Predicting Credit Consumption.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "Customer_Demo = pd.read_excel('CustomerDemographics.xlsx')\n",
    "Customer_Behavior_Data =  pd.read_excel('CustomerBehaviorData.xlsx')\n",
    "Credit_Consumption_Data = pd.read_excel('CreditConsumptionData.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>cc_cons</th>\n",
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       "  </thead>\n",
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       "      <th>19996</th>\n",
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       "      <th>19997</th>\n",
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       "<p>20000 rows × 2 columns</p>\n",
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      "text/plain": [
       "          ID  cc_cons\n",
       "0      12554  20014.0\n",
       "1      17645  10173.0\n",
       "2       7604  16095.0\n",
       "3       1590   7707.0\n",
       "4      16556  96408.0\n",
       "...      ...      ...\n",
       "19995  12512      NaN\n",
       "19996  14224      NaN\n",
       "19997  18740      NaN\n",
       "19998  18134      NaN\n",
       "19999  12413      NaN\n",
       "\n",
       "[20000 rows x 2 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Credit_Consumption_Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Perform the merge\n",
    "merged_table = pd.merge(Customer_Demo, Customer_Behavior_Data,on='ID')\n",
    "merged_table = pd.merge(merged_table, Credit_Consumption_Data, on='ID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <td>41672.0</td>\n",
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       "      <td>21</td>\n",
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       "      <td>MEDIUM</td>\n",
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       "      <td>43</td>\n",
       "      <td>11</td>\n",
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       "      <td>51929.0</td>\n",
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       "      <td>20201.00</td>\n",
       "      <td>47</td>\n",
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       "      <td>8</td>\n",
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       "      <td>38503.00</td>\n",
       "      <td>5</td>\n",
       "      <td>42</td>\n",
       "      <td>47504.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>5311.54</td>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "          ID account_type gender  age  Income  Emp_Tenure_Years  \\\n",
       "0      19427      current      M   63  MEDIUM              30.1   \n",
       "1      16150      current      M   36  MEDIUM              14.4   \n",
       "2      11749      current      F   28  MEDIUM               4.8   \n",
       "3      11635      current      M   32  MEDIUM               9.6   \n",
       "4       8908      current      M   32    HIGH              12.0   \n",
       "...      ...          ...    ...  ...     ...               ...   \n",
       "19995   1270      current      F   66  MEDIUM              32.2   \n",
       "19996  15992      current      M   53  MEDIUM              19.8   \n",
       "19997   7081      current      F   66  MEDIUM              18.4   \n",
       "19998   6821      current      M   32     LOW               6.0   \n",
       "19999  13730      current      F  118     LOW              78.4   \n",
       "\n",
       "       Tenure_with_Bank  region_code  NetBanking_Flag  \\\n",
       "0                    10        628.0                1   \n",
       "1                    10        656.0                0   \n",
       "2                    10        314.0                1   \n",
       "3                     2        614.0                1   \n",
       "4                     7        750.0                1   \n",
       "...                 ...          ...              ...   \n",
       "19995                 6        354.0                1   \n",
       "19996                 5        809.0                0   \n",
       "19997                 1        466.0                0   \n",
       "19998                 8        619.0                0   \n",
       "19999                 7        882.0                1   \n",
       "\n",
       "       Avg_days_between_transaction  ...  debit_count_may  \\\n",
       "0                               5.0  ...               14   \n",
       "1                              12.0  ...               63   \n",
       "2                              13.0  ...               82   \n",
       "3                              19.0  ...               78   \n",
       "4                              18.0  ...              171   \n",
       "...                             ...  ...              ...   \n",
       "19995                           7.0  ...                8   \n",
       "19996                          14.0  ...               11   \n",
       "19997                          10.0  ...              171   \n",
       "19998                          17.0  ...               82   \n",
       "19999                          10.0  ...                8   \n",
       "\n",
       "       max_credit_amount_may  debit_amount_jun  credit_amount_jun  \\\n",
       "0                    20770.0          44884.90          369000.75   \n",
       "1                    78627.0          91073.84          243182.32   \n",
       "2                     1260.0          96552.00           35467.00   \n",
       "3                    23332.0          18250.00           87204.35   \n",
       "4                    41672.0          83525.75           83992.00   \n",
       "...                      ...               ...                ...   \n",
       "19995                24438.0         153772.21           31284.00   \n",
       "19996               213000.0          18098.47           93108.00   \n",
       "19997                34327.0          32198.63           46800.00   \n",
       "19998                51929.0          27334.14           20201.00   \n",
       "19999                32431.0          23464.59           38503.00   \n",
       "\n",
       "       credit_count_jun  debit_count_jun  max_credit_amount_jun  loan_enq  \\\n",
       "0                    40               96                46088.0         Y   \n",
       "1                     7               12                17953.0         Y   \n",
       "2                    16               42                41121.0         Y   \n",
       "3                     2               10                32003.0         Y   \n",
       "4                    21               12                 9626.0         Y   \n",
       "...                 ...              ...                    ...       ...   \n",
       "19995                 3               38                42872.0         Y   \n",
       "19996                43               11                36747.0         Y   \n",
       "19997                 3               38                18810.0         Y   \n",
       "19998                47               80                11443.0         Y   \n",
       "19999                 5               42                47504.0         Y   \n",
       "\n",
       "       emi_active  cc_cons  \n",
       "0         2646.72      NaN  \n",
       "1         5469.79      NaN  \n",
       "2         7207.85      NaN  \n",
       "3          591.34      NaN  \n",
       "4         2621.39      NaN  \n",
       "...           ...      ...  \n",
       "19995     6334.20      NaN  \n",
       "19996   102216.02      NaN  \n",
       "19997     1930.19      NaN  \n",
       "19998    24499.91      NaN  \n",
       "19999     5311.54      NaN  \n",
       "\n",
       "[20000 rows x 49 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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     "text": [
      "DataFrame info:\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 20000 entries, 0 to 19999\n",
      "Data columns (total 49 columns):\n",
      " #   Column                        Non-Null Count  Dtype  \n",
      "---  ------                        --------------  -----  \n",
      " 0   ID                            20000 non-null  int64  \n",
      " 1   account_type                  19999 non-null  object \n",
      " 2   gender                        19999 non-null  object \n",
      " 3   age                           20000 non-null  int64  \n",
      " 4   Income                        19999 non-null  object \n",
      " 5   Emp_Tenure_Years              20000 non-null  float64\n",
      " 6   Tenure_with_Bank              20000 non-null  int64  \n",
      " 7   region_code                   19999 non-null  float64\n",
      " 8   NetBanking_Flag               20000 non-null  int64  \n",
      " 9   Avg_days_between_transaction  19997 non-null  float64\n",
      " 10  cc_cons_apr                   20000 non-null  float64\n",
      " 11  dc_cons_apr                   20000 non-null  float64\n",
      " 12  cc_cons_may                   19999 non-null  float64\n",
      " 13  dc_cons_may                   20000 non-null  float64\n",
      " 14  cc_cons_jun                   20000 non-null  float64\n",
      " 15  dc_cons_jun                   19999 non-null  float64\n",
      " 16  cc_count_apr                  19999 non-null  float64\n",
      " 17  cc_count_may                  20000 non-null  int64  \n",
      " 18  cc_count_jun                  20000 non-null  int64  \n",
      " 19  dc_count_apr                  20000 non-null  int64  \n",
      " 20  dc_count_may                  20000 non-null  int64  \n",
      " 21  dc_count_jun                  20000 non-null  int64  \n",
      " 22  card_lim                      20000 non-null  int64  \n",
      " 23  personal_loan_active          20000 non-null  int64  \n",
      " 24  vehicle_loan_active           20000 non-null  int64  \n",
      " 25  personal_loan_closed          19999 non-null  float64\n",
      " 26  vehicle_loan_closed           20000 non-null  int64  \n",
      " 27  investment_1                  20000 non-null  float64\n",
      " 28  investment_2                  20000 non-null  float64\n",
      " 29  investment_3                  19998 non-null  float64\n",
      " 30  investment_4                  20000 non-null  float64\n",
      " 31  debit_amount_apr              20000 non-null  float64\n",
      " 32  credit_amount_apr             20000 non-null  float64\n",
      " 33  debit_count_apr               19999 non-null  float64\n",
      " 34  credit_count_apr              20000 non-null  int64  \n",
      " 35  max_credit_amount_apr         20000 non-null  float64\n",
      " 36  debit_amount_may              20000 non-null  float64\n",
      " 37  credit_amount_may             20000 non-null  float64\n",
      " 38  credit_count_may              20000 non-null  int64  \n",
      " 39  debit_count_may               20000 non-null  int64  \n",
      " 40  max_credit_amount_may         20000 non-null  float64\n",
      " 41  debit_amount_jun              20000 non-null  float64\n",
      " 42  credit_amount_jun             20000 non-null  float64\n",
      " 43  credit_count_jun              20000 non-null  int64  \n",
      " 44  debit_count_jun               20000 non-null  int64  \n",
      " 45  max_credit_amount_jun         20000 non-null  float64\n",
      " 46  loan_enq                      19998 non-null  object \n",
      " 47  emi_active                    19999 non-null  float64\n",
      " 48  cc_cons                       15000 non-null  float64\n",
      "dtypes: float64(27), int64(18), object(4)\n",
      "memory usage: 7.6+ MB\n"
     ]
    }
   ],
   "source": [
    "# Get metadata using info() method\n",
    "print(\"DataFrame info:\")\n",
    "merged_table.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame summary statistics:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>age</th>\n",
       "      <th>Emp_Tenure_Years</th>\n",
       "      <th>Tenure_with_Bank</th>\n",
       "      <th>region_code</th>\n",
       "      <th>NetBanking_Flag</th>\n",
       "      <th>Avg_days_between_transaction</th>\n",
       "      <th>cc_cons_apr</th>\n",
       "      <th>dc_cons_apr</th>\n",
       "      <th>cc_cons_may</th>\n",
       "      <th>...</th>\n",
       "      <th>credit_count_may</th>\n",
       "      <th>debit_count_may</th>\n",
       "      <th>max_credit_amount_may</th>\n",
       "      <th>debit_amount_jun</th>\n",
       "      <th>credit_amount_jun</th>\n",
       "      <th>credit_count_jun</th>\n",
       "      <th>debit_count_jun</th>\n",
       "      <th>max_credit_amount_jun</th>\n",
       "      <th>emi_active</th>\n",
       "      <th>cc_cons</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20000.000000</td>\n",
       "      <td>20000.000000</td>\n",
       "      <td>20000.000000</td>\n",
       "      <td>20000.000000</td>\n",
       "      <td>19999.000000</td>\n",
       "      <td>20000.000000</td>\n",
       "      <td>19997.000000</td>\n",
       "      <td>20000.000000</td>\n",
       "      <td>20000.000000</td>\n",
       "      <td>1.999900e+04</td>\n",
       "      <td>...</td>\n",
       "      <td>20000.00000</td>\n",
       "      <td>20000.000000</td>\n",
       "      <td>2.000000e+04</td>\n",
       "      <td>2.000000e+04</td>\n",
       "      <td>2.000000e+04</td>\n",
       "      <td>20000.000000</td>\n",
       "      <td>20000.000000</td>\n",
       "      <td>2.000000e+04</td>\n",
       "      <td>1.999900e+04</td>\n",
       "      <td>15000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9999.500000</td>\n",
       "      <td>47.672300</td>\n",
       "      <td>15.217250</td>\n",
       "      <td>5.496050</td>\n",
       "      <td>598.959348</td>\n",
       "      <td>0.718000</td>\n",
       "      <td>11.044757</td>\n",
       "      <td>17856.990922</td>\n",
       "      <td>8130.453414</td>\n",
       "      <td>1.618836e+04</td>\n",
       "      <td>...</td>\n",
       "      <td>11.91900</td>\n",
       "      <td>40.974850</td>\n",
       "      <td>5.820799e+04</td>\n",
       "      <td>7.812280e+04</td>\n",
       "      <td>7.881693e+04</td>\n",
       "      <td>19.905450</td>\n",
       "      <td>44.529100</td>\n",
       "      <td>5.284985e+04</td>\n",
       "      <td>1.539887e+04</td>\n",
       "      <td>55157.729867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5773.647028</td>\n",
       "      <td>30.359771</td>\n",
       "      <td>20.481455</td>\n",
       "      <td>2.870452</td>\n",
       "      <td>220.105230</td>\n",
       "      <td>0.449985</td>\n",
       "      <td>5.480552</td>\n",
       "      <td>25158.878234</td>\n",
       "      <td>14572.188829</td>\n",
       "      <td>2.651068e+04</td>\n",
       "      <td>...</td>\n",
       "      <td>16.85576</td>\n",
       "      <td>40.561202</td>\n",
       "      <td>2.085914e+05</td>\n",
       "      <td>2.448983e+05</td>\n",
       "      <td>1.569872e+05</td>\n",
       "      <td>27.055323</td>\n",
       "      <td>35.080453</td>\n",
       "      <td>1.343175e+05</td>\n",
       "      <td>1.202541e+05</td>\n",
       "      <td>97670.972252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>123.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>253.000000</td>\n",
       "      <td>19.350000</td>\n",
       "      <td>1.570000e+02</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.500000e-01</td>\n",
       "      <td>5.230000e+00</td>\n",
       "      <td>1.188000e+01</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000e-02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4999.750000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>424.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4377.000000</td>\n",
       "      <td>1624.650000</td>\n",
       "      <td>3.635910e+03</td>\n",
       "      <td>...</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>2.020225e+04</td>\n",
       "      <td>2.576311e+04</td>\n",
       "      <td>2.492008e+04</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.896450e+04</td>\n",
       "      <td>1.545940e+03</td>\n",
       "      <td>6768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>9999.500000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>9.900000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>623.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>10214.642500</td>\n",
       "      <td>3964.750000</td>\n",
       "      <td>8.635230e+03</td>\n",
       "      <td>...</td>\n",
       "      <td>4.00000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>3.149650e+04</td>\n",
       "      <td>4.572505e+04</td>\n",
       "      <td>4.618300e+04</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>3.025000e+04</td>\n",
       "      <td>3.934660e+03</td>\n",
       "      <td>13364.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>14999.250000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>799.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>21189.000000</td>\n",
       "      <td>9339.020000</td>\n",
       "      <td>1.865531e+04</td>\n",
       "      <td>...</td>\n",
       "      <td>11.00000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>5.642100e+04</td>\n",
       "      <td>8.529653e+04</td>\n",
       "      <td>8.602000e+04</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>5.344900e+04</td>\n",
       "      <td>9.376390e+03</td>\n",
       "      <td>20105.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>19999.000000</td>\n",
       "      <td>223.000000</td>\n",
       "      <td>203.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>974.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>441031.330000</td>\n",
       "      <td>229255.000000</td>\n",
       "      <td>1.117246e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>71.00000</td>\n",
       "      <td>171.000000</td>\n",
       "      <td>1.000000e+07</td>\n",
       "      <td>2.642915e+07</td>\n",
       "      <td>6.611953e+06</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>133.000000</td>\n",
       "      <td>5.200000e+06</td>\n",
       "      <td>7.447125e+06</td>\n",
       "      <td>408382.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ID           age  Emp_Tenure_Years  Tenure_with_Bank  \\\n",
       "count  20000.000000  20000.000000      20000.000000      20000.000000   \n",
       "mean    9999.500000     47.672300         15.217250          5.496050   \n",
       "std     5773.647028     30.359771         20.481455          2.870452   \n",
       "min        0.000000     25.000000          0.500000          1.000000   \n",
       "25%     4999.750000     33.000000          5.100000          3.000000   \n",
       "50%     9999.500000     40.000000          9.900000          6.000000   \n",
       "75%    14999.250000     53.000000         18.000000          8.000000   \n",
       "max    19999.000000    223.000000        203.000000         10.000000   \n",
       "\n",
       "        region_code  NetBanking_Flag  Avg_days_between_transaction  \\\n",
       "count  19999.000000     20000.000000                  19997.000000   \n",
       "mean     598.959348         0.718000                     11.044757   \n",
       "std      220.105230         0.449985                      5.480552   \n",
       "min      123.000000         0.000000                      2.000000   \n",
       "25%      424.000000         0.000000                      6.000000   \n",
       "50%      623.000000         1.000000                     11.000000   \n",
       "75%      799.000000         1.000000                     16.000000   \n",
       "max      974.000000         1.000000                     20.000000   \n",
       "\n",
       "         cc_cons_apr    dc_cons_apr   cc_cons_may  ...  credit_count_may  \\\n",
       "count   20000.000000   20000.000000  1.999900e+04  ...       20000.00000   \n",
       "mean    17856.990922    8130.453414  1.618836e+04  ...          11.91900   \n",
       "std     25158.878234   14572.188829  2.651068e+04  ...          16.85576   \n",
       "min       253.000000      19.350000  1.570000e+02  ...           0.00000   \n",
       "25%      4377.000000    1624.650000  3.635910e+03  ...           2.00000   \n",
       "50%     10214.642500    3964.750000  8.635230e+03  ...           4.00000   \n",
       "75%     21189.000000    9339.020000  1.865531e+04  ...          11.00000   \n",
       "max    441031.330000  229255.000000  1.117246e+06  ...          71.00000   \n",
       "\n",
       "       debit_count_may  max_credit_amount_may  debit_amount_jun  \\\n",
       "count     20000.000000           2.000000e+04      2.000000e+04   \n",
       "mean         40.974850           5.820799e+04      7.812280e+04   \n",
       "std          40.561202           2.085914e+05      2.448983e+05   \n",
       "min           0.000000           2.500000e-01      5.230000e+00   \n",
       "25%          11.000000           2.020225e+04      2.576311e+04   \n",
       "50%          23.000000           3.149650e+04      4.572505e+04   \n",
       "75%          69.000000           5.642100e+04      8.529653e+04   \n",
       "max         171.000000           1.000000e+07      2.642915e+07   \n",
       "\n",
       "       credit_amount_jun  credit_count_jun  debit_count_jun  \\\n",
       "count       2.000000e+04      20000.000000     20000.000000   \n",
       "mean        7.881693e+04         19.905450        44.529100   \n",
       "std         1.569872e+05         27.055323        35.080453   \n",
       "min         1.188000e+01          1.000000         0.000000   \n",
       "25%         2.492008e+04          4.000000        12.000000   \n",
       "50%         4.618300e+04          7.000000        37.000000   \n",
       "75%         8.602000e+04         21.000000        73.000000   \n",
       "max         6.611953e+06        117.000000       133.000000   \n",
       "\n",
       "       max_credit_amount_jun    emi_active        cc_cons  \n",
       "count           2.000000e+04  1.999900e+04   15000.000000  \n",
       "mean            5.284985e+04  1.539887e+04   55157.729867  \n",
       "std             1.343175e+05  1.202541e+05   97670.972252  \n",
       "min             6.000000e-02  0.000000e+00       0.000000  \n",
       "25%             1.896450e+04  1.545940e+03    6768.000000  \n",
       "50%             3.025000e+04  3.934660e+03   13364.000000  \n",
       "75%             5.344900e+04  9.376390e+03   20105.750000  \n",
       "max             5.200000e+06  7.447125e+06  408382.000000  \n",
       "\n",
       "[8 rows x 45 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get summary statistics using describe() method\n",
    "print(\"DataFrame summary statistics:\")\n",
    "merged_table.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def continuous_var_summary( x ):\n",
    "    \n",
    "    # freq and missings\n",
    "    n_total = x.shape[0]\n",
    "    n_miss = x.isna().sum()\n",
    "    perc_miss = n_miss * 100 / n_total\n",
    "    \n",
    "    # outliers - iqr\n",
    "    q1 = x.quantile(0.25)\n",
    "    q3 = x.quantile(0.75)\n",
    "    iqr = q3 - q1\n",
    "    lc_iqr = q1 - 1.5 * iqr\n",
    "    uc_iqr = q3 + 1.5 * iqr\n",
    "    \n",
    "    \n",
    "    return pd.Series( [ x.dtype, x.nunique(), n_total, x.count(), n_miss, perc_miss,\n",
    "                       x.sum(), x.mean(), x.std(), x.var(), \n",
    "                       lc_iqr, uc_iqr, \n",
    "                       x.min(), x.quantile(0.01), x.quantile(0.05), x.quantile(0.10), \n",
    "                       x.quantile(0.25), x.quantile(0.5), x.quantile(0.75), \n",
    "                       x.quantile(0.90), x.quantile(0.95), x.quantile(0.99), x.max() ], \n",
    "                     \n",
    "                    index = ['dtype', 'cardinality', 'n_tot', 'n', 'nmiss', 'perc_miss',\n",
    "                             'sum', 'mean', 'std', 'var',\n",
    "                        'lc_iqr', 'uc_iqr',\n",
    "                        'min', 'p1', 'p5', 'p10', 'p25', 'p50', 'p75', 'p90', 'p95', 'p99', 'max']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# An utility function to create dummy variable\n",
    "def create_dummies(df, colname):\n",
    "    col_dummies = pd.get_dummies(df[colname], \n",
    "                                 prefix = colname, \n",
    "                                 drop_first = True)\n",
    "    df = pd.concat([df, col_dummies], axis = 1)\n",
    "    df.drop(colname, axis = 1, inplace = True )\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for null value\n",
    "merged_table.isnull().any().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "mis_val_col =['account_type' , 'gender' , 'Income' , 'region_code', 'Avg_days_between_transaction', \n",
    "'cc_cons_may', 'dc_cons_jun', 'cc_count_apr', 'personal_loan_closed', 'investment_3',\n",
    "'debit_count_apr', 'loan_enq', 'emi_active']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_table[mis_val_col].isnull().any().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop rows where the values are null\n",
    "# merged_table = merged_table.dropna(subset=mis_val_col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for null value\n",
    "merged_table.isnull().any().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 20000 entries, 0 to 19999\n",
      "Data columns (total 49 columns):\n",
      " #   Column                        Non-Null Count  Dtype  \n",
      "---  ------                        --------------  -----  \n",
      " 0   ID                            20000 non-null  int64  \n",
      " 1   account_type                  19999 non-null  object \n",
      " 2   gender                        19999 non-null  object \n",
      " 3   age                           20000 non-null  int64  \n",
      " 4   Income                        19999 non-null  object \n",
      " 5   Emp_Tenure_Years              20000 non-null  float64\n",
      " 6   Tenure_with_Bank              20000 non-null  int64  \n",
      " 7   region_code                   19999 non-null  float64\n",
      " 8   NetBanking_Flag               20000 non-null  int64  \n",
      " 9   Avg_days_between_transaction  19997 non-null  float64\n",
      " 10  cc_cons_apr                   20000 non-null  float64\n",
      " 11  dc_cons_apr                   20000 non-null  float64\n",
      " 12  cc_cons_may                   19999 non-null  float64\n",
      " 13  dc_cons_may                   20000 non-null  float64\n",
      " 14  cc_cons_jun                   20000 non-null  float64\n",
      " 15  dc_cons_jun                   19999 non-null  float64\n",
      " 16  cc_count_apr                  19999 non-null  float64\n",
      " 17  cc_count_may                  20000 non-null  int64  \n",
      " 18  cc_count_jun                  20000 non-null  int64  \n",
      " 19  dc_count_apr                  20000 non-null  int64  \n",
      " 20  dc_count_may                  20000 non-null  int64  \n",
      " 21  dc_count_jun                  20000 non-null  int64  \n",
      " 22  card_lim                      20000 non-null  int64  \n",
      " 23  personal_loan_active          20000 non-null  int64  \n",
      " 24  vehicle_loan_active           20000 non-null  int64  \n",
      " 25  personal_loan_closed          19999 non-null  float64\n",
      " 26  vehicle_loan_closed           20000 non-null  int64  \n",
      " 27  investment_1                  20000 non-null  float64\n",
      " 28  investment_2                  20000 non-null  float64\n",
      " 29  investment_3                  19998 non-null  float64\n",
      " 30  investment_4                  20000 non-null  float64\n",
      " 31  debit_amount_apr              20000 non-null  float64\n",
      " 32  credit_amount_apr             20000 non-null  float64\n",
      " 33  debit_count_apr               19999 non-null  float64\n",
      " 34  credit_count_apr              20000 non-null  int64  \n",
      " 35  max_credit_amount_apr         20000 non-null  float64\n",
      " 36  debit_amount_may              20000 non-null  float64\n",
      " 37  credit_amount_may             20000 non-null  float64\n",
      " 38  credit_count_may              20000 non-null  int64  \n",
      " 39  debit_count_may               20000 non-null  int64  \n",
      " 40  max_credit_amount_may         20000 non-null  float64\n",
      " 41  debit_amount_jun              20000 non-null  float64\n",
      " 42  credit_amount_jun             20000 non-null  float64\n",
      " 43  credit_count_jun              20000 non-null  int64  \n",
      " 44  debit_count_jun               20000 non-null  int64  \n",
      " 45  max_credit_amount_jun         20000 non-null  float64\n",
      " 46  loan_enq                      19998 non-null  object \n",
      " 47  emi_active                    19999 non-null  float64\n",
      " 48  cc_cons                       15000 non-null  float64\n",
      "dtypes: float64(27), int64(18), object(4)\n",
      "memory usage: 7.6+ MB\n"
     ]
    }
   ],
   "source": [
    "merged_table.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# merged_table.to_excel('output.xlsx', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>account_type</th>\n",
       "      <th>gender</th>\n",
       "      <th>Income</th>\n",
       "      <th>region_code</th>\n",
       "      <th>Avg_days_between_transaction</th>\n",
       "      <th>cc_cons_may</th>\n",
       "      <th>dc_cons_jun</th>\n",
       "      <th>cc_count_apr</th>\n",
       "      <th>personal_loan_closed</th>\n",
       "      <th>investment_3</th>\n",
       "      <th>debit_count_apr</th>\n",
       "      <th>loan_enq</th>\n",
       "      <th>emi_active</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>current</td>\n",
       "      <td>M</td>\n",
       "      <td>MEDIUM</td>\n",
       "      <td>628.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9553.00</td>\n",
       "      <td>3124.00</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>2646.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>current</td>\n",
       "      <td>M</td>\n",
       "      <td>MEDIUM</td>\n",
       "      <td>656.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7386.49</td>\n",
       "      <td>6738.00</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28912.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>5469.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>current</td>\n",
       "      <td>F</td>\n",
       "      <td>MEDIUM</td>\n",
       "      <td>314.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4170.53</td>\n",
       "      <td>3778.00</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1036.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>7207.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>current</td>\n",
       "      <td>M</td>\n",
       "      <td>MEDIUM</td>\n",
       "      <td>614.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>5261.00</td>\n",
       "      <td>293.00</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>678.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>591.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>current</td>\n",
       "      <td>M</td>\n",
       "      <td>HIGH</td>\n",
       "      <td>750.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>19193.13</td>\n",
       "      <td>4933.00</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>2621.39</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19995</th>\n",
       "      <td>current</td>\n",
       "      <td>F</td>\n",
       "      <td>MEDIUM</td>\n",
       "      <td>354.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>69121.74</td>\n",
       "      <td>828.04</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>51520.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>6334.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19996</th>\n",
       "      <td>current</td>\n",
       "      <td>M</td>\n",
       "      <td>MEDIUM</td>\n",
       "      <td>809.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4269.27</td>\n",
       "      <td>5299.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15477.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>102216.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19997</th>\n",
       "      <td>current</td>\n",
       "      <td>F</td>\n",
       "      <td>MEDIUM</td>\n",
       "      <td>466.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11019.00</td>\n",
       "      <td>83247.00</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13873.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>1930.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19998</th>\n",
       "      <td>current</td>\n",
       "      <td>M</td>\n",
       "      <td>LOW</td>\n",
       "      <td>619.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2650.08</td>\n",
       "      <td>11827.46</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3975.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>24499.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19999</th>\n",
       "      <td>current</td>\n",
       "      <td>F</td>\n",
       "      <td>LOW</td>\n",
       "      <td>882.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>15746.06</td>\n",
       "      <td>831.88</td>\n",
       "      <td>531.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16114.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>5311.54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20000 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      account_type gender  Income  region_code  Avg_days_between_transaction  \\\n",
       "0          current      M  MEDIUM        628.0                           5.0   \n",
       "1          current      M  MEDIUM        656.0                          12.0   \n",
       "2          current      F  MEDIUM        314.0                          13.0   \n",
       "3          current      M  MEDIUM        614.0                          19.0   \n",
       "4          current      M    HIGH        750.0                          18.0   \n",
       "...            ...    ...     ...          ...                           ...   \n",
       "19995      current      F  MEDIUM        354.0                           7.0   \n",
       "19996      current      M  MEDIUM        809.0                          14.0   \n",
       "19997      current      F  MEDIUM        466.0                          10.0   \n",
       "19998      current      M     LOW        619.0                          17.0   \n",
       "19999      current      F     LOW        882.0                          10.0   \n",
       "\n",
       "       cc_cons_may  dc_cons_jun  cc_count_apr  personal_loan_closed  \\\n",
       "0          9553.00      3124.00           8.0                   1.0   \n",
       "1          7386.49      6738.00          19.0                   1.0   \n",
       "2          4170.53      3778.00          12.0                   1.0   \n",
       "3          5261.00       293.00          24.0                   1.0   \n",
       "4         19193.13      4933.00         100.0                   1.0   \n",
       "...            ...          ...           ...                   ...   \n",
       "19995     69121.74       828.04           5.0                   1.0   \n",
       "19996      4269.27      5299.00           1.0                   1.0   \n",
       "19997     11019.00     83247.00           6.0                   1.0   \n",
       "19998      2650.08     11827.46           5.0                   1.0   \n",
       "19999     15746.06       831.88         531.0                   1.0   \n",
       "\n",
       "       investment_3  debit_count_apr loan_enq  emi_active  \n",
       "0           80000.0             63.0        Y     2646.72  \n",
       "1           28912.0             10.0        Y     5469.79  \n",
       "2            1036.0             55.0        Y     7207.85  \n",
       "3             678.0             32.0        Y      591.34  \n",
       "4           12000.0             67.0        Y     2621.39  \n",
       "...             ...              ...      ...         ...  \n",
       "19995       51520.0             29.0        Y     6334.20  \n",
       "19996       15477.0              5.0        Y   102216.02  \n",
       "19997       13873.0             11.0        Y     1930.19  \n",
       "19998        3975.0              5.0        Y    24499.91  \n",
       "19999       16114.0             24.0        Y     5311.54  \n",
       "\n",
       "[20000 rows x 13 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_table[mis_val_col]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "miss_int_col=['region_code', 'Avg_days_between_transaction' ,'cc_cons_may', \n",
    "              'dc_cons_jun', 'cc_count_apr', 'personal_loan_closed', 'investment_3',\n",
    "              'debit_count_apr', 'emi_active' ]\n",
    "miss_obj_col=['account_type', 'gender', 'Income','loan_enq']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in miss_int_col:\n",
    "    merged_table[i] = np.where(pd.isnull(merged_table[i]), \n",
    "                               merged_table[i].mean(),\n",
    "                               merged_table[i])\n",
    "for j in miss_obj_col:\n",
    "    merged_table[j] = np.where(pd.isnull(merged_table[j]), \n",
    "                               merged_table[j].mode().values[0],\n",
    "                               merged_table[j])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 20000 entries, 0 to 19999\n",
      "Data columns (total 49 columns):\n",
      " #   Column                        Non-Null Count  Dtype  \n",
      "---  ------                        --------------  -----  \n",
      " 0   ID                            20000 non-null  int64  \n",
      " 1   account_type                  20000 non-null  object \n",
      " 2   gender                        20000 non-null  object \n",
      " 3   age                           20000 non-null  int64  \n",
      " 4   Income                        20000 non-null  object \n",
      " 5   Emp_Tenure_Years              20000 non-null  float64\n",
      " 6   Tenure_with_Bank              20000 non-null  int64  \n",
      " 7   region_code                   20000 non-null  float64\n",
      " 8   NetBanking_Flag               20000 non-null  int64  \n",
      " 9   Avg_days_between_transaction  20000 non-null  float64\n",
      " 10  cc_cons_apr                   20000 non-null  float64\n",
      " 11  dc_cons_apr                   20000 non-null  float64\n",
      " 12  cc_cons_may                   20000 non-null  float64\n",
      " 13  dc_cons_may                   20000 non-null  float64\n",
      " 14  cc_cons_jun                   20000 non-null  float64\n",
      " 15  dc_cons_jun                   20000 non-null  float64\n",
      " 16  cc_count_apr                  20000 non-null  float64\n",
      " 17  cc_count_may                  20000 non-null  int64  \n",
      " 18  cc_count_jun                  20000 non-null  int64  \n",
      " 19  dc_count_apr                  20000 non-null  int64  \n",
      " 20  dc_count_may                  20000 non-null  int64  \n",
      " 21  dc_count_jun                  20000 non-null  int64  \n",
      " 22  card_lim                      20000 non-null  int64  \n",
      " 23  personal_loan_active          20000 non-null  int64  \n",
      " 24  vehicle_loan_active           20000 non-null  int64  \n",
      " 25  personal_loan_closed          20000 non-null  float64\n",
      " 26  vehicle_loan_closed           20000 non-null  int64  \n",
      " 27  investment_1                  20000 non-null  float64\n",
      " 28  investment_2                  20000 non-null  float64\n",
      " 29  investment_3                  20000 non-null  float64\n",
      " 30  investment_4                  20000 non-null  float64\n",
      " 31  debit_amount_apr              20000 non-null  float64\n",
      " 32  credit_amount_apr             20000 non-null  float64\n",
      " 33  debit_count_apr               20000 non-null  float64\n",
      " 34  credit_count_apr              20000 non-null  int64  \n",
      " 35  max_credit_amount_apr         20000 non-null  float64\n",
      " 36  debit_amount_may              20000 non-null  float64\n",
      " 37  credit_amount_may             20000 non-null  float64\n",
      " 38  credit_count_may              20000 non-null  int64  \n",
      " 39  debit_count_may               20000 non-null  int64  \n",
      " 40  max_credit_amount_may         20000 non-null  float64\n",
      " 41  debit_amount_jun              20000 non-null  float64\n",
      " 42  credit_amount_jun             20000 non-null  float64\n",
      " 43  credit_count_jun              20000 non-null  int64  \n",
      " 44  debit_count_jun               20000 non-null  int64  \n",
      " 45  max_credit_amount_jun         20000 non-null  float64\n",
      " 46  loan_enq                      20000 non-null  object \n",
      " 47  emi_active                    20000 non-null  float64\n",
      " 48  cc_cons                       15000 non-null  float64\n",
      "dtypes: float64(27), int64(18), object(4)\n",
      "memory usage: 7.6+ MB\n"
     ]
    }
   ],
   "source": [
    "merged_table.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_col = merged_table.select_dtypes(include=['int','float']).columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### One hot encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "314a6f38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['account_type', 'gender', 'Income', 'loan_enq'], dtype='object')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj_var = merged_table.select_dtypes(include=['object']).columns\n",
    "obj_var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e579bab3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "current    16926\n",
       "saving      3074\n",
       "Name: account_type, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_table['account_type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "017d11da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "M    17288\n",
       "F     2712\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_table['gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5da47050",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MEDIUM    12775\n",
       "LOW        5612\n",
       "HIGH       1613\n",
       "Name: Income, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_table['Income'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "92d1f7dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Y    20000\n",
       "Name: loan_enq, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_table['loan_enq'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "49fc526e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# lets drop loan_enq as its cardinality is lowest\n",
    "# and lets drop ID as its cardinality is very very high\n",
    "merged_table.drop(columns=['ID','loan_enq'] , inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "046f5083",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20000, 47)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_table.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d24942e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_table = create_dummies(merged_table,['account_type','gender','Income'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "46883b8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20000, 48)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_table.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21602e3a",
   "metadata": {},
   "source": [
    "#### Outlier treatment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9a37cc32",
   "metadata": {},
   "outputs": [],
   "source": [
    "# separate the numeric and object variables\n",
    "num_var = merged_table.select_dtypes(['float64', 'int64'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "5a41e4be",
   "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>age</th>\n",
       "      <th>Emp_Tenure_Years</th>\n",
       "      <th>Tenure_with_Bank</th>\n",
       "      <th>region_code</th>\n",
       "      <th>NetBanking_Flag</th>\n",
       "      <th>Avg_days_between_transaction</th>\n",
       "      <th>cc_cons_apr</th>\n",
       "      <th>dc_cons_apr</th>\n",
       "      <th>cc_cons_may</th>\n",
       "      <th>dc_cons_may</th>\n",
       "      <th>...</th>\n",
       "      <th>credit_count_may</th>\n",
       "      <th>debit_count_may</th>\n",
       "      <th>max_credit_amount_may</th>\n",
       "      <th>debit_amount_jun</th>\n",
       "      <th>credit_amount_jun</th>\n",
       "      <th>credit_count_jun</th>\n",
       "      <th>debit_count_jun</th>\n",
       "      <th>max_credit_amount_jun</th>\n",
       "      <th>emi_active</th>\n",
       "      <th>cc_cons</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>dtype</th>\n",
       "      <td>int64</td>\n",
       "      <td>float64</td>\n",
       "      <td>int64</td>\n",
       "      <td>float64</td>\n",
       "      <td>int64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>...</td>\n",
       "      <td>int64</td>\n",
       "      <td>int64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>int64</td>\n",
       "      <td>int64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cardinality</th>\n",
       "      <td>43</td>\n",
       "      <td>270</td>\n",
       "      <td>10</td>\n",
       "      <td>211</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>8459</td>\n",
       "      <td>3861</td>\n",
       "      <td>8505</td>\n",
       "      <td>4053</td>\n",
       "      <td>...</td>\n",
       "      <td>31</td>\n",
       "      <td>57</td>\n",
       "      <td>5299</td>\n",
       "      <td>8632</td>\n",
       "      <td>8311</td>\n",
       "      <td>39</td>\n",
       "      <td>66</td>\n",
       "      <td>5833</td>\n",
       "      <td>5941</td>\n",
       "      <td>12260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n_tot</th>\n",
       "      <td>20000</td>\n",
       "      <td>20000</td>\n",
       "      <td>20000</td>\n",
       "      <td>20000</td>\n",
       "      <td>20000</td>\n",
       "      <td>20000</td>\n",
       "      <td>20000</td>\n",
       "      <td>20000</td>\n",
       "      <td>20000</td>\n",
       "      <td>20000</td>\n",
       "      <td>...</td>\n",
       "      <td>20000</td>\n",
       "      <td>20000</td>\n",
       "      <td>20000</td>\n",
       "      <td>20000</td>\n",
       "      <td>20000</td>\n",
       "      <td>20000</td>\n",
       "      <td>20000</td>\n",
       "      <td>20000</td>\n",
       "      <td>20000</td>\n",
       "      <td>20000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n</th>\n",
       "      <td>20000</td>\n",
       "      <td>20000</td>\n",
       "      <td>20000</td>\n",
       "      <td>20000</td>\n",
       "      <td>20000</td>\n",
       "      <td>20000</td>\n",
       "      <td>20000</td>\n",
       "      <td>20000</td>\n",
       "      <td>20000</td>\n",
       "      <td>20000</td>\n",
       "      <td>...</td>\n",
       "      <td>20000</td>\n",
       "      <td>20000</td>\n",
       "      <td>20000</td>\n",
       "      <td>20000</td>\n",
       "      <td>20000</td>\n",
       "      <td>20000</td>\n",
       "      <td>20000</td>\n",
       "      <td>20000</td>\n",
       "      <td>20000</td>\n",
       "      <td>15000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nmiss</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>perc_miss</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sum</th>\n",
       "      <td>953446</td>\n",
       "      <td>304345.0</td>\n",
       "      <td>109921</td>\n",
       "      <td>11979186.959348</td>\n",
       "      <td>14360</td>\n",
       "      <td>220895.13427</td>\n",
       "      <td>357139818.44</td>\n",
       "      <td>162609068.27</td>\n",
       "      <td>323767294.334717</td>\n",
       "      <td>154048543.82</td>\n",
       "      <td>...</td>\n",
       "      <td>238380</td>\n",
       "      <td>819497</td>\n",
       "      <td>1164159702.76</td>\n",
       "      <td>1562455943.19</td>\n",
       "      <td>1576338552.15</td>\n",
       "      <td>398109</td>\n",
       "      <td>890582</td>\n",
       "      <td>1056996951.42</td>\n",
       "      <td>307977375.468773</td>\n",
       "      <td>827365948.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>47.6723</td>\n",
       "      <td>15.21725</td>\n",
       "      <td>5.49605</td>\n",
       "      <td>598.959348</td>\n",
       "      <td>0.718</td>\n",
       "      <td>11.044757</td>\n",
       "      <td>17856.990922</td>\n",
       "      <td>8130.453414</td>\n",
       "      <td>16188.364717</td>\n",
       "      <td>7702.427191</td>\n",
       "      <td>...</td>\n",
       "      <td>11.919</td>\n",
       "      <td>40.97485</td>\n",
       "      <td>58207.985138</td>\n",
       "      <td>78122.79716</td>\n",
       "      <td>78816.927607</td>\n",
       "      <td>19.90545</td>\n",
       "      <td>44.5291</td>\n",
       "      <td>52849.847571</td>\n",
       "      <td>15398.868773</td>\n",
       "      <td>55157.729867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>30.359771</td>\n",
       "      <td>20.481455</td>\n",
       "      <td>2.870452</td>\n",
       "      <td>220.099727</td>\n",
       "      <td>0.449985</td>\n",
       "      <td>5.480141</td>\n",
       "      <td>25158.878234</td>\n",
       "      <td>14572.188829</td>\n",
       "      <td>26510.018788</td>\n",
       "      <td>17892.7318</td>\n",
       "      <td>...</td>\n",
       "      <td>16.85576</td>\n",
       "      <td>40.561202</td>\n",
       "      <td>208591.362703</td>\n",
       "      <td>244898.308732</td>\n",
       "      <td>156987.241764</td>\n",
       "      <td>27.055323</td>\n",
       "      <td>35.080453</td>\n",
       "      <td>134317.525107</td>\n",
       "      <td>120251.069229</td>\n",
       "      <td>97670.972252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>var</th>\n",
       "      <td>921.715698</td>\n",
       "      <td>419.489979</td>\n",
       "      <td>8.239496</td>\n",
       "      <td>48443.889692</td>\n",
       "      <td>0.202486</td>\n",
       "      <td>30.031949</td>\n",
       "      <td>632969153.988079</td>\n",
       "      <td>212348687.260089</td>\n",
       "      <td>702781096.121192</td>\n",
       "      <td>320149851.281652</td>\n",
       "      <td>...</td>\n",
       "      <td>284.116645</td>\n",
       "      <td>1645.211078</td>\n",
       "      <td>43510356594.345634</td>\n",
       "      <td>59975181619.710663</td>\n",
       "      <td>24644994076.810459</td>\n",
       "      <td>731.99051</td>\n",
       "      <td>1230.638185</td>\n",
       "      <td>18041197550.945358</td>\n",
       "      <td>14460319650.712879</td>\n",
       "      <td>9539618820.721489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lc_iqr</th>\n",
       "      <td>3.0</td>\n",
       "      <td>-14.25</td>\n",
       "      <td>-4.5</td>\n",
       "      <td>-138.5</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>-20841.0</td>\n",
       "      <td>-9946.905</td>\n",
       "      <td>-18880.20375</td>\n",
       "      <td>-8309.735</td>\n",
       "      <td>...</td>\n",
       "      <td>-11.5</td>\n",
       "      <td>-76.0</td>\n",
       "      <td>-34125.875</td>\n",
       "      <td>-63537.02</td>\n",
       "      <td>-66729.8</td>\n",
       "      <td>-21.5</td>\n",
       "      <td>-79.5</td>\n",
       "      <td>-32762.25</td>\n",
       "      <td>-10199.735</td>\n",
       "      <td>-13238.625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>uc_iqr</th>\n",
       "      <td>83.0</td>\n",
       "      <td>37.35</td>\n",
       "      <td>15.5</td>\n",
       "      <td>1361.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>31.0</td>\n",
       "      <td>46407.0</td>\n",
       "      <td>20910.575</td>\n",
       "      <td>41162.76625</td>\n",
       "      <td>18207.585</td>\n",
       "      <td>...</td>\n",
       "      <td>24.5</td>\n",
       "      <td>156.0</td>\n",
       "      <td>110749.125</td>\n",
       "      <td>174596.66</td>\n",
       "      <td>177669.88</td>\n",
       "      <td>46.5</td>\n",
       "      <td>164.5</td>\n",
       "      <td>105175.75</td>\n",
       "      <td>21122.065</td>\n",
       "      <td>40112.375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>25</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>123.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>253.0</td>\n",
       "      <td>19.35</td>\n",
       "      <td>157.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>5.23</td>\n",
       "      <td>11.88</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p1</th>\n",
       "      <td>27.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>233.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>641.9886</td>\n",
       "      <td>153.1286</td>\n",
       "      <td>509.495</td>\n",
       "      <td>152.1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>120.34</td>\n",
       "      <td>1077.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>26.7395</td>\n",
       "      <td>271.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p5</th>\n",
       "      <td>28.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>249.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1219.0</td>\n",
       "      <td>378.0</td>\n",
       "      <td>1035.817</td>\n",
       "      <td>410.97</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5675.0</td>\n",
       "      <td>6972.0</td>\n",
       "      <td>4020.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1679.5</td>\n",
       "      <td>198.005</td>\n",
       "      <td>1290.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p10</th>\n",
       "      <td>29.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>269.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1908.0</td>\n",
       "      <td>643.85</td>\n",
       "      <td>1666.364</td>\n",
       "      <td>731.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12003.0</td>\n",
       "      <td>12573.958</td>\n",
       "      <td>11509.04</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11200.0</td>\n",
       "      <td>461.06</td>\n",
       "      <td>2633.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p25</th>\n",
       "      <td>33.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>424.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4377.0</td>\n",
       "      <td>1624.65</td>\n",
       "      <td>3635.91</td>\n",
       "      <td>1634.26</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>20202.25</td>\n",
       "      <td>25763.11</td>\n",
       "      <td>24920.08</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>18964.5</td>\n",
       "      <td>1545.94</td>\n",
       "      <td>6768.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p50</th>\n",
       "      <td>40.0</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6.0</td>\n",
       "      <td>623.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10214.6425</td>\n",
       "      <td>3964.75</td>\n",
       "      <td>8638.87</td>\n",
       "      <td>3791.72</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>31496.5</td>\n",
       "      <td>45725.05</td>\n",
       "      <td>46183.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>30250.0</td>\n",
       "      <td>3934.85</td>\n",
       "      <td>13364.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p75</th>\n",
       "      <td>53.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>799.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>21189.0</td>\n",
       "      <td>9339.02</td>\n",
       "      <td>18646.6525</td>\n",
       "      <td>8263.59</td>\n",
       "      <td>...</td>\n",
       "      <td>11.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>56421.0</td>\n",
       "      <td>85296.53</td>\n",
       "      <td>86020.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>53449.0</td>\n",
       "      <td>9376.39</td>\n",
       "      <td>20105.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p90</th>\n",
       "      <td>64.0</td>\n",
       "      <td>31.5</td>\n",
       "      <td>10.0</td>\n",
       "      <td>883.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>40640.9</td>\n",
       "      <td>18443.691</td>\n",
       "      <td>36139.224</td>\n",
       "      <td>16144.76</td>\n",
       "      <td>...</td>\n",
       "      <td>43.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>103500.0</td>\n",
       "      <td>151821.74</td>\n",
       "      <td>158148.58</td>\n",
       "      <td>58.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>100183.0</td>\n",
       "      <td>19713.876</td>\n",
       "      <td>219688.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p95</th>\n",
       "      <td>66.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>937.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>59106.728</td>\n",
       "      <td>27835.0</td>\n",
       "      <td>53962.38</td>\n",
       "      <td>25584.0</td>\n",
       "      <td>...</td>\n",
       "      <td>47.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>157048.0</td>\n",
       "      <td>220638.7625</td>\n",
       "      <td>234766.7625</td>\n",
       "      <td>94.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>150006.83</td>\n",
       "      <td>36144.92</td>\n",
       "      <td>309852.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p99</th>\n",
       "      <td>222.0</td>\n",
       "      <td>121.2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>968.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>125259.5</td>\n",
       "      <td>64470.75</td>\n",
       "      <td>125727.9665</td>\n",
       "      <td>65963.0603</td>\n",
       "      <td>...</td>\n",
       "      <td>70.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>385963.0</td>\n",
       "      <td>535233.77</td>\n",
       "      <td>534467.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>377905.0</td>\n",
       "      <td>155037.0995</td>\n",
       "      <td>395290.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>223</td>\n",
       "      <td>203.0</td>\n",
       "      <td>10</td>\n",
       "      <td>974.0</td>\n",
       "      <td>1</td>\n",
       "      <td>20.0</td>\n",
       "      <td>441031.33</td>\n",
       "      <td>229255.0</td>\n",
       "      <td>1117246.31</td>\n",
       "      <td>911223.49</td>\n",
       "      <td>...</td>\n",
       "      <td>71</td>\n",
       "      <td>171</td>\n",
       "      <td>10000000.0</td>\n",
       "      <td>26429152.75</td>\n",
       "      <td>6611953.0</td>\n",
       "      <td>117</td>\n",
       "      <td>133</td>\n",
       "      <td>5200000.0</td>\n",
       "      <td>7447125.13</td>\n",
       "      <td>408382.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23 rows × 44 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    age Emp_Tenure_Years Tenure_with_Bank      region_code  \\\n",
       "dtype             int64          float64            int64          float64   \n",
       "cardinality          43              270               10              211   \n",
       "n_tot             20000            20000            20000            20000   \n",
       "n                 20000            20000            20000            20000   \n",
       "nmiss                 0                0                0                0   \n",
       "perc_miss           0.0              0.0              0.0              0.0   \n",
       "sum              953446         304345.0           109921  11979186.959348   \n",
       "mean            47.6723         15.21725          5.49605       598.959348   \n",
       "std           30.359771        20.481455         2.870452       220.099727   \n",
       "var          921.715698       419.489979         8.239496     48443.889692   \n",
       "lc_iqr              3.0           -14.25             -4.5           -138.5   \n",
       "uc_iqr             83.0            37.35             15.5           1361.5   \n",
       "min                  25              0.5                1            123.0   \n",
       "p1                 27.0              0.9              1.0            233.0   \n",
       "p5                 28.0              1.7              1.0            249.0   \n",
       "p10                29.0              2.7              2.0            269.0   \n",
       "p25                33.0              5.1              3.0            424.0   \n",
       "p50                40.0              9.9              6.0            623.0   \n",
       "p75                53.0             18.0              8.0            799.0   \n",
       "p90                64.0             31.5             10.0            883.0   \n",
       "p95                66.0             40.0             10.0            937.0   \n",
       "p99               222.0            121.2             10.0            968.0   \n",
       "max                 223            203.0               10            974.0   \n",
       "\n",
       "            NetBanking_Flag Avg_days_between_transaction       cc_cons_apr  \\\n",
       "dtype                 int64                      float64           float64   \n",
       "cardinality               2                           20              8459   \n",
       "n_tot                 20000                        20000             20000   \n",
       "n                     20000                        20000             20000   \n",
       "nmiss                     0                            0                 0   \n",
       "perc_miss               0.0                          0.0               0.0   \n",
       "sum                   14360                 220895.13427      357139818.44   \n",
       "mean                  0.718                    11.044757      17856.990922   \n",
       "std                0.449985                     5.480141      25158.878234   \n",
       "var                0.202486                    30.031949  632969153.988079   \n",
       "lc_iqr                 -1.5                         -9.0          -20841.0   \n",
       "uc_iqr                  2.5                         31.0           46407.0   \n",
       "min                       0                          2.0             253.0   \n",
       "p1                      0.0                          2.0          641.9886   \n",
       "p5                      0.0                          2.0            1219.0   \n",
       "p10                     0.0                          3.0            1908.0   \n",
       "p25                     0.0                          6.0            4377.0   \n",
       "p50                     1.0                         11.0        10214.6425   \n",
       "p75                     1.0                         16.0           21189.0   \n",
       "p90                     1.0                         19.0           40640.9   \n",
       "p95                     1.0                         20.0         59106.728   \n",
       "p99                     1.0                         20.0          125259.5   \n",
       "max                       1                         20.0         441031.33   \n",
       "\n",
       "                  dc_cons_apr       cc_cons_may       dc_cons_may  ...  \\\n",
       "dtype                 float64           float64           float64  ...   \n",
       "cardinality              3861              8505              4053  ...   \n",
       "n_tot                   20000             20000             20000  ...   \n",
       "n                       20000             20000             20000  ...   \n",
       "nmiss                       0                 0                 0  ...   \n",
       "perc_miss                 0.0               0.0               0.0  ...   \n",
       "sum              162609068.27  323767294.334717      154048543.82  ...   \n",
       "mean              8130.453414      16188.364717       7702.427191  ...   \n",
       "std              14572.188829      26510.018788        17892.7318  ...   \n",
       "var          212348687.260089  702781096.121192  320149851.281652  ...   \n",
       "lc_iqr              -9946.905      -18880.20375         -8309.735  ...   \n",
       "uc_iqr              20910.575       41162.76625         18207.585  ...   \n",
       "min                     19.35             157.0              22.0  ...   \n",
       "p1                   153.1286           509.495             152.1  ...   \n",
       "p5                      378.0          1035.817            410.97  ...   \n",
       "p10                    643.85          1666.364             731.0  ...   \n",
       "p25                   1624.65           3635.91           1634.26  ...   \n",
       "p50                   3964.75           8638.87           3791.72  ...   \n",
       "p75                   9339.02        18646.6525           8263.59  ...   \n",
       "p90                 18443.691         36139.224          16144.76  ...   \n",
       "p95                   27835.0          53962.38           25584.0  ...   \n",
       "p99                  64470.75       125727.9665        65963.0603  ...   \n",
       "max                  229255.0        1117246.31         911223.49  ...   \n",
       "\n",
       "            credit_count_may debit_count_may max_credit_amount_may  \\\n",
       "dtype                  int64           int64               float64   \n",
       "cardinality               31              57                  5299   \n",
       "n_tot                  20000           20000                 20000   \n",
       "n                      20000           20000                 20000   \n",
       "nmiss                      0               0                     0   \n",
       "perc_miss                0.0             0.0                   0.0   \n",
       "sum                   238380          819497         1164159702.76   \n",
       "mean                  11.919        40.97485          58207.985138   \n",
       "std                 16.85576       40.561202         208591.362703   \n",
       "var               284.116645     1645.211078    43510356594.345634   \n",
       "lc_iqr                 -11.5           -76.0            -34125.875   \n",
       "uc_iqr                  24.5           156.0            110749.125   \n",
       "min                        0               0                  0.25   \n",
       "p1                       0.0             1.0                120.34   \n",
       "p5                       1.0             4.0                5675.0   \n",
       "p10                      1.0             6.0               12003.0   \n",
       "p25                      2.0            11.0              20202.25   \n",
       "p50                      4.0            23.0               31496.5   \n",
       "p75                     11.0            69.0               56421.0   \n",
       "p90                     43.0            84.0              103500.0   \n",
       "p95                     47.0           108.0              157048.0   \n",
       "p99                     70.0           171.0              385963.0   \n",
       "max                       71             171            10000000.0   \n",
       "\n",
       "               debit_amount_jun   credit_amount_jun credit_count_jun  \\\n",
       "dtype                   float64             float64            int64   \n",
       "cardinality                8632                8311               39   \n",
       "n_tot                     20000               20000            20000   \n",
       "n                         20000               20000            20000   \n",
       "nmiss                         0                   0                0   \n",
       "perc_miss                   0.0                 0.0              0.0   \n",
       "sum               1562455943.19       1576338552.15           398109   \n",
       "mean                78122.79716        78816.927607         19.90545   \n",
       "std               244898.308732       156987.241764        27.055323   \n",
       "var          59975181619.710663  24644994076.810459        731.99051   \n",
       "lc_iqr                -63537.02            -66729.8            -21.5   \n",
       "uc_iqr                174596.66           177669.88             46.5   \n",
       "min                        5.23               11.88                1   \n",
       "p1                       1077.0               296.0              1.0   \n",
       "p5                       6972.0              4020.9              2.0   \n",
       "p10                   12573.958            11509.04              2.0   \n",
       "p25                    25763.11            24920.08              4.0   \n",
       "p50                    45725.05             46183.0              7.0   \n",
       "p75                    85296.53             86020.0             21.0   \n",
       "p90                   151821.74           158148.58             58.0   \n",
       "p95                 220638.7625         234766.7625             94.0   \n",
       "p99                   535233.77            534467.0            117.0   \n",
       "max                 26429152.75           6611953.0              117   \n",
       "\n",
       "            debit_count_jun max_credit_amount_jun          emi_active  \\\n",
       "dtype                 int64               float64             float64   \n",
       "cardinality              66                  5833                5941   \n",
       "n_tot                 20000                 20000               20000   \n",
       "n                     20000                 20000               20000   \n",
       "nmiss                     0                     0                   0   \n",
       "perc_miss               0.0                   0.0                 0.0   \n",
       "sum                  890582         1056996951.42    307977375.468773   \n",
       "mean                44.5291          52849.847571        15398.868773   \n",
       "std               35.080453         134317.525107       120251.069229   \n",
       "var             1230.638185    18041197550.945358  14460319650.712879   \n",
       "lc_iqr                -79.5             -32762.25          -10199.735   \n",
       "uc_iqr                164.5             105175.75           21122.065   \n",
       "min                       0                  0.06                 0.0   \n",
       "p1                      0.0                 160.0             26.7395   \n",
       "p5                      2.0                1679.5             198.005   \n",
       "p10                     7.0               11200.0              461.06   \n",
       "p25                    12.0               18964.5             1545.94   \n",
       "p50                    37.0               30250.0             3934.85   \n",
       "p75                    73.0               53449.0             9376.39   \n",
       "p90                   103.0              100183.0           19713.876   \n",
       "p95                   106.0             150006.83            36144.92   \n",
       "p99                   124.0              377905.0         155037.0995   \n",
       "max                     133             5200000.0          7447125.13   \n",
       "\n",
       "                       cc_cons  \n",
       "dtype                  float64  \n",
       "cardinality              12260  \n",
       "n_tot                    20000  \n",
       "n                        15000  \n",
       "nmiss                     5000  \n",
       "perc_miss                 25.0  \n",
       "sum                827365948.0  \n",
       "mean              55157.729867  \n",
       "std               97670.972252  \n",
       "var          9539618820.721489  \n",
       "lc_iqr              -13238.625  \n",
       "uc_iqr               40112.375  \n",
       "min                        0.0  \n",
       "p1                      271.99  \n",
       "p5                     1290.85  \n",
       "p10                     2633.6  \n",
       "p25                     6768.0  \n",
       "p50                    13364.0  \n",
       "p75                   20105.75  \n",
       "p90                   219688.7  \n",
       "p95                   309852.0  \n",
       "p99                  395290.55  \n",
       "max                   408382.0  \n",
       "\n",
       "[23 rows x 44 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the data audit report for all the variables\n",
    "num_var.apply(continuous_var_summary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "323f7a14",
   "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>age</th>\n",
       "      <th>Emp_Tenure_Years</th>\n",
       "      <th>Tenure_with_Bank</th>\n",
       "      <th>region_code</th>\n",
       "      <th>NetBanking_Flag</th>\n",
       "      <th>Avg_days_between_transaction</th>\n",
       "      <th>cc_cons_apr</th>\n",
       "      <th>dc_cons_apr</th>\n",
       "      <th>cc_cons_may</th>\n",
       "      <th>dc_cons_may</th>\n",
       "      <th>...</th>\n",
       "      <th>credit_count_may</th>\n",
       "      <th>debit_count_may</th>\n",
       "      <th>max_credit_amount_may</th>\n",
       "      <th>debit_amount_jun</th>\n",
       "      <th>credit_amount_jun</th>\n",
       "      <th>credit_count_jun</th>\n",
       "      <th>debit_count_jun</th>\n",
       "      <th>max_credit_amount_jun</th>\n",
       "      <th>emi_active</th>\n",
       "      <th>cc_cons</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>30.1</td>\n",
       "      <td>10</td>\n",
       "      <td>628.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7998.48</td>\n",
       "      <td>2289.00</td>\n",
       "      <td>9553.00</td>\n",
       "      <td>2879.58</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>20770.0</td>\n",
       "      <td>44884.90</td>\n",
       "      <td>369000.75</td>\n",
       "      <td>40</td>\n",
       "      <td>96</td>\n",
       "      <td>46088.0</td>\n",
       "      <td>2646.72</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>36</td>\n",
       "      <td>14.4</td>\n",
       "      <td>10</td>\n",
       "      <td>656.0</td>\n",
       "      <td>0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>16479.64</td>\n",
       "      <td>415.00</td>\n",
       "      <td>7386.49</td>\n",
       "      <td>6763.00</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>63</td>\n",
       "      <td>78627.0</td>\n",
       "      <td>91073.84</td>\n",
       "      <td>243182.32</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>17953.0</td>\n",
       "      <td>5469.79</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>4.8</td>\n",
       "      <td>10</td>\n",
       "      <td>314.0</td>\n",
       "      <td>1</td>\n",
       "      <td>13.0</td>\n",
       "      <td>29272.03</td>\n",
       "      <td>40994.00</td>\n",
       "      <td>4170.53</td>\n",
       "      <td>26717.30</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>82</td>\n",
       "      <td>1260.0</td>\n",
       "      <td>96552.00</td>\n",
       "      <td>35467.00</td>\n",
       "      <td>16</td>\n",
       "      <td>42</td>\n",
       "      <td>41121.0</td>\n",
       "      <td>7207.85</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32</td>\n",
       "      <td>9.6</td>\n",
       "      <td>2</td>\n",
       "      <td>614.0</td>\n",
       "      <td>1</td>\n",
       "      <td>19.0</td>\n",
       "      <td>9662.31</td>\n",
       "      <td>5306.39</td>\n",
       "      <td>5261.00</td>\n",
       "      <td>11934.79</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>78</td>\n",
       "      <td>23332.0</td>\n",
       "      <td>18250.00</td>\n",
       "      <td>87204.35</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>32003.0</td>\n",
       "      <td>591.34</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7</td>\n",
       "      <td>750.0</td>\n",
       "      <td>1</td>\n",
       "      <td>18.0</td>\n",
       "      <td>13239.86</td>\n",
       "      <td>7063.00</td>\n",
       "      <td>19193.13</td>\n",
       "      <td>22304.86</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>171</td>\n",
       "      <td>41672.0</td>\n",
       "      <td>83525.75</td>\n",
       "      <td>83992.00</td>\n",
       "      <td>21</td>\n",
       "      <td>12</td>\n",
       "      <td>9626.0</td>\n",
       "      <td>2621.39</td>\n",
       "      <td>NaN</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19995</th>\n",
       "      <td>66</td>\n",
       "      <td>32.2</td>\n",
       "      <td>6</td>\n",
       "      <td>354.0</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4611.00</td>\n",
       "      <td>12015.50</td>\n",
       "      <td>69121.74</td>\n",
       "      <td>550.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>24438.0</td>\n",
       "      <td>153772.21</td>\n",
       "      <td>31284.00</td>\n",
       "      <td>3</td>\n",
       "      <td>38</td>\n",
       "      <td>42872.0</td>\n",
       "      <td>6334.20</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19996</th>\n",
       "      <td>53</td>\n",
       "      <td>19.8</td>\n",
       "      <td>5</td>\n",
       "      <td>809.0</td>\n",
       "      <td>0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>9545.00</td>\n",
       "      <td>2061.00</td>\n",
       "      <td>4269.27</td>\n",
       "      <td>901.00</td>\n",
       "      <td>...</td>\n",
       "      <td>47</td>\n",
       "      <td>11</td>\n",
       "      <td>213000.0</td>\n",
       "      <td>18098.47</td>\n",
       "      <td>93108.00</td>\n",
       "      <td>43</td>\n",
       "      <td>11</td>\n",
       "      <td>36747.0</td>\n",
       "      <td>102216.02</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19997</th>\n",
       "      <td>66</td>\n",
       "      <td>18.4</td>\n",
       "      <td>1</td>\n",
       "      <td>466.0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2028.93</td>\n",
       "      <td>9454.99</td>\n",
       "      <td>11019.00</td>\n",
       "      <td>4774.50</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>171</td>\n",
       "      <td>34327.0</td>\n",
       "      <td>32198.63</td>\n",
       "      <td>46800.00</td>\n",
       "      <td>3</td>\n",
       "      <td>38</td>\n",
       "      <td>18810.0</td>\n",
       "      <td>1930.19</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19998</th>\n",
       "      <td>32</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8</td>\n",
       "      <td>619.0</td>\n",
       "      <td>0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>11456.23</td>\n",
       "      <td>3999.00</td>\n",
       "      <td>2650.08</td>\n",
       "      <td>290.00</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>82</td>\n",
       "      <td>51929.0</td>\n",
       "      <td>27334.14</td>\n",
       "      <td>20201.00</td>\n",
       "      <td>47</td>\n",
       "      <td>80</td>\n",
       "      <td>11443.0</td>\n",
       "      <td>24499.91</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19999</th>\n",
       "      <td>118</td>\n",
       "      <td>78.4</td>\n",
       "      <td>7</td>\n",
       "      <td>882.0</td>\n",
       "      <td>1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>23551.00</td>\n",
       "      <td>16821.30</td>\n",
       "      <td>15746.06</td>\n",
       "      <td>1310.59</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>32431.0</td>\n",
       "      <td>23464.59</td>\n",
       "      <td>38503.00</td>\n",
       "      <td>5</td>\n",
       "      <td>42</td>\n",
       "      <td>47504.0</td>\n",
       "      <td>5311.54</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20000 rows × 44 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  Emp_Tenure_Years  Tenure_with_Bank  region_code  NetBanking_Flag  \\\n",
       "0       63              30.1                10        628.0                1   \n",
       "1       36              14.4                10        656.0                0   \n",
       "2       28               4.8                10        314.0                1   \n",
       "3       32               9.6                 2        614.0                1   \n",
       "4       32              12.0                 7        750.0                1   \n",
       "...    ...               ...               ...          ...              ...   \n",
       "19995   66              32.2                 6        354.0                1   \n",
       "19996   53              19.8                 5        809.0                0   \n",
       "19997   66              18.4                 1        466.0                0   \n",
       "19998   32               6.0                 8        619.0                0   \n",
       "19999  118              78.4                 7        882.0                1   \n",
       "\n",
       "       Avg_days_between_transaction  cc_cons_apr  dc_cons_apr  cc_cons_may  \\\n",
       "0                               5.0      7998.48      2289.00      9553.00   \n",
       "1                              12.0     16479.64       415.00      7386.49   \n",
       "2                              13.0     29272.03     40994.00      4170.53   \n",
       "3                              19.0      9662.31      5306.39      5261.00   \n",
       "4                              18.0     13239.86      7063.00     19193.13   \n",
       "...                             ...          ...          ...          ...   \n",
       "19995                           7.0      4611.00     12015.50     69121.74   \n",
       "19996                          14.0      9545.00      2061.00      4269.27   \n",
       "19997                          10.0      2028.93      9454.99     11019.00   \n",
       "19998                          17.0     11456.23      3999.00      2650.08   \n",
       "19999                          10.0     23551.00     16821.30     15746.06   \n",
       "\n",
       "       dc_cons_may  ...  credit_count_may  debit_count_may  \\\n",
       "0          2879.58  ...                 5               14   \n",
       "1          6763.00  ...                 4               63   \n",
       "2         26717.30  ...                 6               82   \n",
       "3         11934.79  ...                 2               78   \n",
       "4         22304.86  ...                 3              171   \n",
       "...            ...  ...               ...              ...   \n",
       "19995       550.50  ...                 0                8   \n",
       "19996       901.00  ...                47               11   \n",
       "19997      4774.50  ...                 2              171   \n",
       "19998       290.00  ...                 3               82   \n",
       "19999      1310.59  ...                 5                8   \n",
       "\n",
       "       max_credit_amount_may  debit_amount_jun  credit_amount_jun  \\\n",
       "0                    20770.0          44884.90          369000.75   \n",
       "1                    78627.0          91073.84          243182.32   \n",
       "2                     1260.0          96552.00           35467.00   \n",
       "3                    23332.0          18250.00           87204.35   \n",
       "4                    41672.0          83525.75           83992.00   \n",
       "...                      ...               ...                ...   \n",
       "19995                24438.0         153772.21           31284.00   \n",
       "19996               213000.0          18098.47           93108.00   \n",
       "19997                34327.0          32198.63           46800.00   \n",
       "19998                51929.0          27334.14           20201.00   \n",
       "19999                32431.0          23464.59           38503.00   \n",
       "\n",
       "       credit_count_jun  debit_count_jun  max_credit_amount_jun  emi_active  \\\n",
       "0                    40               96                46088.0     2646.72   \n",
       "1                     7               12                17953.0     5469.79   \n",
       "2                    16               42                41121.0     7207.85   \n",
       "3                     2               10                32003.0      591.34   \n",
       "4                    21               12                 9626.0     2621.39   \n",
       "...                 ...              ...                    ...         ...   \n",
       "19995                 3               38                42872.0     6334.20   \n",
       "19996                43               11                36747.0   102216.02   \n",
       "19997                 3               38                18810.0     1930.19   \n",
       "19998                47               80                11443.0    24499.91   \n",
       "19999                 5               42                47504.0     5311.54   \n",
       "\n",
       "       cc_cons  \n",
       "0          NaN  \n",
       "1          NaN  \n",
       "2          NaN  \n",
       "3          NaN  \n",
       "4          NaN  \n",
       "...        ...  \n",
       "19995      NaN  \n",
       "19996      NaN  \n",
       "19997      NaN  \n",
       "19998      NaN  \n",
       "19999      NaN  \n",
       "\n",
       "[20000 rows x 44 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b7947fcd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x2880 with 44 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Determine the number of rows and columns for the subplot grid\n",
    "num_rows = 12\n",
    "num_cols = 4\n",
    "\n",
    "# Create the subplot grid\n",
    "fig, axes = plt.subplots(num_rows, num_cols, figsize=(20, 40))\n",
    "\n",
    "# Flatten the axes array for easier indexing\n",
    "axes = axes.flatten()\n",
    "\n",
    "# Iterate over each numeric column and create a box plot\n",
    "for i, column in enumerate(num_var.columns):\n",
    "    # Select the corresponding axis for the current box plot\n",
    "    ax = axes[i]\n",
    "    \n",
    "    # Create the box plot for the current column\n",
    "    ax.boxplot(num_var[column])\n",
    "    \n",
    "    # Set the title of the box plot to the column name\n",
    "    ax.set_title(column)\n",
    "    \n",
    "    # Remove x-axis ticks and labels for better visualization\n",
    "    ax.set_xticks([])\n",
    "    ax.set_xticklabels([])\n",
    "    \n",
    "    # Adjust the subplot spacing\n",
    "    fig.subplots_adjust(hspace=0.5)\n",
    "    \n",
    "# Remove any extra subplots if the number of columns is not divisible by the number of rows\n",
    "if len(num_var.columns) < num_rows * num_cols:\n",
    "    for j in range(len(num_var.columns), num_rows * num_cols):\n",
    "        fig.delaxes(axes[j])\n",
    "\n",
    "# Display the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "0f623222",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "223"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# maximum age cannot be more than 100\n",
    "merged_table.age.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "29735522",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "222.0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_table.age.quantile(0.99)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "4e4d036b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# clealy there are many attributes which require outlier treatment\n",
    "\n",
    "outl_col= ['age', 'Emp_Tenure_Years', 'cc_cons_apr','dc_cons_apr', \n",
    "          'cc_cons_may','dc_cons_may','cc_cons_jun','dc_cons_jun','cc_count_apr', \n",
    "          'cc_count_may','cc_count_jun','dc_count_apr', 'dc_count_may',\n",
    "          'card_lim','investment_1', 'investment_2','investment_3','investment_4',\n",
    "          'debit_amount_apr','credit_amount_apr','debit_count_apr',\n",
    "          'credit_count_apr', 'max_credit_amount_apr','debit_amount_may',\n",
    "          'credit_amount_may','credit_count_may','debit_count_may',\n",
    "          'max_credit_amount_may', 'debit_amount_jun','credit_amount_jun',\n",
    "          'credit_count_jun','max_credit_amount_jun', 'emi_active']        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f74994b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This method is not working as the outliers are more than expected.\n",
    "\n",
    "#for i in outl_col:\n",
    "#    merged_table[i].clip(lower = merged_table[i].quantile(0.01),\n",
    "#                         upper = merged_table[i].quantile(0.99), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "cb379960",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set the multiplier to adjust the IQR threshold\n",
    "multiplier = 1.5\n",
    "\n",
    "# Iterate over each column in the DataFrame\n",
    "for col in outl_col:\n",
    "# Calculate the first quartile (Q1) and third quartile (Q3) of the column\n",
    "    Q1 = merged_table[col].quantile(0.25)\n",
    "    Q3 = merged_table[col].quantile(0.75)\n",
    "    \n",
    "# Calculate the IQR (Interquartile Range)\n",
    "    IQR = Q3 - Q1\n",
    "    \n",
    "# Define the lower and upper bounds for outliers\n",
    "    lower_bound = Q1 - multiplier * IQR\n",
    "    upper_bound = Q3 + multiplier * IQR\n",
    "    \n",
    "# Identify and treat outliers by capping them within the lower and upper bounds\n",
    "    merged_table[col] = merged_table[col].apply(lambda x: lower_bound if \n",
    "                                  x < lower_bound else (upper_bound if \n",
    "                                                        x > upper_bound else x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "4fd402da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x2160 with 33 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create an 11x4 subplot grid\n",
    "fig, axes = plt.subplots(11, 4, figsize=(15, 30))\n",
    "\n",
    "# Flatten the axes array for easier indexing\n",
    "axes = axes.flatten()\n",
    "\n",
    "# Iterate over each numeric column and create a box plot\n",
    "for i, column in enumerate(outl_col):\n",
    "    # Select the corresponding axis for the current box plot\n",
    "    ax = axes[i]\n",
    "    \n",
    "    # Create the box plot for the current column\n",
    "    ax.boxplot(merged_table[column])\n",
    "    \n",
    "    # Set the title of the box plot to the column name\n",
    "    ax.set_title(column)\n",
    "    \n",
    "    # Remove x-axis ticks and labels for better visualization\n",
    "    ax.set_xticks([])\n",
    "    ax.set_xticklabels([])\n",
    "    \n",
    "    # Adjust the subplot spacing\n",
    "    fig.subplots_adjust(hspace=0.4)\n",
    "    \n",
    "# Remove any extra subplots if the number of columns is not divisible by 4\n",
    "if len(outl_col) < 44:\n",
    "    for j in range(len(outl_col), 44):\n",
    "        fig.delaxes(axes[j])\n",
    "\n",
    "# Display the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f717f0c2",
   "metadata": {},
   "source": [
    "#### Now lets do the feature selection using Ridge and Lasso regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "26c7aa42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'Emp_Tenure_Years', 'Tenure_with_Bank', 'region_code',\n",
       "       'NetBanking_Flag', 'Avg_days_between_transaction', 'cc_cons_apr',\n",
       "       'dc_cons_apr', 'cc_cons_may', 'dc_cons_may', 'cc_cons_jun',\n",
       "       'dc_cons_jun', 'cc_count_apr', 'cc_count_may', 'cc_count_jun',\n",
       "       'dc_count_apr', 'dc_count_may', 'dc_count_jun', 'card_lim',\n",
       "       'personal_loan_active', 'vehicle_loan_active', 'personal_loan_closed',\n",
       "       'vehicle_loan_closed', 'investment_1', 'investment_2', 'investment_3',\n",
       "       'investment_4', 'debit_amount_apr', 'credit_amount_apr',\n",
       "       'debit_count_apr', 'credit_count_apr', 'max_credit_amount_apr',\n",
       "       'debit_amount_may', 'credit_amount_may', 'credit_count_may',\n",
       "       'debit_count_may', 'max_credit_amount_may', 'debit_amount_jun',\n",
       "       'credit_amount_jun', 'credit_count_jun', 'debit_count_jun',\n",
       "       'max_credit_amount_jun', 'emi_active', 'cc_cons', 'account_type_saving',\n",
       "       'gender_M', 'Income_LOW', 'Income_MEDIUM'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_table.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "3564d008",
   "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>age</th>\n",
       "      <th>Emp_Tenure_Years</th>\n",
       "      <th>Tenure_with_Bank</th>\n",
       "      <th>region_code</th>\n",
       "      <th>NetBanking_Flag</th>\n",
       "      <th>Avg_days_between_transaction</th>\n",
       "      <th>cc_cons_apr</th>\n",
       "      <th>dc_cons_apr</th>\n",
       "      <th>cc_cons_may</th>\n",
       "      <th>dc_cons_may</th>\n",
       "      <th>...</th>\n",
       "      <th>credit_amount_jun</th>\n",
       "      <th>credit_count_jun</th>\n",
       "      <th>debit_count_jun</th>\n",
       "      <th>max_credit_amount_jun</th>\n",
       "      <th>emi_active</th>\n",
       "      <th>cc_cons</th>\n",
       "      <th>account_type_saving</th>\n",
       "      <th>gender_M</th>\n",
       "      <th>Income_LOW</th>\n",
       "      <th>Income_MEDIUM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63.0</td>\n",
       "      <td>30.10</td>\n",
       "      <td>10</td>\n",
       "      <td>628.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7998.48</td>\n",
       "      <td>2289.000</td>\n",
       "      <td>9553.00000</td>\n",
       "      <td>2879.580</td>\n",
       "      <td>...</td>\n",
       "      <td>177669.88</td>\n",
       "      <td>40.0</td>\n",
       "      <td>96</td>\n",
       "      <td>46088.0</td>\n",
       "      <td>2646.720</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>36.0</td>\n",
       "      <td>14.40</td>\n",
       "      <td>10</td>\n",
       "      <td>656.0</td>\n",
       "      <td>0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>16479.64</td>\n",
       "      <td>415.000</td>\n",
       "      <td>7386.49000</td>\n",
       "      <td>6763.000</td>\n",
       "      <td>...</td>\n",
       "      <td>177669.88</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12</td>\n",
       "      <td>17953.0</td>\n",
       "      <td>5469.790</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28.0</td>\n",
       "      <td>4.80</td>\n",
       "      <td>10</td>\n",
       "      <td>314.0</td>\n",
       "      <td>1</td>\n",
       "      <td>13.0</td>\n",
       "      <td>29272.03</td>\n",
       "      <td>20910.575</td>\n",
       "      <td>4170.53000</td>\n",
       "      <td>18207.585</td>\n",
       "      <td>...</td>\n",
       "      <td>35467.00</td>\n",
       "      <td>16.0</td>\n",
       "      <td>42</td>\n",
       "      <td>41121.0</td>\n",
       "      <td>7207.850</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32.0</td>\n",
       "      <td>9.60</td>\n",
       "      <td>2</td>\n",
       "      <td>614.0</td>\n",
       "      <td>1</td>\n",
       "      <td>19.0</td>\n",
       "      <td>9662.31</td>\n",
       "      <td>5306.390</td>\n",
       "      <td>5261.00000</td>\n",
       "      <td>11934.790</td>\n",
       "      <td>...</td>\n",
       "      <td>87204.35</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10</td>\n",
       "      <td>32003.0</td>\n",
       "      <td>591.340</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32.0</td>\n",
       "      <td>12.00</td>\n",
       "      <td>7</td>\n",
       "      <td>750.0</td>\n",
       "      <td>1</td>\n",
       "      <td>18.0</td>\n",
       "      <td>13239.86</td>\n",
       "      <td>7063.000</td>\n",
       "      <td>19193.13000</td>\n",
       "      <td>18207.585</td>\n",
       "      <td>...</td>\n",
       "      <td>83992.00</td>\n",
       "      <td>21.0</td>\n",
       "      <td>12</td>\n",
       "      <td>9626.0</td>\n",
       "      <td>2621.390</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19995</th>\n",
       "      <td>66.0</td>\n",
       "      <td>32.20</td>\n",
       "      <td>6</td>\n",
       "      <td>354.0</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4611.00</td>\n",
       "      <td>12015.500</td>\n",
       "      <td>41162.76625</td>\n",
       "      <td>550.500</td>\n",
       "      <td>...</td>\n",
       "      <td>31284.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>38</td>\n",
       "      <td>42872.0</td>\n",
       "      <td>6334.200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19996</th>\n",
       "      <td>53.0</td>\n",
       "      <td>19.80</td>\n",
       "      <td>5</td>\n",
       "      <td>809.0</td>\n",
       "      <td>0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>9545.00</td>\n",
       "      <td>2061.000</td>\n",
       "      <td>4269.27000</td>\n",
       "      <td>901.000</td>\n",
       "      <td>...</td>\n",
       "      <td>93108.00</td>\n",
       "      <td>43.0</td>\n",
       "      <td>11</td>\n",
       "      <td>36747.0</td>\n",
       "      <td>21122.065</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19997</th>\n",
       "      <td>66.0</td>\n",
       "      <td>18.40</td>\n",
       "      <td>1</td>\n",
       "      <td>466.0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2028.93</td>\n",
       "      <td>9454.990</td>\n",
       "      <td>11019.00000</td>\n",
       "      <td>4774.500</td>\n",
       "      <td>...</td>\n",
       "      <td>46800.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>38</td>\n",
       "      <td>18810.0</td>\n",
       "      <td>1930.190</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19998</th>\n",
       "      <td>32.0</td>\n",
       "      <td>6.00</td>\n",
       "      <td>8</td>\n",
       "      <td>619.0</td>\n",
       "      <td>0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>11456.23</td>\n",
       "      <td>3999.000</td>\n",
       "      <td>2650.08000</td>\n",
       "      <td>290.000</td>\n",
       "      <td>...</td>\n",
       "      <td>20201.00</td>\n",
       "      <td>46.5</td>\n",
       "      <td>80</td>\n",
       "      <td>11443.0</td>\n",
       "      <td>21122.065</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19999</th>\n",
       "      <td>83.0</td>\n",
       "      <td>37.35</td>\n",
       "      <td>7</td>\n",
       "      <td>882.0</td>\n",
       "      <td>1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>23551.00</td>\n",
       "      <td>16821.300</td>\n",
       "      <td>15746.06000</td>\n",
       "      <td>1310.590</td>\n",
       "      <td>...</td>\n",
       "      <td>38503.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>42</td>\n",
       "      <td>47504.0</td>\n",
       "      <td>5311.540</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20000 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        age  Emp_Tenure_Years  Tenure_with_Bank  region_code  NetBanking_Flag  \\\n",
       "0      63.0             30.10                10        628.0                1   \n",
       "1      36.0             14.40                10        656.0                0   \n",
       "2      28.0              4.80                10        314.0                1   \n",
       "3      32.0              9.60                 2        614.0                1   \n",
       "4      32.0             12.00                 7        750.0                1   \n",
       "...     ...               ...               ...          ...              ...   \n",
       "19995  66.0             32.20                 6        354.0                1   \n",
       "19996  53.0             19.80                 5        809.0                0   \n",
       "19997  66.0             18.40                 1        466.0                0   \n",
       "19998  32.0              6.00                 8        619.0                0   \n",
       "19999  83.0             37.35                 7        882.0                1   \n",
       "\n",
       "       Avg_days_between_transaction  cc_cons_apr  dc_cons_apr  cc_cons_may  \\\n",
       "0                               5.0      7998.48     2289.000   9553.00000   \n",
       "1                              12.0     16479.64      415.000   7386.49000   \n",
       "2                              13.0     29272.03    20910.575   4170.53000   \n",
       "3                              19.0      9662.31     5306.390   5261.00000   \n",
       "4                              18.0     13239.86     7063.000  19193.13000   \n",
       "...                             ...          ...          ...          ...   \n",
       "19995                           7.0      4611.00    12015.500  41162.76625   \n",
       "19996                          14.0      9545.00     2061.000   4269.27000   \n",
       "19997                          10.0      2028.93     9454.990  11019.00000   \n",
       "19998                          17.0     11456.23     3999.000   2650.08000   \n",
       "19999                          10.0     23551.00    16821.300  15746.06000   \n",
       "\n",
       "       dc_cons_may  ...  credit_amount_jun  credit_count_jun  debit_count_jun  \\\n",
       "0         2879.580  ...          177669.88              40.0               96   \n",
       "1         6763.000  ...          177669.88               7.0               12   \n",
       "2        18207.585  ...           35467.00              16.0               42   \n",
       "3        11934.790  ...           87204.35               2.0               10   \n",
       "4        18207.585  ...           83992.00              21.0               12   \n",
       "...            ...  ...                ...               ...              ...   \n",
       "19995      550.500  ...           31284.00               3.0               38   \n",
       "19996      901.000  ...           93108.00              43.0               11   \n",
       "19997     4774.500  ...           46800.00               3.0               38   \n",
       "19998      290.000  ...           20201.00              46.5               80   \n",
       "19999     1310.590  ...           38503.00               5.0               42   \n",
       "\n",
       "       max_credit_amount_jun  emi_active  cc_cons  account_type_saving  \\\n",
       "0                    46088.0    2646.720      NaN                    0   \n",
       "1                    17953.0    5469.790      NaN                    0   \n",
       "2                    41121.0    7207.850      NaN                    0   \n",
       "3                    32003.0     591.340      NaN                    0   \n",
       "4                     9626.0    2621.390      NaN                    0   \n",
       "...                      ...         ...      ...                  ...   \n",
       "19995                42872.0    6334.200      NaN                    0   \n",
       "19996                36747.0   21122.065      NaN                    0   \n",
       "19997                18810.0    1930.190      NaN                    0   \n",
       "19998                11443.0   21122.065      NaN                    0   \n",
       "19999                47504.0    5311.540      NaN                    0   \n",
       "\n",
       "       gender_M  Income_LOW  Income_MEDIUM  \n",
       "0             1           0              1  \n",
       "1             1           0              1  \n",
       "2             0           0              1  \n",
       "3             1           0              1  \n",
       "4             1           0              0  \n",
       "...         ...         ...            ...  \n",
       "19995         0           0              1  \n",
       "19996         1           0              1  \n",
       "19997         0           0              1  \n",
       "19998         1           1              0  \n",
       "19999         0           1              0  \n",
       "\n",
       "[20000 rows x 48 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "4f3c5b7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the ML libraries\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "f50acda3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        True\n",
       "1        True\n",
       "2        True\n",
       "3        True\n",
       "4        True\n",
       "         ... \n",
       "19995    True\n",
       "19996    True\n",
       "19997    True\n",
       "19998    True\n",
       "19999    True\n",
       "Name: cc_cons, Length: 20000, dtype: bool"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_table['cc_cons'].isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "a93073ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "Non_null_df = merged_table[~merged_table['cc_cons'].isnull()]\n",
    "validation_df= merged_table[merged_table['cc_cons'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "c605d17c",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = Non_null_df.loc[:, Non_null_df.columns == 'cc_cons']\n",
    "X = Non_null_df.loc[:, Non_null_df.columns != 'cc_cons']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "ea556a1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#from sklearn.tree import DecisionTreeRegressor\n",
    "#from sklearn.model_selection import train_test_split\n",
    "#from sklearn.metrics import mean_squared_error\n",
    "#\n",
    "## Assuming 'X' is your feature matrix and 'y' is your target variable\n",
    "#\n",
    "## Split the data into training and testing sets\n",
    "#X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
    "#\n",
    "## Create the Decision Tree Regressor model\n",
    "#tree_reg = DecisionTreeRegressor()\n",
    "#\n",
    "## Train the model\n",
    "#tree_reg.fit(X_train, y_train)\n",
    "#\n",
    "## Make predictions on the test set\n",
    "#y_pred = tree_reg.predict(X_test)\n",
    "#\n",
    "## Calculate the Mean Squared Error\n",
    "#mse = mean_squared_error(y_test, y_pred)\n",
    "#print(\"Mean Squared Error:\", mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "7045947d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#### Clarly Decision tree regressor is not giving a good MSE \n",
    "# so let go with linear regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "5e03ca71",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Before linear regression we will do feature reduction using\n",
    "# using ridge and lasso regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "723451ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "\n",
    "#scale all column of data, this will give a numpy array\n",
    "x_scaled = preprocessing.scale(X)\n",
    "x_scaled = pd.DataFrame(x_scaled, columns= X.columns)\n",
    "\n",
    "y_scaled = preprocessing.scale(y)\n",
    "y_scaled = pd.DataFrame(y_scaled, columns= y.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "51784f4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,\n",
    "                                                    y,\n",
    "                                                    test_size=0.3,\n",
    "                                                    random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "c6ccbac9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train: (10500, 47)\n",
      "X_test: (4500, 47)\n",
      "y_train: (10500, 1)\n",
      "y_test: (4500, 1)\n"
     ]
    }
   ],
   "source": [
    "print('X_train:', X_train.shape)\n",
    "print('X_test:', X_test.shape)\n",
    "print('y_train:', y_train.shape)\n",
    "print('y_test:', y_test.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "bbc74b6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build a regression model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "6cdb3650",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_reg = LinearRegression()\n",
    "lin_reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "ed8d195a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The coeficient for age is 53.31983921035553\n",
      "The coeficient for Emp_Tenure_Years is -205.49167410783042\n",
      "The coeficient for Tenure_with_Bank is 323.18531906721097\n",
      "The coeficient for region_code is -7.479038270540438\n",
      "The coeficient for NetBanking_Flag is 1311.6854755351944\n",
      "The coeficient for Avg_days_between_transaction is -165.2474356356223\n",
      "The coeficient for cc_cons_apr is -0.04950591028034235\n",
      "The coeficient for dc_cons_apr is 0.12950802648897097\n",
      "The coeficient for cc_cons_may is 0.15272801452186568\n",
      "The coeficient for dc_cons_may is -0.24769085185310402\n",
      "The coeficient for cc_cons_jun is 0.1104674477446359\n",
      "The coeficient for dc_cons_jun is -0.09190950331074887\n",
      "The coeficient for cc_count_apr is -22.283835221459082\n",
      "The coeficient for cc_count_may is 73.8756407480301\n",
      "The coeficient for cc_count_jun is 5.16962665418685\n",
      "The coeficient for dc_count_apr is -7.912540773178816\n",
      "The coeficient for dc_count_may is 105.4634236201698\n",
      "The coeficient for dc_count_jun is 0.04368986734082081\n",
      "The coeficient for card_lim is -0.004056162777516192\n",
      "The coeficient for personal_loan_active is -1.4102852219366468e-10\n",
      "The coeficient for vehicle_loan_active is 2.5437429940211587e-11\n",
      "The coeficient for personal_loan_closed is 1.037008257043226e-10\n",
      "The coeficient for vehicle_loan_closed is -1.3726442205097555e-09\n",
      "The coeficient for investment_1 is 0.00024727996636642047\n",
      "The coeficient for investment_2 is -0.00244965529361707\n",
      "The coeficient for investment_3 is -0.031311686010539574\n",
      "The coeficient for investment_4 is -0.12806849345203553\n",
      "The coeficient for debit_amount_apr is -0.0234041773871958\n",
      "The coeficient for credit_amount_apr is 0.030921467551841886\n",
      "The coeficient for debit_count_apr is -1.468746698152279\n",
      "The coeficient for credit_count_apr is 5.783835100805628\n",
      "The coeficient for max_credit_amount_apr is 0.021453321433643424\n",
      "The coeficient for debit_amount_may is 0.004861357590721482\n",
      "The coeficient for credit_amount_may is -0.012529589828915277\n",
      "The coeficient for credit_count_may is 60.00002177183333\n",
      "The coeficient for debit_count_may is 9.655456148371549\n",
      "The coeficient for max_credit_amount_may is 0.029255213507216382\n",
      "The coeficient for debit_amount_jun is 0.011323450193842177\n",
      "The coeficient for credit_amount_jun is 0.00446624820374808\n",
      "The coeficient for credit_count_jun is 10.037563580590925\n",
      "The coeficient for debit_count_jun is -7.3966900020137505\n",
      "The coeficient for max_credit_amount_jun is 0.015166392001916051\n",
      "The coeficient for emi_active is 0.2520569548390128\n",
      "The coeficient for account_type_saving is 2509.1198668040547\n",
      "The coeficient for gender_M is 590.9995821131226\n",
      "The coeficient for Income_LOW is -219.6249734707248\n",
      "The coeficient for Income_MEDIUM is -1311.7872651050259\n"
     ]
    }
   ],
   "source": [
    "for idx, col_name in enumerate(X_train.columns):\n",
    "    print(\"The coeficient for {} is {}\".format(col_name, lin_reg.coef_[0][idx]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "3505f16a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The intercept of our model using linear regression is 50874.81821186285\n"
     ]
    }
   ],
   "source": [
    "intercept = lin_reg.intercept_[0]\n",
    "\n",
    "print('The intercept of our model using linear regression is {}'.format(intercept))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bad27e7b",
   "metadata": {},
   "source": [
    "### Ridge Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "8f833095",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge(alpha=0.3)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge = Ridge(alpha = .3) \n",
    "#coefficients are prevented to be too big by this alpha value\n",
    "ridge.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "7d6d2648",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ridge model coefficient for age is :53.3197379900505\n",
      "Ridge model coefficient for Emp_Tenure_Years is :-205.49162249775853\n",
      "Ridge model coefficient for Tenure_with_Bank is :323.1849897956099\n",
      "Ridge model coefficient for region_code is :-7.47904159111954\n",
      "Ridge model coefficient for NetBanking_Flag is :1311.502383117764\n",
      "Ridge model coefficient for Avg_days_between_transaction is :-165.2476217616334\n",
      "Ridge model coefficient for cc_cons_apr is :-0.049505616139724706\n",
      "Ridge model coefficient for dc_cons_apr is :0.1295076330974963\n",
      "Ridge model coefficient for cc_cons_may is :0.1527281103295366\n",
      "Ridge model coefficient for dc_cons_may is :-0.24769013447701418\n",
      "Ridge model coefficient for cc_cons_jun is :0.11046760493322152\n",
      "Ridge model coefficient for dc_cons_jun is :-0.09190969610731715\n",
      "Ridge model coefficient for cc_count_apr is :-22.28388885619922\n",
      "Ridge model coefficient for cc_count_may is :73.8756549782463\n",
      "Ridge model coefficient for cc_count_jun is :5.169525209313887\n",
      "Ridge model coefficient for dc_count_apr is :-7.912463070301792\n",
      "Ridge model coefficient for dc_count_may is :105.46360332323722\n",
      "Ridge model coefficient for dc_count_jun is :0.04348118507239454\n",
      "Ridge model coefficient for card_lim is :-0.004056150041621431\n",
      "Ridge model coefficient for personal_loan_active is :0.0\n",
      "Ridge model coefficient for vehicle_loan_active is :0.0\n",
      "Ridge model coefficient for personal_loan_closed is :0.0\n",
      "Ridge model coefficient for vehicle_loan_closed is :0.0\n",
      "Ridge model coefficient for investment_1 is :0.0002472553489601957\n",
      "Ridge model coefficient for investment_2 is :-0.0024496595135893515\n",
      "Ridge model coefficient for investment_3 is :-0.031311659842248395\n",
      "Ridge model coefficient for investment_4 is :-0.12806887559204094\n",
      "Ridge model coefficient for debit_amount_apr is :-0.02340425742669956\n",
      "Ridge model coefficient for credit_amount_apr is :0.030921485389918416\n",
      "Ridge model coefficient for debit_count_apr is :-1.4687270764128508\n",
      "Ridge model coefficient for credit_count_apr is :5.783831450859495\n",
      "Ridge model coefficient for max_credit_amount_apr is :0.021453098475633453\n",
      "Ridge model coefficient for debit_amount_may is :0.004861369719170574\n",
      "Ridge model coefficient for credit_amount_may is :-0.012529599388339521\n",
      "Ridge model coefficient for credit_count_may is :60.00030889089571\n",
      "Ridge model coefficient for debit_count_may is :9.655457206363844\n",
      "Ridge model coefficient for max_credit_amount_may is :0.029255269661737363\n",
      "Ridge model coefficient for debit_amount_jun is :0.01132336217821812\n",
      "Ridge model coefficient for credit_amount_jun is :0.004466263217493209\n",
      "Ridge model coefficient for credit_count_jun is :10.037716935285756\n",
      "Ridge model coefficient for debit_count_jun is :-7.396643386520949\n",
      "Ridge model coefficient for max_credit_amount_jun is :0.015166481837214797\n",
      "Ridge model coefficient for emi_active is :0.2520570534015489\n",
      "Ridge model coefficient for account_type_saving is :2508.5712819491796\n",
      "Ridge model coefficient for gender_M is :590.8465070826431\n",
      "Ridge model coefficient for Income_LOW is :-219.04986605179252\n",
      "Ridge model coefficient for Income_MEDIUM is :-1311.1731148851545\n"
     ]
    }
   ],
   "source": [
    "for i,col in enumerate(X_train.columns):\n",
    "    print(\"Ridge model coefficient for {} is :{}\".format(col, ridge.coef_[0][i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "6dd4ffc2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The intercept of our model using ridge regression is 50874.6243237836\n"
     ]
    }
   ],
   "source": [
    "r_intercept = ridge.intercept_[0]\n",
    "print('The intercept of our model using ridge regression is {}'.format(r_intercept))"
   ]
  },
  {
   "cell_type": "raw",
   "id": "4e507483",
   "metadata": {},
   "source": [
    "If the intercept term in a ridge regression model is negative, it simply means that the model predicts a negative value for the target variable when all the features (independent variables) are zero. The intercept in a linear regression model represents the estimated value of the dependent variable when all the independent variables are zero.\n",
    "\n",
    "In the context of ridge regression, the negative intercept does not carry a specific meaning on its own. It is primarily an artifact of the model fitting process and the regularization technique. Ridge regression applies a penalty term to the coefficients of the features, which can affect the intercept as well."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "841a6220",
   "metadata": {},
   "source": [
    "### Lasso Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "56d79066",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lasso(alpha=0.1)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso = Lasso(alpha = .1) \n",
    "#coefficients are prevented to be too big by this alpha value\n",
    "lasso.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "f0e4467e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lasso model coefficient for age is :53.317715402401774\n",
      "Lasso model coefficient for Emp_Tenure_Years is :-205.4891799866099\n",
      "Lasso model coefficient for Tenure_with_Bank is :323.1750769156077\n",
      "Lasso model coefficient for region_code is :-7.479013314107411\n",
      "Lasso model coefficient for NetBanking_Flag is :1311.2061160392068\n",
      "Lasso model coefficient for Avg_days_between_transaction is :-165.24446655269801\n",
      "Lasso model coefficient for cc_cons_apr is :-0.04950508781179882\n",
      "Lasso model coefficient for dc_cons_apr is :0.12950631178669858\n",
      "Lasso model coefficient for cc_cons_may is :0.15272893844098845\n",
      "Lasso model coefficient for dc_cons_may is :-0.24768817101739954\n",
      "Lasso model coefficient for cc_cons_jun is :0.11046866727634509\n",
      "Lasso model coefficient for dc_cons_jun is :-0.09190953141891171\n",
      "Lasso model coefficient for cc_count_apr is :-22.283817255992986\n",
      "Lasso model coefficient for cc_count_may is :73.87553290273677\n",
      "Lasso model coefficient for cc_count_jun is :5.169462013831336\n",
      "Lasso model coefficient for dc_count_apr is :-7.912170125718337\n",
      "Lasso model coefficient for dc_count_may is :105.46370901507903\n",
      "Lasso model coefficient for dc_count_jun is :0.043039443498639716\n",
      "Lasso model coefficient for card_lim is :-0.004056098392364688\n",
      "Lasso model coefficient for personal_loan_active is :0.0\n",
      "Lasso model coefficient for vehicle_loan_active is :0.0\n",
      "Lasso model coefficient for personal_loan_closed is :0.0\n",
      "Lasso model coefficient for vehicle_loan_closed is :0.0\n",
      "Lasso model coefficient for investment_1 is :0.00024721206310430945\n",
      "Lasso model coefficient for investment_2 is :-0.002449683227518887\n",
      "Lasso model coefficient for investment_3 is :-0.031311564539402204\n",
      "Lasso model coefficient for investment_4 is :-0.12806955107673362\n",
      "Lasso model coefficient for debit_amount_apr is :-0.02340447826421801\n",
      "Lasso model coefficient for credit_amount_apr is :0.03092152030254736\n",
      "Lasso model coefficient for debit_count_apr is :-1.4686758041512022\n",
      "Lasso model coefficient for credit_count_apr is :5.782858976792293\n",
      "Lasso model coefficient for max_credit_amount_apr is :0.021452808370838742\n",
      "Lasso model coefficient for debit_amount_may is :0.004861342022818073\n",
      "Lasso model coefficient for credit_amount_may is :-0.012529606385602753\n",
      "Lasso model coefficient for credit_count_may is :59.99905164093946\n",
      "Lasso model coefficient for debit_count_may is :9.65549272612475\n",
      "Lasso model coefficient for max_credit_amount_may is :0.029255514582223897\n",
      "Lasso model coefficient for debit_amount_jun is :0.011323235144886673\n",
      "Lasso model coefficient for credit_amount_jun is :0.004466376037854869\n",
      "Lasso model coefficient for credit_count_jun is :10.037715248120303\n",
      "Lasso model coefficient for debit_count_jun is :-7.396360086162177\n",
      "Lasso model coefficient for max_credit_amount_jun is :0.015166637428778634\n",
      "Lasso model coefficient for emi_active is :0.25205670120880347\n",
      "Lasso model coefficient for account_type_saving is :2508.3575953345994\n",
      "Lasso model coefficient for gender_M is :590.1134201120744\n",
      "Lasso model coefficient for Income_LOW is :-216.7227106915837\n",
      "Lasso model coefficient for Income_MEDIUM is :-1309.0762863351501\n"
     ]
    }
   ],
   "source": [
    "for i,col in enumerate(X_train.columns):\n",
    "    print(\"Lasso model coefficient for {} is :{}\".format(col, lasso.coef_[i]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bceeb333",
   "metadata": {},
   "source": [
    "#### The coeficients which are coming as close to zero are not needed by the model ie. they are least important features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "97e7af5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Based on lasso regression we have come up with the selected features based on 0.01 threshold\n",
    "\n",
    "selected_features = ['age','Emp_Tenure_Years','Tenure_with_Bank','region_code','NetBanking_Flag',\n",
    "                     'Avg_days_between_transaction','cc_cons_apr','dc_cons_apr','cc_cons_may',\n",
    "                     'dc_cons_may','cc_cons_jun','dc_cons_jun','cc_count_apr','dc_count_apr',\n",
    "                     'dc_count_may','investment_4','debit_count_apr','credit_count_apr','credit_count_may',\n",
    "                     'debit_count_may','credit_count_jun','debit_count_jun','emi_active',\n",
    "                     'account_type_saving','gender_M','Income_LOW','Income_MEDIUM','cc_cons']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "6e618b2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#feature reduced dataset\n",
    "fr_merged = merged_table[selected_features]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85d1f481",
   "metadata": {},
   "source": [
    "### We need to create clusters using K-means."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "3b7151aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.stats as stats\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "3d5b99c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Intitlaize the standard scaler model\n",
    "sc = StandardScaler()\n",
    "# You can use min-max scaler aswell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "334b9d5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit the model on the train dataset\n",
    "sc_model = sc.fit(fr_merged)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "dbeb2cec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler()"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sc_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "a25f027b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Perfrom Transformation using the model object\n",
    "\n",
    "fr_merged_scaled = sc_model.transform(fr_merged)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "b428a917",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.34082862,  1.64023862,  1.56911251, ..., -0.62453764,\n",
       "         0.75203572,         nan],\n",
       "       [-0.5708024 ,  0.12644559,  1.56911251, ..., -0.62453764,\n",
       "         0.75203572,         nan],\n",
       "       [-1.1372116 , -0.79918582,  1.56911251, ..., -0.62453764,\n",
       "         0.75203572,         nan],\n",
       "       ...,\n",
       "       [ 1.55323207,  0.51212534, -1.56636026, ..., -0.62453764,\n",
       "         0.75203572,         nan],\n",
       "       [-0.854007  , -0.68348189,  0.87234078, ...,  1.60118452,\n",
       "        -1.32972407,         nan],\n",
       "       [ 2.7568516 ,  2.33928318,  0.52395492, ...,  1.60118452,\n",
       "        -1.32972407,         nan]])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fr_merged_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "d6f8b12b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting the output into a dataframe and adding proper column names\n",
    "fr_merged_scaled = pd.DataFrame(fr_merged_scaled)\n",
    "fr_merged_scaled.columns = list(fr_merged.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "d16d8108",
   "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>age</th>\n",
       "      <th>Emp_Tenure_Years</th>\n",
       "      <th>Tenure_with_Bank</th>\n",
       "      <th>region_code</th>\n",
       "      <th>NetBanking_Flag</th>\n",
       "      <th>Avg_days_between_transaction</th>\n",
       "      <th>cc_cons_apr</th>\n",
       "      <th>dc_cons_apr</th>\n",
       "      <th>cc_cons_may</th>\n",
       "      <th>dc_cons_may</th>\n",
       "      <th>...</th>\n",
       "      <th>credit_count_may</th>\n",
       "      <th>debit_count_may</th>\n",
       "      <th>credit_count_jun</th>\n",
       "      <th>debit_count_jun</th>\n",
       "      <th>emi_active</th>\n",
       "      <th>account_type_saving</th>\n",
       "      <th>gender_M</th>\n",
       "      <th>Income_LOW</th>\n",
       "      <th>Income_MEDIUM</th>\n",
       "      <th>cc_cons</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.340829</td>\n",
       "      <td>1.640239</td>\n",
       "      <td>1.569113</td>\n",
       "      <td>0.131946</td>\n",
       "      <td>0.626704</td>\n",
       "      <td>-1.103057</td>\n",
       "      <td>-0.509852</td>\n",
       "      <td>-0.653404</td>\n",
       "      <td>-0.292422</td>\n",
       "      <td>-0.535186</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.371390</td>\n",
       "      <td>-0.682123</td>\n",
       "      <td>1.498848</td>\n",
       "      <td>1.467261</td>\n",
       "      <td>-0.586325</td>\n",
       "      <td>-0.426162</td>\n",
       "      <td>0.396070</td>\n",
       "      <td>-0.624538</td>\n",
       "      <td>0.752036</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.570802</td>\n",
       "      <td>0.126446</td>\n",
       "      <td>1.569113</td>\n",
       "      <td>0.259165</td>\n",
       "      <td>-1.595650</td>\n",
       "      <td>0.174314</td>\n",
       "      <td>0.107963</td>\n",
       "      <td>-0.951421</td>\n",
       "      <td>-0.468388</td>\n",
       "      <td>0.178802</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.486587</td>\n",
       "      <td>0.586120</td>\n",
       "      <td>-0.531684</td>\n",
       "      <td>-0.927295</td>\n",
       "      <td>-0.154302</td>\n",
       "      <td>-0.426162</td>\n",
       "      <td>0.396070</td>\n",
       "      <td>-0.624538</td>\n",
       "      <td>0.752036</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.137212</td>\n",
       "      <td>-0.799186</td>\n",
       "      <td>1.569113</td>\n",
       "      <td>-1.294715</td>\n",
       "      <td>0.626704</td>\n",
       "      <td>0.356796</td>\n",
       "      <td>1.039832</td>\n",
       "      <td>2.307933</td>\n",
       "      <td>-0.729590</td>\n",
       "      <td>2.282952</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.256192</td>\n",
       "      <td>1.077887</td>\n",
       "      <td>0.022097</td>\n",
       "      <td>-0.072096</td>\n",
       "      <td>0.111678</td>\n",
       "      <td>-0.426162</td>\n",
       "      <td>-2.524803</td>\n",
       "      <td>-0.624538</td>\n",
       "      <td>0.752036</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.854007</td>\n",
       "      <td>-0.336370</td>\n",
       "      <td>-1.217974</td>\n",
       "      <td>0.068337</td>\n",
       "      <td>0.626704</td>\n",
       "      <td>1.451686</td>\n",
       "      <td>-0.388650</td>\n",
       "      <td>-0.173557</td>\n",
       "      <td>-0.641022</td>\n",
       "      <td>1.129664</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.716981</td>\n",
       "      <td>0.974357</td>\n",
       "      <td>-0.839341</td>\n",
       "      <td>-0.984308</td>\n",
       "      <td>-0.900866</td>\n",
       "      <td>-0.426162</td>\n",
       "      <td>0.396070</td>\n",
       "      <td>-0.624538</td>\n",
       "      <td>0.752036</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.854007</td>\n",
       "      <td>-0.104962</td>\n",
       "      <td>0.523955</td>\n",
       "      <td>0.686255</td>\n",
       "      <td>0.626704</td>\n",
       "      <td>1.269204</td>\n",
       "      <td>-0.128041</td>\n",
       "      <td>0.105792</td>\n",
       "      <td>0.490556</td>\n",
       "      <td>2.282952</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.601784</td>\n",
       "      <td>2.993192</td>\n",
       "      <td>0.329754</td>\n",
       "      <td>-0.927295</td>\n",
       "      <td>-0.590201</td>\n",
       "      <td>-0.426162</td>\n",
       "      <td>0.396070</td>\n",
       "      <td>-0.624538</td>\n",
       "      <td>-1.329724</td>\n",
       "      <td>NaN</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19995</th>\n",
       "      <td>1.553232</td>\n",
       "      <td>1.842720</td>\n",
       "      <td>0.175569</td>\n",
       "      <td>-1.112975</td>\n",
       "      <td>0.626704</td>\n",
       "      <td>-0.738094</td>\n",
       "      <td>-0.756615</td>\n",
       "      <td>0.893374</td>\n",
       "      <td>2.274946</td>\n",
       "      <td>-0.963400</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.947375</td>\n",
       "      <td>-0.837418</td>\n",
       "      <td>-0.777809</td>\n",
       "      <td>-0.186123</td>\n",
       "      <td>-0.022019</td>\n",
       "      <td>-0.426162</td>\n",
       "      <td>-2.524803</td>\n",
       "      <td>-0.624538</td>\n",
       "      <td>0.752036</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19996</th>\n",
       "      <td>0.632817</td>\n",
       "      <td>0.647113</td>\n",
       "      <td>-0.172817</td>\n",
       "      <td>0.954322</td>\n",
       "      <td>-1.595650</td>\n",
       "      <td>0.539278</td>\n",
       "      <td>-0.397195</td>\n",
       "      <td>-0.689662</td>\n",
       "      <td>-0.721571</td>\n",
       "      <td>-0.898958</td>\n",
       "      <td>...</td>\n",
       "      <td>1.874953</td>\n",
       "      <td>-0.759770</td>\n",
       "      <td>1.683442</td>\n",
       "      <td>-0.955801</td>\n",
       "      <td>2.241010</td>\n",
       "      <td>-0.426162</td>\n",
       "      <td>0.396070</td>\n",
       "      <td>-0.624538</td>\n",
       "      <td>0.752036</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19997</th>\n",
       "      <td>1.553232</td>\n",
       "      <td>0.512125</td>\n",
       "      <td>-1.566360</td>\n",
       "      <td>-0.604102</td>\n",
       "      <td>-1.595650</td>\n",
       "      <td>-0.190649</td>\n",
       "      <td>-0.944708</td>\n",
       "      <td>0.486184</td>\n",
       "      <td>-0.173353</td>\n",
       "      <td>-0.186794</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.716981</td>\n",
       "      <td>2.993192</td>\n",
       "      <td>-0.777809</td>\n",
       "      <td>-0.186123</td>\n",
       "      <td>-0.695978</td>\n",
       "      <td>-0.426162</td>\n",
       "      <td>-2.524803</td>\n",
       "      <td>-0.624538</td>\n",
       "      <td>0.752036</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19998</th>\n",
       "      <td>-0.854007</td>\n",
       "      <td>-0.683482</td>\n",
       "      <td>0.872341</td>\n",
       "      <td>0.091055</td>\n",
       "      <td>-1.595650</td>\n",
       "      <td>1.086722</td>\n",
       "      <td>-0.257970</td>\n",
       "      <td>-0.381467</td>\n",
       "      <td>-0.853083</td>\n",
       "      <td>-1.011294</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.601784</td>\n",
       "      <td>1.077887</td>\n",
       "      <td>1.898802</td>\n",
       "      <td>1.011155</td>\n",
       "      <td>2.241010</td>\n",
       "      <td>-0.426162</td>\n",
       "      <td>0.396070</td>\n",
       "      <td>1.601185</td>\n",
       "      <td>-1.329724</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19999</th>\n",
       "      <td>2.756852</td>\n",
       "      <td>2.339283</td>\n",
       "      <td>0.523955</td>\n",
       "      <td>1.285998</td>\n",
       "      <td>0.626704</td>\n",
       "      <td>-0.190649</td>\n",
       "      <td>0.623081</td>\n",
       "      <td>1.657627</td>\n",
       "      <td>0.210583</td>\n",
       "      <td>-0.823653</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.371390</td>\n",
       "      <td>-0.837418</td>\n",
       "      <td>-0.654747</td>\n",
       "      <td>-0.072096</td>\n",
       "      <td>-0.178520</td>\n",
       "      <td>-0.426162</td>\n",
       "      <td>-2.524803</td>\n",
       "      <td>1.601185</td>\n",
       "      <td>-1.329724</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20000 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            age  Emp_Tenure_Years  Tenure_with_Bank  region_code  \\\n",
       "0      1.340829          1.640239          1.569113     0.131946   \n",
       "1     -0.570802          0.126446          1.569113     0.259165   \n",
       "2     -1.137212         -0.799186          1.569113    -1.294715   \n",
       "3     -0.854007         -0.336370         -1.217974     0.068337   \n",
       "4     -0.854007         -0.104962          0.523955     0.686255   \n",
       "...         ...               ...               ...          ...   \n",
       "19995  1.553232          1.842720          0.175569    -1.112975   \n",
       "19996  0.632817          0.647113         -0.172817     0.954322   \n",
       "19997  1.553232          0.512125         -1.566360    -0.604102   \n",
       "19998 -0.854007         -0.683482          0.872341     0.091055   \n",
       "19999  2.756852          2.339283          0.523955     1.285998   \n",
       "\n",
       "       NetBanking_Flag  Avg_days_between_transaction  cc_cons_apr  \\\n",
       "0             0.626704                     -1.103057    -0.509852   \n",
       "1            -1.595650                      0.174314     0.107963   \n",
       "2             0.626704                      0.356796     1.039832   \n",
       "3             0.626704                      1.451686    -0.388650   \n",
       "4             0.626704                      1.269204    -0.128041   \n",
       "...                ...                           ...          ...   \n",
       "19995         0.626704                     -0.738094    -0.756615   \n",
       "19996        -1.595650                      0.539278    -0.397195   \n",
       "19997        -1.595650                     -0.190649    -0.944708   \n",
       "19998        -1.595650                      1.086722    -0.257970   \n",
       "19999         0.626704                     -0.190649     0.623081   \n",
       "\n",
       "       dc_cons_apr  cc_cons_may  dc_cons_may  ...  credit_count_may  \\\n",
       "0        -0.653404    -0.292422    -0.535186  ...         -0.371390   \n",
       "1        -0.951421    -0.468388     0.178802  ...         -0.486587   \n",
       "2         2.307933    -0.729590     2.282952  ...         -0.256192   \n",
       "3        -0.173557    -0.641022     1.129664  ...         -0.716981   \n",
       "4         0.105792     0.490556     2.282952  ...         -0.601784   \n",
       "...            ...          ...          ...  ...               ...   \n",
       "19995     0.893374     2.274946    -0.963400  ...         -0.947375   \n",
       "19996    -0.689662    -0.721571    -0.898958  ...          1.874953   \n",
       "19997     0.486184    -0.173353    -0.186794  ...         -0.716981   \n",
       "19998    -0.381467    -0.853083    -1.011294  ...         -0.601784   \n",
       "19999     1.657627     0.210583    -0.823653  ...         -0.371390   \n",
       "\n",
       "       debit_count_may  credit_count_jun  debit_count_jun  emi_active  \\\n",
       "0            -0.682123          1.498848         1.467261   -0.586325   \n",
       "1             0.586120         -0.531684        -0.927295   -0.154302   \n",
       "2             1.077887          0.022097        -0.072096    0.111678   \n",
       "3             0.974357         -0.839341        -0.984308   -0.900866   \n",
       "4             2.993192          0.329754        -0.927295   -0.590201   \n",
       "...                ...               ...              ...         ...   \n",
       "19995        -0.837418         -0.777809        -0.186123   -0.022019   \n",
       "19996        -0.759770          1.683442        -0.955801    2.241010   \n",
       "19997         2.993192         -0.777809        -0.186123   -0.695978   \n",
       "19998         1.077887          1.898802         1.011155    2.241010   \n",
       "19999        -0.837418         -0.654747        -0.072096   -0.178520   \n",
       "\n",
       "       account_type_saving  gender_M  Income_LOW  Income_MEDIUM  cc_cons  \n",
       "0                -0.426162  0.396070   -0.624538       0.752036      NaN  \n",
       "1                -0.426162  0.396070   -0.624538       0.752036      NaN  \n",
       "2                -0.426162 -2.524803   -0.624538       0.752036      NaN  \n",
       "3                -0.426162  0.396070   -0.624538       0.752036      NaN  \n",
       "4                -0.426162  0.396070   -0.624538      -1.329724      NaN  \n",
       "...                    ...       ...         ...            ...      ...  \n",
       "19995            -0.426162 -2.524803   -0.624538       0.752036      NaN  \n",
       "19996            -0.426162  0.396070   -0.624538       0.752036      NaN  \n",
       "19997            -0.426162 -2.524803   -0.624538       0.752036      NaN  \n",
       "19998            -0.426162  0.396070    1.601185      -1.329724      NaN  \n",
       "19999            -0.426162 -2.524803    1.601185      -1.329724      NaN  \n",
       "\n",
       "[20000 rows x 28 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fr_merged_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "793825ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([        nan, -0.35982954, -0.46058955, ...,  3.57404967,\n",
       "       -0.26973836, -0.3983479 ])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fr_merged_scaled.cc_cons.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "c586f22d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([    nan,  20014.,  10173., ..., 404227.,  28813.,  16252.])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fr_merged.cc_cons.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "c11d16ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1.500000e+04\n",
       "mean     1.845191e-17\n",
       "std      1.000033e+00\n",
       "min     -5.647488e-01\n",
       "25%     -4.954527e-01\n",
       "50%     -4.279175e-01\n",
       "75%     -3.588901e-01\n",
       "max      3.616592e+00\n",
       "Name: cc_cons, dtype: float64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fr_merged_scaled.cc_cons.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "bdcacb6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     15000.000000\n",
       "mean      55157.729867\n",
       "std       97670.972252\n",
       "min           0.000000\n",
       "25%        6768.000000\n",
       "50%       13364.000000\n",
       "75%       20105.750000\n",
       "max      408382.000000\n",
       "Name: cc_cons, dtype: float64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fr_merged.cc_cons.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "ee911274",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_fr_merged_scaled = fr_merged_scaled.loc[:, fr_merged_scaled.columns != 'cc_cons']\n",
    "y_fr_merged_scaled = fr_merged_scaled.loc[:, fr_merged_scaled.columns == 'cc_cons']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b3d3a0b",
   "metadata": {},
   "source": [
    "#### Silhouette Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "2f0508f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We have to apply k-means clustering to X_fr_merged_scaled, \n",
    "# as kmeans is an unsuppervised learning method.\n",
    "\n",
    "k_range = range(2,21)\n",
    "silhouette_scores = []\n",
    "\n",
    "for k in k_range:\n",
    "    km = KMeans(n_clusters=k, random_state=123)\n",
    "    km.fit(X_fr_merged_scaled)\n",
    "    silhouette_scores.append(metrics.silhouette_score(X_fr_merged_scaled, km.labels_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "503275d4",
   "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": [
    "### Creating a plot to check how the scores are looking\n",
    "\n",
    "plt.plot(k_range,silhouette_scores)\n",
    "plt.xlabel('No. of Clusters')\n",
    "plt.ylabel('Silhouette Scores')\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92b7d844",
   "metadata": {},
   "source": [
    "#### Pseudo F Value (WCSS Score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "1467023a",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Creating a loop to find the silhouette score fro models where K is from 2 to 20\n",
    "\n",
    "k_range = range(2,21)\n",
    "wcss_scores = []\n",
    "\n",
    "for k in k_range:\n",
    "    km = KMeans(n_clusters=k, random_state=123)\n",
    "    km.fit(X_fr_merged_scaled)\n",
    "    wcss_scores.append(km.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "49ce4792",
   "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": [
    "### Creating a plot to check how the scores are looking\n",
    "\n",
    "plt.plot(k_range,wcss_scores)\n",
    "plt.xlabel('No. of Clusters')\n",
    "plt.ylabel('WCSS Scores')\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "28138320",
   "metadata": {},
   "outputs": [],
   "source": [
    "#### We have decided to go for a range of K from 8 to 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "80ae8230",
   "metadata": {},
   "outputs": [],
   "source": [
    "km_6 = KMeans(n_clusters=6, random_state=123).fit(X_fr_merged_scaled)\n",
    "#km_9 = KMeans(n_clusters=9, random_state=123).fit(X_fr_merged_scaled)\n",
    "#km_10 = KMeans(n_clusters=10, random_state=123).fit(X_fr_merged_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "b133dfe1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1798\n",
       "1    2524\n",
       "2    3024\n",
       "3    2818\n",
       "4    4284\n",
       "5    5552\n",
       "dtype: int64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(km_6.labels_).value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "3f47b635",
   "metadata": {},
   "outputs": [],
   "source": [
    "fr_merged_scaled['Cluster_6'] = pd.Series(km_6.labels_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "292d6ad2",
   "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>age</th>\n",
       "      <th>Emp_Tenure_Years</th>\n",
       "      <th>Tenure_with_Bank</th>\n",
       "      <th>region_code</th>\n",
       "      <th>NetBanking_Flag</th>\n",
       "      <th>Avg_days_between_transaction</th>\n",
       "      <th>cc_cons_apr</th>\n",
       "      <th>dc_cons_apr</th>\n",
       "      <th>cc_cons_may</th>\n",
       "      <th>dc_cons_may</th>\n",
       "      <th>...</th>\n",
       "      <th>debit_count_may</th>\n",
       "      <th>credit_count_jun</th>\n",
       "      <th>debit_count_jun</th>\n",
       "      <th>emi_active</th>\n",
       "      <th>account_type_saving</th>\n",
       "      <th>gender_M</th>\n",
       "      <th>Income_LOW</th>\n",
       "      <th>Income_MEDIUM</th>\n",
       "      <th>cc_cons</th>\n",
       "      <th>Cluster_6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.340829</td>\n",
       "      <td>1.640239</td>\n",
       "      <td>1.569113</td>\n",
       "      <td>0.131946</td>\n",
       "      <td>0.626704</td>\n",
       "      <td>-1.103057</td>\n",
       "      <td>-0.509852</td>\n",
       "      <td>-0.653404</td>\n",
       "      <td>-0.292422</td>\n",
       "      <td>-0.535186</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.682123</td>\n",
       "      <td>1.498848</td>\n",
       "      <td>1.467261</td>\n",
       "      <td>-0.586325</td>\n",
       "      <td>-0.426162</td>\n",
       "      <td>0.396070</td>\n",
       "      <td>-0.624538</td>\n",
       "      <td>0.752036</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.570802</td>\n",
       "      <td>0.126446</td>\n",
       "      <td>1.569113</td>\n",
       "      <td>0.259165</td>\n",
       "      <td>-1.595650</td>\n",
       "      <td>0.174314</td>\n",
       "      <td>0.107963</td>\n",
       "      <td>-0.951421</td>\n",
       "      <td>-0.468388</td>\n",
       "      <td>0.178802</td>\n",
       "      <td>...</td>\n",
       "      <td>0.586120</td>\n",
       "      <td>-0.531684</td>\n",
       "      <td>-0.927295</td>\n",
       "      <td>-0.154302</td>\n",
       "      <td>-0.426162</td>\n",
       "      <td>0.396070</td>\n",
       "      <td>-0.624538</td>\n",
       "      <td>0.752036</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.137212</td>\n",
       "      <td>-0.799186</td>\n",
       "      <td>1.569113</td>\n",
       "      <td>-1.294715</td>\n",
       "      <td>0.626704</td>\n",
       "      <td>0.356796</td>\n",
       "      <td>1.039832</td>\n",
       "      <td>2.307933</td>\n",
       "      <td>-0.729590</td>\n",
       "      <td>2.282952</td>\n",
       "      <td>...</td>\n",
       "      <td>1.077887</td>\n",
       "      <td>0.022097</td>\n",
       "      <td>-0.072096</td>\n",
       "      <td>0.111678</td>\n",
       "      <td>-0.426162</td>\n",
       "      <td>-2.524803</td>\n",
       "      <td>-0.624538</td>\n",
       "      <td>0.752036</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.854007</td>\n",
       "      <td>-0.336370</td>\n",
       "      <td>-1.217974</td>\n",
       "      <td>0.068337</td>\n",
       "      <td>0.626704</td>\n",
       "      <td>1.451686</td>\n",
       "      <td>-0.388650</td>\n",
       "      <td>-0.173557</td>\n",
       "      <td>-0.641022</td>\n",
       "      <td>1.129664</td>\n",
       "      <td>...</td>\n",
       "      <td>0.974357</td>\n",
       "      <td>-0.839341</td>\n",
       "      <td>-0.984308</td>\n",
       "      <td>-0.900866</td>\n",
       "      <td>-0.426162</td>\n",
       "      <td>0.396070</td>\n",
       "      <td>-0.624538</td>\n",
       "      <td>0.752036</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.854007</td>\n",
       "      <td>-0.104962</td>\n",
       "      <td>0.523955</td>\n",
       "      <td>0.686255</td>\n",
       "      <td>0.626704</td>\n",
       "      <td>1.269204</td>\n",
       "      <td>-0.128041</td>\n",
       "      <td>0.105792</td>\n",
       "      <td>0.490556</td>\n",
       "      <td>2.282952</td>\n",
       "      <td>...</td>\n",
       "      <td>2.993192</td>\n",
       "      <td>0.329754</td>\n",
       "      <td>-0.927295</td>\n",
       "      <td>-0.590201</td>\n",
       "      <td>-0.426162</td>\n",
       "      <td>0.396070</td>\n",
       "      <td>-0.624538</td>\n",
       "      <td>-1.329724</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19995</th>\n",
       "      <td>1.553232</td>\n",
       "      <td>1.842720</td>\n",
       "      <td>0.175569</td>\n",
       "      <td>-1.112975</td>\n",
       "      <td>0.626704</td>\n",
       "      <td>-0.738094</td>\n",
       "      <td>-0.756615</td>\n",
       "      <td>0.893374</td>\n",
       "      <td>2.274946</td>\n",
       "      <td>-0.963400</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.837418</td>\n",
       "      <td>-0.777809</td>\n",
       "      <td>-0.186123</td>\n",
       "      <td>-0.022019</td>\n",
       "      <td>-0.426162</td>\n",
       "      <td>-2.524803</td>\n",
       "      <td>-0.624538</td>\n",
       "      <td>0.752036</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19996</th>\n",
       "      <td>0.632817</td>\n",
       "      <td>0.647113</td>\n",
       "      <td>-0.172817</td>\n",
       "      <td>0.954322</td>\n",
       "      <td>-1.595650</td>\n",
       "      <td>0.539278</td>\n",
       "      <td>-0.397195</td>\n",
       "      <td>-0.689662</td>\n",
       "      <td>-0.721571</td>\n",
       "      <td>-0.898958</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.759770</td>\n",
       "      <td>1.683442</td>\n",
       "      <td>-0.955801</td>\n",
       "      <td>2.241010</td>\n",
       "      <td>-0.426162</td>\n",
       "      <td>0.396070</td>\n",
       "      <td>-0.624538</td>\n",
       "      <td>0.752036</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19997</th>\n",
       "      <td>1.553232</td>\n",
       "      <td>0.512125</td>\n",
       "      <td>-1.566360</td>\n",
       "      <td>-0.604102</td>\n",
       "      <td>-1.595650</td>\n",
       "      <td>-0.190649</td>\n",
       "      <td>-0.944708</td>\n",
       "      <td>0.486184</td>\n",
       "      <td>-0.173353</td>\n",
       "      <td>-0.186794</td>\n",
       "      <td>...</td>\n",
       "      <td>2.993192</td>\n",
       "      <td>-0.777809</td>\n",
       "      <td>-0.186123</td>\n",
       "      <td>-0.695978</td>\n",
       "      <td>-0.426162</td>\n",
       "      <td>-2.524803</td>\n",
       "      <td>-0.624538</td>\n",
       "      <td>0.752036</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19998</th>\n",
       "      <td>-0.854007</td>\n",
       "      <td>-0.683482</td>\n",
       "      <td>0.872341</td>\n",
       "      <td>0.091055</td>\n",
       "      <td>-1.595650</td>\n",
       "      <td>1.086722</td>\n",
       "      <td>-0.257970</td>\n",
       "      <td>-0.381467</td>\n",
       "      <td>-0.853083</td>\n",
       "      <td>-1.011294</td>\n",
       "      <td>...</td>\n",
       "      <td>1.077887</td>\n",
       "      <td>1.898802</td>\n",
       "      <td>1.011155</td>\n",
       "      <td>2.241010</td>\n",
       "      <td>-0.426162</td>\n",
       "      <td>0.396070</td>\n",
       "      <td>1.601185</td>\n",
       "      <td>-1.329724</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19999</th>\n",
       "      <td>2.756852</td>\n",
       "      <td>2.339283</td>\n",
       "      <td>0.523955</td>\n",
       "      <td>1.285998</td>\n",
       "      <td>0.626704</td>\n",
       "      <td>-0.190649</td>\n",
       "      <td>0.623081</td>\n",
       "      <td>1.657627</td>\n",
       "      <td>0.210583</td>\n",
       "      <td>-0.823653</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.837418</td>\n",
       "      <td>-0.654747</td>\n",
       "      <td>-0.072096</td>\n",
       "      <td>-0.178520</td>\n",
       "      <td>-0.426162</td>\n",
       "      <td>-2.524803</td>\n",
       "      <td>1.601185</td>\n",
       "      <td>-1.329724</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20000 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            age  Emp_Tenure_Years  Tenure_with_Bank  region_code  \\\n",
       "0      1.340829          1.640239          1.569113     0.131946   \n",
       "1     -0.570802          0.126446          1.569113     0.259165   \n",
       "2     -1.137212         -0.799186          1.569113    -1.294715   \n",
       "3     -0.854007         -0.336370         -1.217974     0.068337   \n",
       "4     -0.854007         -0.104962          0.523955     0.686255   \n",
       "...         ...               ...               ...          ...   \n",
       "19995  1.553232          1.842720          0.175569    -1.112975   \n",
       "19996  0.632817          0.647113         -0.172817     0.954322   \n",
       "19997  1.553232          0.512125         -1.566360    -0.604102   \n",
       "19998 -0.854007         -0.683482          0.872341     0.091055   \n",
       "19999  2.756852          2.339283          0.523955     1.285998   \n",
       "\n",
       "       NetBanking_Flag  Avg_days_between_transaction  cc_cons_apr  \\\n",
       "0             0.626704                     -1.103057    -0.509852   \n",
       "1            -1.595650                      0.174314     0.107963   \n",
       "2             0.626704                      0.356796     1.039832   \n",
       "3             0.626704                      1.451686    -0.388650   \n",
       "4             0.626704                      1.269204    -0.128041   \n",
       "...                ...                           ...          ...   \n",
       "19995         0.626704                     -0.738094    -0.756615   \n",
       "19996        -1.595650                      0.539278    -0.397195   \n",
       "19997        -1.595650                     -0.190649    -0.944708   \n",
       "19998        -1.595650                      1.086722    -0.257970   \n",
       "19999         0.626704                     -0.190649     0.623081   \n",
       "\n",
       "       dc_cons_apr  cc_cons_may  dc_cons_may  ...  debit_count_may  \\\n",
       "0        -0.653404    -0.292422    -0.535186  ...        -0.682123   \n",
       "1        -0.951421    -0.468388     0.178802  ...         0.586120   \n",
       "2         2.307933    -0.729590     2.282952  ...         1.077887   \n",
       "3        -0.173557    -0.641022     1.129664  ...         0.974357   \n",
       "4         0.105792     0.490556     2.282952  ...         2.993192   \n",
       "...            ...          ...          ...  ...              ...   \n",
       "19995     0.893374     2.274946    -0.963400  ...        -0.837418   \n",
       "19996    -0.689662    -0.721571    -0.898958  ...        -0.759770   \n",
       "19997     0.486184    -0.173353    -0.186794  ...         2.993192   \n",
       "19998    -0.381467    -0.853083    -1.011294  ...         1.077887   \n",
       "19999     1.657627     0.210583    -0.823653  ...        -0.837418   \n",
       "\n",
       "       credit_count_jun  debit_count_jun  emi_active  account_type_saving  \\\n",
       "0              1.498848         1.467261   -0.586325            -0.426162   \n",
       "1             -0.531684        -0.927295   -0.154302            -0.426162   \n",
       "2              0.022097        -0.072096    0.111678            -0.426162   \n",
       "3             -0.839341        -0.984308   -0.900866            -0.426162   \n",
       "4              0.329754        -0.927295   -0.590201            -0.426162   \n",
       "...                 ...              ...         ...                  ...   \n",
       "19995         -0.777809        -0.186123   -0.022019            -0.426162   \n",
       "19996          1.683442        -0.955801    2.241010            -0.426162   \n",
       "19997         -0.777809        -0.186123   -0.695978            -0.426162   \n",
       "19998          1.898802         1.011155    2.241010            -0.426162   \n",
       "19999         -0.654747        -0.072096   -0.178520            -0.426162   \n",
       "\n",
       "       gender_M  Income_LOW  Income_MEDIUM  cc_cons  Cluster_6  \n",
       "0      0.396070   -0.624538       0.752036      NaN          3  \n",
       "1      0.396070   -0.624538       0.752036      NaN          2  \n",
       "2     -2.524803   -0.624538       0.752036      NaN          1  \n",
       "3      0.396070   -0.624538       0.752036      NaN          5  \n",
       "4      0.396070   -0.624538      -1.329724      NaN          5  \n",
       "...         ...         ...            ...      ...        ...  \n",
       "19995 -2.524803   -0.624538       0.752036      NaN          1  \n",
       "19996  0.396070   -0.624538       0.752036      NaN          2  \n",
       "19997 -2.524803   -0.624538       0.752036      NaN          1  \n",
       "19998  0.396070    1.601185      -1.329724      NaN          4  \n",
       "19999 -2.524803    1.601185      -1.329724      NaN          3  \n",
       "\n",
       "[20000 rows x 29 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fr_merged_scaled"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "024adaa1",
   "metadata": {},
   "source": [
    "#### Create different datesets for each cluster to perform clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "b665dfbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "fr_merged_scaled_c1 = fr_merged_scaled[fr_merged_scaled.Cluster_6==0]\n",
    "fr_merged_scaled_c2 = fr_merged_scaled[fr_merged_scaled.Cluster_6==1]\n",
    "fr_merged_scaled_c3 = fr_merged_scaled[fr_merged_scaled.Cluster_6==2]\n",
    "fr_merged_scaled_c4 = fr_merged_scaled[fr_merged_scaled.Cluster_6==3]\n",
    "fr_merged_scaled_c5 = fr_merged_scaled[fr_merged_scaled.Cluster_6==4]\n",
    "fr_merged_scaled_c6 = fr_merged_scaled[fr_merged_scaled.Cluster_6==5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "9e3c20ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "# cluster_for_excel = fr_merged_scaled.groupby('Cluster_6').apply(lambda x: x.mean()).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "f297508d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# we will check if allthe cluters have null values in cc_cons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "3d7a6d72",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 5552 entries, 3 to 19993\n",
      "Data columns (total 29 columns):\n",
      " #   Column                        Non-Null Count  Dtype  \n",
      "---  ------                        --------------  -----  \n",
      " 0   age                           5552 non-null   float64\n",
      " 1   Emp_Tenure_Years              5552 non-null   float64\n",
      " 2   Tenure_with_Bank              5552 non-null   float64\n",
      " 3   region_code                   5552 non-null   float64\n",
      " 4   NetBanking_Flag               5552 non-null   float64\n",
      " 5   Avg_days_between_transaction  5552 non-null   float64\n",
      " 6   cc_cons_apr                   5552 non-null   float64\n",
      " 7   dc_cons_apr                   5552 non-null   float64\n",
      " 8   cc_cons_may                   5552 non-null   float64\n",
      " 9   dc_cons_may                   5552 non-null   float64\n",
      " 10  cc_cons_jun                   5552 non-null   float64\n",
      " 11  dc_cons_jun                   5552 non-null   float64\n",
      " 12  cc_count_apr                  5552 non-null   float64\n",
      " 13  dc_count_apr                  5552 non-null   float64\n",
      " 14  dc_count_may                  5552 non-null   float64\n",
      " 15  investment_4                  5552 non-null   float64\n",
      " 16  debit_count_apr               5552 non-null   float64\n",
      " 17  credit_count_apr              5552 non-null   float64\n",
      " 18  credit_count_may              5552 non-null   float64\n",
      " 19  debit_count_may               5552 non-null   float64\n",
      " 20  credit_count_jun              5552 non-null   float64\n",
      " 21  debit_count_jun               5552 non-null   float64\n",
      " 22  emi_active                    5552 non-null   float64\n",
      " 23  account_type_saving           5552 non-null   float64\n",
      " 24  gender_M                      5552 non-null   float64\n",
      " 25  Income_LOW                    5552 non-null   float64\n",
      " 26  Income_MEDIUM                 5552 non-null   float64\n",
      " 27  cc_cons                       4179 non-null   float64\n",
      " 28  Cluster_6                     5552 non-null   int32  \n",
      "dtypes: float64(28), int32(1)\n",
      "memory usage: 1.2 MB\n"
     ]
    }
   ],
   "source": [
    "fr_merged_scaled_c6.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "dfbd01b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Joshi\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:4308: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  return super().drop(\n"
     ]
    }
   ],
   "source": [
    "#remove the Cluster_6 variable from all data sets\n",
    "\n",
    "for i in range(1, 7):  \n",
    "    dataset_name = \"fr_merged_scaled_c{}\".format(i)\n",
    "    dataset = globals()[dataset_name]\n",
    "    if \"Cluster_6\" in dataset.columns:\n",
    "        dataset.drop(\"Cluster_6\", axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "840846fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error, r2_score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "639bc044",
   "metadata": {},
   "source": [
    "#### Linear regression for cluster 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "3110f6f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>Emp_Tenure_Years</th>\n",
       "      <th>Tenure_with_Bank</th>\n",
       "      <th>region_code</th>\n",
       "      <th>NetBanking_Flag</th>\n",
       "      <th>Avg_days_between_transaction</th>\n",
       "      <th>cc_cons_apr</th>\n",
       "      <th>dc_cons_apr</th>\n",
       "      <th>cc_cons_may</th>\n",
       "      <th>dc_cons_may</th>\n",
       "      <th>...</th>\n",
       "      <th>credit_count_may</th>\n",
       "      <th>debit_count_may</th>\n",
       "      <th>credit_count_jun</th>\n",
       "      <th>debit_count_jun</th>\n",
       "      <th>emi_active</th>\n",
       "      <th>account_type_saving</th>\n",
       "      <th>gender_M</th>\n",
       "      <th>Income_LOW</th>\n",
       "      <th>Income_MEDIUM</th>\n",
       "      <th>cc_cons</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>-0.287598</td>\n",
       "      <td>0.280717</td>\n",
       "      <td>0.872341</td>\n",
       "      <td>0.758951</td>\n",
       "      <td>0.626704</td>\n",
       "      <td>-1.650502</td>\n",
       "      <td>-0.184704</td>\n",
       "      <td>-0.982590</td>\n",
       "      <td>-0.434608</td>\n",
       "      <td>0.415930</td>\n",
       "      <td>...</td>\n",
       "      <td>0.319793</td>\n",
       "      <td>-0.345650</td>\n",
       "      <td>1.498848</td>\n",
       "      <td>-0.357162</td>\n",
       "      <td>0.096306</td>\n",
       "      <td>-0.426162</td>\n",
       "      <td>0.39607</td>\n",
       "      <td>-0.624538</td>\n",
       "      <td>0.752036</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0.632817</td>\n",
       "      <td>0.647113</td>\n",
       "      <td>-1.217974</td>\n",
       "      <td>-0.699516</td>\n",
       "      <td>0.626704</td>\n",
       "      <td>-0.738094</td>\n",
       "      <td>2.288040</td>\n",
       "      <td>-0.855607</td>\n",
       "      <td>0.118988</td>\n",
       "      <td>-0.535108</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.716981</td>\n",
       "      <td>-1.018595</td>\n",
       "      <td>1.898802</td>\n",
       "      <td>-0.756255</td>\n",
       "      <td>-0.220396</td>\n",
       "      <td>-0.426162</td>\n",
       "      <td>0.39607</td>\n",
       "      <td>-0.624538</td>\n",
       "      <td>0.752036</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>-0.500001</td>\n",
       "      <td>0.377137</td>\n",
       "      <td>-0.521203</td>\n",
       "      <td>-0.658624</td>\n",
       "      <td>0.626704</td>\n",
       "      <td>0.904241</td>\n",
       "      <td>-0.096051</td>\n",
       "      <td>-0.608719</td>\n",
       "      <td>2.274946</td>\n",
       "      <td>-1.055418</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.947375</td>\n",
       "      <td>-0.578593</td>\n",
       "      <td>1.898802</td>\n",
       "      <td>-0.927295</td>\n",
       "      <td>0.551066</td>\n",
       "      <td>-0.426162</td>\n",
       "      <td>0.39607</td>\n",
       "      <td>-0.624538</td>\n",
       "      <td>0.752036</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>-0.854007</td>\n",
       "      <td>-0.104962</td>\n",
       "      <td>-0.521203</td>\n",
       "      <td>0.563580</td>\n",
       "      <td>0.626704</td>\n",
       "      <td>-1.468020</td>\n",
       "      <td>-0.652883</td>\n",
       "      <td>-0.726084</td>\n",
       "      <td>-0.580300</td>\n",
       "      <td>-0.760699</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.716981</td>\n",
       "      <td>-0.397415</td>\n",
       "      <td>1.898802</td>\n",
       "      <td>0.127450</td>\n",
       "      <td>0.056087</td>\n",
       "      <td>-0.426162</td>\n",
       "      <td>0.39607</td>\n",
       "      <td>-0.624538</td>\n",
       "      <td>0.752036</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>-0.287598</td>\n",
       "      <td>0.280717</td>\n",
       "      <td>0.523955</td>\n",
       "      <td>-1.044822</td>\n",
       "      <td>0.626704</td>\n",
       "      <td>0.904241</td>\n",
       "      <td>1.428057</td>\n",
       "      <td>-0.617858</td>\n",
       "      <td>-0.232456</td>\n",
       "      <td>2.282952</td>\n",
       "      <td>...</td>\n",
       "      <td>1.010975</td>\n",
       "      <td>-0.915065</td>\n",
       "      <td>1.252723</td>\n",
       "      <td>0.384010</td>\n",
       "      <td>-0.753487</td>\n",
       "      <td>-0.426162</td>\n",
       "      <td>0.39607</td>\n",
       "      <td>-0.624538</td>\n",
       "      <td>0.752036</td>\n",
       "      <td>NaN</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19967</th>\n",
       "      <td>0.349613</td>\n",
       "      <td>1.254559</td>\n",
       "      <td>-1.566360</td>\n",
       "      <td>-0.113403</td>\n",
       "      <td>0.626704</td>\n",
       "      <td>-0.008167</td>\n",
       "      <td>-0.964444</td>\n",
       "      <td>1.381668</td>\n",
       "      <td>-0.354015</td>\n",
       "      <td>-0.161146</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.832178</td>\n",
       "      <td>-0.397415</td>\n",
       "      <td>1.898802</td>\n",
       "      <td>0.583556</td>\n",
       "      <td>-0.633308</td>\n",
       "      <td>-0.426162</td>\n",
       "      <td>0.39607</td>\n",
       "      <td>-0.624538</td>\n",
       "      <td>-1.329724</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19971</th>\n",
       "      <td>-0.924808</td>\n",
       "      <td>-1.049878</td>\n",
       "      <td>-0.521203</td>\n",
       "      <td>-1.108431</td>\n",
       "      <td>0.626704</td>\n",
       "      <td>0.904241</td>\n",
       "      <td>-0.438102</td>\n",
       "      <td>-0.694115</td>\n",
       "      <td>0.016217</td>\n",
       "      <td>2.282952</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.486587</td>\n",
       "      <td>-0.112708</td>\n",
       "      <td>1.898802</td>\n",
       "      <td>-0.556709</td>\n",
       "      <td>-0.751054</td>\n",
       "      <td>-0.426162</td>\n",
       "      <td>0.39607</td>\n",
       "      <td>-0.624538</td>\n",
       "      <td>0.752036</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19973</th>\n",
       "      <td>0.632817</td>\n",
       "      <td>0.010742</td>\n",
       "      <td>0.872341</td>\n",
       "      <td>-1.508260</td>\n",
       "      <td>0.626704</td>\n",
       "      <td>-0.190649</td>\n",
       "      <td>0.323511</td>\n",
       "      <td>-0.749456</td>\n",
       "      <td>-0.971394</td>\n",
       "      <td>-0.991254</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.371390</td>\n",
       "      <td>1.052005</td>\n",
       "      <td>1.898802</td>\n",
       "      <td>1.096675</td>\n",
       "      <td>0.782988</td>\n",
       "      <td>-0.426162</td>\n",
       "      <td>0.39607</td>\n",
       "      <td>-0.624538</td>\n",
       "      <td>0.752036</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19980</th>\n",
       "      <td>-0.783206</td>\n",
       "      <td>-0.008542</td>\n",
       "      <td>0.872341</td>\n",
       "      <td>-0.690429</td>\n",
       "      <td>0.626704</td>\n",
       "      <td>0.539278</td>\n",
       "      <td>0.689618</td>\n",
       "      <td>-0.399652</td>\n",
       "      <td>-1.006515</td>\n",
       "      <td>2.021402</td>\n",
       "      <td>...</td>\n",
       "      <td>0.089399</td>\n",
       "      <td>1.052005</td>\n",
       "      <td>1.898802</td>\n",
       "      <td>-0.955801</td>\n",
       "      <td>0.991695</td>\n",
       "      <td>-0.426162</td>\n",
       "      <td>0.39607</td>\n",
       "      <td>-0.624538</td>\n",
       "      <td>0.752036</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19988</th>\n",
       "      <td>-0.854007</td>\n",
       "      <td>-0.567778</td>\n",
       "      <td>-0.172817</td>\n",
       "      <td>-0.276969</td>\n",
       "      <td>0.626704</td>\n",
       "      <td>-1.468020</td>\n",
       "      <td>-0.756615</td>\n",
       "      <td>-0.971776</td>\n",
       "      <td>0.579234</td>\n",
       "      <td>-0.424244</td>\n",
       "      <td>...</td>\n",
       "      <td>1.817355</td>\n",
       "      <td>-0.397415</td>\n",
       "      <td>1.898802</td>\n",
       "      <td>1.011155</td>\n",
       "      <td>-0.651650</td>\n",
       "      <td>-0.426162</td>\n",
       "      <td>0.39607</td>\n",
       "      <td>-0.624538</td>\n",
       "      <td>0.752036</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1798 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            age  Emp_Tenure_Years  Tenure_with_Bank  region_code  \\\n",
       "30    -0.287598          0.280717          0.872341     0.758951   \n",
       "34     0.632817          0.647113         -1.217974    -0.699516   \n",
       "49    -0.500001          0.377137         -0.521203    -0.658624   \n",
       "50    -0.854007         -0.104962         -0.521203     0.563580   \n",
       "59    -0.287598          0.280717          0.523955    -1.044822   \n",
       "...         ...               ...               ...          ...   \n",
       "19967  0.349613          1.254559         -1.566360    -0.113403   \n",
       "19971 -0.924808         -1.049878         -0.521203    -1.108431   \n",
       "19973  0.632817          0.010742          0.872341    -1.508260   \n",
       "19980 -0.783206         -0.008542          0.872341    -0.690429   \n",
       "19988 -0.854007         -0.567778         -0.172817    -0.276969   \n",
       "\n",
       "       NetBanking_Flag  Avg_days_between_transaction  cc_cons_apr  \\\n",
       "30            0.626704                     -1.650502    -0.184704   \n",
       "34            0.626704                     -0.738094     2.288040   \n",
       "49            0.626704                      0.904241    -0.096051   \n",
       "50            0.626704                     -1.468020    -0.652883   \n",
       "59            0.626704                      0.904241     1.428057   \n",
       "...                ...                           ...          ...   \n",
       "19967         0.626704                     -0.008167    -0.964444   \n",
       "19971         0.626704                      0.904241    -0.438102   \n",
       "19973         0.626704                     -0.190649     0.323511   \n",
       "19980         0.626704                      0.539278     0.689618   \n",
       "19988         0.626704                     -1.468020    -0.756615   \n",
       "\n",
       "       dc_cons_apr  cc_cons_may  dc_cons_may  ...  credit_count_may  \\\n",
       "30       -0.982590    -0.434608     0.415930  ...          0.319793   \n",
       "34       -0.855607     0.118988    -0.535108  ...         -0.716981   \n",
       "49       -0.608719     2.274946    -1.055418  ...         -0.947375   \n",
       "50       -0.726084    -0.580300    -0.760699  ...         -0.716981   \n",
       "59       -0.617858    -0.232456     2.282952  ...          1.010975   \n",
       "...            ...          ...          ...  ...               ...   \n",
       "19967     1.381668    -0.354015    -0.161146  ...         -0.832178   \n",
       "19971    -0.694115     0.016217     2.282952  ...         -0.486587   \n",
       "19973    -0.749456    -0.971394    -0.991254  ...         -0.371390   \n",
       "19980    -0.399652    -1.006515     2.021402  ...          0.089399   \n",
       "19988    -0.971776     0.579234    -0.424244  ...          1.817355   \n",
       "\n",
       "       debit_count_may  credit_count_jun  debit_count_jun  emi_active  \\\n",
       "30           -0.345650          1.498848        -0.357162    0.096306   \n",
       "34           -1.018595          1.898802        -0.756255   -0.220396   \n",
       "49           -0.578593          1.898802        -0.927295    0.551066   \n",
       "50           -0.397415          1.898802         0.127450    0.056087   \n",
       "59           -0.915065          1.252723         0.384010   -0.753487   \n",
       "...                ...               ...              ...         ...   \n",
       "19967        -0.397415          1.898802         0.583556   -0.633308   \n",
       "19971        -0.112708          1.898802        -0.556709   -0.751054   \n",
       "19973         1.052005          1.898802         1.096675    0.782988   \n",
       "19980         1.052005          1.898802        -0.955801    0.991695   \n",
       "19988        -0.397415          1.898802         1.011155   -0.651650   \n",
       "\n",
       "       account_type_saving  gender_M  Income_LOW  Income_MEDIUM  cc_cons  \n",
       "30               -0.426162   0.39607   -0.624538       0.752036      NaN  \n",
       "34               -0.426162   0.39607   -0.624538       0.752036      NaN  \n",
       "49               -0.426162   0.39607   -0.624538       0.752036      NaN  \n",
       "50               -0.426162   0.39607   -0.624538       0.752036      NaN  \n",
       "59               -0.426162   0.39607   -0.624538       0.752036      NaN  \n",
       "...                    ...       ...         ...            ...      ...  \n",
       "19967            -0.426162   0.39607   -0.624538      -1.329724      NaN  \n",
       "19971            -0.426162   0.39607   -0.624538       0.752036      NaN  \n",
       "19973            -0.426162   0.39607   -0.624538       0.752036      NaN  \n",
       "19980            -0.426162   0.39607   -0.624538       0.752036      NaN  \n",
       "19988            -0.426162   0.39607   -0.624538       0.752036      NaN  \n",
       "\n",
       "[1798 rows x 28 columns]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fr_merged_scaled_c1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "f3665765",
   "metadata": {},
   "outputs": [],
   "source": [
    "Non_null_fr_scaled_c1 = fr_merged_scaled_c1[~fr_merged_scaled_c1['cc_cons'].isnull()]\n",
    "validation_fr_scaled_c1 = fr_merged_scaled_c1[fr_merged_scaled_c1['cc_cons'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "ca43ced7",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = Non_null_fr_scaled_c1.loc[:, Non_null_fr_scaled_c1.columns == 'cc_cons']\n",
    "X = Non_null_fr_scaled_c1.loc[:, Non_null_fr_scaled_c1.columns != 'cc_cons']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "86c291f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y,\n",
    "                                                    test_size=0.3,\n",
    "                                                    random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "1d2be190",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train: (945, 27)\n",
      "X_test: (406, 27)\n",
      "y_train: (945, 1)\n",
      "y_test: (406, 1)\n"
     ]
    }
   ],
   "source": [
    "print('X_train:', X_train.shape)\n",
    "print('X_test:', X_test.shape)\n",
    "print('y_train:', y_train.shape)\n",
    "print('y_test:', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "f74787c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 0.9762258568590634\n",
      "Root Mean Squared Error: 0.9880414246675406\n",
      "R-squared Score: 0.011965730632726324\n"
     ]
    }
   ],
   "source": [
    "#'X' is feature matrix and 'y' is your target variable\n",
    "\n",
    "# Create and train the linear regression model\n",
    "reg_model = LinearRegression()\n",
    "reg_model.fit(X, y)\n",
    "\n",
    "# Make predictions on the test set\n",
    "y_pred = reg_model.predict(X_test)\n",
    "\n",
    "# Calculate Mean Squared Error\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "print(\"Mean Squared Error:\", mse)\n",
    "\n",
    "# Calculate Root Mean Squared Error\n",
    "rmse = np.sqrt(mse)\n",
    "print(\"Root Mean Squared Error:\", rmse)\n",
    "\n",
    "# Calculate R-squared score\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "print(\"R-squared Score:\", r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "7061812b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train accuracy: 0.019406443420497133\n",
      "test accuracy: 0.011965730632726324\n"
     ]
    }
   ],
   "source": [
    "print(\"train accuracy:\" ,reg_model.score(X_train, y_train))\n",
    "print(\"test accuracy:\",reg_model.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "e2418ecd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The intercept of our model using linear regression is -25879807458361.44\n"
     ]
    }
   ],
   "source": [
    "intercept = reg_model.intercept_[0]\n",
    "\n",
    "print('The intercept of our model using linear regression is {}'.format(intercept))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f37724cc",
   "metadata": {},
   "source": [
    "Clearly, the Kmeans clustering did not created the homogeneous groups, as the output on cluster 1 is not giving a good accuracy, like we thought it would give."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3d46d05",
   "metadata": {},
   "source": [
    "Let us employee all the regression techniques and let the machine decide which is the best regression method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "91d48001",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression, Ridge, Lasso, ElasticNet, SGDRegressor, BayesianRidge\n",
    "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor, ExtraTreesRegressor, BaggingRegressor,AdaBoostRegressor\n",
    "from sklearn.linear_model import ElasticNet, Lasso, Ridge\n",
    "from sklearn.svm import SVR, LinearSVR\n",
    "from xgboost import XGBRegressor\n",
    "from catboost import CatBoostRegressor\n",
    "from lightgbm import LGBMRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "import lightgbm as lgb\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
    "from sklearn.metrics import  make_scorer, mean_squared_error,mean_squared_log_error\n",
    "from sklearn.model_selection import cross_val_score, GridSearchCV, KFold, RandomizedSearchCV, train_test_split\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "208bd1d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches\n",
    "import matplotlib.gridspec as gridspec \n",
    "from scipy import stats\n",
    "import seaborn as sns\n",
    "sns.set_style('whitegrid')\n",
    "import warnings\n",
    "warnings.simplefilter(action='ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "b6c75d14",
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters = [{'kernel': ['rbf'],\n",
    "                'epsilon': [0.1, 0.2, 0.5],\n",
    "                'gamma': [1e-4, 0.01, 0.1, 0.5],\n",
    "                'C': [1, 10, 100]},\n",
    "              {'kernel': ['linear'], 'C': [1, 10, 100, 1000]}]           "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "d3b59040",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'Emp_Tenure_Years', 'Tenure_with_Bank', 'region_code',\n",
       "       'NetBanking_Flag', 'Avg_days_between_transaction', 'cc_cons_apr',\n",
       "       'dc_cons_apr', 'cc_cons_may', 'dc_cons_may', 'cc_cons_jun',\n",
       "       'dc_cons_jun', 'cc_count_apr', 'dc_count_apr', 'dc_count_may',\n",
       "       'investment_4', 'debit_count_apr', 'credit_count_apr',\n",
       "       'credit_count_may', 'debit_count_may', 'credit_count_jun',\n",
       "       'debit_count_jun', 'emi_active', 'account_type_saving', 'gender_M',\n",
       "       'Income_LOW', 'Income_MEDIUM', 'cc_cons'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# complete data set\n",
    "fr_merged.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "55ce8c8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "fr_merged_Non_Null = fr_merged[~fr_merged['cc_cons'].isnull()]\n",
    "fr_merged_Null= fr_merged[fr_merged['cc_cons'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "e2db02f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>age</th>\n",
       "      <th>Emp_Tenure_Years</th>\n",
       "      <th>Tenure_with_Bank</th>\n",
       "      <th>region_code</th>\n",
       "      <th>NetBanking_Flag</th>\n",
       "      <th>Avg_days_between_transaction</th>\n",
       "      <th>cc_cons_apr</th>\n",
       "      <th>dc_cons_apr</th>\n",
       "      <th>cc_cons_may</th>\n",
       "      <th>dc_cons_may</th>\n",
       "      <th>...</th>\n",
       "      <th>credit_count_may</th>\n",
       "      <th>debit_count_may</th>\n",
       "      <th>credit_count_jun</th>\n",
       "      <th>debit_count_jun</th>\n",
       "      <th>emi_active</th>\n",
       "      <th>account_type_saving</th>\n",
       "      <th>gender_M</th>\n",
       "      <th>Income_LOW</th>\n",
       "      <th>Income_MEDIUM</th>\n",
       "      <th>cc_cons</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>35.0</td>\n",
       "      <td>15.00</td>\n",
       "      <td>1</td>\n",
       "      <td>708.0</td>\n",
       "      <td>0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>24893.00</td>\n",
       "      <td>378.00</td>\n",
       "      <td>10288.00000</td>\n",
       "      <td>18207.585</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>65</td>\n",
       "      <td>1674.090</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>20014.0</td>\n",
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       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>35.0</td>\n",
       "      <td>15.00</td>\n",
       "      <td>6</td>\n",
       "      <td>123.0</td>\n",
       "      <td>0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>18941.62</td>\n",
       "      <td>966.00</td>\n",
       "      <td>20672.00000</td>\n",
       "      <td>287.000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>63</td>\n",
       "      <td>13043.340</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10173.0</td>\n",
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       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>55.0</td>\n",
       "      <td>24.50</td>\n",
       "      <td>1</td>\n",
       "      <td>802.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5678.87</td>\n",
       "      <td>2724.00</td>\n",
       "      <td>1964.50000</td>\n",
       "      <td>3933.110</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
       "      <td>21122.065</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>16095.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>29.0</td>\n",
       "      <td>4.50</td>\n",
       "      <td>6</td>\n",
       "      <td>867.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>30489.50</td>\n",
       "      <td>1236.00</td>\n",
       "      <td>12609.88000</td>\n",
       "      <td>9138.140</td>\n",
       "      <td>...</td>\n",
       "      <td>23.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>83</td>\n",
       "      <td>3544.330</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7707.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>34.0</td>\n",
       "      <td>12.60</td>\n",
       "      <td>4</td>\n",
       "      <td>802.0</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7441.40</td>\n",
       "      <td>6906.04</td>\n",
       "      <td>4364.00000</td>\n",
       "      <td>1939.000</td>\n",
       "      <td>...</td>\n",
       "      <td>24.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>32</td>\n",
       "      <td>12780.440</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>96408.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <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",
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       "    <tr>\n",
       "      <th>15106</th>\n",
       "      <td>53.0</td>\n",
       "      <td>13.20</td>\n",
       "      <td>9</td>\n",
       "      <td>653.0</td>\n",
       "      <td>1</td>\n",
       "      <td>20.0</td>\n",
       "      <td>10443.76</td>\n",
       "      <td>1218.85</td>\n",
       "      <td>7252.24000</td>\n",
       "      <td>3873.000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>14.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4263.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15107</th>\n",
       "      <td>44.0</td>\n",
       "      <td>2.40</td>\n",
       "      <td>9</td>\n",
       "      <td>535.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>29890.33</td>\n",
       "      <td>6308.00</td>\n",
       "      <td>16025.50000</td>\n",
       "      <td>7610.000</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>18</td>\n",
       "      <td>4762.100</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15108</th>\n",
       "      <td>37.0</td>\n",
       "      <td>3.40</td>\n",
       "      <td>9</td>\n",
       "      <td>464.0</td>\n",
       "      <td>1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4148.98</td>\n",
       "      <td>948.74</td>\n",
       "      <td>19360.26000</td>\n",
       "      <td>494.000</td>\n",
       "      <td>...</td>\n",
       "      <td>24.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>73</td>\n",
       "      <td>2877.330</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>404227.0</td>\n",
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       "    <tr>\n",
       "      <th>15109</th>\n",
       "      <td>83.0</td>\n",
       "      <td>37.35</td>\n",
       "      <td>10</td>\n",
       "      <td>536.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>46407.00</td>\n",
       "      <td>7191.05</td>\n",
       "      <td>41162.76625</td>\n",
       "      <td>18207.585</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>80</td>\n",
       "      <td>13006.840</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>28813.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15110</th>\n",
       "      <td>66.0</td>\n",
       "      <td>4.60</td>\n",
       "      <td>2</td>\n",
       "      <td>269.0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7375.67</td>\n",
       "      <td>306.80</td>\n",
       "      <td>5859.49000</td>\n",
       "      <td>6702.400</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>46.5</td>\n",
       "      <td>50</td>\n",
       "      <td>1588.600</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>16252.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15000 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        age  Emp_Tenure_Years  Tenure_with_Bank  region_code  NetBanking_Flag  \\\n",
       "111    35.0             15.00                 1        708.0                0   \n",
       "112    35.0             15.00                 6        123.0                0   \n",
       "113    55.0             24.50                 1        802.0                1   \n",
       "114    29.0              4.50                 6        867.0                1   \n",
       "115    34.0             12.60                 4        802.0                1   \n",
       "...     ...               ...               ...          ...              ...   \n",
       "15106  53.0             13.20                 9        653.0                1   \n",
       "15107  44.0              2.40                 9        535.0                1   \n",
       "15108  37.0              3.40                 9        464.0                1   \n",
       "15109  83.0             37.35                10        536.0                1   \n",
       "15110  66.0              4.60                 2        269.0                0   \n",
       "\n",
       "       Avg_days_between_transaction  cc_cons_apr  dc_cons_apr  cc_cons_may  \\\n",
       "111                            17.0     24893.00       378.00  10288.00000   \n",
       "112                            14.0     18941.62       966.00  20672.00000   \n",
       "113                             3.0      5678.87      2724.00   1964.50000   \n",
       "114                             4.0     30489.50      1236.00  12609.88000   \n",
       "115                             7.0      7441.40      6906.04   4364.00000   \n",
       "...                             ...          ...          ...          ...   \n",
       "15106                          20.0     10443.76      1218.85   7252.24000   \n",
       "15107                           2.0     29890.33      6308.00  16025.50000   \n",
       "15108                           8.0      4148.98       948.74  19360.26000   \n",
       "15109                           3.0     46407.00      7191.05  41162.76625   \n",
       "15110                           8.0      7375.67       306.80   5859.49000   \n",
       "\n",
       "       dc_cons_may  ...  credit_count_may  debit_count_may  credit_count_jun  \\\n",
       "111      18207.585  ...               2.0             40.0               2.0   \n",
       "112        287.000  ...               2.0             78.0              21.0   \n",
       "113       3933.110  ...               2.0             82.0               7.0   \n",
       "114       9138.140  ...              23.0             38.0              21.0   \n",
       "115       1939.000  ...              24.0             50.0               8.0   \n",
       "...            ...  ...               ...              ...               ...   \n",
       "15106     3873.000  ...               1.0              8.0              14.0   \n",
       "15107     7610.000  ...               4.0             20.0              11.0   \n",
       "15108      494.000  ...              24.5              5.0               4.0   \n",
       "15109    18207.585  ...               3.0             25.0               3.0   \n",
       "15110     6702.400  ...               2.0             82.0              46.5   \n",
       "\n",
       "       debit_count_jun  emi_active  account_type_saving  gender_M  Income_LOW  \\\n",
       "111                 65    1674.090                    0         1           0   \n",
       "112                 63   13043.340                    0         1           0   \n",
       "113                  1   21122.065                    0         1           0   \n",
       "114                 83    3544.330                    0         0           0   \n",
       "115                 32   12780.440                    0         1           0   \n",
       "...                ...         ...                  ...       ...         ...   \n",
       "15106                8   21122.065                    0         0           1   \n",
       "15107               18    4762.100                    0         1           0   \n",
       "15108               73    2877.330                    0         1           0   \n",
       "15109               80   13006.840                    0         1           0   \n",
       "15110               50    1588.600                    0         1           0   \n",
       "\n",
       "       Income_MEDIUM   cc_cons  \n",
       "111                1   20014.0  \n",
       "112                0   10173.0  \n",
       "113                1   16095.0  \n",
       "114                1    7707.0  \n",
       "115                1   96408.0  \n",
       "...              ...       ...  \n",
       "15106              0    4263.0  \n",
       "15107              1   11019.0  \n",
       "15108              1  404227.0  \n",
       "15109              1   28813.0  \n",
       "15110              1   16252.0  \n",
       "\n",
       "[15000 rows x 28 columns]"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# complete data for train-test\n",
    "fr_merged_Non_Null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "ba7d3521",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>age</th>\n",
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       "      <th>Tenure_with_Bank</th>\n",
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       "      <th>NetBanking_Flag</th>\n",
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       "      <th>dc_cons_may</th>\n",
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       "      <td>12.0</td>\n",
       "      <td>16479.64</td>\n",
       "      <td>415.000</td>\n",
       "      <td>7386.49000</td>\n",
       "      <td>6763.000</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12</td>\n",
       "      <td>5469.790</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28.0</td>\n",
       "      <td>4.80</td>\n",
       "      <td>10</td>\n",
       "      <td>314.0</td>\n",
       "      <td>1</td>\n",
       "      <td>13.0</td>\n",
       "      <td>29272.03</td>\n",
       "      <td>20910.575</td>\n",
       "      <td>4170.53000</td>\n",
       "      <td>18207.585</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>42</td>\n",
       "      <td>7207.850</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32.0</td>\n",
       "      <td>9.60</td>\n",
       "      <td>2</td>\n",
       "      <td>614.0</td>\n",
       "      <td>1</td>\n",
       "      <td>19.0</td>\n",
       "      <td>9662.31</td>\n",
       "      <td>5306.390</td>\n",
       "      <td>5261.00000</td>\n",
       "      <td>11934.790</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10</td>\n",
       "      <td>591.340</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32.0</td>\n",
       "      <td>12.00</td>\n",
       "      <td>7</td>\n",
       "      <td>750.0</td>\n",
       "      <td>1</td>\n",
       "      <td>18.0</td>\n",
       "      <td>13239.86</td>\n",
       "      <td>7063.000</td>\n",
       "      <td>19193.13000</td>\n",
       "      <td>18207.585</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>12</td>\n",
       "      <td>2621.390</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19995</th>\n",
       "      <td>66.0</td>\n",
       "      <td>32.20</td>\n",
       "      <td>6</td>\n",
       "      <td>354.0</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4611.00</td>\n",
       "      <td>12015.500</td>\n",
       "      <td>41162.76625</td>\n",
       "      <td>550.500</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>38</td>\n",
       "      <td>6334.200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19996</th>\n",
       "      <td>53.0</td>\n",
       "      <td>19.80</td>\n",
       "      <td>5</td>\n",
       "      <td>809.0</td>\n",
       "      <td>0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>9545.00</td>\n",
       "      <td>2061.000</td>\n",
       "      <td>4269.27000</td>\n",
       "      <td>901.000</td>\n",
       "      <td>...</td>\n",
       "      <td>24.5</td>\n",
       "      <td>11.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>11</td>\n",
       "      <td>21122.065</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19997</th>\n",
       "      <td>66.0</td>\n",
       "      <td>18.40</td>\n",
       "      <td>1</td>\n",
       "      <td>466.0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2028.93</td>\n",
       "      <td>9454.990</td>\n",
       "      <td>11019.00000</td>\n",
       "      <td>4774.500</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>38</td>\n",
       "      <td>1930.190</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19998</th>\n",
       "      <td>32.0</td>\n",
       "      <td>6.00</td>\n",
       "      <td>8</td>\n",
       "      <td>619.0</td>\n",
       "      <td>0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>11456.23</td>\n",
       "      <td>3999.000</td>\n",
       "      <td>2650.08000</td>\n",
       "      <td>290.000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>46.5</td>\n",
       "      <td>80</td>\n",
       "      <td>21122.065</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19999</th>\n",
       "      <td>83.0</td>\n",
       "      <td>37.35</td>\n",
       "      <td>7</td>\n",
       "      <td>882.0</td>\n",
       "      <td>1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>23551.00</td>\n",
       "      <td>16821.300</td>\n",
       "      <td>15746.06000</td>\n",
       "      <td>1310.590</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>42</td>\n",
       "      <td>5311.540</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5000 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        age  Emp_Tenure_Years  Tenure_with_Bank  region_code  NetBanking_Flag  \\\n",
       "0      63.0             30.10                10        628.0                1   \n",
       "1      36.0             14.40                10        656.0                0   \n",
       "2      28.0              4.80                10        314.0                1   \n",
       "3      32.0              9.60                 2        614.0                1   \n",
       "4      32.0             12.00                 7        750.0                1   \n",
       "...     ...               ...               ...          ...              ...   \n",
       "19995  66.0             32.20                 6        354.0                1   \n",
       "19996  53.0             19.80                 5        809.0                0   \n",
       "19997  66.0             18.40                 1        466.0                0   \n",
       "19998  32.0              6.00                 8        619.0                0   \n",
       "19999  83.0             37.35                 7        882.0                1   \n",
       "\n",
       "       Avg_days_between_transaction  cc_cons_apr  dc_cons_apr  cc_cons_may  \\\n",
       "0                               5.0      7998.48     2289.000   9553.00000   \n",
       "1                              12.0     16479.64      415.000   7386.49000   \n",
       "2                              13.0     29272.03    20910.575   4170.53000   \n",
       "3                              19.0      9662.31     5306.390   5261.00000   \n",
       "4                              18.0     13239.86     7063.000  19193.13000   \n",
       "...                             ...          ...          ...          ...   \n",
       "19995                           7.0      4611.00    12015.500  41162.76625   \n",
       "19996                          14.0      9545.00     2061.000   4269.27000   \n",
       "19997                          10.0      2028.93     9454.990  11019.00000   \n",
       "19998                          17.0     11456.23     3999.000   2650.08000   \n",
       "19999                          10.0     23551.00    16821.300  15746.06000   \n",
       "\n",
       "       dc_cons_may  ...  credit_count_may  debit_count_may  credit_count_jun  \\\n",
       "0         2879.580  ...               5.0             14.0              40.0   \n",
       "1         6763.000  ...               4.0             63.0               7.0   \n",
       "2        18207.585  ...               6.0             82.0              16.0   \n",
       "3        11934.790  ...               2.0             78.0               2.0   \n",
       "4        18207.585  ...               3.0            156.0              21.0   \n",
       "...            ...  ...               ...              ...               ...   \n",
       "19995      550.500  ...               0.0              8.0               3.0   \n",
       "19996      901.000  ...              24.5             11.0              43.0   \n",
       "19997     4774.500  ...               2.0            156.0               3.0   \n",
       "19998      290.000  ...               3.0             82.0              46.5   \n",
       "19999     1310.590  ...               5.0              8.0               5.0   \n",
       "\n",
       "       debit_count_jun  emi_active  account_type_saving  gender_M  Income_LOW  \\\n",
       "0                   96    2646.720                    0         1           0   \n",
       "1                   12    5469.790                    0         1           0   \n",
       "2                   42    7207.850                    0         0           0   \n",
       "3                   10     591.340                    0         1           0   \n",
       "4                   12    2621.390                    0         1           0   \n",
       "...                ...         ...                  ...       ...         ...   \n",
       "19995               38    6334.200                    0         0           0   \n",
       "19996               11   21122.065                    0         1           0   \n",
       "19997               38    1930.190                    0         0           0   \n",
       "19998               80   21122.065                    0         1           1   \n",
       "19999               42    5311.540                    0         0           1   \n",
       "\n",
       "       Income_MEDIUM  cc_cons  \n",
       "0                  1      NaN  \n",
       "1                  1      NaN  \n",
       "2                  1      NaN  \n",
       "3                  1      NaN  \n",
       "4                  0      NaN  \n",
       "...              ...      ...  \n",
       "19995              1      NaN  \n",
       "19996              1      NaN  \n",
       "19997              1      NaN  \n",
       "19998              0      NaN  \n",
       "19999              0      NaN  \n",
       "\n",
       "[5000 rows x 28 columns]"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# complete data for prediction of null cc_cons value\n",
    "fr_merged_Null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "d9ab319d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fr_merged_scaled.drop(columns=\"Cluster_6\", inplace=True)\n",
    "# fr_merged_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "57009741",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "c8010d85",
   "metadata": {},
   "outputs": [],
   "source": [
    "# split train-test\n",
    "\n",
    "X = fr_merged_Non_Null.drop(columns='cc_cons')\n",
    "y = fr_merged_Non_Null.loc[:,'cc_cons']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "71cf2a6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "## taking too long to work\n",
    "\n",
    "#svr_cv = SVR()\n",
    "#regressor_cv = GridSearchCV(svr_cv, param_grid = parameters)\n",
    "#regressor_cv.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "4f9845e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def rmsle(y, y0):\n",
    "    return np.sqrt(np.mean(np.square(np.log1p(y) - np.log1p(y0))))\n",
    "rmsle_score = make_scorer(rmsle, greater_is_better=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "692bd52f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.002149 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 2752\n",
      "[LightGBM] [Info] Number of data points in the train set: 10500, number of used features: 27\n",
      "[LightGBM] [Info] Start training from score 55409.393333\n",
      "Learning rate set to 0.059365\n",
      "0:\tlearn: 97771.5461195\ttotal: 193ms\tremaining: 3m 12s\n",
      "1:\tlearn: 97728.7837775\ttotal: 204ms\tremaining: 1m 41s\n",
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     ]
    }
   ],
   "source": [
    "models=[RandomForestRegressor(),AdaBoostRegressor(),BaggingRegressor(),SVR(),KNeighborsRegressor(),\n",
    "       XGBRegressor(),ElasticNet(),GradientBoostingRegressor(),DecisionTreeRegressor(), Lasso(),\n",
    "        LinearRegression(),LGBMRegressor(), CatBoostRegressor(),Ridge()]\n",
    "\n",
    "model_names=['RandomForestRegressor','AdaBoostRegressor','BaggingRegressor','SVR',\n",
    "             'KNeighborsRegressor','XGBRegressor','ElasticNet', 'GradientBoostingRegressor',\n",
    "             'DecisionTreeRegressor',' Lasso','LinearRegression','LGBMRegressor',\n",
    "             'CatBoostRegressor','Ridge']\n",
    "rmsle=[]\n",
    "d={}\n",
    "for model in range (len(models)):\n",
    "    clf=models[model]\n",
    "    clf.fit(X_train,y_train)\n",
    "    test_pred=clf.predict(X_test)\n",
    "    rmsle.append(np.sqrt(np.mean(np.square(np.log1p(test_pred) - np.log1p(y_test)))))\n",
    "d={'Modelling Algo':model_names,'RMSLE':rmsle}  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "3f18ed38",
   "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>Modelling Algo</th>\n",
       "      <th>RMSLE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RandomForestRegressor</td>\n",
       "      <td>2.148069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AdaBoostRegressor</td>\n",
       "      <td>2.384668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BaggingRegressor</td>\n",
       "      <td>2.151507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SVR</td>\n",
       "      <td>1.610467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>KNeighborsRegressor</td>\n",
       "      <td>2.058863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>XGBRegressor</td>\n",
       "      <td>2.15071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ElasticNet</td>\n",
       "      <td>2.05421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>GradientBoostingRegressor</td>\n",
       "      <td>2.051598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>DecisionTreeRegressor</td>\n",
       "      <td>2.288845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Lasso</td>\n",
       "      <td>2.053991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>LinearRegression</td>\n",
       "      <td>2.053986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>LGBMRegressor</td>\n",
       "      <td>2.05204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>CatBoostRegressor</td>\n",
       "      <td>2.070225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Ridge</td>\n",
       "      <td>2.053987</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Modelling Algo     RMSLE\n",
       "0       RandomForestRegressor  2.148069\n",
       "1           AdaBoostRegressor  2.384668\n",
       "2            BaggingRegressor  2.151507\n",
       "3                         SVR  1.610467\n",
       "4         KNeighborsRegressor  2.058863\n",
       "5                XGBRegressor   2.15071\n",
       "6                  ElasticNet   2.05421\n",
       "7   GradientBoostingRegressor  2.051598\n",
       "8       DecisionTreeRegressor  2.288845\n",
       "9                       Lasso  2.053991\n",
       "10           LinearRegression  2.053986\n",
       "11              LGBMRegressor   2.05204\n",
       "12          CatBoostRegressor  2.070225\n",
       "13                      Ridge  2.053987"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmsle_frame=pd.DataFrame.from_dict(d, orient='index').transpose()\n",
    "rmsle_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "9e4a96bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2b60322c3a0>"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(y='Modelling Algo',x='RMSLE',data=rmsle_frame,kind='bar',size=5,aspect=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "5ef69f22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2b6081d9dc0>"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.factorplot(x='Modelling Algo',y='RMSLE',data=rmsle_frame,kind='point',size=5,aspect=2)\n",
    "g.set_xticklabels(rotation=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce285d13",
   "metadata": {},
   "source": [
    "As we can see that SVR is having least rmsle score, we will do some hyperparameter tunning to check if the model improves."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "bb91b2e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# X_fr_merged_scaled_val = fr_merged_scaled_Non_null.drop(columns='cc_cons')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "c1c90943",
   "metadata": {},
   "outputs": [],
   "source": [
    "svr = SVR().fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "ef1ca251",
   "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>age</th>\n",
       "      <th>Emp_Tenure_Years</th>\n",
       "      <th>Tenure_with_Bank</th>\n",
       "      <th>region_code</th>\n",
       "      <th>NetBanking_Flag</th>\n",
       "      <th>Avg_days_between_transaction</th>\n",
       "      <th>cc_cons_apr</th>\n",
       "      <th>dc_cons_apr</th>\n",
       "      <th>cc_cons_may</th>\n",
       "      <th>dc_cons_may</th>\n",
       "      <th>...</th>\n",
       "      <th>credit_count_apr</th>\n",
       "      <th>credit_count_may</th>\n",
       "      <th>debit_count_may</th>\n",
       "      <th>credit_count_jun</th>\n",
       "      <th>debit_count_jun</th>\n",
       "      <th>emi_active</th>\n",
       "      <th>account_type_saving</th>\n",
       "      <th>gender_M</th>\n",
       "      <th>Income_LOW</th>\n",
       "      <th>Income_MEDIUM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63.0</td>\n",
       "      <td>30.10</td>\n",
       "      <td>10</td>\n",
       "      <td>628.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7998.48</td>\n",
       "      <td>2289.000</td>\n",
       "      <td>9553.00000</td>\n",
       "      <td>2879.580</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>96</td>\n",
       "      <td>2646.720</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>36.0</td>\n",
       "      <td>14.40</td>\n",
       "      <td>10</td>\n",
       "      <td>656.0</td>\n",
       "      <td>0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>16479.64</td>\n",
       "      <td>415.000</td>\n",
       "      <td>7386.49000</td>\n",
       "      <td>6763.000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12</td>\n",
       "      <td>5469.790</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28.0</td>\n",
       "      <td>4.80</td>\n",
       "      <td>10</td>\n",
       "      <td>314.0</td>\n",
       "      <td>1</td>\n",
       "      <td>13.0</td>\n",
       "      <td>29272.03</td>\n",
       "      <td>20910.575</td>\n",
       "      <td>4170.53000</td>\n",
       "      <td>18207.585</td>\n",
       "      <td>...</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>42</td>\n",
       "      <td>7207.850</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32.0</td>\n",
       "      <td>9.60</td>\n",
       "      <td>2</td>\n",
       "      <td>614.0</td>\n",
       "      <td>1</td>\n",
       "      <td>19.0</td>\n",
       "      <td>9662.31</td>\n",
       "      <td>5306.390</td>\n",
       "      <td>5261.00000</td>\n",
       "      <td>11934.790</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10</td>\n",
       "      <td>591.340</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32.0</td>\n",
       "      <td>12.00</td>\n",
       "      <td>7</td>\n",
       "      <td>750.0</td>\n",
       "      <td>1</td>\n",
       "      <td>18.0</td>\n",
       "      <td>13239.86</td>\n",
       "      <td>7063.000</td>\n",
       "      <td>19193.13000</td>\n",
       "      <td>18207.585</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>12</td>\n",
       "      <td>2621.390</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19995</th>\n",
       "      <td>66.0</td>\n",
       "      <td>32.20</td>\n",
       "      <td>6</td>\n",
       "      <td>354.0</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4611.00</td>\n",
       "      <td>12015.500</td>\n",
       "      <td>41162.76625</td>\n",
       "      <td>550.500</td>\n",
       "      <td>...</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>38</td>\n",
       "      <td>6334.200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19996</th>\n",
       "      <td>53.0</td>\n",
       "      <td>19.80</td>\n",
       "      <td>5</td>\n",
       "      <td>809.0</td>\n",
       "      <td>0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>9545.00</td>\n",
       "      <td>2061.000</td>\n",
       "      <td>4269.27000</td>\n",
       "      <td>901.000</td>\n",
       "      <td>...</td>\n",
       "      <td>13.0</td>\n",
       "      <td>24.5</td>\n",
       "      <td>11.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>11</td>\n",
       "      <td>21122.065</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19997</th>\n",
       "      <td>66.0</td>\n",
       "      <td>18.40</td>\n",
       "      <td>1</td>\n",
       "      <td>466.0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2028.93</td>\n",
       "      <td>9454.990</td>\n",
       "      <td>11019.00000</td>\n",
       "      <td>4774.500</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>38</td>\n",
       "      <td>1930.190</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19998</th>\n",
       "      <td>32.0</td>\n",
       "      <td>6.00</td>\n",
       "      <td>8</td>\n",
       "      <td>619.0</td>\n",
       "      <td>0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>11456.23</td>\n",
       "      <td>3999.000</td>\n",
       "      <td>2650.08000</td>\n",
       "      <td>290.000</td>\n",
       "      <td>...</td>\n",
       "      <td>26.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>46.5</td>\n",
       "      <td>80</td>\n",
       "      <td>21122.065</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19999</th>\n",
       "      <td>83.0</td>\n",
       "      <td>37.35</td>\n",
       "      <td>7</td>\n",
       "      <td>882.0</td>\n",
       "      <td>1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>23551.00</td>\n",
       "      <td>16821.300</td>\n",
       "      <td>15746.06000</td>\n",
       "      <td>1310.590</td>\n",
       "      <td>...</td>\n",
       "      <td>35.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>42</td>\n",
       "      <td>5311.540</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5000 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        age  Emp_Tenure_Years  Tenure_with_Bank  region_code  NetBanking_Flag  \\\n",
       "0      63.0             30.10                10        628.0                1   \n",
       "1      36.0             14.40                10        656.0                0   \n",
       "2      28.0              4.80                10        314.0                1   \n",
       "3      32.0              9.60                 2        614.0                1   \n",
       "4      32.0             12.00                 7        750.0                1   \n",
       "...     ...               ...               ...          ...              ...   \n",
       "19995  66.0             32.20                 6        354.0                1   \n",
       "19996  53.0             19.80                 5        809.0                0   \n",
       "19997  66.0             18.40                 1        466.0                0   \n",
       "19998  32.0              6.00                 8        619.0                0   \n",
       "19999  83.0             37.35                 7        882.0                1   \n",
       "\n",
       "       Avg_days_between_transaction  cc_cons_apr  dc_cons_apr  cc_cons_may  \\\n",
       "0                               5.0      7998.48     2289.000   9553.00000   \n",
       "1                              12.0     16479.64      415.000   7386.49000   \n",
       "2                              13.0     29272.03    20910.575   4170.53000   \n",
       "3                              19.0      9662.31     5306.390   5261.00000   \n",
       "4                              18.0     13239.86     7063.000  19193.13000   \n",
       "...                             ...          ...          ...          ...   \n",
       "19995                           7.0      4611.00    12015.500  41162.76625   \n",
       "19996                          14.0      9545.00     2061.000   4269.27000   \n",
       "19997                          10.0      2028.93     9454.990  11019.00000   \n",
       "19998                          17.0     11456.23     3999.000   2650.08000   \n",
       "19999                          10.0     23551.00    16821.300  15746.06000   \n",
       "\n",
       "       dc_cons_may  ...  credit_count_apr  credit_count_may  debit_count_may  \\\n",
       "0         2879.580  ...               1.0               5.0             14.0   \n",
       "1         6763.000  ...               1.0               4.0             63.0   \n",
       "2        18207.585  ...              33.0               6.0             82.0   \n",
       "3        11934.790  ...               7.0               2.0             78.0   \n",
       "4        18207.585  ...               1.0               3.0            156.0   \n",
       "...            ...  ...               ...               ...              ...   \n",
       "19995      550.500  ...              35.0               0.0              8.0   \n",
       "19996      901.000  ...              13.0              24.5             11.0   \n",
       "19997     4774.500  ...               3.0               2.0            156.0   \n",
       "19998      290.000  ...              26.0               3.0             82.0   \n",
       "19999     1310.590  ...              35.0               5.0              8.0   \n",
       "\n",
       "       credit_count_jun  debit_count_jun  emi_active  account_type_saving  \\\n",
       "0                  40.0               96    2646.720                    0   \n",
       "1                   7.0               12    5469.790                    0   \n",
       "2                  16.0               42    7207.850                    0   \n",
       "3                   2.0               10     591.340                    0   \n",
       "4                  21.0               12    2621.390                    0   \n",
       "...                 ...              ...         ...                  ...   \n",
       "19995               3.0               38    6334.200                    0   \n",
       "19996              43.0               11   21122.065                    0   \n",
       "19997               3.0               38    1930.190                    0   \n",
       "19998              46.5               80   21122.065                    0   \n",
       "19999               5.0               42    5311.540                    0   \n",
       "\n",
       "       gender_M  Income_LOW  Income_MEDIUM  \n",
       "0             1           0              1  \n",
       "1             1           0              1  \n",
       "2             0           0              1  \n",
       "3             1           0              1  \n",
       "4             1           0              0  \n",
       "...         ...         ...            ...  \n",
       "19995         0           0              1  \n",
       "19996         1           0              1  \n",
       "19997         0           0              1  \n",
       "19998         1           1              0  \n",
       "19999         0           1              0  \n",
       "\n",
       "[5000 rows x 27 columns]"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Only the X variables of the data where cc_cons is null, this will be fed in the model to predict values\n",
    "X_fr_merged_Null = fr_merged_Null.drop(columns='cc_cons')\n",
    "X_fr_merged_Null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "bfe99fb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_svr = svr.predict(X_fr_merged_Null)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "ff108483",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([13355.24002053, 13363.67000331, 13365.3101417 , ...,\n",
       "       13348.10614537, 13376.80152235, 13365.70438044])"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_svr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "3eae5545",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = pd.DataFrame({'cc_cons': y_pred_svr})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "a6036938",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set the index of dataset_2 to match dataset_1's index\n",
    "y_pred_matched = y_pred.set_index(X_fr_merged_Null.index)\n",
    "\n",
    "# Merge the datasets based on the index of dataset_1\n",
    "fr_merged_Null_new = X_fr_merged_Null.merge(y_pred_matched, left_index=True, right_index=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "383c80f0",
   "metadata": {},
   "source": [
    "#### After jumping from linear regression, ridge regression, lasso regression etc. The final predicted values for the record where the cc_cons was set to null based on support vector regressor can be seen in the below mentioned data set: \"fr_merged_Null_new\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "67002be2",
   "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>age</th>\n",
       "      <th>Emp_Tenure_Years</th>\n",
       "      <th>Tenure_with_Bank</th>\n",
       "      <th>region_code</th>\n",
       "      <th>NetBanking_Flag</th>\n",
       "      <th>Avg_days_between_transaction</th>\n",
       "      <th>cc_cons_apr</th>\n",
       "      <th>dc_cons_apr</th>\n",
       "      <th>cc_cons_may</th>\n",
       "      <th>dc_cons_may</th>\n",
       "      <th>...</th>\n",
       "      <th>credit_count_may</th>\n",
       "      <th>debit_count_may</th>\n",
       "      <th>credit_count_jun</th>\n",
       "      <th>debit_count_jun</th>\n",
       "      <th>emi_active</th>\n",
       "      <th>account_type_saving</th>\n",
       "      <th>gender_M</th>\n",
       "      <th>Income_LOW</th>\n",
       "      <th>Income_MEDIUM</th>\n",
       "      <th>cc_cons</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63.0</td>\n",
       "      <td>30.10</td>\n",
       "      <td>10</td>\n",
       "      <td>628.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7998.48</td>\n",
       "      <td>2289.000</td>\n",
       "      <td>9553.00000</td>\n",
       "      <td>2879.580</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>96</td>\n",
       "      <td>2646.720</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>13355.240021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>36.0</td>\n",
       "      <td>14.40</td>\n",
       "      <td>10</td>\n",
       "      <td>656.0</td>\n",
       "      <td>0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>16479.64</td>\n",
       "      <td>415.000</td>\n",
       "      <td>7386.49000</td>\n",
       "      <td>6763.000</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12</td>\n",
       "      <td>5469.790</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>13363.670003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28.0</td>\n",
       "      <td>4.80</td>\n",
       "      <td>10</td>\n",
       "      <td>314.0</td>\n",
       "      <td>1</td>\n",
       "      <td>13.0</td>\n",
       "      <td>29272.03</td>\n",
       "      <td>20910.575</td>\n",
       "      <td>4170.53000</td>\n",
       "      <td>18207.585</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>42</td>\n",
       "      <td>7207.850</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>13365.310142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32.0</td>\n",
       "      <td>9.60</td>\n",
       "      <td>2</td>\n",
       "      <td>614.0</td>\n",
       "      <td>1</td>\n",
       "      <td>19.0</td>\n",
       "      <td>9662.31</td>\n",
       "      <td>5306.390</td>\n",
       "      <td>5261.00000</td>\n",
       "      <td>11934.790</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10</td>\n",
       "      <td>591.340</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>13345.178204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32.0</td>\n",
       "      <td>12.00</td>\n",
       "      <td>7</td>\n",
       "      <td>750.0</td>\n",
       "      <td>1</td>\n",
       "      <td>18.0</td>\n",
       "      <td>13239.86</td>\n",
       "      <td>7063.000</td>\n",
       "      <td>19193.13000</td>\n",
       "      <td>18207.585</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>12</td>\n",
       "      <td>2621.390</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13346.397722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <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>19995</th>\n",
       "      <td>66.0</td>\n",
       "      <td>32.20</td>\n",
       "      <td>6</td>\n",
       "      <td>354.0</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4611.00</td>\n",
       "      <td>12015.500</td>\n",
       "      <td>41162.76625</td>\n",
       "      <td>550.500</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>38</td>\n",
       "      <td>6334.200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>13364.768043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19996</th>\n",
       "      <td>53.0</td>\n",
       "      <td>19.80</td>\n",
       "      <td>5</td>\n",
       "      <td>809.0</td>\n",
       "      <td>0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>9545.00</td>\n",
       "      <td>2061.000</td>\n",
       "      <td>4269.27000</td>\n",
       "      <td>901.000</td>\n",
       "      <td>...</td>\n",
       "      <td>24.5</td>\n",
       "      <td>11.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>11</td>\n",
       "      <td>21122.065</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>13370.443980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19997</th>\n",
       "      <td>66.0</td>\n",
       "      <td>18.40</td>\n",
       "      <td>1</td>\n",
       "      <td>466.0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2028.93</td>\n",
       "      <td>9454.990</td>\n",
       "      <td>11019.00000</td>\n",
       "      <td>4774.500</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>38</td>\n",
       "      <td>1930.190</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>13348.106145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19998</th>\n",
       "      <td>32.0</td>\n",
       "      <td>6.00</td>\n",
       "      <td>8</td>\n",
       "      <td>619.0</td>\n",
       "      <td>0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>11456.23</td>\n",
       "      <td>3999.000</td>\n",
       "      <td>2650.08000</td>\n",
       "      <td>290.000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>46.5</td>\n",
       "      <td>80</td>\n",
       "      <td>21122.065</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>13376.801522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19999</th>\n",
       "      <td>83.0</td>\n",
       "      <td>37.35</td>\n",
       "      <td>7</td>\n",
       "      <td>882.0</td>\n",
       "      <td>1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>23551.00</td>\n",
       "      <td>16821.300</td>\n",
       "      <td>15746.06000</td>\n",
       "      <td>1310.590</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>42</td>\n",
       "      <td>5311.540</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>13365.704380</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5000 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        age  Emp_Tenure_Years  Tenure_with_Bank  region_code  NetBanking_Flag  \\\n",
       "0      63.0             30.10                10        628.0                1   \n",
       "1      36.0             14.40                10        656.0                0   \n",
       "2      28.0              4.80                10        314.0                1   \n",
       "3      32.0              9.60                 2        614.0                1   \n",
       "4      32.0             12.00                 7        750.0                1   \n",
       "...     ...               ...               ...          ...              ...   \n",
       "19995  66.0             32.20                 6        354.0                1   \n",
       "19996  53.0             19.80                 5        809.0                0   \n",
       "19997  66.0             18.40                 1        466.0                0   \n",
       "19998  32.0              6.00                 8        619.0                0   \n",
       "19999  83.0             37.35                 7        882.0                1   \n",
       "\n",
       "       Avg_days_between_transaction  cc_cons_apr  dc_cons_apr  cc_cons_may  \\\n",
       "0                               5.0      7998.48     2289.000   9553.00000   \n",
       "1                              12.0     16479.64      415.000   7386.49000   \n",
       "2                              13.0     29272.03    20910.575   4170.53000   \n",
       "3                              19.0      9662.31     5306.390   5261.00000   \n",
       "4                              18.0     13239.86     7063.000  19193.13000   \n",
       "...                             ...          ...          ...          ...   \n",
       "19995                           7.0      4611.00    12015.500  41162.76625   \n",
       "19996                          14.0      9545.00     2061.000   4269.27000   \n",
       "19997                          10.0      2028.93     9454.990  11019.00000   \n",
       "19998                          17.0     11456.23     3999.000   2650.08000   \n",
       "19999                          10.0     23551.00    16821.300  15746.06000   \n",
       "\n",
       "       dc_cons_may  ...  credit_count_may  debit_count_may  credit_count_jun  \\\n",
       "0         2879.580  ...               5.0             14.0              40.0   \n",
       "1         6763.000  ...               4.0             63.0               7.0   \n",
       "2        18207.585  ...               6.0             82.0              16.0   \n",
       "3        11934.790  ...               2.0             78.0               2.0   \n",
       "4        18207.585  ...               3.0            156.0              21.0   \n",
       "...            ...  ...               ...              ...               ...   \n",
       "19995      550.500  ...               0.0              8.0               3.0   \n",
       "19996      901.000  ...              24.5             11.0              43.0   \n",
       "19997     4774.500  ...               2.0            156.0               3.0   \n",
       "19998      290.000  ...               3.0             82.0              46.5   \n",
       "19999     1310.590  ...               5.0              8.0               5.0   \n",
       "\n",
       "       debit_count_jun  emi_active  account_type_saving  gender_M  Income_LOW  \\\n",
       "0                   96    2646.720                    0         1           0   \n",
       "1                   12    5469.790                    0         1           0   \n",
       "2                   42    7207.850                    0         0           0   \n",
       "3                   10     591.340                    0         1           0   \n",
       "4                   12    2621.390                    0         1           0   \n",
       "...                ...         ...                  ...       ...         ...   \n",
       "19995               38    6334.200                    0         0           0   \n",
       "19996               11   21122.065                    0         1           0   \n",
       "19997               38    1930.190                    0         0           0   \n",
       "19998               80   21122.065                    0         1           1   \n",
       "19999               42    5311.540                    0         0           1   \n",
       "\n",
       "       Income_MEDIUM       cc_cons  \n",
       "0                  1  13355.240021  \n",
       "1                  1  13363.670003  \n",
       "2                  1  13365.310142  \n",
       "3                  1  13345.178204  \n",
       "4                  0  13346.397722  \n",
       "...              ...           ...  \n",
       "19995              1  13364.768043  \n",
       "19996              1  13370.443980  \n",
       "19997              1  13348.106145  \n",
       "19998              0  13376.801522  \n",
       "19999              0  13365.704380  \n",
       "\n",
       "[5000 rows x 28 columns]"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fr_merged_Null_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0bb15a00",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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