diff --git a/Assignment 1/211175_Vishal_1.ipynb b/Assignment 1/211175_Vishal_1.ipynb
new file mode 100644
index 0000000..2eb5851
--- /dev/null
+++ b/Assignment 1/211175_Vishal_1.ipynb	
@@ -0,0 +1,2119 @@
+{
+ "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",
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+       "\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",
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+       "      <td>0.000000</td>\n",
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+       "      <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",
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+       "      <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": [
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+       "  <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",
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+       "      <td>0.222222</td>\n",
+       "      <td>12</td>\n",
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+       "    <tr>\n",
+       "      <th>2</th>\n",
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+       "      <td>1.000000</td>\n",
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+       "      <td>0.000000</td>\n",
+       "      <td>12</td>\n",
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+       "    <tr>\n",
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+       "      <td>0.636364</td>\n",
+       "      <td>1499.00</td>\n",
+       "      <td>1499.00</td>\n",
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+       "    <tr>\n",
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+       "      <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": "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_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"
+     ]
+    },
+    {
+     "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",
+      "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",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for col in df:\n",
+    "    plt.figure()\n",
+    "    sns.displot(df[col])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "8aab7983",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for col in df:\n",
+    "    df[col]=np.sqrt(df[col])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "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": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.skew(axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "6bfd5aea",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "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"
+     ]
+    },
+    {
+     "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",
+      "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",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for col in df:\n",
+    "    plt.figure()\n",
+    "    sns.displot(df[col])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "1fb22bc6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Correlation between different fearures')"
+      ]
+     },
+     "execution_count": 13,
+     "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=\"YlGnBu\")\n",
+    "\n",
+    "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": 14,
+   "id": "82883ccc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler\n",
+    "df_std = StandardScaler().fit_transform(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "e150e382",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Cumulative explained variance')"
+      ]
+     },
+     "execution_count": 15,
+     "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",
+    "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')\n",
+    "#Through Scikit learn we are plotting graph for no. of components over our data after data cleaning."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "6a3a942c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.decomposition import PCA \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": 17,
+   "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": 18,
+   "id": "44f2459b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(8950, 7)"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Y_sklearn.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "bb8b8a5b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df=pd.DataFrame(Y_sklearn,columns=['PC1','PC2','PC3','PC4','PC5','PC6','PC7'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "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",
+       "  </tbody>\n",
+       "</table>\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"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "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": 22,
+   "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": [
+    "#elbow method for finding optimal no. of K\n",
+    "plt.plot(m,wcss,'bx-')\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": 23,
+   "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=\"r\")\n",
+    "plt.xlabel(\"PC1\")\n",
+    "plt.ylabel(\"PC2\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "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 #through elbow method\n",
+    "\n",
+    "Centroids = (X.sample(n=K))\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()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "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>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>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>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",
+       "</div>"
+      ],
+      "text/plain": [
+       "           PC1       PC2       PC3       PC4       PC5       PC6       PC7\n",
+       "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,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Centroids"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "f25df742",
+   "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": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "a23db718",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x241ed2d9f70>"
+      ]
+     },
+     "execution_count": 27,
+     "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.cluster import KMeans\n",
+    "kmeans = KMeans(n_clusters=3)\n",
+    "kmeans.fit(X)\n",
+    "plt.scatter(X[\"PC1\"],X[\"PC2\"], label='True Position') \n",
+    "#applying Kmeans to our data through Scikit learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "b6aec8f9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1 2 3 ... 1 2 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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\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.xlabel('PC1')\n",
+    "plt.ylabel('PC2')"
+   ]
+  }
+ ],
+ "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
+}
diff --git a/Assignment 2/211175_Vishal_2.ipynb b/Assignment 2/211175_Vishal_2.ipynb
new file mode 100644
index 0000000..9e5056b
--- /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
+}