STT_811_Semester_Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1b30804f",
   "metadata": {},
   "source": [
    "# STT 811\n",
    "# Semester Project"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8672a98",
   "metadata": {},
   "source": [
    "## Default of Credit Card Clients Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "af6961ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"UCI_Credit_Card.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "293dc30c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>LIMIT_BAL</th>\n",
       "      <th>SEX</th>\n",
       "      <th>EDUCATION</th>\n",
       "      <th>MARRIAGE</th>\n",
       "      <th>AGE</th>\n",
       "      <th>PAY_0</th>\n",
       "      <th>PAY_2</th>\n",
       "      <th>PAY_3</th>\n",
       "      <th>PAY_4</th>\n",
       "      <th>...</th>\n",
       "      <th>BILL_AMT4</th>\n",
       "      <th>BILL_AMT5</th>\n",
       "      <th>BILL_AMT6</th>\n",
       "      <th>PAY_AMT1</th>\n",
       "      <th>PAY_AMT2</th>\n",
       "      <th>PAY_AMT3</th>\n",
       "      <th>PAY_AMT4</th>\n",
       "      <th>PAY_AMT5</th>\n",
       "      <th>PAY_AMT6</th>\n",
       "      <th>default.payment.next.month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>689.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>120000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3272.0</td>\n",
       "      <td>3455.0</td>\n",
       "      <td>3261.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>90000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>14331.0</td>\n",
       "      <td>14948.0</td>\n",
       "      <td>15549.0</td>\n",
       "      <td>1518.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>28314.0</td>\n",
       "      <td>28959.0</td>\n",
       "      <td>29547.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>1069.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>20940.0</td>\n",
       "      <td>19146.0</td>\n",
       "      <td>19131.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>36681.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9000.0</td>\n",
       "      <td>689.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29995</th>\n",
       "      <td>29996</td>\n",
       "      <td>220000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>88004.0</td>\n",
       "      <td>31237.0</td>\n",
       "      <td>15980.0</td>\n",
       "      <td>8500.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>5003.0</td>\n",
       "      <td>3047.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29996</th>\n",
       "      <td>29997</td>\n",
       "      <td>150000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>43</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>8979.0</td>\n",
       "      <td>5190.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1837.0</td>\n",
       "      <td>3526.0</td>\n",
       "      <td>8998.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29997</th>\n",
       "      <td>29998</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>37</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>20878.0</td>\n",
       "      <td>20582.0</td>\n",
       "      <td>19357.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22000.0</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>3100.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29998</th>\n",
       "      <td>29999</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>52774.0</td>\n",
       "      <td>11855.0</td>\n",
       "      <td>48944.0</td>\n",
       "      <td>85900.0</td>\n",
       "      <td>3409.0</td>\n",
       "      <td>1178.0</td>\n",
       "      <td>1926.0</td>\n",
       "      <td>52964.0</td>\n",
       "      <td>1804.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29999</th>\n",
       "      <td>30000</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>36535.0</td>\n",
       "      <td>32428.0</td>\n",
       "      <td>15313.0</td>\n",
       "      <td>2078.0</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>1430.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30000 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          ID  LIMIT_BAL  SEX  EDUCATION  MARRIAGE  AGE  PAY_0  PAY_2  PAY_3  \\\n",
       "0          1    20000.0    2          2         1   24      2      2     -1   \n",
       "1          2   120000.0    2          2         2   26     -1      2      0   \n",
       "2          3    90000.0    2          2         2   34      0      0      0   \n",
       "3          4    50000.0    2          2         1   37      0      0      0   \n",
       "4          5    50000.0    1          2         1   57     -1      0     -1   \n",
       "...      ...        ...  ...        ...       ...  ...    ...    ...    ...   \n",
       "29995  29996   220000.0    1          3         1   39      0      0      0   \n",
       "29996  29997   150000.0    1          3         2   43     -1     -1     -1   \n",
       "29997  29998    30000.0    1          2         2   37      4      3      2   \n",
       "29998  29999    80000.0    1          3         1   41      1     -1      0   \n",
       "29999  30000    50000.0    1          2         1   46      0      0      0   \n",
       "\n",
       "       PAY_4  ...  BILL_AMT4  BILL_AMT5  BILL_AMT6  PAY_AMT1  PAY_AMT2  \\\n",
       "0         -1  ...        0.0        0.0        0.0       0.0     689.0   \n",
       "1          0  ...     3272.0     3455.0     3261.0       0.0    1000.0   \n",
       "2          0  ...    14331.0    14948.0    15549.0    1518.0    1500.0   \n",
       "3          0  ...    28314.0    28959.0    29547.0    2000.0    2019.0   \n",
       "4          0  ...    20940.0    19146.0    19131.0    2000.0   36681.0   \n",
       "...      ...  ...        ...        ...        ...       ...       ...   \n",
       "29995      0  ...    88004.0    31237.0    15980.0    8500.0   20000.0   \n",
       "29996     -1  ...     8979.0     5190.0        0.0    1837.0    3526.0   \n",
       "29997     -1  ...    20878.0    20582.0    19357.0       0.0       0.0   \n",
       "29998      0  ...    52774.0    11855.0    48944.0   85900.0    3409.0   \n",
       "29999      0  ...    36535.0    32428.0    15313.0    2078.0    1800.0   \n",
       "\n",
       "       PAY_AMT3  PAY_AMT4  PAY_AMT5  PAY_AMT6  default.payment.next.month  \n",
       "0           0.0       0.0       0.0       0.0                           1  \n",
       "1        1000.0    1000.0       0.0    2000.0                           1  \n",
       "2        1000.0    1000.0    1000.0    5000.0                           0  \n",
       "3        1200.0    1100.0    1069.0    1000.0                           0  \n",
       "4       10000.0    9000.0     689.0     679.0                           0  \n",
       "...         ...       ...       ...       ...                         ...  \n",
       "29995    5003.0    3047.0    5000.0    1000.0                           0  \n",
       "29996    8998.0     129.0       0.0       0.0                           0  \n",
       "29997   22000.0    4200.0    2000.0    3100.0                           1  \n",
       "29998    1178.0    1926.0   52964.0    1804.0                           1  \n",
       "29999    1430.0    1000.0    1000.0    1000.0                           1  \n",
       "\n",
       "[30000 rows x 25 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d64a766f",
   "metadata": {},
   "source": [
    "## Initial Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08be4004",
   "metadata": {},
   "source": [
    "### Check missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "233a3c0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                            0\n",
       "LIMIT_BAL                     0\n",
       "SEX                           0\n",
       "EDUCATION                     0\n",
       "MARRIAGE                      0\n",
       "AGE                           0\n",
       "PAY_0                         0\n",
       "PAY_2                         0\n",
       "PAY_3                         0\n",
       "PAY_4                         0\n",
       "PAY_5                         0\n",
       "PAY_6                         0\n",
       "BILL_AMT1                     0\n",
       "BILL_AMT2                     0\n",
       "BILL_AMT3                     0\n",
       "BILL_AMT4                     0\n",
       "BILL_AMT5                     0\n",
       "BILL_AMT6                     0\n",
       "PAY_AMT1                      0\n",
       "PAY_AMT2                      0\n",
       "PAY_AMT3                      0\n",
       "PAY_AMT4                      0\n",
       "PAY_AMT5                      0\n",
       "PAY_AMT6                      0\n",
       "default.payment.next.month    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "98ac769b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>LIMIT_BAL</th>\n",
       "      <th>SEX</th>\n",
       "      <th>EDUCATION</th>\n",
       "      <th>MARRIAGE</th>\n",
       "      <th>AGE</th>\n",
       "      <th>PAY_0</th>\n",
       "      <th>PAY_2</th>\n",
       "      <th>PAY_3</th>\n",
       "      <th>PAY_4</th>\n",
       "      <th>...</th>\n",
       "      <th>BILL_AMT4</th>\n",
       "      <th>BILL_AMT5</th>\n",
       "      <th>BILL_AMT6</th>\n",
       "      <th>PAY_AMT1</th>\n",
       "      <th>PAY_AMT2</th>\n",
       "      <th>PAY_AMT3</th>\n",
       "      <th>PAY_AMT4</th>\n",
       "      <th>PAY_AMT5</th>\n",
       "      <th>PAY_AMT6</th>\n",
       "      <th>default.payment.next.month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>3.000000e+04</td>\n",
       "      <td>30000.00000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>15000.500000</td>\n",
       "      <td>167484.322667</td>\n",
       "      <td>1.603733</td>\n",
       "      <td>1.853133</td>\n",
       "      <td>1.551867</td>\n",
       "      <td>35.485500</td>\n",
       "      <td>-0.016700</td>\n",
       "      <td>-0.133767</td>\n",
       "      <td>-0.166200</td>\n",
       "      <td>-0.220667</td>\n",
       "      <td>...</td>\n",
       "      <td>43262.948967</td>\n",
       "      <td>40311.400967</td>\n",
       "      <td>38871.760400</td>\n",
       "      <td>5663.580500</td>\n",
       "      <td>5.921163e+03</td>\n",
       "      <td>5225.68150</td>\n",
       "      <td>4826.076867</td>\n",
       "      <td>4799.387633</td>\n",
       "      <td>5215.502567</td>\n",
       "      <td>0.221200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8660.398374</td>\n",
       "      <td>129747.661567</td>\n",
       "      <td>0.489129</td>\n",
       "      <td>0.790349</td>\n",
       "      <td>0.521970</td>\n",
       "      <td>9.217904</td>\n",
       "      <td>1.123802</td>\n",
       "      <td>1.197186</td>\n",
       "      <td>1.196868</td>\n",
       "      <td>1.169139</td>\n",
       "      <td>...</td>\n",
       "      <td>64332.856134</td>\n",
       "      <td>60797.155770</td>\n",
       "      <td>59554.107537</td>\n",
       "      <td>16563.280354</td>\n",
       "      <td>2.304087e+04</td>\n",
       "      <td>17606.96147</td>\n",
       "      <td>15666.159744</td>\n",
       "      <td>15278.305679</td>\n",
       "      <td>17777.465775</td>\n",
       "      <td>0.415062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-170000.000000</td>\n",
       "      <td>-81334.000000</td>\n",
       "      <td>-339603.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7500.750000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2326.750000</td>\n",
       "      <td>1763.000000</td>\n",
       "      <td>1256.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>8.330000e+02</td>\n",
       "      <td>390.00000</td>\n",
       "      <td>296.000000</td>\n",
       "      <td>252.500000</td>\n",
       "      <td>117.750000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>15000.500000</td>\n",
       "      <td>140000.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>19052.000000</td>\n",
       "      <td>18104.500000</td>\n",
       "      <td>17071.000000</td>\n",
       "      <td>2100.000000</td>\n",
       "      <td>2.009000e+03</td>\n",
       "      <td>1800.00000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>22500.250000</td>\n",
       "      <td>240000.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>54506.000000</td>\n",
       "      <td>50190.500000</td>\n",
       "      <td>49198.250000</td>\n",
       "      <td>5006.000000</td>\n",
       "      <td>5.000000e+03</td>\n",
       "      <td>4505.00000</td>\n",
       "      <td>4013.250000</td>\n",
       "      <td>4031.500000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>30000.000000</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>891586.000000</td>\n",
       "      <td>927171.000000</td>\n",
       "      <td>961664.000000</td>\n",
       "      <td>873552.000000</td>\n",
       "      <td>1.684259e+06</td>\n",
       "      <td>896040.00000</td>\n",
       "      <td>621000.000000</td>\n",
       "      <td>426529.000000</td>\n",
       "      <td>528666.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ID       LIMIT_BAL           SEX     EDUCATION      MARRIAGE  \\\n",
       "count  30000.000000    30000.000000  30000.000000  30000.000000  30000.000000   \n",
       "mean   15000.500000   167484.322667      1.603733      1.853133      1.551867   \n",
       "std     8660.398374   129747.661567      0.489129      0.790349      0.521970   \n",
       "min        1.000000    10000.000000      1.000000      0.000000      0.000000   \n",
       "25%     7500.750000    50000.000000      1.000000      1.000000      1.000000   \n",
       "50%    15000.500000   140000.000000      2.000000      2.000000      2.000000   \n",
       "75%    22500.250000   240000.000000      2.000000      2.000000      2.000000   \n",
       "max    30000.000000  1000000.000000      2.000000      6.000000      3.000000   \n",
       "\n",
       "                AGE         PAY_0         PAY_2         PAY_3         PAY_4  \\\n",
       "count  30000.000000  30000.000000  30000.000000  30000.000000  30000.000000   \n",
       "mean      35.485500     -0.016700     -0.133767     -0.166200     -0.220667   \n",
       "std        9.217904      1.123802      1.197186      1.196868      1.169139   \n",
       "min       21.000000     -2.000000     -2.000000     -2.000000     -2.000000   \n",
       "25%       28.000000     -1.000000     -1.000000     -1.000000     -1.000000   \n",
       "50%       34.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "75%       41.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "max       79.000000      8.000000      8.000000      8.000000      8.000000   \n",
       "\n",
       "       ...      BILL_AMT4      BILL_AMT5      BILL_AMT6       PAY_AMT1  \\\n",
       "count  ...   30000.000000   30000.000000   30000.000000   30000.000000   \n",
       "mean   ...   43262.948967   40311.400967   38871.760400    5663.580500   \n",
       "std    ...   64332.856134   60797.155770   59554.107537   16563.280354   \n",
       "min    ... -170000.000000  -81334.000000 -339603.000000       0.000000   \n",
       "25%    ...    2326.750000    1763.000000    1256.000000    1000.000000   \n",
       "50%    ...   19052.000000   18104.500000   17071.000000    2100.000000   \n",
       "75%    ...   54506.000000   50190.500000   49198.250000    5006.000000   \n",
       "max    ...  891586.000000  927171.000000  961664.000000  873552.000000   \n",
       "\n",
       "           PAY_AMT2      PAY_AMT3       PAY_AMT4       PAY_AMT5  \\\n",
       "count  3.000000e+04   30000.00000   30000.000000   30000.000000   \n",
       "mean   5.921163e+03    5225.68150    4826.076867    4799.387633   \n",
       "std    2.304087e+04   17606.96147   15666.159744   15278.305679   \n",
       "min    0.000000e+00       0.00000       0.000000       0.000000   \n",
       "25%    8.330000e+02     390.00000     296.000000     252.500000   \n",
       "50%    2.009000e+03    1800.00000    1500.000000    1500.000000   \n",
       "75%    5.000000e+03    4505.00000    4013.250000    4031.500000   \n",
       "max    1.684259e+06  896040.00000  621000.000000  426529.000000   \n",
       "\n",
       "            PAY_AMT6  default.payment.next.month  \n",
       "count   30000.000000                30000.000000  \n",
       "mean     5215.502567                    0.221200  \n",
       "std     17777.465775                    0.415062  \n",
       "min         0.000000                    0.000000  \n",
       "25%       117.750000                    0.000000  \n",
       "50%      1500.000000                    0.000000  \n",
       "75%      4000.000000                    0.000000  \n",
       "max    528666.000000                    1.000000  \n",
       "\n",
       "[8 rows x 25 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5e1a6ea",
   "metadata": {},
   "source": [
    "## Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fafc01b",
   "metadata": {},
   "source": [
    "### Rename dependent variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "61d23913",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>LIMIT_BAL</th>\n",
       "      <th>SEX</th>\n",
       "      <th>EDUCATION</th>\n",
       "      <th>MARRIAGE</th>\n",
       "      <th>AGE</th>\n",
       "      <th>PAY_0</th>\n",
       "      <th>PAY_2</th>\n",
       "      <th>PAY_3</th>\n",
       "      <th>PAY_4</th>\n",
       "      <th>...</th>\n",
       "      <th>BILL_AMT4</th>\n",
       "      <th>BILL_AMT5</th>\n",
       "      <th>BILL_AMT6</th>\n",
       "      <th>PAY_AMT1</th>\n",
       "      <th>PAY_AMT2</th>\n",
       "      <th>PAY_AMT3</th>\n",
       "      <th>PAY_AMT4</th>\n",
       "      <th>PAY_AMT5</th>\n",
       "      <th>PAY_AMT6</th>\n",
       "      <th>IsDefaulter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>689.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>120000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3272.0</td>\n",
       "      <td>3455.0</td>\n",
       "      <td>3261.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>90000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>14331.0</td>\n",
       "      <td>14948.0</td>\n",
       "      <td>15549.0</td>\n",
       "      <td>1518.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>28314.0</td>\n",
       "      <td>28959.0</td>\n",
       "      <td>29547.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>1069.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>20940.0</td>\n",
       "      <td>19146.0</td>\n",
       "      <td>19131.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>36681.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9000.0</td>\n",
       "      <td>689.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29995</th>\n",
       "      <td>29996</td>\n",
       "      <td>220000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>88004.0</td>\n",
       "      <td>31237.0</td>\n",
       "      <td>15980.0</td>\n",
       "      <td>8500.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>5003.0</td>\n",
       "      <td>3047.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29996</th>\n",
       "      <td>29997</td>\n",
       "      <td>150000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>43</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>8979.0</td>\n",
       "      <td>5190.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1837.0</td>\n",
       "      <td>3526.0</td>\n",
       "      <td>8998.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29997</th>\n",
       "      <td>29998</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>37</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>20878.0</td>\n",
       "      <td>20582.0</td>\n",
       "      <td>19357.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22000.0</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>3100.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29998</th>\n",
       "      <td>29999</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>52774.0</td>\n",
       "      <td>11855.0</td>\n",
       "      <td>48944.0</td>\n",
       "      <td>85900.0</td>\n",
       "      <td>3409.0</td>\n",
       "      <td>1178.0</td>\n",
       "      <td>1926.0</td>\n",
       "      <td>52964.0</td>\n",
       "      <td>1804.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29999</th>\n",
       "      <td>30000</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>36535.0</td>\n",
       "      <td>32428.0</td>\n",
       "      <td>15313.0</td>\n",
       "      <td>2078.0</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>1430.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30000 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          ID  LIMIT_BAL  SEX  EDUCATION  MARRIAGE  AGE  PAY_0  PAY_2  PAY_3  \\\n",
       "0          1    20000.0    2          2         1   24      2      2     -1   \n",
       "1          2   120000.0    2          2         2   26     -1      2      0   \n",
       "2          3    90000.0    2          2         2   34      0      0      0   \n",
       "3          4    50000.0    2          2         1   37      0      0      0   \n",
       "4          5    50000.0    1          2         1   57     -1      0     -1   \n",
       "...      ...        ...  ...        ...       ...  ...    ...    ...    ...   \n",
       "29995  29996   220000.0    1          3         1   39      0      0      0   \n",
       "29996  29997   150000.0    1          3         2   43     -1     -1     -1   \n",
       "29997  29998    30000.0    1          2         2   37      4      3      2   \n",
       "29998  29999    80000.0    1          3         1   41      1     -1      0   \n",
       "29999  30000    50000.0    1          2         1   46      0      0      0   \n",
       "\n",
       "       PAY_4  ...  BILL_AMT4  BILL_AMT5  BILL_AMT6  PAY_AMT1  PAY_AMT2  \\\n",
       "0         -1  ...        0.0        0.0        0.0       0.0     689.0   \n",
       "1          0  ...     3272.0     3455.0     3261.0       0.0    1000.0   \n",
       "2          0  ...    14331.0    14948.0    15549.0    1518.0    1500.0   \n",
       "3          0  ...    28314.0    28959.0    29547.0    2000.0    2019.0   \n",
       "4          0  ...    20940.0    19146.0    19131.0    2000.0   36681.0   \n",
       "...      ...  ...        ...        ...        ...       ...       ...   \n",
       "29995      0  ...    88004.0    31237.0    15980.0    8500.0   20000.0   \n",
       "29996     -1  ...     8979.0     5190.0        0.0    1837.0    3526.0   \n",
       "29997     -1  ...    20878.0    20582.0    19357.0       0.0       0.0   \n",
       "29998      0  ...    52774.0    11855.0    48944.0   85900.0    3409.0   \n",
       "29999      0  ...    36535.0    32428.0    15313.0    2078.0    1800.0   \n",
       "\n",
       "       PAY_AMT3  PAY_AMT4  PAY_AMT5  PAY_AMT6  IsDefaulter  \n",
       "0           0.0       0.0       0.0       0.0            1  \n",
       "1        1000.0    1000.0       0.0    2000.0            1  \n",
       "2        1000.0    1000.0    1000.0    5000.0            0  \n",
       "3        1200.0    1100.0    1069.0    1000.0            0  \n",
       "4       10000.0    9000.0     689.0     679.0            0  \n",
       "...         ...       ...       ...       ...          ...  \n",
       "29995    5003.0    3047.0    5000.0    1000.0            0  \n",
       "29996    8998.0     129.0       0.0       0.0            0  \n",
       "29997   22000.0    4200.0    2000.0    3100.0            1  \n",
       "29998    1178.0    1926.0   52964.0    1804.0            1  \n",
       "29999    1430.0    1000.0    1000.0    1000.0            1  \n",
       "\n",
       "[30000 rows x 25 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['IsDefaulter'] =df ['default.payment.next.month']\n",
    "df = df.drop('default.payment.next.month',axis = 1)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "811017f4",
   "metadata": {},
   "source": [
    "### Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dd72b0fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'SEX': array([2, 1]), 'EDUCATION': array([2, 1, 3, 5, 4, 6, 0]), 'MARRIAGE': array([1, 2, 3, 0]), 'PAY_0': array([ 2, -1,  0, -2,  1,  3,  4,  8,  7,  5,  6]), 'PAY_2': array([ 2,  0, -1, -2,  3,  5,  7,  4,  1,  6,  8]), 'PAY_3': array([-1,  0,  2, -2,  3,  4,  6,  7,  1,  5,  8]), 'PAY_4': array([-1,  0, -2,  2,  3,  4,  5,  7,  6,  1,  8]), 'PAY_5': array([-2,  0, -1,  2,  3,  5,  4,  7,  8,  6]), 'PAY_6': array([-2,  2,  0, -1,  3,  6,  4,  7,  8,  5]), 'IsDefaulter': array([1, 0])}\n"
     ]
    }
   ],
   "source": [
    "columns_to_check = ['SEX', 'EDUCATION', 'MARRIAGE', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6', 'IsDefaulter']\n",
    "\n",
    "unique_values = {col: df[col].unique() for col in columns_to_check}\n",
    "\n",
    "print(unique_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "00db4923",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 0])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['MARRIAGE'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0272f792",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['MARRIAGE'] = df['MARRIAGE'].replace(0, 3)\n",
    "df = df[df['MARRIAGE'] != 3]\n",
    "df['MARRIAGE'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d9dd66cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 1, 3, 5, 4, 6, 0])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['EDUCATION'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3bca082e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/8f/qwr1knqs71ldwc5svff0zrd00000gn/T/ipykernel_6713/60113906.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['EDUCATION'] = df['EDUCATION'].apply(lambda x: 4 if x in [0, 5, 6] else x)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([2, 1, 3])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['EDUCATION'] = df['EDUCATION'].apply(lambda x: 4 if x in [0, 5, 6] else x)\n",
    "df = df[df['EDUCATION'] != 4]\n",
    "df['EDUCATION'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "24892d8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>LIMIT_BAL</th>\n",
       "      <th>SEX</th>\n",
       "      <th>EDUCATION</th>\n",
       "      <th>MARRIAGE</th>\n",
       "      <th>AGE</th>\n",
       "      <th>PAY_0</th>\n",
       "      <th>PAY_2</th>\n",
       "      <th>PAY_3</th>\n",
       "      <th>PAY_4</th>\n",
       "      <th>...</th>\n",
       "      <th>BILL_AMT4</th>\n",
       "      <th>BILL_AMT5</th>\n",
       "      <th>BILL_AMT6</th>\n",
       "      <th>PAY_AMT1</th>\n",
       "      <th>PAY_AMT2</th>\n",
       "      <th>PAY_AMT3</th>\n",
       "      <th>PAY_AMT4</th>\n",
       "      <th>PAY_AMT5</th>\n",
       "      <th>PAY_AMT6</th>\n",
       "      <th>IsDefaulter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>1</td>\n",
       "      <td>24</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>120000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3272.0</td>\n",
       "      <td>3455.0</td>\n",
       "      <td>3261.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2000.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>90000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>14331.0</td>\n",
       "      <td>14948.0</td>\n",
       "      <td>15549.0</td>\n",
       "      <td>1518.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>28314.0</td>\n",
       "      <td>28959.0</td>\n",
       "      <td>29547.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>1069.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>20940.0</td>\n",
       "      <td>19146.0</td>\n",
       "      <td>19131.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>36681.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9000.0</td>\n",
       "      <td>689.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>29995</th>\n",
       "      <td>29996</td>\n",
       "      <td>220000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>88004.0</td>\n",
       "      <td>31237.0</td>\n",
       "      <td>15980.0</td>\n",
       "      <td>8500.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>5003.0</td>\n",
       "      <td>3047.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29996</th>\n",
       "      <td>29997</td>\n",
       "      <td>150000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>43</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>8979.0</td>\n",
       "      <td>5190.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1837.0</td>\n",
       "      <td>3526.0</td>\n",
       "      <td>8998.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29997</th>\n",
       "      <td>29998</td>\n",
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       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>37</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22000.0</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>3100.0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>29998</th>\n",
       "      <td>29999</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>52774.0</td>\n",
       "      <td>11855.0</td>\n",
       "      <td>48944.0</td>\n",
       "      <td>85900.0</td>\n",
       "      <td>3409.0</td>\n",
       "      <td>1178.0</td>\n",
       "      <td>1926.0</td>\n",
       "      <td>52964.0</td>\n",
       "      <td>1804.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29999</th>\n",
       "      <td>30000</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>36535.0</td>\n",
       "      <td>32428.0</td>\n",
       "      <td>15313.0</td>\n",
       "      <td>2078.0</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>1430.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>29163 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          ID  LIMIT_BAL  SEX  EDUCATION  MARRIAGE  AGE  PAY_0  PAY_2  PAY_3  \\\n",
       "0          1    20000.0    2          2         1   24      2      2     -1   \n",
       "1          2   120000.0    2          2         2   26     -1      2      0   \n",
       "2          3    90000.0    2          2         2   34      0      0      0   \n",
       "3          4    50000.0    2          2         1   37      0      0      0   \n",
       "4          5    50000.0    1          2         1   57     -1      0     -1   \n",
       "...      ...        ...  ...        ...       ...  ...    ...    ...    ...   \n",
       "29995  29996   220000.0    1          3         1   39      0      0      0   \n",
       "29996  29997   150000.0    1          3         2   43     -1     -1     -1   \n",
       "29997  29998    30000.0    1          2         2   37      4      3      2   \n",
       "29998  29999    80000.0    1          3         1   41      1     -1      0   \n",
       "29999  30000    50000.0    1          2         1   46      0      0      0   \n",
       "\n",
       "       PAY_4  ...  BILL_AMT4  BILL_AMT5  BILL_AMT6  PAY_AMT1  PAY_AMT2  \\\n",
       "0         -1  ...        0.0        0.0        0.0       0.0     689.0   \n",
       "1          0  ...     3272.0     3455.0     3261.0       0.0    1000.0   \n",
       "2          0  ...    14331.0    14948.0    15549.0    1518.0    1500.0   \n",
       "3          0  ...    28314.0    28959.0    29547.0    2000.0    2019.0   \n",
       "4          0  ...    20940.0    19146.0    19131.0    2000.0   36681.0   \n",
       "...      ...  ...        ...        ...        ...       ...       ...   \n",
       "29995      0  ...    88004.0    31237.0    15980.0    8500.0   20000.0   \n",
       "29996     -1  ...     8979.0     5190.0        0.0    1837.0    3526.0   \n",
       "29997     -1  ...    20878.0    20582.0    19357.0       0.0       0.0   \n",
       "29998      0  ...    52774.0    11855.0    48944.0   85900.0    3409.0   \n",
       "29999      0  ...    36535.0    32428.0    15313.0    2078.0    1800.0   \n",
       "\n",
       "       PAY_AMT3  PAY_AMT4  PAY_AMT5  PAY_AMT6  IsDefaulter  \n",
       "0           0.0       0.0       0.0       0.0            1  \n",
       "1        1000.0    1000.0       0.0    2000.0            1  \n",
       "2        1000.0    1000.0    1000.0    5000.0            0  \n",
       "3        1200.0    1100.0    1069.0    1000.0            0  \n",
       "4       10000.0    9000.0     689.0     679.0            0  \n",
       "...         ...       ...       ...       ...          ...  \n",
       "29995    5003.0    3047.0    5000.0    1000.0            0  \n",
       "29996    8998.0     129.0       0.0       0.0            0  \n",
       "29997   22000.0    4200.0    2000.0    3100.0            1  \n",
       "29998    1178.0    1926.0   52964.0    1804.0            1  \n",
       "29999    1430.0    1000.0    1000.0    1000.0            1  \n",
       "\n",
       "[29163 rows x 25 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74f22753",
   "metadata": {},
   "source": [
    "### Rename few independent column varaibles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fbff462c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(columns={'PAY_0':'PAY_SEPT','PAY_2':'PAY_AUG','PAY_3':'PAY_JUL','PAY_4':'PAY_JUN','PAY_5':'PAY_MAY','PAY_6':'PAY_APR'},inplace=True)\n",
    "df.rename(columns={'BILL_AMT1':'BILL_AMT_SEPT','BILL_AMT2':'BILL_AMT_AUG','BILL_AMT3':'BILL_AMT_JUL','BILL_AMT4':'BILL_AMT_JUN','BILL_AMT5':'BILL_AMT_MAY','BILL_AMT6':'BILL_AMT_APR'}, inplace = True)\n",
    "df.rename(columns={'PAY_AMT1':'PAY_AMT_SEPT','PAY_AMT2':'PAY_AMT_AUG','PAY_AMT3':'PAY_AMT_JUL','PAY_AMT4':'PAY_AMT_JUN','PAY_AMT5':'PAY_AMT_MAY','PAY_AMT6':'PAY_AMT_APR'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f1bc7035",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>LIMIT_BAL</th>\n",
       "      <th>SEX</th>\n",
       "      <th>EDUCATION</th>\n",
       "      <th>MARRIAGE</th>\n",
       "      <th>AGE</th>\n",
       "      <th>PAY_SEPT</th>\n",
       "      <th>PAY_AUG</th>\n",
       "      <th>PAY_JUL</th>\n",
       "      <th>PAY_JUN</th>\n",
       "      <th>...</th>\n",
       "      <th>BILL_AMT_JUN</th>\n",
       "      <th>BILL_AMT_MAY</th>\n",
       "      <th>BILL_AMT_APR</th>\n",
       "      <th>PAY_AMT_SEPT</th>\n",
       "      <th>PAY_AMT_AUG</th>\n",
       "      <th>PAY_AMT_JUL</th>\n",
       "      <th>PAY_AMT_JUN</th>\n",
       "      <th>PAY_AMT_MAY</th>\n",
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       "      <td>120000.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2000.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>90000.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>14331.0</td>\n",
       "      <td>14948.0</td>\n",
       "      <td>15549.0</td>\n",
       "      <td>1518.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>50000.0</td>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>28959.0</td>\n",
       "      <td>29547.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>1069.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>20940.0</td>\n",
       "      <td>19146.0</td>\n",
       "      <td>19131.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>36681.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9000.0</td>\n",
       "      <td>689.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
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       "      <th>29995</th>\n",
       "      <td>29996</td>\n",
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       "    <tr>\n",
       "      <th>29996</th>\n",
       "      <td>29997</td>\n",
       "      <td>150000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
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       "      <td>-1</td>\n",
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       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1837.0</td>\n",
       "      <td>3526.0</td>\n",
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       "    <tr>\n",
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       "      <td>29998</td>\n",
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       "    <tr>\n",
       "      <th>29998</th>\n",
       "      <td>29999</td>\n",
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       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>52774.0</td>\n",
       "      <td>11855.0</td>\n",
       "      <td>48944.0</td>\n",
       "      <td>85900.0</td>\n",
       "      <td>3409.0</td>\n",
       "      <td>1178.0</td>\n",
       "      <td>1926.0</td>\n",
       "      <td>52964.0</td>\n",
       "      <td>1804.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29999</th>\n",
       "      <td>30000</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>36535.0</td>\n",
       "      <td>32428.0</td>\n",
       "      <td>15313.0</td>\n",
       "      <td>2078.0</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>1430.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>29163 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          ID  LIMIT_BAL  SEX  EDUCATION  MARRIAGE  AGE  PAY_SEPT  PAY_AUG  \\\n",
       "0          1    20000.0    2          2         1   24         2        2   \n",
       "1          2   120000.0    2          2         2   26        -1        2   \n",
       "2          3    90000.0    2          2         2   34         0        0   \n",
       "3          4    50000.0    2          2         1   37         0        0   \n",
       "4          5    50000.0    1          2         1   57        -1        0   \n",
       "...      ...        ...  ...        ...       ...  ...       ...      ...   \n",
       "29995  29996   220000.0    1          3         1   39         0        0   \n",
       "29996  29997   150000.0    1          3         2   43        -1       -1   \n",
       "29997  29998    30000.0    1          2         2   37         4        3   \n",
       "29998  29999    80000.0    1          3         1   41         1       -1   \n",
       "29999  30000    50000.0    1          2         1   46         0        0   \n",
       "\n",
       "       PAY_JUL  PAY_JUN  ...  BILL_AMT_JUN  BILL_AMT_MAY  BILL_AMT_APR  \\\n",
       "0           -1       -1  ...           0.0           0.0           0.0   \n",
       "1            0        0  ...        3272.0        3455.0        3261.0   \n",
       "2            0        0  ...       14331.0       14948.0       15549.0   \n",
       "3            0        0  ...       28314.0       28959.0       29547.0   \n",
       "4           -1        0  ...       20940.0       19146.0       19131.0   \n",
       "...        ...      ...  ...           ...           ...           ...   \n",
       "29995        0        0  ...       88004.0       31237.0       15980.0   \n",
       "29996       -1       -1  ...        8979.0        5190.0           0.0   \n",
       "29997        2       -1  ...       20878.0       20582.0       19357.0   \n",
       "29998        0        0  ...       52774.0       11855.0       48944.0   \n",
       "29999        0        0  ...       36535.0       32428.0       15313.0   \n",
       "\n",
       "       PAY_AMT_SEPT  PAY_AMT_AUG  PAY_AMT_JUL  PAY_AMT_JUN  PAY_AMT_MAY  \\\n",
       "0               0.0        689.0          0.0          0.0          0.0   \n",
       "1               0.0       1000.0       1000.0       1000.0          0.0   \n",
       "2            1518.0       1500.0       1000.0       1000.0       1000.0   \n",
       "3            2000.0       2019.0       1200.0       1100.0       1069.0   \n",
       "4            2000.0      36681.0      10000.0       9000.0        689.0   \n",
       "...             ...          ...          ...          ...          ...   \n",
       "29995        8500.0      20000.0       5003.0       3047.0       5000.0   \n",
       "29996        1837.0       3526.0       8998.0        129.0          0.0   \n",
       "29997           0.0          0.0      22000.0       4200.0       2000.0   \n",
       "29998       85900.0       3409.0       1178.0       1926.0      52964.0   \n",
       "29999        2078.0       1800.0       1430.0       1000.0       1000.0   \n",
       "\n",
       "       PAY_AMT_APR  IsDefaulter  \n",
       "0              0.0            1  \n",
       "1           2000.0            1  \n",
       "2           5000.0            0  \n",
       "3           1000.0            0  \n",
       "4            679.0            0  \n",
       "...            ...          ...  \n",
       "29995       1000.0            0  \n",
       "29996          0.0            0  \n",
       "29997       3100.0            1  \n",
       "29998       1804.0            1  \n",
       "29999       1000.0            1  \n",
       "\n",
       "[29163 rows x 25 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa3fb206",
   "metadata": {},
   "source": [
    "### Drop ID column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "aac3a8a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop('ID', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a05e1e5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LIMIT_BAL</th>\n",
       "      <th>SEX</th>\n",
       "      <th>EDUCATION</th>\n",
       "      <th>MARRIAGE</th>\n",
       "      <th>AGE</th>\n",
       "      <th>PAY_SEPT</th>\n",
       "      <th>PAY_AUG</th>\n",
       "      <th>PAY_JUL</th>\n",
       "      <th>PAY_JUN</th>\n",
       "      <th>PAY_MAY</th>\n",
       "      <th>...</th>\n",
       "      <th>BILL_AMT_JUN</th>\n",
       "      <th>BILL_AMT_MAY</th>\n",
       "      <th>BILL_AMT_APR</th>\n",
       "      <th>PAY_AMT_SEPT</th>\n",
       "      <th>PAY_AMT_AUG</th>\n",
       "      <th>PAY_AMT_JUL</th>\n",
       "      <th>PAY_AMT_JUN</th>\n",
       "      <th>PAY_AMT_MAY</th>\n",
       "      <th>PAY_AMT_APR</th>\n",
       "      <th>IsDefaulter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>689.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>120000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3272.0</td>\n",
       "      <td>3455.0</td>\n",
       "      <td>3261.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>90000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>14331.0</td>\n",
       "      <td>14948.0</td>\n",
       "      <td>15549.0</td>\n",
       "      <td>1518.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>28314.0</td>\n",
       "      <td>28959.0</td>\n",
       "      <td>29547.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>1069.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>20940.0</td>\n",
       "      <td>19146.0</td>\n",
       "      <td>19131.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>36681.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9000.0</td>\n",
       "      <td>689.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29995</th>\n",
       "      <td>220000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>88004.0</td>\n",
       "      <td>31237.0</td>\n",
       "      <td>15980.0</td>\n",
       "      <td>8500.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>5003.0</td>\n",
       "      <td>3047.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29996</th>\n",
       "      <td>150000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>43</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8979.0</td>\n",
       "      <td>5190.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1837.0</td>\n",
       "      <td>3526.0</td>\n",
       "      <td>8998.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29997</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>37</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>20878.0</td>\n",
       "      <td>20582.0</td>\n",
       "      <td>19357.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22000.0</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>3100.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29998</th>\n",
       "      <td>80000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>52774.0</td>\n",
       "      <td>11855.0</td>\n",
       "      <td>48944.0</td>\n",
       "      <td>85900.0</td>\n",
       "      <td>3409.0</td>\n",
       "      <td>1178.0</td>\n",
       "      <td>1926.0</td>\n",
       "      <td>52964.0</td>\n",
       "      <td>1804.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29999</th>\n",
       "      <td>50000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>36535.0</td>\n",
       "      <td>32428.0</td>\n",
       "      <td>15313.0</td>\n",
       "      <td>2078.0</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>1430.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>29163 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       LIMIT_BAL  SEX  EDUCATION  MARRIAGE  AGE  PAY_SEPT  PAY_AUG  PAY_JUL  \\\n",
       "0        20000.0    2          2         1   24         2        2       -1   \n",
       "1       120000.0    2          2         2   26        -1        2        0   \n",
       "2        90000.0    2          2         2   34         0        0        0   \n",
       "3        50000.0    2          2         1   37         0        0        0   \n",
       "4        50000.0    1          2         1   57        -1        0       -1   \n",
       "...          ...  ...        ...       ...  ...       ...      ...      ...   \n",
       "29995   220000.0    1          3         1   39         0        0        0   \n",
       "29996   150000.0    1          3         2   43        -1       -1       -1   \n",
       "29997    30000.0    1          2         2   37         4        3        2   \n",
       "29998    80000.0    1          3         1   41         1       -1        0   \n",
       "29999    50000.0    1          2         1   46         0        0        0   \n",
       "\n",
       "       PAY_JUN  PAY_MAY  ...  BILL_AMT_JUN  BILL_AMT_MAY  BILL_AMT_APR  \\\n",
       "0           -1       -2  ...           0.0           0.0           0.0   \n",
       "1            0        0  ...        3272.0        3455.0        3261.0   \n",
       "2            0        0  ...       14331.0       14948.0       15549.0   \n",
       "3            0        0  ...       28314.0       28959.0       29547.0   \n",
       "4            0        0  ...       20940.0       19146.0       19131.0   \n",
       "...        ...      ...  ...           ...           ...           ...   \n",
       "29995        0        0  ...       88004.0       31237.0       15980.0   \n",
       "29996       -1        0  ...        8979.0        5190.0           0.0   \n",
       "29997       -1        0  ...       20878.0       20582.0       19357.0   \n",
       "29998        0        0  ...       52774.0       11855.0       48944.0   \n",
       "29999        0        0  ...       36535.0       32428.0       15313.0   \n",
       "\n",
       "       PAY_AMT_SEPT  PAY_AMT_AUG  PAY_AMT_JUL  PAY_AMT_JUN  PAY_AMT_MAY  \\\n",
       "0               0.0        689.0          0.0          0.0          0.0   \n",
       "1               0.0       1000.0       1000.0       1000.0          0.0   \n",
       "2            1518.0       1500.0       1000.0       1000.0       1000.0   \n",
       "3            2000.0       2019.0       1200.0       1100.0       1069.0   \n",
       "4            2000.0      36681.0      10000.0       9000.0        689.0   \n",
       "...             ...          ...          ...          ...          ...   \n",
       "29995        8500.0      20000.0       5003.0       3047.0       5000.0   \n",
       "29996        1837.0       3526.0       8998.0        129.0          0.0   \n",
       "29997           0.0          0.0      22000.0       4200.0       2000.0   \n",
       "29998       85900.0       3409.0       1178.0       1926.0      52964.0   \n",
       "29999        2078.0       1800.0       1430.0       1000.0       1000.0   \n",
       "\n",
       "       PAY_AMT_APR  IsDefaulter  \n",
       "0              0.0            1  \n",
       "1           2000.0            1  \n",
       "2           5000.0            0  \n",
       "3           1000.0            0  \n",
       "4            679.0            0  \n",
       "...            ...          ...  \n",
       "29995       1000.0            0  \n",
       "29996          0.0            0  \n",
       "29997       3100.0            1  \n",
       "29998       1804.0            1  \n",
       "29999       1000.0            1  \n",
       "\n",
       "[29163 rows x 24 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7267a43e",
   "metadata": {},
   "source": [
    "### Class imbalance: Oversampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ee6cb395",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IsDefaulter\n",
      "0    22649\n",
      "1     6514\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "category_counts = df['IsDefaulter'].value_counts()\n",
    "print(category_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f925987c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "IsDefaulter\n",
       "1    22649\n",
       "0    22649\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from imblearn.over_sampling import RandomOverSampler\n",
    "\n",
    "oversampler = RandomOverSampler(random_state=42)\n",
    "X_over, y_over = oversampler.fit_resample(df.drop('IsDefaulter', axis=1), df['IsDefaulter'])\n",
    "df = pd.concat([X_over, y_over], axis=1)\n",
    "df['IsDefaulter'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "240c7bf0",
   "metadata": {},
   "source": [
    "### Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f2626f25",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f9ced979",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final['Dues'] = (df_final['BILL_AMT_APR']+df_final['BILL_AMT_MAY']+df_final['BILL_AMT_JUN']+df_final['BILL_AMT_JUL']+df_final['BILL_AMT_SEPT'])-(df_final['PAY_AMT_APR']+df_final['PAY_AMT_MAY']+df_final['PAY_AMT_JUN']+df_final['PAY_AMT_JUL']+df_final['PAY_AMT_AUG']+df_final['PAY_AMT_SEPT'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d30cab4d",
   "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>LIMIT_BAL</th>\n",
       "      <th>SEX</th>\n",
       "      <th>EDUCATION</th>\n",
       "      <th>MARRIAGE</th>\n",
       "      <th>AGE</th>\n",
       "      <th>PAY_SEPT</th>\n",
       "      <th>PAY_AUG</th>\n",
       "      <th>PAY_JUL</th>\n",
       "      <th>PAY_JUN</th>\n",
       "      <th>PAY_MAY</th>\n",
       "      <th>...</th>\n",
       "      <th>BILL_AMT_MAY</th>\n",
       "      <th>BILL_AMT_APR</th>\n",
       "      <th>PAY_AMT_SEPT</th>\n",
       "      <th>PAY_AMT_AUG</th>\n",
       "      <th>PAY_AMT_JUL</th>\n",
       "      <th>PAY_AMT_JUN</th>\n",
       "      <th>PAY_AMT_MAY</th>\n",
       "      <th>PAY_AMT_APR</th>\n",
       "      <th>IsDefaulter</th>\n",
       "      <th>Dues</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>689.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3913.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>120000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3455.0</td>\n",
       "      <td>3261.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>10352.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>90000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>14948.0</td>\n",
       "      <td>15549.0</td>\n",
       "      <td>1518.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>76608.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>28959.0</td>\n",
       "      <td>29547.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>1069.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>174713.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>19146.0</td>\n",
       "      <td>19131.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>36681.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9000.0</td>\n",
       "      <td>689.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>0</td>\n",
       "      <td>44620.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45293</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>4883.0</td>\n",
       "      <td>5001.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1528.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>730.0</td>\n",
       "      <td>199.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1</td>\n",
       "      <td>39838.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45294</th>\n",
       "      <td>140000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>115590.0</td>\n",
       "      <td>118221.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5600.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>4500.0</td>\n",
       "      <td>4600.0</td>\n",
       "      <td>1</td>\n",
       "      <td>548923.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45295</th>\n",
       "      <td>50000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>16519.0</td>\n",
       "      <td>19231.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>140672.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45296</th>\n",
       "      <td>220000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>780.0</td>\n",
       "      <td>390.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>780.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>780.0</td>\n",
       "      <td>390.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45297</th>\n",
       "      <td>80000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>102147.0</td>\n",
       "      <td>91829.0</td>\n",
       "      <td>3560.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3912.0</td>\n",
       "      <td>3832.0</td>\n",
       "      <td>3508.0</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>1</td>\n",
       "      <td>474003.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>45298 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       LIMIT_BAL  SEX  EDUCATION  MARRIAGE  AGE  PAY_SEPT  PAY_AUG  PAY_JUL  \\\n",
       "0        20000.0    2          2         1   24         2        2       -1   \n",
       "1       120000.0    2          2         2   26        -1        2        0   \n",
       "2        90000.0    2          2         2   34         0        0        0   \n",
       "3        50000.0    2          2         1   37         0        0        0   \n",
       "4        50000.0    1          2         1   57        -1        0       -1   \n",
       "...          ...  ...        ...       ...  ...       ...      ...      ...   \n",
       "45293    10000.0    1          2         2   24         0        0        0   \n",
       "45294   140000.0    2          2         1   60         2        2        0   \n",
       "45295    50000.0    1          2         1   28         2        0        0   \n",
       "45296   220000.0    1          3         1   30         2       -1       -1   \n",
       "45297    80000.0    2          3         1   37         1        2        2   \n",
       "\n",
       "       PAY_JUN  PAY_MAY  ...  BILL_AMT_MAY  BILL_AMT_APR  PAY_AMT_SEPT  \\\n",
       "0           -1       -2  ...           0.0           0.0           0.0   \n",
       "1            0        0  ...        3455.0        3261.0           0.0   \n",
       "2            0        0  ...       14948.0       15549.0        1518.0   \n",
       "3            0        0  ...       28959.0       29547.0        2000.0   \n",
       "4            0        0  ...       19146.0       19131.0        2000.0   \n",
       "...        ...      ...  ...           ...           ...           ...   \n",
       "45293        2        0  ...        4883.0        5001.0        1500.0   \n",
       "45294        0        0  ...      115590.0      118221.0           0.0   \n",
       "45295        0        0  ...       16519.0       19231.0        3000.0   \n",
       "45296       -1       -1  ...           0.0         780.0         390.0   \n",
       "45297        0        0  ...      102147.0       91829.0        3560.0   \n",
       "\n",
       "       PAY_AMT_AUG  PAY_AMT_JUL  PAY_AMT_JUN  PAY_AMT_MAY  PAY_AMT_APR  \\\n",
       "0            689.0          0.0          0.0          0.0          0.0   \n",
       "1           1000.0       1000.0       1000.0          0.0       2000.0   \n",
       "2           1500.0       1000.0       1000.0       1000.0       5000.0   \n",
       "3           2019.0       1200.0       1100.0       1069.0       1000.0   \n",
       "4          36681.0      10000.0       9000.0        689.0        679.0   \n",
       "...            ...          ...          ...          ...          ...   \n",
       "45293       1528.0          0.0        730.0        199.0        100.0   \n",
       "45294       5600.0       6000.0       4200.0       4500.0       4600.0   \n",
       "45295       2000.0       2000.0       2000.0       3000.0       1000.0   \n",
       "45296          0.0        780.0          0.0        780.0        390.0   \n",
       "45297          0.0       3912.0       3832.0       3508.0       2800.0   \n",
       "\n",
       "       IsDefaulter      Dues  \n",
       "0                1    3913.0  \n",
       "1                1   10352.0  \n",
       "2                0   76608.0  \n",
       "3                0  174713.0  \n",
       "4                0   44620.0  \n",
       "...            ...       ...  \n",
       "45293            1   39838.0  \n",
       "45294            1  548923.0  \n",
       "45295            1  140672.0  \n",
       "45296            1       0.0  \n",
       "45297            1  474003.0  \n",
       "\n",
       "[45298 rows x 25 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9d7941c",
   "metadata": {},
   "source": [
    "### One-hot encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7eb82608",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final = pd.get_dummies(df_final,columns=['SEX','EDUCATION','MARRIAGE'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d92da629",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>LIMIT_BAL</th>\n",
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       "      <td>5000.0</td>\n",
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       "      <th>3</th>\n",
       "      <td>50000.0</td>\n",
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       "      <td>1000.0</td>\n",
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       "    <tr>\n",
       "      <th>45295</th>\n",
       "      <td>50000.0</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>42435.0</td>\n",
       "      <td>44030.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>140672.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45296</th>\n",
       "      <td>220000.0</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>780.0</td>\n",
       "      <td>390.0</td>\n",
       "      <td>...</td>\n",
       "      <td>390.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45297</th>\n",
       "      <td>80000.0</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>99396.0</td>\n",
       "      <td>100495.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>1</td>\n",
       "      <td>474003.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>45298 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       LIMIT_BAL  AGE  PAY_SEPT  PAY_AUG  PAY_JUL  PAY_JUN  PAY_MAY  PAY_APR  \\\n",
       "0        20000.0   24         2        2       -1       -1       -2       -2   \n",
       "1       120000.0   26        -1        2        0        0        0        2   \n",
       "2        90000.0   34         0        0        0        0        0        0   \n",
       "3        50000.0   37         0        0        0        0        0        0   \n",
       "4        50000.0   57        -1        0       -1        0        0        0   \n",
       "...          ...  ...       ...      ...      ...      ...      ...      ...   \n",
       "45293    10000.0   24         0        0        0        2        0        0   \n",
       "45294   140000.0   60         2        2        0        0        0        0   \n",
       "45295    50000.0   28         2        0        0        0        0        0   \n",
       "45296   220000.0   30         2       -1       -1       -1       -1       -1   \n",
       "45297    80000.0   37         1        2        2        0        0        0   \n",
       "\n",
       "       BILL_AMT_SEPT  BILL_AMT_AUG  ...  PAY_AMT_APR  IsDefaulter      Dues  \\\n",
       "0             3913.0        3102.0  ...          0.0            1    3913.0   \n",
       "1             2682.0        1725.0  ...       2000.0            1   10352.0   \n",
       "2            29239.0       14027.0  ...       5000.0            0   76608.0   \n",
       "3            46990.0       48233.0  ...       1000.0            0  174713.0   \n",
       "4             8617.0        5670.0  ...        679.0            0   44620.0   \n",
       "...              ...           ...  ...          ...          ...       ...   \n",
       "45293        11035.0       12349.0  ...        100.0            1   39838.0   \n",
       "45294       113079.0      109796.0  ...       4600.0            1  548923.0   \n",
       "45295        42435.0       44030.0  ...       1000.0            1  140672.0   \n",
       "45296          780.0         390.0  ...        390.0            1       0.0   \n",
       "45297        99396.0      100495.0  ...       2800.0            1  474003.0   \n",
       "\n",
       "       SEX_1  SEX_2  EDUCATION_1  EDUCATION_2  EDUCATION_3  MARRIAGE_1  \\\n",
       "0      False   True        False         True        False        True   \n",
       "1      False   True        False         True        False       False   \n",
       "2      False   True        False         True        False       False   \n",
       "3      False   True        False         True        False        True   \n",
       "4       True  False        False         True        False        True   \n",
       "...      ...    ...          ...          ...          ...         ...   \n",
       "45293   True  False        False         True        False       False   \n",
       "45294  False   True        False         True        False        True   \n",
       "45295   True  False        False         True        False        True   \n",
       "45296   True  False        False        False         True        True   \n",
       "45297  False   True        False        False         True        True   \n",
       "\n",
       "       MARRIAGE_2  \n",
       "0           False  \n",
       "1            True  \n",
       "2            True  \n",
       "3           False  \n",
       "4           False  \n",
       "...           ...  \n",
       "45293        True  \n",
       "45294       False  \n",
       "45295       False  \n",
       "45296       False  \n",
       "45297       False  \n",
       "\n",
       "[45298 rows x 29 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e1ff3e18",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final = pd.get_dummies(df_final, columns = ['PAY_SEPT', 'PAY_AUG', 'PAY_JUL', 'PAY_JUN', 'PAY_MAY', 'PAY_APR'], drop_first = True )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "fb9cffd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LIMIT_BAL</th>\n",
       "      <th>AGE</th>\n",
       "      <th>BILL_AMT_SEPT</th>\n",
       "      <th>BILL_AMT_AUG</th>\n",
       "      <th>BILL_AMT_JUL</th>\n",
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       "      <td>26</td>\n",
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       "      <td>1725.0</td>\n",
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       "      <td>14027.0</td>\n",
       "      <td>13559.0</td>\n",
       "      <td>14331.0</td>\n",
       "      <td>14948.0</td>\n",
       "      <td>15549.0</td>\n",
       "      <td>1518.0</td>\n",
       "      <td>1500.0</td>\n",
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       "      <th>3</th>\n",
       "      <td>50000.0</td>\n",
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       "      <td>48233.0</td>\n",
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       "      <td>28314.0</td>\n",
       "      <td>28959.0</td>\n",
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       "      <td>2000.0</td>\n",
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       "      <td>5670.0</td>\n",
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       "      <td>19131.0</td>\n",
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       "      <td>97982.0</td>\n",
       "      <td>100261.0</td>\n",
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       "      <td>91829.0</td>\n",
       "      <td>3560.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>45298 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       LIMIT_BAL  AGE  BILL_AMT_SEPT  BILL_AMT_AUG  BILL_AMT_JUL  \\\n",
       "0        20000.0   24         3913.0        3102.0         689.0   \n",
       "1       120000.0   26         2682.0        1725.0        2682.0   \n",
       "2        90000.0   34        29239.0       14027.0       13559.0   \n",
       "3        50000.0   37        46990.0       48233.0       49291.0   \n",
       "4        50000.0   57         8617.0        5670.0       35835.0   \n",
       "...          ...  ...            ...           ...           ...   \n",
       "45293    10000.0   24        11035.0       12349.0       13598.0   \n",
       "45294   140000.0   60       113079.0      109796.0      112144.0   \n",
       "45295    50000.0   28        42435.0       44030.0       43979.0   \n",
       "45296   220000.0   30          780.0         390.0           0.0   \n",
       "45297    80000.0   37        99396.0      100495.0       97982.0   \n",
       "\n",
       "       BILL_AMT_JUN  BILL_AMT_MAY  BILL_AMT_APR  PAY_AMT_SEPT  PAY_AMT_AUG  \\\n",
       "0               0.0           0.0           0.0           0.0        689.0   \n",
       "1            3272.0        3455.0        3261.0           0.0       1000.0   \n",
       "2           14331.0       14948.0       15549.0        1518.0       1500.0   \n",
       "3           28314.0       28959.0       29547.0        2000.0       2019.0   \n",
       "4           20940.0       19146.0       19131.0        2000.0      36681.0   \n",
       "...             ...           ...           ...           ...          ...   \n",
       "45293        9378.0        4883.0        5001.0        1500.0       1528.0   \n",
       "45294      114789.0      115590.0      118221.0           0.0       5600.0   \n",
       "45295       31508.0       16519.0       19231.0        3000.0       2000.0   \n",
       "45296         780.0           0.0         780.0         390.0          0.0   \n",
       "45297      100261.0      102147.0       91829.0        3560.0          0.0   \n",
       "\n",
       "       ...  PAY_MAY_8  PAY_APR_-1  PAY_APR_0  PAY_APR_2  PAY_APR_3  PAY_APR_4  \\\n",
       "0      ...      False       False      False      False      False      False   \n",
       "1      ...      False       False      False       True      False      False   \n",
       "2      ...      False       False       True      False      False      False   \n",
       "3      ...      False       False       True      False      False      False   \n",
       "4      ...      False       False       True      False      False      False   \n",
       "...    ...        ...         ...        ...        ...        ...        ...   \n",
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       "45294  ...      False       False       True      False      False      False   \n",
       "45295  ...      False       False       True      False      False      False   \n",
       "45296  ...      False        True      False      False      False      False   \n",
       "45297  ...      False       False       True      False      False      False   \n",
       "\n",
       "       PAY_APR_5  PAY_APR_6  PAY_APR_7  PAY_APR_8  \n",
       "0          False      False      False      False  \n",
       "1          False      False      False      False  \n",
       "2          False      False      False      False  \n",
       "3          False      False      False      False  \n",
       "4          False      False      False      False  \n",
       "...          ...        ...        ...        ...  \n",
       "45293      False      False      False      False  \n",
       "45294      False      False      False      False  \n",
       "45295      False      False      False      False  \n",
       "45296      False      False      False      False  \n",
       "45297      False      False      False      False  \n",
       "\n",
       "[45298 rows x 81 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "bbd394cc",
   "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>LIMIT_BAL</th>\n",
       "      <th>AGE</th>\n",
       "      <th>BILL_AMT_SEPT</th>\n",
       "      <th>BILL_AMT_AUG</th>\n",
       "      <th>BILL_AMT_JUL</th>\n",
       "      <th>BILL_AMT_JUN</th>\n",
       "      <th>BILL_AMT_MAY</th>\n",
       "      <th>BILL_AMT_APR</th>\n",
       "      <th>PAY_AMT_SEPT</th>\n",
       "      <th>PAY_AMT_AUG</th>\n",
       "      <th>...</th>\n",
       "      <th>PAY_MAY_8</th>\n",
       "      <th>PAY_APR_-1</th>\n",
       "      <th>PAY_APR_0</th>\n",
       "      <th>PAY_APR_2</th>\n",
       "      <th>PAY_APR_3</th>\n",
       "      <th>PAY_APR_4</th>\n",
       "      <th>PAY_APR_5</th>\n",
       "      <th>PAY_APR_6</th>\n",
       "      <th>PAY_APR_7</th>\n",
       "      <th>PAY_APR_8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20000</td>\n",
       "      <td>24</td>\n",
       "      <td>3913</td>\n",
       "      <td>3102</td>\n",
       "      <td>689</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>689</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>120000</td>\n",
       "      <td>26</td>\n",
       "      <td>2682</td>\n",
       "      <td>1725</td>\n",
       "      <td>2682</td>\n",
       "      <td>3272</td>\n",
       "      <td>3455</td>\n",
       "      <td>3261</td>\n",
       "      <td>0</td>\n",
       "      <td>1000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>90000</td>\n",
       "      <td>34</td>\n",
       "      <td>29239</td>\n",
       "      <td>14027</td>\n",
       "      <td>13559</td>\n",
       "      <td>14331</td>\n",
       "      <td>14948</td>\n",
       "      <td>15549</td>\n",
       "      <td>1518</td>\n",
       "      <td>1500</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50000</td>\n",
       "      <td>37</td>\n",
       "      <td>46990</td>\n",
       "      <td>48233</td>\n",
       "      <td>49291</td>\n",
       "      <td>28314</td>\n",
       "      <td>28959</td>\n",
       "      <td>29547</td>\n",
       "      <td>2000</td>\n",
       "      <td>2019</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50000</td>\n",
       "      <td>57</td>\n",
       "      <td>8617</td>\n",
       "      <td>5670</td>\n",
       "      <td>35835</td>\n",
       "      <td>20940</td>\n",
       "      <td>19146</td>\n",
       "      <td>19131</td>\n",
       "      <td>2000</td>\n",
       "      <td>36681</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45293</th>\n",
       "      <td>10000</td>\n",
       "      <td>24</td>\n",
       "      <td>11035</td>\n",
       "      <td>12349</td>\n",
       "      <td>13598</td>\n",
       "      <td>9378</td>\n",
       "      <td>4883</td>\n",
       "      <td>5001</td>\n",
       "      <td>1500</td>\n",
       "      <td>1528</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45294</th>\n",
       "      <td>140000</td>\n",
       "      <td>60</td>\n",
       "      <td>113079</td>\n",
       "      <td>109796</td>\n",
       "      <td>112144</td>\n",
       "      <td>114789</td>\n",
       "      <td>115590</td>\n",
       "      <td>118221</td>\n",
       "      <td>0</td>\n",
       "      <td>5600</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45295</th>\n",
       "      <td>50000</td>\n",
       "      <td>28</td>\n",
       "      <td>42435</td>\n",
       "      <td>44030</td>\n",
       "      <td>43979</td>\n",
       "      <td>31508</td>\n",
       "      <td>16519</td>\n",
       "      <td>19231</td>\n",
       "      <td>3000</td>\n",
       "      <td>2000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45296</th>\n",
       "      <td>220000</td>\n",
       "      <td>30</td>\n",
       "      <td>780</td>\n",
       "      <td>390</td>\n",
       "      <td>0</td>\n",
       "      <td>780</td>\n",
       "      <td>0</td>\n",
       "      <td>780</td>\n",
       "      <td>390</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45297</th>\n",
       "      <td>80000</td>\n",
       "      <td>37</td>\n",
       "      <td>99396</td>\n",
       "      <td>100495</td>\n",
       "      <td>97982</td>\n",
       "      <td>100261</td>\n",
       "      <td>102147</td>\n",
       "      <td>91829</td>\n",
       "      <td>3560</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>45298 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       LIMIT_BAL  AGE  BILL_AMT_SEPT  BILL_AMT_AUG  BILL_AMT_JUL  \\\n",
       "0          20000   24           3913          3102           689   \n",
       "1         120000   26           2682          1725          2682   \n",
       "2          90000   34          29239         14027         13559   \n",
       "3          50000   37          46990         48233         49291   \n",
       "4          50000   57           8617          5670         35835   \n",
       "...          ...  ...            ...           ...           ...   \n",
       "45293      10000   24          11035         12349         13598   \n",
       "45294     140000   60         113079        109796        112144   \n",
       "45295      50000   28          42435         44030         43979   \n",
       "45296     220000   30            780           390             0   \n",
       "45297      80000   37          99396        100495         97982   \n",
       "\n",
       "       BILL_AMT_JUN  BILL_AMT_MAY  BILL_AMT_APR  PAY_AMT_SEPT  PAY_AMT_AUG  \\\n",
       "0                 0             0             0             0          689   \n",
       "1              3272          3455          3261             0         1000   \n",
       "2             14331         14948         15549          1518         1500   \n",
       "3             28314         28959         29547          2000         2019   \n",
       "4             20940         19146         19131          2000        36681   \n",
       "...             ...           ...           ...           ...          ...   \n",
       "45293          9378          4883          5001          1500         1528   \n",
       "45294        114789        115590        118221             0         5600   \n",
       "45295         31508         16519         19231          3000         2000   \n",
       "45296           780             0           780           390            0   \n",
       "45297        100261        102147         91829          3560            0   \n",
       "\n",
       "       ...  PAY_MAY_8  PAY_APR_-1  PAY_APR_0  PAY_APR_2  PAY_APR_3  PAY_APR_4  \\\n",
       "0      ...          0           0          0          0          0          0   \n",
       "1      ...          0           0          0          1          0          0   \n",
       "2      ...          0           0          1          0          0          0   \n",
       "3      ...          0           0          1          0          0          0   \n",
       "4      ...          0           0          1          0          0          0   \n",
       "...    ...        ...         ...        ...        ...        ...        ...   \n",
       "45293  ...          0           0          1          0          0          0   \n",
       "45294  ...          0           0          1          0          0          0   \n",
       "45295  ...          0           0          1          0          0          0   \n",
       "45296  ...          0           1          0          0          0          0   \n",
       "45297  ...          0           0          1          0          0          0   \n",
       "\n",
       "       PAY_APR_5  PAY_APR_6  PAY_APR_7  PAY_APR_8  \n",
       "0              0          0          0          0  \n",
       "1              0          0          0          0  \n",
       "2              0          0          0          0  \n",
       "3              0          0          0          0  \n",
       "4              0          0          0          0  \n",
       "...          ...        ...        ...        ...  \n",
       "45293          0          0          0          0  \n",
       "45294          0          0          0          0  \n",
       "45295          0          0          0          0  \n",
       "45296          0          0          0          0  \n",
       "45297          0          0          0          0  \n",
       "\n",
       "[45298 rows x 81 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_names = {\n",
    "    'SEX_1': 'Male',\n",
    "    'SEX_2': 'Female',\n",
    "    'EDUCATION_1': 'Graduate School',\n",
    "    'EDUCATION_2': 'University',\n",
    "    'EDUCATION_3': 'High School',\n",
    "    'MARRIAGE_1': 'Married',\n",
    "    'MARRIAGE_2': 'Single'\n",
    "}\n",
    "\n",
    "df_final = df_final.rename(columns=column_names).astype(int)\n",
    "df_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0b4a707d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['LIMIT_BAL', 'AGE', 'BILL_AMT_SEPT', 'BILL_AMT_AUG', 'BILL_AMT_JUL',\n",
       "       'BILL_AMT_JUN', 'BILL_AMT_MAY', 'BILL_AMT_APR', 'PAY_AMT_SEPT',\n",
       "       'PAY_AMT_AUG', 'PAY_AMT_JUL', 'PAY_AMT_JUN', 'PAY_AMT_MAY',\n",
       "       'PAY_AMT_APR', 'IsDefaulter', 'Dues', 'Male', 'Female',\n",
       "       'Graduate School', 'University', 'High School', 'Married', 'Single',\n",
       "       'PAY_SEPT_-1', 'PAY_SEPT_0', 'PAY_SEPT_1', 'PAY_SEPT_2', 'PAY_SEPT_3',\n",
       "       'PAY_SEPT_4', 'PAY_SEPT_5', 'PAY_SEPT_6', 'PAY_SEPT_7', 'PAY_SEPT_8',\n",
       "       'PAY_AUG_-1', 'PAY_AUG_0', 'PAY_AUG_1', 'PAY_AUG_2', 'PAY_AUG_3',\n",
       "       'PAY_AUG_4', 'PAY_AUG_5', 'PAY_AUG_6', 'PAY_AUG_7', 'PAY_AUG_8',\n",
       "       'PAY_JUL_-1', 'PAY_JUL_0', 'PAY_JUL_1', 'PAY_JUL_2', 'PAY_JUL_3',\n",
       "       'PAY_JUL_4', 'PAY_JUL_5', 'PAY_JUL_6', 'PAY_JUL_7', 'PAY_JUL_8',\n",
       "       'PAY_JUN_-1', 'PAY_JUN_0', 'PAY_JUN_1', 'PAY_JUN_2', 'PAY_JUN_3',\n",
       "       'PAY_JUN_4', 'PAY_JUN_5', 'PAY_JUN_6', 'PAY_JUN_7', 'PAY_JUN_8',\n",
       "       'PAY_MAY_-1', 'PAY_MAY_0', 'PAY_MAY_2', 'PAY_MAY_3', 'PAY_MAY_4',\n",
       "       'PAY_MAY_5', 'PAY_MAY_6', 'PAY_MAY_7', 'PAY_MAY_8', 'PAY_APR_-1',\n",
       "       'PAY_APR_0', 'PAY_APR_2', 'PAY_APR_3', 'PAY_APR_4', 'PAY_APR_5',\n",
       "       'PAY_APR_6', 'PAY_APR_7', 'PAY_APR_8'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0c47223",
   "metadata": {},
   "source": [
    "### Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6bb373e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "X = df_final.drop('IsDefaulter', axis=1) \n",
    "y = df_final['IsDefaulter']\n",
    "\n",
    "\n",
    "scaler = StandardScaler()\n",
    "for column in X.columns:\n",
    "    X[column] = scaler.fit_transform(X[[column]])\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d42c82da",
   "metadata": {},
   "source": [
    "## Feature Selection/ Dimensionality Reduction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7ede4de1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Principal Component Analysis (PCA)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca = PCA()\n",
    "X_pca = pca.fit_transform(X)\n",
    "\n",
    "explained_variance_ratio = pca.explained_variance_ratio_\n",
    "\n",
    "cumulative_variance = np.cumsum(explained_variance_ratio)\n",
    "\n",
    "# Plot explained variance ratio\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(range(1, len(explained_variance_ratio) + 1), explained_variance_ratio, alpha=0.5, align='center', label='Explained Variance Ratio')\n",
    "plt.step(range(1, len(cumulative_variance) + 1), cumulative_variance, where='mid', label='Cumulative Explained Variance')\n",
    "plt.xlabel('Number of Principal Components')\n",
    "plt.ylabel('Explained Variance Ratio / Cumulative Explained Variance')\n",
    "plt.title('Explained Variance Ratio and Cumulative Explained Variance')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e2bdedc9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sandhyakilari/anaconda3/lib/python3.11/site-packages/seaborn/axisgrid.py:118: UserWarning: The figure layout has changed to tight\n",
      "  self._figure.tight_layout(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1394x1250 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "pca = PCA(n_components=5)\n",
    "X_pca = pca.fit_transform(X)\n",
    "\n",
    "# Create a DataFrame for the PCA results\n",
    "pca_df = pd.DataFrame(data=X_pca, columns=['PC1', 'PC2', 'PC3', 'PC4', 'PC5'])\n",
    "pca_df['Default'] = y.map({0: 'Not Defaulter', 1: 'Defaulter'})  # Assigning meaningful labels to hue elements\n",
    "\n",
    "\n",
    "# Plot the scatter plot matrix of all principal components\n",
    "sns.pairplot(pca_df, hue='Default', diag_kind='kde', plot_kws={'alpha':0.5})\n",
    "plt.suptitle('Scatter Plot Matrix of Principal Components')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56472987",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis (EDA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2da449d1",
   "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>LIMIT_BAL</th>\n",
       "      <th>AGE</th>\n",
       "      <th>BILL_AMT_SEPT</th>\n",
       "      <th>BILL_AMT_AUG</th>\n",
       "      <th>BILL_AMT_JUL</th>\n",
       "      <th>BILL_AMT_JUN</th>\n",
       "      <th>BILL_AMT_MAY</th>\n",
       "      <th>BILL_AMT_APR</th>\n",
       "      <th>PAY_AMT_SEPT</th>\n",
       "      <th>PAY_AMT_AUG</th>\n",
       "      <th>...</th>\n",
       "      <th>PAY_MAY_8</th>\n",
       "      <th>PAY_APR_-1</th>\n",
       "      <th>PAY_APR_0</th>\n",
       "      <th>PAY_APR_2</th>\n",
       "      <th>PAY_APR_3</th>\n",
       "      <th>PAY_APR_4</th>\n",
       "      <th>PAY_APR_5</th>\n",
       "      <th>PAY_APR_6</th>\n",
       "      <th>PAY_APR_7</th>\n",
       "      <th>PAY_APR_8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20000</td>\n",
       "      <td>24</td>\n",
       "      <td>3913</td>\n",
       "      <td>3102</td>\n",
       "      <td>689</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>689</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>120000</td>\n",
       "      <td>26</td>\n",
       "      <td>2682</td>\n",
       "      <td>1725</td>\n",
       "      <td>2682</td>\n",
       "      <td>3272</td>\n",
       "      <td>3455</td>\n",
       "      <td>3261</td>\n",
       "      <td>0</td>\n",
       "      <td>1000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>90000</td>\n",
       "      <td>34</td>\n",
       "      <td>29239</td>\n",
       "      <td>14027</td>\n",
       "      <td>13559</td>\n",
       "      <td>14331</td>\n",
       "      <td>14948</td>\n",
       "      <td>15549</td>\n",
       "      <td>1518</td>\n",
       "      <td>1500</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50000</td>\n",
       "      <td>37</td>\n",
       "      <td>46990</td>\n",
       "      <td>48233</td>\n",
       "      <td>49291</td>\n",
       "      <td>28314</td>\n",
       "      <td>28959</td>\n",
       "      <td>29547</td>\n",
       "      <td>2000</td>\n",
       "      <td>2019</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50000</td>\n",
       "      <td>57</td>\n",
       "      <td>8617</td>\n",
       "      <td>5670</td>\n",
       "      <td>35835</td>\n",
       "      <td>20940</td>\n",
       "      <td>19146</td>\n",
       "      <td>19131</td>\n",
       "      <td>2000</td>\n",
       "      <td>36681</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   LIMIT_BAL  AGE  BILL_AMT_SEPT  BILL_AMT_AUG  BILL_AMT_JUL  BILL_AMT_JUN  \\\n",
       "0      20000   24           3913          3102           689             0   \n",
       "1     120000   26           2682          1725          2682          3272   \n",
       "2      90000   34          29239         14027         13559         14331   \n",
       "3      50000   37          46990         48233         49291         28314   \n",
       "4      50000   57           8617          5670         35835         20940   \n",
       "\n",
       "   BILL_AMT_MAY  BILL_AMT_APR  PAY_AMT_SEPT  PAY_AMT_AUG  ...  PAY_MAY_8  \\\n",
       "0             0             0             0          689  ...          0   \n",
       "1          3455          3261             0         1000  ...          0   \n",
       "2         14948         15549          1518         1500  ...          0   \n",
       "3         28959         29547          2000         2019  ...          0   \n",
       "4         19146         19131          2000        36681  ...          0   \n",
       "\n",
       "   PAY_APR_-1  PAY_APR_0  PAY_APR_2  PAY_APR_3  PAY_APR_4  PAY_APR_5  \\\n",
       "0           0          0          0          0          0          0   \n",
       "1           0          0          1          0          0          0   \n",
       "2           0          1          0          0          0          0   \n",
       "3           0          1          0          0          0          0   \n",
       "4           0          1          0          0          0          0   \n",
       "\n",
       "   PAY_APR_6  PAY_APR_7  PAY_APR_8  \n",
       "0          0          0          0  \n",
       "1          0          0          0  \n",
       "2          0          0          0  \n",
       "3          0          0          0  \n",
       "4          0          0          0  \n",
       "\n",
       "[5 rows x 81 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8c1152b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1400x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Separate the ages based on defaulting status\n",
    "age_defaulters = df[df['IsDefaulter'] == 1]['AGE']\n",
    "age_non_defaulters = df[df['IsDefaulter'] == 0]['AGE']\n",
    "\n",
    "# Set up the figure and axes\n",
    "fig, axes = plt.subplots(1, 2, figsize=(14, 7))\n",
    "\n",
    "# Plot histogram for defaulters\n",
    "axes[0].hist(age_defaulters, bins=40, color='blue', edgecolor='black')\n",
    "axes[0].set_title('Distribution of Age of defaulters')\n",
    "axes[0].set_xlabel('Age')\n",
    "axes[0].set_ylabel('Frequency')\n",
    "\n",
    "# Plot histogram for non-defaulters\n",
    "axes[1].hist(age_non_defaulters, bins=40, color='blue', edgecolor='black')\n",
    "axes[1].set_title('Distribution of Age of non-defaulters')\n",
    "axes[1].set_xlabel('Age')\n",
    "axes[1].set_ylabel('Frequency')\n",
    "\n",
    "# Overall title\n",
    "plt.suptitle('Univariate Analysis of continuous data in Application data')\n",
    "\n",
    "# Show plot\n",
    "plt.tight_layout(rect=[0, 0.03, 1, 0.95]) # Adjust the rect to fit the overall title\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d8065241",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Assuming df is your dataframe and that defaulting is indicated by non-zero values in PAY_APR_* columns\n",
    "# Create a default indicator column for April as an example\n",
    "pay_apr_columns = [col for col in df_final.columns if 'PAY_APR_' in col]\n",
    "df_final['defaulted_apr'] = df_final[pay_apr_columns].sum(axis=1) > 0\n",
    "\n",
    "# Now separate the ages based on the defaulting status for April\n",
    "age_defaulters = df_final[df_final['defaulted_apr']]['AGE']\n",
    "age_non_defaulters = df_final[~df_final['defaulted_apr']]['AGE']\n",
    "\n",
    "# Set up the figure and axes\n",
    "fig, axes = plt.subplots(1, 2, figsize=(14, 7))\n",
    "\n",
    "# Plot histogram for defaulters\n",
    "axes[0].hist(age_defaulters, bins=40, color='blue', edgecolor='black')\n",
    "axes[0].set_title('Distribution of Age of defaulters')\n",
    "axes[0].set_xlabel('Age')\n",
    "axes[0].set_ylabel('Frequency')\n",
    "\n",
    "# Plot histogram for non-defaulters\n",
    "axes[1].hist(age_non_defaulters, bins=40, color='blue', edgecolor='black')\n",
    "axes[1].set_title('Distribution of Age of non-defaulters')\n",
    "axes[1].set_xlabel('Age')\n",
    "axes[1].set_ylabel('Frequency')\n",
    "\n",
    "# Overall title\n",
    "plt.suptitle('Univariate Analysis of continuous data in Application data')\n",
    "\n",
    "# Show plot\n",
    "plt.tight_layout(rect=[0, 0.03, 1, 0.95]) # Adjust the rect to fit the overall title\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc12b2c5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "4bb8df79",
   "metadata": {},
   "source": [
    "## Modeling Approaches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5df6530b",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_final.drop(['IsDefaulter','Dues'],axis=1)\n",
    "y = df_final['IsDefaulter']\n",
    "\n",
    "columns = X.columns\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X = scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8c83a530",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import accuracy_score, recall_score, precision_score, f1_score, roc_auc_score, confusion_matrix, roc_curve, auc\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify = y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c4423ec",
   "metadata": {},
   "source": [
    "## Implementing Logistic Regression¶"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "96768e09",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0641ab0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_log_reg = df_final.copy()\n",
    "df_log_reg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "84bb73d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = {'penalty':['l1','l2'], 'C' : [0.001, 0.01, 0.1, 1, 10, 100, 1000] }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9dd7add9",
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_lr_clf = GridSearchCV(LogisticRegression(), param_grid, scoring = 'accuracy', n_jobs = -1, verbose = 3, cv = 3)\n",
    "grid_lr_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d06eaaf3",
   "metadata": {},
   "outputs": [],
   "source": [
    "optimized_clf = grid_lr_clf.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fbd9b04c",
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_lr_clf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e50d548",
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_lr_clf.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a0af702",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicted Probability\n",
    "train_preds = optimized_clf.predict_proba(X_train)[:,1]\n",
    "test_preds = optimized_clf.predict_proba(X_test)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1cc5d274",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the predicted classes\n",
    "train_class_preds = optimized_clf.predict(X_train)\n",
    "test_class_preds = optimized_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dad32475",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the accuracy scores\n",
    "train_accuracy_lr = accuracy_score(train_class_preds,y_train)\n",
    "test_accuracy_lr = accuracy_score(test_class_preds,y_test)\n",
    "\n",
    "print(\"The accuracy on train data is \", train_accuracy_lr)\n",
    "print(\"The accuracy on test data is \", test_accuracy_lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "77290d58",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_accuracy_lr = accuracy_score(test_class_preds,y_test)\n",
    "test_precision_score_lr = precision_score(test_class_preds,y_test)\n",
    "test_recall_score_lr = recall_score(test_class_preds,y_test)\n",
    "test_f1_score_lr = f1_score(test_class_preds,y_test)\n",
    "test_roc_score_lr = roc_auc_score(test_class_preds,y_test)\n",
    "\n",
    "print(\"The accuracy on test data is \", test_accuracy_lr)\n",
    "print(\"The precision on test data is \", test_precision_score_lr)\n",
    "print(\"The recall on test data is \", test_recall_score_lr)\n",
    "print(\"The f1 on test data is \", test_f1_score_lr)\n",
    "print(\"The roc_score on test data is \", test_roc_score_lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ac86b81",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the confusion matrix for both train and test\n",
    "\n",
    "labels = ['Not Defaulter', 'Defaulter']\n",
    "cm = confusion_matrix(y_test, test_class_preds)\n",
    "print(cm)\n",
    "\n",
    "ax= plt.subplot()\n",
    "sns.heatmap(cm, annot=True, ax = ax) #annot=True to annotate cells\n",
    "\n",
    "# labels, title and ticks\n",
    "ax.set_xlabel('Predicted labels')\n",
    "ax.set_ylabel('True labels')\n",
    "ax.set_title('Confusion Matrix')\n",
    "ax.xaxis.set_ticklabels(labels)\n",
    "ax.yaxis.set_ticklabels(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f648a69f",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_importance = pd.DataFrame({'Features':columns, 'Importance':np.abs(optimized_clf.coef_).ravel() })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b176c443",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_importance = feature_importance.sort_values(by = 'Importance', ascending=False)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f99dda3",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.bar(height=feature_importance['Importance'], x= feature_importance['Features'])\n",
    "plt.xticks(rotation=80)\n",
    "plt.title(\"Feature importances via coefficients\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a4364297",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_preds_proba_lr = optimized_clf.predict_proba(X_test)[::,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8262d85d",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_proba = y_preds_proba_lr\n",
    "fpr, tpr, _ = roc_curve(y_test,  y_pred_proba)\n",
    "auc = roc_auc_score(y_test, y_pred_proba)\n",
    "plt.plot(fpr,tpr,label=\"data 1, auc=\"+str(auc))\n",
    "plt.legend(loc=4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7342988",
   "metadata": {},
   "source": [
    "### Implementing Decision Tree¶"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3002336",
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = {'max_depth': [20,30,50,100], 'min_samples_split':[0.1,0.2,0.4]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a42d1298",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b78ef6b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_DTC_clf = GridSearchCV(DecisionTreeClassifier(), param_grid, scoring = 'accuracy', n_jobs = -1, verbose = 3, cv = 3)\n",
    "grid_DTC_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1899edc",
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_DTC_clf.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "52f000ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "optimal_DTC_clf = grid_DTC_clf.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8474a9fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the predicted classes\n",
    "train_class_preds = optimal_DTC_clf.predict(X_train)\n",
    "test_class_preds = optimal_DTC_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "05be67b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_DTC_clf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8c34bf59",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the accuracy scores\n",
    "train_accuracy_DTC = accuracy_score(train_class_preds,y_train)\n",
    "test_accuracy_DTC = accuracy_score(test_class_preds,y_test)\n",
    "\n",
    "print(\"The accuracy on train data is \", train_accuracy_DTC)\n",
    "print(\"The accuracy on test data is \", test_accuracy_DTC)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fe59c1f",
   "metadata": {},
   "source": [
    "### Implementing RandomForest¶"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a3e19266",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e20a2683",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn import metrics\n",
    "\n",
    "rf_model = RandomForestClassifier(random_state = 42)\n",
    "\n",
    "rf_model.fit(X_train, y_train)\n",
    "pred_rf = rf_model.predict(X_test)\n",
    "\n",
    "print(\"Test Accuracy: \",metrics.accuracy_score(y_test, pred_rf))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d68cd5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_confusion_matrix = metrics.confusion_matrix(y_test, pred_rf)\n",
    "sns.heatmap(rf_confusion_matrix, annot=True, fmt=\"d\")\n",
    "\n",
    "plt.xlabel(\"Predicted Label\", fontsize= 12)\n",
    "plt.ylabel(\"True Label\", fontsize= 12)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "00585b58",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(metrics.classification_report(y_test, pred_rf, labels = [0, 1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5426c05f",
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_pred_proba = rf_model.predict_proba(X_test)[:,1]\n",
    "\n",
    "rf_roc_auc = metrics.roc_auc_score(y_test, rf_pred_proba)\n",
    "print('ROC_AUC: ', rf_roc_auc)\n",
    "\n",
    "rf_fpr, rf_tpr, thresholds = metrics.roc_curve(y_test, rf_pred_proba)\n",
    "\n",
    "plt.plot(rf_fpr,rf_tpr, label = 'ROC_AUC = %0.3f' % rf_roc_auc)\n",
    "\n",
    "plt.xlabel(\"False Positive Rate\", fontsize= 12)\n",
    "plt.ylabel(\"True Positive Rate\", fontsize= 12)\n",
    "plt.legend(loc=\"lower right\")\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e44e0d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_importances_rf = pd.DataFrame(rf_model.feature_importances_,\n",
    "                                   index = columns,\n",
    "                                    columns=['importance_rf']).sort_values('importance_rf',\n",
    "                                                                        ascending=False)[:10]\n",
    "                                    \n",
    "plt.subplots(figsize=(17,6))\n",
    "plt.title(\"Feature importances\")\n",
    "plt.bar(feature_importances_rf.index, feature_importances_rf['importance_rf'],\n",
    "        color=\"g\",  align=\"center\")\n",
    "plt.xticks(feature_importances_rf.index, rotation = 85)\n",
    "#plt.xlim([-1, X.shape[1]])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cdac851d",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_importances_rf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31b83e15",
   "metadata": {},
   "source": [
    "### Implementing XGBoost¶"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c767d53",
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBClassifier\n",
    "\n",
    "xgb = XGBClassifier()\n",
    "xgb.fit(X_train, y_train)\n",
    "y_pred_xgb = xgb.predict(X_test)\n",
    "\n",
    "\n",
    "print(f'Accuracy: {accuracy_score(y_test, y_pred_xgb)}')\n",
    "print(f'Precision: {precision_score(y_test, y_pred_xgb)}')\n",
    "print(f'Recall: {recall_score(y_test, y_pred_xgb)}')\n",
    "print(f'F1-score: {f1_score(y_test, y_pred_xgb)}')\n",
    "\n",
    "\n",
    "# Calculate confusion matrix\n",
    "conf_matrix = confusion_matrix(y_test, y_pred_xgb)\n",
    "\n",
    "# Plot confusion matrix as a heatmap\n",
    "plt.figure(figsize=(5, 4))\n",
    "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Reds')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Actual')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a2d596d",
   "metadata": {},
   "source": [
    "###### 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "efcd2b2a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2ca23118",
   "metadata": {},
   "outputs": [],
   "source": [
    "dtrain=xgb.DMatrix(X_train,label=y_train)\n",
    "dtest=xgb.DMatrix(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d2c5182",
   "metadata": {},
   "outputs": [],
   "source": [
    "#setting parameters for xgboost\n",
    "parameters={'max_depth':7, 'eta':1, 'silent':1,'objective':'binary:logistic','eval_metric':'auc','learning_rate':.05}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "afcd3174",
   "metadata": {},
   "outputs": [],
   "source": [
    "#training our model \n",
    "num_round=50\n",
    "from datetime import datetime \n",
    "start = datetime.now() \n",
    "xg=xgb.train(parameters,dtrain,num_round) \n",
    "stop = datetime.now()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a7941b6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Execution time of the model \n",
    "execution_time_xgb = stop-start \n",
    "execution_time_xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e03b6033",
   "metadata": {},
   "outputs": [],
   "source": [
    "#now predicting our model on train set \n",
    "train_class_preds_probs=xg.predict(dtrain) \n",
    "#now predicting our model on test set \n",
    "test_class_preds_probs =xg.predict(dtest) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa33f598",
   "metadata": {},
   "outputs": [],
   "source": [
    "len(train_class_preds_probs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4769d5af",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_class_preds = []\n",
    "test_class_preds = []\n",
    "for i in range(0,len(train_class_preds_probs)):\n",
    "    if train_class_preds_probs[i] >= 0.5:\n",
    "        train_class_preds.append(1)\n",
    "    else:\n",
    "        train_class_preds.append(0)\n",
    "\n",
    "for i in range(0,len(test_class_preds_probs)):\n",
    "    if test_class_preds_probs[i] >= 0.5:\n",
    "        test_class_preds.append(1)\n",
    "    else:\n",
    "        test_class_preds.append(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b7909e3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_class_preds_probs[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "89067a53",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_class_preds[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f794b5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "len(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "94f10215",
   "metadata": {},
   "outputs": [],
   "source": [
    "len(train_class_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35ec4894",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the accuracy scores\n",
    "train_accuracy_xgb = accuracy_score(train_class_preds,y_train)\n",
    "test_accuracy_xgb = accuracy_score(test_class_preds,y_test)\n",
    "\n",
    "print(\"The accuracy on train data is \", train_accuracy_xgb)\n",
    "print(\"The accuracy on test data is \", test_accuracy_xgb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e96112fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_accuracy_xgb = accuracy_score(test_class_preds,y_test)\n",
    "test_precision_xgb = precision_score(test_class_preds,y_test)\n",
    "test_recall_score_xgb = recall_score(test_class_preds,y_test)\n",
    "test_f1_score_xgb = f1_score(test_class_preds,y_test)\n",
    "test_roc_score_xgb = roc_auc_score(test_class_preds,y_test)\n",
    "\n",
    "print(\"The accuracy on test data is \", test_accuracy_xgb)\n",
    "print(\"The precision on test data is \", test_precision_xgb)\n",
    "print(\"The recall on test data is \", test_recall_score_xgb)\n",
    "print(\"The f1 on test data is \", test_f1_score_xgb)\n",
    "print(\"The roc_score on train data is \", test_roc_score_xgb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c07e610f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import  XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce1e9fe3",
   "metadata": {},
   "outputs": [],
   "source": [
    "param_test1 = {\n",
    " 'max_depth':range(3,10,2),\n",
    " 'min_child_weight':range(1,6,2)\n",
    "}\n",
    "gsearch1 = GridSearchCV(estimator = XGBClassifier( learning_rate =0.1, n_estimators=140, max_depth=5,\n",
    " min_child_weight=1, gamma=0, subsample=0.8, colsample_bytree=0.8,\n",
    " objective= 'binary:logistic', nthread=4, scale_pos_weight=1, seed=27), \n",
    " param_grid = param_test1, scoring='accuracy',n_jobs=-1, cv=3, verbose = 2)\n",
    "gsearch1.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7137e88b",
   "metadata": {},
   "outputs": [],
   "source": [
    "gsearch1.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "415b6aa0",
   "metadata": {},
   "outputs": [],
   "source": [
    "optimal_xgb = gsearch1.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8b0d0838",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the predicted classes\n",
    "train_class_preds = optimal_xgb.predict(X_train)\n",
    "test_class_preds = optimal_xgb.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "51ac532b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the accuracy scores\n",
    "train_accuracy_xgb_tuned = accuracy_score(train_class_preds,y_train)\n",
    "test_accuracy_xgb_tuned = accuracy_score(test_class_preds,y_test)\n",
    "\n",
    "print(\"The accuracy on train data is \", train_accuracy_xgb_tuned)\n",
    "print(\"The accuracy on test data is \", test_accuracy_xgb_tuned)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c1844f04",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_accuracy_xgb_tuned = accuracy_score(test_class_preds,y_test)\n",
    "test_precision_xgb_tuned = precision_score(test_class_preds,y_test)\n",
    "test_recall_score_xgb_tuned = recall_score(test_class_preds,y_test)\n",
    "test_f1_score_xgb_tuned = f1_score(test_class_preds,y_test)\n",
    "test_roc_score_xgb_tuned = roc_auc_score(test_class_preds,y_test)\n",
    "\n",
    "print(\"The accuracy on test data is \", test_accuracy_xgb_tuned)\n",
    "print(\"The precision on test data is \", test_precision_xgb_tuned)\n",
    "print(\"The recall on test data is \", test_recall_score_xgb_tuned)\n",
    "print(\"The f1 on test data is \", test_f1_score_xgb_tuned)\n",
    "print(\"The roc_score on train data is \", test_roc_score_xgb_tuned)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "78eea605",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(optimal_xgb.feature_importances_,\n",
    "                                   index = columns,\n",
    "                                    columns=['importance_xgb']).sort_values('importance_xgb',\n",
    "                                                                        ascending=False)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f4007e69",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Feature Importance\n",
    "feature_importances_xgb = pd.DataFrame(optimal_xgb.feature_importances_,\n",
    "                                   index = columns,\n",
    "                                    columns=['importance_xgb']).sort_values('importance_xgb',\n",
    "                                                                        ascending=False)[:10]\n",
    "                                    \n",
    "plt.subplots(figsize=(17,6))\n",
    "plt.title(\"Feature importances\")\n",
    "plt.bar(feature_importances_xgb.index, feature_importances_xgb['importance_xgb'],\n",
    "        color=\"b\",  align=\"center\")\n",
    "plt.xticks(feature_importances_rf.index, rotation = 85)\n",
    "#plt.xlim([-1, X.shape[1]])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a65462b",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_preds_proba_xgb = optimal_xgb.predict_proba(X_test)[::,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "63d85235",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_proba = y_preds_proba_xgb\n",
    "fpr, tpr, _ = metrics.roc_curve(y_test,  y_pred_proba)\n",
    "auc = metrics.roc_auc_score(y_test, y_pred_proba)\n",
    "plt.plot(fpr,tpr,label=\"data 1, auc=\"+str(auc))\n",
    "plt.legend(loc=4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c712b9b",
   "metadata": {},
   "source": [
    "### Support Vector Machine (SVM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2efa2272",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.inspection import permutation_importance\n",
    "\n",
    "svm_model = SVC(kernel='rbf') # non-linear kernel\n",
    "svm_model.fit(X_train, y_train)\n",
    "\n",
    "y_pred = svm_model.predict(X_test)\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "precision = precision_score(y_test, y_pred)\n",
    "recall = recall_score(y_test, y_pred)\n",
    "\n",
    "print(\"Accuracy:\", accuracy)\n",
    "print(\"Precision:\", precision)\n",
    "print(\"Recall:\", recall)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed7ce2f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = ['Not Defaulter', 'Defaulter']\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "print(cm)\n",
    "\n",
    "ax= plt.subplot()\n",
    "sns.heatmap(cm, annot=True, ax = ax) #annot=True to annotate cells\n",
    "\n",
    "# labels, title and ticks\n",
    "ax.set_xlabel('Predicted labels')\n",
    "ax.set_ylabel('True labels')\n",
    "ax.set_title('Confusion Matrix')\n",
    "ax.xaxis.set_ticklabels(labels)\n",
    "ax.yaxis.set_ticklabels(labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a6af6cd",
   "metadata": {},
   "source": [
    "### Neural Networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "499bfae0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Dropout\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "\n",
    "\n",
    "X_train_scaled = X_train\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "# Define the MLP model\n",
    "model = Sequential([\n",
    "    Dense(64, activation='relu', input_shape=(X_train_scaled.shape[1],)),\n",
    "    Dropout(0.5),\n",
    "    Dense(32, activation='relu'),\n",
    "    Dropout(0.5),\n",
    "    Dense(1, activation='sigmoid')\n",
    "])\n",
    "\n",
    "# Compile the model\n",
    "model.compile(optimizer=Adam(lr=0.001), loss='binary_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "# Train the model\n",
    "history = model.fit(X_train_scaled, y_train, epochs=10, batch_size=32, validation_split=0.1)\n",
    "\n",
    "# Evaluate the model\n",
    "y_pred_proba = model.predict(X_test_scaled)\n",
    "y_pred = (y_pred_proba > 0.5).astype(int)\n",
    "\n",
    "# Compute evaluation metrics\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "precision = precision_score(y_test, y_pred)\n",
    "recall = recall_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "print(\"Accuracy:\", accuracy)\n",
    "print(\"Precision:\", precision)\n",
    "print(\"Recall:\", recall)\n",
    "print(\"F1-score:\", f1)\n",
    "print(\"Confusion Matrix:\\n\", conf_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "49026c4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "results[\"Neural Network\"] = {\n",
    "    \"confusion_matrix\": conf_matrix,\n",
    "    \"accuracy\": accuracy,\n",
    "    \"precision\": precision,\n",
    "    \"recall\": recall,\n",
    "    \"f1_score\": f1\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b2788ede",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "X_train_scaled = X_train\n",
    "\n",
    "# Transform the test data\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "# Build the model\n",
    "model = tf.keras.models.Sequential([\n",
    "  tf.keras.layers.Dense(16, activation='relu', input_shape=(X_train_scaled.shape[1],)),\n",
    "  tf.keras.layers.Dense(16, activation='relu'),\n",
    "  tf.keras.layers.Dense(1, activation='sigmoid')\n",
    "])\n",
    "\n",
    "# Compile the model\n",
    "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "# Train the model\n",
    "model.fit(X_train_scaled, y_train, batch_size=32, epochs=10, validation_split=0.2)\n",
    "\n",
    "# Evaluate the model on test data\n",
    "loss, accuracy = model.evaluate(X_test_scaled, y_test)\n",
    "\n",
    "print(\"Test Loss:\", loss)\n",
    "print(\"Test Accuracy:\", accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d25cc93",
   "metadata": {},
   "source": [
    "### Gaussian Naive Bayes Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ac9a32f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3bae16b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, precision_score, recall_score, f1_score\n",
    "\n",
    "\n",
    "gnb = GaussianNB()\n",
    "\n",
    "gnb.fit(X_train, y_train)\n",
    "\n",
    "y_pred = gnb.predict(X_test)\n",
    "\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "precision = precision_score(y_test, y_pred)\n",
    "recall = recall_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "print(\"Confusion Matrix:\")\n",
    "print(conf_matrix)\n",
    "print(f\"Accuracy: {accuracy:.2f}\")\n",
    "print(f\"Precision: {precision:.2f}\")\n",
    "print(f\"Recall: {recall:.2f}\")\n",
    "print(f\"F1 Score: {f1:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efb5ce05",
   "metadata": {},
   "source": [
    "## Analysis of Model Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7483c694",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9351d134",
   "metadata": {},
   "outputs": [],
   "source": [
    "models = {\n",
    "    \"Logistic Regression\": LogisticRegression(),\n",
    "    \"Support Vector Machine (SVM)\": SVC(kernel='rbf'),\n",
    "    \"Random Forest\": RandomForestClassifier(random_state=42),\n",
    "    \"XGBoost\": xgb.XGBClassifier(),\n",
    "    \"Gaussian Naive Bayes\": GaussianNB()\n",
    "}\n",
    "\n",
    "results = {}\n",
    "\n",
    "for model_name, model in models.items():\n",
    "    model.fit(X_train, y_train)\n",
    "    y_pred = model.predict(X_test)\n",
    "    \n",
    "    conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "    plt.figure(figsize=(3, 3))\n",
    "    sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', cbar=False)\n",
    "    plt.title('Confusion Matrix')\n",
    "    plt.xlabel('Predicted labels')\n",
    "    plt.ylabel('True labels')\n",
    "    plt.show()\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    precision = precision_score(y_test, y_pred)\n",
    "    recall = recall_score(y_test, y_pred)\n",
    "    f1 = f1_score(y_test, y_pred)\n",
    "    \n",
    "    results[model_name] = {\n",
    "        \"confusion_matrix\": conf_matrix,\n",
    "        \"accuracy\": accuracy,\n",
    "        \"precision\": precision,\n",
    "        \"recall\": recall,\n",
    "        \"f1_score\": f1\n",
    "    }\n",
    "\n",
    "for model_name, metrics in results.items():\n",
    "    print(f\"Model: {model_name}\")\n",
    "    print(\"Confusion Matrix:\")\n",
    "    print(metrics[\"confusion_matrix\"])\n",
    "    print(\"Accuracy:\", metrics[\"accuracy\"])\n",
    "    print(\"Precision:\", metrics[\"precision\"])\n",
    "    print(\"Recall:\", metrics[\"recall\"])\n",
    "    print(\"F1 Score:\", metrics[\"f1_score\"])\n",
    "    print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d60c5529",
   "metadata": {},
   "outputs": [],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "72d30633",
   "metadata": {},
   "outputs": [],
   "source": [
    "models[\"Neural Network\"] = model\n",
    "models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a24c48b",
   "metadata": {},
   "outputs": [],
   "source": [
    "metrics_names = [\"Accuracy\", \"Precision\", \"Recall\", \"F1-score\"]\n",
    "models_names = list(results.keys())\n",
    "metrics_values = np.array([[results[model_name][\"accuracy\"], \n",
    "                            results[model_name][\"precision\"], \n",
    "                            results[model_name][\"recall\"], \n",
    "                            results[model_name][\"f1_score\"]] \n",
    "                            for model_name in models_names])\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "bar_width = 0.15\n",
    "index = np.arange(len(models_names))\n",
    "\n",
    "for i in range(len(metrics_names)):\n",
    "    plt.bar(index + i * bar_width, metrics_values[:, i], bar_width, label=metrics_names[i])\n",
    "\n",
    "plt.xlabel('Models')\n",
    "plt.ylabel('Scores')\n",
    "plt.title('Evaluation Metrics by Model')\n",
    "plt.xticks(index + 2 * bar_width, models_names, rotation=45)\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "01a2b857",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve, auc\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# Plot ROC curve for each model\n",
    "for model_name, metrics in results.items():\n",
    "    if model_name == \"Neural Network\":\n",
    "        fpr, tpr, _ = roc_curve(y_test, y_pred_proba)\n",
    "    else:\n",
    "        tn, fp, fn, tp = metrics[\"confusion_matrix\"].ravel()\n",
    "        fpr = np.array([0, fp / (fp + tn), 1])  # Adding 0 and 1 for lower left and upper right corners\n",
    "        tpr = np.array([0, tp / (tp + fn), 1])  # Adding 0 and 1 for lower left and upper right corners\n",
    "    roc_auc = auc(fpr, tpr)\n",
    "    plt.plot(fpr, tpr, lw=2, label='%s (AUC = %0.2f)' % (model_name, roc_auc))\n",
    "\n",
    "plt.plot([0, 1], [0, 1], color='gray', linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Curve')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "871c26da",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve, auc\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# Plot ROC curve for each model\n",
    "for model_name, metrics in results.items():\n",
    "    if model_name == \"Neural Network\":\n",
    "        fpr, tpr, thresholds = roc_curve(y_test, y_pred_proba)\n",
    "    else:\n",
    "        tn, fp, fn, tp = metrics[\"confusion_matrix\"].ravel()\n",
    "        fpr = np.array([0, fp / (fp + tn), 1])  # Adding 0 and 1 for lower left and upper right corners\n",
    "        tpr = np.array([0, tp / (tp + fn), 1])  # Adding 0 and 1 for lower left and upper right corners\n",
    "        thresholds = [0, 0.5, 1]  # Manually setting thresholds for non-NN models\n",
    "        \n",
    "    roc_auc = auc(fpr, tpr)\n",
    "    plt.plot(fpr, tpr, lw=2, label='%s (AUC = %0.2f)' % (model_name, roc_auc))\n",
    "    \n",
    "    # Annotate points with thresholds\n",
    "    for i, threshold in enumerate(thresholds):\n",
    "        plt.annotate(f'{threshold:.2f}', (fpr[i], tpr[i]), textcoords=\"offset points\", xytext=(0,10), ha='center')\n",
    "\n",
    "plt.plot([0, 1], [0, 1], color='gray', linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Curve with Thresholds')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "63073382",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_recall_curve\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Compute precision and recall for Random Forest model\n",
    "precision_rf, recall_rf, thresholds_rf = precision_recall_curve(y_test, rf_pred_proba)\n",
    "\n",
    "# Compute precision and recall for Random Forest model with threshold adjustment\n",
    "y_pred_rf_threshold = (rf_pred_proba > threshold).astype(int)\n",
    "precision_rf_threshold, recall_rf_threshold, _ = precision_recall_curve(y_test, y_pred_rf_threshold)\n",
    "\n",
    "# Plot precision-recall curve for Random Forest model\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(recall_rf, precision_rf, label='Random Forest')\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.title('Precision-Recall Curve (Random Forest)')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "# Plot precision-recall curve for Random Forest model with threshold adjustment\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(recall_rf_threshold, precision_rf_threshold, label='Random Forest (Threshold Adjusted)')\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.title('Precision-Recall Curve with Threshold Adjustment (Random Forest)')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5d5c6d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_recall_curve\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "precision_rf, recall_rf, thresholds_rf = precision_recall_curve(y_test, rf_pred_proba)\n",
    "\n",
    "y_pred_rf_threshold = (rf_pred_proba > threshold).astype(int)\n",
    "\n",
    "plt.figure(figsize=(16, 6))\n",
    "plt.plot(thresholds_rf, precision_rf[:-1], label='Precision')\n",
    "plt.plot(thresholds_rf, recall_rf[:-1], label='Recall')\n",
    "plt.xlabel('Threshold')\n",
    "plt.ylabel('Precision, Recall')\n",
    "plt.title('Precision-Recall Curve (Random Forest)')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "adf55a28",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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