diff --git a/Assignment 1/210237_Avinash_1.ipynb b/Assignment 1/210237_Avinash_1.ipynb
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+++ b/Assignment 1/210237_Avinash_1.ipynb	
@@ -0,0 +1,3175 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "49e2b796",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "##import numpy, pandas, matplotlib, seaborn, \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",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "20de3a29",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "##Load the dataset\n",
+    "df = pd.read_csv('Dataset (2).csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "2e61e7de",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Columns names:\n",
+      "['CUST_ID', 'BALANCE', 'BALANCE_FREQUENCY', 'PURCHASES', 'ONEOFF_PURCHASES', 'INSTALLMENTS_PURCHASES', 'CASH_ADVANCE', 'PURCHASES_FREQUENCY', 'ONEOFF_PURCHASES_FREQUENCY', 'PURCHASES_INSTALLMENTS_FREQUENCY', 'CASH_ADVANCE_FREQUENCY', 'CASH_ADVANCE_TRX', 'PURCHASES_TRX', 'CREDIT_LIMIT', 'PAYMENTS', 'MINIMUM_PAYMENTS', 'PRC_FULL_PAYMENT', 'TENURE']\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Look at the dataset, its columns, range of those columns,\n",
+    "columns_names=df.columns.tolist()\n",
+    "print(\"Columns names:\")\n",
+    "print(columns_names)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "0de6d3ed",
+   "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>CUST_ID</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>C10001</td>\n",
+       "      <td>40.900749</td>\n",
+       "      <td>0.818182</td>\n",
+       "      <td>95.40</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>95.4</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.166667</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.083333</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1000.0</td>\n",
+       "      <td>201.802084</td>\n",
+       "      <td>139.509787</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>C10002</td>\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>C10003</td>\n",
+       "      <td>2495.148862</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>773.17</td>\n",
+       "      <td>773.17</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>12</td>\n",
+       "      <td>7500.0</td>\n",
+       "      <td>622.066742</td>\n",
+       "      <td>627.284787</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>C10004</td>\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>C10005</td>\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": [
+       "  CUST_ID      BALANCE  BALANCE_FREQUENCY  PURCHASES  ONEOFF_PURCHASES  \\\n",
+       "0  C10001    40.900749           0.818182      95.40              0.00   \n",
+       "1  C10002  3202.467416           0.909091       0.00              0.00   \n",
+       "2  C10003  2495.148862           1.000000     773.17            773.17   \n",
+       "3  C10004  1666.670542           0.636364    1499.00           1499.00   \n",
+       "4  C10005   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": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "b1aa0753",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(8950, 17)"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Drop those columns wbich bave data in non-int or non-float form and whose data wouldn't be useful even if we convert it to a number\n",
+    "to_be_dropped=pd.DataFrame(df.CUST_ID).columns\n",
+    "df= df.drop(to_be_dropped,axis=1)\n",
+    "df.shape\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "4eb7a84e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
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+       "</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",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1000.0</td>\n",
+       "      <td>201.802084</td>\n",
+       "      <td>139.509787</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3202.467416</td>\n",
+       "      <td>0.909091</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
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+       "      <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>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",
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+       "      <td>205.788017</td>\n",
+       "      <td>0.083333</td>\n",
+       "      <td>0.083333</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.083333</td>\n",
+       "      <td>1</td>\n",
+       "      <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",
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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               NaN          0.000000      12  \n",
+       "4   678.334763        244.791237          0.000000      12  "
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "71ca4647",
+   "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": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Find missing values in data\n",
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "e191118c",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>BALANCE</th>\n",
+       "      <th>BALANCE_FREQUENCY</th>\n",
+       "      <th>PURCHASES</th>\n",
+       "      <th>ONEOFF_PURCHASES</th>\n",
+       "      <th>INSTALLMENTS_PURCHASES</th>\n",
+       "      <th>CASH_ADVANCE</th>\n",
+       "      <th>PURCHASES_FREQUENCY</th>\n",
+       "      <th>ONEOFF_PURCHASES_FREQUENCY</th>\n",
+       "      <th>PURCHASES_INSTALLMENTS_FREQUENCY</th>\n",
+       "      <th>CASH_ADVANCE_FREQUENCY</th>\n",
+       "      <th>CASH_ADVANCE_TRX</th>\n",
+       "      <th>PURCHASES_TRX</th>\n",
+       "      <th>CREDIT_LIMIT</th>\n",
+       "      <th>PAYMENTS</th>\n",
+       "      <th>MINIMUM_PAYMENTS</th>\n",
+       "      <th>PRC_FULL_PAYMENT</th>\n",
+       "      <th>TENURE</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>40.900749</td>\n",
+       "      <td>0.818182</td>\n",
+       "      <td>95.40</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>95.40</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.166667</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.083333</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1000.0</td>\n",
+       "      <td>201.802084</td>\n",
+       "      <td>139.509787</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3202.467416</td>\n",
+       "      <td>0.909091</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>6442.945483</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.250000</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7000.0</td>\n",
+       "      <td>4103.032597</td>\n",
+       "      <td>1072.340217</td>\n",
+       "      <td>0.222222</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2495.148862</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>773.17</td>\n",
+       "      <td>773.17</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>12</td>\n",
+       "      <td>7500.0</td>\n",
+       "      <td>622.066742</td>\n",
+       "      <td>627.284787</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1666.670542</td>\n",
+       "      <td>0.636364</td>\n",
+       "      <td>1499.00</td>\n",
+       "      <td>1499.00</td>\n",
+       "      <td>0.00</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>864.206542</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>817.714335</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>16.00</td>\n",
+       "      <td>16.00</td>\n",
+       "      <td>0.00</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",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <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>28.493517</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>291.12</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>291.12</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.833333</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1000.0</td>\n",
+       "      <td>325.594462</td>\n",
+       "      <td>48.886365</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8946</th>\n",
+       "      <td>19.183215</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>300.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>300.00</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.833333</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1000.0</td>\n",
+       "      <td>275.861322</td>\n",
+       "      <td>864.206542</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8947</th>\n",
+       "      <td>23.398673</td>\n",
+       "      <td>0.833333</td>\n",
+       "      <td>144.40</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>144.40</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.833333</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1000.0</td>\n",
+       "      <td>81.270775</td>\n",
+       "      <td>82.418369</td>\n",
+       "      <td>0.250000</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8948</th>\n",
+       "      <td>13.457564</td>\n",
+       "      <td>0.833333</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>36.558778</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.166667</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>500.0</td>\n",
+       "      <td>52.549959</td>\n",
+       "      <td>55.755628</td>\n",
+       "      <td>0.250000</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8949</th>\n",
+       "      <td>372.708075</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>1093.25</td>\n",
+       "      <td>1093.25</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>127.040008</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>2</td>\n",
+       "      <td>23</td>\n",
+       "      <td>1200.0</td>\n",
+       "      <td>63.165404</td>\n",
+       "      <td>88.288956</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>8950 rows × 17 columns</p>\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",
+       "8945    28.493517           1.000000     291.12              0.00   \n",
+       "8946    19.183215           1.000000     300.00              0.00   \n",
+       "8947    23.398673           0.833333     144.40              0.00   \n",
+       "8948    13.457564           0.833333       0.00              0.00   \n",
+       "8949   372.708075           0.666667    1093.25           1093.25   \n",
+       "\n",
+       "      INSTALLMENTS_PURCHASES  CASH_ADVANCE  PURCHASES_FREQUENCY  \\\n",
+       "0                      95.40      0.000000             0.166667   \n",
+       "1                       0.00   6442.945483             0.000000   \n",
+       "2                       0.00      0.000000             1.000000   \n",
+       "3                       0.00    205.788017             0.083333   \n",
+       "4                       0.00      0.000000             0.083333   \n",
+       "...                      ...           ...                  ...   \n",
+       "8945                  291.12      0.000000             1.000000   \n",
+       "8946                  300.00      0.000000             1.000000   \n",
+       "8947                  144.40      0.000000             0.833333   \n",
+       "8948                    0.00     36.558778             0.000000   \n",
+       "8949                    0.00    127.040008             0.666667   \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",
+       "8945                    0.000000                          0.833333   \n",
+       "8946                    0.000000                          0.833333   \n",
+       "8947                    0.000000                          0.666667   \n",
+       "8948                    0.000000                          0.000000   \n",
+       "8949                    0.666667                          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",
+       "8945                0.000000                 0              6        1000.0   \n",
+       "8946                0.000000                 0              6        1000.0   \n",
+       "8947                0.000000                 0              5        1000.0   \n",
+       "8948                0.166667                 2              0         500.0   \n",
+       "8949                0.333333                 2             23        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  \n",
+       "...           ...               ...               ...     ...  \n",
+       "8945   325.594462         48.886365          0.500000       6  \n",
+       "8946   275.861322        864.206542          0.000000       6  \n",
+       "8947    81.270775         82.418369          0.250000       6  \n",
+       "8948    52.549959         55.755628          0.250000       6  \n",
+       "8949    63.165404         88.288956          0.000000       6  \n",
+       "\n",
+       "[8950 rows x 17 columns]"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#If a column has more than 70% missing vaues then drop it\n",
+    "\n",
+    "#Otherwise fill it with mean (experiment with both mean and median)\n",
+    "\n",
+    "df['MINIMUM_PAYMENTS'].fillna(df['MINIMUM_PAYMENTS'].mean(), inplace = True)\n",
+    "df['CREDIT_LIMIT'].fillna(df['CREDIT_LIMIT'].mean(), inplace = True)\n",
+    "df\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "b0b90a2f",
+   "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": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isna().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "729e3e8c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "BALANCE                              2.393386\n",
+       "BALANCE_FREQUENCY                   -2.023266\n",
+       "PURCHASES                            8.144269\n",
+       "ONEOFF_PURCHASES                    10.045083\n",
+       "INSTALLMENTS_PURCHASES               7.299120\n",
+       "CASH_ADVANCE                         5.166609\n",
+       "PURCHASES_FREQUENCY                  0.060164\n",
+       "ONEOFF_PURCHASES_FREQUENCY           1.535613\n",
+       "PURCHASES_INSTALLMENTS_FREQUENCY     0.509201\n",
+       "CASH_ADVANCE_FREQUENCY               1.828686\n",
+       "CASH_ADVANCE_TRX                     5.721298\n",
+       "PURCHASES_TRX                        4.630655\n",
+       "CREDIT_LIMIT                         1.522549\n",
+       "PAYMENTS                             5.907620\n",
+       "MINIMUM_PAYMENTS                    13.867357\n",
+       "PRC_FULL_PAYMENT                     1.942820\n",
+       "TENURE                              -2.943017\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Find skewness in data of different columns\n",
+    "df.skew(axis = 0,skipna=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "95e136f0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "BALANCE\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\ASUS\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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ZXRmfneBRxXnVDcsPAJeN8rufHkdzY3XgSB/DDo0jnFMBWDqjhl9sO25mHBGRSSHO4S/J0NEVzGozUk8FoHlmDfte76OzV1eAicjko1ApkI7uPmD0nspbZgUn6zUEJiKTkUKlQNq7wlAZpaeybHbw5MdNuztjb5OISNQUKgWS7qmMdJ8KBLMVN1aXsXFPrnk8RURObgqVAmnv6qO8NEFVKjniembG8rnT2KieiohMQgqVAknfTX+iKVoyLZ9Ty9b93RwdGCpAy0REoqNQKZCO7v5Rz6ekLZ9by9Cw87ImlxSRSUahUiDpySTzsXzuNAANgYnIpKNQKZDRJpPMNLeugrrKUoWKiEw6CpUCGBwa5mBP/sNfZsbZc2t5bufheBsmIhIxhUoBHDzSjzs0VZ94Msls71gynS37uo7d3yIiMhkoVAqgPc97VDL92umNAPxS84CJyCSiUCmAfO+mz3TWnFpqK0r5RatCRUQmD4VKAXR0h5NJjqGnkkwY71g8nV+0HiCYzFlE5OSnUCmAY5NJjqGnAnBRcyO7D/ey40BPHM0SEYmcQqUA2rv6qChNUlU2tsfXXLq0CYB1L+6No1kiIpGL9SFdEhjLPSqZ5jdUsqixint+/ip1FaVvmuLl+gtOi7KJIiKRUE+lADq6+2gcw+XEmc5bUM+BI/0aAhORSUGhUgBjmaIl2/I5tZSVJFi/42DErRIRiZ5CpQA6uvvHNfwFkCpJcO5pdWxo6+RAt26EFJGTm0IlZgNDwxw8kv8ULblcunQGiQQ8snlfhC0TEYmeQiVmB4+M/R6VbNMqSrloSSMb2jrZfag3qqaJiEROoRKz8dxNn8slS5uoKith7Qu7GdbNkCJyktIlxTF7Y96v0a/+uu/JnSdcVl6a5Mrls/jWM208veMgH7xwQWRtFBGJinoqMesIeypN1eUT3tY58+tY3FjFw5t+pdmLReSkpFCJWXrer8Y8eiqjMTNWnTOXgSHnc+s2T3h7IiJRU6jErL2rj6pUkspUNCONTTVlXNLcyHee280T2w9Esk0RkagoVGLW0d1H4wSu/Mrl0rfMYG5dBZ9bt1kzGIvISUWhErOJ3E1/IqXJBJ94TzMvtHXy8KZfRbptEZGJUKjErKO7j6aIQwXgt8+dy+kzqvm7H73C8LB6KyJyclCoxCwY/pr4SfpsD65v47wF9bTu7+avvrdpxMuRRUQKRaESo4GhYQ71DEQ+/JW2fE4t9ZWl/GxreyzbFxEZK4VKjA6M4zHCY5FMGBed3shrB3vYeeBILL8hIjIWsYaKmV1hZlvMrNXMVudYbmZ2e7h8g5mtGK2umTWY2SNmtjV8rw/LLzezZ8zsxfD93XHuWz7G+xjhsThvQT0VpUl+1toR22+IiOQrtlAxsyRwB7ASWAZcZ2bLslZbCTSHr5uAO/Oouxp41N2bgUfD7wAdwG+6+9nAjcA3Ytq1vKXveo+rpwJQVpLkgsUNvLTndV7tUG9FRIorzp7K+UCru293937gfmBV1jqrgHs98ARQZ2azR6m7ClgTfl4DvB/A3Z9z9z1h+Sag3Mzi+9c8D8fm/YqxpwLwjsXTSSSMr/18e6y/IyIymjhDZS6wK+N7W1iWzzoj1Z3p7nsBwvcZOX77A8Bz7n7cBFlmdpOZrTez9e3t8Z7gjmqG4tHUlJdy7vw6vrW+7dhU+yIixRBnqFiOsuwbKk60Tj51c/+o2VnA3wJ/kGu5u9/t7i3u3tLU1JTPJseto7uP6rISKlLJWH8H4KLTG+kbHOabT+nSYhEpnjhDpQ2Yn/F9HrAnz3VGqrsvHCIjfN+fXsnM5gEPATe4+7YI9mFCOrr7aayO/h6VXGZOK+eSpU2s+eUO+geHC/KbIiLZ4gyVp4FmM1tkZingWmBt1jprgRvCq8AuBDrDIa2R6q4lOBFP+P49ADOrA34A3Oruv4hxv/LW3nU09qGvTB+5aCH7u/pY9+Legv2miEim2ELF3QeBW4CHgc3Ag+6+ycxuNrObw9XWAduBVuArwMdGqhvWuQ243My2ApeH3wnXPx34SzN7PnzlOt9SMB3d/bFe+ZXtXUubOH1GNV/7+auaaFJEiiLWJz+6+zqC4MgsuyvjswMfz7duWH4AuCxH+V8Dfz3BJkeqo7uPdyyeXrDfMzM+fNFCPvXQRta/doi3L2wo2G+LiIDuqI9N/+Awh2OcouVEfvvcedRVlvK1n71a0N8VEQGFSmz2vX4UgNm1E3+M8FhUpJJcf/5p/OilX7FDN0OKSIHFOvw1le053AvA7LrChUp6puLailISZvzZtzdw1blzuf6C0wrWBhGZ2tRTicneznRPpaLgv11TXsp5C+p5duchOnsHCv77IjJ1KVRisqcz6KnMKWBPJdPFzU24u6bFF5GCUqjEZM/hXmorSqlMFWeEsaEqxbmn1fPUqwfZHQ7FiYjETaESk72Hjxb8JH22y86YgQNf+vErRW2HiEwdCpWY7Ok8ypy6wp9PyVRXmeLCRQ18+5k2Nu99vahtEZGpQaESk72dvUXvqQD8pzNmUFeZ4i++u5HhYd1lLyLxUqjEoLd/iMM9A0XvqQBUpkpYvfIMnnntEA+u3zV6BRGRCVCoxCB95dfJ0FMBuHrFPC5Y1MD/+reX9HRIEYmVQiUGew8X7x6VXBIJ4x+uPYfSkgS33PcsRweGit0kETlFKVRiUOx7VHKZXVvBF655Gy/tfZ1b7nuWwSE9c0VEoqdQiUG6pzLrJBn+SrvszJn8z986ix9v3s8fPfA8fYPqsYhItDT3Vwx2HephRk0ZZSXxP0Y4H+k5wQCSiQRXnDWLf9uwl/1dfXz5unOZMe3kCj8RmbzUU4lB6/5uljRVF7sZJ3TJ0ia+dO05vLDrMO/54k/5zrNteqiXiERCoRIxd2dbezenzzh5QwVg1Tlz+eEnLqZ5Zg1/8uAL/N6a9fwqnARTRGS8FCoRa+/uo+voIEuaqordlBHd9+ROnth+kKvOncuvnz2bx7a2867//RO+8cRruklSRMZN51Qi1rq/G4AlJ3lPJS1hxkWnN3LGrBq++/xu/vK7G/nqY9u5+rx5TA+fWqnnsYhIvtRTidi29uDmwpN9+Cvb9OoyPnLRIq5eMS84gf+TVl5oO1zsZonIJKNQidi2/d1UpZLMmoRXVJkZKxbU84fvPp2Z08p54OldPPRcG739uvRYRPKjUInYtvZulsyoxsyK3ZRxq6tM8fsXL+ZdS5t4eschVt3xc7bu6yp2s0RkElCoRGzbSX45cb6SCeN9Z83iQ+9cyIHufn7zyz9nzS936CS+iIxIoRKh7r5B9nQePemv/BqLpTNr+OEnLubCxdP5H2s3ccM9T7G3U0+SFJHcFCoRem7nIQCWz60tckuiNWNaOV//0Nv5m6uW88xrh3jvFx/jH3+6TdO8iMhxFCoRenL7QZIJo2VhQ7GbEqn7ntzJN5/ahWF87NIlzKmr4HM/fJlLPv8T7n5sGweP9Be7iSJyktB9KhF6YvsBls+tpbrs1D2s06vLuPGdC9nW3s3mva/z2XUv83cPv8I7T5/OJc1NXLK0kSVNk/tCBREZv1P3X78C6+0f4oW2w3zkokXFbkpBLGmqZklTNS0LGnh25yE27u7k37e0AzCtvIS3zKqheWYNS5qqWdBQyWnTKzmtoZLy0pNjkk0RiYdCJSLP7TzEwJBz4eLpxW5KQc2qLefKs2dz5dmzOXSkn4bqFBt3d/LKvi5+sGEvnb0Db1q/pryEhqoUDZUpLn3LDBY3VbGkqZrFTVUKHJFTgEIlIk9sP0DCoGVhfbGbUjT1VSnc4aw5tZw1pxZ3p6d/iINH+oNXTz8Hu/s5cKSf7R1HeG7XK8fqmsHcugoWTq+isTpFU00ZjdXhq6aMxuoUjdVlNFSlKE3qVKDIyUqhEoGhYeeh53fTsrCBmvLSYjfnpGFmVJWVUFVWwvyGyuOWDwwN09HdR3tXH+3h+2sHjrDjwBE6uvs4OpD76ZRVZSU0VZcxc1oZv/HW2Zw9r44zZ9ecNM+vEZnKFCoReHTzPnYd7OXWlWcWuymTSmkywezaCmbXVhy3zN3pHxymu2+Q7r5Buo4OHvvc2TtAe1cfz+86zJOvHgQgacas2nLm1ldw9Yp5vHV+Lc0zakgmdMGASCEpVCLw9V/sYE5tOe9dNrPYTTllmBllpUnKSpPHZkvO5u4c7h1g96Fe2g710naohxd2HeapMGgqU0mWz6nl7Hm1zJpWTm1lKXUVpVSXl1CZKqEqlaSyLHxPlZAq0bCayEQpVCbokZf28fj2A3zyijMo0Vh/QZkZ9ZUp6itTx244HfbgYokNbYfZ0NbJC22H+cYTr9E/mHsoLVNp0qhMlVBRmmRRYxX1VaXUVaaoryylvjJFXWWKuopS6quC7w1VKaaVl5JQb0jkmFhDxcyuAL4EJIGvuvttWcstXH4l0AN8yN2fHamumTUADwALgR3A77j7oXDZrcBHgSHgv7r7w3Hu38bdnXzi/ud467xaPvTOhXH+lOQpYXasp7J0Zg1LZ9bwgRXBUFpv/xA9/UP0DQ3RPzj8xmtomL7BYY6Gy3v6B9lzuJfW/d309A/SOzDEiaY8M6ChKkVdZSkNVSlqK0qprUi/l1JVliSZMBJmJBJGwoKhukTCSCUTlCYTlCaN0pIE1WUl1FWUhj2qlHpOMinFFipmlgTuAC4H2oCnzWytu7+UsdpKoDl8XQDcCVwwSt3VwKPufpuZrQ6/f9LMlgHXAmcBc4Afm9lSd498LpED3X3c/uhW/uXJnTTVlPHVG1qoSOkk8ckqYUZ5aZLy0iT145iWzd05OjBM70AQOEf6gvd0AB3pH6Knb5CO7n7aDvXS2z/E4LDT3Tc4oXZXpZLUVaaYVlFKTVkJNeXpVyk15cEFEMmEURK+kskEpYl0eBlGcFVdwoz0vajpz0YQcEG5vbFeRh2C/x1Xh3B5MpHehgVBaUYi8eZliXR51jIzwGHYwXHcg16mO8ErLCNsj5GxD4k32pkuD3fj2PfMfbFwR9LrWfYyOLbtY++Z282qk/4uucXZUzkfaHX37QBmdj+wCsgMlVXAve7uwBNmVmdmswl6ISequwq4NKy/Bvh34JNh+f3u3ge8amatYRsej3rH9nYe5b6ndvK7b5/PJ97TzIyayffsFMmfmVGRSlKRStJQlcq73tBw0EN60z+aBP9oDg87Q+4MDb/x6hscPhZWvQNBUPX0D3F0YIj27j52HeoJelQDQZkmjC6+N0LojeBJl6fD7I3lx4dYMa1cPpsv/M7bIt9unKEyF9iV8b2NoDcy2jpzR6k70933Arj7XjObkbGtJ3Js603M7CbgpvBrt5ltyXeHsn02fI1DI9Ax3t+dAnR8RqdjNDIdn5E1boaOL/7uuOsvONGCOEMlVxZn/7fVidbJp+54fg93vxu4e5RtxcrM1rt7SzHbcDLT8RmdjtHIdHxGFufxifNMYBswP+P7PGBPnuuMVHdfOERG+L5/DL8nIiIxijNUngaazWyRmaUITqKvzVpnLXCDBS4EOsOhrZHqrgVuDD/fCHwvo/xaMyszs0UEJ/+fimvnRETkeLENf7n7oJndAjxMcFnwPe6+ycxuDpffBawjuJy4leCS4g+PVDfc9G3Ag2b2UWAncE1YZ5OZPUhwMn8Q+HgcV35FpKjDb5OAjs/odIxGpuMzstiOj7nrEhIREYmG7q4SEZHIKFRERCQyCpUCM7MrzGyLmbWGMwJMCWa2w8xeNLPnzWx9WNZgZo+Y2dbwvT5j/VvDY7TFzN6XUX5euJ1WM7vdJvGtzWZ2j5ntN7ONGWWRHZPwopUHwvInzWxhQXdwgk5wfD5tZrvDv6PnzezKjGVT7fjMN7OfmNlmM9tkZp8Iy4v7N+TuehXoRXDRwTZgMZACXgCWFbtdBdr3HUBjVtnngdXh59XA34afl4XHpgxYFB6zZLjsKeAdBPcl/RBYWex9m8AxuQRYAWyM45gAHwPuCj9fCzxQ7H2O4Ph8GvjTHOtOxeMzG1gRfq4BXgmPQ1H/htRTKaxjU9e4ez+Qnn5mqlpFMNUO4fv7M8rvd/c+d3+V4OrA88P7kqa5++Me/JXfm1Fn0nH3x4CDWcVRHpPMbX0buGwy9exOcHxOZCoen70eTsDr7l3AZoJZRIr6N6RQKawTTUszFTjwIzN7xoKpciBryh0gc8qdE03f05aj/FQS5TE5VsfdB4FOYHpsLS+cW8xsQzg8lh7amdLHJxyWOhd4kiL/DSlUCms808+cKi5y9xUEM1N/3MwuGWHdKKfvOVWM55icisfrTmAJcA6wF/hCWD5lj4+ZVQP/CvyRu78+0qo5yiI/RgqVwpqyU8m4+57wfT/wEMFQ4Fin3GkLP2eXn0qiPCbH6phZCVBL/sNJJyV33+fuQ+4+DHyF4O8IpujxMbNSgkD5F3f/Tlhc1L8hhUph5TN1zSnHzKrMrCb9GXgvsJExTrkTduW7zOzCcFz3how6p4ooj0nmtq4G/n84Zj5ppf+xDF1F8HcEU/D4hPvzNWCzu38xY1Fx/4aKfQXDVHsRTEvzCsGVF58qdnsKtM+LCa46eQHYlN5vgrHZR4Gt4XtDRp1PhcdoCxlXeAEtBP+QbAO+TDgrxGR8Ad8kGMIZIPgvwo9GeUyAcuBbBCdknwIWF3ufIzg+3wBeBDaE/+DNnsLH59cIhqI2AM+HryuL/TekaVpERCQyGv4SEZHIKFRERCQyChUREYmMQkVERCKjUBERkcgoVEQmwMyGwtlyXzCzZ83snVnL/9jMjppZbUbZpWb2byNs8wUz+2ZW2T+Fs/OWhd8bzWxHxvKlZrYunE12s5k9aGYzw9/qtDdm9X3ezN4T2QEQyaJQEZmYXnc/x93fBtwKfC5r+XUEN71elc/GzOxMgv9fXhLeKJppCPhIjjrlwA+AO939dHc/k2A6k6ZwlZ+FbUy/fpzvzomMlUJFJDrTgEPpL2a2BKgG/oIgXPJxPcENfj8Cfitr2T8AfxxOl5Fd53F3/366wN1/4u4bESmw7D9OERmbCjN7nuDO49nAuzOWXUdwV/jPgLeY2QwP5j4bye8ClwNvAW4J66ftBH4O/Bfg+xnly4FnRtjmxWEb0z7g7ttGaYfIuKinIjIx6eGvM4ArgHsznjdxLcHzK4aB7wDXjLQhM3s70O7urxFMr7Ei86l9oc8Cf8bY/r+bPfylQJHYqKciEhF3f9zMGoEmM5tFMGHfI2HGpIDtwB0jbOI64IyME/DTgA8AX834jdaw1/E7GfU2Ae+KaDdEJkQ9FZGImNkZBI+MPkAQEJ9294Xhaw4w18wWnKBugqAn89Z0HYKn7uU6F/M3wJ9mfL8PeKeZ/XrG9q4ws7Oj2C+RsVCoiExMRfpSXeAB4EZ3HyIY+nooa92HwnIIHsvaln4BnwR2u/vujPUfA5ZlTfeOu28Cns343gv8BvCHZrbVzF4CPsQbz9G4OOuS4qsj2G+RnDRLsYiIREY9FRERiYxCRUREIqNQERGRyChUREQkMgoVERGJjEJFREQio1AREZHI/AdN8DXTQVY4eQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "BALANCE_FREQUENCY\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\ASUS\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PURCHASES\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\ASUS\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ONEOFF_PURCHASES\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\ASUS\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INSTALLMENTS_PURCHASES\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\ASUS\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CASH_ADVANCE\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\ASUS\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PURCHASES_FREQUENCY\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\ASUS\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ONEOFF_PURCHASES_FREQUENCY\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\ASUS\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PURCHASES_INSTALLMENTS_FREQUENCY\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\ASUS\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CASH_ADVANCE_FREQUENCY\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\ASUS\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CASH_ADVANCE_TRX\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\ASUS\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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F1drIYxBmZgUGBEBEbAA2NGxbl/n6UZLuo5bOnSzjz4OodzG5BWFm7cszqXO0PEg96BaEmbUvB0SO4UrzLqbe7hLCLQgza28OiByD6QS4WT35dzGVJPp6Suz1GISZtTEHRI76DOlZvfkBAUk3kwepzaydOSBy1FsQ9dtZ8/R3d3minJm1NQdEjsFKjd7uEqVS3oTuRH9PyWMQZtbWHBA5hisj9I8xQF3nLiYza3cOiByD5VrT8QdwQJhZ+3NA5Bis1Ma8g6muv6fkMQgza2sOiBxDldroZLix9HcnLYiIVheoNTObWRwQOQZbCYieLmojMXrHk5lZu3FA5BhqqYvJC/aZWXtzQOQYqoyMLsg3lvr+vYMehzCz9uSAyDFYae0uJsDLbZhZ23JA5Biq1JrOogZG50n4TiYza1cOiBxDlRr947QgjurvAWDXU8OTUZKZ2aRzQOQYLI8/SD23P3nW0o4nhiajJDOzSeeAaBARDFXHH6Tu7iqx4Kg+dj45OEmVmZlNLgdEg0otqI3EuC0IgMXz+tnxpFsQZtaeHBANRpf6biEgTjhmFjuecAvCzNqTA6LB8KEExLx+dj4x6OU2zKwtOSAajPe40azF82axr1zzXAgza0sOiAaDLTxutO6EY2YBuJvJzNqSA6LBUGUEYNy7mCDpYgJ8J5OZtSUHRIPBcutjEIvn1VsQvpPJzNqPA6LBULX1gFhwVB/dJbmLyczakgOiwVC59UHqrpI4fm4/Oz0XwszakAOiwaHcxQRJN5NbEGbWjgoNCEkXSbpP0lZJV+Xsl6S/S/dvlnRWZt+Dku6SdKekTUXWmXVgkLq1gFh67Gy27drnuRBm1nYKCwhJXcDVwMXASuBSSSsbDrsYWJ5+rAU+3bD//Ih4YUSsLqrORofagli19BgGnhr2khtm1naKbEGcDWyNiG0RUQZuBC5pOOYS4POR+DEwT9IJBdY0rqH6TOre1n40Zy6dD8AdD+8prCYzs6lQZEAsBh7JvN6ebmv1mABukXS7pLWFVdlgqFJDgt6u1n40K044mr7uEnc8/ESxhZmZTbLuAq+tnG2NHfXNjjkvInZIehZwq6R7I+IHB71JEh5rAZYtW3Yk9QIHngUh5ZV2sJ6uEi9YfIxbEGbWdopsQWwHlmZeLwF2tHpMRNQ/PwbcRNJldZCIuDYiVkfE6oULFx5x0YOVWssD1HVnLpvH3Tv2Uq6OHPH7m5lNF0UGxG3AckmnSOoF1gDrG45ZD7wpvZvpXODJiNgpaY6kowEkzQFeCdxdYK2jhiojLQ9Q1525bD7l6gg/37m3oKrMzCZfYV1MEVGVdAVwM9AFXB8RWyS9I92/DtgAvBrYCuwH3pqefjxwU9rN0w18MSK+VVStWUOVWkvrMGW96KRkoPo/t+5i1dJ5BVRlZjb5ihyDICI2kIRAdtu6zNcBXJ5z3jZgVZG1jeVwupiOn9vPWcvm8Y3NO7n8/GcXVJmZ2eTyTOoGQ5XaIXcxAbzmjBO5Z+deHti1r4CqzMwmnwOiwWCl1tKzIBq9+gWLANhw186JLsnMbEo4IBoMVUbo6z70gDjhmFm86KT5fP1nO7zshpm1BQdEg6HDbEEA/O5Zi7n30ae445EnJrYoM7MpUOgg9Uw0WK7R391abn7xJw8/43W5OkJfd4kv/Oghzlo2v4jyzMwmjVsQDZ4crHDMrJ7DOrevu4uzls3nG5t38vjTwxNcmZnZ5HILImOwXGOwUmP+nN7DvsY5pxzLj7Y9zvu+spnfXHH8M/a94ZwjXwrEzGyyuAWRsWd/GYBjjyAgnjW3nxWLjmbjLx/30htmNqM5IDJ270sCYv7sww8IgFc8ZyH7yzV++uDuiSjLzGxKOCAy6gFx3FFHFhDLjpvDqQvm8MNfDLgVYWYzlgMio97FdKQtCIALTj+ep4aqbPzlriO+lpnZVHBAZNRbEEcyBlF3yoI5nH7CXL5//wBPD1eP+HpmZpPNAZGxZ18ZicO+zbXRRc9bRKU2wi1bHp2Q65mZTSYHRMbu/WXmz+6lq9Ta0+TGs/DoPs579gI2PbTHi/iZ2YzjgMjYva/M/NkT03qou2DF8cyf3cPX7vgVQ5XahF7bzKxIDoiM3fvKEzL+kNXbXeJ3zlzMwNPDfPSb907otc3MiuSAyNizrzIhdzA1Wv6so3nJacfx2Y0P8p17fz3h1zczK4IDImP3/olvQdS96nmLOP2Eubz7hju5/9dPFfIeZmYTyQGRigj2FNDFVNfTVeK6N6+mr6eLt37mNrYNPF3I+5iZTRQHRGrvUJXqSBQWEACL583iM295MYOVGq+/ZiO3bHnUDxcys2nLq7mm9kzQOkzjecGSY/i3y8/jDz+/ibVfuJ2XnHYcZy2bz4nzZj3jOK/8amZTzS2I1O4JWMm1VUuPnc3X/+SlfOR1z+PnO/dy9Xe38uXbH+HJwUrh721m1iq3IFK7ny4+IBqfQNfTVeKK85fzvfsfY+MvH+euXz3JBSuO57xnLyisBjOzVjkgUpPZgsia1dvFxc8/gXNPOY5v3LWTb215lDsfeYLnLZ7rx5aa2ZRyF1Nq28A+ukti4dF9U/L+8+f0ctm5J3HZOcvYX67yu9dsZO3nN3HHw3umpB4zM7cgUj954HFWLZ1Hf0/XlNax8sRjOG3hUezZX+ZzP3qIW+75NeeeeixrXryMC1cez5w+/yczs8nhvzbAvuEqm7c/yR+97NSpLgWAvp4u3vvK57L25adx408f5jP/70Gu/NKd9PeUuOD043n5cxbyG6cex9JjZ091qWbWxhwQwKaH9lAbCc499bipLmVUfUB7dm83f/yK03j48f38bPsTfO++Ab6xeScA82f3cOqCozh14Rze/+rTJ338xMzamwMC+Mm2x+kuiRedND0HhUsSJy+Yw8kL5vDbq07ksaeG2TbwNA/s2sc9O/dy+8N7+PJ/beesZfP5b8sXsGLR0Sw6ZhbzZvUwb3YPR/f3TNgS5mbWOQoNCEkXAZ8CuoDrIuKjDfuV7n81sB94S0T8VyvnTqQfb3ucM5YcMyP690sSi+b2s2huPy85bQEjEex4YpCerhLfufcx/vY/fnHQOfWHIM2b1cMxs3tHg2P+7N5k++z0Y1YvR/V3c1Rf5qO/m54u38tg1okK+4soqQu4GrgQ2A7cJml9RNyTOexiYHn6cQ7waeCcFs+dEEOVGlt27OVtLz1loi89KUoSS+YnYxGXnr2M363WGHhqmKeHq+wv1xgs15LPleT1/uEqu54aZrBSY3+5ylBlZNz36O0ucXQaFnN6u58ZIpmv5/R1Hziur5s5vV1IoiSQhEjCSij53PB1KXMM6fbSWOc1bC8JyB6T/mzq+8l5j9w64Bk1m3WyIv/JfDawNSK2AUi6EbgEyP6RvwT4fCQLEv1Y0jxJJwAnt3DuhOjv6WLTX/wWlVp7rInU1901GhitqI0EQ5U0SCo1his1hqojlKs1hiojDFdHGK7Wks+V5PPAU8Ns372f4erI6LHt8vPLc1Bw1AOHxtBJPqMD4TQyEtRGgloEIyMQBCWJrlLmQ6JUEt0lje6b7tk0zcvrOPPn9HLTO8+b8OsWGRCLgUcyr7eTtBLGO2Zxi+cCIGktsDZ9+bSk+46g5mYWALsKunbRZmrtM7VucO1TpWNr1+WH/b4njbWjyIDI+0dG4z8zxzqmlXOTjRHXAtceWmmHTtKmiFhd9PsUYabWPlPrBtc+VVz7xCoyILYDSzOvlwA7Wjymt4VzzcysQEXennIbsFzSKZJ6gTXA+oZj1gNvUuJc4MmI2NniuWZmVqDCWhARUZV0BXAzya2q10fEFknvSPevAzaQ3OK6leQ217c2O7eoWltUeDdWgWZq7TO1bnDtU8W1TyD5iWZmZpbHM6DMzCyXA8LMzHI5IMYh6SJJ90naKumqqa6nGUlLJX1X0s8lbZH07nT7sZJulfSL9PP0XHSKZAa+pDsk/Xv6ekbUnk7y/LKke9Of/2/MoNrfk/7/crekGyT1T9faJV0v6TFJd2e2jVmrpPenv7v3SXrV1FQ9Wkte7R9L/5/ZLOkmSfMy+6a8dgdEE5klPy4GVgKXSlo5tVU1VQX+NCJOB84FLk/rvQr4dkQsB76dvp6u3g38PPN6ptT+KeBbEbECWEXyPUz72iUtBt4FrI6I55PcFLKG6Vv7Z4GLGrbl1pr+v78GeF56zjXp7/RU+SwH134r8PyIOAO4H3g/TJ/aHRDNjS4XEhFloL7kx7QUETvrix1GxFMkf6QWk9T8ufSwzwG/MyUFjkPSEuA1wHWZzdO+dklzgZcB/wgQEeWIeIIZUHuqG5glqRuYTTLnaFrWHhE/AHY3bB6r1kuAGyNiOCIeILlb8uzJqDNPXu0RcUtEVNOXPyaZ8wXTpHYHRHNjLQUy7Uk6GTgT+AlwfDq/hPTzs6awtGb+FvhzILuC4Eyo/VRgAPhM2j12naQ5zIDaI+JXwMeBh4GdJHORbmEG1J4xVq0z7ff3fwLfTL+eFrU7IJprecmP6UTSUcBXgCsjYu9U19MKSa8FHouI26e6lsPQDZwFfDoizgT2MX26ZJpK++svAU4BTgTmSLpsaquaMDPm91fSB0m6iP+5vinnsEmv3QHRXCvLhUwrknpIwuGfI+Kr6eZfp6vkkn5+bKrqa+I84HWSHiTpyvtNSf/EzKh9O7A9In6Svv4ySWDMhNp/C3ggIgYiogJ8FXgJM6P2urFqnRG/v5LeDLwWeGMcmJg2LWp3QDQ3o5b8kCSSfvCfR8QnMrvWA29Ov34z8G+TXdt4IuL9EbEkIk4m+Tl/JyIuY2bU/ijwiKTnppsuIFmaftrXTtK1dK6k2en/PxeQjF3NhNrrxqp1PbBGUp+kU0ieO/PTKahvTEoejPY+4HURsT+za3rUHhH+aPJBshTI/cAvgQ9OdT3j1PpSkmboZuDO9OPVwHEkd3f8Iv187FTXOs738Qrg39OvZ0TtwAuBTenP/mvA/BlU+0eAe4G7gS8AfdO1duAGkrGSCsm/st/WrFbgg+nv7n3AxdOw9q0kYw3139d106l2L7VhZma53MVkZma5HBBmZpbLAWFmZrkcEGZmlssBYWZmuRwQZmaWywFh05akRZJulPRLSfdI2iDpOem+90gaknRM5vjZkv5Z0l3p0tX/mS47gqSnG679Fkl/30INP5N0Q8O2z0p6IN13v6TPp6uiIul7jUszS7pS0jXp1wslVST9UcMxD0r6Sub1f5f02czriyVtUrKU+L2SPp5u/7CkX0m6M/MxL+f7eFVm/9PpEtJ3prW/QtKT6TpSo9dOz3uvpH/MvH6jpG+M93Oz9uCAsGkpndV7E/C9iDgtIlYCHwCOTw+5lGSm++szp70b+HVEvCCSpavfRjIp6XBrOJ3kd+Rl6eJ7WX8WEauA5wJ3AN9NZ9vfQDITPGtNuh3g90lW7bw05y1XS3peTh3PB/4euCySpdyfD2zLHPLJiHhh5uOJxmtExM31/SQT+t6Yvn5TesgPI1lH6kzgtZLOS7f/HfAiSeelwfOXwJ/k1G5tyAFh09X5QCUi1tU3RMSdEfFDSacBRwF/wTP/0J4A/Cpz/H0RMXwENbyBZGbxLcDr8g6IxCeBR0meG/Jlkj+wfTC6qu6JwH+mp1wK/CmwpN7qyPg4SQg2+nPgryLi3vQ9qxFxzRF8X2OKiEGSGb2L6+8FvJPkuSh/A1wfEdvGvIC1FQeETVfPB8Za2fVSkn+R/xB4rqT68s7XA++T9CNJfylpeeacWdluGOB/t1DD/wC+lL5X3r/4s/4LWBERj5OsmVN/MMwa4EsREZKWAosi4qfAv6TXz/oX4CxJz27Y3uxnAfCezPf23XG/qybS1V2XAz+ob4uIjSTrM/0WSUhYh3BA2Ey0huRhKiMkq4/+PiQtDJJnM3wMOBa4Le0mAhjMdsMAH2r2BpJeDAxExEMk6/ucpeaP3cwuz5ztZsp2L60hCQFIVqxtDJ1aWvv7m9WWI9vFdP4hnlv33yRtJmkJ/XskCxACo8vHrwZ6gIWHeX2bgRwQNl1tAV7UuFHSGST/wr01XRp8DZk/tBHxdER8NSLeCfwTyWKFh+NSYEX6Hr8E5gK/1+T4MznwqNSvARdIOguYFelT/tJrviW95npgVUMrB5IurZcByzLbcn8WE+yHkTz28gXAH0t6YWbfR0h+ln8FfLLgOmwacUDYdPUdoE/SH9Y3pP+q/xTw4Yg4Of04EVgs6aR0IHV+emwvyXPEHzrUN5ZUImmVnFF/H5KH6hzUzaTEu0jGP74FSUgB3yPp8rohPe65wJyIWJy55v+lYUA7kmcyfBK4MrP5Y8AHMndwlSS991C/r1ZExP1pXe9L3+sFJI+B/WvgWuAkSRcW8d42/TggbFqKZJnh1wMXpre5bgE+TLIU+E0Nh99E8of2NOD7ku4iubNoE8nDkw7Vy4BfRfI4zrofACuVPpgG+Jikn5EsBf9i4PxInltedwOwiqQrCZJwaaz7K+SPbfwjyVPqAIiIzSSBcYOkn5Msy31C5vjsGMSd6cD4kVhHcufWKcCngfdExFDapfdO4FNpAFub83LfZmaWyy0IMzPL1T3+IWbtS8nD4n+/YfO/RsRfTUU9EyGdyf3XDZsfiIjX5x1vNhZ3MZmZWS53MZmZWS4HhJmZ5XJAmJlZLgeEmZnl+v8eT+H1zeHkNQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PURCHASES_TRX\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\ASUS\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CREDIT_LIMIT\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\ASUS\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PAYMENTS\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\ASUS\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MINIMUM_PAYMENTS\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\ASUS\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PRC_FULL_PAYMENT\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\ASUS\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "TENURE\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\ASUS\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for columns_names in df:\n",
+    "      print(columns_names)\n",
+    "      \n",
+    "      plt.figure()\n",
+    "      sns.distplot(df[columns_names]) \n",
+    "      plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "8ede293d",
+   "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>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>6.395369</td>\n",
+       "      <td>0.904534</td>\n",
+       "      <td>9.767292</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>9.767292</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.408249</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.288675</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.414214</td>\n",
+       "      <td>31.622777</td>\n",
+       "      <td>14.205706</td>\n",
+       "      <td>11.811426</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3.464102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>56.590347</td>\n",
+       "      <td>0.953463</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>80.267961</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>83.666003</td>\n",
+       "      <td>64.054919</td>\n",
+       "      <td>32.746606</td>\n",
+       "      <td>0.471404</td>\n",
+       "      <td>3.464102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>49.951465</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>27.805935</td>\n",
+       "      <td>27.805935</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.464102</td>\n",
+       "      <td>86.602540</td>\n",
+       "      <td>24.941266</td>\n",
+       "      <td>25.045654</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3.464102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>40.824877</td>\n",
+       "      <td>0.797724</td>\n",
+       "      <td>38.716921</td>\n",
+       "      <td>38.716921</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>14.345313</td>\n",
+       "      <td>0.288675</td>\n",
+       "      <td>0.288675</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.288675</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>86.602540</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>29.397390</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3.464102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>28.595705</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.288675</td>\n",
+       "      <td>0.288675</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>34.641016</td>\n",
+       "      <td>26.044861</td>\n",
+       "      <td>15.645806</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3.464102</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     BALANCE  BALANCE_FREQUENCY  PURCHASES  ONEOFF_PURCHASES  \\\n",
+       "0   6.395369           0.904534   9.767292          0.000000   \n",
+       "1  56.590347           0.953463   0.000000          0.000000   \n",
+       "2  49.951465           1.000000  27.805935         27.805935   \n",
+       "3  40.824877           0.797724  38.716921         38.716921   \n",
+       "4  28.595705           1.000000   4.000000          4.000000   \n",
+       "\n",
+       "   INSTALLMENTS_PURCHASES  CASH_ADVANCE  PURCHASES_FREQUENCY  \\\n",
+       "0                9.767292      0.000000             0.408249   \n",
+       "1                0.000000     80.267961             0.000000   \n",
+       "2                0.000000      0.000000             1.000000   \n",
+       "3                0.000000     14.345313             0.288675   \n",
+       "4                0.000000      0.000000             0.288675   \n",
+       "\n",
+       "   ONEOFF_PURCHASES_FREQUENCY  PURCHASES_INSTALLMENTS_FREQUENCY  \\\n",
+       "0                    0.000000                          0.288675   \n",
+       "1                    0.000000                          0.000000   \n",
+       "2                    1.000000                          0.000000   \n",
+       "3                    0.288675                          0.000000   \n",
+       "4                    0.288675                          0.000000   \n",
+       "\n",
+       "   CASH_ADVANCE_FREQUENCY  CASH_ADVANCE_TRX  PURCHASES_TRX  CREDIT_LIMIT  \\\n",
+       "0                0.000000               0.0       1.414214     31.622777   \n",
+       "1                0.500000               2.0       0.000000     83.666003   \n",
+       "2                0.000000               0.0       3.464102     86.602540   \n",
+       "3                0.288675               1.0       1.000000     86.602540   \n",
+       "4                0.000000               0.0       1.000000     34.641016   \n",
+       "\n",
+       "    PAYMENTS  MINIMUM_PAYMENTS  PRC_FULL_PAYMENT    TENURE  \n",
+       "0  14.205706         11.811426          0.000000  3.464102  \n",
+       "1  64.054919         32.746606          0.471404  3.464102  \n",
+       "2  24.941266         25.045654          0.000000  3.464102  \n",
+       "3   0.000000         29.397390          0.000000  3.464102  \n",
+       "4  26.044861         15.645806          0.000000  3.464102  "
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Remove skewness using square root transform(try out different methods like log transform\n",
+    "df_sqrt=np.sqrt(df)\n",
+    "df_sqrt.head()\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "fd91e0e8",
+   "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": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Plot the data and find skewness again to check how much it was corrected\n",
+    "df_sqrt.skew(axis = 0,skipna=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "91681f29",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "BALANCE\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\ASUS\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "BALANCE_FREQUENCY\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\ASUS\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PURCHASES\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\ASUS\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ONEOFF_PURCHASES\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\ASUS\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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Zgc4emuu8B2Fmp448A2IdsEzSUknVwLXAmpI+a4Dr07OZLgHaI2JnRPxxRCyMiCXpev8aEe/Ksdbjigjaunppbhh+D6KqUEF1ocIBYWZTRmVeLxwRfZJuBh4ACsCdEbFR0o3p8tXAWuBqYCvQCbw3r3peiq7efnr6BoadZmNQQ03BQ0xmNmXkFhAAEbGWJASK21YXPQ7gphFe4wfAD3Ior2xHJ+obZpqNQfXVld6DMLMpw1dSl2Fwor7jncUEyR5Ep/cgzGyKcECUoW2Eqb4HNVRX+q5yZjZlOCDKcGCEqb4HNdYmQ0yej8nMpgIHRBnaRpiob1BTTSV9A8HBI96LMLOTnwOiDIPHIKaPEBCNtcnyPYe6c6/JzCxvDogyHOjspb66QE1l9kyug5pqk5PCHBBmNhU4IMrQ1tk74imuAI3p1dR7DjsgzOzk54AoQ1tnz4inuIL3IMxsanFAlKGtq5cZx5lmY1BdVYGC5IAwsynBAVGGkSbqGySJxtpKB4SZTQkOiDK0pXeTK0dTbaWPQZjZlOCAGMHAQNDW2TPiRXKDGmu8B2FmU4MDYgSHuvsYiJGn2RjU5CEmM5siHBAjKHeivkGNNVXs7+imf8DTbZjZyc0BMYK2Mqf6HtRUW8lAwL4O70WY2cnNATGCoxP1lXGaKxRdLOdhJjM7yTkgRlDuTK6DfLGcmU0VDogR7DucBMTMhpqy+jelE/btdkCY2UnOATGC/R09VFaIaXXl3Z11cA9iZ9uRPMsyM8udA2IE+w73MKOhGkll9a8qVDB3Wi3b9nfkXJmZWb4cECPY19HDzIbyjj8MOn1mPdv3d+ZUkZnZ+HBAjGB/RzczG0cZEC31bNvngDCzk5sDYgT7O3poKfMA9aDFLfXsPtRNV09/TlWZmeXPATGCEx1iAnjOw0xmdhJzQBxHd18/h4700TLagGhxQJjZyS/XgJB0paQtkrZKujVjuSR9Ll3+uKQL0vZFkr4vabOkjZJuybPO4RzoSKbZGO0xiMUzGwDYts9nMpnZySu3gJBUAG4DrgJWANdJWlHS7SpgWfpzA3B72t4HfDAiXg5cAtyUsW7uBudTGu0Q04z6KppqKr0HYWYntTz3IC4CtkbE0xHRA9wDrCrpswr4ciQeBJolzYuInRGxASAiDgGbgQU51pppf0dyFfVoD1JL4vSZ9Q4IMzup5RkQC4DtRc93MPRLfsQ+kpYA5wMPZb2JpBskrZe0fs+ePS+15mO8GBCj24OA5DjEcz7V1cxOYnkGRNalx6U3SThuH0mNwL3A+yPiYNabRMQdEbEyIlbOnj37hIvNsjedh2nWKI9BAJwxu4Hn9nf6VFczO2nlGRA7gEVFzxcCreX2kVRFEg53RcR9OdY5rP0d3RQqxLTa8qb6LrZycQt9A8GG5w7kUJmZWf7yDIh1wDJJSyVVA9cCa0r6rAGuT89mugRoj4idSiY++iKwOSI+k2ONx7W/I7kXdUVFefMwFVu5ZAYVgoee3pdDZWZm+StvitITEBF9km4GHgAKwJ0RsVHSjeny1cBa4GpgK9AJvDdd/VLg3cDPJT2atn0kItbmVW+WfYdHf5HcoKbaKl6xYDoPPr1/jKsyMxsfuQUEQPqFvrakbXXR4wBuyljvx2QfnxhXyTQbJxYQABcvbeFLP9nGkd5+aqsKY1iZmVn+fCX1cew9PPqJ+opdvHQmPf0DPPJc29gVZWY2ThwQw4gIWtuPML+57oRf48KlLRQqxP2Plx6bNzOb/HIdYjqZ7evooadvgPnTa0/4NabXVfHuSxbzpZ8+y3UXns4rF04f0uerDz03pO03Lz79hN/TzGysOCCG0drWBfCS9iAAPnDFy/jW4zv50D8+xoffcjZVlRU8tesQW3cf5qndh3lq1yEqJBprK5ndWMOZcxp58zmnMatxdFdvm5mNNQfEMFrTe0q/1ICYXlfFJ9/5Sv7g7g38zpfXH21vqC4wZ1oty+Y0EQQHu/r4xa5DPLK9ja8/vINzF07n7ecv4O3nLWDGSzhQbmZ2ohwQwxirPQiAK1acxkeufjnP7O2gUCHmNNXSWDN00w9EsLP9CHVVFTywcRcfv38Tn1z7JG95xVyuWbmIXzlz5gldk2FmdiIcEMNobeuitqqCGfWjv4o6S2VFBcvmNB23T4XEgjSQrrvodC5r72L9tgN8d9Mu7n+slYUz6rhm5SJ+Y+VC5k1/6cFlZnY8DohhtLZ3Mb+5juSi7okxb3odb3tVHVeeM5dNrQfZ0dbJX37nF/zVd3/BZWfP4ZoLF/HG5XOoKvhkNDMbew6IYbS2HWH+JPlXelWhgnMXNXPuomZec8Ys1m/bz/pn9/OvT+5mVmMN77xgAW85Zy7nL2r2EJSZjRkHxDBa27q47OyxnR12LLQ0VPPmFXO5fPlpPLXrEK3tR7jzx89wx789zeymGt708tN4w9mzufSsWTRkHOcwMyuXv0Ey9PQNsOdw95gcoM5LoUIsnzeN5fOm8ZozZrJl1yE27TzIvRt2cPfPnqMgsWRWPS87rYlXzJ/OjIZqX19hZqPigMiw6+ARIjjhIaasi9/yVFdd4LxFzZy3qJm+gQG27evkFy8cYsuuQ/zfJ17g20+8wJlzGplWV8mbV8ylutLHLMxsZA6IDM+P4Smu462yooIzZzdy5uxGrnrlPA509LDhuQOs33aAm7/6CAua63jf5WfxzgsW+uC2mR2XvyEybE/vJT2/+cSn2ZgsZjRUc/nLT+PDbzmbO9+zkllNNfzRvT/nTZ/5If/06PMkE+qamQ3lgMiweechaiorOL2lfqJLGTMVEi+0d/MfXr2Q6y9ZTE/fALfc8yiv//T3fdc7M8vkgMiwsbWd5fOmUTkFh2Ck5OD2TW84i1+/YAFtnb288/M/4X13P3J0aM3MDHwMYoiIYNPOg7zt3PkTXUquKiRevbiFVyyYzv6OHu74t6d5YOML/O7rzuA/X3amT5E1M+9BlNpxoItDR/o4Z/60iS5lXNRUFpg3vY5bLl/G8rlN/PX3t3LJn36PD37tUQYGfHzC7FTmgCixsbUdgHPmD713w1TWXF/NNReezo2/eibN9VXcu+F53vbXP+ZHT+3xgWyzU5THEUpsbD2YXIQ29/gT601Vp7fUc+OvnsnjO9r50VN7ePcXf8ZZcxp518Wn885XL2Ra7dhMXmhmk58DosTG1oOcObuB2qrCRJcyYSRx7qJmPr7qHO5/rJWvPLiNj92/iT9d+yQXn9HC5cvncNnZc1g8s35CJzM0s3w5IIr0DwSPbW/j9S+bfHMwTYT7NjwPwDUXns6lZ3Xy+I52nm/r4mP3b4L7NzF3Wi0XLm3hoqUtXLy0hbNmN3qyQLMpxAFR5P9t3cu+jh6uWHHaRJcy6SycUc/CGcl1IfsOd/PU7sM8u6+DH27Zzf2PtQLQXF/FhUuSsLhoaQsrpuipwmanCgdEkW888jxNtZW8cfmciS5lUpvZWMPMxhouOWMmEcGBzl6e2dvBs3s72LDtAN/ZtAtIbqt6weIZnLeomWWnNfGy0xpZOquBmspTd/jO7GTigEh1dPfx7Sde4O3nzz+ljz+MliRaGqppaajm1YtnAHCwq5dn93VQXVnBz57Zz+d/8Ev601NmCxVicUs9S2Y1sHhmPYtb6lk8q4HFLckeiicSNJs8HBCpr63fTldvP++8YOFEl3LSm1ZXxasWNgOwfO40+vqT6dN3H+pm98Ej7D7UzabWg/z4qb309A8cXa9CyQSJi2fWs3hmEhqLZ6ZBMrOe+mp/XM3GU67/x0m6EvgsUAC+EBGfKlmudPnVQCfwnojYUM66Y+nR7W18cu2TvG7ZLFam/wq2sVNZqGDe9Loh99GOCA5397G/o4d9HT3sO9zD/o5utu3r5JHn2ujs6T+m/+ymmiGhsXhmA6e31NNcV+UD5GZjLLeAkFQAbgOuAHYA6yStiYhNRd2uApalPxcDtwMXl7numGjr7OHGf3iYOdNq+Ny15/u0zXEkiabaKppqq1g8s2HI8q6e/jQ8ul8MkY4enm/r4t4NR47pW1nx4lBXTVWBmkLF0eGqIIgg+Sl5DMktXWsqK6ipLFBTVUFt+nuwrbbqxWU1lRXUVhVe7F9ZQU1VBRUSkqgQCCGRthX/Bkj6lC4rVIgKJT/JY6ioEIX0uUTRY39GbXzkuQdxEbA1Ip4GkHQPsAoo/pJfBXw5kkt1H5TULGkesKSMdcfE9Loq3nPpEl571ixmNFSP9cvbS1BXXWBBdR0LZgy9L0dv/wD7O3qO/nR093G4u4+Onn46u/s4OBBHj3sApN/NCI5+wQ5+zfZH0Ncf9PYP0DcQ9KW/e/sH6OsfjJHJYzBYChIVFS8+lhjz8BjLlxvrWCv9W0tff2jtGnbZ8dZVunSwTUf7DH09aWj/8dDSUM03fv/SMX/dPANiAbC96PkOkr2EkfosKHNdACTdANyQPj0sactLqHm0ZgF7x/H9TgbeJkN5mwzlbXKsl7w9dNMJr7p4uAV5BkRWfpb+Y2y4PuWsmzRG3AHcMbrSxoak9RGxciLee7LyNhnK22Qob5NjTdbtkWdA7AAWFT1fCLSW2ae6jHXNzCxHeZ50vg5YJmmppGrgWmBNSZ81wPVKXAK0R8TOMtc1M7Mc5bYHERF9km4GHiA5VfXOiNgo6cZ0+WpgLckprltJTnN97/HWzavWl2BChrYmOW+TobxNhvI2Odak3B7yXP9mZpbF8xqYmVkmB4SZmWVyQJwASVdK2iJpq6RbJ7qeiSLpWUk/l/SopPVpW4uk70h6Kv09pecukXSnpN2SnihqG3YbSPrj9HOzRdJbJqbqfA2zTT4m6fn0s/KopKuLlp0K22SRpO9L2ixpo6Rb0vZJ/VlxQIxS0TQgVwErgOskrZjYqibUGyLivKJzuG8FvhcRy4Dvpc+nsr8Hrixpy9wG6efkWuCcdJ3Pp5+nqebvGbpNAP4q/aycFxFr4ZTaJn3AByPi5cAlwE3p3z6pPysOiNE7OoVIRPQAg9OAWGIV8KX08ZeAt09cKfmLiH8D9pc0D7cNVgH3RER3RDxDcvbeReNR53gaZpsM51TZJjsHJyKNiEPAZpIZIyb1Z8UBMXrDTQ9yKgrgXyQ9nE55AnBaei0L6e9T8e5Lw22DU/2zc7Okx9MhqMGhlFNum0haApwPPMQk/6w4IEav7GlATgGXRsQFJMNtN0l6/UQXNMmdyp+d24EzgfOAncBfpu2n1DaR1AjcC7w/Ig4er2tG27hvFwfE6JUzhcgpISJa09+7gW+Q7ALvSmfkJf29e+IqnDDDbYNT9rMTEbsioj8iBoC/5cXhklNmm0iqIgmHuyLivrR5Un9WHBCj52lAAEkNkpoGHwNvBp4g2Ra/nXb7beCfJqbCCTXcNlgDXCupRtJSkvug/GwC6ht3g1+CqXeQfFbgFNkm6c3RvghsjojPFC2a1J8V38NxlE6iaUDydhrwjXRO/ErgqxHxbUnrgK9J+k/Ac8C/n8AacyfpbuAyYJakHcBHgU+RsQ3SqWa+RnJfkz7gpojoz3zhk9gw2+QySeeRDJM8C/wenDrbBLgUeDfwc0mPpm0fYZJ/VjzVhpmZZfIQk5mZZXJAmJlZJgeEmZllckCYmVkmB4SZmWVyQJiZWSYHhE1akhZK+qd0KuRfSvqspGpJl0kKSW8r6vstSZelj3+QTpE8OLX014v63SDpyfTnZ5JeW7SsdL3fSNv7i9oeTefSyar3Mkntkh5Jp3X+aNr+Hkl/XdL3B5JWpo8Hp01/XNIPJS0u6ne9pCfSKaI3SfpQ2v73g/UV9T1c8vwDko5Iml7UVi/prvT9npD043T6h6y/c3Bm0bemf9NjaQ2/V8Z/PpsCfKGcTUrplaf3AbdHxKp0quM7gD8B/plkKoL/Ctw/zEv8VkSsL3nNt5JcoPXaiNgr6QLgm5IuiogXhlsP6IqI88os/UcR8db06vJHJX2rzPXekNb0ceC/Ab8r6Srg/cCbI6JVUi3JxVbluo7kyv93kEzBDXALsCsiXgkg6WygN1025O9Mp4e4A7goInZIqgGWjKIGO4l5D8ImqzcCRyLi7wDSq0g/APxHoB54DGiXdMUoXvOPgA9HxN70NTeQTLF801gWnr52B/AwyQR1o/FTXpy184+BDxXNeXUkIv62nBeRdCbQSBI21xUtmgc8X1TnlojoPs5LNZH8Q3Jf2r87IraU+bfYSc4BYZPVOSRfsEels18+B5yVNn2C5Aswy11FQyV/PtxrAuvT9qz1ZqZtdUVt3yin+HTdS4DRTsNyJfDN9PErMuot9ufFQ0Ily64D7gZ+BJwtaXAa6TuBP5L0U0mfkLSsaJ3iv/NRSddExH6SeYG2Sbpb0m9J8vfGKcJDTDZZiezpjY+2R8SPJCHpdRn9soaKynmflzrE9DpJjwADwKfSOXVWDtO3+H2/L+k0ktk8hwu9Uh+OiOLjK8XHIK4F3hERA5LuI5nj57aIeFTSGSSTK74JWCfpNRGxmWH+zoj4HUmvTPt/CLgCeE+ZNdpJzP8SsMlqI3DMF6ukaSRTIP+yqPlPSI5FlGMT8OqStgvS9rHyo4g4PyJeHRGr07Z9QOm9uVuAvUXP3wAsJvm7/2fatjGj3hFJehXJ7J/fkfQsSVgcHWaKiMMRcV9E/D7wFeDqzBcqEhE/j4i/IgmHXx9tTXZyckDYZPU9oF7S9XD0XuB/SXKwtXOwU0T8C8mX77llvOangT8bHDpSMrvoe4DPj2HdWdYBl0qam77vSqCGY+8YRkR0kRyUvl5SC/BJ4NNF69VIel8Z73cd8LGIWJL+zAcWSFos6VKld3NTMl39CmDbcC8kqVHp2WGp847X36YWDzHZpBQRIekdJDdr/+8k/5hZSzJF8mtKuv8JQ+87cZekrvTx3oh4U0SskbQA+ImkAA4B7xq85WNeImKXpFuAten4/WHguvTmOaV9dyqZLvumiPhf6bDTd9OzuoLkGMJIriW5y1+xb6TtO4Hb09erIDkj7N60T13JsYxvk2zbP5T0N0AX0IGHl04Znu7bzMwyeYjJzMwyeYjJbJQkvQX4s5LmZyLiHRNRj1lePMRkZmaZPMRkZmaZHBBmZpbJAWFmZpkcEGZmlun/A2r3lbdxj+JMAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INSTALLMENTS_PURCHASES\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\ASUS\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CASH_ADVANCE\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\ASUS\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PURCHASES_FREQUENCY\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\ASUS\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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WFHP9rJEMS+15QkIgzB+fzbbDxyitcc90SQt4PzhYVc+b28q4cXZev9euHp2VyNCUOJbvqkBtXrwZBH75rx2IBHfsvbP53rNn3dSLt4D3g7+s3E+EyIDWrhYRzhyTSdmxJg5UufsyYsZsLK5h8YclfGbuqF6nEwfKuJwkhqXG8fYO9wS8HWQdoObWdp4tOsiFp+QwNHVgB4lOG5HKyxtKKdp/lHxbiMyEuP4cVAe4cfZI/ueVLWQkxnDnuaHRewfvdMlx2byyoZSWtnai+3gsLRSF/08QZEu3l3G0voVPzhw54H3FRkVy2ohUNhbX0GQnPhmXWry2hNV7q7jvwvGkxEUHu5yPmT8+m2NNra45u9wCfoAWry0mKymWeYX+mVo1Mz+d5rZ2NpbU+GV/xoSS+uZWHliylel5adwwa+CdIn+bOzaLqAhhmUuGaSzgB+BoXTNvbSvjqqm5fZ4aeTJ5GQlkJcVS5JIehDGdvbyhlJqGFh64+rSArRjZF8lx0czIT3fNgVbHAl5EHhWRMhHZ5FQbwfbShkO0tCnX+HH1OxFhZn46B6rqKatt9Nt+jQm2DcXVrDtYzd3nFTJxWEqwyzmp+eOHsKW01hXvPyd78I8BFzu4/6BbtLaEicNSOCXXv7+s0/LSiBBYY7144xJVdc28sO4QI9LjuSuEDqx255xxntUl3TBM41jAq+o7gLsWduhkV9lx1h+s5tp+ntjUk+S4aMYPTWHtwWra2m1OvAlvLW3tPLVqP4py/cyRfhvOdMrEYcnkpMS6Ypgm6K+0iNwhIkUiUlReHj4v6OK1xURGCJ+YmuvI/mfmp1PX1MrOI8cc2b8xgdCuynNrijlU08gnZ4wkMyk22CX1qmO65Ls7y2kN86VDgh7wqvqwqs5U1ZnZ2dnBLscn7e3KPz4s4ezCLMeWNy3MSSIhJpK1B2yYxoQnVWXJxlI2ltRw8aShTAjhcfeu5o8fQm1jK+vCfOmQoAd8OFqxp5LSmka/HlztKioigikj09h6+Bg19S2OtWOMEzrC/f3dlcwdk+m3acSBMndsFpERwrIwH6axgO+HRWuLSY6L4oJTnF3ad/rIdNralZc3HnK0HWP8qbWtnUVri3lvdyVnjMnk0tOGIRJ6UyJ7khofzfS8NJbtCO+rPDk5TfJvwApgvIgUi8hnnWorkOqaWvnnpsNcPnkYcdHOXjsyNy2OIcmxLF5b4mg7xvjL0fpmFi7fy9oD1Zw3YQiXh2G4d5g/fgibSmopOxa+0yWdnEVzo6oOU9VoVR2hqo841VYg/XPTYeqb2xwdnukgIkzLS2fN/qPsq6hzvD1j+qtdlaJ9Vfz2rZ0crm3khlkjOX9iTtiGO/x7uuS7O8L3cpo2RNNHiz8sJi8jgZn56QFpb+rINERg8YfWizehR1XZeeQYf3x7N4s/LCEnJY67zytk8oi0YJc2YJNyU8hOjg3r+fC2mmQfHKpu4P3dlXx5QWHAeiap8dGcNTaLxWuLuWdBYUie3m0Gn8rjTazeW8XqvZUcqmkkNT6a/5gxwtshccfvaMd0yTe2HqGtXYkMw/eeBXwf/OPDElThmmnOD890ds304dz7zHo+2FfFnNGZAW3bGIDGlja2ltbywb4q3thSRtH+KtrVc7nJq6cOZ1pems8nMPV3meFgOGdcNs+tKWbdwWpmBOhTuz9ZwPtIVVm0tpjZBRnkZSYEtO2LJg0lIWYTi9eWWMAbx6gqlXXN7K+sY39lvferjt3ldWw7XEtLm+es6glDk/nSuWNpa/dMBHBLj707ZxdmExkhvLXtiAW8m60vrmFPeR13zBsd8LYTYqK45NRhvLKxlO9fOcnx2TvhoL+9wJvm5Pm5kvCiqlQcb2ZfZR17Kzxf+yvr2FfhCfO65n9fh0AEclPjGZWVyGfPGs3UkWlMHZn20YVtwqkn3l+pCdGcPjqD1zYf4asXTQh2OX1mAe+jRWuKiY2K4NLJw4LS/rUzhrNobTH/2nKET0xxZnkEE966Bm5zazuHaxs5VN3AoeoGSmsaqTjeRFPrv0+/jxAoyEwkPzOB2aMyyM9MoCAzkbzMBEakx/f7GsNuctGkofz3C5vZVXaMsUOSg11On1jA+6CptY2XNhziwklDg3YFmtNHZZKbGsfitcUW8KZb1fXNnmGVKs8Qy+GaRjqWqouPjmRYWhzT8tLJSoohMzGWrKQY0hJiuPWM/l9LeDC48BRPwL+2+YgFvBst3VZGdX0L1ziwcqSvIiKEq6cP5w/LdlN2rNGxNXBMeGhvV3aUHeODfUcp2ldF0b6jlFQ3ABATFUFeRgLzxw9heFocw9LiSYuPdvVYuZOGpsYxLS+Nf246zF3njg12OX1iAe+DZz44yJDkWOaNDe56GldPG8FDS3fz4rpDfC4IxwIGs0CPN3c9VnC8qZX1B6sp2neUNQeO8uH+oxxragVgSHIsswoymJaXRn5mIkNT4sJySl8ou3jSUH786jYOVNYHfJLFQFjA96KkuoFlO8r50rljg76O9dghSUwZmcaitSVhG/Bt7eoZE65poLahlX2VdURFCNnJsRRkJTJlRBoZiTHBLjOomlvbWbO/ii2Hatl8qJYNxTVsO1xLu3oOfI7PSeaKqbnMyEtnVkEGIzPiEZF+/xEaDAdLB+ryKbn8+NVtvLThUFj14i3ge/HMas8v//UhcoHg66YP5zsvbGZTSQ2nDk8Ndjk+O1TdwMo9lWw6VENji+cgnwAr9lTQ2qa0drqwyYj0eGbkpzN3bBbzCrMYlhofpKqd09TaxtG6Fqrqmqiqa6ayrpmj9c1UHm+mqq75o7HztIRoTs1N5UvnFTIjP52pI9NIjQ/OcaDBbHhaPDPz03lxnQW8a7S2tfNM0UHOGZfNiPTQ+Fj2iSnDeWDJVp5afYAfXX1asMvp1dH6Zl7ZUMqW0lpiIiOYlJvChGEpjEiPJyUumlvPyEdVqaprZseR42wsqWZ9cQ3v767khXWeVTRHZycyb2wWc8dmcfqYzKAd6O4LVeVYUytVx5upqveEduev497hlQ6xURFkJsYwLDWOKSPTuGHWSCYNTyU31d3zzMPJFVNy+e6Lm9l++Bjjh4bHwVYL+B4s3V7OkdomfnBl6MydTk2I5orJuTz/YQnfuGQCySEadqrKqr1VvLqpFIAFE4Zw5pgs4mNOnHYnImQmxXJGUixnjMn86Pnbjxxj+c4Klu+q4O9FxTy+Yj+REcKUEamkJcQwNjuJkRkJQRtvbm5tp7re2/Oua+aotyde5e2Nd5wYBJ5PK6nx0aQnxjBhaDIZiTEf+4qPjvxYkFccb3bFJePc5NLThvH9lzbz/LoSvn5xeMyJt4Dvwd9WH2BIciwLJgwJdikfc/Pp+Ty7ppjn1x3i1tNDb4pbS1s7z60pZmNJDYVDkrh62nDSEvo2ri4iTBiawoShKXxu3miaW9tZe+DoR4G/dFsZb20rIyYqgmGpceQkx5GTEktGYgypCTGkxUcTGxXRr96vqlLb0EqFd/ik/FgTy3eWc7ShhZr6Fqobmqmub6G+00lBANGRQmZiLJlJsRQOSSIjKZaMhBgyE2NIS4gO+jEcMzDZybHMHz+ExWuLue/C8WFxINsC/iRKqhtYtr2Mu0Lg4GpXU0akcurwFJ54fx+3zMkLqY/w9c2tPLFiPwer6rlo0lDmFWYR4Yf6YqIiOH10JqePzuS+i8bzyLt72VNxnN3lxymtaWRjSQ2r9308cCNFiI2OIC46krioCGKiInlp/SFEIEIEEWhtUxpa2mhobqOhpY365laq61s+dkygcw1p8dGkJUQzIj3ho9vpCZ5eeFJsVEj9Xxj/+48ZI3hrWxnv7Czn3PGh1fHrjgX8STy9+gBK6Bxc7UxE+MzcUXzl7+t5Z2fFR+tWB1t1fTN/fn8fVXXN3DA7j9McPAgcHxPJpNxUJuV62ugY866ua6a6oYWaBk8Pu7GljabW9o/+bWtX2lVRPGuYR4qQHBdFTkos8dGRxMdEkZ4QTUZiDJlJMWQkxpKdFMuK3ZXERffvE4FxjwUTc8hIjOG5omIL+HDV0NzGX1fuZ8GEnJA5uNrV5ZNz+cmr23hk+d6QCPiDVfX86Z09NLa0cfuZBYzOTgpo+yJCSlw0KXHR9HTEpL9r0YT7xZeNf8RERXDV1OH8ZeU+Ko43kZUUG+ySehRaYw8hYtHaYo7Wt/D5eaOCXcpJxURF8Kkz8nlnRznbDtcGtZYjtY3cvHAVza3t3HH26ICHuzGBdNOcPFralL+FwfkDFvBdtLcrjy7fy+QRqcwelRHscnp0y+n5JMZE8ru3dgWthqq6Zm5ZuIrK403cPrfAlXPWjels7JAk5hVm8ddV+2lpa+/9CUFkAd/Fkk2l7Kmo446zR4f8eGtaQgy3nVnAKxtL2VV2LODtH2ts4bZHV3Ogqp6Ft80K2eEsY/zt02cWcKS2idc2Hw52KT2ygO+kvV35zZs7KRySxCWnBmdZ4L763LzRxEdH8ps3A9uLb2xp47OPF7G1tJY/3DL9o/nrxgwG544fQkFmAn96ew+qJ864ChUW8J28uukwO44c5+4FhWExxxUgIzGGT59ZwIvrD7GxuCYgbTa3tvPFv67hg31V/PL6qZw3IScg7RoTKiIihDvnj2VjSQ1vh/BFuS3gvZpb2/n5v7ZTOCSJS08Lj957hy/OH0NmYgw/fGWL472JtnblK39fx9Lt5Txw1Wm2Nr0ZtK6aNpzhafH89q1dIduLt4D3+uvK/eytqOObl00Mm957h+S4aO45v5BVe6tYstG5McH2duXrizbw8oZSvnnphEF/+TszuMVERfCF+WNYs/8oS7eXBbucblnA45kJ8us3dzKvMIv5ITCnvD9unJ3HqcNT+O6Lm6mub/b7/lWVbz2/iefWFHPv+eO44+wxfm/DmHBzw6yRjM5K5IFXtobkjBoLeOCHL2+hrqmVb192SsjPnDmZqMgIfnrtZI7WN/ODl7b4dd+qyvde3MzfVh/grnPHcPeC8Fku1RgnRUdGcP8lE9hdXheS6+oP+oB/e0c5iz8s4c75Y8JmCdCTmZSbyl3njmXxhyX8veigX/bZ0tbO157bwOMr9vP5eaO478LxYftH0BgnXHBKDnPHZvLga9s55L1sYqgY1AFfcbyJrz67nrFDkrjrPHf0Sr+8oJAzx2Tynec3saG4ekD7Ot7UymcfL+LZNcV8eUEh37x0ooW7MV2ICD++ejJt7co3/7ExpA64DtqAb29X7n1mHTUNLfz2xmnERp24Tnk4iowQfnPjNLKSYrnt0dXsONK/E6AOVTdw/Z9W8N6uCn567Wnce8E4C3djTiIvM4GvXTyeZdvL+fN7+4JdzkcGbcA/sGQr7+6s4LtXTGLisJRgl+NXWUmxPPX5OURHRnDjwyv5YF+Vz89VVZZsLOWSX7/Lvoo6Fn5qJtfPstkyxvTmtjMKOH9iDj9asrVP7zknDcqAf/id3TyyfC+3zy3gxtmhtxywP+RnJvL0HaeTEh/NTf+3koeW7qKpta3H52w/fIzPPl7EnU+uJS8jgZfvnse5IXaxE2NCVUSE8ItPTmFkRgKff6Ko35+e/VpTsAsIJFXld2/t5EdLtnHZacPCetaML0ZnJ/H8nXO54JQcHnxtO+f9/G1+++ZO1h+s5lhjC3VNrewqO8bfPzjIrY+s4qJfvcOqPZV869KJLL7zTEZlJQb7RzAmrKTGR/P47bOJiYzgloWrgh7yg2Y9+MaWNr71j00sWlvMNdOG87PrJofdCU39kZoQze9vnsE7O8r549u7+cXrO/jF6ztO2G5Eejz3nj+O287M7/Pl9QYiFKeWGTMQeZkJ/PVzc7h54Squ/cP7/OHmGZxVmBWUWgZFwK/ZX8X9izays+w4d583lnvOH0fEIAj3zs4el83Z47I5VN3AhuJq9lfW064wNDXWe+3TZFd/mjEmkMblJPP8XXO5/c+rufXRVXx+3mjuPX9ctxedd5KrA37b4Vp++9YuXtlQSm5qHI9/ZnZIXP0omHLT4slNszXbjXHa8LR4nr9rLg+8spWH39nDi+sOcc/5hVw1bThx0YEJekcDXkQuBn4NRAILVfUnTranquytqGPZ9nJe3nCItQeqSYyJ5EvnjuWL88eQGOvqv2fGBzYkZAIpISaKB672LMr3wJKt3L94Iw++tp3LJw/jwklDmZaXRkKMc7nk2J5FJBJ4CLgAKAY+EJEXVdWv59E3t7bzqzd2sKG4hg3F1dQ2tgIwLieJb106ketmjCA9MXBjysYY09Wc0Zm8cNdcVuyu5IkV+/nbBwd5fMV+IiOEU4alMCM/nf++/BS/Dx072aWdDexS1T0AIvI0cCXg14CPjhQWry0hMymGyybnMnlEKmeMzqTAZoAYY0KIiHDm2CzOHJvF8aZWPthXxdr9Rynad5Q1+486clzQyYAfDnReEKUYmNN1IxG5A7jD++1xEdnehzaygIqOb5b0o8gA+1i9vrrZgUL6oF81B5HV66xwq5ebw6Rmufujm32tN/9kDzgZ8N39OTphkQZVfRh4uF8NiBSp6sz+PDcYwq1eCL+arV5nhVu9EH41+7NeJ090KgY6nyY6AjjkYHvGGGM6cTLgPwAKRWSUiMQANwAvOtieMcaYThwbolHVVhH5EvAanmmSj6rqZj8306+hnSAKt3oh/Gq2ep0VbvVC+NXst3ollNYuNsYY4z+DarExY4wZTCzgjTHGpcIq4EUkQ0ReF5Gd3n/Tu9lmpIgsFZGtIrJZRL4chDovFpHtIrJLRO7v5nERkd94H98gItMDXWOXenqr92ZvnRtE5H0RmRKMOjvV02O9nbabJSJtInJdIOs7SS291iwi80Vknff39u1A19illt5+J1JF5CURWe+t9/Zg1NmpnkdFpExENp3k8VB7z/VWr3/ec6oaNl/Az4D7vbfvB37azTbDgOne28nADuCUANYYCewGRgMxwPqu7QOXAq/iOVfgdGBVEF9TX+o9E0j33r4k1OvttN1beM5/uy5Y9fbhNU7Dc5Z3nvf7ISFe7zc73n9ANlAFxASx5rOB6cCmkzweMu85H+v1y3surHrweJY6eNx7+3Hgqq4bqGqpqq713j4GbMVzVm2gfLREg6o2Ax1LNHR2JfCEeqwE0kRkWABr7KzXelX1fVU96v12JZ5zGoLFl9cX4L+ARUBZIIs7CV9qvglYrKoHAFQ1mHX7Uq8CyeJZYzoJT8C3BrbMTsWovuOt4WRC6T3Xa73+es+FW8DnqGopeIIc6PF6ciJSAEwDVjlf2ke6W6Kh6x8YX7YJlL7W8lk8PaFg6bVeERkOXA38MYB19cSX13gckC4iy0RkjYh8KmDVnciXen8HTMRz8uJG4Muq2h6Y8vollN5zfdXv91zIrZ8rIm8AQ7t56Ft93E8Snh7cPapa64/afG26m/u6zkX1aRmHAPG5FhE5F88v21mOVtQzX+r9FfB1VW0LkYuY+FJzFDADWADEAytEZKWqnnj5Lef5Uu9FwDrgPGAM8LqIvBvg91pfhNJ7zmcDfc+FXMCr6vkne0xEjojIMFUt9X686vZjrIhE4wn3J1V1sUOlnowvSzSE0jIOPtUiIpOBhcAlqloZoNq640u9M4GnveGeBVwqIq2q+nxAKjyRr78TFapaB9SJyDvAFDzHkALNl3pvB36inkHiXSKyF5gArA5MiX0WSu85n/jjPRduQzQvArd5b98GvNB1A++Y4CPAVlX9ZQBr6+DLEg0vAp/yHtk/HajpGHoKgl7rFZE8YDFwa5B6lJ31Wq+qjlLVAlUtAJ4D7gxiuINvvxMvAPNEJEpEEvCsvLo1wHV28KXeA3g+bSAiOcB4YE9Aq+ybUHrP9cpv77lgHknux5HnTOBNYKf33wzv/bnAEu/ts/B89NqA5yPkOuDSANd5KZ6e127gW977vgB8wXtb8FwMZTee8cuZQX5de6t3IXC00+tZFMr1dtn2MYI8i8bXmoGv4plJswnP0GLI1ut9z/3L+/u7CbglyPX+DSgFWvD01j8b4u+53ur1y3vOliowxhiXCrchGmOMMT6ygDfGGJeygDfGGJeygDfGGJeygDfGGJeygDfGGJeygDd94l1+d52IbBKRZ0UkQUQKui57KiLfE5H7vLcfE5G93uetF5EFnbabLSLveJem3SYiC737/LSI/K7LPpeJyMxO308TERWRi7ps9y3vErYbvG3O6fT87d771onIc977x3sfWyeeZaZPesk08SzpW9NpH290+nlLvPdtEZEbOz2n88+/TkTe7/TYVd46t3lf0+s6Pdb15/3ode6mjnUicr73MRWRX3R63n0i8r1O33/K29Zmb633iciPROSnnbbJF5E9IpJ2stfChL6QW6rAhLwGVZ0KICJP4jk5w5flIL6qqs+JZ22Nh/GcKZkDPAvcoKorvGchX4tnmWdf3Ags9/77mremM4DL8SwZ3SQiWXiWvO1ws6oWddnPb4D/VdUXvPs4rZd231XVy7u5/39V9eciUgisEZHnVLWl88/feWPxrPH9c+ACVd0rIqOAN0Rkr6qu6e2H76GOJuAaEfmxqlZ0afMS4B7gQlU9JCJxwK14TgL6UEQeU9WtwK+B76hqtQ91mBBlPXgzEO8CY/v4nBX8exW/u4DHVXUFgHo8p6pHetuJ94/BdcCngQu9QQWe6wFUqGqTd58VqtrbmiPD8JxNiPc5G/vw85xAVXcC9cAJF6Tp4j7gR6q61/u8vcCPgP83kPbxLNv7MHBvN499A7iv4zVR1UZV/T9VbQC+Avze+0cgWVWfHGAdJsgs4E2/iEgUngsR9DUMLwae994+Feipp3p95yEIPIuIdZgL7FXV3cAyPKfWg+f0+ZEiskNEfi8i53TZ55Od9vmg977/Bd4SkVdF5F4fhiXmddrHCauciudqQTv142u6P9jpOR3BOambn78IOKWX9rurY52IjOn02EPAzSKS2uU5J33NVXUJnjXKnwDu9LEGE8JsiMb0Vbw3bMHTg38ETw+4O53XwXhQRH6GZw3/031s6xlV/VLHNyKyrNNjN+K5EAXef2/Fc8GM4yIyA5gHnAs8IyL3q+pj3m1PGKJR1T+LyGt4/vhcCfyniEzp+BTQjZMNjdwrIp/HcyWki7s8dsIQDZ71UXpaSrq7dUQ633eyOlDVWhF5ArgbaOhum5N4CIhX1e19eI4JUdaDN33VoKpTvV//pZ4r/lRy4nBEBtB5/PereIZzvs2/r8q1Gc8a6H0iIpF4xur/W0T2Ab8FLhGRZABVbVPVZar6XeBL3m17pKqHVPVRVb0SzxDHqX2tC88Y/HjgeuCJTsNGJ7OZj38qAc9l3Dr+AHV9Xbu+pr35FZ5FrBK7tNnTa97u/TIuYAFvBkxVjwOl4p0dIyIZeHqwy7ts147n4F2Ed+bL74DbOma5eJ97i4h0d8GXzs4H1qvqSPUsC5yPZ/3/q7wzYgo7bTsV2N/TzsRzgelo7+2heFYtLent5z4Z9VyDoIh/L219Mj8HviGeK491XIHsHqBj6GgZcIv3eAPe/S3tQx1VwN/xhHyHHwM/63iNRSRWRO72dZ8mvNgQjfGXTwEPdZqe933v+PjHqKqKyA+Br6nqAhG5Afi5iAzB03N8h95n5dwI/KPLfYuAL+JZbve33nH0VmAXcEen7Z4UkY4hiwr1XGDmQuDXItLovf+rqnq49x+5Rz8AnhKR//N+/6CIfLvT47NVdZ2IfB14SURigQLg3E7DIw/juYjGehFRPH80vtFpH/M6DZcB/LCbYaBf4PkUA3jG2b2zl97w/uFQ4NGB/KAmdNlywcaECBH5CZ4LfVzkHfoyZkAs4I0xxqVsiMaYbniPEfy0y917VfXqYNRjTH9YD94YY1zKZtEYY4xLWcAbY4xLWcAbY4xLWcAbY4xL/X/YZ0su917ZngAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ONEOFF_PURCHASES_FREQUENCY\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\ASUS\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PURCHASES_INSTALLMENTS_FREQUENCY\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\ASUS\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CASH_ADVANCE_FREQUENCY\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\ASUS\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CASH_ADVANCE_TRX\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\ASUS\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PURCHASES_TRX\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\ASUS\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CREDIT_LIMIT\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\ASUS\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PAYMENTS\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\ASUS\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MINIMUM_PAYMENTS\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\ASUS\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PRC_FULL_PAYMENT\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\ASUS\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "TENURE\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\ASUS\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for columns_names in df_sqrt:\n",
+    "      print(columns_names)\n",
+    "      \n",
+    "      plt.figure()\n",
+    "      sns.distplot(df_sqrt[columns_names]) \n",
+    "      plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "84fc8cef",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Plot correlation heatmap to see if there are many correlated features\n",
+    "correlation = df_sqrt.corr()\n",
+    "plt.figure(figsize=(10,10))\n",
+    "sns.heatmap(correlation, vmax=1, square=True,annot=True,cmap='cubehelix')\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "0d4f90c9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-1.0780545 , -0.09712559, -0.59690132, ..., -0.66790184,\n",
+       "        -0.63618182,  0.35348056],\n",
+       "       [ 1.05361908,  0.19053935, -1.0423371 , ...,  0.52254404,\n",
+       "         0.78887411,  0.35348056],\n",
+       "       [ 0.77167992,  0.46414608,  0.22574803, ...,  0.08464159,\n",
+       "        -0.63618182,  0.35348056],\n",
+       "       ...,\n",
+       "       [-1.14422589, -0.04811222, -0.49431949, ..., -0.82330785,\n",
+       "         0.87531901, -4.27612743],\n",
+       "       [-1.19386072, -0.04811222, -1.0423371 , ..., -0.91494252,\n",
+       "         0.87531901, -4.27612743],\n",
+       "       [-0.52978249, -0.61472285,  0.46555636, ..., -0.8052387 ,\n",
+       "        -0.63618182, -4.27612743]])"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Standardize the data\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "df= StandardScaler().fit_transform(df_sqrt)\n",
+    "df "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "367d7c61",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Cumulative explained variance')"
+      ]
+     },
+     "execution_count": 44,
+     "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": [
+    "#Apply PCA and find out many principal components are needed to explain how much variance then take required  number of principal components so that you reduce the number of columns as well as retain a lot variance of original data as well\n",
+    "from sklearn.decomposition import PCA\n",
+    "pca = PCA().fit(df)\n",
+    "plt.plot(np.cumsum(pca.explained_variance_ratio_))\n",
+    "plt.xlim(0,18,1)\n",
+    "plt.xlabel('Number of components')\n",
+    "plt.ylabel('Cumulative explained variance')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "f392d8c6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-0.88512191, -2.48301693,  0.23090242, ...,  0.04336989,\n",
+       "        -0.38202562, -0.35720617],\n",
+       "       [-3.00034342,  2.01508943, -0.16533426, ...,  1.67093843,\n",
+       "        -0.28801526,  0.94274938],\n",
+       "       [ 1.19172624,  0.38517395, -1.92678896, ..., -0.55010278,\n",
+       "        -0.23006842,  0.52287556],\n",
+       "       ...,\n",
+       "       [ 0.10596162, -3.06675754,  1.18931984, ..., -2.96585047,\n",
+       "         1.26333337,  1.97973232],\n",
+       "       [-2.84716017, -2.51797947, -0.29519488, ..., -2.99036079,\n",
+       "         0.69668999,  1.77427724],\n",
+       "       [-0.16460436, -0.5243077 , -1.64424995, ..., -4.69253162,\n",
+       "         1.53231934,  0.09281539]])"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.decomposition import PCA \n",
+    "sklearn_pca = PCA(n_components=7)\n",
+    "Y_sklearn = sklearn_pca.fit_transform(df)\n",
+    "Y_sklearn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "f6d98e2b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(8950, 7)"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Y_sklearn.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "39ea467b",
+   "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": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df=pd.DataFrame(Y_sklearn,columns=['PC1','PC2','PC3','PC4','PC5','PC6','PC7'])\n",
+    "df                                  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "5890ea51",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.cluster import KMeans"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "id": "edc5eb32",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "arr = []\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",
+    "    arr.append(kmeans.inertia_)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "id": "6d138bae",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(m,arr,'bx-')\n",
+    "plt.plot(range(1,11), arr)\n",
+    "plt.xlabel('pc')\n",
+    "plt.ylabel('Values of Inertia')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "id": "5a89eeb2",
+   "metadata": {
+    "scrolled": false
+   },
+   "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": [
+    "A = df[[\"PC1\",\"PC2\",\"PC3\",\"PC4\",\"PC5\",\"PC6\",\"PC7\"]]\n",
+    "plt.scatter(X1[\"PC1\"], X1[\"PC2\"], c=\"m\")\n",
+    "plt.xlabel(\"pc1\")\n",
+    "plt.ylabel(\"pc2\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "id": "cd09b4d4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "k=3\n",
+    "\n",
+    "centroids=(A.sample (n=k))\n",
+    "plt.scatter(A[\"PC1\"],A[\"PC2\"], c=\"y\")\n",
+    "plt.scatter(centroids[\"PC1\"],centroids[\"PC2\"], c=\"blue\")\n",
+    "plt.xlabel(\"pc1\")\n",
+    "plt.ylabel(\"pc2\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "id": "4fbc12f7",
+   "metadata": {
+    "scrolled": false
+   },
+   "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>2798</th>\n",
+       "      <td>-2.765325</td>\n",
+       "      <td>0.174076</td>\n",
+       "      <td>-0.272335</td>\n",
+       "      <td>0.602304</td>\n",
+       "      <td>0.604267</td>\n",
+       "      <td>-0.455717</td>\n",
+       "      <td>0.583418</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6295</th>\n",
+       "      <td>0.767719</td>\n",
+       "      <td>-2.237786</td>\n",
+       "      <td>0.834423</td>\n",
+       "      <td>-0.124922</td>\n",
+       "      <td>0.690676</td>\n",
+       "      <td>0.897724</td>\n",
+       "      <td>-1.143067</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4000</th>\n",
+       "      <td>0.431495</td>\n",
+       "      <td>2.012042</td>\n",
+       "      <td>-0.495208</td>\n",
+       "      <td>1.010099</td>\n",
+       "      <td>-0.546225</td>\n",
+       "      <td>-0.098397</td>\n",
+       "      <td>-0.139177</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           PC1       PC2       PC3       PC4       PC5       PC6       PC7\n",
+       "2798 -2.765325  0.174076 -0.272335  0.602304  0.604267 -0.455717  0.583418\n",
+       "6295  0.767719 -2.237786  0.834423 -0.124922  0.690676  0.897724 -1.143067\n",
+       "4000  0.431495  2.012042 -0.495208  1.010099 -0.546225 -0.098397 -0.139177"
+      ]
+     },
+     "execution_count": 110,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "centroids\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "id": "60bc05ce",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.4708266848615437\n",
+      "0.3879895071058796\n",
+      "0.3076712634310339\n",
+      "0.2218456052530988\n",
+      "0.13056006937907935\n",
+      "0.07628873321822409\n",
+      "0.05475637336362141\n",
+      "0.023438486034222345\n",
+      "0.02122059219140853\n",
+      "0.014382864734112714\n",
+      "0.009425406349192422\n",
+      "0.009568313409212726\n",
+      "0.004227241002541696\n",
+      "0.011324875376103431\n",
+      "0.01189244038657502\n",
+      "0.01505106390407005\n",
+      "0.007273977132700504\n",
+      "0.005456305655349337\n",
+      "0.003045674795264595\n",
+      "0.0044628162985719646\n",
+      "0.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "from math import sqrt\n",
+    "\n",
+    "diff = 1\n",
+    "j=0\n",
+    "\n",
+    "while(diff!=0):\n",
+    "    XD=A\n",
+    "    i=1\n",
+    "    for index1, row_c in centroids.iterrows():\n",
+    "        ED=[]\n",
+    "        for index2, row_d in XD.iterrows():\n",
+    "            d1 = (row_c[\"PC1\"]-row_d[\"PC1\"])**2\n",
+    "            d2 = (row_c[\"PC2\"]-row_d[\"PC2\"])**2\n",
+    "            d3 = (row_c[\"PC3\"]-row_d[\"PC3\"])**2\n",
+    "            d4 = (row_c[\"PC4\"]-row_d[\"PC4\"])**2\n",
+    "            d5 = (row_c[\"PC5\"]-row_d[\"PC5\"])**2\n",
+    "            d6 = (row_c[\"PC6\"]-row_d[\"PC6\"])**2\n",
+    "            d7 = (row_c[\"PC7\"]-row_d[\"PC7\"])**2\n",
+    "            d = sqrt(d1+d2+d3+d4+d5+d5+d7)\n",
+    "            ED.append(d)\n",
+    "        A[i] = ED\n",
+    "        i = i+1\n",
+    "    \n",
+    "    C = []\n",
+    "    for index, row in A.iterrows():\n",
+    "        min_dist=row[1]\n",
+    "        pos=1\n",
+    "        for i in range(k):\n",
+    "            if row[i+1] < min_dist:\n",
+    "                min_dist = row[i+1]\n",
+    "                pos = i+1\n",
+    "        C.append(pos)\n",
+    "    A[\"Cluster\"]=C\n",
+    "    Centroids_new = A.groupby([\"Cluster\"]).mean()[[\"PC1\",\"PC2\",\"PC3\",\"PC4\",\"PC5\",\"PC6\",\"PC7\"]]\n",
+    "    if j == 0:\n",
+    "        diff = 1\n",
+    "        j = j+1\n",
+    "    else:\n",
+    "        diff = (Centroids_new['PC1'] - centroids['PC1']).sum() + (Centroids_new['PC2'] - centroids['PC2']).sum() + (Centroids_new['PC3'] - centroids['PC3']).sum() + (Centroids_new['PC4'] - centroids['PC4']).sum() + (Centroids_new['PC5'] - centroids['PC5']).sum() + (Centroids_new['PC6'] - centroids['PC6']).sum() + (Centroids_new['PC7'] - centroids['PC7']).sum()\n",
+    "        print(diff.sum())\n",
+    "    centroids = A.groupby([\"Cluster\"]).mean()[[\"PC1\",\"PC2\",\"PC3\",\"PC4\",\"PC5\",\"PC6\",\"PC7\"]]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "id": "391fd937",
+   "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>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",
+       "    <tr>\n",
+       "      <th>Cluster</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>1</th>\n",
+       "      <td>-2.499732</td>\n",
+       "      <td>0.896508</td>\n",
+       "      <td>-0.024622</td>\n",
+       "      <td>-0.159911</td>\n",
+       "      <td>-0.003804</td>\n",
+       "      <td>-0.113843</td>\n",
+       "      <td>0.035023</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.583201</td>\n",
+       "      <td>-1.502786</td>\n",
+       "      <td>0.166498</td>\n",
+       "      <td>0.110215</td>\n",
+       "      <td>0.075530</td>\n",
+       "      <td>0.062477</td>\n",
+       "      <td>0.028198</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2.920789</td>\n",
+       "      <td>1.611112</td>\n",
+       "      <td>-0.301608</td>\n",
+       "      <td>0.037009</td>\n",
+       "      <td>-0.148960</td>\n",
+       "      <td>0.059203</td>\n",
+       "      <td>-0.115667</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              PC1       PC2       PC3       PC4       PC5       PC6       PC7\n",
+       "Cluster                                                                      \n",
+       "1       -2.499732  0.896508 -0.024622 -0.159911 -0.003804 -0.113843  0.035023\n",
+       "2        0.583201 -1.502786  0.166498  0.110215  0.075530  0.062477  0.028198\n",
+       "3        2.920789  1.611112 -0.301608  0.037009 -0.148960  0.059203 -0.115667"
+      ]
+     },
+     "execution_count": 113,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Centroids_new"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "id": "5bae8916",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PC1</th>\n",
+       "      <th>PC2</th>\n",
+       "      <th>PC3</th>\n",
+       "      <th>PC4</th>\n",
+       "      <th>PC5</th>\n",
+       "      <th>PC6</th>\n",
+       "      <th>PC7</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>Cluster</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-0.885122</td>\n",
+       "      <td>-2.483017</td>\n",
+       "      <td>0.230902</td>\n",
+       "      <td>0.807129</td>\n",
+       "      <td>0.043370</td>\n",
+       "      <td>-0.382026</td>\n",
+       "      <td>-0.357206</td>\n",
+       "      <td>3.897038</td>\n",
+       "      <td>1.938367</td>\n",
+       "      <td>5.679421</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-3.000343</td>\n",
+       "      <td>2.015089</td>\n",
+       "      <td>-0.165334</td>\n",
+       "      <td>-1.087171</td>\n",
+       "      <td>1.670938</td>\n",
+       "      <td>-0.288015</td>\n",
+       "      <td>0.942749</td>\n",
+       "      <td>2.968996</td>\n",
+       "      <td>5.717354</td>\n",
+       "      <td>6.652042</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.191726</td>\n",
+       "      <td>0.385174</td>\n",
+       "      <td>-1.926789</td>\n",
+       "      <td>1.859338</td>\n",
+       "      <td>-0.550103</td>\n",
+       "      <td>-0.230068</td>\n",
+       "      <td>0.522876</td>\n",
+       "      <td>4.734852</td>\n",
+       "      <td>3.521862</td>\n",
+       "      <td>3.344286</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-0.794805</td>\n",
+       "      <td>0.218433</td>\n",
+       "      <td>-1.661542</td>\n",
+       "      <td>1.195618</td>\n",
+       "      <td>0.058950</td>\n",
+       "      <td>0.798510</td>\n",
+       "      <td>-0.086756</td>\n",
+       "      <td>2.811802</td>\n",
+       "      <td>3.065141</td>\n",
+       "      <td>4.361683</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-1.265058</td>\n",
+       "      <td>-1.593317</td>\n",
+       "      <td>-0.689436</td>\n",
+       "      <td>1.339644</td>\n",
+       "      <td>-0.114019</td>\n",
+       "      <td>-0.837737</td>\n",
+       "      <td>0.231600</td>\n",
+       "      <td>3.236854</td>\n",
+       "      <td>2.404497</td>\n",
+       "      <td>5.455272</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8945</th>\n",
+       "      <td>0.668484</td>\n",
+       "      <td>-2.871696</td>\n",
+       "      <td>1.452469</td>\n",
+       "      <td>-2.236975</td>\n",
+       "      <td>-2.854943</td>\n",
+       "      <td>0.766981</td>\n",
+       "      <td>2.343375</td>\n",
+       "      <td>7.233217</td>\n",
+       "      <td>5.619573</td>\n",
+       "      <td>7.355801</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8946</th>\n",
+       "      <td>0.262604</td>\n",
+       "      <td>-2.240280</td>\n",
+       "      <td>1.844972</td>\n",
+       "      <td>-0.568118</td>\n",
+       "      <td>-3.339266</td>\n",
+       "      <td>1.706628</td>\n",
+       "      <td>1.774529</td>\n",
+       "      <td>6.812375</td>\n",
+       "      <td>5.504129</td>\n",
+       "      <td>7.127559</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8947</th>\n",
+       "      <td>0.105962</td>\n",
+       "      <td>-3.066758</td>\n",
+       "      <td>1.189320</td>\n",
+       "      <td>-1.775107</td>\n",
+       "      <td>-2.965850</td>\n",
+       "      <td>1.263333</td>\n",
+       "      <td>1.979732</td>\n",
+       "      <td>6.921630</td>\n",
+       "      <td>5.439012</td>\n",
+       "      <td>7.454716</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8948</th>\n",
+       "      <td>-2.847160</td>\n",
+       "      <td>-2.517979</td>\n",
+       "      <td>-0.295195</td>\n",
+       "      <td>-2.148352</td>\n",
+       "      <td>-2.990361</td>\n",
+       "      <td>0.696690</td>\n",
+       "      <td>1.774277</td>\n",
+       "      <td>6.055624</td>\n",
+       "      <td>6.321428</td>\n",
+       "      <td>8.649479</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8949</th>\n",
+       "      <td>-0.164604</td>\n",
+       "      <td>-0.524308</td>\n",
+       "      <td>-1.644250</td>\n",
+       "      <td>-1.315554</td>\n",
+       "      <td>-4.692532</td>\n",
+       "      <td>1.532319</td>\n",
+       "      <td>0.092815</td>\n",
+       "      <td>7.443247</td>\n",
+       "      <td>7.231961</td>\n",
+       "      <td>7.683966</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>8950 rows × 11 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           PC1       PC2       PC3       PC4       PC5       PC6       PC7  \\\n",
+       "0    -0.885122 -2.483017  0.230902  0.807129  0.043370 -0.382026 -0.357206   \n",
+       "1    -3.000343  2.015089 -0.165334 -1.087171  1.670938 -0.288015  0.942749   \n",
+       "2     1.191726  0.385174 -1.926789  1.859338 -0.550103 -0.230068  0.522876   \n",
+       "3    -0.794805  0.218433 -1.661542  1.195618  0.058950  0.798510 -0.086756   \n",
+       "4    -1.265058 -1.593317 -0.689436  1.339644 -0.114019 -0.837737  0.231600   \n",
+       "...        ...       ...       ...       ...       ...       ...       ...   \n",
+       "8945  0.668484 -2.871696  1.452469 -2.236975 -2.854943  0.766981  2.343375   \n",
+       "8946  0.262604 -2.240280  1.844972 -0.568118 -3.339266  1.706628  1.774529   \n",
+       "8947  0.105962 -3.066758  1.189320 -1.775107 -2.965850  1.263333  1.979732   \n",
+       "8948 -2.847160 -2.517979 -0.295195 -2.148352 -2.990361  0.696690  1.774277   \n",
+       "8949 -0.164604 -0.524308 -1.644250 -1.315554 -4.692532  1.532319  0.092815   \n",
+       "\n",
+       "             1         2         3  Cluster  \n",
+       "0     3.897038  1.938367  5.679421        2  \n",
+       "1     2.968996  5.717354  6.652042        1  \n",
+       "2     4.734852  3.521862  3.344286        3  \n",
+       "3     2.811802  3.065141  4.361683        1  \n",
+       "4     3.236854  2.404497  5.455272        2  \n",
+       "...        ...       ...       ...      ...  \n",
+       "8945  7.233217  5.619573  7.355801        2  \n",
+       "8946  6.812375  5.504129  7.127559        2  \n",
+       "8947  6.921630  5.439012  7.454716        2  \n",
+       "8948  6.055624  6.321428  8.649479        1  \n",
+       "8949  7.443247  7.231961  7.683966        2  \n",
+       "\n",
+       "[8950 rows x 11 columns]"
+      ]
+     },
+     "execution_count": 114,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "id": "842019e1",
+   "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": [
+    "color=['red','green','blue']\n",
+    "for h in range(k):\n",
+    "    data=A[A[\"Cluster\"]==h+1]\n",
+    "    plt.scatter(data[\"PC1\"],data[\"PC2\"],c=color[h])\n",
+    "plt.scatter(centroids[\"PC1\"],centroids[\"PC2\"],c='white')\n",
+    "plt.xlabel('PC1')\n",
+    "plt.ylabel('PC2')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2922202f",
+   "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",
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+}