       5000         5000           4975.0   36 months   \n",
+       "1  1077430    1314167       2500         2500           2500.0   60 months   \n",
+       "2  1077175    1313524       2400         2400           2400.0   36 months   \n",
+       "3  1076863    1277178      10000        10000          10000.0   36 months   \n",
+       "4  1075358    1311748       3000         3000           3000.0   60 months   \n",
+       "\n",
+       "   int_rate  installment grade sub_grade  ... next_pymnt_d last_credit_pull_d  \\\n",
+       "0     10.65       162.87     B        B2  ...          NaN             Jan-16   \n",
+       "1     15.27        59.83     C        C4  ...          NaN             Sep-13   \n",
+       "2     15.96        84.33     C        C5  ...          NaN             Jan-16   \n",
+       "3     13.49       339.31     C        C1  ...          NaN             Jan-15   \n",
+       "4     12.69        67.79     B        B5  ...       Feb-16             Jan-16   \n",
+       "\n",
+       "  collections_12_mths_ex_med  mths_since_last_major_derog policy_code  \\\n",
+       "0                        0.0                          NaN           1   \n",
+       "1                        0.0                          NaN           1   \n",
+       "2                        0.0                          NaN           1   \n",
+       "3                        0.0                          NaN           1   \n",
+       "4                        0.0                          NaN           1   \n",
+       "\n",
+       "  application_type acc_now_delinq tot_coll_amt tot_cur_bal total_rev_hi_lim  \n",
+       "0       INDIVIDUAL            0.0          NaN         NaN              NaN  \n",
+       "1       INDIVIDUAL            0.0          NaN         NaN              NaN  \n",
+       "2       INDIVIDUAL            0.0          NaN         NaN              NaN  \n",
+       "3       INDIVIDUAL            0.0          NaN         NaN              NaN  \n",
+       "4       INDIVIDUAL            0.0          NaN         NaN              NaN  \n",
+       "\n",
+       "[5 rows x 56 columns]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# drop columns with more than 80% null values\n",
+    "df=df[df.columns[df.isnull().mean() < 0.8]]\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "a1534071",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 466285 entries, 0 to 466284\n",
+      "Data columns (total 56 columns):\n",
+      " #   Column                       Non-Null Count   Dtype  \n",
+      "---  ------                       --------------   -----  \n",
+      " 0   id                           466285 non-null  int64  \n",
+      " 1   member_id                    466285 non-null  int64  \n",
+      " 2   loan_amnt                    466285 non-null  int64  \n",
+      " 3   funded_amnt                  466285 non-null  int64  \n",
+      " 4   funded_amnt_inv              466285 non-null  float64\n",
+      " 5   term                         466285 non-null  object \n",
+      " 6   int_rate                     466285 non-null  float64\n",
+      " 7   installment                  466285 non-null  float64\n",
+      " 8   grade                        466285 non-null  object \n",
+      " 9   sub_grade                    466285 non-null  object \n",
+      " 10  emp_title                    438697 non-null  object \n",
+      " 11  emp_length                   445277 non-null  object \n",
+      " 12  home_ownership               466285 non-null  object \n",
+      " 13  annual_inc                   466281 non-null  float64\n",
+      " 14  verification_status          466285 non-null  object \n",
+      " 15  issue_d                      466285 non-null  object \n",
+      " 16  loan_status                  466285 non-null  object \n",
+      " 17  pymnt_plan                   466285 non-null  object \n",
+      " 18  url                          466285 non-null  object \n",
+      " 19  desc                         125983 non-null  object \n",
+      " 20  purpose                      466285 non-null  object \n",
+      " 21  title                        466265 non-null  object \n",
+      " 22  zip_code                     466285 non-null  object \n",
+      " 23  addr_state                   466285 non-null  object \n",
+      " 24  dti                          466285 non-null  float64\n",
+      " 25  delinq_2yrs                  466256 non-null  float64\n",
+      " 26  earliest_cr_line             466256 non-null  object \n",
+      " 27  inq_last_6mths               466256 non-null  float64\n",
+      " 28  mths_since_last_delinq       215934 non-null  float64\n",
+      " 29  open_acc                     466256 non-null  float64\n",
+      " 30  pub_rec                      466256 non-null  float64\n",
+      " 31  revol_bal                    466285 non-null  int64  \n",
+      " 32  revol_util                   465945 non-null  float64\n",
+      " 33  total_acc                    466256 non-null  float64\n",
+      " 34  initial_list_status          466285 non-null  object \n",
+      " 35  out_prncp                    466285 non-null  float64\n",
+      " 36  out_prncp_inv                466285 non-null  float64\n",
+      " 37  total_pymnt                  466285 non-null  float64\n",
+      " 38  total_pymnt_inv              466285 non-null  float64\n",
+      " 39  total_rec_prncp              466285 non-null  float64\n",
+      " 40  total_rec_int                466285 non-null  float64\n",
+      " 41  total_rec_late_fee           466285 non-null  float64\n",
+      " 42  recoveries                   466285 non-null  float64\n",
+      " 43  collection_recovery_fee      466285 non-null  float64\n",
+      " 44  last_pymnt_d                 465909 non-null  object \n",
+      " 45  last_pymnt_amnt              466285 non-null  float64\n",
+      " 46  next_pymnt_d                 239071 non-null  object \n",
+      " 47  last_credit_pull_d           466243 non-null  object \n",
+      " 48  collections_12_mths_ex_med   466140 non-null  float64\n",
+      " 49  mths_since_last_major_derog  98974 non-null   float64\n",
+      " 50  policy_code                  466285 non-null  int64  \n",
+      " 51  application_type             466285 non-null  object \n",
+      " 52  acc_now_delinq               466256 non-null  float64\n",
+      " 53  tot_coll_amt                 396009 non-null  float64\n",
+      " 54  tot_cur_bal                  396009 non-null  float64\n",
+      " 55  total_rev_hi_lim             396009 non-null  float64\n",
+      "dtypes: float64(28), int64(6), object(22)\n",
+      "memory usage: 199.2+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# drop redundant and forward-looking columns\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "7b868980",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#dropping redundant column isn't done completely for now\n",
+    "df_drop=df.drop(labels=['id','member_id','zip_code','emp_title','verification_status','issue_d','pymnt_plan','url','addr_state','delinq_2yrs','application_type','desc'],axis=1,inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "f3a847b0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 466285 entries, 0 to 466284\n",
+      "Data columns (total 44 columns):\n",
+      " #   Column                       Non-Null Count   Dtype  \n",
+      "---  ------                       --------------   -----  \n",
+      " 0   loan_amnt                    466285 non-null  int64  \n",
+      " 1   funded_amnt                  466285 non-null  int64  \n",
+      " 2   funded_amnt_inv              466285 non-null  float64\n",
+      " 3   term                         466285 non-null  object \n",
+      " 4   int_rate                     466285 non-null  float64\n",
+      " 5   installment                  466285 non-null  float64\n",
+      " 6   grade                        466285 non-null  object \n",
+      " 7   sub_grade                    466285 non-null  object \n",
+      " 8   emp_length                   445277 non-null  object \n",
+      " 9   home_ownership               466285 non-null  object \n",
+      " 10  annual_inc                   466281 non-null  float64\n",
+      " 11  loan_status                  466285 non-null  object \n",
+      " 12  purpose                      466285 non-null  object \n",
+      " 13  title                        466265 non-null  object \n",
+      " 14  dti                          466285 non-null  float64\n",
+      " 15  earliest_cr_line             466256 non-null  object \n",
+      " 16  inq_last_6mths               466256 non-null  float64\n",
+      " 17  mths_since_last_delinq       215934 non-null  float64\n",
+      " 18  open_acc                     466256 non-null  float64\n",
+      " 19  pub_rec                      466256 non-null  float64\n",
+      " 20  revol_bal                    466285 non-null  int64  \n",
+      " 21  revol_util                   465945 non-null  float64\n",
+      " 22  total_acc                    466256 non-null  float64\n",
+      " 23  initial_list_status          466285 non-null  object \n",
+      " 24  out_prncp                    466285 non-null  float64\n",
+      " 25  out_prncp_inv                466285 non-null  float64\n",
+      " 26  total_pymnt                  466285 non-null  float64\n",
+      " 27  total_pymnt_inv              466285 non-null  float64\n",
+      " 28  total_rec_prncp              466285 non-null  float64\n",
+      " 29  total_rec_int                466285 non-null  float64\n",
+      " 30  total_rec_late_fee           466285 non-null  float64\n",
+      " 31  recoveries                   466285 non-null  float64\n",
+      " 32  collection_recovery_fee      466285 non-null  float64\n",
+      " 33  last_pymnt_d                 465909 non-null  object \n",
+      " 34  last_pymnt_amnt              466285 non-null  float64\n",
+      " 35  next_pymnt_d                 239071 non-null  object \n",
+      " 36  last_credit_pull_d           466243 non-null  object \n",
+      " 37  collections_12_mths_ex_med   466140 non-null  float64\n",
+      " 38  mths_since_last_major_derog  98974 non-null   float64\n",
+      " 39  policy_code                  466285 non-null  int64  \n",
+      " 40  acc_now_delinq               466256 non-null  float64\n",
+      " 41  tot_coll_amt                 396009 non-null  float64\n",
+      " 42  tot_cur_bal                  396009 non-null  float64\n",
+      " 43  total_rev_hi_lim             396009 non-null  float64\n",
+      "dtypes: float64(27), int64(4), object(13)\n",
+      "memory usage: 156.5+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# re-explore the dataset\n",
+    "#df.describe()\n",
+    "#df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e7300df0",
+   "metadata": {},
+   "source": [
+    "## Identify the target variable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "6ac92171",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# identify the target variable(target column) no need to code this just identify\n",
+    "# Here the target column is loan_status."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "5f0d11eb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Fully Paid', 'Charged Off', 'Current', 'Default',\n",
+       "       'Late (31-120 days)', 'In Grace Period', 'Late (16-30 days)',\n",
+       "       'Does not meet the credit policy. Status:Fully Paid',\n",
+       "       'Does not meet the credit policy. Status:Charged Off'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# explore the unique values in the target column\n",
+    "df['loan_status'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "37f20874",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>loan_amnt</th>\n",
+       "      <th>funded_amnt</th>\n",
+       "      <th>funded_amnt_inv</th>\n",
+       "      <th>int_rate</th>\n",
+       "      <th>installment</th>\n",
+       "      <th>annual_inc</th>\n",
+       "      <th>dti</th>\n",
+       "      <th>inq_last_6mths</th>\n",
+       "      <th>mths_since_last_delinq</th>\n",
+       "      <th>open_acc</th>\n",
+       "      <th>...</th>\n",
+       "      <th>recoveries</th>\n",
+       "      <th>collection_recovery_fee</th>\n",
+       "      <th>last_pymnt_amnt</th>\n",
+       "      <th>collections_12_mths_ex_med</th>\n",
+       "      <th>mths_since_last_major_derog</th>\n",
+       "      <th>policy_code</th>\n",
+       "      <th>acc_now_delinq</th>\n",
+       "      <th>tot_coll_amt</th>\n",
+       "      <th>tot_cur_bal</th>\n",
+       "      <th>total_rev_hi_lim</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>loan_status</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Charged Off</th>\n",
+       "      <td>14518.758682</td>\n",
+       "      <td>14470.241318</td>\n",
+       "      <td>14342.342678</td>\n",
+       "      <td>15.998834</td>\n",
+       "      <td>439.992667</td>\n",
+       "      <td>64750.370887</td>\n",
+       "      <td>18.219970</td>\n",
+       "      <td>0.994585</td>\n",
+       "      <td>34.308502</td>\n",
+       "      <td>10.999317</td>\n",
+       "      <td>...</td>\n",
+       "      <td>926.523955</td>\n",
+       "      <td>96.189387</td>\n",
+       "      <td>454.438219</td>\n",
+       "      <td>0.006993</td>\n",
+       "      <td>42.481076</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.003602</td>\n",
+       "      <td>127.370317</td>\n",
+       "      <td>114522.093018</td>\n",
+       "      <td>26195.836948</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Current</th>\n",
+       "      <td>15177.366474</td>\n",
+       "      <td>15172.779584</td>\n",
+       "      <td>15164.954547</td>\n",
+       "      <td>13.773271</td>\n",
+       "      <td>447.410438</td>\n",
+       "      <td>74703.804790</td>\n",
+       "      <td>18.026668</td>\n",
+       "      <td>0.695584</td>\n",
+       "      <td>33.349143</td>\n",
+       "      <td>11.529832</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>457.937174</td>\n",
+       "      <td>0.012376</td>\n",
+       "      <td>42.782234</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.005191</td>\n",
+       "      <td>184.464786</td>\n",
+       "      <td>140668.380119</td>\n",
+       "      <td>31438.554947</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Default</th>\n",
+       "      <td>15284.885817</td>\n",
+       "      <td>15284.885817</td>\n",
+       "      <td>15279.967293</td>\n",
+       "      <td>16.136394</td>\n",
+       "      <td>455.537296</td>\n",
+       "      <td>65611.847500</td>\n",
+       "      <td>19.253089</td>\n",
+       "      <td>0.917067</td>\n",
+       "      <td>33.953202</td>\n",
+       "      <td>11.971154</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>477.220733</td>\n",
+       "      <td>0.008413</td>\n",
+       "      <td>43.317757</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.006010</td>\n",
+       "      <td>132.382064</td>\n",
+       "      <td>109985.603194</td>\n",
+       "      <td>27184.632678</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Does not meet the credit policy. Status:Charged Off</th>\n",
+       "      <td>9527.233903</td>\n",
+       "      <td>9248.127464</td>\n",
+       "      <td>5807.001406</td>\n",
+       "      <td>14.597148</td>\n",
+       "      <td>305.158804</td>\n",
+       "      <td>69525.915033</td>\n",
+       "      <td>14.343732</td>\n",
+       "      <td>4.672823</td>\n",
+       "      <td>25.903433</td>\n",
+       "      <td>10.001319</td>\n",
+       "      <td>...</td>\n",
+       "      <td>579.002047</td>\n",
+       "      <td>122.187443</td>\n",
+       "      <td>305.497911</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Does not meet the credit policy. Status:Fully Paid</th>\n",
+       "      <td>8853.231891</td>\n",
+       "      <td>8679.376258</td>\n",
+       "      <td>6411.141563</td>\n",
+       "      <td>13.978642</td>\n",
+       "      <td>287.069225</td>\n",
+       "      <td>72145.418271</td>\n",
+       "      <td>14.107173</td>\n",
+       "      <td>4.000510</td>\n",
+       "      <td>27.466790</td>\n",
+       "      <td>10.102446</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2178.940287</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.002039</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Fully Paid</th>\n",
+       "      <td>13214.394226</td>\n",
+       "      <td>13169.988064</td>\n",
+       "      <td>13072.610934</td>\n",
+       "      <td>13.255943</td>\n",
+       "      <td>411.086245</td>\n",
+       "      <td>73709.612302</td>\n",
+       "      <td>15.937997</td>\n",
+       "      <td>0.839346</td>\n",
+       "      <td>35.339155</td>\n",
+       "      <td>10.794407</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>7165.937941</td>\n",
+       "      <td>0.005469</td>\n",
+       "      <td>43.037954</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.002598</td>\n",
+       "      <td>219.721618</td>\n",
+       "      <td>143006.253262</td>\n",
+       "      <td>29996.098672</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>In Grace Period</th>\n",
+       "      <td>16128.289892</td>\n",
+       "      <td>16128.289892</td>\n",
+       "      <td>16120.110393</td>\n",
+       "      <td>15.827072</td>\n",
+       "      <td>486.777092</td>\n",
+       "      <td>74033.926462</td>\n",
+       "      <td>18.801208</td>\n",
+       "      <td>0.831214</td>\n",
+       "      <td>32.568089</td>\n",
+       "      <td>11.719644</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>546.211116</td>\n",
+       "      <td>0.013350</td>\n",
+       "      <td>43.729143</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.003179</td>\n",
+       "      <td>219.261688</td>\n",
+       "      <td>130041.024675</td>\n",
+       "      <td>25975.458766</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Late (16-30 days)</th>\n",
+       "      <td>15800.779967</td>\n",
+       "      <td>15800.779967</td>\n",
+       "      <td>15793.313843</td>\n",
+       "      <td>15.964466</td>\n",
+       "      <td>477.734885</td>\n",
+       "      <td>72390.530665</td>\n",
+       "      <td>18.625952</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>30.740469</td>\n",
+       "      <td>11.692939</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>534.378596</td>\n",
+       "      <td>0.021346</td>\n",
+       "      <td>42.072193</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.004926</td>\n",
+       "      <td>215.194794</td>\n",
+       "      <td>133734.067170</td>\n",
+       "      <td>26412.624685</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Late (31-120 days)</th>\n",
+       "      <td>15553.757246</td>\n",
+       "      <td>15549.630435</td>\n",
+       "      <td>15542.374393</td>\n",
+       "      <td>15.947754</td>\n",
+       "      <td>465.948607</td>\n",
+       "      <td>69316.828822</td>\n",
+       "      <td>19.090399</td>\n",
+       "      <td>0.880145</td>\n",
+       "      <td>32.760579</td>\n",
+       "      <td>11.729275</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>506.048239</td>\n",
+       "      <td>0.011304</td>\n",
+       "      <td>43.028380</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.006377</td>\n",
+       "      <td>196.418793</td>\n",
+       "      <td>121340.395781</td>\n",
+       "      <td>26409.483552</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>9 rows × 31 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                       loan_amnt  \\\n",
+       "loan_status                                                        \n",
+       "Charged Off                                         14518.758682   \n",
+       "Current                                             15177.366474   \n",
+       "Default                                             15284.885817   \n",
+       "Does not meet the credit policy. Status:Charged...   9527.233903   \n",
+       "Does not meet the credit policy. Status:Fully Paid   8853.231891   \n",
+       "Fully Paid                                          13214.394226   \n",
+       "In Grace Period                                     16128.289892   \n",
+       "Late (16-30 days)                                   15800.779967   \n",
+       "Late (31-120 days)                                  15553.757246   \n",
+       "\n",
+       "                                                     funded_amnt  \\\n",
+       "loan_status                                                        \n",
+       "Charged Off                                         14470.241318   \n",
+       "Current                                             15172.779584   \n",
+       "Default                                             15284.885817   \n",
+       "Does not meet the credit policy. Status:Charged...   9248.127464   \n",
+       "Does not meet the credit policy. Status:Fully Paid   8679.376258   \n",
+       "Fully Paid                                          13169.988064   \n",
+       "In Grace Period                                     16128.289892   \n",
+       "Late (16-30 days)                                   15800.779967   \n",
+       "Late (31-120 days)                                  15549.630435   \n",
+       "\n",
+       "                                                    funded_amnt_inv  \\\n",
+       "loan_status                                                           \n",
+       "Charged Off                                            14342.342678   \n",
+       "Current                                                15164.954547   \n",
+       "Default                                                15279.967293   \n",
+       "Does not meet the credit policy. Status:Charged...      5807.001406   \n",
+       "Does not meet the credit policy. Status:Fully Paid      6411.141563   \n",
+       "Fully Paid                                             13072.610934   \n",
+       "In Grace Period                                        16120.110393   \n",
+       "Late (16-30 days)                                      15793.313843   \n",
+       "Late (31-120 days)                                     15542.374393   \n",
+       "\n",
+       "                                                     int_rate  installment  \\\n",
+       "loan_status                                                                  \n",
+       "Charged Off                                         15.998834   439.992667   \n",
+       "Current                                             13.773271   447.410438   \n",
+       "Default                                             16.136394   455.537296   \n",
+       "Does not meet the credit policy. Status:Charged...  14.597148   305.158804   \n",
+       "Does not meet the credit policy. Status:Fully Paid  13.978642   287.069225   \n",
+       "Fully Paid                                          13.255943   411.086245   \n",
+       "In Grace Period                                     15.827072   486.777092   \n",
+       "Late (16-30 days)                                   15.964466   477.734885   \n",
+       "Late (31-120 days)                                  15.947754   465.948607   \n",
+       "\n",
+       "                                                      annual_inc        dti  \\\n",
+       "loan_status                                                                   \n",
+       "Charged Off                                         64750.370887  18.219970   \n",
+       "Current                                             74703.804790  18.026668   \n",
+       "Default                                             65611.847500  19.253089   \n",
+       "Does not meet the credit policy. Status:Charged...  69525.915033  14.343732   \n",
+       "Does not meet the credit policy. Status:Fully Paid  72145.418271  14.107173   \n",
+       "Fully Paid                                          73709.612302  15.937997   \n",
+       "In Grace Period                                     74033.926462  18.801208   \n",
+       "Late (16-30 days)                                   72390.530665  18.625952   \n",
+       "Late (31-120 days)                                  69316.828822  19.090399   \n",
+       "\n",
+       "                                                    inq_last_6mths  \\\n",
+       "loan_status                                                          \n",
+       "Charged Off                                               0.994585   \n",
+       "Current                                                   0.695584   \n",
+       "Default                                                   0.917067   \n",
+       "Does not meet the credit policy. Status:Charged...        4.672823   \n",
+       "Does not meet the credit policy. Status:Fully Paid        4.000510   \n",
+       "Fully Paid                                                0.839346   \n",
+       "In Grace Period                                           0.831214   \n",
+       "Late (16-30 days)                                         0.904762   \n",
+       "Late (31-120 days)                                        0.880145   \n",
+       "\n",
+       "                                                    mths_since_last_delinq  \\\n",
+       "loan_status                                                                  \n",
+       "Charged Off                                                      34.308502   \n",
+       "Current                                                          33.349143   \n",
+       "Default                                                          33.953202   \n",
+       "Does not meet the credit policy. Status:Charged...               25.903433   \n",
+       "Does not meet the credit policy. Status:Fully Paid               27.466790   \n",
+       "Fully Paid                                                       35.339155   \n",
+       "In Grace Period                                                  32.568089   \n",
+       "Late (16-30 days)                                                30.740469   \n",
+       "Late (31-120 days)                                               32.760579   \n",
+       "\n",
+       "                                                     open_acc  ...  \\\n",
+       "loan_status                                                    ...   \n",
+       "Charged Off                                         10.999317  ...   \n",
+       "Current                                             11.529832  ...   \n",
+       "Default                                             11.971154  ...   \n",
+       "Does not meet the credit policy. Status:Charged...  10.001319  ...   \n",
+       "Does not meet the credit policy. Status:Fully Paid  10.102446  ...   \n",
+       "Fully Paid                                          10.794407  ...   \n",
+       "In Grace Period                                     11.719644  ...   \n",
+       "Late (16-30 days)                                   11.692939  ...   \n",
+       "Late (31-120 days)                                  11.729275  ...   \n",
+       "\n",
+       "                                                    recoveries  \\\n",
+       "loan_status                                                      \n",
+       "Charged Off                                         926.523955   \n",
+       "Current                                               0.000000   \n",
+       "Default                                               0.000000   \n",
+       "Does not meet the credit policy. Status:Charged...  579.002047   \n",
+       "Does not meet the credit policy. Status:Fully Paid    0.000000   \n",
+       "Fully Paid                                            0.000000   \n",
+       "In Grace Period                                       0.000000   \n",
+       "Late (16-30 days)                                     0.000000   \n",
+       "Late (31-120 days)                                    0.000000   \n",
+       "\n",
+       "                                                    collection_recovery_fee  \\\n",
+       "loan_status                                                                   \n",
+       "Charged Off                                                       96.189387   \n",
+       "Current                                                            0.000000   \n",
+       "Default                                                            0.000000   \n",
+       "Does not meet the credit policy. Status:Charged...               122.187443   \n",
+       "Does not meet the credit policy. Status:Fully Paid                 0.000000   \n",
+       "Fully Paid                                                         0.000000   \n",
+       "In Grace Period                                                    0.000000   \n",
+       "Late (16-30 days)                                                  0.000000   \n",
+       "Late (31-120 days)                                                 0.000000   \n",
+       "\n",
+       "                                                    last_pymnt_amnt  \\\n",
+       "loan_status                                                           \n",
+       "Charged Off                                              454.438219   \n",
+       "Current                                                  457.937174   \n",
+       "Default                                                  477.220733   \n",
+       "Does not meet the credit policy. Status:Charged...       305.497911   \n",
+       "Does not meet the credit policy. Status:Fully Paid      2178.940287   \n",
+       "Fully Paid                                              7165.937941   \n",
+       "In Grace Period                                          546.211116   \n",
+       "Late (16-30 days)                                        534.378596   \n",
+       "Late (31-120 days)                                       506.048239   \n",
+       "\n",
+       "                                                    collections_12_mths_ex_med  \\\n",
+       "loan_status                                                                      \n",
+       "Charged Off                                                           0.006993   \n",
+       "Current                                                               0.012376   \n",
+       "Default                                                               0.008413   \n",
+       "Does not meet the credit policy. Status:Charged...                    0.000000   \n",
+       "Does not meet the credit policy. Status:Fully Paid                    0.000000   \n",
+       "Fully Paid                                                            0.005469   \n",
+       "In Grace Period                                                       0.013350   \n",
+       "Late (16-30 days)                                                     0.021346   \n",
+       "Late (31-120 days)                                                    0.011304   \n",
+       "\n",
+       "                                                    mths_since_last_major_derog  \\\n",
+       "loan_status                                                                       \n",
+       "Charged Off                                                           42.481076   \n",
+       "Current                                                               42.782234   \n",
+       "Default                                                               43.317757   \n",
+       "Does not meet the credit policy. Status:Charged...                          NaN   \n",
+       "Does not meet the credit policy. Status:Fully Paid                          NaN   \n",
+       "Fully Paid                                                            43.037954   \n",
+       "In Grace Period                                                       43.729143   \n",
+       "Late (16-30 days)                                                     42.072193   \n",
+       "Late (31-120 days)                                                    43.028380   \n",
+       "\n",
+       "                                                    policy_code  \\\n",
+       "loan_status                                                       \n",
+       "Charged Off                                                 1.0   \n",
+       "Current                                                     1.0   \n",
+       "Default                                                     1.0   \n",
+       "Does not meet the credit policy. Status:Charged...          1.0   \n",
+       "Does not meet the credit policy. Status:Fully Paid          1.0   \n",
+       "Fully Paid                                                  1.0   \n",
+       "In Grace Period                                             1.0   \n",
+       "Late (16-30 days)                                           1.0   \n",
+       "Late (31-120 days)                                          1.0   \n",
+       "\n",
+       "                                                    acc_now_delinq  \\\n",
+       "loan_status                                                          \n",
+       "Charged Off                                               0.003602   \n",
+       "Current                                                   0.005191   \n",
+       "Default                                                   0.006010   \n",
+       "Does not meet the credit policy. Status:Charged...        0.000000   \n",
+       "Does not meet the credit policy. Status:Fully Paid        0.002039   \n",
+       "Fully Paid                                                0.002598   \n",
+       "In Grace Period                                           0.003179   \n",
+       "Late (16-30 days)                                         0.004926   \n",
+       "Late (31-120 days)                                        0.006377   \n",
+       "\n",
+       "                                                    tot_coll_amt  \\\n",
+       "loan_status                                                        \n",
+       "Charged Off                                           127.370317   \n",
+       "Current                                               184.464786   \n",
+       "Default                                               132.382064   \n",
+       "Does not meet the credit policy. Status:Charged...           NaN   \n",
+       "Does not meet the credit policy. Status:Fully Paid           NaN   \n",
+       "Fully Paid                                            219.721618   \n",
+       "In Grace Period                                       219.261688   \n",
+       "Late (16-30 days)                                     215.194794   \n",
+       "Late (31-120 days)                                    196.418793   \n",
+       "\n",
+       "                                                      tot_cur_bal  \\\n",
+       "loan_status                                                         \n",
+       "Charged Off                                         114522.093018   \n",
+       "Current                                             140668.380119   \n",
+       "Default                                             109985.603194   \n",
+       "Does not meet the credit policy. Status:Charged...            NaN   \n",
+       "Does not meet the credit policy. Status:Fully Paid            NaN   \n",
+       "Fully Paid                                          143006.253262   \n",
+       "In Grace Period                                     130041.024675   \n",
+       "Late (16-30 days)                                   133734.067170   \n",
+       "Late (31-120 days)                                  121340.395781   \n",
+       "\n",
+       "                                                    total_rev_hi_lim  \n",
+       "loan_status                                                           \n",
+       "Charged Off                                             26195.836948  \n",
+       "Current                                                 31438.554947  \n",
+       "Default                                                 27184.632678  \n",
+       "Does not meet the credit policy. Status:Charged...               NaN  \n",
+       "Does not meet the credit policy. Status:Fully Paid               NaN  \n",
+       "Fully Paid                                              29996.098672  \n",
+       "In Grace Period                                         25975.458766  \n",
+       "Late (16-30 days)                                       26412.624685  \n",
+       "Late (31-120 days)                                      26409.483552  \n",
+       "\n",
+       "[9 rows x 31 columns]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# create a new column based on the target column that will be our target variable\n",
+    "loan_status=df.groupby('loan_status').mean()\n",
+    "loan_status"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "d7a50128",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>loan_amnt</th>\n",
+       "      <th>funded_amnt</th>\n",
+       "      <th>funded_amnt_inv</th>\n",
+       "      <th>term</th>\n",
+       "      <th>int_rate</th>\n",
+       "      <th>installment</th>\n",
+       "      <th>grade</th>\n",
+       "      <th>sub_grade</th>\n",
+       "      <th>emp_length</th>\n",
+       "      <th>home_ownership</th>\n",
+       "      <th>...</th>\n",
+       "      <th>last_pymnt_amnt</th>\n",
+       "      <th>next_pymnt_d</th>\n",
+       "      <th>last_credit_pull_d</th>\n",
+       "      <th>collections_12_mths_ex_med</th>\n",
+       "      <th>mths_since_last_major_derog</th>\n",
+       "      <th>policy_code</th>\n",
+       "      <th>acc_now_delinq</th>\n",
+       "      <th>tot_coll_amt</th>\n",
+       "      <th>tot_cur_bal</th>\n",
+       "      <th>total_rev_hi_lim</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5000</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>4975.0</td>\n",
+       "      <td>36 months</td>\n",
+       "      <td>10.65</td>\n",
+       "      <td>162.87</td>\n",
+       "      <td>B</td>\n",
+       "      <td>B2</td>\n",
+       "      <td>10+ years</td>\n",
+       "      <td>RENT</td>\n",
+       "      <td>...</td>\n",
+       "      <td>171.62</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Jan-16</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2500</td>\n",
+       "      <td>2500</td>\n",
+       "      <td>2500.0</td>\n",
+       "      <td>60 months</td>\n",
+       "      <td>15.27</td>\n",
+       "      <td>59.83</td>\n",
+       "      <td>C</td>\n",
+       "      <td>C4</td>\n",
+       "      <td>&lt; 1 year</td>\n",
+       "      <td>RENT</td>\n",
+       "      <td>...</td>\n",
+       "      <td>119.66</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Sep-13</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2400</td>\n",
+       "      <td>2400</td>\n",
+       "      <td>2400.0</td>\n",
+       "      <td>36 months</td>\n",
+       "      <td>15.96</td>\n",
+       "      <td>84.33</td>\n",
+       "      <td>C</td>\n",
+       "      <td>C5</td>\n",
+       "      <td>10+ years</td>\n",
+       "      <td>RENT</td>\n",
+       "      <td>...</td>\n",
+       "      <td>649.91</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Jan-16</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10000</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>10000.0</td>\n",
+       "      <td>36 months</td>\n",
+       "      <td>13.49</td>\n",
+       "      <td>339.31</td>\n",
+       "      <td>C</td>\n",
+       "      <td>C1</td>\n",
+       "      <td>10+ years</td>\n",
+       "      <td>RENT</td>\n",
+       "      <td>...</td>\n",
+       "      <td>357.48</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Jan-15</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3000</td>\n",
+       "      <td>3000</td>\n",
+       "      <td>3000.0</td>\n",
+       "      <td>60 months</td>\n",
+       "      <td>12.69</td>\n",
+       "      <td>67.79</td>\n",
+       "      <td>B</td>\n",
+       "      <td>B5</td>\n",
+       "      <td>1 year</td>\n",
+       "      <td>RENT</td>\n",
+       "      <td>...</td>\n",
+       "      <td>67.79</td>\n",
+       "      <td>Feb-16</td>\n",
+       "      <td>Jan-16</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 43 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   loan_amnt  funded_amnt  funded_amnt_inv        term  int_rate  installment  \\\n",
+       "0       5000         5000           4975.0   36 months     10.65       162.87   \n",
+       "1       2500         2500           2500.0   60 months     15.27        59.83   \n",
+       "2       2400         2400           2400.0   36 months     15.96        84.33   \n",
+       "3      10000        10000          10000.0   36 months     13.49       339.31   \n",
+       "4       3000         3000           3000.0   60 months     12.69        67.79   \n",
+       "\n",
+       "  grade sub_grade emp_length home_ownership  ...  last_pymnt_amnt  \\\n",
+       "0     B        B2  10+ years           RENT  ...           171.62   \n",
+       "1     C        C4   < 1 year           RENT  ...           119.66   \n",
+       "2     C        C5  10+ years           RENT  ...           649.91   \n",
+       "3     C        C1  10+ years           RENT  ...           357.48   \n",
+       "4     B        B5     1 year           RENT  ...            67.79   \n",
+       "\n",
+       "  next_pymnt_d last_credit_pull_d  collections_12_mths_ex_med  \\\n",
+       "0          NaN             Jan-16                         0.0   \n",
+       "1          NaN             Sep-13                         0.0   \n",
+       "2          NaN             Jan-16                         0.0   \n",
+       "3          NaN             Jan-15                         0.0   \n",
+       "4       Feb-16             Jan-16                         0.0   \n",
+       "\n",
+       "  mths_since_last_major_derog  policy_code  acc_now_delinq  tot_coll_amt  \\\n",
+       "0                         NaN            1             0.0           NaN   \n",
+       "1                         NaN            1             0.0           NaN   \n",
+       "2                         NaN            1             0.0           NaN   \n",
+       "3                         NaN            1             0.0           NaN   \n",
+       "4                         NaN            1             0.0           NaN   \n",
+       "\n",
+       "   tot_cur_bal  total_rev_hi_lim  \n",
+       "0          NaN               NaN  \n",
+       "1          NaN               NaN  \n",
+       "2          NaN               NaN  \n",
+       "3          NaN               NaN  \n",
+       "4          NaN               NaN  \n",
+       "\n",
+       "[5 rows x 43 columns]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Drop the original target column\n",
+    "df_drop=df.drop(labels=['loan_status'],axis=1)\n",
+    "df_drop.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d62da664",
+   "metadata": {},
+   "source": [
+    "## Split Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "0af569dd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# split data into 80/20 while keeping the distribution of bad loans in test set same as that in the pre-split dataset(X_train, y_train, etc)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "d4ba9e6b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# specifically hard copying the training sets to avoid Pandas' SetttingWithCopyWarning when we play around with this data later on\n",
+    "# you can refer to this link https://github.com/scikit-learn/scikit-learn/issues/8723\n",
+    "# this is currently an open issue between Pandas and Scikit-Learn teams"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "45df823e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create a helper function clean up a column which has values given along with years, assign 0 to NANs and convert to numeric"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "4cf50e36",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# apply to X_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "6f21a969",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# confirm our transformation by looking at unique values of this column"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d86f2544",
+   "metadata": {},
+   "source": [
+    "### Date columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "ba10320e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create a function to convert date columns to datetime format and create a new column as a difference between today and the respective date\n",
+    "# details of the function\n",
+    "    # store current month\n",
+    "    # convert to datetime format\n",
+    "    # calculate the difference in months and add to a new column\n",
+    "    # make any resulting -ve values to be equal to the max date\n",
+    "    # drop the original date column\n",
+    "# Note : In current date use the same data to maintain uniformilty across everyone's code. Use : 2020-08-01"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "99a24bb3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# apply to X_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "4acfad7d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# check these new columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "baf48e3e",
+   "metadata": {},
+   "source": [
+    "### Term column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "7832bb43",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# write a function to remove 'months' string from the 'term' column and convert it to numeric \n",
+    "# use the function on 'term' column of X_Train"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "168c666d",
+   "metadata": {},
+   "source": [
+    "## Feature Selection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "726b6bf8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# first divide training data into categorical and numerical subsets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "08784212",
+   "metadata": {},
+   "source": [
+    "## Chi-squared statistic for categorical features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "1224a634",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# define an empty dictionary to store chi-squared test results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "11d6040a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# loop over each column in the training set to calculate chi-statistic with the target variable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "79a44211",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# convert the dictionary to a DF"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "d11c692d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# keep only the top four categorical features"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8949f3fa",
+   "metadata": {},
+   "source": [
+    "## ANOVA F-Statistic for numerical feature"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "f7c8d70f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# since f_class_if does not accept missing values, we will do a very crude imputation of missing values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "815594f8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Calculate F Statistic and corresponding p values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "66d26e6f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# convert to a DF"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "a0e14369",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# keep only the top 20 features and calculate pair-wise correlations between them\n",
+    "# save the top 20 numerical features in a list"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5a831c81",
+   "metadata": {},
+   "source": [
+    "## Pair wise correlations to detect multicollinearity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "fb639daf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# calculate pair-wise correlations between them"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "59b405e0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# drop 2 features based on their multicollinearity with other features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "315f9251",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define a helper function to drop the 4 categorical features with least p-values for chi squared test, 14 numerical features with least F-Statistic\n",
+    "# and 2 numerical features with high multicollinearity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "a483be96",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# apply to X_train"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7b40d71b",
+   "metadata": {},
+   "source": [
+    "## creating dummy variables\n",
+    "### convert discrete variables to dummy variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "cbb5d957",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# write a function to create dummy variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "65ad5814",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# apply to our final four categorical variables"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "87b2556e",
+   "metadata": {},
+   "source": [
+    "## Update the test data set with all data cleaning procedures performed so far"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "f2ce52f0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# also reindex the dummied test set variables to make sure all the feature columns in the train set are also available in the test set"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d96c459f",
+   "metadata": {},
+   "source": [
+    "## WoE Binning/Feature Engineering"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "d91849df",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# we will analyze both categorical and numerical features based on their categorical/binned WoEs and IVs and then combine some of these binned categories together through a custom python class with fit_transform method"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "9098de60",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create copies of the 4 training sets(by the names X_train_prepr, y_train_prepr, etc) to be preprocessed using WoE"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "556ef2ee",
+   "metadata": {},
+   "source": [
+    "## analyze WoEs and IVs of discrete features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "dd203042",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# write a function that takes 3 arguments: a dataframe (X_train_prepr), a string (column name), and a dataframe (y_train_prepr)\n",
+    "# the function should returns a dataframe as a result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "136285cd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# set the default style of the graphs to the seaborn style. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "8cc8ddac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# define a function for plotting WoE across categories that takes 2 arguments: a dataframe and a number"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "ff26c024",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# apply these on all four categorical columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "09c44927",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# observe graphs of WOE. If there is a continuous increase in WoE across the different categories then we do not need to combine any features together and should leave all these categories as they are"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "c8b8839b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# if there are no missing values in the grade column leave it as it is, otherwise createa a separate and independent category for all Missing values that would never be combined with any other category\n",
+    "# you will come across this scenario when working through features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "c524dd5d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# define a function to calculate WoE of continuous variables\n",
+    "# this is same as the function we defined earlier for discrete variables\n",
+    "# the only difference are the 2 lines of code that need to be commented in the function that results in the df being sorted by continuous variable values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "c176da96",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# apply this on continuous variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "82436e3d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# fine classing using the cut method if there are a large number of unique values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "6e66c07f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# don't use the features which have\n",
+    "    # very low IV\n",
+    "    # which have unusually high IV\n",
+    "    # WoE ranges between a very small range, implying low power of differentiating between good and bad loans"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "d9c14866",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# if there is a feature for which most of the values are inside a particular range and very few outside then \n",
+    "    # create one category for values outside that range\n",
+    "    # apply your approach to all other values(which are inside the range)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "7052db33",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# for some of the columns values would feel out of place like utilization being greater than 1 in some values which is very rare so filter those out"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "b055d6fe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# while plotting WOE if you have some doubt in curve you can also zoom on some portion to understand the nature of graph in that area"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "9aac8722",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# if the IV is borderline close to the minimum or maximum ideal threshold, you can proceed without ignoring that feature"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9865d6b8",
+   "metadata": {},
+   "source": [
+    "## Define Custom Class for WoE Binning/Reengineering"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "3ecec1c7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# here we will create a custom scikit-learn class to take care of all binning transformations on any given data set\n",
+    "# this custom class will help us in performing k fold cross validation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "bfeb5b21",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create a list of all the reference categories, i.e. one category from each of the global features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "b9d62257",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# this custom class will create new categorical dummy features based on the cut-off points that we manually identified based on the WoE plots and IV above"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "260e7f4b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# structure this class so that it also allows a fit_transform method to be implemented on it, thereby allowing you to use it as part of a scikit-learn Pipeline "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8594f479",
+   "metadata": {},
+   "source": [
+    "## PD Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "1c007251",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# reconfirm shape of the 4 datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "400573de",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# define modeling pipeline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "46bbec7d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# define cross-validation criteria\n",
+    "# RepeatedStratifiedKFold automatially takes care of the class imbalance while splitting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "e76c496a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# fit and evaluate the logistic regression pipeline with cross-validation as defined in cv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "81862473",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# print the mean AUROC score and Gini"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "ab3c480b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# fit the pipeline on the whole training set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "9426b9ed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create a transformed training set through our WoE_Binning custom class"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "a0a4d556",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# store the column names in X_train as a list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "96f2bfd1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create a summary table of our logistic regression model(name it summary_table)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "0b02d02b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create a new column in the dataframe, called 'Coefficients', with row values the transposed coefficients from the 'LogisticRegression' model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "bbefe8f3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# increase the index of every row of the dataframe with 1 to store our model intercept in 1st row"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "35d11b18",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# assign our model intercept to this new row"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "1bdc664a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# sort the dataframe by index"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e39044f",
+   "metadata": {},
+   "source": [
+    "## Prediction Time!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "0fb857da",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# make preditions on our test set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "d751fae8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get the predicted probabilities(name it y_hat_test_proba)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "457c48b4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# select the probabilities of only the positive class (class 1 - default) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "1221e651",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# we will now create a new DF with actual classes and the predicted probabilities\n",
+    "# create a temp y_test DF to reset its index to allow proper concaternation with y_hat_test_proba"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "120e4119",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# check the shape to make sure the number of rows is same as that in y_test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "eff09b21",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# rename the columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "4a340a2b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# makes the index of one dataframe equal to the index of another dataframe"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "67f3f5c7",
+   "metadata": {},
+   "source": [
+    "## Confusion Matrix and AUROC on Test Set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "id": "bcedd377",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# assign a threshold value to differentiate good with bad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "42884466",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# crate a new column for the predicted class based on predicted probabilities and threshold\n",
+    "# we will determine this optimal threshold later in this project"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "id": "a1b467c7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create the confusion matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "id": "32ae63bd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get the values required to plot a ROC curve"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "id": "32a48f01",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# plot the ROC curve"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "id": "32926203",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# plot a secondary diagonal line, with dashed line style and black color to represent a no-skill classifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "id": "b029d7fe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# calculate the Area Under the Receiver Operating Characteristic Curve (AUROC) on our test set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "id": "e421c4b8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# calculate Gini from AUROC"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "id": "a67d6a4a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# draw a PR curve\n",
+    "# calculate the no skill line as the proportion of the positive class"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "id": "e4408762",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# plot the no skill precision-recall curve"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "id": "9419ed09",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# calculate inputs for the PR curve"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "id": "b5a5bae3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# calculate inputs for the PR curve"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "id": "7d78d0fe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# plot PR curve"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "id": "a78dd2ac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# calculate PR AUC"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9dd5c621",
+   "metadata": {},
+   "source": [
+    "## Applying the Model - Scorecard Creation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "id": "37913e85",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# print the summary_table"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "id": "e56fea64",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create a new dataframe with one column\n",
+    "# its values are the values from the 'reference_categories' list\n",
+    "# name it 'Feature name'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "id": "a26ea5f4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create a second column called 'Coefficients' which contains only 0 values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "id": "8da85214",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# concatenates two dataframes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "id": "1b6bceac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# we reset the index of a dataframe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "id": "8674e55b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create a new column called 'Original feature name' which contains the value of the 'Feature name' column up to the column symbol"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "id": "5c9e748e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# define the min and max threshholds for our scorecard"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "id": "86b900c9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# calculate the sum of the minimum coefficients of each category within the original feature name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "id": "a346dbdc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# calculate the sum of the maximum coefficients of each category within the original feature name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "id": "ac8691f9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create a new columns that has the imputed calculated Score based on the multiplication of the coefficient by the ratio of the differences between\n",
+    "# maximum & minimum score and maximum & minimum sum of cefficients"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "id": "1bf24313",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# update the calculated score of the Intercept (i.e. the default score for each loan)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "id": "dc2657e3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# round the values of the 'Score - Calculation' column and store them in a new column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "id": "543f897d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# check the min and max possible scores of our scorecard"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "id": "88818024",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# both our min and max scores are out by +1\n",
+    "# we need to manually adjust this\n",
+    "# to decide which one we'll evaluate based on the rounding differences of the minimum category within each Original Feature Name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "id": "e188b1a5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# we can get by deducting 1 from the Intercept"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "id": "feb1f6f1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# recheck min and max possible scores"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cd043803",
+   "metadata": {},
+   "source": [
+    "## Calculating credit scores for all observations in the test data set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "id": "7ff8f92a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create a transformed test set through our WoE_Binning custom class"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "id": "95d3ba61",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# insert an Intercept column in its beginning to align with the # of rows in scorecard"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "id": "e6229a6f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get the list of our final scorecard scores"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "id": "bf4b59e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# check the shapes of test set and scorecard before doing matrix dot multiplication"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "id": "9ee9b571",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# we can see that the test set has a few less columns than the rows in scorecard due to the reference categories\n",
+    "# since the reference categories will always be scored as 0 based on the scorecard, it is safe to add these categories to the end of test set with 0 values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "id": "9cb4b22e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# need to reshape scorecard_scores so that it is of proper shape to allow for matrix dot multiplication"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "id": "60a94f3a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# matrix dot multiplication of test set with scorecard scores"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1d8c99b3",
+   "metadata": {},
+   "source": [
+    "## Setting loan approval cut-offs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "id": "869dcdea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# calculate Youden's J-Statistic to identify the best threshhold"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "id": "cc9bdb99",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# locate the index of the largest J"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "id": "0401c25d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# print the best threshold"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "id": "13cbdedb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# this means that based on the Youden's J statistic, this is the ideal probability threshold which minimizes the FPR and maximimizes the TPR\n",
+    "# which means all samples with a predicted probability higher than this should be classified as in Default and vice versa\n",
+    "# this ideal threshold might appear to be counterintuitive compared to the default probability threshold of 0.5 but remember that we used the class_weight parameter when fitting our logistic regression model that would have helped us\n",
+    "\n",
+    "# we can confirm this by looking at our original confusion matrix with the updated threshold"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "id": "8577ee5f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# update the threshold value"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "id": "fae9f7ec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# crate a new column for the predicted class based on predicted probabilities and threshold"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "id": "e9cf41a8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create the confusion matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "id": "ddfcf9bc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# the updated confusion matrix would show a marked improvement in the TPR at marginal cost of lower TNR but at the same time, FNR has improved drastically with a corresponding marginal increase in FPR\n",
+    "\n",
+    "# find the corresponding acceptance and rejection rates on the test set at this ideal threshold"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "id": "b0794619",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create a new DF comprising of the thresholds from the ROC output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "id": "b8b07a3a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# calculate Score corresponding to each threshold"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "id": "a838b102",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# define a function called 'n_approved' which assigns a value of 1 if a predicted probability is greater than the parameter p which is a threshold and a value of 0 if it is not\n",
+    "# then it sums the column.\n",
+    "# for given any percentage values the function will return the number of rows wih estimated probabilites greater than the threshold"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "id": "00b90a17",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# assuming that all credit applications above a given probability of being 'good' will be approved\n",
+    "# when we apply the 'n_approved' function to a threshold it will return the number of approved applications\n",
+    "# here we calculate the number of approved appliations for all thresholds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "id": "cb181639",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# then we calculate the number of rejected applications for each threshold\n",
+    "# it is the difference between the total number of applications and the approved applications for that threshold"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "id": "1630dc56",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# approval rate equals the ratio of the approved applications and all applications"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "id": "b5873b1c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# rejection rate equals one minus approval rate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "id": "3d7a0362",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# look at the approval and rejection rates at our ideal threshold"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "id": "b63291ad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# compare the above rates with the case of the default 0.5 threshold"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "id": "b26cc736",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# you would find that 0.5 threshold would result in a very high rejection rate with a corresponding loss of business\n",
+    "\n",
+    "# we will stick with our ideal threshold and the corresponding Creidt Score of (fill yourself) and can also monitor the model's performance in production if more data were available"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bb92abd1",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 0ad86812387a51c105af2bf78f89a6abc3498a8b Mon Sep 17 00:00:00 2001
From: vishalh21 <106445212+vishalh21@users.noreply.github.com>
Date: Wed, 13 Jul 2022 00:00:25 +0530
Subject: [PATCH 09/14] Create Assignment 2

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+

From c9a8663ecdec8e9311376b4e0e0fae3cfac46dac Mon Sep 17 00:00:00 2001
From: vishalh21 <106445212+vishalh21@users.noreply.github.com>
Date: Wed, 13 Jul 2022 00:03:12 +0530
Subject: [PATCH 10/14] Delete Assignment 2

---
 Assignment 2 | 1 -
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From 958d1565bb80ac82ac33253fd245dfe2f288bda9 Mon Sep 17 00:00:00 2001
From: vishalh21 <106445212+vishalh21@users.noreply.github.com>
Date: Wed, 13 Jul 2022 00:11:39 +0530
Subject: [PATCH 11/14] Create 211175_Vishal_1

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+

From 23b3c2a69ac6994d9c4fc2bc70b16e779b7eb17b Mon Sep 17 00:00:00 2001
From: vishalh21 <106445212+vishalh21@users.noreply.github.com>
Date: Wed, 13 Jul 2022 00:12:02 +0530
Subject: [PATCH 12/14] Add files via upload

---
 Assignment 2/211175_Vishal_2.ipynb | 3366 ++++++++++++++++++++++++++++
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diff --git a/Assignment 2/211175_Vishal_2.ipynb b/Assignment 2/211175_Vishal_2.ipynb
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--- /dev/null
+++ b/Assignment 2/211175_Vishal_2.ipynb	
@@ -0,0 +1,3366 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "0d5f58b8",
+   "metadata": {},
+   "source": [
+    "## Environment Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "c0d00887",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import pandas, numpy, seaborn, pyplot\n",
+    "import numpy as np \n",
+    "import pandas as pd\n",
+    "import matplotlib as mpl\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from math import sqrt\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "8718c756",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import train_test_split, RepeatedStratifiedKFold, cross_val_score, LogisticRegression, roc_curve, roc_auc_score,\n",
+    "# confusion_matrix, precision_recall_curve, auc, f_classif, Pipeline, BaseEstimator, TransformerMixin, chi2_contingency\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.model_selection import RepeatedStratifiedKFold\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn import metrics\n",
+    "from sklearn.metrics import roc_curve\n",
+    "from sklearn.metrics import roc_auc_score\n",
+    "from sklearn.metrics import confusion_matrix\n",
+    "from sklearn.metrics import precision_recall_curve\n",
+    "from sklearn.feature_selection import f_classif\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.base import BaseEstimator\n",
+    "from sklearn.base import TransformerMixin\n",
+    "from scipy.stats import chi2_contingency"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "452fe749",
+   "metadata": {},
+   "source": [
+    "## Import Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "8dfd3c5f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load the dataset\n",
+    "df=pd.read_csv('credit_risk_dataset.csv',low_memory=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "88db398b",
+   "metadata": {},
+   "source": [
+    "## Data Exploration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0a185963",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>member_id</th>\n",
+       "      <th>loan_amnt</th>\n",
+       "      <th>funded_amnt</th>\n",
+       "      <th>funded_amnt_inv</th>\n",
+       "      <th>term</th>\n",
+       "      <th>int_rate</th>\n",
+       "      <th>installment</th>\n",
+       "      <th>grade</th>\n",
+       "      <th>sub_grade</th>\n",
+       "      <th>...</th>\n",
+       "      <th>total_bal_il</th>\n",
+       "      <th>il_util</th>\n",
+       "      <th>open_rv_12m</th>\n",
+       "      <th>open_rv_24m</th>\n",
+       "      <th>max_bal_bc</th>\n",
+       "      <th>all_util</th>\n",
+       "      <th>total_rev_hi_lim</th>\n",
+       "      <th>inq_fi</th>\n",
+       "      <th>total_cu_tl</th>\n",
+       "      <th>inq_last_12m</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1077501</td>\n",
+       "      <td>1296599</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>4975.0</td>\n",
+       "      <td>36 months</td>\n",
+       "      <td>10.65</td>\n",
+       "      <td>162.87</td>\n",
+       "      <td>B</td>\n",
+       "      <td>B2</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1077430</td>\n",
+       "      <td>1314167</td>\n",
+       "      <td>2500</td>\n",
+       "      <td>2500</td>\n",
+       "      <td>2500.0</td>\n",
+       "      <td>60 months</td>\n",
+       "      <td>15.27</td>\n",
+       "      <td>59.83</td>\n",
+       "      <td>C</td>\n",
+       "      <td>C4</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1077175</td>\n",
+       "      <td>1313524</td>\n",
+       "      <td>2400</td>\n",
+       "      <td>2400</td>\n",
+       "      <td>2400.0</td>\n",
+       "      <td>36 months</td>\n",
+       "      <td>15.96</td>\n",
+       "      <td>84.33</td>\n",
+       "      <td>C</td>\n",
+       "      <td>C5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1076863</td>\n",
+       "      <td>1277178</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>10000.0</td>\n",
+       "      <td>36 months</td>\n",
+       "      <td>13.49</td>\n",
+       "      <td>339.31</td>\n",
+       "      <td>C</td>\n",
+       "      <td>C1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1075358</td>\n",
+       "      <td>1311748</td>\n",
+       "      <td>3000</td>\n",
+       "      <td>3000</td>\n",
+       "      <td>3000.0</td>\n",
+       "      <td>60 months</td>\n",
+       "      <td>12.69</td>\n",
+       "      <td>67.79</td>\n",
+       "      <td>B</td>\n",
+       "      <td>B5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 74 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        id  member_id  loan_amnt  funded_amnt  funded_amnt_inv        term  \\\n",
+       "0  1077501    1296599       5000         5000           4975.0   36 months   \n",
+       "1  1077430    1314167       2500         2500           2500.0   60 months   \n",
+       "2  1077175    1313524       2400         2400           2400.0   36 months   \n",
+       "3  1076863    1277178      10000        10000          10000.0   36 months   \n",
+       "4  1075358    1311748       3000         3000           3000.0   60 months   \n",
+       "\n",
+       "   int_rate  installment grade sub_grade  ... total_bal_il il_util  \\\n",
+       "0     10.65       162.87     B        B2  ...          NaN     NaN   \n",
+       "1     15.27        59.83     C        C4  ...          NaN     NaN   \n",
+       "2     15.96        84.33     C        C5  ...          NaN     NaN   \n",
+       "3     13.49       339.31     C        C1  ...          NaN     NaN   \n",
+       "4     12.69        67.79     B        B5  ...          NaN     NaN   \n",
+       "\n",
+       "  open_rv_12m  open_rv_24m max_bal_bc all_util total_rev_hi_lim inq_fi  \\\n",
+       "0         NaN          NaN        NaN      NaN              NaN    NaN   \n",
+       "1         NaN          NaN        NaN      NaN              NaN    NaN   \n",
+       "2         NaN          NaN        NaN      NaN              NaN    NaN   \n",
+       "3         NaN          NaN        NaN      NaN              NaN    NaN   \n",
+       "4         NaN          NaN        NaN      NaN              NaN    NaN   \n",
+       "\n",
+       "  total_cu_tl inq_last_12m  \n",
+       "0         NaN          NaN  \n",
+       "1         NaN          NaN  \n",
+       "2         NaN          NaN  \n",
+       "3         NaN          NaN  \n",
+       "4         NaN          NaN  \n",
+       "\n",
+       "[5 rows x 74 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# explore the dataset(use the data dictionary)\n",
+    "# look at the columns, which data type is present in a column, range of values in each column, their mean, etc.\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "de2273c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(466285, 74)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "1c1516bf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 466285 entries, 0 to 466284\n",
+      "Data columns (total 74 columns):\n",
+      " #   Column                       Non-Null Count   Dtype  \n",
+      "---  ------                       --------------   -----  \n",
+      " 0   id                           466285 non-null  int64  \n",
+      " 1   member_id                    466285 non-null  int64  \n",
+      " 2   loan_amnt                    466285 non-null  int64  \n",
+      " 3   funded_amnt                  466285 non-null  int64  \n",
+      " 4   funded_amnt_inv              466285 non-null  float64\n",
+      " 5   term                         466285 non-null  object \n",
+      " 6   int_rate                     466285 non-null  float64\n",
+      " 7   installment                  466285 non-null  float64\n",
+      " 8   grade                        466285 non-null  object \n",
+      " 9   sub_grade                    466285 non-null  object \n",
+      " 10  emp_title                    438697 non-null  object \n",
+      " 11  emp_length                   445277 non-null  object \n",
+      " 12  home_ownership               466285 non-null  object \n",
+      " 13  annual_inc                   466281 non-null  float64\n",
+      " 14  verification_status          466285 non-null  object \n",
+      " 15  issue_d                      466285 non-null  object \n",
+      " 16  loan_status                  466285 non-null  object \n",
+      " 17  pymnt_plan                   466285 non-null  object \n",
+      " 18  url                          466285 non-null  object \n",
+      " 19  desc                         125983 non-null  object \n",
+      " 20  purpose                      466285 non-null  object \n",
+      " 21  title                        466265 non-null  object \n",
+      " 22  zip_code                     466285 non-null  object \n",
+      " 23  addr_state                   466285 non-null  object \n",
+      " 24  dti                          466285 non-null  float64\n",
+      " 25  delinq_2yrs                  466256 non-null  float64\n",
+      " 26  earliest_cr_line             466256 non-null  object \n",
+      " 27  inq_last_6mths               466256 non-null  float64\n",
+      " 28  mths_since_last_delinq       215934 non-null  float64\n",
+      " 29  mths_since_last_record       62638 non-null   float64\n",
+      " 30  open_acc                     466256 non-null  float64\n",
+      " 31  pub_rec                      466256 non-null  float64\n",
+      " 32  revol_bal                    466285 non-null  int64  \n",
+      " 33  revol_util                   465945 non-null  float64\n",
+      " 34  total_acc                    466256 non-null  float64\n",
+      " 35  initial_list_status          466285 non-null  object \n",
+      " 36  out_prncp                    466285 non-null  float64\n",
+      " 37  out_prncp_inv                466285 non-null  float64\n",
+      " 38  total_pymnt                  466285 non-null  float64\n",
+      " 39  total_pymnt_inv              466285 non-null  float64\n",
+      " 40  total_rec_prncp              466285 non-null  float64\n",
+      " 41  total_rec_int                466285 non-null  float64\n",
+      " 42  total_rec_late_fee           466285 non-null  float64\n",
+      " 43  recoveries                   466285 non-null  float64\n",
+      " 44  collection_recovery_fee      466285 non-null  float64\n",
+      " 45  last_pymnt_d                 465909 non-null  object \n",
+      " 46  last_pymnt_amnt              466285 non-null  float64\n",
+      " 47  next_pymnt_d                 239071 non-null  object \n",
+      " 48  last_credit_pull_d           466243 non-null  object \n",
+      " 49  collections_12_mths_ex_med   466140 non-null  float64\n",
+      " 50  mths_since_last_major_derog  98974 non-null   float64\n",
+      " 51  policy_code                  466285 non-null  int64  \n",
+      " 52  application_type             466285 non-null  object \n",
+      " 53  annual_inc_joint             0 non-null       float64\n",
+      " 54  dti_joint                    0 non-null       float64\n",
+      " 55  verification_status_joint    0 non-null       float64\n",
+      " 56  acc_now_delinq               466256 non-null  float64\n",
+      " 57  tot_coll_amt                 396009 non-null  float64\n",
+      " 58  tot_cur_bal                  396009 non-null  float64\n",
+      " 59  open_acc_6m                  0 non-null       float64\n",
+      " 60  open_il_6m                   0 non-null       float64\n",
+      " 61  open_il_12m                  0 non-null       float64\n",
+      " 62  open_il_24m                  0 non-null       float64\n",
+      " 63  mths_since_rcnt_il           0 non-null       float64\n",
+      " 64  total_bal_il                 0 non-null       float64\n",
+      " 65  il_util                      0 non-null       float64\n",
+      " 66  open_rv_12m                  0 non-null       float64\n",
+      " 67  open_rv_24m                  0 non-null       float64\n",
+      " 68  max_bal_bc                   0 non-null       float64\n",
+      " 69  all_util                     0 non-null       float64\n",
+      " 70  total_rev_hi_lim             396009 non-null  float64\n",
+      " 71  inq_fi                       0 non-null       float64\n",
+      " 72  total_cu_tl                  0 non-null       float64\n",
+      " 73  inq_last_12m                 0 non-null       float64\n",
+      "dtypes: float64(46), int64(6), object(22)\n",
+      "memory usage: 263.3+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "88a46339",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>member_id</th>\n",
+       "      <th>loan_amnt</th>\n",
+       "      <th>funded_amnt</th>\n",
+       "      <th>funded_amnt_inv</th>\n",
+       "      <th>int_rate</th>\n",
+       "      <th>installment</th>\n",
+       "      <th>annual_inc</th>\n",
+       "      <th>dti</th>\n",
+       "      <th>delinq_2yrs</th>\n",
+       "      <th>...</th>\n",
+       "      <th>total_bal_il</th>\n",
+       "      <th>il_util</th>\n",
+       "      <th>open_rv_12m</th>\n",
+       "      <th>open_rv_24m</th>\n",
+       "      <th>max_bal_bc</th>\n",
+       "      <th>all_util</th>\n",
+       "      <th>total_rev_hi_lim</th>\n",
+       "      <th>inq_fi</th>\n",
+       "      <th>total_cu_tl</th>\n",
+       "      <th>inq_last_12m</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>4.662850e+05</td>\n",
+       "      <td>4.662850e+05</td>\n",
+       "      <td>466285.000000</td>\n",
+       "      <td>466285.000000</td>\n",
+       "      <td>466285.000000</td>\n",
+       "      <td>466285.000000</td>\n",
+       "      <td>466285.000000</td>\n",
+       "      <td>4.662810e+05</td>\n",
+       "      <td>466285.000000</td>\n",
+       "      <td>466256.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.960090e+05</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>1.307973e+07</td>\n",
+       "      <td>1.459766e+07</td>\n",
+       "      <td>14317.277577</td>\n",
+       "      <td>14291.801044</td>\n",
+       "      <td>14222.329888</td>\n",
+       "      <td>13.829236</td>\n",
+       "      <td>432.061201</td>\n",
+       "      <td>7.327738e+04</td>\n",
+       "      <td>17.218758</td>\n",
+       "      <td>0.284678</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.037909e+04</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>1.089371e+07</td>\n",
+       "      <td>1.168237e+07</td>\n",
+       "      <td>8286.509164</td>\n",
+       "      <td>8274.371300</td>\n",
+       "      <td>8297.637788</td>\n",
+       "      <td>4.357587</td>\n",
+       "      <td>243.485550</td>\n",
+       "      <td>5.496357e+04</td>\n",
+       "      <td>7.851121</td>\n",
+       "      <td>0.797365</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.724713e+04</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>5.473400e+04</td>\n",
+       "      <td>7.047300e+04</td>\n",
+       "      <td>500.000000</td>\n",
+       "      <td>500.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.420000</td>\n",
+       "      <td>15.670000</td>\n",
+       "      <td>1.896000e+03</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>3.639987e+06</td>\n",
+       "      <td>4.379705e+06</td>\n",
+       "      <td>8000.000000</td>\n",
+       "      <td>8000.000000</td>\n",
+       "      <td>8000.000000</td>\n",
+       "      <td>10.990000</td>\n",
+       "      <td>256.690000</td>\n",
+       "      <td>4.500000e+04</td>\n",
+       "      <td>11.360000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.350000e+04</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>1.010790e+07</td>\n",
+       "      <td>1.194108e+07</td>\n",
+       "      <td>12000.000000</td>\n",
+       "      <td>12000.000000</td>\n",
+       "      <td>12000.000000</td>\n",
+       "      <td>13.660000</td>\n",
+       "      <td>379.890000</td>\n",
+       "      <td>6.300000e+04</td>\n",
+       "      <td>16.870000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.280000e+04</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>2.073121e+07</td>\n",
+       "      <td>2.300154e+07</td>\n",
+       "      <td>20000.000000</td>\n",
+       "      <td>20000.000000</td>\n",
+       "      <td>19950.000000</td>\n",
+       "      <td>16.490000</td>\n",
+       "      <td>566.580000</td>\n",
+       "      <td>8.896000e+04</td>\n",
+       "      <td>22.780000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.790000e+04</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>3.809811e+07</td>\n",
+       "      <td>4.086083e+07</td>\n",
+       "      <td>35000.000000</td>\n",
+       "      <td>35000.000000</td>\n",
+       "      <td>35000.000000</td>\n",
+       "      <td>26.060000</td>\n",
+       "      <td>1409.990000</td>\n",
+       "      <td>7.500000e+06</td>\n",
+       "      <td>39.990000</td>\n",
+       "      <td>29.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9.999999e+06</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>8 rows × 52 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 id     member_id      loan_amnt    funded_amnt  \\\n",
+       "count  4.662850e+05  4.662850e+05  466285.000000  466285.000000   \n",
+       "mean   1.307973e+07  1.459766e+07   14317.277577   14291.801044   \n",
+       "std    1.089371e+07  1.168237e+07    8286.509164    8274.371300   \n",
+       "min    5.473400e+04  7.047300e+04     500.000000     500.000000   \n",
+       "25%    3.639987e+06  4.379705e+06    8000.000000    8000.000000   \n",
+       "50%    1.010790e+07  1.194108e+07   12000.000000   12000.000000   \n",
+       "75%    2.073121e+07  2.300154e+07   20000.000000   20000.000000   \n",
+       "max    3.809811e+07  4.086083e+07   35000.000000   35000.000000   \n",
+       "\n",
+       "       funded_amnt_inv       int_rate    installment    annual_inc  \\\n",
+       "count    466285.000000  466285.000000  466285.000000  4.662810e+05   \n",
+       "mean      14222.329888      13.829236     432.061201  7.327738e+04   \n",
+       "std        8297.637788       4.357587     243.485550  5.496357e+04   \n",
+       "min           0.000000       5.420000      15.670000  1.896000e+03   \n",
+       "25%        8000.000000      10.990000     256.690000  4.500000e+04   \n",
+       "50%       12000.000000      13.660000     379.890000  6.300000e+04   \n",
+       "75%       19950.000000      16.490000     566.580000  8.896000e+04   \n",
+       "max       35000.000000      26.060000    1409.990000  7.500000e+06   \n",
+       "\n",
+       "                 dti    delinq_2yrs  ...  total_bal_il  il_util  open_rv_12m  \\\n",
+       "count  466285.000000  466256.000000  ...           0.0      0.0          0.0   \n",
+       "mean       17.218758       0.284678  ...           NaN      NaN          NaN   \n",
+       "std         7.851121       0.797365  ...           NaN      NaN          NaN   \n",
+       "min         0.000000       0.000000  ...           NaN      NaN          NaN   \n",
+       "25%        11.360000       0.000000  ...           NaN      NaN          NaN   \n",
+       "50%        16.870000       0.000000  ...           NaN      NaN          NaN   \n",
+       "75%        22.780000       0.000000  ...           NaN      NaN          NaN   \n",
+       "max        39.990000      29.000000  ...           NaN      NaN          NaN   \n",
+       "\n",
+       "       open_rv_24m  max_bal_bc  all_util  total_rev_hi_lim  inq_fi  \\\n",
+       "count          0.0         0.0       0.0      3.960090e+05     0.0   \n",
+       "mean           NaN         NaN       NaN      3.037909e+04     NaN   \n",
+       "std            NaN         NaN       NaN      3.724713e+04     NaN   \n",
+       "min            NaN         NaN       NaN      0.000000e+00     NaN   \n",
+       "25%            NaN         NaN       NaN      1.350000e+04     NaN   \n",
+       "50%            NaN         NaN       NaN      2.280000e+04     NaN   \n",
+       "75%            NaN         NaN       NaN      3.790000e+04     NaN   \n",
+       "max            NaN         NaN       NaN      9.999999e+06     NaN   \n",
+       "\n",
+       "       total_cu_tl  inq_last_12m  \n",
+       "count          0.0           0.0  \n",
+       "mean           NaN           NaN  \n",
+       "std            NaN           NaN  \n",
+       "min            NaN           NaN  \n",
+       "25%            NaN           NaN  \n",
+       "50%            NaN           NaN  \n",
+       "75%            NaN           NaN  \n",
+       "max            NaN           NaN  \n",
+       "\n",
+       "[8 rows x 52 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "cc753927",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['id', 'member_id', 'loan_amnt', 'funded_amnt', 'funded_amnt_inv',\n",
+       "       'term', 'int_rate', 'installment', 'grade', 'sub_grade', 'emp_title',\n",
+       "       'emp_length', 'home_ownership', 'annual_inc', 'verification_status',\n",
+       "       'issue_d', 'loan_status', 'pymnt_plan', 'url', 'desc', 'purpose',\n",
+       "       'title', 'zip_code', 'addr_state', 'dti', 'delinq_2yrs',\n",
+       "       'earliest_cr_line', 'inq_last_6mths', 'mths_since_last_delinq',\n",
+       "       'open_acc', 'pub_rec', 'revol_bal', 'revol_util', 'total_acc',\n",
+       "       'initial_list_status', 'out_prncp', 'out_prncp_inv', 'total_pymnt',\n",
+       "       'total_pymnt_inv', 'total_rec_prncp', 'total_rec_int',\n",
+       "       'total_rec_late_fee', 'recoveries', 'collection_recovery_fee',\n",
+       "       'last_pymnt_d', 'last_pymnt_amnt', 'next_pymnt_d', 'last_credit_pull_d',\n",
+       "       'collections_12_mths_ex_med', 'mths_since_last_major_derog',\n",
+       "       'policy_code', 'application_type', 'acc_now_delinq', 'tot_coll_amt',\n",
+       "       'tot_cur_bal', 'total_rev_hi_lim'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# get a list of columns that have more than 80% null values\n",
+    "df.columns[df.isnull().mean() < 0.8]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "bb70a075",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>member_id</th>\n",
+       "      <th>loan_amnt</th>\n",
+       "      <th>funded_amnt</th>\n",
+       "      <th>funded_amnt_inv</th>\n",
+       "      <th>term</th>\n",
+       "      <th>int_rate</th>\n",
+       "      <th>installment</th>\n",
+       "      <th>grade</th>\n",
+       "      <th>sub_grade</th>\n",
+       "      <th>...</th>\n",
+       "      <th>next_pymnt_d</th>\n",
+       "      <th>last_credit_pull_d</th>\n",
+       "      <th>collections_12_mths_ex_med</th>\n",
+       "      <th>mths_since_last_major_derog</th>\n",
+       "      <th>policy_code</th>\n",
+       "      <th>application_type</th>\n",
+       "      <th>acc_now_delinq</th>\n",
+       "      <th>tot_coll_amt</th>\n",
+       "      <th>tot_cur_bal</th>\n",
+       "      <th>total_rev_hi_lim</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1077501</td>\n",
+       "      <td>1296599</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>4975.0</td>\n",
+       "      <td>36 months</td>\n",
+       "      <td>10.65</td>\n",
+       "      <td>162.87</td>\n",
+       "      <td>B</td>\n",
+       "      <td>B2</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Jan-16</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>INDIVIDUAL</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1077430</td>\n",
+       "      <td>1314167</td>\n",
+       "      <td>2500</td>\n",
+       "      <td>2500</td>\n",
+       "      <td>2500.0</td>\n",
+       "      <td>60 months</td>\n",
+       "      <td>15.27</td>\n",
+       "      <td>59.83</td>\n",
+       "      <td>C</td>\n",
+       "      <td>C4</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Sep-13</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>INDIVIDUAL</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1077175</td>\n",
+       "      <td>1313524</td>\n",
+       "      <td>2400</td>\n",
+       "      <td>2400</td>\n",
+       "      <td>2400.0</td>\n",
+       "      <td>36 months</td>\n",
+       "      <td>15.96</td>\n",
+       "      <td>84.33</td>\n",
+       "      <td>C</td>\n",
+       "      <td>C5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Jan-16</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>INDIVIDUAL</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1076863</td>\n",
+       "      <td>1277178</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>10000.0</td>\n",
+       "      <td>36 months</td>\n",
+       "      <td>13.49</td>\n",
+       "      <td>339.31</td>\n",
+       "      <td>C</td>\n",
+       "      <td>C1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Jan-15</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>INDIVIDUAL</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1075358</td>\n",
+       "      <td>1311748</td>\n",
+       "      <td>3000</td>\n",
+       "      <td>3000</td>\n",
+       "      <td>3000.0</td>\n",
+       "      <td>60 months</td>\n",
+       "      <td>12.69</td>\n",
+       "      <td>67.79</td>\n",
+       "      <td>B</td>\n",
+       "      <td>B5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Feb-16</td>\n",
+       "      <td>Jan-16</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>INDIVIDUAL</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 56 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        id  member_id  loan_amnt  funded_amnt  funded_amnt_inv        term  \\\n",
+       "0  1077501    1296599       5000         5000           4975.0   36 months   \n",
+       "1  1077430    1314167       2500         2500           2500.0   60 months   \n",
+       "2  1077175    1313524       2400         2400           2400.0   36 months   \n",
+       "3  1076863    1277178      10000        10000          10000.0   36 months   \n",
+       "4  1075358    1311748       3000         3000           3000.0   60 months   \n",
+       "\n",
+       "   int_rate  installment grade sub_grade  ... next_pymnt_d last_credit_pull_d  \\\n",
+       "0     10.65       162.87     B        B2  ...          NaN             Jan-16   \n",
+       "1     15.27        59.83     C        C4  ...          NaN             Sep-13   \n",
+       "2     15.96        84.33     C        C5  ...          NaN             Jan-16   \n",
+       "3     13.49       339.31     C        C1  ...          NaN             Jan-15   \n",
+       "4     12.69        67.79     B        B5  ...       Feb-16             Jan-16   \n",
+       "\n",
+       "  collections_12_mths_ex_med  mths_since_last_major_derog policy_code  \\\n",
+       "0                        0.0                          NaN           1   \n",
+       "1                        0.0                          NaN           1   \n",
+       "2                        0.0                          NaN           1   \n",
+       "3                        0.0                          NaN           1   \n",
+       "4                        0.0                          NaN           1   \n",
+       "\n",
+       "  application_type acc_now_delinq tot_coll_amt tot_cur_bal total_rev_hi_lim  \n",
+       "0       INDIVIDUAL            0.0          NaN         NaN              NaN  \n",
+       "1       INDIVIDUAL            0.0          NaN         NaN              NaN  \n",
+       "2       INDIVIDUAL            0.0          NaN         NaN              NaN  \n",
+       "3       INDIVIDUAL            0.0          NaN         NaN              NaN  \n",
+       "4       INDIVIDUAL            0.0          NaN         NaN              NaN  \n",
+       "\n",
+       "[5 rows x 56 columns]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# drop columns with more than 80% null values\n",
+    "df=df[df.columns[df.isnull().mean() < 0.8]]\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "a1534071",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 466285 entries, 0 to 466284\n",
+      "Data columns (total 56 columns):\n",
+      " #   Column                       Non-Null Count   Dtype  \n",
+      "---  ------                       --------------   -----  \n",
+      " 0   id                           466285 non-null  int64  \n",
+      " 1   member_id                    466285 non-null  int64  \n",
+      " 2   loan_amnt                    466285 non-null  int64  \n",
+      " 3   funded_amnt                  466285 non-null  int64  \n",
+      " 4   funded_amnt_inv              466285 non-null  float64\n",
+      " 5   term                         466285 non-null  object \n",
+      " 6   int_rate                     466285 non-null  float64\n",
+      " 7   installment                  466285 non-null  float64\n",
+      " 8   grade                        466285 non-null  object \n",
+      " 9   sub_grade                    466285 non-null  object \n",
+      " 10  emp_title                    438697 non-null  object \n",
+      " 11  emp_length                   445277 non-null  object \n",
+      " 12  home_ownership               466285 non-null  object \n",
+      " 13  annual_inc                   466281 non-null  float64\n",
+      " 14  verification_status          466285 non-null  object \n",
+      " 15  issue_d                      466285 non-null  object \n",
+      " 16  loan_status                  466285 non-null  object \n",
+      " 17  pymnt_plan                   466285 non-null  object \n",
+      " 18  url                          466285 non-null  object \n",
+      " 19  desc                         125983 non-null  object \n",
+      " 20  purpose                      466285 non-null  object \n",
+      " 21  title                        466265 non-null  object \n",
+      " 22  zip_code                     466285 non-null  object \n",
+      " 23  addr_state                   466285 non-null  object \n",
+      " 24  dti                          466285 non-null  float64\n",
+      " 25  delinq_2yrs                  466256 non-null  float64\n",
+      " 26  earliest_cr_line             466256 non-null  object \n",
+      " 27  inq_last_6mths               466256 non-null  float64\n",
+      " 28  mths_since_last_delinq       215934 non-null  float64\n",
+      " 29  open_acc                     466256 non-null  float64\n",
+      " 30  pub_rec                      466256 non-null  float64\n",
+      " 31  revol_bal                    466285 non-null  int64  \n",
+      " 32  revol_util                   465945 non-null  float64\n",
+      " 33  total_acc                    466256 non-null  float64\n",
+      " 34  initial_list_status          466285 non-null  object \n",
+      " 35  out_prncp                    466285 non-null  float64\n",
+      " 36  out_prncp_inv                466285 non-null  float64\n",
+      " 37  total_pymnt                  466285 non-null  float64\n",
+      " 38  total_pymnt_inv              466285 non-null  float64\n",
+      " 39  total_rec_prncp              466285 non-null  float64\n",
+      " 40  total_rec_int                466285 non-null  float64\n",
+      " 41  total_rec_late_fee           466285 non-null  float64\n",
+      " 42  recoveries                   466285 non-null  float64\n",
+      " 43  collection_recovery_fee      466285 non-null  float64\n",
+      " 44  last_pymnt_d                 465909 non-null  object \n",
+      " 45  last_pymnt_amnt              466285 non-null  float64\n",
+      " 46  next_pymnt_d                 239071 non-null  object \n",
+      " 47  last_credit_pull_d           466243 non-null  object \n",
+      " 48  collections_12_mths_ex_med   466140 non-null  float64\n",
+      " 49  mths_since_last_major_derog  98974 non-null   float64\n",
+      " 50  policy_code                  466285 non-null  int64  \n",
+      " 51  application_type             466285 non-null  object \n",
+      " 52  acc_now_delinq               466256 non-null  float64\n",
+      " 53  tot_coll_amt                 396009 non-null  float64\n",
+      " 54  tot_cur_bal                  396009 non-null  float64\n",
+      " 55  total_rev_hi_lim             396009 non-null  float64\n",
+      "dtypes: float64(28), int64(6), object(22)\n",
+      "memory usage: 199.2+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# drop redundant and forward-looking columns\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "7b868980",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#dropping redundant column isn't done completely for now\n",
+    "df_drop=df.drop(labels=['id','member_id','zip_code','emp_title','verification_status','issue_d','pymnt_plan','url','addr_state','delinq_2yrs','application_type','desc'],axis=1,inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "f3a847b0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 466285 entries, 0 to 466284\n",
+      "Data columns (total 44 columns):\n",
+      " #   Column                       Non-Null Count   Dtype  \n",
+      "---  ------                       --------------   -----  \n",
+      " 0   loan_amnt                    466285 non-null  int64  \n",
+      " 1   funded_amnt                  466285 non-null  int64  \n",
+      " 2   funded_amnt_inv              466285 non-null  float64\n",
+      " 3   term                         466285 non-null  object \n",
+      " 4   int_rate                     466285 non-null  float64\n",
+      " 5   installment                  466285 non-null  float64\n",
+      " 6   grade                        466285 non-null  object \n",
+      " 7   sub_grade                    466285 non-null  object \n",
+      " 8   emp_length                   445277 non-null  object \n",
+      " 9   home_ownership               466285 non-null  object \n",
+      " 10  annual_inc                   466281 non-null  float64\n",
+      " 11  loan_status                  466285 non-null  object \n",
+      " 12  purpose                      466285 non-null  object \n",
+      " 13  title                        466265 non-null  object \n",
+      " 14  dti                          466285 non-null  float64\n",
+      " 15  earliest_cr_line             466256 non-null  object \n",
+      " 16  inq_last_6mths               466256 non-null  float64\n",
+      " 17  mths_since_last_delinq       215934 non-null  float64\n",
+      " 18  open_acc                     466256 non-null  float64\n",
+      " 19  pub_rec                      466256 non-null  float64\n",
+      " 20  revol_bal                    466285 non-null  int64  \n",
+      " 21  revol_util                   465945 non-null  float64\n",
+      " 22  total_acc                    466256 non-null  float64\n",
+      " 23  initial_list_status          466285 non-null  object \n",
+      " 24  out_prncp                    466285 non-null  float64\n",
+      " 25  out_prncp_inv                466285 non-null  float64\n",
+      " 26  total_pymnt                  466285 non-null  float64\n",
+      " 27  total_pymnt_inv              466285 non-null  float64\n",
+      " 28  total_rec_prncp              466285 non-null  float64\n",
+      " 29  total_rec_int                466285 non-null  float64\n",
+      " 30  total_rec_late_fee           466285 non-null  float64\n",
+      " 31  recoveries                   466285 non-null  float64\n",
+      " 32  collection_recovery_fee      466285 non-null  float64\n",
+      " 33  last_pymnt_d                 465909 non-null  object \n",
+      " 34  last_pymnt_amnt              466285 non-null  float64\n",
+      " 35  next_pymnt_d                 239071 non-null  object \n",
+      " 36  last_credit_pull_d           466243 non-null  object \n",
+      " 37  collections_12_mths_ex_med   466140 non-null  float64\n",
+      " 38  mths_since_last_major_derog  98974 non-null   float64\n",
+      " 39  policy_code                  466285 non-null  int64  \n",
+      " 40  acc_now_delinq               466256 non-null  float64\n",
+      " 41  tot_coll_amt                 396009 non-null  float64\n",
+      " 42  tot_cur_bal                  396009 non-null  float64\n",
+      " 43  total_rev_hi_lim             396009 non-null  float64\n",
+      "dtypes: float64(27), int64(4), object(13)\n",
+      "memory usage: 156.5+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# re-explore the dataset\n",
+    "#df.describe()\n",
+    "#df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e7300df0",
+   "metadata": {},
+   "source": [
+    "## Identify the target variable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "6ac92171",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# identify the target variable(target column) no need to code this just identify\n",
+    "# Here the target column is loan_status."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "5f0d11eb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Fully Paid', 'Charged Off', 'Current', 'Default',\n",
+       "       'Late (31-120 days)', 'In Grace Period', 'Late (16-30 days)',\n",
+       "       'Does not meet the credit policy. Status:Fully Paid',\n",
+       "       'Does not meet the credit policy. Status:Charged Off'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# explore the unique values in the target column\n",
+    "df['loan_status'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "37f20874",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>loan_amnt</th>\n",
+       "      <th>funded_amnt</th>\n",
+       "      <th>funded_amnt_inv</th>\n",
+       "      <th>int_rate</th>\n",
+       "      <th>installment</th>\n",
+       "      <th>annual_inc</th>\n",
+       "      <th>dti</th>\n",
+       "      <th>inq_last_6mths</th>\n",
+       "      <th>mths_since_last_delinq</th>\n",
+       "      <th>open_acc</th>\n",
+       "      <th>...</th>\n",
+       "      <th>recoveries</th>\n",
+       "      <th>collection_recovery_fee</th>\n",
+       "      <th>last_pymnt_amnt</th>\n",
+       "      <th>collections_12_mths_ex_med</th>\n",
+       "      <th>mths_since_last_major_derog</th>\n",
+       "      <th>policy_code</th>\n",
+       "      <th>acc_now_delinq</th>\n",
+       "      <th>tot_coll_amt</th>\n",
+       "      <th>tot_cur_bal</th>\n",
+       "      <th>total_rev_hi_lim</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>loan_status</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Charged Off</th>\n",
+       "      <td>14518.758682</td>\n",
+       "      <td>14470.241318</td>\n",
+       "      <td>14342.342678</td>\n",
+       "      <td>15.998834</td>\n",
+       "      <td>439.992667</td>\n",
+       "      <td>64750.370887</td>\n",
+       "      <td>18.219970</td>\n",
+       "      <td>0.994585</td>\n",
+       "      <td>34.308502</td>\n",
+       "      <td>10.999317</td>\n",
+       "      <td>...</td>\n",
+       "      <td>926.523955</td>\n",
+       "      <td>96.189387</td>\n",
+       "      <td>454.438219</td>\n",
+       "      <td>0.006993</td>\n",
+       "      <td>42.481076</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.003602</td>\n",
+       "      <td>127.370317</td>\n",
+       "      <td>114522.093018</td>\n",
+       "      <td>26195.836948</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Current</th>\n",
+       "      <td>15177.366474</td>\n",
+       "      <td>15172.779584</td>\n",
+       "      <td>15164.954547</td>\n",
+       "      <td>13.773271</td>\n",
+       "      <td>447.410438</td>\n",
+       "      <td>74703.804790</td>\n",
+       "      <td>18.026668</td>\n",
+       "      <td>0.695584</td>\n",
+       "      <td>33.349143</td>\n",
+       "      <td>11.529832</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>457.937174</td>\n",
+       "      <td>0.012376</td>\n",
+       "      <td>42.782234</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.005191</td>\n",
+       "      <td>184.464786</td>\n",
+       "      <td>140668.380119</td>\n",
+       "      <td>31438.554947</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Default</th>\n",
+       "      <td>15284.885817</td>\n",
+       "      <td>15284.885817</td>\n",
+       "      <td>15279.967293</td>\n",
+       "      <td>16.136394</td>\n",
+       "      <td>455.537296</td>\n",
+       "      <td>65611.847500</td>\n",
+       "      <td>19.253089</td>\n",
+       "      <td>0.917067</td>\n",
+       "      <td>33.953202</td>\n",
+       "      <td>11.971154</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>477.220733</td>\n",
+       "      <td>0.008413</td>\n",
+       "      <td>43.317757</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.006010</td>\n",
+       "      <td>132.382064</td>\n",
+       "      <td>109985.603194</td>\n",
+       "      <td>27184.632678</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Does not meet the credit policy. Status:Charged Off</th>\n",
+       "      <td>9527.233903</td>\n",
+       "      <td>9248.127464</td>\n",
+       "      <td>5807.001406</td>\n",
+       "      <td>14.597148</td>\n",
+       "      <td>305.158804</td>\n",
+       "      <td>69525.915033</td>\n",
+       "      <td>14.343732</td>\n",
+       "      <td>4.672823</td>\n",
+       "      <td>25.903433</td>\n",
+       "      <td>10.001319</td>\n",
+       "      <td>...</td>\n",
+       "      <td>579.002047</td>\n",
+       "      <td>122.187443</td>\n",
+       "      <td>305.497911</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Does not meet the credit policy. Status:Fully Paid</th>\n",
+       "      <td>8853.231891</td>\n",
+       "      <td>8679.376258</td>\n",
+       "      <td>6411.141563</td>\n",
+       "      <td>13.978642</td>\n",
+       "      <td>287.069225</td>\n",
+       "      <td>72145.418271</td>\n",
+       "      <td>14.107173</td>\n",
+       "      <td>4.000510</td>\n",
+       "      <td>27.466790</td>\n",
+       "      <td>10.102446</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2178.940287</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.002039</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Fully Paid</th>\n",
+       "      <td>13214.394226</td>\n",
+       "      <td>13169.988064</td>\n",
+       "      <td>13072.610934</td>\n",
+       "      <td>13.255943</td>\n",
+       "      <td>411.086245</td>\n",
+       "      <td>73709.612302</td>\n",
+       "      <td>15.937997</td>\n",
+       "      <td>0.839346</td>\n",
+       "      <td>35.339155</td>\n",
+       "      <td>10.794407</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>7165.937941</td>\n",
+       "      <td>0.005469</td>\n",
+       "      <td>43.037954</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.002598</td>\n",
+       "      <td>219.721618</td>\n",
+       "      <td>143006.253262</td>\n",
+       "      <td>29996.098672</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>In Grace Period</th>\n",
+       "      <td>16128.289892</td>\n",
+       "      <td>16128.289892</td>\n",
+       "      <td>16120.110393</td>\n",
+       "      <td>15.827072</td>\n",
+       "      <td>486.777092</td>\n",
+       "      <td>74033.926462</td>\n",
+       "      <td>18.801208</td>\n",
+       "      <td>0.831214</td>\n",
+       "      <td>32.568089</td>\n",
+       "      <td>11.719644</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>546.211116</td>\n",
+       "      <td>0.013350</td>\n",
+       "      <td>43.729143</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.003179</td>\n",
+       "      <td>219.261688</td>\n",
+       "      <td>130041.024675</td>\n",
+       "      <td>25975.458766</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Late (16-30 days)</th>\n",
+       "      <td>15800.779967</td>\n",
+       "      <td>15800.779967</td>\n",
+       "      <td>15793.313843</td>\n",
+       "      <td>15.964466</td>\n",
+       "      <td>477.734885</td>\n",
+       "      <td>72390.530665</td>\n",
+       "      <td>18.625952</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>30.740469</td>\n",
+       "      <td>11.692939</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>534.378596</td>\n",
+       "      <td>0.021346</td>\n",
+       "      <td>42.072193</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.004926</td>\n",
+       "      <td>215.194794</td>\n",
+       "      <td>133734.067170</td>\n",
+       "      <td>26412.624685</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Late (31-120 days)</th>\n",
+       "      <td>15553.757246</td>\n",
+       "      <td>15549.630435</td>\n",
+       "      <td>15542.374393</td>\n",
+       "      <td>15.947754</td>\n",
+       "      <td>465.948607</td>\n",
+       "      <td>69316.828822</td>\n",
+       "      <td>19.090399</td>\n",
+       "      <td>0.880145</td>\n",
+       "      <td>32.760579</td>\n",
+       "      <td>11.729275</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>506.048239</td>\n",
+       "      <td>0.011304</td>\n",
+       "      <td>43.028380</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.006377</td>\n",
+       "      <td>196.418793</td>\n",
+       "      <td>121340.395781</td>\n",
+       "      <td>26409.483552</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>9 rows × 31 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                       loan_amnt  \\\n",
+       "loan_status                                                        \n",
+       "Charged Off                                         14518.758682   \n",
+       "Current                                             15177.366474   \n",
+       "Default                                             15284.885817   \n",
+       "Does not meet the credit policy. Status:Charged...   9527.233903   \n",
+       "Does not meet the credit policy. Status:Fully Paid   8853.231891   \n",
+       "Fully Paid                                          13214.394226   \n",
+       "In Grace Period                                     16128.289892   \n",
+       "Late (16-30 days)                                   15800.779967   \n",
+       "Late (31-120 days)                                  15553.757246   \n",
+       "\n",
+       "                                                     funded_amnt  \\\n",
+       "loan_status                                                        \n",
+       "Charged Off                                         14470.241318   \n",
+       "Current                                             15172.779584   \n",
+       "Default                                             15284.885817   \n",
+       "Does not meet the credit policy. Status:Charged...   9248.127464   \n",
+       "Does not meet the credit policy. Status:Fully Paid   8679.376258   \n",
+       "Fully Paid                                          13169.988064   \n",
+       "In Grace Period                                     16128.289892   \n",
+       "Late (16-30 days)                                   15800.779967   \n",
+       "Late (31-120 days)                                  15549.630435   \n",
+       "\n",
+       "                                                    funded_amnt_inv  \\\n",
+       "loan_status                                                           \n",
+       "Charged Off                                            14342.342678   \n",
+       "Current                                                15164.954547   \n",
+       "Default                                                15279.967293   \n",
+       "Does not meet the credit policy. Status:Charged...      5807.001406   \n",
+       "Does not meet the credit policy. Status:Fully Paid      6411.141563   \n",
+       "Fully Paid                                             13072.610934   \n",
+       "In Grace Period                                        16120.110393   \n",
+       "Late (16-30 days)                                      15793.313843   \n",
+       "Late (31-120 days)                                     15542.374393   \n",
+       "\n",
+       "                                                     int_rate  installment  \\\n",
+       "loan_status                                                                  \n",
+       "Charged Off                                         15.998834   439.992667   \n",
+       "Current                                             13.773271   447.410438   \n",
+       "Default                                             16.136394   455.537296   \n",
+       "Does not meet the credit policy. Status:Charged...  14.597148   305.158804   \n",
+       "Does not meet the credit policy. Status:Fully Paid  13.978642   287.069225   \n",
+       "Fully Paid                                          13.255943   411.086245   \n",
+       "In Grace Period                                     15.827072   486.777092   \n",
+       "Late (16-30 days)                                   15.964466   477.734885   \n",
+       "Late (31-120 days)                                  15.947754   465.948607   \n",
+       "\n",
+       "                                                      annual_inc        dti  \\\n",
+       "loan_status                                                                   \n",
+       "Charged Off                                         64750.370887  18.219970   \n",
+       "Current                                             74703.804790  18.026668   \n",
+       "Default                                             65611.847500  19.253089   \n",
+       "Does not meet the credit policy. Status:Charged...  69525.915033  14.343732   \n",
+       "Does not meet the credit policy. Status:Fully Paid  72145.418271  14.107173   \n",
+       "Fully Paid                                          73709.612302  15.937997   \n",
+       "In Grace Period                                     74033.926462  18.801208   \n",
+       "Late (16-30 days)                                   72390.530665  18.625952   \n",
+       "Late (31-120 days)                                  69316.828822  19.090399   \n",
+       "\n",
+       "                                                    inq_last_6mths  \\\n",
+       "loan_status                                                          \n",
+       "Charged Off                                               0.994585   \n",
+       "Current                                                   0.695584   \n",
+       "Default                                                   0.917067   \n",
+       "Does not meet the credit policy. Status:Charged...        4.672823   \n",
+       "Does not meet the credit policy. Status:Fully Paid        4.000510   \n",
+       "Fully Paid                                                0.839346   \n",
+       "In Grace Period                                           0.831214   \n",
+       "Late (16-30 days)                                         0.904762   \n",
+       "Late (31-120 days)                                        0.880145   \n",
+       "\n",
+       "                                                    mths_since_last_delinq  \\\n",
+       "loan_status                                                                  \n",
+       "Charged Off                                                      34.308502   \n",
+       "Current                                                          33.349143   \n",
+       "Default                                                          33.953202   \n",
+       "Does not meet the credit policy. Status:Charged...               25.903433   \n",
+       "Does not meet the credit policy. Status:Fully Paid               27.466790   \n",
+       "Fully Paid                                                       35.339155   \n",
+       "In Grace Period                                                  32.568089   \n",
+       "Late (16-30 days)                                                30.740469   \n",
+       "Late (31-120 days)                                               32.760579   \n",
+       "\n",
+       "                                                     open_acc  ...  \\\n",
+       "loan_status                                                    ...   \n",
+       "Charged Off                                         10.999317  ...   \n",
+       "Current                                             11.529832  ...   \n",
+       "Default                                             11.971154  ...   \n",
+       "Does not meet the credit policy. Status:Charged...  10.001319  ...   \n",
+       "Does not meet the credit policy. Status:Fully Paid  10.102446  ...   \n",
+       "Fully Paid                                          10.794407  ...   \n",
+       "In Grace Period                                     11.719644  ...   \n",
+       "Late (16-30 days)                                   11.692939  ...   \n",
+       "Late (31-120 days)                                  11.729275  ...   \n",
+       "\n",
+       "                                                    recoveries  \\\n",
+       "loan_status                                                      \n",
+       "Charged Off                                         926.523955   \n",
+       "Current                                               0.000000   \n",
+       "Default                                               0.000000   \n",
+       "Does not meet the credit policy. Status:Charged...  579.002047   \n",
+       "Does not meet the credit policy. Status:Fully Paid    0.000000   \n",
+       "Fully Paid                                            0.000000   \n",
+       "In Grace Period                                       0.000000   \n",
+       "Late (16-30 days)                                     0.000000   \n",
+       "Late (31-120 days)                                    0.000000   \n",
+       "\n",
+       "                                                    collection_recovery_fee  \\\n",
+       "loan_status                                                                   \n",
+       "Charged Off                                                       96.189387   \n",
+       "Current                                                            0.000000   \n",
+       "Default                                                            0.000000   \n",
+       "Does not meet the credit policy. Status:Charged...               122.187443   \n",
+       "Does not meet the credit policy. Status:Fully Paid                 0.000000   \n",
+       "Fully Paid                                                         0.000000   \n",
+       "In Grace Period                                                    0.000000   \n",
+       "Late (16-30 days)                                                  0.000000   \n",
+       "Late (31-120 days)                                                 0.000000   \n",
+       "\n",
+       "                                                    last_pymnt_amnt  \\\n",
+       "loan_status                                                           \n",
+       "Charged Off                                              454.438219   \n",
+       "Current                                                  457.937174   \n",
+       "Default                                                  477.220733   \n",
+       "Does not meet the credit policy. Status:Charged...       305.497911   \n",
+       "Does not meet the credit policy. Status:Fully Paid      2178.940287   \n",
+       "Fully Paid                                              7165.937941   \n",
+       "In Grace Period                                          546.211116   \n",
+       "Late (16-30 days)                                        534.378596   \n",
+       "Late (31-120 days)                                       506.048239   \n",
+       "\n",
+       "                                                    collections_12_mths_ex_med  \\\n",
+       "loan_status                                                                      \n",
+       "Charged Off                                                           0.006993   \n",
+       "Current                                                               0.012376   \n",
+       "Default                                                               0.008413   \n",
+       "Does not meet the credit policy. Status:Charged...                    0.000000   \n",
+       "Does not meet the credit policy. Status:Fully Paid                    0.000000   \n",
+       "Fully Paid                                                            0.005469   \n",
+       "In Grace Period                                                       0.013350   \n",
+       "Late (16-30 days)                                                     0.021346   \n",
+       "Late (31-120 days)                                                    0.011304   \n",
+       "\n",
+       "                                                    mths_since_last_major_derog  \\\n",
+       "loan_status                                                                       \n",
+       "Charged Off                                                           42.481076   \n",
+       "Current                                                               42.782234   \n",
+       "Default                                                               43.317757   \n",
+       "Does not meet the credit policy. Status:Charged...                          NaN   \n",
+       "Does not meet the credit policy. Status:Fully Paid                          NaN   \n",
+       "Fully Paid                                                            43.037954   \n",
+       "In Grace Period                                                       43.729143   \n",
+       "Late (16-30 days)                                                     42.072193   \n",
+       "Late (31-120 days)                                                    43.028380   \n",
+       "\n",
+       "                                                    policy_code  \\\n",
+       "loan_status                                                       \n",
+       "Charged Off                                                 1.0   \n",
+       "Current                                                     1.0   \n",
+       "Default                                                     1.0   \n",
+       "Does not meet the credit policy. Status:Charged...          1.0   \n",
+       "Does not meet the credit policy. Status:Fully Paid          1.0   \n",
+       "Fully Paid                                                  1.0   \n",
+       "In Grace Period                                             1.0   \n",
+       "Late (16-30 days)                                           1.0   \n",
+       "Late (31-120 days)                                          1.0   \n",
+       "\n",
+       "                                                    acc_now_delinq  \\\n",
+       "loan_status                                                          \n",
+       "Charged Off                                               0.003602   \n",
+       "Current                                                   0.005191   \n",
+       "Default                                                   0.006010   \n",
+       "Does not meet the credit policy. Status:Charged...        0.000000   \n",
+       "Does not meet the credit policy. Status:Fully Paid        0.002039   \n",
+       "Fully Paid                                                0.002598   \n",
+       "In Grace Period                                           0.003179   \n",
+       "Late (16-30 days)                                         0.004926   \n",
+       "Late (31-120 days)                                        0.006377   \n",
+       "\n",
+       "                                                    tot_coll_amt  \\\n",
+       "loan_status                                                        \n",
+       "Charged Off                                           127.370317   \n",
+       "Current                                               184.464786   \n",
+       "Default                                               132.382064   \n",
+       "Does not meet the credit policy. Status:Charged...           NaN   \n",
+       "Does not meet the credit policy. Status:Fully Paid           NaN   \n",
+       "Fully Paid                                            219.721618   \n",
+       "In Grace Period                                       219.261688   \n",
+       "Late (16-30 days)                                     215.194794   \n",
+       "Late (31-120 days)                                    196.418793   \n",
+       "\n",
+       "                                                      tot_cur_bal  \\\n",
+       "loan_status                                                         \n",
+       "Charged Off                                         114522.093018   \n",
+       "Current                                             140668.380119   \n",
+       "Default                                             109985.603194   \n",
+       "Does not meet the credit policy. Status:Charged...            NaN   \n",
+       "Does not meet the credit policy. Status:Fully Paid            NaN   \n",
+       "Fully Paid                                          143006.253262   \n",
+       "In Grace Period                                     130041.024675   \n",
+       "Late (16-30 days)                                   133734.067170   \n",
+       "Late (31-120 days)                                  121340.395781   \n",
+       "\n",
+       "                                                    total_rev_hi_lim  \n",
+       "loan_status                                                           \n",
+       "Charged Off                                             26195.836948  \n",
+       "Current                                                 31438.554947  \n",
+       "Default                                                 27184.632678  \n",
+       "Does not meet the credit policy. Status:Charged...               NaN  \n",
+       "Does not meet the credit policy. Status:Fully Paid               NaN  \n",
+       "Fully Paid                                              29996.098672  \n",
+       "In Grace Period                                         25975.458766  \n",
+       "Late (16-30 days)                                       26412.624685  \n",
+       "Late (31-120 days)                                      26409.483552  \n",
+       "\n",
+       "[9 rows x 31 columns]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# create a new column based on the target column that will be our target variable\n",
+    "loan_status=df.groupby('loan_status').mean()\n",
+    "loan_status"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "d7a50128",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>loan_amnt</th>\n",
+       "      <th>funded_amnt</th>\n",
+       "      <th>funded_amnt_inv</th>\n",
+       "      <th>term</th>\n",
+       "      <th>int_rate</th>\n",
+       "      <th>installment</th>\n",
+       "      <th>grade</th>\n",
+       "      <th>sub_grade</th>\n",
+       "      <th>emp_length</th>\n",
+       "      <th>home_ownership</th>\n",
+       "      <th>...</th>\n",
+       "      <th>last_pymnt_amnt</th>\n",
+       "      <th>next_pymnt_d</th>\n",
+       "      <th>last_credit_pull_d</th>\n",
+       "      <th>collections_12_mths_ex_med</th>\n",
+       "      <th>mths_since_last_major_derog</th>\n",
+       "      <th>policy_code</th>\n",
+       "      <th>acc_now_delinq</th>\n",
+       "      <th>tot_coll_amt</th>\n",
+       "      <th>tot_cur_bal</th>\n",
+       "      <th>total_rev_hi_lim</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5000</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>4975.0</td>\n",
+       "      <td>36 months</td>\n",
+       "      <td>10.65</td>\n",
+       "      <td>162.87</td>\n",
+       "      <td>B</td>\n",
+       "      <td>B2</td>\n",
+       "      <td>10+ years</td>\n",
+       "      <td>RENT</td>\n",
+       "      <td>...</td>\n",
+       "      <td>171.62</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Jan-16</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2500</td>\n",
+       "      <td>2500</td>\n",
+       "      <td>2500.0</td>\n",
+       "      <td>60 months</td>\n",
+       "      <td>15.27</td>\n",
+       "      <td>59.83</td>\n",
+       "      <td>C</td>\n",
+       "      <td>C4</td>\n",
+       "      <td>&lt; 1 year</td>\n",
+       "      <td>RENT</td>\n",
+       "      <td>...</td>\n",
+       "      <td>119.66</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Sep-13</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2400</td>\n",
+       "      <td>2400</td>\n",
+       "      <td>2400.0</td>\n",
+       "      <td>36 months</td>\n",
+       "      <td>15.96</td>\n",
+       "      <td>84.33</td>\n",
+       "      <td>C</td>\n",
+       "      <td>C5</td>\n",
+       "      <td>10+ years</td>\n",
+       "      <td>RENT</td>\n",
+       "      <td>...</td>\n",
+       "      <td>649.91</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Jan-16</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10000</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>10000.0</td>\n",
+       "      <td>36 months</td>\n",
+       "      <td>13.49</td>\n",
+       "      <td>339.31</td>\n",
+       "      <td>C</td>\n",
+       "      <td>C1</td>\n",
+       "      <td>10+ years</td>\n",
+       "      <td>RENT</td>\n",
+       "      <td>...</td>\n",
+       "      <td>357.48</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Jan-15</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3000</td>\n",
+       "      <td>3000</td>\n",
+       "      <td>3000.0</td>\n",
+       "      <td>60 months</td>\n",
+       "      <td>12.69</td>\n",
+       "      <td>67.79</td>\n",
+       "      <td>B</td>\n",
+       "      <td>B5</td>\n",
+       "      <td>1 year</td>\n",
+       "      <td>RENT</td>\n",
+       "      <td>...</td>\n",
+       "      <td>67.79</td>\n",
+       "      <td>Feb-16</td>\n",
+       "      <td>Jan-16</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 43 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   loan_amnt  funded_amnt  funded_amnt_inv        term  int_rate  installment  \\\n",
+       "0       5000         5000           4975.0   36 months     10.65       162.87   \n",
+       "1       2500         2500           2500.0   60 months     15.27        59.83   \n",
+       "2       2400         2400           2400.0   36 months     15.96        84.33   \n",
+       "3      10000        10000          10000.0   36 months     13.49       339.31   \n",
+       "4       3000         3000           3000.0   60 months     12.69        67.79   \n",
+       "\n",
+       "  grade sub_grade emp_length home_ownership  ...  last_pymnt_amnt  \\\n",
+       "0     B        B2  10+ years           RENT  ...           171.62   \n",
+       "1     C        C4   < 1 year           RENT  ...           119.66   \n",
+       "2     C        C5  10+ years           RENT  ...           649.91   \n",
+       "3     C        C1  10+ years           RENT  ...           357.48   \n",
+       "4     B        B5     1 year           RENT  ...            67.79   \n",
+       "\n",
+       "  next_pymnt_d last_credit_pull_d  collections_12_mths_ex_med  \\\n",
+       "0          NaN             Jan-16                         0.0   \n",
+       "1          NaN             Sep-13                         0.0   \n",
+       "2          NaN             Jan-16                         0.0   \n",
+       "3          NaN             Jan-15                         0.0   \n",
+       "4       Feb-16             Jan-16                         0.0   \n",
+       "\n",
+       "  mths_since_last_major_derog  policy_code  acc_now_delinq  tot_coll_amt  \\\n",
+       "0                         NaN            1             0.0           NaN   \n",
+       "1                         NaN            1             0.0           NaN   \n",
+       "2                         NaN            1             0.0           NaN   \n",
+       "3                         NaN            1             0.0           NaN   \n",
+       "4                         NaN            1             0.0           NaN   \n",
+       "\n",
+       "   tot_cur_bal  total_rev_hi_lim  \n",
+       "0          NaN               NaN  \n",
+       "1          NaN               NaN  \n",
+       "2          NaN               NaN  \n",
+       "3          NaN               NaN  \n",
+       "4          NaN               NaN  \n",
+       "\n",
+       "[5 rows x 43 columns]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Drop the original target column\n",
+    "df_drop=df.drop(labels=['loan_status'],axis=1)\n",
+    "df_drop.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d62da664",
+   "metadata": {},
+   "source": [
+    "## Split Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "0af569dd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# split data into 80/20 while keeping the distribution of bad loans in test set same as that in the pre-split dataset(X_train, y_train, etc)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "d4ba9e6b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# specifically hard copying the training sets to avoid Pandas' SetttingWithCopyWarning when we play around with this data later on\n",
+    "# you can refer to this link https://github.com/scikit-learn/scikit-learn/issues/8723\n",
+    "# this is currently an open issue between Pandas and Scikit-Learn teams"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "45df823e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create a helper function clean up a column which has values given along with years, assign 0 to NANs and convert to numeric"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "4cf50e36",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# apply to X_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "6f21a969",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# confirm our transformation by looking at unique values of this column"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d86f2544",
+   "metadata": {},
+   "source": [
+    "### Date columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "ba10320e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create a function to convert date columns to datetime format and create a new column as a difference between today and the respective date\n",
+    "# details of the function\n",
+    "    # store current month\n",
+    "    # convert to datetime format\n",
+    "    # calculate the difference in months and add to a new column\n",
+    "    # make any resulting -ve values to be equal to the max date\n",
+    "    # drop the original date column\n",
+    "# Note : In current date use the same data to maintain uniformilty across everyone's code. Use : 2020-08-01"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "99a24bb3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# apply to X_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "4acfad7d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# check these new columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "baf48e3e",
+   "metadata": {},
+   "source": [
+    "### Term column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "7832bb43",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# write a function to remove 'months' string from the 'term' column and convert it to numeric \n",
+    "# use the function on 'term' column of X_Train"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "168c666d",
+   "metadata": {},
+   "source": [
+    "## Feature Selection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "726b6bf8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# first divide training data into categorical and numerical subsets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "08784212",
+   "metadata": {},
+   "source": [
+    "## Chi-squared statistic for categorical features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "1224a634",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# define an empty dictionary to store chi-squared test results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "11d6040a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# loop over each column in the training set to calculate chi-statistic with the target variable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "79a44211",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# convert the dictionary to a DF"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "d11c692d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# keep only the top four categorical features"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8949f3fa",
+   "metadata": {},
+   "source": [
+    "## ANOVA F-Statistic for numerical feature"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "f7c8d70f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# since f_class_if does not accept missing values, we will do a very crude imputation of missing values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "815594f8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Calculate F Statistic and corresponding p values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "66d26e6f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# convert to a DF"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "a0e14369",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# keep only the top 20 features and calculate pair-wise correlations between them\n",
+    "# save the top 20 numerical features in a list"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5a831c81",
+   "metadata": {},
+   "source": [
+    "## Pair wise correlations to detect multicollinearity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "fb639daf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# calculate pair-wise correlations between them"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "59b405e0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# drop 2 features based on their multicollinearity with other features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "315f9251",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define a helper function to drop the 4 categorical features with least p-values for chi squared test, 14 numerical features with least F-Statistic\n",
+    "# and 2 numerical features with high multicollinearity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "a483be96",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# apply to X_train"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7b40d71b",
+   "metadata": {},
+   "source": [
+    "## creating dummy variables\n",
+    "### convert discrete variables to dummy variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "cbb5d957",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# write a function to create dummy variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "65ad5814",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# apply to our final four categorical variables"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "87b2556e",
+   "metadata": {},
+   "source": [
+    "## Update the test data set with all data cleaning procedures performed so far"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "f2ce52f0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# also reindex the dummied test set variables to make sure all the feature columns in the train set are also available in the test set"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d96c459f",
+   "metadata": {},
+   "source": [
+    "## WoE Binning/Feature Engineering"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "d91849df",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# we will analyze both categorical and numerical features based on their categorical/binned WoEs and IVs and then combine some of these binned categories together through a custom python class with fit_transform method"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "9098de60",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create copies of the 4 training sets(by the names X_train_prepr, y_train_prepr, etc) to be preprocessed using WoE"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "556ef2ee",
+   "metadata": {},
+   "source": [
+    "## analyze WoEs and IVs of discrete features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "dd203042",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# write a function that takes 3 arguments: a dataframe (X_train_prepr), a string (column name), and a dataframe (y_train_prepr)\n",
+    "# the function should returns a dataframe as a result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "136285cd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# set the default style of the graphs to the seaborn style. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "8cc8ddac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# define a function for plotting WoE across categories that takes 2 arguments: a dataframe and a number"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "ff26c024",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# apply these on all four categorical columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "09c44927",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# observe graphs of WOE. If there is a continuous increase in WoE across the different categories then we do not need to combine any features together and should leave all these categories as they are"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "c8b8839b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# if there are no missing values in the grade column leave it as it is, otherwise createa a separate and independent category for all Missing values that would never be combined with any other category\n",
+    "# you will come across this scenario when working through features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "c524dd5d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# define a function to calculate WoE of continuous variables\n",
+    "# this is same as the function we defined earlier for discrete variables\n",
+    "# the only difference are the 2 lines of code that need to be commented in the function that results in the df being sorted by continuous variable values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "c176da96",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# apply this on continuous variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "82436e3d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# fine classing using the cut method if there are a large number of unique values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "6e66c07f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# don't use the features which have\n",
+    "    # very low IV\n",
+    "    # which have unusually high IV\n",
+    "    # WoE ranges between a very small range, implying low power of differentiating between good and bad loans"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "d9c14866",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# if there is a feature for which most of the values are inside a particular range and very few outside then \n",
+    "    # create one category for values outside that range\n",
+    "    # apply your approach to all other values(which are inside the range)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "7052db33",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# for some of the columns values would feel out of place like utilization being greater than 1 in some values which is very rare so filter those out"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "b055d6fe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# while plotting WOE if you have some doubt in curve you can also zoom on some portion to understand the nature of graph in that area"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "9aac8722",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# if the IV is borderline close to the minimum or maximum ideal threshold, you can proceed without ignoring that feature"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9865d6b8",
+   "metadata": {},
+   "source": [
+    "## Define Custom Class for WoE Binning/Reengineering"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "3ecec1c7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# here we will create a custom scikit-learn class to take care of all binning transformations on any given data set\n",
+    "# this custom class will help us in performing k fold cross validation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "bfeb5b21",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create a list of all the reference categories, i.e. one category from each of the global features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "b9d62257",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# this custom class will create new categorical dummy features based on the cut-off points that we manually identified based on the WoE plots and IV above"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "260e7f4b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# structure this class so that it also allows a fit_transform method to be implemented on it, thereby allowing you to use it as part of a scikit-learn Pipeline "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8594f479",
+   "metadata": {},
+   "source": [
+    "## PD Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "1c007251",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# reconfirm shape of the 4 datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "400573de",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# define modeling pipeline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "46bbec7d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# define cross-validation criteria\n",
+    "# RepeatedStratifiedKFold automatially takes care of the class imbalance while splitting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "e76c496a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# fit and evaluate the logistic regression pipeline with cross-validation as defined in cv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "81862473",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# print the mean AUROC score and Gini"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "ab3c480b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# fit the pipeline on the whole training set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "9426b9ed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create a transformed training set through our WoE_Binning custom class"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "a0a4d556",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# store the column names in X_train as a list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "96f2bfd1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create a summary table of our logistic regression model(name it summary_table)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "0b02d02b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create a new column in the dataframe, called 'Coefficients', with row values the transposed coefficients from the 'LogisticRegression' model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "bbefe8f3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# increase the index of every row of the dataframe with 1 to store our model intercept in 1st row"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "35d11b18",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# assign our model intercept to this new row"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "1bdc664a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# sort the dataframe by index"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e39044f",
+   "metadata": {},
+   "source": [
+    "## Prediction Time!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "0fb857da",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# make preditions on our test set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "d751fae8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get the predicted probabilities(name it y_hat_test_proba)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "457c48b4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# select the probabilities of only the positive class (class 1 - default) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "1221e651",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# we will now create a new DF with actual classes and the predicted probabilities\n",
+    "# create a temp y_test DF to reset its index to allow proper concaternation with y_hat_test_proba"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "120e4119",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# check the shape to make sure the number of rows is same as that in y_test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "eff09b21",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# rename the columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "4a340a2b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# makes the index of one dataframe equal to the index of another dataframe"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "67f3f5c7",
+   "metadata": {},
+   "source": [
+    "## Confusion Matrix and AUROC on Test Set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "id": "bcedd377",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# assign a threshold value to differentiate good with bad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "42884466",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# crate a new column for the predicted class based on predicted probabilities and threshold\n",
+    "# we will determine this optimal threshold later in this project"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "id": "a1b467c7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create the confusion matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "id": "32ae63bd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get the values required to plot a ROC curve"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "id": "32a48f01",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# plot the ROC curve"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "id": "32926203",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# plot a secondary diagonal line, with dashed line style and black color to represent a no-skill classifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "id": "b029d7fe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# calculate the Area Under the Receiver Operating Characteristic Curve (AUROC) on our test set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "id": "e421c4b8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# calculate Gini from AUROC"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "id": "a67d6a4a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# draw a PR curve\n",
+    "# calculate the no skill line as the proportion of the positive class"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "id": "e4408762",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# plot the no skill precision-recall curve"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "id": "9419ed09",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# calculate inputs for the PR curve"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "id": "b5a5bae3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# calculate inputs for the PR curve"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "id": "7d78d0fe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# plot PR curve"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "id": "a78dd2ac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# calculate PR AUC"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9dd5c621",
+   "metadata": {},
+   "source": [
+    "## Applying the Model - Scorecard Creation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "id": "37913e85",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# print the summary_table"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "id": "e56fea64",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create a new dataframe with one column\n",
+    "# its values are the values from the 'reference_categories' list\n",
+    "# name it 'Feature name'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "id": "a26ea5f4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create a second column called 'Coefficients' which contains only 0 values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "id": "8da85214",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# concatenates two dataframes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "id": "1b6bceac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# we reset the index of a dataframe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "id": "8674e55b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create a new column called 'Original feature name' which contains the value of the 'Feature name' column up to the column symbol"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "id": "5c9e748e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# define the min and max threshholds for our scorecard"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "id": "86b900c9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# calculate the sum of the minimum coefficients of each category within the original feature name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "id": "a346dbdc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# calculate the sum of the maximum coefficients of each category within the original feature name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "id": "ac8691f9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create a new columns that has the imputed calculated Score based on the multiplication of the coefficient by the ratio of the differences between\n",
+    "# maximum & minimum score and maximum & minimum sum of cefficients"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "id": "1bf24313",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# update the calculated score of the Intercept (i.e. the default score for each loan)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "id": "dc2657e3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# round the values of the 'Score - Calculation' column and store them in a new column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "id": "543f897d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# check the min and max possible scores of our scorecard"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "id": "88818024",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# both our min and max scores are out by +1\n",
+    "# we need to manually adjust this\n",
+    "# to decide which one we'll evaluate based on the rounding differences of the minimum category within each Original Feature Name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "id": "e188b1a5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# we can get by deducting 1 from the Intercept"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "id": "feb1f6f1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# recheck min and max possible scores"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cd043803",
+   "metadata": {},
+   "source": [
+    "## Calculating credit scores for all observations in the test data set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "id": "7ff8f92a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create a transformed test set through our WoE_Binning custom class"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "id": "95d3ba61",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# insert an Intercept column in its beginning to align with the # of rows in scorecard"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "id": "e6229a6f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get the list of our final scorecard scores"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "id": "bf4b59e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# check the shapes of test set and scorecard before doing matrix dot multiplication"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "id": "9ee9b571",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# we can see that the test set has a few less columns than the rows in scorecard due to the reference categories\n",
+    "# since the reference categories will always be scored as 0 based on the scorecard, it is safe to add these categories to the end of test set with 0 values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "id": "9cb4b22e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# need to reshape scorecard_scores so that it is of proper shape to allow for matrix dot multiplication"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "id": "60a94f3a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# matrix dot multiplication of test set with scorecard scores"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1d8c99b3",
+   "metadata": {},
+   "source": [
+    "## Setting loan approval cut-offs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "id": "869dcdea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# calculate Youden's J-Statistic to identify the best threshhold"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "id": "cc9bdb99",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# locate the index of the largest J"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "id": "0401c25d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# print the best threshold"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "id": "13cbdedb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# this means that based on the Youden's J statistic, this is the ideal probability threshold which minimizes the FPR and maximimizes the TPR\n",
+    "# which means all samples with a predicted probability higher than this should be classified as in Default and vice versa\n",
+    "# this ideal threshold might appear to be counterintuitive compared to the default probability threshold of 0.5 but remember that we used the class_weight parameter when fitting our logistic regression model that would have helped us\n",
+    "\n",
+    "# we can confirm this by looking at our original confusion matrix with the updated threshold"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "id": "8577ee5f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# update the threshold value"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "id": "fae9f7ec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# crate a new column for the predicted class based on predicted probabilities and threshold"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "id": "e9cf41a8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create the confusion matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "id": "ddfcf9bc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# the updated confusion matrix would show a marked improvement in the TPR at marginal cost of lower TNR but at the same time, FNR has improved drastically with a corresponding marginal increase in FPR\n",
+    "\n",
+    "# find the corresponding acceptance and rejection rates on the test set at this ideal threshold"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "id": "b0794619",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create a new DF comprising of the thresholds from the ROC output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "id": "b8b07a3a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# calculate Score corresponding to each threshold"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "id": "a838b102",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# define a function called 'n_approved' which assigns a value of 1 if a predicted probability is greater than the parameter p which is a threshold and a value of 0 if it is not\n",
+    "# then it sums the column.\n",
+    "# for given any percentage values the function will return the number of rows wih estimated probabilites greater than the threshold"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "id": "00b90a17",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# assuming that all credit applications above a given probability of being 'good' will be approved\n",
+    "# when we apply the 'n_approved' function to a threshold it will return the number of approved applications\n",
+    "# here we calculate the number of approved appliations for all thresholds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "id": "cb181639",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# then we calculate the number of rejected applications for each threshold\n",
+    "# it is the difference between the total number of applications and the approved applications for that threshold"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "id": "1630dc56",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# approval rate equals the ratio of the approved applications and all applications"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "id": "b5873b1c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# rejection rate equals one minus approval rate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "id": "3d7a0362",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# look at the approval and rejection rates at our ideal threshold"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "id": "b63291ad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# compare the above rates with the case of the default 0.5 threshold"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "id": "b26cc736",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# you would find that 0.5 threshold would result in a very high rejection rate with a corresponding loss of business\n",
+    "\n",
+    "# we will stick with our ideal threshold and the corresponding Creidt Score of (fill yourself) and can also monitor the model's performance in production if more data were available"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bb92abd1",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 518046b11725b7d854839aa5af34f18232e5d6d6 Mon Sep 17 00:00:00 2001
From: vishalh21 <106445212+vishalh21@users.noreply.github.com>
Date: Wed, 13 Jul 2022 00:12:18 +0530
Subject: [PATCH 13/14] Delete 211175_Vishal_2.ipynb

---
 211175_Vishal_2.ipynb | 3366 -----------------------------------------
 1 file changed, 3366 deletions(-)
 delete mode 100644 211175_Vishal_2.ipynb

diff --git a/211175_Vishal_2.ipynb b/211175_Vishal_2.ipynb
deleted file mode 100644
index 9e5056b..0000000
--- a/211175_Vishal_2.ipynb
+++ /dev/null
@@ -1,3366 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "0d5f58b8",
-   "metadata": {},
-   "source": [
-    "## Environment Setup"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "c0d00887",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# import pandas, numpy, seaborn, pyplot\n",
-    "import numpy as np \n",
-    "import pandas as pd\n",
-    "import matplotlib as mpl\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "from math import sqrt\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "8718c756",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# import train_test_split, RepeatedStratifiedKFold, cross_val_score, LogisticRegression, roc_curve, roc_auc_score,\n",
-    "# confusion_matrix, precision_recall_curve, auc, f_classif, Pipeline, BaseEstimator, TransformerMixin, chi2_contingency\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.model_selection import RepeatedStratifiedKFold\n",
-    "from sklearn.model_selection import cross_val_score\n",
-    "from sklearn.linear_model import LogisticRegression\n",
-    "from sklearn import metrics\n",
-    "from sklearn.metrics import roc_curve\n",
-    "from sklearn.metrics import roc_auc_score\n",
-    "from sklearn.metrics import confusion_matrix\n",
-    "from sklearn.metrics import precision_recall_curve\n",
-    "from sklearn.feature_selection import f_classif\n",
-    "from sklearn.pipeline import Pipeline\n",
-    "from sklearn.base import BaseEstimator\n",
-    "from sklearn.base import TransformerMixin\n",
-    "from scipy.stats import chi2_contingency"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "452fe749",
-   "metadata": {},
-   "source": [
-    "## Import Data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "8dfd3c5f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# load the dataset\n",
-    "df=pd.read_csv('credit_risk_dataset.csv',low_memory=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "88db398b",
-   "metadata": {},
-   "source": [
-    "## Data Exploration"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "0a185963",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>member_id</th>\n",
-       "      <th>loan_amnt</th>\n",
-       "      <th>funded_amnt</th>\n",
-       "      <th>funded_amnt_inv</th>\n",
-       "      <th>term</th>\n",
-       "      <th>int_rate</th>\n",
-       "      <th>installment</th>\n",
-       "      <th>grade</th>\n",
-       "      <th>sub_grade</th>\n",
-       "      <th>...</th>\n",
-       "      <th>total_bal_il</th>\n",
-       "      <th>il_util</th>\n",
-       "      <th>open_rv_12m</th>\n",
-       "      <th>open_rv_24m</th>\n",
-       "      <th>max_bal_bc</th>\n",
-       "      <th>all_util</th>\n",
-       "      <th>total_rev_hi_lim</th>\n",
-       "      <th>inq_fi</th>\n",
-       "      <th>total_cu_tl</th>\n",
-       "      <th>inq_last_12m</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1077501</td>\n",
-       "      <td>1296599</td>\n",
-       "      <td>5000</td>\n",
-       "      <td>5000</td>\n",
-       "      <td>4975.0</td>\n",
-       "      <td>36 months</td>\n",
-       "      <td>10.65</td>\n",
-       "      <td>162.87</td>\n",
-       "      <td>B</td>\n",
-       "      <td>B2</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1077430</td>\n",
-       "      <td>1314167</td>\n",
-       "      <td>2500</td>\n",
-       "      <td>2500</td>\n",
-       "      <td>2500.0</td>\n",
-       "      <td>60 months</td>\n",
-       "      <td>15.27</td>\n",
-       "      <td>59.83</td>\n",
-       "      <td>C</td>\n",
-       "      <td>C4</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1077175</td>\n",
-       "      <td>1313524</td>\n",
-       "      <td>2400</td>\n",
-       "      <td>2400</td>\n",
-       "      <td>2400.0</td>\n",
-       "      <td>36 months</td>\n",
-       "      <td>15.96</td>\n",
-       "      <td>84.33</td>\n",
-       "      <td>C</td>\n",
-       "      <td>C5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1076863</td>\n",
-       "      <td>1277178</td>\n",
-       "      <td>10000</td>\n",
-       "      <td>10000</td>\n",
-       "      <td>10000.0</td>\n",
-       "      <td>36 months</td>\n",
-       "      <td>13.49</td>\n",
-       "      <td>339.31</td>\n",
-       "      <td>C</td>\n",
-       "      <td>C1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1075358</td>\n",
-       "      <td>1311748</td>\n",
-       "      <td>3000</td>\n",
-       "      <td>3000</td>\n",
-       "      <td>3000.0</td>\n",
-       "      <td>60 months</td>\n",
-       "      <td>12.69</td>\n",
-       "      <td>67.79</td>\n",
-       "      <td>B</td>\n",
-       "      <td>B5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 74 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        id  member_id  loan_amnt  funded_amnt  funded_amnt_inv        term  \\\n",
-       "0  1077501    1296599       5000         5000           4975.0   36 months   \n",
-       "1  1077430    1314167       2500         2500           2500.0   60 months   \n",
-       "2  1077175    1313524       2400         2400           2400.0   36 months   \n",
-       "3  1076863    1277178      10000        10000          10000.0   36 months   \n",
-       "4  1075358    1311748       3000         3000           3000.0   60 months   \n",
-       "\n",
-       "   int_rate  installment grade sub_grade  ... total_bal_il il_util  \\\n",
-       "0     10.65       162.87     B        B2  ...          NaN     NaN   \n",
-       "1     15.27        59.83     C        C4  ...          NaN     NaN   \n",
-       "2     15.96        84.33     C        C5  ...          NaN     NaN   \n",
-       "3     13.49       339.31     C        C1  ...          NaN     NaN   \n",
-       "4     12.69        67.79     B        B5  ...          NaN     NaN   \n",
-       "\n",
-       "  open_rv_12m  open_rv_24m max_bal_bc all_util total_rev_hi_lim inq_fi  \\\n",
-       "0         NaN          NaN        NaN      NaN              NaN    NaN   \n",
-       "1         NaN          NaN        NaN      NaN              NaN    NaN   \n",
-       "2         NaN          NaN        NaN      NaN              NaN    NaN   \n",
-       "3         NaN          NaN        NaN      NaN              NaN    NaN   \n",
-       "4         NaN          NaN        NaN      NaN              NaN    NaN   \n",
-       "\n",
-       "  total_cu_tl inq_last_12m  \n",
-       "0         NaN          NaN  \n",
-       "1         NaN          NaN  \n",
-       "2         NaN          NaN  \n",
-       "3         NaN          NaN  \n",
-       "4         NaN          NaN  \n",
-       "\n",
-       "[5 rows x 74 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# explore the dataset(use the data dictionary)\n",
-    "# look at the columns, which data type is present in a column, range of values in each column, their mean, etc.\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "de2273c7",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(466285, 74)"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "1c1516bf",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 466285 entries, 0 to 466284\n",
-      "Data columns (total 74 columns):\n",
-      " #   Column                       Non-Null Count   Dtype  \n",
-      "---  ------                       --------------   -----  \n",
-      " 0   id                           466285 non-null  int64  \n",
-      " 1   member_id                    466285 non-null  int64  \n",
-      " 2   loan_amnt                    466285 non-null  int64  \n",
-      " 3   funded_amnt                  466285 non-null  int64  \n",
-      " 4   funded_amnt_inv              466285 non-null  float64\n",
-      " 5   term                         466285 non-null  object \n",
-      " 6   int_rate                     466285 non-null  float64\n",
-      " 7   installment                  466285 non-null  float64\n",
-      " 8   grade                        466285 non-null  object \n",
-      " 9   sub_grade                    466285 non-null  object \n",
-      " 10  emp_title                    438697 non-null  object \n",
-      " 11  emp_length                   445277 non-null  object \n",
-      " 12  home_ownership               466285 non-null  object \n",
-      " 13  annual_inc                   466281 non-null  float64\n",
-      " 14  verification_status          466285 non-null  object \n",
-      " 15  issue_d                      466285 non-null  object \n",
-      " 16  loan_status                  466285 non-null  object \n",
-      " 17  pymnt_plan                   466285 non-null  object \n",
-      " 18  url                          466285 non-null  object \n",
-      " 19  desc                         125983 non-null  object \n",
-      " 20  purpose                      466285 non-null  object \n",
-      " 21  title                        466265 non-null  object \n",
-      " 22  zip_code                     466285 non-null  object \n",
-      " 23  addr_state                   466285 non-null  object \n",
-      " 24  dti                          466285 non-null  float64\n",
-      " 25  delinq_2yrs                  466256 non-null  float64\n",
-      " 26  earliest_cr_line             466256 non-null  object \n",
-      " 27  inq_last_6mths               466256 non-null  float64\n",
-      " 28  mths_since_last_delinq       215934 non-null  float64\n",
-      " 29  mths_since_last_record       62638 non-null   float64\n",
-      " 30  open_acc                     466256 non-null  float64\n",
-      " 31  pub_rec                      466256 non-null  float64\n",
-      " 32  revol_bal                    466285 non-null  int64  \n",
-      " 33  revol_util                   465945 non-null  float64\n",
-      " 34  total_acc                    466256 non-null  float64\n",
-      " 35  initial_list_status          466285 non-null  object \n",
-      " 36  out_prncp                    466285 non-null  float64\n",
-      " 37  out_prncp_inv                466285 non-null  float64\n",
-      " 38  total_pymnt                  466285 non-null  float64\n",
-      " 39  total_pymnt_inv              466285 non-null  float64\n",
-      " 40  total_rec_prncp              466285 non-null  float64\n",
-      " 41  total_rec_int                466285 non-null  float64\n",
-      " 42  total_rec_late_fee           466285 non-null  float64\n",
-      " 43  recoveries                   466285 non-null  float64\n",
-      " 44  collection_recovery_fee      466285 non-null  float64\n",
-      " 45  last_pymnt_d                 465909 non-null  object \n",
-      " 46  last_pymnt_amnt              466285 non-null  float64\n",
-      " 47  next_pymnt_d                 239071 non-null  object \n",
-      " 48  last_credit_pull_d           466243 non-null  object \n",
-      " 49  collections_12_mths_ex_med   466140 non-null  float64\n",
-      " 50  mths_since_last_major_derog  98974 non-null   float64\n",
-      " 51  policy_code                  466285 non-null  int64  \n",
-      " 52  application_type             466285 non-null  object \n",
-      " 53  annual_inc_joint             0 non-null       float64\n",
-      " 54  dti_joint                    0 non-null       float64\n",
-      " 55  verification_status_joint    0 non-null       float64\n",
-      " 56  acc_now_delinq               466256 non-null  float64\n",
-      " 57  tot_coll_amt                 396009 non-null  float64\n",
-      " 58  tot_cur_bal                  396009 non-null  float64\n",
-      " 59  open_acc_6m                  0 non-null       float64\n",
-      " 60  open_il_6m                   0 non-null       float64\n",
-      " 61  open_il_12m                  0 non-null       float64\n",
-      " 62  open_il_24m                  0 non-null       float64\n",
-      " 63  mths_since_rcnt_il           0 non-null       float64\n",
-      " 64  total_bal_il                 0 non-null       float64\n",
-      " 65  il_util                      0 non-null       float64\n",
-      " 66  open_rv_12m                  0 non-null       float64\n",
-      " 67  open_rv_24m                  0 non-null       float64\n",
-      " 68  max_bal_bc                   0 non-null       float64\n",
-      " 69  all_util                     0 non-null       float64\n",
-      " 70  total_rev_hi_lim             396009 non-null  float64\n",
-      " 71  inq_fi                       0 non-null       float64\n",
-      " 72  total_cu_tl                  0 non-null       float64\n",
-      " 73  inq_last_12m                 0 non-null       float64\n",
-      "dtypes: float64(46), int64(6), object(22)\n",
-      "memory usage: 263.3+ MB\n"
-     ]
-    }
-   ],
-   "source": [
-    "df.info()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "88a46339",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>member_id</th>\n",
-       "      <th>loan_amnt</th>\n",
-       "      <th>funded_amnt</th>\n",
-       "      <th>funded_amnt_inv</th>\n",
-       "      <th>int_rate</th>\n",
-       "      <th>installment</th>\n",
-       "      <th>annual_inc</th>\n",
-       "      <th>dti</th>\n",
-       "      <th>delinq_2yrs</th>\n",
-       "      <th>...</th>\n",
-       "      <th>total_bal_il</th>\n",
-       "      <th>il_util</th>\n",
-       "      <th>open_rv_12m</th>\n",
-       "      <th>open_rv_24m</th>\n",
-       "      <th>max_bal_bc</th>\n",
-       "      <th>all_util</th>\n",
-       "      <th>total_rev_hi_lim</th>\n",
-       "      <th>inq_fi</th>\n",
-       "      <th>total_cu_tl</th>\n",
-       "      <th>inq_last_12m</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>4.662850e+05</td>\n",
-       "      <td>4.662850e+05</td>\n",
-       "      <td>466285.000000</td>\n",
-       "      <td>466285.000000</td>\n",
-       "      <td>466285.000000</td>\n",
-       "      <td>466285.000000</td>\n",
-       "      <td>466285.000000</td>\n",
-       "      <td>4.662810e+05</td>\n",
-       "      <td>466285.000000</td>\n",
-       "      <td>466256.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>3.960090e+05</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>1.307973e+07</td>\n",
-       "      <td>1.459766e+07</td>\n",
-       "      <td>14317.277577</td>\n",
-       "      <td>14291.801044</td>\n",
-       "      <td>14222.329888</td>\n",
-       "      <td>13.829236</td>\n",
-       "      <td>432.061201</td>\n",
-       "      <td>7.327738e+04</td>\n",
-       "      <td>17.218758</td>\n",
-       "      <td>0.284678</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.037909e+04</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>1.089371e+07</td>\n",
-       "      <td>1.168237e+07</td>\n",
-       "      <td>8286.509164</td>\n",
-       "      <td>8274.371300</td>\n",
-       "      <td>8297.637788</td>\n",
-       "      <td>4.357587</td>\n",
-       "      <td>243.485550</td>\n",
-       "      <td>5.496357e+04</td>\n",
-       "      <td>7.851121</td>\n",
-       "      <td>0.797365</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.724713e+04</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>5.473400e+04</td>\n",
-       "      <td>7.047300e+04</td>\n",
-       "      <td>500.000000</td>\n",
-       "      <td>500.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>5.420000</td>\n",
-       "      <td>15.670000</td>\n",
-       "      <td>1.896000e+03</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>3.639987e+06</td>\n",
-       "      <td>4.379705e+06</td>\n",
-       "      <td>8000.000000</td>\n",
-       "      <td>8000.000000</td>\n",
-       "      <td>8000.000000</td>\n",
-       "      <td>10.990000</td>\n",
-       "      <td>256.690000</td>\n",
-       "      <td>4.500000e+04</td>\n",
-       "      <td>11.360000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.350000e+04</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>1.010790e+07</td>\n",
-       "      <td>1.194108e+07</td>\n",
-       "      <td>12000.000000</td>\n",
-       "      <td>12000.000000</td>\n",
-       "      <td>12000.000000</td>\n",
-       "      <td>13.660000</td>\n",
-       "      <td>379.890000</td>\n",
-       "      <td>6.300000e+04</td>\n",
-       "      <td>16.870000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2.280000e+04</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>2.073121e+07</td>\n",
-       "      <td>2.300154e+07</td>\n",
-       "      <td>20000.000000</td>\n",
-       "      <td>20000.000000</td>\n",
-       "      <td>19950.000000</td>\n",
-       "      <td>16.490000</td>\n",
-       "      <td>566.580000</td>\n",
-       "      <td>8.896000e+04</td>\n",
-       "      <td>22.780000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.790000e+04</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>3.809811e+07</td>\n",
-       "      <td>4.086083e+07</td>\n",
-       "      <td>35000.000000</td>\n",
-       "      <td>35000.000000</td>\n",
-       "      <td>35000.000000</td>\n",
-       "      <td>26.060000</td>\n",
-       "      <td>1409.990000</td>\n",
-       "      <td>7.500000e+06</td>\n",
-       "      <td>39.990000</td>\n",
-       "      <td>29.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>9.999999e+06</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>8 rows × 52 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 id     member_id      loan_amnt    funded_amnt  \\\n",
-       "count  4.662850e+05  4.662850e+05  466285.000000  466285.000000   \n",
-       "mean   1.307973e+07  1.459766e+07   14317.277577   14291.801044   \n",
-       "std    1.089371e+07  1.168237e+07    8286.509164    8274.371300   \n",
-       "min    5.473400e+04  7.047300e+04     500.000000     500.000000   \n",
-       "25%    3.639987e+06  4.379705e+06    8000.000000    8000.000000   \n",
-       "50%    1.010790e+07  1.194108e+07   12000.000000   12000.000000   \n",
-       "75%    2.073121e+07  2.300154e+07   20000.000000   20000.000000   \n",
-       "max    3.809811e+07  4.086083e+07   35000.000000   35000.000000   \n",
-       "\n",
-       "       funded_amnt_inv       int_rate    installment    annual_inc  \\\n",
-       "count    466285.000000  466285.000000  466285.000000  4.662810e+05   \n",
-       "mean      14222.329888      13.829236     432.061201  7.327738e+04   \n",
-       "std        8297.637788       4.357587     243.485550  5.496357e+04   \n",
-       "min           0.000000       5.420000      15.670000  1.896000e+03   \n",
-       "25%        8000.000000      10.990000     256.690000  4.500000e+04   \n",
-       "50%       12000.000000      13.660000     379.890000  6.300000e+04   \n",
-       "75%       19950.000000      16.490000     566.580000  8.896000e+04   \n",
-       "max       35000.000000      26.060000    1409.990000  7.500000e+06   \n",
-       "\n",
-       "                 dti    delinq_2yrs  ...  total_bal_il  il_util  open_rv_12m  \\\n",
-       "count  466285.000000  466256.000000  ...           0.0      0.0          0.0   \n",
-       "mean       17.218758       0.284678  ...           NaN      NaN          NaN   \n",
-       "std         7.851121       0.797365  ...           NaN      NaN          NaN   \n",
-       "min         0.000000       0.000000  ...           NaN      NaN          NaN   \n",
-       "25%        11.360000       0.000000  ...           NaN      NaN          NaN   \n",
-       "50%        16.870000       0.000000  ...           NaN      NaN          NaN   \n",
-       "75%        22.780000       0.000000  ...           NaN      NaN          NaN   \n",
-       "max        39.990000      29.000000  ...           NaN      NaN          NaN   \n",
-       "\n",
-       "       open_rv_24m  max_bal_bc  all_util  total_rev_hi_lim  inq_fi  \\\n",
-       "count          0.0         0.0       0.0      3.960090e+05     0.0   \n",
-       "mean           NaN         NaN       NaN      3.037909e+04     NaN   \n",
-       "std            NaN         NaN       NaN      3.724713e+04     NaN   \n",
-       "min            NaN         NaN       NaN      0.000000e+00     NaN   \n",
-       "25%            NaN         NaN       NaN      1.350000e+04     NaN   \n",
-       "50%            NaN         NaN       NaN      2.280000e+04     NaN   \n",
-       "75%            NaN         NaN       NaN      3.790000e+04     NaN   \n",
-       "max            NaN         NaN       NaN      9.999999e+06     NaN   \n",
-       "\n",
-       "       total_cu_tl  inq_last_12m  \n",
-       "count          0.0           0.0  \n",
-       "mean           NaN           NaN  \n",
-       "std            NaN           NaN  \n",
-       "min            NaN           NaN  \n",
-       "25%            NaN           NaN  \n",
-       "50%            NaN           NaN  \n",
-       "75%            NaN           NaN  \n",
-       "max            NaN           NaN  \n",
-       "\n",
-       "[8 rows x 52 columns]"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "cc753927",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['id', 'member_id', 'loan_amnt', 'funded_amnt', 'funded_amnt_inv',\n",
-       "       'term', 'int_rate', 'installment', 'grade', 'sub_grade', 'emp_title',\n",
-       "       'emp_length', 'home_ownership', 'annual_inc', 'verification_status',\n",
-       "       'issue_d', 'loan_status', 'pymnt_plan', 'url', 'desc', 'purpose',\n",
-       "       'title', 'zip_code', 'addr_state', 'dti', 'delinq_2yrs',\n",
-       "       'earliest_cr_line', 'inq_last_6mths', 'mths_since_last_delinq',\n",
-       "       'open_acc', 'pub_rec', 'revol_bal', 'revol_util', 'total_acc',\n",
-       "       'initial_list_status', 'out_prncp', 'out_prncp_inv', 'total_pymnt',\n",
-       "       'total_pymnt_inv', 'total_rec_prncp', 'total_rec_int',\n",
-       "       'total_rec_late_fee', 'recoveries', 'collection_recovery_fee',\n",
-       "       'last_pymnt_d', 'last_pymnt_amnt', 'next_pymnt_d', 'last_credit_pull_d',\n",
-       "       'collections_12_mths_ex_med', 'mths_since_last_major_derog',\n",
-       "       'policy_code', 'application_type', 'acc_now_delinq', 'tot_coll_amt',\n",
-       "       'tot_cur_bal', 'total_rev_hi_lim'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# get a list of columns that have more than 80% null values\n",
-    "df.columns[df.isnull().mean() < 0.8]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "bb70a075",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>member_id</th>\n",
-       "      <th>loan_amnt</th>\n",
-       "      <th>funded_amnt</th>\n",
-       "      <th>funded_amnt_inv</th>\n",
-       "      <th>term</th>\n",
-       "      <th>int_rate</th>\n",
-       "      <th>installment</th>\n",
-       "      <th>grade</th>\n",
-       "      <th>sub_grade</th>\n",
-       "      <th>...</th>\n",
-       "      <th>next_pymnt_d</th>\n",
-       "      <th>last_credit_pull_d</th>\n",
-       "      <th>collections_12_mths_ex_med</th>\n",
-       "      <th>mths_since_last_major_derog</th>\n",
-       "      <th>policy_code</th>\n",
-       "      <th>application_type</th>\n",
-       "      <th>acc_now_delinq</th>\n",
-       "      <th>tot_coll_amt</th>\n",
-       "      <th>tot_cur_bal</th>\n",
-       "      <th>total_rev_hi_lim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1077501</td>\n",
-       "      <td>1296599</td>\n",
-       "      <td>5000</td>\n",
-       "      <td>5000</td>\n",
-       "      <td>4975.0</td>\n",
-       "      <td>36 months</td>\n",
-       "      <td>10.65</td>\n",
-       "      <td>162.87</td>\n",
-       "      <td>B</td>\n",
-       "      <td>B2</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Jan-16</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1</td>\n",
-       "      <td>INDIVIDUAL</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1077430</td>\n",
-       "      <td>1314167</td>\n",
-       "      <td>2500</td>\n",
-       "      <td>2500</td>\n",
-       "      <td>2500.0</td>\n",
-       "      <td>60 months</td>\n",
-       "      <td>15.27</td>\n",
-       "      <td>59.83</td>\n",
-       "      <td>C</td>\n",
-       "      <td>C4</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Sep-13</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1</td>\n",
-       "      <td>INDIVIDUAL</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1077175</td>\n",
-       "      <td>1313524</td>\n",
-       "      <td>2400</td>\n",
-       "      <td>2400</td>\n",
-       "      <td>2400.0</td>\n",
-       "      <td>36 months</td>\n",
-       "      <td>15.96</td>\n",
-       "      <td>84.33</td>\n",
-       "      <td>C</td>\n",
-       "      <td>C5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Jan-16</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1</td>\n",
-       "      <td>INDIVIDUAL</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1076863</td>\n",
-       "      <td>1277178</td>\n",
-       "      <td>10000</td>\n",
-       "      <td>10000</td>\n",
-       "      <td>10000.0</td>\n",
-       "      <td>36 months</td>\n",
-       "      <td>13.49</td>\n",
-       "      <td>339.31</td>\n",
-       "      <td>C</td>\n",
-       "      <td>C1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Jan-15</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1</td>\n",
-       "      <td>INDIVIDUAL</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1075358</td>\n",
-       "      <td>1311748</td>\n",
-       "      <td>3000</td>\n",
-       "      <td>3000</td>\n",
-       "      <td>3000.0</td>\n",
-       "      <td>60 months</td>\n",
-       "      <td>12.69</td>\n",
-       "      <td>67.79</td>\n",
-       "      <td>B</td>\n",
-       "      <td>B5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>Feb-16</td>\n",
-       "      <td>Jan-16</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1</td>\n",
-       "      <td>INDIVIDUAL</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 56 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        id  member_id  loan_amnt  funded_amnt  funded_amnt_inv        term  \\\n",
-       "0  1077501    1296599       5000         5000           4975.0   36 months   \n",
-       "1  1077430    1314167       2500         2500           2500.0   60 months   \n",
-       "2  1077175    1313524       2400         2400           2400.0   36 months   \n",
-       "3  1076863    1277178      10000        10000          10000.0   36 months   \n",
-       "4  1075358    1311748       3000         3000           3000.0   60 months   \n",
-       "\n",
-       "   int_rate  installment grade sub_grade  ... next_pymnt_d last_credit_pull_d  \\\n",
-       "0     10.65       162.87     B        B2  ...          NaN             Jan-16   \n",
-       "1     15.27        59.83     C        C4  ...          NaN             Sep-13   \n",
-       "2     15.96        84.33     C        C5  ...          NaN             Jan-16   \n",
-       "3     13.49       339.31     C        C1  ...          NaN             Jan-15   \n",
-       "4     12.69        67.79     B        B5  ...       Feb-16             Jan-16   \n",
-       "\n",
-       "  collections_12_mths_ex_med  mths_since_last_major_derog policy_code  \\\n",
-       "0                        0.0                          NaN           1   \n",
-       "1                        0.0                          NaN           1   \n",
-       "2                        0.0                          NaN           1   \n",
-       "3                        0.0                          NaN           1   \n",
-       "4                        0.0                          NaN           1   \n",
-       "\n",
-       "  application_type acc_now_delinq tot_coll_amt tot_cur_bal total_rev_hi_lim  \n",
-       "0       INDIVIDUAL            0.0          NaN         NaN              NaN  \n",
-       "1       INDIVIDUAL            0.0          NaN         NaN              NaN  \n",
-       "2       INDIVIDUAL            0.0          NaN         NaN              NaN  \n",
-       "3       INDIVIDUAL            0.0          NaN         NaN              NaN  \n",
-       "4       INDIVIDUAL            0.0          NaN         NaN              NaN  \n",
-       "\n",
-       "[5 rows x 56 columns]"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# drop columns with more than 80% null values\n",
-    "df=df[df.columns[df.isnull().mean() < 0.8]]\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "a1534071",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 466285 entries, 0 to 466284\n",
-      "Data columns (total 56 columns):\n",
-      " #   Column                       Non-Null Count   Dtype  \n",
-      "---  ------                       --------------   -----  \n",
-      " 0   id                           466285 non-null  int64  \n",
-      " 1   member_id                    466285 non-null  int64  \n",
-      " 2   loan_amnt                    466285 non-null  int64  \n",
-      " 3   funded_amnt                  466285 non-null  int64  \n",
-      " 4   funded_amnt_inv              466285 non-null  float64\n",
-      " 5   term                         466285 non-null  object \n",
-      " 6   int_rate                     466285 non-null  float64\n",
-      " 7   installment                  466285 non-null  float64\n",
-      " 8   grade                        466285 non-null  object \n",
-      " 9   sub_grade                    466285 non-null  object \n",
-      " 10  emp_title                    438697 non-null  object \n",
-      " 11  emp_length                   445277 non-null  object \n",
-      " 12  home_ownership               466285 non-null  object \n",
-      " 13  annual_inc                   466281 non-null  float64\n",
-      " 14  verification_status          466285 non-null  object \n",
-      " 15  issue_d                      466285 non-null  object \n",
-      " 16  loan_status                  466285 non-null  object \n",
-      " 17  pymnt_plan                   466285 non-null  object \n",
-      " 18  url                          466285 non-null  object \n",
-      " 19  desc                         125983 non-null  object \n",
-      " 20  purpose                      466285 non-null  object \n",
-      " 21  title                        466265 non-null  object \n",
-      " 22  zip_code                     466285 non-null  object \n",
-      " 23  addr_state                   466285 non-null  object \n",
-      " 24  dti                          466285 non-null  float64\n",
-      " 25  delinq_2yrs                  466256 non-null  float64\n",
-      " 26  earliest_cr_line             466256 non-null  object \n",
-      " 27  inq_last_6mths               466256 non-null  float64\n",
-      " 28  mths_since_last_delinq       215934 non-null  float64\n",
-      " 29  open_acc                     466256 non-null  float64\n",
-      " 30  pub_rec                      466256 non-null  float64\n",
-      " 31  revol_bal                    466285 non-null  int64  \n",
-      " 32  revol_util                   465945 non-null  float64\n",
-      " 33  total_acc                    466256 non-null  float64\n",
-      " 34  initial_list_status          466285 non-null  object \n",
-      " 35  out_prncp                    466285 non-null  float64\n",
-      " 36  out_prncp_inv                466285 non-null  float64\n",
-      " 37  total_pymnt                  466285 non-null  float64\n",
-      " 38  total_pymnt_inv              466285 non-null  float64\n",
-      " 39  total_rec_prncp              466285 non-null  float64\n",
-      " 40  total_rec_int                466285 non-null  float64\n",
-      " 41  total_rec_late_fee           466285 non-null  float64\n",
-      " 42  recoveries                   466285 non-null  float64\n",
-      " 43  collection_recovery_fee      466285 non-null  float64\n",
-      " 44  last_pymnt_d                 465909 non-null  object \n",
-      " 45  last_pymnt_amnt              466285 non-null  float64\n",
-      " 46  next_pymnt_d                 239071 non-null  object \n",
-      " 47  last_credit_pull_d           466243 non-null  object \n",
-      " 48  collections_12_mths_ex_med   466140 non-null  float64\n",
-      " 49  mths_since_last_major_derog  98974 non-null   float64\n",
-      " 50  policy_code                  466285 non-null  int64  \n",
-      " 51  application_type             466285 non-null  object \n",
-      " 52  acc_now_delinq               466256 non-null  float64\n",
-      " 53  tot_coll_amt                 396009 non-null  float64\n",
-      " 54  tot_cur_bal                  396009 non-null  float64\n",
-      " 55  total_rev_hi_lim             396009 non-null  float64\n",
-      "dtypes: float64(28), int64(6), object(22)\n",
-      "memory usage: 199.2+ MB\n"
-     ]
-    }
-   ],
-   "source": [
-    "# drop redundant and forward-looking columns\n",
-    "df.info()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "7b868980",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#dropping redundant column isn't done completely for now\n",
-    "df_drop=df.drop(labels=['id','member_id','zip_code','emp_title','verification_status','issue_d','pymnt_plan','url','addr_state','delinq_2yrs','application_type','desc'],axis=1,inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "f3a847b0",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 466285 entries, 0 to 466284\n",
-      "Data columns (total 44 columns):\n",
-      " #   Column                       Non-Null Count   Dtype  \n",
-      "---  ------                       --------------   -----  \n",
-      " 0   loan_amnt                    466285 non-null  int64  \n",
-      " 1   funded_amnt                  466285 non-null  int64  \n",
-      " 2   funded_amnt_inv              466285 non-null  float64\n",
-      " 3   term                         466285 non-null  object \n",
-      " 4   int_rate                     466285 non-null  float64\n",
-      " 5   installment                  466285 non-null  float64\n",
-      " 6   grade                        466285 non-null  object \n",
-      " 7   sub_grade                    466285 non-null  object \n",
-      " 8   emp_length                   445277 non-null  object \n",
-      " 9   home_ownership               466285 non-null  object \n",
-      " 10  annual_inc                   466281 non-null  float64\n",
-      " 11  loan_status                  466285 non-null  object \n",
-      " 12  purpose                      466285 non-null  object \n",
-      " 13  title                        466265 non-null  object \n",
-      " 14  dti                          466285 non-null  float64\n",
-      " 15  earliest_cr_line             466256 non-null  object \n",
-      " 16  inq_last_6mths               466256 non-null  float64\n",
-      " 17  mths_since_last_delinq       215934 non-null  float64\n",
-      " 18  open_acc                     466256 non-null  float64\n",
-      " 19  pub_rec                      466256 non-null  float64\n",
-      " 20  revol_bal                    466285 non-null  int64  \n",
-      " 21  revol_util                   465945 non-null  float64\n",
-      " 22  total_acc                    466256 non-null  float64\n",
-      " 23  initial_list_status          466285 non-null  object \n",
-      " 24  out_prncp                    466285 non-null  float64\n",
-      " 25  out_prncp_inv                466285 non-null  float64\n",
-      " 26  total_pymnt                  466285 non-null  float64\n",
-      " 27  total_pymnt_inv              466285 non-null  float64\n",
-      " 28  total_rec_prncp              466285 non-null  float64\n",
-      " 29  total_rec_int                466285 non-null  float64\n",
-      " 30  total_rec_late_fee           466285 non-null  float64\n",
-      " 31  recoveries                   466285 non-null  float64\n",
-      " 32  collection_recovery_fee      466285 non-null  float64\n",
-      " 33  last_pymnt_d                 465909 non-null  object \n",
-      " 34  last_pymnt_amnt              466285 non-null  float64\n",
-      " 35  next_pymnt_d                 239071 non-null  object \n",
-      " 36  last_credit_pull_d           466243 non-null  object \n",
-      " 37  collections_12_mths_ex_med   466140 non-null  float64\n",
-      " 38  mths_since_last_major_derog  98974 non-null   float64\n",
-      " 39  policy_code                  466285 non-null  int64  \n",
-      " 40  acc_now_delinq               466256 non-null  float64\n",
-      " 41  tot_coll_amt                 396009 non-null  float64\n",
-      " 42  tot_cur_bal                  396009 non-null  float64\n",
-      " 43  total_rev_hi_lim             396009 non-null  float64\n",
-      "dtypes: float64(27), int64(4), object(13)\n",
-      "memory usage: 156.5+ MB\n"
-     ]
-    }
-   ],
-   "source": [
-    "# re-explore the dataset\n",
-    "#df.describe()\n",
-    "#df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e7300df0",
-   "metadata": {},
-   "source": [
-    "## Identify the target variable"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "6ac92171",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# identify the target variable(target column) no need to code this just identify\n",
-    "# Here the target column is loan_status."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "5f0d11eb",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array(['Fully Paid', 'Charged Off', 'Current', 'Default',\n",
-       "       'Late (31-120 days)', 'In Grace Period', 'Late (16-30 days)',\n",
-       "       'Does not meet the credit policy. Status:Fully Paid',\n",
-       "       'Does not meet the credit policy. Status:Charged Off'],\n",
-       "      dtype=object)"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# explore the unique values in the target column\n",
-    "df['loan_status'].unique()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "37f20874",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>loan_amnt</th>\n",
-       "      <th>funded_amnt</th>\n",
-       "      <th>funded_amnt_inv</th>\n",
-       "      <th>int_rate</th>\n",
-       "      <th>installment</th>\n",
-       "      <th>annual_inc</th>\n",
-       "      <th>dti</th>\n",
-       "      <th>inq_last_6mths</th>\n",
-       "      <th>mths_since_last_delinq</th>\n",
-       "      <th>open_acc</th>\n",
-       "      <th>...</th>\n",
-       "      <th>recoveries</th>\n",
-       "      <th>collection_recovery_fee</th>\n",
-       "      <th>last_pymnt_amnt</th>\n",
-       "      <th>collections_12_mths_ex_med</th>\n",
-       "      <th>mths_since_last_major_derog</th>\n",
-       "      <th>policy_code</th>\n",
-       "      <th>acc_now_delinq</th>\n",
-       "      <th>tot_coll_amt</th>\n",
-       "      <th>tot_cur_bal</th>\n",
-       "      <th>total_rev_hi_lim</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>loan_status</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Charged Off</th>\n",
-       "      <td>14518.758682</td>\n",
-       "      <td>14470.241318</td>\n",
-       "      <td>14342.342678</td>\n",
-       "      <td>15.998834</td>\n",
-       "      <td>439.992667</td>\n",
-       "      <td>64750.370887</td>\n",
-       "      <td>18.219970</td>\n",
-       "      <td>0.994585</td>\n",
-       "      <td>34.308502</td>\n",
-       "      <td>10.999317</td>\n",
-       "      <td>...</td>\n",
-       "      <td>926.523955</td>\n",
-       "      <td>96.189387</td>\n",
-       "      <td>454.438219</td>\n",
-       "      <td>0.006993</td>\n",
-       "      <td>42.481076</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.003602</td>\n",
-       "      <td>127.370317</td>\n",
-       "      <td>114522.093018</td>\n",
-       "      <td>26195.836948</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Current</th>\n",
-       "      <td>15177.366474</td>\n",
-       "      <td>15172.779584</td>\n",
-       "      <td>15164.954547</td>\n",
-       "      <td>13.773271</td>\n",
-       "      <td>447.410438</td>\n",
-       "      <td>74703.804790</td>\n",
-       "      <td>18.026668</td>\n",
-       "      <td>0.695584</td>\n",
-       "      <td>33.349143</td>\n",
-       "      <td>11.529832</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>457.937174</td>\n",
-       "      <td>0.012376</td>\n",
-       "      <td>42.782234</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.005191</td>\n",
-       "      <td>184.464786</td>\n",
-       "      <td>140668.380119</td>\n",
-       "      <td>31438.554947</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Default</th>\n",
-       "      <td>15284.885817</td>\n",
-       "      <td>15284.885817</td>\n",
-       "      <td>15279.967293</td>\n",
-       "      <td>16.136394</td>\n",
-       "      <td>455.537296</td>\n",
-       "      <td>65611.847500</td>\n",
-       "      <td>19.253089</td>\n",
-       "      <td>0.917067</td>\n",
-       "      <td>33.953202</td>\n",
-       "      <td>11.971154</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>477.220733</td>\n",
-       "      <td>0.008413</td>\n",
-       "      <td>43.317757</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.006010</td>\n",
-       "      <td>132.382064</td>\n",
-       "      <td>109985.603194</td>\n",
-       "      <td>27184.632678</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Does not meet the credit policy. Status:Charged Off</th>\n",
-       "      <td>9527.233903</td>\n",
-       "      <td>9248.127464</td>\n",
-       "      <td>5807.001406</td>\n",
-       "      <td>14.597148</td>\n",
-       "      <td>305.158804</td>\n",
-       "      <td>69525.915033</td>\n",
-       "      <td>14.343732</td>\n",
-       "      <td>4.672823</td>\n",
-       "      <td>25.903433</td>\n",
-       "      <td>10.001319</td>\n",
-       "      <td>...</td>\n",
-       "      <td>579.002047</td>\n",
-       "      <td>122.187443</td>\n",
-       "      <td>305.497911</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Does not meet the credit policy. Status:Fully Paid</th>\n",
-       "      <td>8853.231891</td>\n",
-       "      <td>8679.376258</td>\n",
-       "      <td>6411.141563</td>\n",
-       "      <td>13.978642</td>\n",
-       "      <td>287.069225</td>\n",
-       "      <td>72145.418271</td>\n",
-       "      <td>14.107173</td>\n",
-       "      <td>4.000510</td>\n",
-       "      <td>27.466790</td>\n",
-       "      <td>10.102446</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>2178.940287</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.002039</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Fully Paid</th>\n",
-       "      <td>13214.394226</td>\n",
-       "      <td>13169.988064</td>\n",
-       "      <td>13072.610934</td>\n",
-       "      <td>13.255943</td>\n",
-       "      <td>411.086245</td>\n",
-       "      <td>73709.612302</td>\n",
-       "      <td>15.937997</td>\n",
-       "      <td>0.839346</td>\n",
-       "      <td>35.339155</td>\n",
-       "      <td>10.794407</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>7165.937941</td>\n",
-       "      <td>0.005469</td>\n",
-       "      <td>43.037954</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.002598</td>\n",
-       "      <td>219.721618</td>\n",
-       "      <td>143006.253262</td>\n",
-       "      <td>29996.098672</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>In Grace Period</th>\n",
-       "      <td>16128.289892</td>\n",
-       "      <td>16128.289892</td>\n",
-       "      <td>16120.110393</td>\n",
-       "      <td>15.827072</td>\n",
-       "      <td>486.777092</td>\n",
-       "      <td>74033.926462</td>\n",
-       "      <td>18.801208</td>\n",
-       "      <td>0.831214</td>\n",
-       "      <td>32.568089</td>\n",
-       "      <td>11.719644</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>546.211116</td>\n",
-       "      <td>0.013350</td>\n",
-       "      <td>43.729143</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.003179</td>\n",
-       "      <td>219.261688</td>\n",
-       "      <td>130041.024675</td>\n",
-       "      <td>25975.458766</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Late (16-30 days)</th>\n",
-       "      <td>15800.779967</td>\n",
-       "      <td>15800.779967</td>\n",
-       "      <td>15793.313843</td>\n",
-       "      <td>15.964466</td>\n",
-       "      <td>477.734885</td>\n",
-       "      <td>72390.530665</td>\n",
-       "      <td>18.625952</td>\n",
-       "      <td>0.904762</td>\n",
-       "      <td>30.740469</td>\n",
-       "      <td>11.692939</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>534.378596</td>\n",
-       "      <td>0.021346</td>\n",
-       "      <td>42.072193</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.004926</td>\n",
-       "      <td>215.194794</td>\n",
-       "      <td>133734.067170</td>\n",
-       "      <td>26412.624685</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Late (31-120 days)</th>\n",
-       "      <td>15553.757246</td>\n",
-       "      <td>15549.630435</td>\n",
-       "      <td>15542.374393</td>\n",
-       "      <td>15.947754</td>\n",
-       "      <td>465.948607</td>\n",
-       "      <td>69316.828822</td>\n",
-       "      <td>19.090399</td>\n",
-       "      <td>0.880145</td>\n",
-       "      <td>32.760579</td>\n",
-       "      <td>11.729275</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>506.048239</td>\n",
-       "      <td>0.011304</td>\n",
-       "      <td>43.028380</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.006377</td>\n",
-       "      <td>196.418793</td>\n",
-       "      <td>121340.395781</td>\n",
-       "      <td>26409.483552</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>9 rows × 31 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                       loan_amnt  \\\n",
-       "loan_status                                                        \n",
-       "Charged Off                                         14518.758682   \n",
-       "Current                                             15177.366474   \n",
-       "Default                                             15284.885817   \n",
-       "Does not meet the credit policy. Status:Charged...   9527.233903   \n",
-       "Does not meet the credit policy. Status:Fully Paid   8853.231891   \n",
-       "Fully Paid                                          13214.394226   \n",
-       "In Grace Period                                     16128.289892   \n",
-       "Late (16-30 days)                                   15800.779967   \n",
-       "Late (31-120 days)                                  15553.757246   \n",
-       "\n",
-       "                                                     funded_amnt  \\\n",
-       "loan_status                                                        \n",
-       "Charged Off                                         14470.241318   \n",
-       "Current                                             15172.779584   \n",
-       "Default                                             15284.885817   \n",
-       "Does not meet the credit policy. Status:Charged...   9248.127464   \n",
-       "Does not meet the credit policy. Status:Fully Paid   8679.376258   \n",
-       "Fully Paid                                          13169.988064   \n",
-       "In Grace Period                                     16128.289892   \n",
-       "Late (16-30 days)                                   15800.779967   \n",
-       "Late (31-120 days)                                  15549.630435   \n",
-       "\n",
-       "                                                    funded_amnt_inv  \\\n",
-       "loan_status                                                           \n",
-       "Charged Off                                            14342.342678   \n",
-       "Current                                                15164.954547   \n",
-       "Default                                                15279.967293   \n",
-       "Does not meet the credit policy. Status:Charged...      5807.001406   \n",
-       "Does not meet the credit policy. Status:Fully Paid      6411.141563   \n",
-       "Fully Paid                                             13072.610934   \n",
-       "In Grace Period                                        16120.110393   \n",
-       "Late (16-30 days)                                      15793.313843   \n",
-       "Late (31-120 days)                                     15542.374393   \n",
-       "\n",
-       "                                                     int_rate  installment  \\\n",
-       "loan_status                                                                  \n",
-       "Charged Off                                         15.998834   439.992667   \n",
-       "Current                                             13.773271   447.410438   \n",
-       "Default                                             16.136394   455.537296   \n",
-       "Does not meet the credit policy. Status:Charged...  14.597148   305.158804   \n",
-       "Does not meet the credit policy. Status:Fully Paid  13.978642   287.069225   \n",
-       "Fully Paid                                          13.255943   411.086245   \n",
-       "In Grace Period                                     15.827072   486.777092   \n",
-       "Late (16-30 days)                                   15.964466   477.734885   \n",
-       "Late (31-120 days)                                  15.947754   465.948607   \n",
-       "\n",
-       "                                                      annual_inc        dti  \\\n",
-       "loan_status                                                                   \n",
-       "Charged Off                                         64750.370887  18.219970   \n",
-       "Current                                             74703.804790  18.026668   \n",
-       "Default                                             65611.847500  19.253089   \n",
-       "Does not meet the credit policy. Status:Charged...  69525.915033  14.343732   \n",
-       "Does not meet the credit policy. Status:Fully Paid  72145.418271  14.107173   \n",
-       "Fully Paid                                          73709.612302  15.937997   \n",
-       "In Grace Period                                     74033.926462  18.801208   \n",
-       "Late (16-30 days)                                   72390.530665  18.625952   \n",
-       "Late (31-120 days)                                  69316.828822  19.090399   \n",
-       "\n",
-       "                                                    inq_last_6mths  \\\n",
-       "loan_status                                                          \n",
-       "Charged Off                                               0.994585   \n",
-       "Current                                                   0.695584   \n",
-       "Default                                                   0.917067   \n",
-       "Does not meet the credit policy. Status:Charged...        4.672823   \n",
-       "Does not meet the credit policy. Status:Fully Paid        4.000510   \n",
-       "Fully Paid                                                0.839346   \n",
-       "In Grace Period                                           0.831214   \n",
-       "Late (16-30 days)                                         0.904762   \n",
-       "Late (31-120 days)                                        0.880145   \n",
-       "\n",
-       "                                                    mths_since_last_delinq  \\\n",
-       "loan_status                                                                  \n",
-       "Charged Off                                                      34.308502   \n",
-       "Current                                                          33.349143   \n",
-       "Default                                                          33.953202   \n",
-       "Does not meet the credit policy. Status:Charged...               25.903433   \n",
-       "Does not meet the credit policy. Status:Fully Paid               27.466790   \n",
-       "Fully Paid                                                       35.339155   \n",
-       "In Grace Period                                                  32.568089   \n",
-       "Late (16-30 days)                                                30.740469   \n",
-       "Late (31-120 days)                                               32.760579   \n",
-       "\n",
-       "                                                     open_acc  ...  \\\n",
-       "loan_status                                                    ...   \n",
-       "Charged Off                                         10.999317  ...   \n",
-       "Current                                             11.529832  ...   \n",
-       "Default                                             11.971154  ...   \n",
-       "Does not meet the credit policy. Status:Charged...  10.001319  ...   \n",
-       "Does not meet the credit policy. Status:Fully Paid  10.102446  ...   \n",
-       "Fully Paid                                          10.794407  ...   \n",
-       "In Grace Period                                     11.719644  ...   \n",
-       "Late (16-30 days)                                   11.692939  ...   \n",
-       "Late (31-120 days)                                  11.729275  ...   \n",
-       "\n",
-       "                                                    recoveries  \\\n",
-       "loan_status                                                      \n",
-       "Charged Off                                         926.523955   \n",
-       "Current                                               0.000000   \n",
-       "Default                                               0.000000   \n",
-       "Does not meet the credit policy. Status:Charged...  579.002047   \n",
-       "Does not meet the credit policy. Status:Fully Paid    0.000000   \n",
-       "Fully Paid                                            0.000000   \n",
-       "In Grace Period                                       0.000000   \n",
-       "Late (16-30 days)                                     0.000000   \n",
-       "Late (31-120 days)                                    0.000000   \n",
-       "\n",
-       "                                                    collection_recovery_fee  \\\n",
-       "loan_status                                                                   \n",
-       "Charged Off                                                       96.189387   \n",
-       "Current                                                            0.000000   \n",
-       "Default                                                            0.000000   \n",
-       "Does not meet the credit policy. Status:Charged...               122.187443   \n",
-       "Does not meet the credit policy. Status:Fully Paid                 0.000000   \n",
-       "Fully Paid                                                         0.000000   \n",
-       "In Grace Period                                                    0.000000   \n",
-       "Late (16-30 days)                                                  0.000000   \n",
-       "Late (31-120 days)                                                 0.000000   \n",
-       "\n",
-       "                                                    last_pymnt_amnt  \\\n",
-       "loan_status                                                           \n",
-       "Charged Off                                              454.438219   \n",
-       "Current                                                  457.937174   \n",
-       "Default                                                  477.220733   \n",
-       "Does not meet the credit policy. Status:Charged...       305.497911   \n",
-       "Does not meet the credit policy. Status:Fully Paid      2178.940287   \n",
-       "Fully Paid                                              7165.937941   \n",
-       "In Grace Period                                          546.211116   \n",
-       "Late (16-30 days)                                        534.378596   \n",
-       "Late (31-120 days)                                       506.048239   \n",
-       "\n",
-       "                                                    collections_12_mths_ex_med  \\\n",
-       "loan_status                                                                      \n",
-       "Charged Off                                                           0.006993   \n",
-       "Current                                                               0.012376   \n",
-       "Default                                                               0.008413   \n",
-       "Does not meet the credit policy. Status:Charged...                    0.000000   \n",
-       "Does not meet the credit policy. Status:Fully Paid                    0.000000   \n",
-       "Fully Paid                                                            0.005469   \n",
-       "In Grace Period                                                       0.013350   \n",
-       "Late (16-30 days)                                                     0.021346   \n",
-       "Late (31-120 days)                                                    0.011304   \n",
-       "\n",
-       "                                                    mths_since_last_major_derog  \\\n",
-       "loan_status                                                                       \n",
-       "Charged Off                                                           42.481076   \n",
-       "Current                                                               42.782234   \n",
-       "Default                                                               43.317757   \n",
-       "Does not meet the credit policy. Status:Charged...                          NaN   \n",
-       "Does not meet the credit policy. Status:Fully Paid                          NaN   \n",
-       "Fully Paid                                                            43.037954   \n",
-       "In Grace Period                                                       43.729143   \n",
-       "Late (16-30 days)                                                     42.072193   \n",
-       "Late (31-120 days)                                                    43.028380   \n",
-       "\n",
-       "                                                    policy_code  \\\n",
-       "loan_status                                                       \n",
-       "Charged Off                                                 1.0   \n",
-       "Current                                                     1.0   \n",
-       "Default                                                     1.0   \n",
-       "Does not meet the credit policy. Status:Charged...          1.0   \n",
-       "Does not meet the credit policy. Status:Fully Paid          1.0   \n",
-       "Fully Paid                                                  1.0   \n",
-       "In Grace Period                                             1.0   \n",
-       "Late (16-30 days)                                           1.0   \n",
-       "Late (31-120 days)                                          1.0   \n",
-       "\n",
-       "                                                    acc_now_delinq  \\\n",
-       "loan_status                                                          \n",
-       "Charged Off                                               0.003602   \n",
-       "Current                                                   0.005191   \n",
-       "Default                                                   0.006010   \n",
-       "Does not meet the credit policy. Status:Charged...        0.000000   \n",
-       "Does not meet the credit policy. Status:Fully Paid        0.002039   \n",
-       "Fully Paid                                                0.002598   \n",
-       "In Grace Period                                           0.003179   \n",
-       "Late (16-30 days)                                         0.004926   \n",
-       "Late (31-120 days)                                        0.006377   \n",
-       "\n",
-       "                                                    tot_coll_amt  \\\n",
-       "loan_status                                                        \n",
-       "Charged Off                                           127.370317   \n",
-       "Current                                               184.464786   \n",
-       "Default                                               132.382064   \n",
-       "Does not meet the credit policy. Status:Charged...           NaN   \n",
-       "Does not meet the credit policy. Status:Fully Paid           NaN   \n",
-       "Fully Paid                                            219.721618   \n",
-       "In Grace Period                                       219.261688   \n",
-       "Late (16-30 days)                                     215.194794   \n",
-       "Late (31-120 days)                                    196.418793   \n",
-       "\n",
-       "                                                      tot_cur_bal  \\\n",
-       "loan_status                                                         \n",
-       "Charged Off                                         114522.093018   \n",
-       "Current                                             140668.380119   \n",
-       "Default                                             109985.603194   \n",
-       "Does not meet the credit policy. Status:Charged...            NaN   \n",
-       "Does not meet the credit policy. Status:Fully Paid            NaN   \n",
-       "Fully Paid                                          143006.253262   \n",
-       "In Grace Period                                     130041.024675   \n",
-       "Late (16-30 days)                                   133734.067170   \n",
-       "Late (31-120 days)                                  121340.395781   \n",
-       "\n",
-       "                                                    total_rev_hi_lim  \n",
-       "loan_status                                                           \n",
-       "Charged Off                                             26195.836948  \n",
-       "Current                                                 31438.554947  \n",
-       "Default                                                 27184.632678  \n",
-       "Does not meet the credit policy. Status:Charged...               NaN  \n",
-       "Does not meet the credit policy. Status:Fully Paid               NaN  \n",
-       "Fully Paid                                              29996.098672  \n",
-       "In Grace Period                                         25975.458766  \n",
-       "Late (16-30 days)                                       26412.624685  \n",
-       "Late (31-120 days)                                      26409.483552  \n",
-       "\n",
-       "[9 rows x 31 columns]"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# create a new column based on the target column that will be our target variable\n",
-    "loan_status=df.groupby('loan_status').mean()\n",
-    "loan_status"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "d7a50128",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>loan_amnt</th>\n",
-       "      <th>funded_amnt</th>\n",
-       "      <th>funded_amnt_inv</th>\n",
-       "      <th>term</th>\n",
-       "      <th>int_rate</th>\n",
-       "      <th>installment</th>\n",
-       "      <th>grade</th>\n",
-       "      <th>sub_grade</th>\n",
-       "      <th>emp_length</th>\n",
-       "      <th>home_ownership</th>\n",
-       "      <th>...</th>\n",
-       "      <th>last_pymnt_amnt</th>\n",
-       "      <th>next_pymnt_d</th>\n",
-       "      <th>last_credit_pull_d</th>\n",
-       "      <th>collections_12_mths_ex_med</th>\n",
-       "      <th>mths_since_last_major_derog</th>\n",
-       "      <th>policy_code</th>\n",
-       "      <th>acc_now_delinq</th>\n",
-       "      <th>tot_coll_amt</th>\n",
-       "      <th>tot_cur_bal</th>\n",
-       "      <th>total_rev_hi_lim</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>5000</td>\n",
-       "      <td>5000</td>\n",
-       "      <td>4975.0</td>\n",
-       "      <td>36 months</td>\n",
-       "      <td>10.65</td>\n",
-       "      <td>162.87</td>\n",
-       "      <td>B</td>\n",
-       "      <td>B2</td>\n",
-       "      <td>10+ years</td>\n",
-       "      <td>RENT</td>\n",
-       "      <td>...</td>\n",
-       "      <td>171.62</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Jan-16</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2500</td>\n",
-       "      <td>2500</td>\n",
-       "      <td>2500.0</td>\n",
-       "      <td>60 months</td>\n",
-       "      <td>15.27</td>\n",
-       "      <td>59.83</td>\n",
-       "      <td>C</td>\n",
-       "      <td>C4</td>\n",
-       "      <td>&lt; 1 year</td>\n",
-       "      <td>RENT</td>\n",
-       "      <td>...</td>\n",
-       "      <td>119.66</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Sep-13</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2400</td>\n",
-       "      <td>2400</td>\n",
-       "      <td>2400.0</td>\n",
-       "      <td>36 months</td>\n",
-       "      <td>15.96</td>\n",
-       "      <td>84.33</td>\n",
-       "      <td>C</td>\n",
-       "      <td>C5</td>\n",
-       "      <td>10+ years</td>\n",
-       "      <td>RENT</td>\n",
-       "      <td>...</td>\n",
-       "      <td>649.91</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Jan-16</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>10000</td>\n",
-       "      <td>10000</td>\n",
-       "      <td>10000.0</td>\n",
-       "      <td>36 months</td>\n",
-       "      <td>13.49</td>\n",
-       "      <td>339.31</td>\n",
-       "      <td>C</td>\n",
-       "      <td>C1</td>\n",
-       "      <td>10+ years</td>\n",
-       "      <td>RENT</td>\n",
-       "      <td>...</td>\n",
-       "      <td>357.48</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Jan-15</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>3000</td>\n",
-       "      <td>3000</td>\n",
-       "      <td>3000.0</td>\n",
-       "      <td>60 months</td>\n",
-       "      <td>12.69</td>\n",
-       "      <td>67.79</td>\n",
-       "      <td>B</td>\n",
-       "      <td>B5</td>\n",
-       "      <td>1 year</td>\n",
-       "      <td>RENT</td>\n",
-       "      <td>...</td>\n",
-       "      <td>67.79</td>\n",
-       "      <td>Feb-16</td>\n",
-       "      <td>Jan-16</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 43 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   loan_amnt  funded_amnt  funded_amnt_inv        term  int_rate  installment  \\\n",
-       "0       5000         5000           4975.0   36 months     10.65       162.87   \n",
-       "1       2500         2500           2500.0   60 months     15.27        59.83   \n",
-       "2       2400         2400           2400.0   36 months     15.96        84.33   \n",
-       "3      10000        10000          10000.0   36 months     13.49       339.31   \n",
-       "4       3000         3000           3000.0   60 months     12.69        67.79   \n",
-       "\n",
-       "  grade sub_grade emp_length home_ownership  ...  last_pymnt_amnt  \\\n",
-       "0     B        B2  10+ years           RENT  ...           171.62   \n",
-       "1     C        C4   < 1 year           RENT  ...           119.66   \n",
-       "2     C        C5  10+ years           RENT  ...           649.91   \n",
-       "3     C        C1  10+ years           RENT  ...           357.48   \n",
-       "4     B        B5     1 year           RENT  ...            67.79   \n",
-       "\n",
-       "  next_pymnt_d last_credit_pull_d  collections_12_mths_ex_med  \\\n",
-       "0          NaN             Jan-16                         0.0   \n",
-       "1          NaN             Sep-13                         0.0   \n",
-       "2          NaN             Jan-16                         0.0   \n",
-       "3          NaN             Jan-15                         0.0   \n",
-       "4       Feb-16             Jan-16                         0.0   \n",
-       "\n",
-       "  mths_since_last_major_derog  policy_code  acc_now_delinq  tot_coll_amt  \\\n",
-       "0                         NaN            1             0.0           NaN   \n",
-       "1                         NaN            1             0.0           NaN   \n",
-       "2                         NaN            1             0.0           NaN   \n",
-       "3                         NaN            1             0.0           NaN   \n",
-       "4                         NaN            1             0.0           NaN   \n",
-       "\n",
-       "   tot_cur_bal  total_rev_hi_lim  \n",
-       "0          NaN               NaN  \n",
-       "1          NaN               NaN  \n",
-       "2          NaN               NaN  \n",
-       "3          NaN               NaN  \n",
-       "4          NaN               NaN  \n",
-       "\n",
-       "[5 rows x 43 columns]"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Drop the original target column\n",
-    "df_drop=df.drop(labels=['loan_status'],axis=1)\n",
-    "df_drop.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d62da664",
-   "metadata": {},
-   "source": [
-    "## Split Data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "0af569dd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# split data into 80/20 while keeping the distribution of bad loans in test set same as that in the pre-split dataset(X_train, y_train, etc)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "d4ba9e6b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# specifically hard copying the training sets to avoid Pandas' SetttingWithCopyWarning when we play around with this data later on\n",
-    "# you can refer to this link https://github.com/scikit-learn/scikit-learn/issues/8723\n",
-    "# this is currently an open issue between Pandas and Scikit-Learn teams"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "45df823e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# create a helper function clean up a column which has values given along with years, assign 0 to NANs and convert to numeric"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "4cf50e36",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# apply to X_train"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "6f21a969",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# confirm our transformation by looking at unique values of this column"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d86f2544",
-   "metadata": {},
-   "source": [
-    "### Date columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "ba10320e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# create a function to convert date columns to datetime format and create a new column as a difference between today and the respective date\n",
-    "# details of the function\n",
-    "    # store current month\n",
-    "    # convert to datetime format\n",
-    "    # calculate the difference in months and add to a new column\n",
-    "    # make any resulting -ve values to be equal to the max date\n",
-    "    # drop the original date column\n",
-    "# Note : In current date use the same data to maintain uniformilty across everyone's code. Use : 2020-08-01"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "99a24bb3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# apply to X_train"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "4acfad7d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# check these new columns"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "baf48e3e",
-   "metadata": {},
-   "source": [
-    "### Term column"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "7832bb43",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# write a function to remove 'months' string from the 'term' column and convert it to numeric \n",
-    "# use the function on 'term' column of X_Train"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "168c666d",
-   "metadata": {},
-   "source": [
-    "## Feature Selection"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "726b6bf8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# first divide training data into categorical and numerical subsets"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "08784212",
-   "metadata": {},
-   "source": [
-    "## Chi-squared statistic for categorical features"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "1224a634",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# define an empty dictionary to store chi-squared test results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "11d6040a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# loop over each column in the training set to calculate chi-statistic with the target variable"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "79a44211",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# convert the dictionary to a DF"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "d11c692d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# keep only the top four categorical features"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8949f3fa",
-   "metadata": {},
-   "source": [
-    "## ANOVA F-Statistic for numerical feature"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "f7c8d70f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# since f_class_if does not accept missing values, we will do a very crude imputation of missing values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "815594f8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Calculate F Statistic and corresponding p values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "66d26e6f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# convert to a DF"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "a0e14369",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# keep only the top 20 features and calculate pair-wise correlations between them\n",
-    "# save the top 20 numerical features in a list"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5a831c81",
-   "metadata": {},
-   "source": [
-    "## Pair wise correlations to detect multicollinearity"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "fb639daf",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# calculate pair-wise correlations between them"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "59b405e0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# drop 2 features based on their multicollinearity with other features"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "315f9251",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Define a helper function to drop the 4 categorical features with least p-values for chi squared test, 14 numerical features with least F-Statistic\n",
-    "# and 2 numerical features with high multicollinearity"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "a483be96",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# apply to X_train"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7b40d71b",
-   "metadata": {},
-   "source": [
-    "## creating dummy variables\n",
-    "### convert discrete variables to dummy variables"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "cbb5d957",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# write a function to create dummy variables"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "id": "65ad5814",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# apply to our final four categorical variables"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "87b2556e",
-   "metadata": {},
-   "source": [
-    "## Update the test data set with all data cleaning procedures performed so far"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "f2ce52f0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# also reindex the dummied test set variables to make sure all the feature columns in the train set are also available in the test set"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d96c459f",
-   "metadata": {},
-   "source": [
-    "## WoE Binning/Feature Engineering"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "d91849df",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# we will analyze both categorical and numerical features based on their categorical/binned WoEs and IVs and then combine some of these binned categories together through a custom python class with fit_transform method"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "9098de60",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# create copies of the 4 training sets(by the names X_train_prepr, y_train_prepr, etc) to be preprocessed using WoE"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "556ef2ee",
-   "metadata": {},
-   "source": [
-    "## analyze WoEs and IVs of discrete features"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "id": "dd203042",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# write a function that takes 3 arguments: a dataframe (X_train_prepr), a string (column name), and a dataframe (y_train_prepr)\n",
-    "# the function should returns a dataframe as a result"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "id": "136285cd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# set the default style of the graphs to the seaborn style. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "8cc8ddac",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# define a function for plotting WoE across categories that takes 2 arguments: a dataframe and a number"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "id": "ff26c024",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# apply these on all four categorical columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "id": "09c44927",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# observe graphs of WOE. If there is a continuous increase in WoE across the different categories then we do not need to combine any features together and should leave all these categories as they are"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "id": "c8b8839b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# if there are no missing values in the grade column leave it as it is, otherwise createa a separate and independent category for all Missing values that would never be combined with any other category\n",
-    "# you will come across this scenario when working through features"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "id": "c524dd5d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# define a function to calculate WoE of continuous variables\n",
-    "# this is same as the function we defined earlier for discrete variables\n",
-    "# the only difference are the 2 lines of code that need to be commented in the function that results in the df being sorted by continuous variable values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "id": "c176da96",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# apply this on continuous variables"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "id": "82436e3d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# fine classing using the cut method if there are a large number of unique values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "id": "6e66c07f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# don't use the features which have\n",
-    "    # very low IV\n",
-    "    # which have unusually high IV\n",
-    "    # WoE ranges between a very small range, implying low power of differentiating between good and bad loans"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "id": "d9c14866",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# if there is a feature for which most of the values are inside a particular range and very few outside then \n",
-    "    # create one category for values outside that range\n",
-    "    # apply your approach to all other values(which are inside the range)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "id": "7052db33",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# for some of the columns values would feel out of place like utilization being greater than 1 in some values which is very rare so filter those out"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "id": "b055d6fe",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# while plotting WOE if you have some doubt in curve you can also zoom on some portion to understand the nature of graph in that area"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "id": "9aac8722",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# if the IV is borderline close to the minimum or maximum ideal threshold, you can proceed without ignoring that feature"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9865d6b8",
-   "metadata": {},
-   "source": [
-    "## Define Custom Class for WoE Binning/Reengineering"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "id": "3ecec1c7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# here we will create a custom scikit-learn class to take care of all binning transformations on any given data set\n",
-    "# this custom class will help us in performing k fold cross validation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "id": "bfeb5b21",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# create a list of all the reference categories, i.e. one category from each of the global features"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "id": "b9d62257",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# this custom class will create new categorical dummy features based on the cut-off points that we manually identified based on the WoE plots and IV above"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "id": "260e7f4b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# structure this class so that it also allows a fit_transform method to be implemented on it, thereby allowing you to use it as part of a scikit-learn Pipeline "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8594f479",
-   "metadata": {},
-   "source": [
-    "## PD Model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "id": "1c007251",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# reconfirm shape of the 4 datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "id": "400573de",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# define modeling pipeline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "id": "46bbec7d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# define cross-validation criteria\n",
-    "# RepeatedStratifiedKFold automatially takes care of the class imbalance while splitting"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "id": "e76c496a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# fit and evaluate the logistic regression pipeline with cross-validation as defined in cv"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "id": "81862473",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# print the mean AUROC score and Gini"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "id": "ab3c480b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# fit the pipeline on the whole training set"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "id": "9426b9ed",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# create a transformed training set through our WoE_Binning custom class"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "id": "a0a4d556",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# store the column names in X_train as a list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "id": "96f2bfd1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# create a summary table of our logistic regression model(name it summary_table)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "id": "0b02d02b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# create a new column in the dataframe, called 'Coefficients', with row values the transposed coefficients from the 'LogisticRegression' model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "id": "bbefe8f3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# increase the index of every row of the dataframe with 1 to store our model intercept in 1st row"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "id": "35d11b18",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# assign our model intercept to this new row"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "id": "1bdc664a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# sort the dataframe by index"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2e39044f",
-   "metadata": {},
-   "source": [
-    "## Prediction Time!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "id": "0fb857da",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# make preditions on our test set"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "id": "d751fae8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# get the predicted probabilities(name it y_hat_test_proba)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "id": "457c48b4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# select the probabilities of only the positive class (class 1 - default) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "id": "1221e651",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# we will now create a new DF with actual classes and the predicted probabilities\n",
-    "# create a temp y_test DF to reset its index to allow proper concaternation with y_hat_test_proba"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "id": "120e4119",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# check the shape to make sure the number of rows is same as that in y_test"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "id": "eff09b21",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# rename the columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "id": "4a340a2b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# makes the index of one dataframe equal to the index of another dataframe"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "67f3f5c7",
-   "metadata": {},
-   "source": [
-    "## Confusion Matrix and AUROC on Test Set"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 82,
-   "id": "bcedd377",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# assign a threshold value to differentiate good with bad"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 83,
-   "id": "42884466",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# crate a new column for the predicted class based on predicted probabilities and threshold\n",
-    "# we will determine this optimal threshold later in this project"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 84,
-   "id": "a1b467c7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# create the confusion matrix"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 85,
-   "id": "32ae63bd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# get the values required to plot a ROC curve"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 86,
-   "id": "32a48f01",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# plot the ROC curve"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 87,
-   "id": "32926203",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# plot a secondary diagonal line, with dashed line style and black color to represent a no-skill classifier"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 88,
-   "id": "b029d7fe",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# calculate the Area Under the Receiver Operating Characteristic Curve (AUROC) on our test set"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 89,
-   "id": "e421c4b8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# calculate Gini from AUROC"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 90,
-   "id": "a67d6a4a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# draw a PR curve\n",
-    "# calculate the no skill line as the proportion of the positive class"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 91,
-   "id": "e4408762",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# plot the no skill precision-recall curve"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 92,
-   "id": "9419ed09",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# calculate inputs for the PR curve"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 93,
-   "id": "b5a5bae3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# calculate inputs for the PR curve"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 94,
-   "id": "7d78d0fe",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# plot PR curve"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 95,
-   "id": "a78dd2ac",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# calculate PR AUC"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9dd5c621",
-   "metadata": {},
-   "source": [
-    "## Applying the Model - Scorecard Creation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 96,
-   "id": "37913e85",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# print the summary_table"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 97,
-   "id": "e56fea64",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# create a new dataframe with one column\n",
-    "# its values are the values from the 'reference_categories' list\n",
-    "# name it 'Feature name'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 98,
-   "id": "a26ea5f4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# create a second column called 'Coefficients' which contains only 0 values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 99,
-   "id": "8da85214",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# concatenates two dataframes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 100,
-   "id": "1b6bceac",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# we reset the index of a dataframe"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 101,
-   "id": "8674e55b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# create a new column called 'Original feature name' which contains the value of the 'Feature name' column up to the column symbol"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 102,
-   "id": "5c9e748e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# define the min and max threshholds for our scorecard"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 103,
-   "id": "86b900c9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# calculate the sum of the minimum coefficients of each category within the original feature name"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 104,
-   "id": "a346dbdc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# calculate the sum of the maximum coefficients of each category within the original feature name"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 105,
-   "id": "ac8691f9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# create a new columns that has the imputed calculated Score based on the multiplication of the coefficient by the ratio of the differences between\n",
-    "# maximum & minimum score and maximum & minimum sum of cefficients"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 106,
-   "id": "1bf24313",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# update the calculated score of the Intercept (i.e. the default score for each loan)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 107,
-   "id": "dc2657e3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# round the values of the 'Score - Calculation' column and store them in a new column"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 108,
-   "id": "543f897d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# check the min and max possible scores of our scorecard"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 109,
-   "id": "88818024",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# both our min and max scores are out by +1\n",
-    "# we need to manually adjust this\n",
-    "# to decide which one we'll evaluate based on the rounding differences of the minimum category within each Original Feature Name"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 110,
-   "id": "e188b1a5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# we can get by deducting 1 from the Intercept"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 111,
-   "id": "feb1f6f1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# recheck min and max possible scores"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cd043803",
-   "metadata": {},
-   "source": [
-    "## Calculating credit scores for all observations in the test data set"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 112,
-   "id": "7ff8f92a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# create a transformed test set through our WoE_Binning custom class"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 113,
-   "id": "95d3ba61",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# insert an Intercept column in its beginning to align with the # of rows in scorecard"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 114,
-   "id": "e6229a6f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# get the list of our final scorecard scores"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 115,
-   "id": "bf4b59e4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# check the shapes of test set and scorecard before doing matrix dot multiplication"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 116,
-   "id": "9ee9b571",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# we can see that the test set has a few less columns than the rows in scorecard due to the reference categories\n",
-    "# since the reference categories will always be scored as 0 based on the scorecard, it is safe to add these categories to the end of test set with 0 values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 117,
-   "id": "9cb4b22e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# need to reshape scorecard_scores so that it is of proper shape to allow for matrix dot multiplication"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 118,
-   "id": "60a94f3a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# matrix dot multiplication of test set with scorecard scores"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1d8c99b3",
-   "metadata": {},
-   "source": [
-    "## Setting loan approval cut-offs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 119,
-   "id": "869dcdea",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# calculate Youden's J-Statistic to identify the best threshhold"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 120,
-   "id": "cc9bdb99",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# locate the index of the largest J"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 121,
-   "id": "0401c25d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# print the best threshold"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 122,
-   "id": "13cbdedb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# this means that based on the Youden's J statistic, this is the ideal probability threshold which minimizes the FPR and maximimizes the TPR\n",
-    "# which means all samples with a predicted probability higher than this should be classified as in Default and vice versa\n",
-    "# this ideal threshold might appear to be counterintuitive compared to the default probability threshold of 0.5 but remember that we used the class_weight parameter when fitting our logistic regression model that would have helped us\n",
-    "\n",
-    "# we can confirm this by looking at our original confusion matrix with the updated threshold"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 123,
-   "id": "8577ee5f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# update the threshold value"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 124,
-   "id": "fae9f7ec",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# crate a new column for the predicted class based on predicted probabilities and threshold"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 125,
-   "id": "e9cf41a8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# create the confusion matrix"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 126,
-   "id": "ddfcf9bc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# the updated confusion matrix would show a marked improvement in the TPR at marginal cost of lower TNR but at the same time, FNR has improved drastically with a corresponding marginal increase in FPR\n",
-    "\n",
-    "# find the corresponding acceptance and rejection rates on the test set at this ideal threshold"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 127,
-   "id": "b0794619",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# create a new DF comprising of the thresholds from the ROC output"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 128,
-   "id": "b8b07a3a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# calculate Score corresponding to each threshold"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 129,
-   "id": "a838b102",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# define a function called 'n_approved' which assigns a value of 1 if a predicted probability is greater than the parameter p which is a threshold and a value of 0 if it is not\n",
-    "# then it sums the column.\n",
-    "# for given any percentage values the function will return the number of rows wih estimated probabilites greater than the threshold"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 130,
-   "id": "00b90a17",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# assuming that all credit applications above a given probability of being 'good' will be approved\n",
-    "# when we apply the 'n_approved' function to a threshold it will return the number of approved applications\n",
-    "# here we calculate the number of approved appliations for all thresholds"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 131,
-   "id": "cb181639",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# then we calculate the number of rejected applications for each threshold\n",
-    "# it is the difference between the total number of applications and the approved applications for that threshold"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 132,
-   "id": "1630dc56",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# approval rate equals the ratio of the approved applications and all applications"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 133,
-   "id": "b5873b1c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# rejection rate equals one minus approval rate"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 134,
-   "id": "3d7a0362",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# look at the approval and rejection rates at our ideal threshold"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 135,
-   "id": "b63291ad",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# compare the above rates with the case of the default 0.5 threshold"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 136,
-   "id": "b26cc736",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# you would find that 0.5 threshold would result in a very high rejection rate with a corresponding loss of business\n",
-    "\n",
-    "# we will stick with our ideal threshold and the corresponding Creidt Score of (fill yourself) and can also monitor the model's performance in production if more data were available"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "bb92abd1",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

From 3206ca509b6cabbcff108cf66c17e03be73a51fb Mon Sep 17 00:00:00 2001
From: vishalh21 <106445212+vishalh21@users.noreply.github.com>
Date: Wed, 13 Jul 2022 00:13:40 +0530
Subject: [PATCH 14/14] Delete 211175_Vishal_1

---
 Assignment 2/211175_Vishal_1 | 1 -
 1 file changed, 1 deletion(-)
 delete mode 100644 Assignment 2/211175_Vishal_1

diff --git a/Assignment 2/211175_Vishal_1 b/Assignment 2/211175_Vishal_1
deleted file mode 100644
index 8b13789..0000000
--- a/Assignment 2/211175_Vishal_1	
+++ /dev/null
@@ -1 +0,0 @@
-