Churn_Analytics_Bepec.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bcaad15c",
   "metadata": {},
   "source": [
    "**Imporitng Libraries**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "id": "d7d0ee14",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "#to enable the inline plotting, where the plots/graphs will be displayed just below the cell where your plotting commands are written"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "640971eb",
   "metadata": {},
   "source": [
    "**changing the default number of rows to be displayed**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "id": "5d985d07",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_rows', None)\n",
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d19d007",
   "metadata": {},
   "source": [
    "**Dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "id": "efb74edf",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data = pd.read_excel(\"Dataset.xlsx\") # CHUNK MEANS why customers are not subscribing any OTT platform again in case of bank we can say customer are not sticking to current bank."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a23c0633",
   "metadata": {},
   "source": [
    "**Printing last 5 rows of dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "id": "4d34ba87",
   "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>CIF</th>\n",
       "      <th>CUS_DOB</th>\n",
       "      <th>AGE</th>\n",
       "      <th>CUS_Month_Income</th>\n",
       "      <th>CUS_Gender</th>\n",
       "      <th>CUS_Marital_Status</th>\n",
       "      <th>CUS_Customer_Since</th>\n",
       "      <th>YEARS_WITH_US</th>\n",
       "      <th># total debit transactions for S1</th>\n",
       "      <th># total debit transactions for S2</th>\n",
       "      <th># total debit transactions for S3</th>\n",
       "      <th>total debit amount for S1</th>\n",
       "      <th>total debit amount for S2</th>\n",
       "      <th>total debit amount for S3</th>\n",
       "      <th># total credit transactions for S1</th>\n",
       "      <th># total credit transactions for S2</th>\n",
       "      <th># total credit transactions for S3</th>\n",
       "      <th>total credit amount for S1</th>\n",
       "      <th>total credit amount for S2</th>\n",
       "      <th>total credit amount for S3</th>\n",
       "      <th>total debit amount</th>\n",
       "      <th>total debit transactions</th>\n",
       "      <th>total credit amount</th>\n",
       "      <th>total credit transactions</th>\n",
       "      <th>total transactions</th>\n",
       "      <th>CUS_Target</th>\n",
       "      <th>TAR_Desc</th>\n",
       "      <th>Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1244</th>\n",
       "      <td>XXXXXX</td>\n",
       "      <td>Sep 10 1951 12:00AM</td>\n",
       "      <td>68</td>\n",
       "      <td>4500.0</td>\n",
       "      <td>MALE</td>\n",
       "      <td>SINGLE</td>\n",
       "      <td>2005-07-25</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>3069.60</td>\n",
       "      <td>2376.00</td>\n",
       "      <td>200.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5645.60</td>\n",
       "      <td>26</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>2223</td>\n",
       "      <td>LOW</td>\n",
       "      <td>CHURN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1245</th>\n",
       "      <td>XXXXXX</td>\n",
       "      <td>Mar 23 1984 12:00AM</td>\n",
       "      <td>35</td>\n",
       "      <td>3500000.0</td>\n",
       "      <td>MALE</td>\n",
       "      <td>SINGLE</td>\n",
       "      <td>2005-07-27</td>\n",
       "      <td>14</td>\n",
       "      <td>55</td>\n",
       "      <td>35</td>\n",
       "      <td>37</td>\n",
       "      <td>119297.02</td>\n",
       "      <td>118884.00</td>\n",
       "      <td>147554.88</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>114601.14</td>\n",
       "      <td>119137.62</td>\n",
       "      <td>440891.65</td>\n",
       "      <td>385735.90</td>\n",
       "      <td>127</td>\n",
       "      <td>674630.41</td>\n",
       "      <td>22</td>\n",
       "      <td>149</td>\n",
       "      <td>2222</td>\n",
       "      <td>MIDLE</td>\n",
       "      <td>ACTIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1246</th>\n",
       "      <td>XXXXXX</td>\n",
       "      <td>Feb  4 1985 12:00AM</td>\n",
       "      <td>34</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>MALE</td>\n",
       "      <td>SINGLE</td>\n",
       "      <td>2005-07-25</td>\n",
       "      <td>14</td>\n",
       "      <td>415</td>\n",
       "      <td>368</td>\n",
       "      <td>424</td>\n",
       "      <td>1068684.72</td>\n",
       "      <td>571814.89</td>\n",
       "      <td>833122.07</td>\n",
       "      <td>63</td>\n",
       "      <td>76</td>\n",
       "      <td>78</td>\n",
       "      <td>936134.09</td>\n",
       "      <td>733658.65</td>\n",
       "      <td>971555.27</td>\n",
       "      <td>2473621.68</td>\n",
       "      <td>1207</td>\n",
       "      <td>2641348.01</td>\n",
       "      <td>217</td>\n",
       "      <td>1424</td>\n",
       "      <td>2232</td>\n",
       "      <td>MIDLE</td>\n",
       "      <td>ACTIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1247</th>\n",
       "      <td>XXXXXX</td>\n",
       "      <td>Feb  3 1950 12:00AM</td>\n",
       "      <td>69</td>\n",
       "      <td>2000000.0</td>\n",
       "      <td>MALE</td>\n",
       "      <td>SINGLE</td>\n",
       "      <td>2005-07-26</td>\n",
       "      <td>14</td>\n",
       "      <td>147</td>\n",
       "      <td>202</td>\n",
       "      <td>234</td>\n",
       "      <td>397602.73</td>\n",
       "      <td>514584.25</td>\n",
       "      <td>459665.24</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>23</td>\n",
       "      <td>377245.00</td>\n",
       "      <td>532708.75</td>\n",
       "      <td>400814.60</td>\n",
       "      <td>1371852.22</td>\n",
       "      <td>583</td>\n",
       "      <td>1310768.35</td>\n",
       "      <td>48</td>\n",
       "      <td>631</td>\n",
       "      <td>2222</td>\n",
       "      <td>MIDLE</td>\n",
       "      <td>ACTIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1248</th>\n",
       "      <td>XXXXXX</td>\n",
       "      <td>Feb 23 1961 12:00AM</td>\n",
       "      <td>58</td>\n",
       "      <td>5000000.0</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>SINGLE</td>\n",
       "      <td>2005-07-26</td>\n",
       "      <td>14</td>\n",
       "      <td>76</td>\n",
       "      <td>88</td>\n",
       "      <td>69</td>\n",
       "      <td>59730.64</td>\n",
       "      <td>420105.56</td>\n",
       "      <td>281991.71</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>19</td>\n",
       "      <td>30500.00</td>\n",
       "      <td>422000.00</td>\n",
       "      <td>214350.00</td>\n",
       "      <td>761827.91</td>\n",
       "      <td>233</td>\n",
       "      <td>666850.00</td>\n",
       "      <td>37</td>\n",
       "      <td>270</td>\n",
       "      <td>2222</td>\n",
       "      <td>MIDLE</td>\n",
       "      <td>ACTIVE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         CIF              CUS_DOB  AGE  CUS_Month_Income CUS_Gender  \\\n",
       "1244  XXXXXX  Sep 10 1951 12:00AM   68            4500.0       MALE   \n",
       "1245  XXXXXX  Mar 23 1984 12:00AM   35         3500000.0       MALE   \n",
       "1246  XXXXXX  Feb  4 1985 12:00AM   34            1000.0       MALE   \n",
       "1247  XXXXXX  Feb  3 1950 12:00AM   69         2000000.0       MALE   \n",
       "1248  XXXXXX  Feb 23 1961 12:00AM   58         5000000.0     FEMALE   \n",
       "\n",
       "     CUS_Marital_Status CUS_Customer_Since  YEARS_WITH_US  \\\n",
       "1244             SINGLE         2005-07-25             14   \n",
       "1245             SINGLE         2005-07-27             14   \n",
       "1246             SINGLE         2005-07-25             14   \n",
       "1247             SINGLE         2005-07-26             14   \n",
       "1248             SINGLE         2005-07-26             14   \n",
       "\n",
       "      # total debit transactions for S1  # total debit transactions for S2  \\\n",
       "1244                                 13                                 12   \n",
       "1245                                 55                                 35   \n",
       "1246                                415                                368   \n",
       "1247                                147                                202   \n",
       "1248                                 76                                 88   \n",
       "\n",
       "      # total debit transactions for S3  total debit amount for S1  \\\n",
       "1244                                  1                    3069.60   \n",
       "1245                                 37                  119297.02   \n",
       "1246                                424                 1068684.72   \n",
       "1247                                234                  397602.73   \n",
       "1248                                 69                   59730.64   \n",
       "\n",
       "      total debit amount for S2  total debit amount for S3  \\\n",
       "1244                    2376.00                     200.00   \n",
       "1245                  118884.00                  147554.88   \n",
       "1246                  571814.89                  833122.07   \n",
       "1247                  514584.25                  459665.24   \n",
       "1248                  420105.56                  281991.71   \n",
       "\n",
       "      # total credit transactions for S1  # total credit transactions for S2  \\\n",
       "1244                                   0                                   0   \n",
       "1245                                   7                                   6   \n",
       "1246                                  63                                  76   \n",
       "1247                                  11                                  14   \n",
       "1248                                   4                                  14   \n",
       "\n",
       "      # total credit transactions for S3  total credit amount for S1  \\\n",
       "1244                                   0                        0.00   \n",
       "1245                                   9                   114601.14   \n",
       "1246                                  78                   936134.09   \n",
       "1247                                  23                   377245.00   \n",
       "1248                                  19                    30500.00   \n",
       "\n",
       "      total credit amount for S2  total credit amount for S3  \\\n",
       "1244                        0.00                        0.00   \n",
       "1245                   119137.62                   440891.65   \n",
       "1246                   733658.65                   971555.27   \n",
       "1247                   532708.75                   400814.60   \n",
       "1248                   422000.00                   214350.00   \n",
       "\n",
       "      total debit amount  total debit transactions  total credit amount  \\\n",
       "1244             5645.60                        26                 0.00   \n",
       "1245           385735.90                       127            674630.41   \n",
       "1246          2473621.68                      1207           2641348.01   \n",
       "1247          1371852.22                       583           1310768.35   \n",
       "1248           761827.91                       233            666850.00   \n",
       "\n",
       "      total credit transactions  total transactions  CUS_Target TAR_Desc  \\\n",
       "1244                          0                  26        2223      LOW   \n",
       "1245                         22                 149        2222    MIDLE   \n",
       "1246                        217                1424        2232    MIDLE   \n",
       "1247                         48                 631        2222    MIDLE   \n",
       "1248                         37                 270        2222    MIDLE   \n",
       "\n",
       "      Status  \n",
       "1244   CHURN  \n",
       "1245  ACTIVE  \n",
       "1246  ACTIVE  \n",
       "1247  ACTIVE  \n",
       "1248  ACTIVE  "
      ]
     },
     "execution_count": 341,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8999eb13",
   "metadata": {},
   "source": [
    "## Description of the variables\n",
    "\n",
    "1. Age - Age of the customer\n",
    "2. Years with us - How many years the customer using the services of a particular bank.\n",
    "3. Status - Is customer still using bank services (Target variable contains Active or Churn)\n",
    "4. CUS_DOB - Date of birth of customers.\n",
    "5. CIF  - number is a unique number that the banks assign to each customer (Drop)\n",
    "6. CUS_Month_Income - Monthly income of customer\n",
    "7. CUS_Gender - Gender of customer\n",
    "8. CUS_Martial_Status - Customer is single or married\n",
    "9. CUS_Customer_Since - From how many years is she or he doing business with the banks\n",
    "10. YEARS_WITH_US - Total number of years the customer is associated with the bank\n",
    "11. total debit transactions for S1 - Total Debitted transaction from branch S1\n",
    "12. total debit transactions for S2 - Total  Debited transaction from branch S2\n",
    "13. total debit transactions for S3 - Total Debited transaction from branch S3\n",
    "14. total debit amount for S1 - Total debit amount for branch S1\n",
    "15. total debit amount for S2 - Total debit amount for branch S2\n",
    "16. total debit amount for S1 - Total debit amount for branch S3\n",
    "17. total credit transactions for S1 - Total Creditted transaction from branch S1\n",
    "18. total credit transactions for S2 - Total  Creditted transaction from branch S2\n",
    "19. total credit transactions for S3 - Total Creditted transaction from branch S3\n",
    "20. total credit amount for S1 - Total Credit  amount for branch S3\n",
    "21. total credit amount for S2 - Total credit amount for branch S3\n",
    "22. total credit amount for S3 - Total Credit amount for branch S3\n",
    "23. total credit amount - For all the branches\n",
    "24. total credit transactions - for all the branches\n",
    "25. total debit amount - for all the branches\n",
    "26. total credit transactions - for all the branches \n",
    "27. total transactions - for all the branches\n",
    "28. CUS_target - Customer target\n",
    "29. TAR_Desc - Transaction describe\n",
    "30. Status - Target feature."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "962515aa",
   "metadata": {},
   "source": [
    "**Printing first 5 rows of datasaet**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "id": "fd4f6f84",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CIF</th>\n",
       "      <th>CUS_DOB</th>\n",
       "      <th>AGE</th>\n",
       "      <th>CUS_Month_Income</th>\n",
       "      <th>CUS_Gender</th>\n",
       "      <th>CUS_Marital_Status</th>\n",
       "      <th>CUS_Customer_Since</th>\n",
       "      <th>YEARS_WITH_US</th>\n",
       "      <th># total debit transactions for S1</th>\n",
       "      <th># total debit transactions for S2</th>\n",
       "      <th># total debit transactions for S3</th>\n",
       "      <th>total debit amount for S1</th>\n",
       "      <th>total debit amount for S2</th>\n",
       "      <th>total debit amount for S3</th>\n",
       "      <th># total credit transactions for S1</th>\n",
       "      <th># total credit transactions for S2</th>\n",
       "      <th># total credit transactions for S3</th>\n",
       "      <th>total credit amount for S1</th>\n",
       "      <th>total credit amount for S2</th>\n",
       "      <th>total credit amount for S3</th>\n",
       "      <th>total debit amount</th>\n",
       "      <th>total debit transactions</th>\n",
       "      <th>total credit amount</th>\n",
       "      <th>total credit transactions</th>\n",
       "      <th>total transactions</th>\n",
       "      <th>CUS_Target</th>\n",
       "      <th>TAR_Desc</th>\n",
       "      <th>Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>XXXXXX</td>\n",
       "      <td>Feb 13 1970 12:00AM</td>\n",
       "      <td>49</td>\n",
       "      <td>7116.64</td>\n",
       "      <td>MALE</td>\n",
       "      <td>MARRIED</td>\n",
       "      <td>1994-06-30</td>\n",
       "      <td>25</td>\n",
       "      <td>277</td>\n",
       "      <td>265</td>\n",
       "      <td>345</td>\n",
       "      <td>1459126.64</td>\n",
       "      <td>1230543.08</td>\n",
       "      <td>2068641.91</td>\n",
       "      <td>10</td>\n",
       "      <td>24</td>\n",
       "      <td>31</td>\n",
       "      <td>1516981.10</td>\n",
       "      <td>1764079.61</td>\n",
       "      <td>2378592.62</td>\n",
       "      <td>4758311.63</td>\n",
       "      <td>887</td>\n",
       "      <td>5659653.33</td>\n",
       "      <td>65</td>\n",
       "      <td>952</td>\n",
       "      <td>2231</td>\n",
       "      <td>EXECUTIVE</td>\n",
       "      <td>ACTIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>XXXXXX</td>\n",
       "      <td>Sep 20 1973 12:00AM</td>\n",
       "      <td>46</td>\n",
       "      <td>1500000.00</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>SINGLE</td>\n",
       "      <td>2005-05-19</td>\n",
       "      <td>14</td>\n",
       "      <td>37</td>\n",
       "      <td>15</td>\n",
       "      <td>45</td>\n",
       "      <td>35372.55</td>\n",
       "      <td>20134.00</td>\n",
       "      <td>83856.67</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>19500.00</td>\n",
       "      <td>57500.00</td>\n",
       "      <td>139363.22</td>\n",
       "      <td>97</td>\n",
       "      <td>87000.00</td>\n",
       "      <td>10</td>\n",
       "      <td>107</td>\n",
       "      <td>2223</td>\n",
       "      <td>LOW</td>\n",
       "      <td>ACTIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>XXXXXX</td>\n",
       "      <td>Jul 18 1966 12:00AM</td>\n",
       "      <td>53</td>\n",
       "      <td>5000000.00</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>SINGLE</td>\n",
       "      <td>2005-05-20</td>\n",
       "      <td>14</td>\n",
       "      <td>31</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>28248.85</td>\n",
       "      <td>4777.66</td>\n",
       "      <td>4348.95</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>709625.00</td>\n",
       "      <td>240311.84</td>\n",
       "      <td>70946.86</td>\n",
       "      <td>37375.46</td>\n",
       "      <td>53</td>\n",
       "      <td>1020883.70</td>\n",
       "      <td>27</td>\n",
       "      <td>80</td>\n",
       "      <td>2222</td>\n",
       "      <td>MIDLE</td>\n",
       "      <td>ACTIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>XXXXXX</td>\n",
       "      <td>Jan  9 2004 12:00AM</td>\n",
       "      <td>15</td>\n",
       "      <td>500.00</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>SINGLE</td>\n",
       "      <td>2005-05-20</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4000.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>30103.05</td>\n",
       "      <td>28089.99</td>\n",
       "      <td>18753.92</td>\n",
       "      <td>4000.00</td>\n",
       "      <td>1</td>\n",
       "      <td>76946.96</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>2223</td>\n",
       "      <td>LOW</td>\n",
       "      <td>ACTIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>XXXXXX</td>\n",
       "      <td>Mar  7 1971 12:00AM</td>\n",
       "      <td>48</td>\n",
       "      <td>9000000.00</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>SINGLE</td>\n",
       "      <td>2014-06-30</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>33</td>\n",
       "      <td>12</td>\n",
       "      <td>69083.38</td>\n",
       "      <td>223007.51</td>\n",
       "      <td>46379.40</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>195600.00</td>\n",
       "      <td>176000.00</td>\n",
       "      <td>70500.00</td>\n",
       "      <td>338470.29</td>\n",
       "      <td>60</td>\n",
       "      <td>442100.00</td>\n",
       "      <td>21</td>\n",
       "      <td>81</td>\n",
       "      <td>2223</td>\n",
       "      <td>LOW</td>\n",
       "      <td>ACTIVE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CIF              CUS_DOB  AGE  CUS_Month_Income CUS_Gender  \\\n",
       "0  XXXXXX  Feb 13 1970 12:00AM   49           7116.64       MALE   \n",
       "1  XXXXXX  Sep 20 1973 12:00AM   46        1500000.00     FEMALE   \n",
       "2  XXXXXX  Jul 18 1966 12:00AM   53        5000000.00     FEMALE   \n",
       "3  XXXXXX  Jan  9 2004 12:00AM   15            500.00     FEMALE   \n",
       "4  XXXXXX  Mar  7 1971 12:00AM   48        9000000.00     FEMALE   \n",
       "\n",
       "  CUS_Marital_Status CUS_Customer_Since  YEARS_WITH_US  \\\n",
       "0            MARRIED         1994-06-30             25   \n",
       "1             SINGLE         2005-05-19             14   \n",
       "2             SINGLE         2005-05-20             14   \n",
       "3             SINGLE         2005-05-20             14   \n",
       "4             SINGLE         2014-06-30              5   \n",
       "\n",
       "   # total debit transactions for S1  # total debit transactions for S2  \\\n",
       "0                                277                                265   \n",
       "1                                 37                                 15   \n",
       "2                                 31                                 14   \n",
       "3                                  0                                  1   \n",
       "4                                 15                                 33   \n",
       "\n",
       "   # total debit transactions for S3  total debit amount for S1  \\\n",
       "0                                345                 1459126.64   \n",
       "1                                 45                   35372.55   \n",
       "2                                  8                   28248.85   \n",
       "3                                  0                       0.00   \n",
       "4                                 12                   69083.38   \n",
       "\n",
       "   total debit amount for S2  total debit amount for S3  \\\n",
       "0                 1230543.08                 2068641.91   \n",
       "1                   20134.00                   83856.67   \n",
       "2                    4777.66                    4348.95   \n",
       "3                    4000.00                       0.00   \n",
       "4                  223007.51                   46379.40   \n",
       "\n",
       "   # total credit transactions for S1  # total credit transactions for S2  \\\n",
       "0                                  10                                  24   \n",
       "1                                   2                                   4   \n",
       "2                                  14                                   8   \n",
       "3                                   5                                   4   \n",
       "4                                   8                                   7   \n",
       "\n",
       "   # total credit transactions for S3  total credit amount for S1  \\\n",
       "0                                  31                  1516981.10   \n",
       "1                                   4                    10000.00   \n",
       "2                                   5                   709625.00   \n",
       "3                                   3                    30103.05   \n",
       "4                                   6                   195600.00   \n",
       "\n",
       "   total credit amount for S2  total credit amount for S3  total debit amount  \\\n",
       "0                  1764079.61                  2378592.62          4758311.63   \n",
       "1                    19500.00                    57500.00           139363.22   \n",
       "2                   240311.84                    70946.86            37375.46   \n",
       "3                    28089.99                    18753.92             4000.00   \n",
       "4                   176000.00                    70500.00           338470.29   \n",
       "\n",
       "   total debit transactions  total credit amount  total credit transactions  \\\n",
       "0                       887           5659653.33                         65   \n",
       "1                        97             87000.00                         10   \n",
       "2                        53           1020883.70                         27   \n",
       "3                         1             76946.96                         12   \n",
       "4                        60            442100.00                         21   \n",
       "\n",
       "   total transactions  CUS_Target   TAR_Desc  Status  \n",
       "0                 952        2231  EXECUTIVE  ACTIVE  \n",
       "1                 107        2223        LOW  ACTIVE  \n",
       "2                  80        2222      MIDLE  ACTIVE  \n",
       "3                  13        2223        LOW  ACTIVE  \n",
       "4                  81        2223        LOW  ACTIVE  "
      ]
     },
     "execution_count": 342,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdeccf51",
   "metadata": {},
   "source": [
    "**Checking for null values in dataset.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "id": "4b7f77bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CIF                                    0\n",
       "CUS_DOB                                0\n",
       "AGE                                    0\n",
       "CUS_Month_Income                      11\n",
       "CUS_Gender                             2\n",
       "CUS_Marital_Status                     0\n",
       "CUS_Customer_Since                     0\n",
       "YEARS_WITH_US                          0\n",
       "# total debit transactions for S1      0\n",
       "# total debit transactions for S2      0\n",
       "# total debit transactions for S3      0\n",
       "total debit amount for S1              0\n",
       "total debit amount for S2              0\n",
       "total debit amount for S3              0\n",
       "# total credit transactions for S1     0\n",
       "# total credit transactions for S2     0\n",
       "# total credit transactions for S3     0\n",
       "total credit amount for S1             0\n",
       "total credit amount for S2             0\n",
       "total credit amount for S3             0\n",
       "total debit amount                     0\n",
       "total debit transactions               0\n",
       "total credit amount                    0\n",
       "total credit transactions              0\n",
       "total transactions                     0\n",
       "CUS_Target                             0\n",
       "TAR_Desc                               0\n",
       "Status                                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 343,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38912ca1",
   "metadata": {},
   "source": [
    "**Box and Distributioon plot of CUS_Monthly_Income**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "id": "d950c1c7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='CUS_Month_Income'>"
      ]
     },
     "execution_count": 344,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data.CUS_Month_Income)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "id": "6d4db969",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='CUS_Month_Income', ylabel='Density'>"
      ]
     },
     "execution_count": 345,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data.CUS_Month_Income)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56cb647f",
   "metadata": {},
   "source": [
    "**looking at the highest monthly income of a customer**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "id": "5b9dfbfa",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Longest 5 Customer monthly income: \n",
      " 1210    80000000.0\n",
      "423     50000000.0\n",
      "425     50000000.0\n",
      "555     50000000.0\n",
      "1104    50000000.0\n",
      "Name: CUS_Month_Income, dtype: float64 \n"
     ]
    }
   ],
   "source": [
    "print('Longest 5 Customer monthly income: \\n {} '.format(data['CUS_Month_Income'].nlargest(5)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9d91665",
   "metadata": {},
   "source": [
    "**Dropping the outliers**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "id": "8139867a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping the outliers\n",
    "data=data[data.CUS_Month_Income!=data.CUS_Month_Income.max()]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89046e90",
   "metadata": {},
   "source": [
    "**Handling the nan values of CUS_Monthly_Income**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "id": "b5a54077",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['CUS_Month_Income'] = data['CUS_Month_Income'].fillna(data['CUS_Month_Income'].mode()[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29b854fd",
   "metadata": {},
   "source": [
    "**counts of unique values.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "id": "7feb7511",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MALE      741\n",
       "FEMALE    505\n",
       "Name: CUS_Gender, dtype: int64"
      ]
     },
     "execution_count": 349,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"CUS_Gender\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04095e27",
   "metadata": {},
   "source": [
    "**Return unique values based on a hash table.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "id": "5f238dc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['MALE', 'FEMALE', nan], dtype=object)"
      ]
     },
     "execution_count": 350,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"CUS_Gender\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b6d2f23",
   "metadata": {},
   "source": [
    "**Converting categorical feature to numerical feature**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "id": "b0c8ed8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"CUS_Gender\"] = data[\"CUS_Gender\"].map({'MALE':1,\"FEMALE\":0})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "id": "18ba3882",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1.0\n",
       "1       0.0\n",
       "2       0.0\n",
       "3       0.0\n",
       "4       0.0\n",
       "5       1.0\n",
       "6       0.0\n",
       "7       1.0\n",
       "8       1.0\n",
       "9       0.0\n",
       "10      1.0\n",
       "11      1.0\n",
       "12      1.0\n",
       "13      0.0\n",
       "14      1.0\n",
       "15      1.0\n",
       "16      1.0\n",
       "17      1.0\n",
       "18      0.0\n",
       "19      1.0\n",
       "20      1.0\n",
       "21      1.0\n",
       "22      0.0\n",
       "23      1.0\n",
       "24      0.0\n",
       "25      0.0\n",
       "26      0.0\n",
       "27      1.0\n",
       "28      1.0\n",
       "29      1.0\n",
       "30      1.0\n",
       "31      1.0\n",
       "32      1.0\n",
       "33      1.0\n",
       "34      0.0\n",
       "35      0.0\n",
       "36      0.0\n",
       "37      1.0\n",
       "38      0.0\n",
       "39      1.0\n",
       "40      1.0\n",
       "41      1.0\n",
       "42      1.0\n",
       "43      0.0\n",
       "44      1.0\n",
       "45      0.0\n",
       "46      1.0\n",
       "47      1.0\n",
       "48      1.0\n",
       "49      1.0\n",
       "50      1.0\n",
       "51      0.0\n",
       "52      1.0\n",
       "53      1.0\n",
       "54      0.0\n",
       "55      0.0\n",
       "56      1.0\n",
       "57      0.0\n",
       "58      1.0\n",
       "59      1.0\n",
       "60      1.0\n",
       "61      1.0\n",
       "62      1.0\n",
       "63      0.0\n",
       "64      0.0\n",
       "65      0.0\n",
       "66      1.0\n",
       "67      0.0\n",
       "68      1.0\n",
       "69      1.0\n",
       "70      0.0\n",
       "71      1.0\n",
       "72      0.0\n",
       "73      0.0\n",
       "74      1.0\n",
       "75      0.0\n",
       "76      1.0\n",
       "77      1.0\n",
       "78      0.0\n",
       "79      1.0\n",
       "80      1.0\n",
       "81      1.0\n",
       "82      0.0\n",
       "83      1.0\n",
       "84      0.0\n",
       "85      0.0\n",
       "86      0.0\n",
       "87      0.0\n",
       "88      0.0\n",
       "89      1.0\n",
       "90      1.0\n",
       "91      0.0\n",
       "92      1.0\n",
       "93      0.0\n",
       "94      1.0\n",
       "95      1.0\n",
       "96      1.0\n",
       "97      0.0\n",
       "98      1.0\n",
       "99      0.0\n",
       "100     1.0\n",
       "101     1.0\n",
       "102     1.0\n",
       "103     0.0\n",
       "104     0.0\n",
       "105     0.0\n",
       "106     0.0\n",
       "107     0.0\n",
       "108     1.0\n",
       "109     1.0\n",
       "110     1.0\n",
       "111     1.0\n",
       "112     0.0\n",
       "113     0.0\n",
       "114     1.0\n",
       "115     1.0\n",
       "116     1.0\n",
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       "1177    0.0\n",
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       "1181    1.0\n",
       "1182    1.0\n",
       "1183    1.0\n",
       "1184    1.0\n",
       "1185    0.0\n",
       "1186    1.0\n",
       "1187    1.0\n",
       "1188    1.0\n",
       "1189    1.0\n",
       "1190    1.0\n",
       "1191    1.0\n",
       "1192    1.0\n",
       "1193    0.0\n",
       "1194    1.0\n",
       "1195    1.0\n",
       "1196    0.0\n",
       "1197    1.0\n",
       "1198    1.0\n",
       "1199    1.0\n",
       "1200    1.0\n",
       "1201    1.0\n",
       "1202    1.0\n",
       "1203    1.0\n",
       "1204    0.0\n",
       "1205    1.0\n",
       "1206    1.0\n",
       "1207    1.0\n",
       "1208    1.0\n",
       "1209    1.0\n",
       "1211    1.0\n",
       "1212    1.0\n",
       "1213    1.0\n",
       "1214    0.0\n",
       "1215    0.0\n",
       "1216    0.0\n",
       "1217    1.0\n",
       "1218    1.0\n",
       "1219    0.0\n",
       "1220    0.0\n",
       "1221    1.0\n",
       "1222    1.0\n",
       "1223    1.0\n",
       "1224    1.0\n",
       "1225    0.0\n",
       "1226    1.0\n",
       "1227    1.0\n",
       "1228    1.0\n",
       "1229    0.0\n",
       "1230    1.0\n",
       "1231    1.0\n",
       "1232    1.0\n",
       "1233    1.0\n",
       "1234    1.0\n",
       "1235    1.0\n",
       "1236    1.0\n",
       "1237    1.0\n",
       "1238    1.0\n",
       "1239    1.0\n",
       "1240    1.0\n",
       "1241    0.0\n",
       "1242    0.0\n",
       "1243    0.0\n",
       "1244    1.0\n",
       "1245    1.0\n",
       "1246    1.0\n",
       "1247    1.0\n",
       "1248    0.0\n",
       "Name: CUS_Gender, dtype: float64"
      ]
     },
     "execution_count": 352,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"CUS_Gender\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c66a112a",
   "metadata": {},
   "source": [
    "**Coverting  DOB to datetime format and set the customer year**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "id": "bb56246e",
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"CUS_DOB_Year\"] = pd.to_datetime(data[\"CUS_DOB\"]).dt.year"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae7bb336",
   "metadata": {},
   "source": [
    "**Handling the nan values**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "id": "c534590f",
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"CUS_Gender\"].fillna(data.CUS_Gender.mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db238cbb",
   "metadata": {},
   "source": [
    "**No Null values is present in dataset**\n",
    "1. Salary one replaced by the Mode.\n",
    "2. Gender one replaced by the mean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "id": "59e5c1dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CIF                                   0\n",
       "CUS_DOB                               0\n",
       "AGE                                   0\n",
       "CUS_Month_Income                      0\n",
       "CUS_Gender                            0\n",
       "CUS_Marital_Status                    0\n",
       "CUS_Customer_Since                    0\n",
       "YEARS_WITH_US                         0\n",
       "# total debit transactions for S1     0\n",
       "# total debit transactions for S2     0\n",
       "# total debit transactions for S3     0\n",
       "total debit amount for S1             0\n",
       "total debit amount for S2             0\n",
       "total debit amount for S3             0\n",
       "# total credit transactions for S1    0\n",
       "# total credit transactions for S2    0\n",
       "# total credit transactions for S3    0\n",
       "total credit amount for S1            0\n",
       "total credit amount for S2            0\n",
       "total credit amount for S3            0\n",
       "total debit amount                    0\n",
       "total debit transactions              0\n",
       "total credit amount                   0\n",
       "total credit transactions             0\n",
       "total transactions                    0\n",
       "CUS_Target                            0\n",
       "TAR_Desc                              0\n",
       "Status                                0\n",
       "CUS_DOB_Year                          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 355,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "id": "827cf731",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "1247    1.000000\n",
       "1248    0.000000\n",
       "Name: CUS_Gender, dtype: float64"
      ]
     },
     "execution_count": 356,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"CUS_Gender\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc7640e4",
   "metadata": {},
   "source": [
    "**Converting TAR_Desc (categorical feature) to (numerical feature)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "id": "dda2cab0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LOW          753\n",
       "MIDLE        406\n",
       "EXECUTIVE     59\n",
       "PLATINUM      30\n",
       "Name: TAR_Desc, dtype: int64"
      ]
     },
     "execution_count": 357,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.TAR_Desc.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "id": "7dded541",
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"TAR_Desc\"] = data[\"TAR_Desc\"].map({'PLATINUM':4,'EXECUTIVE':3,'MIDLE':2,'LOW':1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "id": "b26f62a2",
   "metadata": {},
   "outputs": [
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       "Name: TAR_Desc, dtype: int64"
      ]
     },
     "execution_count": 359,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['TAR_Desc']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "368b0493",
   "metadata": {},
   "source": [
    "**Converting Status (categorical feature) to (numerical feature)**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "id": "d44c938a",
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"Status\"] = data[\"Status\"].map({'ACTIVE':1,'CHURN':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "id": "7b71b335",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "Name: Status, dtype: int64"
      ]
     },
     "execution_count": 361,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"Status\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b91dc514",
   "metadata": {},
   "source": [
    "**Graph between Martial status and status**\n",
    "1. Single are more tend to churn.\n",
    "2. We can ignore other as it's not impacting the target feature.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "id": "54ca10cb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=data, x='CUS_Marital_Status', hue='Status')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13097c80",
   "metadata": {},
   "source": [
    "**Converting CUS_Martial_Status (categorical feature) to (numerical feature)**\n",
    "1. Will use one hot encoding in this \n",
    "2. One hot encoding is applied to nominal categorical feature.\n",
    "3. In this it generally lead to the multicollinearity (it should not be their cause it make our i/p feature dependent on each other with somekind of mathematical function b/w them)\n",
    "4. To overcome this trap (dummy data trap) we basically drop one column (n-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "id": "4a941df0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SINGLE     878\n",
       "MARRIED    328\n",
       "DIVORCE     30\n",
       "WIDOWED     10\n",
       "OTHER        1\n",
       "PARTNER      1\n",
       "Name: CUS_Marital_Status, dtype: int64"
      ]
     },
     "execution_count": 363,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['CUS_Marital_Status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "id": "8c3eccee",
   "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>MARRIED</th>\n",
       "      <th>OTHER</th>\n",
       "      <th>PARTNER</th>\n",
       "      <th>SINGLE</th>\n",
       "      <th>WIDOWED</th>\n",
       "      <th>CIF</th>\n",
       "      <th>CUS_DOB</th>\n",
       "      <th>AGE</th>\n",
       "      <th>CUS_Month_Income</th>\n",
       "      <th>CUS_Gender</th>\n",
       "      <th>CUS_Marital_Status</th>\n",
       "      <th>CUS_Customer_Since</th>\n",
       "      <th>YEARS_WITH_US</th>\n",
       "      <th># total debit transactions for S1</th>\n",
       "      <th># total debit transactions for S2</th>\n",
       "      <th># total debit transactions for S3</th>\n",
       "      <th>total debit amount for S1</th>\n",
       "      <th>total debit amount for S2</th>\n",
       "      <th>total debit amount for S3</th>\n",
       "      <th># total credit transactions for S1</th>\n",
       "      <th># total credit transactions for S2</th>\n",
       "      <th># total credit transactions for S3</th>\n",
       "      <th>total credit amount for S1</th>\n",
       "      <th>total credit amount for S2</th>\n",
       "      <th>total credit amount for S3</th>\n",
       "      <th>total debit amount</th>\n",
       "      <th>total debit transactions</th>\n",
       "      <th>total credit amount</th>\n",
       "      <th>total credit transactions</th>\n",
       "      <th>total transactions</th>\n",
       "      <th>CUS_Target</th>\n",
       "      <th>TAR_Desc</th>\n",
       "      <th>Status</th>\n",
       "      <th>CUS_DOB_Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>XXXXXX</td>\n",
       "      <td>Mar 18 1976 12:00AM</td>\n",
       "      <td>43</td>\n",
       "      <td>4706769.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>SINGLE</td>\n",
       "      <td>1994-08-31</td>\n",
       "      <td>25</td>\n",
       "      <td>119</td>\n",
       "      <td>101</td>\n",
       "      <td>127</td>\n",
       "      <td>657015.16</td>\n",
       "      <td>429208.3</td>\n",
       "      <td>574152.49</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>701716.02</td>\n",
       "      <td>469517.5</td>\n",
       "      <td>623554.18</td>\n",
       "      <td>1660375.95</td>\n",
       "      <td>347</td>\n",
       "      <td>1794787.7</td>\n",
       "      <td>23</td>\n",
       "      <td>370</td>\n",
       "      <td>2232</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1976</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     MARRIED  OTHER  PARTNER  SINGLE  WIDOWED     CIF              CUS_DOB  \\\n",
       "886        0      0        0       1        0  XXXXXX  Mar 18 1976 12:00AM   \n",
       "\n",
       "     AGE  CUS_Month_Income  CUS_Gender CUS_Marital_Status CUS_Customer_Since  \\\n",
       "886   43         4706769.0         0.0             SINGLE         1994-08-31   \n",
       "\n",
       "     YEARS_WITH_US  # total debit transactions for S1  \\\n",
       "886             25                                119   \n",
       "\n",
       "     # total debit transactions for S2  # total debit transactions for S3  \\\n",
       "886                                101                                127   \n",
       "\n",
       "     total debit amount for S1  total debit amount for S2  \\\n",
       "886                  657015.16                   429208.3   \n",
       "\n",
       "     total debit amount for S3  # total credit transactions for S1  \\\n",
       "886                  574152.49                                   9   \n",
       "\n",
       "     # total credit transactions for S2  # total credit transactions for S3  \\\n",
       "886                                   8                                   6   \n",
       "\n",
       "     total credit amount for S1  total credit amount for S2  \\\n",
       "886                   701716.02                    469517.5   \n",
       "\n",
       "     total credit amount for S3  total debit amount  total debit transactions  \\\n",
       "886                   623554.18          1660375.95                       347   \n",
       "\n",
       "     total credit amount  total credit transactions  total transactions  \\\n",
       "886            1794787.7                         23                 370   \n",
       "\n",
       "     CUS_Target  TAR_Desc  Status  CUS_DOB_Year  \n",
       "886        2232         2       1          1976  "
      ]
     },
     "execution_count": 364,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = pd.get_dummies(data['CUS_Marital_Status'], drop_first=True)\n",
    "\n",
    "X.sample()\n",
    "\n",
    "data = pd.concat([X,data], axis=1)\n",
    "\n",
    "data.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "id": "f30714cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['MARRIED', 'OTHER', 'PARTNER', 'SINGLE', 'WIDOWED', 'CIF', 'CUS_DOB',\n",
       "       'AGE', 'CUS_Month_Income', 'CUS_Gender', 'CUS_Marital_Status',\n",
       "       'CUS_Customer_Since', 'YEARS_WITH_US',\n",
       "       '# total debit transactions for S1',\n",
       "       '# total debit transactions for S2',\n",
       "       '# total debit transactions for S3', 'total debit amount for S1',\n",
       "       'total debit amount for S2', 'total debit amount for S3',\n",
       "       '# total credit transactions for S1',\n",
       "       '# total credit transactions for S2',\n",
       "       '# total credit transactions for S3', 'total credit amount for S1',\n",
       "       'total credit amount for S2', 'total credit amount for S3',\n",
       "       'total debit amount', 'total debit transactions', 'total credit amount',\n",
       "       'total credit transactions', 'total transactions', 'CUS_Target',\n",
       "       'TAR_Desc', 'Status', 'CUS_DOB_Year'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 365,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "358d1e7b",
   "metadata": {},
   "source": [
    "**Age between 37 to 60 are more likely to churned**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "id": "f894a816",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1728x1152 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 401.625x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(24,16))\n",
    "sns.displot(data=data, x=\"AGE\", hue=\"Status\",  kde=True, )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "id": "e7c13320",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x1152 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(24,16))\n",
    "sns.countplot(data=data, x=\"AGE\", hue=\"Status\", )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5eefc88",
   "metadata": {},
   "source": [
    "**Dropping irrelevant features** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "id": "2fc2da0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop([\"CIF\", \"CUS_DOB\",\"CUS_Marital_Status\", \"CUS_Customer_Since\", \"# total debit transactions for S1\", \"# total debit transactions for S2\", \"# total debit transactions for S3\",\"total debit amount for S1\", \"total debit amount for S2\",\"total debit amount for S3\", \"# total credit transactions for S1\",\"# total credit transactions for S2\",\"# total credit transactions for S3\", \"total credit amount for S1\",\"total credit amount for S2\",\"total credit amount for S3\",\"OTHER\" ], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "id": "34204316",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "Name: CUS_Gender, dtype: float64"
      ]
     },
     "execution_count": 369,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"CUS_Gender\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "id": "4d5bc21c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MARRIED</th>\n",
       "      <th>PARTNER</th>\n",
       "      <th>SINGLE</th>\n",
       "      <th>WIDOWED</th>\n",
       "      <th>AGE</th>\n",
       "      <th>CUS_Month_Income</th>\n",
       "      <th>CUS_Gender</th>\n",
       "      <th>YEARS_WITH_US</th>\n",
       "      <th>total debit amount</th>\n",
       "      <th>total debit transactions</th>\n",
       "      <th>total credit amount</th>\n",
       "      <th>total credit transactions</th>\n",
       "      <th>total transactions</th>\n",
       "      <th>CUS_Target</th>\n",
       "      <th>TAR_Desc</th>\n",
       "      <th>Status</th>\n",
       "      <th>CUS_DOB_Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1001</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14</td>\n",
       "      <td>69972617.41</td>\n",
       "      <td>1012</td>\n",
       "      <td>1.043919e+08</td>\n",
       "      <td>79</td>\n",
       "      <td>1091</td>\n",
       "      <td>2230</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1972</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      MARRIED  PARTNER  SINGLE  WIDOWED  AGE  CUS_Month_Income  CUS_Gender  \\\n",
       "1001        1        0       0        0   47           10000.0         1.0   \n",
       "\n",
       "      YEARS_WITH_US  total debit amount  total debit transactions  \\\n",
       "1001             14         69972617.41                      1012   \n",
       "\n",
       "      total credit amount  total credit transactions  total transactions  \\\n",
       "1001         1.043919e+08                         79                1091   \n",
       "\n",
       "      CUS_Target  TAR_Desc  Status  CUS_DOB_Year  \n",
       "1001        2230         4       1          1972  "
      ]
     },
     "execution_count": 370,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "id": "08638617",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['MARRIED', 'PARTNER', 'SINGLE', 'WIDOWED', 'AGE', 'CUS_Month_Income',\n",
       "       'CUS_Gender', 'YEARS_WITH_US', 'total debit amount',\n",
       "       'total debit transactions', 'total credit amount',\n",
       "       'total credit transactions', 'total transactions', 'CUS_Target',\n",
       "       'TAR_Desc', 'Status', 'CUS_DOB_Year'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 371,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "id": "2c071566",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1248, 17)"
      ]
     },
     "execution_count": 372,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "baa6033e",
   "metadata": {},
   "source": [
    "**Checking dtypes of each dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "id": "b03f732a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MARRIED                        uint8\n",
       "PARTNER                        uint8\n",
       "SINGLE                         uint8\n",
       "WIDOWED                        uint8\n",
       "AGE                            int64\n",
       "CUS_Month_Income             float64\n",
       "CUS_Gender                   float64\n",
       "YEARS_WITH_US                  int64\n",
       "total debit amount           float64\n",
       "total debit transactions       int64\n",
       "total credit amount          float64\n",
       "total credit transactions      int64\n",
       "total transactions             int64\n",
       "CUS_Target                     int64\n",
       "TAR_Desc                       int64\n",
       "Status                         int64\n",
       "CUS_DOB_Year                   int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 373,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97f43a09",
   "metadata": {},
   "source": [
    "**This graph purely defined as our dataset is imbalanced**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "id": "08becc4c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Status', ylabel='count'>"
      ]
     },
     "execution_count": 374,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=data, x=\"Status\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4970eb9a",
   "metadata": {},
   "source": [
    "**Out of 1249 we have active as 1022 and churn as 227 it's lead to **\n",
    "1. Model will be biased to majority\n",
    "2. Dataset is inbalanced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "id": "23c63c3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#ss =  StandardScaler()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1e44a6b",
   "metadata": {},
   "source": [
    "**Creating Independent and Dependent features**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "id": "db6395f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.drop([\"Status\"], axis=1) # Independent Features\n",
    "y = data[\"Status\"] # Dependent Features\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "id": "8643e489",
   "metadata": {},
   "outputs": [
    {
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       "1247    1.000000\n",
       "1248    0.000000\n",
       "Name: CUS_Gender, dtype: float64"
      ]
     },
     "execution_count": 377,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[\"CUS_Gender\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "id": "8317e050",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method Index.get_indexer of Index(['MARRIED', 'PARTNER', 'SINGLE', 'WIDOWED', 'AGE', 'CUS_Month_Income',\n",
       "       'CUS_Gender', 'YEARS_WITH_US', 'total debit amount',\n",
       "       'total debit transactions', 'total credit amount',\n",
       "       'total credit transactions', 'total transactions', 'CUS_Target',\n",
       "       'TAR_Desc', 'CUS_DOB_Year'],\n",
       "      dtype='object')>"
      ]
     },
     "execution_count": 378,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.columns.get_indexer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "id": "a07ac71b",
   "metadata": {},
   "outputs": [
    {
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       "1226    1\n",
       "1227    1\n",
       "1228    1\n",
       "1229    1\n",
       "1230    1\n",
       "1231    1\n",
       "1232    1\n",
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       "1234    1\n",
       "1235    1\n",
       "1236    0\n",
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       "1238    1\n",
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       "1240    1\n",
       "1241    1\n",
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       "1245    1\n",
       "1246    1\n",
       "1247    1\n",
       "1248    1\n",
       "Name: Status, dtype: int64"
      ]
     },
     "execution_count": 379,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "id": "1b522044",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MARRIED                        uint8\n",
       "PARTNER                        uint8\n",
       "SINGLE                         uint8\n",
       "WIDOWED                        uint8\n",
       "AGE                            int64\n",
       "CUS_Month_Income             float64\n",
       "CUS_Gender                   float64\n",
       "YEARS_WITH_US                  int64\n",
       "total debit amount           float64\n",
       "total debit transactions       int64\n",
       "total credit amount          float64\n",
       "total credit transactions      int64\n",
       "total transactions             int64\n",
       "CUS_Target                     int64\n",
       "TAR_Desc                       int64\n",
       "CUS_DOB_Year                   int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 380,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "id": "b5c57327",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 381,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b0964bc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "id": "571c4272",
   "metadata": {},
   "outputs": [],
   "source": [
    "# data = data.astype('int64')\n",
    "# data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c1830a1",
   "metadata": {},
   "source": [
    "**Conversation of imbalance dataset to balanced dataset by using upsampling.\n",
    "Not using downsampling as it has its own disadvantages of information loss**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27421009",
   "metadata": {},
   "source": [
    "oversampling - we take the minority class sample's and matchup it with the majority class sample\n",
    "1. Random Oversampling\n",
    "2. SMOTE - Synthetic Minority Oversampling Technique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "id": "15a085ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "import imblearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "id": "d5161247",
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE #combination of both under and oversampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "id": "fa544eae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    1022\n",
       "0     226\n",
       "Name: Status, dtype: int64"
      ]
     },
     "execution_count": 385,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "id": "0dad5365",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    1022\n",
       "0    1022\n",
       "Name: Status, dtype: int64"
      ]
     },
     "execution_count": 386,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Implementing Oversampling for Handling Imbalanced \n",
    "smote = SMOTE(sampling_strategy='minority') # code basics \n",
    "X_sm, y_sm = smote.fit_resample(X,y)\n",
    "y_sm.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "id": "eb759a9f",
   "metadata": {},
   "outputs": [
    {
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       "1831    0.336155\n",
       "1832    0.779946\n",
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       "1834    0.793111\n",
       "1835    1.000000\n",
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       "1838    0.722296\n",
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       "1840    1.000000\n",
       "1841    0.091467\n",
       "1842    0.316705\n",
       "1843    1.000000\n",
       "1844    0.135588\n",
       "1845    1.000000\n",
       "1846    0.295499\n",
       "1847    0.357479\n",
       "1848    1.000000\n",
       "1849    0.357508\n",
       "1850    0.774229\n",
       "1851    1.000000\n",
       "1852    0.000000\n",
       "1853    0.618651\n",
       "1854    1.000000\n",
       "1855    1.000000\n",
       "1856    1.000000\n",
       "1857    0.528741\n",
       "1858    0.770427\n",
       "1859    0.595838\n",
       "1860    1.000000\n",
       "1861    1.000000\n",
       "1862    0.000000\n",
       "1863    1.000000\n",
       "1864    1.000000\n",
       "1865    1.000000\n",
       "1866    0.648005\n",
       "1867    1.000000\n",
       "1868    1.000000\n",
       "1869    0.175295\n",
       "1870    1.000000\n",
       "1871    1.000000\n",
       "1872    0.128449\n",
       "1873    1.000000\n",
       "1874    1.000000\n",
       "1875    0.467275\n",
       "1876    1.000000\n",
       "1877    0.809548\n",
       "1878    1.000000\n",
       "1879    1.000000\n",
       "1880    1.000000\n",
       "1881    0.865978\n",
       "1882    1.000000\n",
       "1883    1.000000\n",
       "1884    1.000000\n",
       "1885    0.321560\n",
       "1886    1.000000\n",
       "1887    0.013290\n",
       "1888    1.000000\n",
       "1889    0.403894\n",
       "1890    1.000000\n",
       "1891    1.000000\n",
       "1892    1.000000\n",
       "1893    1.000000\n",
       "1894    0.599164\n",
       "1895    0.374802\n",
       "1896    1.000000\n",
       "1897    0.604862\n",
       "1898    0.792745\n",
       "1899    0.000000\n",
       "1900    0.932727\n",
       "1901    1.000000\n",
       "1902    1.000000\n",
       "1903    1.000000\n",
       "1904    0.093199\n",
       "1905    0.266616\n",
       "1906    0.221120\n",
       "1907    1.000000\n",
       "1908    0.874021\n",
       "1909    0.804626\n",
       "1910    0.438556\n",
       "1911    1.000000\n",
       "1912    1.000000\n",
       "1913    0.000000\n",
       "1914    0.321274\n",
       "1915    1.000000\n",
       "1916    1.000000\n",
       "1917    1.000000\n",
       "1918    0.935560\n",
       "1919    0.401702\n",
       "1920    1.000000\n",
       "1921    0.109212\n",
       "1922    1.000000\n",
       "1923    0.000000\n",
       "1924    1.000000\n",
       "1925    0.883782\n",
       "1926    0.000000\n",
       "1927    0.011351\n",
       "1928    1.000000\n",
       "1929    0.598590\n",
       "1930    0.141859\n",
       "1931    0.089827\n",
       "1932    1.000000\n",
       "1933    1.000000\n",
       "1934    1.000000\n",
       "1935    0.331382\n",
       "1936    1.000000\n",
       "1937    0.614240\n",
       "1938    0.000000\n",
       "1939    1.000000\n",
       "1940    1.000000\n",
       "1941    1.000000\n",
       "1942    0.173211\n",
       "1943    1.000000\n",
       "1944    1.000000\n",
       "1945    0.382043\n",
       "1946    1.000000\n",
       "1947    0.792696\n",
       "1948    0.000000\n",
       "1949    0.564058\n",
       "1950    0.253719\n",
       "1951    0.685392\n",
       "1952    1.000000\n",
       "1953    1.000000\n",
       "1954    0.554037\n",
       "1955    0.221806\n",
       "1956    0.468927\n",
       "1957    0.569710\n",
       "1958    1.000000\n",
       "1959    0.906446\n",
       "1960    0.950579\n",
       "1961    0.588164\n",
       "1962    0.000000\n",
       "1963    0.781083\n",
       "1964    1.000000\n",
       "1965    1.000000\n",
       "1966    1.000000\n",
       "1967    0.243027\n",
       "1968    0.000000\n",
       "1969    0.691449\n",
       "1970    1.000000\n",
       "1971    1.000000\n",
       "1972    0.741185\n",
       "1973    1.000000\n",
       "1974    1.000000\n",
       "1975    0.487457\n",
       "1976    0.176400\n",
       "1977    0.414057\n",
       "1978    1.000000\n",
       "1979    1.000000\n",
       "1980    1.000000\n",
       "1981    1.000000\n",
       "1982    0.078840\n",
       "1983    1.000000\n",
       "1984    1.000000\n",
       "1985    0.102741\n",
       "1986    0.894009\n",
       "1987    0.856826\n",
       "1988    0.242275\n",
       "1989    0.901560\n",
       "1990    1.000000\n",
       "1991    1.000000\n",
       "1992    0.847920\n",
       "1993    0.499334\n",
       "1994    0.662849\n",
       "1995    0.258913\n",
       "1996    0.704999\n",
       "1997    1.000000\n",
       "1998    1.000000\n",
       "1999    1.000000\n",
       "2000    0.456446\n",
       "2001    0.494212\n",
       "2002    0.495766\n",
       "2003    1.000000\n",
       "2004    1.000000\n",
       "2005    0.495550\n",
       "2006    0.479308\n",
       "2007    1.000000\n",
       "2008    0.000000\n",
       "2009    0.411465\n",
       "2010    1.000000\n",
       "2011    0.780753\n",
       "2012    1.000000\n",
       "2013    0.973086\n",
       "2014    1.000000\n",
       "2015    0.683194\n",
       "2016    0.030481\n",
       "2017    0.610207\n",
       "2018    0.000000\n",
       "2019    1.000000\n",
       "2020    1.000000\n",
       "2021    0.923660\n",
       "2022    1.000000\n",
       "2023    1.000000\n",
       "2024    1.000000\n",
       "2025    0.562288\n",
       "2026    1.000000\n",
       "2027    1.000000\n",
       "2028    1.000000\n",
       "2029    1.000000\n",
       "2030    1.000000\n",
       "2031    1.000000\n",
       "2032    1.000000\n",
       "2033    1.000000\n",
       "2034    0.402944\n",
       "2035    0.000000\n",
       "2036    1.000000\n",
       "2037    1.000000\n",
       "2038    1.000000\n",
       "2039    1.000000\n",
       "2040    1.000000\n",
       "2041    0.350197\n",
       "2042    0.000000\n",
       "2043    0.028053\n",
       "Name: CUS_Gender, dtype: float64"
      ]
     },
     "execution_count": 387,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_sm[\"CUS_Gender\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "id": "e70d7bce",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "id": "f74daef6",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split( X_sm, y_sm, test_size=0.2, random_state=15, stratify=y_sm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "id": "047f6f7d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1248,)"
      ]
     },
     "execution_count": 390,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "id": "9af29083",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    1022\n",
       "0     226\n",
       "Name: Status, dtype: int64"
      ]
     },
     "execution_count": 391,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "id": "7d26837d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    1022\n",
       "0    1022\n",
       "Name: Status, dtype: int64"
      ]
     },
     "execution_count": 392,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_sm.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "id": "8bf35b80",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1    818\n",
      "0    817\n",
      "Name: Status, dtype: int64 (1635,)\n"
     ]
    }
   ],
   "source": [
    "print(y_train.value_counts(), y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "id": "5db8cb2c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    205\n",
      "1    204\n",
      "Name: Status, dtype: int64 (409,)\n"
     ]
    }
   ],
   "source": [
    "print(y_test.value_counts(), y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "id": "4e467e14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1635, 16)"
      ]
     },
     "execution_count": 395,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "id": "b307e856",
   "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>MARRIED</th>\n",
       "      <th>PARTNER</th>\n",
       "      <th>SINGLE</th>\n",
       "      <th>WIDOWED</th>\n",
       "      <th>AGE</th>\n",
       "      <th>CUS_Month_Income</th>\n",
       "      <th>CUS_Gender</th>\n",
       "      <th>YEARS_WITH_US</th>\n",
       "      <th>total debit amount</th>\n",
       "      <th>total debit transactions</th>\n",
       "      <th>total credit amount</th>\n",
       "      <th>total credit transactions</th>\n",
       "      <th>total transactions</th>\n",
       "      <th>CUS_Target</th>\n",
       "      <th>TAR_Desc</th>\n",
       "      <th>CUS_DOB_Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1938</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>290899.045160</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14</td>\n",
       "      <td>30494.663898</td>\n",
       "      <td>12</td>\n",
       "      <td>29993.369806</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>2223</td>\n",
       "      <td>1</td>\n",
       "      <td>1993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>914</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>56</td>\n",
       "      <td>2500.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14</td>\n",
       "      <td>2439.520000</td>\n",
       "      <td>5</td>\n",
       "      <td>35.630000</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>2223</td>\n",
       "      <td>1</td>\n",
       "      <td>1963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1428</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>59</td>\n",
       "      <td>316.197156</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14</td>\n",
       "      <td>23851.277982</td>\n",
       "      <td>12</td>\n",
       "      <td>39250.704586</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>2223</td>\n",
       "      <td>1</td>\n",
       "      <td>1959</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      MARRIED  PARTNER  SINGLE  WIDOWED  AGE  CUS_Month_Income  CUS_Gender  \\\n",
       "1938        0        0       1        0   25     290899.045160         0.0   \n",
       "914         0        0       1        0   56       2500.000000         1.0   \n",
       "1428        0        0       1        0   59        316.197156         1.0   \n",
       "\n",
       "      YEARS_WITH_US  total debit amount  total debit transactions  \\\n",
       "1938             14        30494.663898                        12   \n",
       "914              14         2439.520000                         5   \n",
       "1428             14        23851.277982                        12   \n",
       "\n",
       "      total credit amount  total credit transactions  total transactions  \\\n",
       "1938         29993.369806                          2                  15   \n",
       "914             35.630000                          1                   6   \n",
       "1428         39250.704586                          1                  14   \n",
       "\n",
       "      CUS_Target  TAR_Desc  CUS_DOB_Year  \n",
       "1938        2223         1          1993  \n",
       "914         2223         1          1963  \n",
       "1428        2223         1          1959  "
      ]
     },
     "execution_count": 396,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "id": "405ea324",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#X_train.iloc[ :, [4,5,7,8,9,10,11,12,13,14,15]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "id": "1ff6334d",
   "metadata": {},
   "outputs": [],
   "source": [
    "ss = StandardScaler()  # standard normalisation\n",
    "X_train.iloc[ :, [4,5,7,8,9,10,11,12,13,14,15]] = ss.fit_transform(X_train.iloc[ :, [4,5,7,8,9,10,11,12,13,14,15]])\n",
    "X_test.iloc[ :, [4,5,7,8,9,10,11,12,13,14,15]] = ss.transform(X_test.iloc[ :, [4,5,7,8,9,10,11,12,13,14,15]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "id": "7bba433a",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train[\"MARRIED\"] = X_train[\"MARRIED\"].astype('float64')\n",
    "X_train[\"PARTNER\"] = X_train[\"PARTNER\"].astype('float64')\n",
    "X_train[\"SINGLE\"] = X_train[\"SINGLE\"].astype('float64')\n",
    "X_train[\"WIDOWED\"] = X_train[\"WIDOWED\"].astype('float64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "id": "4ccaaab4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MARRIED                      float64\n",
       "PARTNER                      float64\n",
       "SINGLE                       float64\n",
       "WIDOWED                      float64\n",
       "AGE                          float64\n",
       "CUS_Month_Income             float64\n",
       "CUS_Gender                   float64\n",
       "YEARS_WITH_US                float64\n",
       "total debit amount           float64\n",
       "total debit transactions     float64\n",
       "total credit amount          float64\n",
       "total credit transactions    float64\n",
       "total transactions           float64\n",
       "CUS_Target                   float64\n",
       "TAR_Desc                     float64\n",
       "CUS_DOB_Year                 float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 400,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "id": "f2b9e43d",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test[\"MARRIED\"] = X_test[\"MARRIED\"].astype('float64')\n",
    "X_test[\"PARTNER\"] = X_test[\"PARTNER\"].astype('float64')\n",
    "X_test[\"SINGLE\"] = X_test[\"SINGLE\"].astype('float64')\n",
    "X_test[\"WIDOWED\"] = X_test[\"WIDOWED\"].astype('float64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 402,
   "id": "e6ca5e7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MARRIED                      float64\n",
       "PARTNER                      float64\n",
       "SINGLE                       float64\n",
       "WIDOWED                      float64\n",
       "AGE                          float64\n",
       "CUS_Month_Income             float64\n",
       "CUS_Gender                   float64\n",
       "YEARS_WITH_US                float64\n",
       "total debit amount           float64\n",
       "total debit transactions     float64\n",
       "total credit amount          float64\n",
       "total credit transactions    float64\n",
       "total transactions           float64\n",
       "CUS_Target                   float64\n",
       "TAR_Desc                     float64\n",
       "CUS_DOB_Year                 float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 402,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4417012",
   "metadata": {},
   "source": [
    "**Implementing Logistic Regression Classification**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "id": "080273c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "id": "4c1026de",
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "id": "28be974d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 406,
   "id": "d5571c03",
   "metadata": {},
   "outputs": [],
   "source": [
    "parameter = {'penalty':['l1','l2','elasticnet'],\n",
    "             'C':[1,2,3,4,5,6,7,8,9,10,11,12],\n",
    "             'max_iter':[100,200,120]\n",
    "            }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 407,
   "id": "43b18e0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier_regressor = GridSearchCV(classifier,param_grid=parameter, scoring='accuracy', cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 408,
   "id": "8a62b493",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=LogisticRegression(),\n",
       "             param_grid={&#x27;C&#x27;: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],\n",
       "                         &#x27;max_iter&#x27;: [100, 200, 120],\n",
       "                         &#x27;penalty&#x27;: [&#x27;l1&#x27;, &#x27;l2&#x27;, &#x27;elasticnet&#x27;]},\n",
       "             scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=LogisticRegression(),\n",
       "             param_grid={&#x27;C&#x27;: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],\n",
       "                         &#x27;max_iter&#x27;: [100, 200, 120],\n",
       "                         &#x27;penalty&#x27;: [&#x27;l1&#x27;, &#x27;l2&#x27;, &#x27;elasticnet&#x27;]},\n",
       "             scoring=&#x27;accuracy&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=LogisticRegression(),\n",
       "             param_grid={'C': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],\n",
       "                         'max_iter': [100, 200, 120],\n",
       "                         'penalty': ['l1', 'l2', 'elasticnet']},\n",
       "             scoring='accuracy')"
      ]
     },
     "execution_count": 408,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier_regressor.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 409,
   "id": "6bfa393a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'C': 11, 'max_iter': 200, 'penalty': 'l2'}\n"
     ]
    }
   ],
   "source": [
    "print(classifier_regressor.best_params_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "id": "5ee05395",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8844036697247708\n"
     ]
    }
   ],
   "source": [
    "print(classifier_regressor.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 411,
   "id": "f226ec00",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = classifier_regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "id": "f8ebe74e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score,classification_report\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "id": "246812b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8679706601466992\n"
     ]
    }
   ],
   "source": [
    "#We used accuracy maetric because dataset in balanced.\n",
    "#accuracy is 87% for logistic classification.\n",
    "score=accuracy_score(y_pred,y_test)\n",
    "\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "id": "beec02da",
   "metadata": {},
   "outputs": [],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "id": "5f180fe5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.90      0.86       189\n",
      "           1       0.91      0.84      0.87       220\n",
      "\n",
      "    accuracy                           0.87       409\n",
      "   macro avg       0.87      0.87      0.87       409\n",
      "weighted avg       0.87      0.87      0.87       409\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_pred,y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6776058",
   "metadata": {},
   "source": [
    "**Implementing Decision Tree**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 416,
   "id": "5123755a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "id": "5b0da1d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#postpruning - after creating the decision tree we will slice it\n",
    "#preprunning - before creating the decision tree we will slice it (use this)\n",
    "treemodel = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "id": "86bac4db",
   "metadata": {},
   "outputs": [],
   "source": [
    "parameter = {'criterion':['gini','entropy', 'log_loss'],\n",
    "            'splitter':['best', 'random'],\n",
    "            'max_depth':[10,30,50,70,90,110],\n",
    "            'max_features':['auto', 'sqrt', 'log2']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "id": "b09cb6c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier_decision = GridSearchCV(treemodel,param_grid=parameter, scoring='accuracy', cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "id": "e3adfbbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=DecisionTreeClassifier(),\n",
       "             param_grid={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;, &#x27;log_loss&#x27;],\n",
       "                         &#x27;max_depth&#x27;: [10, 30, 50, 70, 90, 110],\n",
       "                         &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;, &#x27;log2&#x27;],\n",
       "                         &#x27;splitter&#x27;: [&#x27;best&#x27;, &#x27;random&#x27;]},\n",
       "             scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=DecisionTreeClassifier(),\n",
       "             param_grid={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;, &#x27;log_loss&#x27;],\n",
       "                         &#x27;max_depth&#x27;: [10, 30, 50, 70, 90, 110],\n",
       "                         &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;, &#x27;log2&#x27;],\n",
       "                         &#x27;splitter&#x27;: [&#x27;best&#x27;, &#x27;random&#x27;]},\n",
       "             scoring=&#x27;accuracy&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=DecisionTreeClassifier(),\n",
       "             param_grid={'criterion': ['gini', 'entropy', 'log_loss'],\n",
       "                         'max_depth': [10, 30, 50, 70, 90, 110],\n",
       "                         'max_features': ['auto', 'sqrt', 'log2'],\n",
       "                         'splitter': ['best', 'random']},\n",
       "             scoring='accuracy')"
      ]
     },
     "execution_count": 420,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier_decision.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 421,
   "id": "e7ee05c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'criterion': 'gini', 'max_depth': 110, 'max_features': 'auto', 'splitter': 'random'}\n"
     ]
    }
   ],
   "source": [
    "print(classifier_decision.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 422,
   "id": "2373c4e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8422018348623853\n"
     ]
    }
   ],
   "source": [
    "print(classifier_decision.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 423,
   "id": "c587a600",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_decison_prediction = classifier_decision.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 424,
   "id": "1b4e6a02",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8484107579462102\n"
     ]
    }
   ],
   "source": [
    "score_decision=accuracy_score(y_decison_prediction,y_test)\n",
    "print(score_decision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 425,
   "id": "66dbe214",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.84      0.85       211\n",
      "           1       0.83      0.86      0.85       198\n",
      "\n",
      "    accuracy                           0.85       409\n",
      "   macro avg       0.85      0.85      0.85       409\n",
      "weighted avg       0.85      0.85      0.85       409\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_decison_prediction,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 426,
   "id": "768689fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn import tree\n",
    "# plt.figure(figsize=(15,10))\n",
    "# tree.plot_tree(treemodel, filled=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c0553d6",
   "metadata": {},
   "source": [
    "**Random Forest Classifier**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 427,
   "id": "fc44d7c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 428,
   "id": "60a82e52",
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "id": "9bba30fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" checked><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 429,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "id": "b1b3e728",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n",
      "0.8850855745721271\n"
     ]
    }
   ],
   "source": [
    "print(rf.score(X_train, y_train)) \n",
    "print(rf.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 431,
   "id": "ba6b4b79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting confusion metrix for logistic regression\n",
    "plt.style.use(\"seaborn\")\n",
    "plt.figure(figsize=(6,6))#, #dpi=90)\n",
    "sns.heatmap(cm, annot=True, fmt=\".1f\")\n",
    "plt.title(\"Confusion Metrix\")\n",
    "plt.ylabel(\"Actual Values\")\n",
    "plt.xlabel(\"Predicted Values\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}