Decision Tree Classifier - Credit Card Payment Default.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Machine Learning Project - Decision Tree Classifier\n",
    "\n",
    "This project aims to build a Machine Learning model that predicts whether a credit card holder will default their next credit card payment based on various factors.\n",
    "\n",
    "Read the more descriptive article I wrote on Medium [here](https://medium.com/@faizyah/machine-learning-will-they-pay-their-credit-card-debt-c9926179feb6).\n",
    "\n",
    "[Link](https://archive.ics.uci.edu/dataset/350/default+of+credit+card+clients) to original dataset.\n",
    "\n",
    "Prepared by: [Faiz Yah](https://www.linkedin.com/in/faizyah/) ^^\n",
    "\n",
    "Klang, Malaysia. 28th September 2024"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 1: Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "from sklearn.utils import resample\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "from sklearn.tree import plot_tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 2: Import Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "path_file = '/Users/.../dataset_credit.xls'\n",
    "\n",
    "df = pd.read_excel(path_file, header=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 3: Exploratory Data Analysis "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30000, 25)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Identify amount of rows (instances) and columns (features)\n",
    "\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>default payment next month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>20000</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</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>689</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>120000</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</td>\n",
       "      <td>3455</td>\n",
       "      <td>3261</td>\n",
       "      <td>0</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>90000</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</td>\n",
       "      <td>14948</td>\n",
       "      <td>15549</td>\n",
       "      <td>1518</td>\n",
       "      <td>1500</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>5000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>50000</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</td>\n",
       "      <td>28959</td>\n",
       "      <td>29547</td>\n",
       "      <td>2000</td>\n",
       "      <td>2019</td>\n",
       "      <td>1200</td>\n",
       "      <td>1100</td>\n",
       "      <td>1069</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>50000</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</td>\n",
       "      <td>19146</td>\n",
       "      <td>19131</td>\n",
       "      <td>2000</td>\n",
       "      <td>36681</td>\n",
       "      <td>10000</td>\n",
       "      <td>9000</td>\n",
       "      <td>689</td>\n",
       "      <td>679</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID  LIMIT_BAL  SEX  EDUCATION  MARRIAGE  AGE  PAY_0  PAY_2  PAY_3  PAY_4  \\\n",
       "0   1      20000    2          2         1   24      2      2     -1     -1   \n",
       "1   2     120000    2          2         2   26     -1      2      0      0   \n",
       "2   3      90000    2          2         2   34      0      0      0      0   \n",
       "3   4      50000    2          2         1   37      0      0      0      0   \n",
       "4   5      50000    1          2         1   57     -1      0     -1      0   \n",
       "\n",
       "   ...  BILL_AMT4  BILL_AMT5  BILL_AMT6  PAY_AMT1  PAY_AMT2  PAY_AMT3  \\\n",
       "0  ...          0          0          0         0       689         0   \n",
       "1  ...       3272       3455       3261         0      1000      1000   \n",
       "2  ...      14331      14948      15549      1518      1500      1000   \n",
       "3  ...      28314      28959      29547      2000      2019      1200   \n",
       "4  ...      20940      19146      19131      2000     36681     10000   \n",
       "\n",
       "   PAY_AMT4  PAY_AMT5  PAY_AMT6  default payment next month  \n",
       "0         0         0         0                           1  \n",
       "1      1000         0      2000                           1  \n",
       "2      1000      1000      5000                           0  \n",
       "3      1100      1069      1000                           0  \n",
       "4      9000       689       679                           0  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check first 5 rows\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Descriptive statistics for each features\n",
    "\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 30000 entries, 0 to 29999\n",
      "Data columns (total 25 columns):\n",
      " #   Column                      Non-Null Count  Dtype\n",
      "---  ------                      --------------  -----\n",
      " 0   ID                          30000 non-null  int64\n",
      " 1   LIMIT_BAL                   30000 non-null  int64\n",
      " 2   SEX                         30000 non-null  int64\n",
      " 3   EDUCATION                   30000 non-null  int64\n",
      " 4   MARRIAGE                    30000 non-null  int64\n",
      " 5   AGE                         30000 non-null  int64\n",
      " 6   PAY_0                       30000 non-null  int64\n",
      " 7   PAY_2                       30000 non-null  int64\n",
      " 8   PAY_3                       30000 non-null  int64\n",
      " 9   PAY_4                       30000 non-null  int64\n",
      " 10  PAY_5                       30000 non-null  int64\n",
      " 11  PAY_6                       30000 non-null  int64\n",
      " 12  BILL_AMT1                   30000 non-null  int64\n",
      " 13  BILL_AMT2                   30000 non-null  int64\n",
      " 14  BILL_AMT3                   30000 non-null  int64\n",
      " 15  BILL_AMT4                   30000 non-null  int64\n",
      " 16  BILL_AMT5                   30000 non-null  int64\n",
      " 17  BILL_AMT6                   30000 non-null  int64\n",
      " 18  PAY_AMT1                    30000 non-null  int64\n",
      " 19  PAY_AMT2                    30000 non-null  int64\n",
      " 20  PAY_AMT3                    30000 non-null  int64\n",
      " 21  PAY_AMT4                    30000 non-null  int64\n",
      " 22  PAY_AMT5                    30000 non-null  int64\n",
      " 23  PAY_AMT6                    30000 non-null  int64\n",
      " 24  default payment next month  30000 non-null  int64\n",
      "dtypes: int64(25)\n",
      "memory usage: 5.7 MB\n"
     ]
    }
   ],
   "source": [
    "#Identify data type of columns, it seems all are integers\n",
    "\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAioAAAHHCAYAAACRAnNyAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAABJF0lEQVR4nO3dd3RU1f7+8WfSEyAJSCiBBEgMhC4gIkVAjFRp1wKIElRAEUUEBbkq1YYUlap4ryAoFgTEH1WIgIJYUKqEaigioYeEECnJ/v3BN3MZk8BMmCEH836tNWsx++w553POGWaenLLHZowxAgAAsCCvgi4AAAAgLwQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVIBcXL17U4MGDFRERIS8vL3Xq1KnAapk9e7ZiY2Pl6+ur0NBQjyxj3759stlsmjlzpkfmj6tr27atevfuXdBl/OM1b95cNWrUuGq/7du3y8fHR9u2bbsOVeFKCCrI0969e/X4448rKipKAQEBCg4OVuPGjfXOO+8oIyOjoMuTJE2dOtUjX64ffPCBxo4dq/vuu08ffvihnn322Tz7Nm/eXDabTTabTV5eXgoODlaVKlX08MMPa8WKFddUx44dO9SzZ09FR0fr/fff1/Tp069pfq5YsmSJRowY4XT/y7eDzWZTiRIlVL9+fX3wwQfKysryXKEW8/3332vEiBFKSUlx+jXr1q3T119/rSFDhtjbVq9ebd+Wv/zyS47X9OzZU0WLFnVHyXl67bXX9OWXXzrVNzvs2mw2vfLKK7n26d69u2w2m8fr/vPPPzVixAht2rQp3/OoVq2a2rVrp2HDhrmvMOSPAXKxaNEiExgYaEJDQ03//v3N9OnTzeTJk03Xrl2Nr6+v6d27d0GXaIwxpnr16qZZs2Zun2+XLl1MuXLlnOrbrFkzU758eTN79mwze/Zs8+6775rnnnvOREVFGUnmgQceMOfPn89XHdOmTTOSzO7du/P1emclJSUZSWbGjBn2tn79+hlXPiL+vh0mTJhgbrnlFiPJDBkyxANVW9PYsWONJJOUlOT0azp27Ghatmzp0LZq1SojyUgy99xzT47XxMfHmyJFilxruVdUpEgREx8f71Tf7PdQQECAqVatWo7pZ86cMUWKFDEBAQEer/vnn3/O8X7O1qxZM1O9enWn5rNkyRIjyezZs8fNFcIVPgWUj2BhSUlJ6tq1qypUqKBvvvlGZcuWtU/r16+f9uzZo8WLFxdghZ539OhRl06zhISE6KGHHnJoe+ONN9S/f39NnTpVFStW1JgxY/JVhySPnfJxt79vh8cff1xVqlTR5MmTNXr0aPn6+hZgddZ09OhRLV68WO+++26u02+55RYtWrRIv/76q+rWrXudq3Nd27ZtNX/+fG3evFm1a9e2ty9cuFDnz59X69at9c033xRghc6Li4tT8eLF9eGHH2rUqFEFXU7hVdBJCdbzxBNPGElm3bp1TvW/cOGCGTVqlImKijJ+fn6mQoUKZujQoeavv/5y6CfJDB8+PMfrK1So4PBX24wZM4wks3btWvPss8+akiVLmqCgINOpUydz9OhRh9fp//7izH5c7ejKmTNnzMCBA0358uWNn5+fqVy5shk7dqzJysoyxvzvr8K/P1atWpXnPK/0F9rFixdNtWrVTFBQkElJSXGYNnv2bFO3bl0TEBBgihcvbrp06WIOHDhwxfXL3n5ffvmladu2rSlbtqzx8/MzUVFRZtSoUebixYtX3LaX13z5tvr7EZX4+Phct8OV5LUd7rvvPiPJHDp0yOzbt8/07dvXVK5c2QQEBJgSJUqY++67z+How969e40kM2HChBzzWrdunZFk5syZY4wxZvjw4UaS2blzp+nevbsJDg42JUuWNC+99JLJysoyBw4cMB06dDDFihUzpUuXNuPGjcsxz7/++ssMGzbMREdHGz8/P1O+fHnz/PPP5/r+7devn1mwYIGpXr268fPzM9WqVTNLly6198mu5++PKx1d+eCDD4wks2/fPof27CMq//3vf03x4sVN+/btHabndURlyZIlpkmTJiYoKMgULVrUtG3b1mzbts0+PSEhwdhsNvPyyy87vO7jjz82kszUqVPt6/v3x5WOrmS/h8aOHWsqVapkBg8e7DC9bdu2pn379nnWPWXKFFOtWjXj5+dnypYta5588klz6tQphz7Z77HffvvNNG/e3AQGBprw8HAzZsyYHNvt74/s97Yz87hc586dTa1atfJcb3geQQU5lCtXzkRFRTndP/tL7b777jNTpkwxPXr0MJJMp06dHPq5GlTq1KljWrRoYSZNmmQGDRpkvL29zQMPPGDvt2DBAlO+fHkTGxtrP93w9ddf51lnVlaWadGihbHZbKZXr15m8uTJpn379kaSGTBggDHmUpCZPXu2iY2NdTiNkZycnOd8r3YoefTo0UaSWbRokb3tlVdeMTabzXTp0sVMnTrVjBw50pQsWdJUrFjR/uG8YMEC07lzZyPJTJs2zcyePdts3rzZGGNMp06dzAMPPGDGjh1rpk2bZu6//34jyTz33HNX3LaX13yloPL999+bu+++20iyb4PZs2fnuY5X2g5169Y13t7eJj093cydO9fUrl3bDBs2zEyfPt38+9//NsWLFzcVKlQw6enp9tc0btzY1KtXL8e8nnzySVOsWDF73+xgcMstt5hu3bqZqVOnmnbt2tmDTpUqVUzfvn3N1KlTTePGjY0ks2bNGvv8MjMzTcuWLU1QUJAZMGCAee+998xTTz1lfHx8TMeOHR2WLcnUrl3blC1b1owePdq8/fbbJioqygQFBZnjx48bY4zZvHmz6datm5Fk3nrrLft2O3PmTJ7brVevXuamm27K0Z79hTt37lwzatQoI8n88ssv9um5feHPmjXL2Gw207p1azNp0iQzZswYU7FiRRMaGuoQlvr162d8fHzs8/vzzz9NiRIlTFxcnD20z5492/j7+5s77rjDvh7ff/99nutxeVD597//bSIjI+3zOnbsmPHx8TGffPJJrnVn78e4uDgzadIk89RTTxlvb29Tv359h9OmzZo1M+Hh4SYiIsI888wzZurUqaZFixZGklmyZIkxxpjk5GT79urTp4+99r179zo9j8u98sorxsvLy5w+fTrPdYdnEVTg4PTp00ZSjg/pvGzatMlIMr169XJof+6554wk880339jbXA0ql39oGmPMs88+a7y9vR2OTLhyjcqXX35pJJlXXnnFof2+++4zNpvN4Ty0K+exr9Z3wYIFRpJ55513jDHG7Nu3z3h7e5tXX33Vod/WrVuNj4+PQ3v2B/ixY8cc+p49ezbHch5//HETFBTkcCQgv0HFmPxdoxIbG2uOHTtmjh07ZhITE03//v2NJPvRgNzqXr9+vZFkZs2aZW977733jCSTmJhobzt//rwpWbKkw/pkb58+ffrY2y5evGjKly9vbDabeeONN+ztp06dMoGBgQ6vnz17tvHy8jLfffedQ03vvvtujqOKkoyfn5/D+2Tz5s1Gkpk0aZK9zdVrVJo0aZJrKLs8qKSkpJjixYubDh062Kf//Qs/LS3NhIaG5rh+LDk52YSEhDi0p6enm5tvvtlUr17d/PXXX6Zdu3YmODjY7N+/3+G1+blGZezYsWbbtm1Gkn27TpkyxRQtWtSkp6fnqPvo0aPGz8/PtGzZ0mRmZtrbJ0+ebCSZDz74wN7WrFmzHO+Vc+fOmTJlyph7773X3na1a1ScmUe2OXPmGEnmxx9/dGo7wP246wcOUlNTJUnFihVzqv+SJUskSQMHDnRoHzRokCRd07Usffr0kc1msz+/4447lJmZqf379+drfkuWLJG3t7f69++fo1ZjjJYuXZrvWq8k+w6HtLQ0SdL8+fOVlZWlBx54QMePH7c/ypQpo5iYGK1ateqq8wwMDLT/Oy0tTcePH9cdd9yhs2fPaseOHR5ZD2fs2LFDYWFhCgsLU9WqVTVp0iS1a9dOH3zwgSTHui9cuKATJ07o5ptvVmhoqH799Vf7tAceeEABAQH6+OOP7W3Lly/X8ePHc1wLJEm9evWy/9vb21u33nqrjDF67LHH7O2hoaGqUqWKfv/9d3vb3LlzVbVqVcXGxjrsixYtWkhSjn0RFxen6Oho+/NatWopODjYYZ6uOnHihIoXL37FPiEhIRowYIC++uorbdy4Mdc+K1asUEpKirp16+awLt7e3mrQoIHDugQFBWnmzJlKTExU06ZNtXjxYr311luKjIzM93pcrnr16qpVq5Y++eQTSdKcOXPUsWNHBQUF5ei7cuVKnT9/XgMGDJCX1/++knr37q3g4OAcnyFFixZ1eA/4+fnptttuc2kfuDKP7H1z/Phxp+cP9yKowEFwcLCk/32pXs3+/fvl5eWlm2++2aG9TJkyCg0NzXeokJTjQzP7A+PUqVP5mt/+/fsVHh6eI4RVrVrVPt0Tzpw5I+l/4W/37t0yxigmJsb+pZ79SExMtF9AeyW//fabOnfurJCQEAUHByssLMz+wXv69GmPrIczKlasqBUrVmjlypVau3atkpOTtWjRIpUsWVKSlJGRoWHDhikiIkL+/v4qWbKkwsLClJKS4lB3aGio2rdvrzlz5tjbPv74Y5UrV84eIi739/dKSEiIAgIC7Mu9vP3y98/u3bv122+/5dgPlStXlqQc+yK3L/LixYvn+z2ZzRhz1T7PPPOMQkND87xlfPfu3ZKkFi1a5Fifr7/+Ose6NG7cWH379tVPP/2kVq1a6dFHH72mdfi7Bx98UHPnztWePXv0/fff68EHH8y1X/b/uypVqji0+/n5KSoqKsf/y/Llyzv8ASO5vg9cmUf2vvl7f1w/3PUDB8HBwQoPD3d5kKNr+U+cmZmZa7u3t3eu7c58qFtJ9rbMDnNZWVmy2WxaunRprut4tTEmUlJS1KxZMwUHB2vUqFGKjo5WQECAfv31Vw0ZMsRhzJK89ktmZmae2/daFClSRHFxcXlOf/rppzVjxgwNGDBADRs2VEhIiGw2m7p27ZpjrJUePXpo7ty5+v7771WzZk199dVXevLJJx3+6s6W27o48/7JyspSzZo1NWHChFz7RkREuDxPV910001OfclmH1UZMWJErkdVsrff7NmzVaZMmRzTfXwcP+7PnTun1atXS7o0ZtLZs2dzPeKRX926ddPQoUPVu3dv3XTTTWrZsqVb5uuOfeDKPLL3zd9DL64fggpyuOeeezR9+nStX79eDRs2vGLfChUqKCsrS7t377YfmZCkI0eOKCUlRRUqVLC3FS9ePMcgWOfPn9fhw4fzXasrAalChQpauXKl0tLSHI6qZJ8qubxWd8nMzNScOXMUFBSkJk2aSJKio6NljFGlSpXsf7m7YvXq1Tpx4oTmz5+vpk2b2tuTkpJy9M1tm0uX/oqNioq64nI88RfkF198ofj4eI0fP97e9tdff+VaY+vWrRUWFqaPP/5YDRo00NmzZ/Xwww+7tZ7o6Ght3rxZd911l9vW19X5xMbGat68eU71HTBggN5++22NHDkyxy3r2aekSpUqdcWwmG348OFKTEzUuHHjNGTIEL3wwguaOHGiQ59r2SaRkZFq3LixVq9erb59++YIStmy/9/t3LnT4T15/vx5JSUlObUuf+fO925SUpK8vLzy9X8V7sGpH+QwePBgFSlSRL169dKRI0dyTN+7d6/eeecdSZfGTJCkt99+26FP9l+o7dq1s7dFR0fr22+/deg3ffr0PI+oOKNIkSJOjwDatm1bZWZmavLkyQ7tb731lmw2m9q0aZPvOnKTmZmp/v37KzExUf3797efVvvXv/4lb29vjRw5MsdfcMYYnThx4orzzf5r8PLXnj9/XlOnTs3RNzo6Wj/88IPOnz9vb1u0aJEOHjx41fqLFCkiSS6NsHo13t7eOdZ50qRJub4HfHx81K1bN33++eeaOXOmatasqVq1armtFunStTCHDh3S+++/n2NaRkaG0tPTXZ6nq9utYcOGOnXqlFPXWGQfVVm4cGGOUVdbtWql4OBgvfbaa7pw4UKO1x47dsz+7x9//FHjxo3TgAEDNGjQID3//POaPHmy1qxZk2NdrmX/v/LKKxo+fLiefvrpPPvExcXJz89PEydOdHhv/Pe//9Xp06cdPkOc5c737i+//KLq1asrJCTkmueF/OGICnKIjo7WnDlz1KVLF1WtWlU9evRQjRo1dP78eX3//feaO3euevbsKUmqXbu24uPjNX36dPspiZ9++kkffvihOnXqpDvvvNM+3169eumJJ57Qvffeq7vvvlubN2/W8uXLr+mQar169TRt2jS98soruvnmm1WqVKlcr2GQpPbt2+vOO+/Uiy++qH379ql27dr6+uuvtXDhQg0YMMDhIklXnT59Wh999JEk6ezZs9qzZ4/mz5+vvXv3qmvXrho9erS9b3R0tF555RUNHTpU+/btU6dOnVSsWDElJSVpwYIF6tOnj5577rk8l9WoUSMVL15c8fHx6t+/v2w2m2bPnp3rYetevXrpiy++UOvWrfXAAw9o7969+uijj5xa13r16kmS+vfvr1atWsnb21tdu3Z1ddM4uOeeezR79myFhISoWrVqWr9+vVauXKmbbrop1/49evTQxIkTtWrVqnwNmHc1Dz/8sD7//HM98cQTWrVqlRo3bqzMzEzt2LFDn3/+uZYvX65bb73VpXlmb7cXX3xRXbt2la+vr9q3b2//8vy7du3aycfHRytXrlSfPn2uOv9nnnlGb731ljZv3uwwz+DgYE2bNk0PP/yw6tatq65duyosLEwHDhzQ4sWL1bhxY02ePFl//fWX4uPjFRMTo1dffVWSNHLkSP2///f/9Mgjj2jr1q32+darV08rV67UhAkTFB4erkqVKqlBgwZOb4tmzZqpWbNmV+wTFhamoUOHauTIkWrdurU6dOignTt3aurUqapfv36uF09fTXR0tEJDQ/Xuu++qWLFiKlKkiBo0aKBKlSq5NJ8LFy5ozZo1evLJJ12uAW503e8zwg1j165dpnfv3qZixYrGz8/PFCtWzDRu3NhMmjTJ4RbYCxcumJEjR5pKlSoZX19fExERkeuAb5mZmWbIkCH2AdxatWpl9uzZk+ftyT///LPD67Nv17x88LXk5GTTrl07U6xYMacGfEtLSzPPPvusCQ8PN76+viYmJsZhwLdsrt6erMsGlipatKiJiYkxDz300BXHdZk3b55p0qSJKVKkiClSpIiJjY01/fr1Mzt37rT3yev25HXr1pnbb7/dPljV4MGDzfLly3MdnG78+PGmXLlyxt/f3zRu3Nhs2LDBqduTL168aJ5++mkTFhZmbDZbvgd8u9ypU6fMI488YkqWLGmKFi1qWrVqZXbs2JHnbdTGXLoF3cvLy/zxxx85puW1ffIaVCy3Gs+fP2/GjBljqlevbvz9/U3x4sVNvXr1zMiRIx3GztD/Dfj2d7nVPnr0aFOuXDnj5eXl1K3KHTp0MHfddZdD2+W3J+e13rmt46pVq0yrVq1MSEiICQgIMNHR0aZnz55mw4YNxpj/3eb/99ttN2zYYHx8fEzfvn3tbTt27DBNmzY1gYGBLg34diV57ZvJkyeb2NhY4+vra0qXLm369u2b54Bvuc2zQoUKDm0LFy401apVMz4+PrkO+ObMPJYuXWokz/+EBa7MZswNdmUigEKlTp06KlGihBISEgq6FI/57rvv1Lx5c+3YsUMxMTEFXQ7+T6dOnWSz2bRgwYKCLqVQI6gAsKwNGzaofv36mjlzpuLj4wu6HI9q06aNypcvn+v1Mrj+EhMTVbNmTW3atEk1atQo6HIKNYIKAMvZtm2bfvnlF40fP17Hjx/X77//roCAgIIuC0AB4K4fAJbzxRdf6JFHHtGFCxf0ySefEFKAQowjKgAAwLI4ogIAACyLoAIAACzrhh7wLSsrS3/++aeKFSvGD0YBAHCDMMYoLS1N4eHhuf5+1+Vu6KDy559/5vjRMAAAcGM4ePCgypcvf8U+N3RQyf5huYMHD9p/RwUAAFhbamqqIiIiHH4gNi83dFDJPt0THBxMUAEA4AbjzGUbXEwLAAAsi6ACAAAsi6ACAAAsi6ACAAAsi6ACAAAsi6ACAAAsi6ACAAAsi6ACAAAsi6ACAAAsi6ACAAAsi6ACAAAsi6ACAAAsi6ACAAAsi6ACAAAsi6ACAAAsi6ACAAAsi6ACAAAsi6ACAAAsi6ACAAAsi6ACAAAsi6ACAAAsi6ACAAAsi6ACAAAsi6ACAAAsi6ACAAAsi6ACAAAsi6ACAAAsi6ACAAAsi6ACAAAsi6ACAAAsi6ACAAAsi6ACAAAsi6ACAAAsi6ACAAAsi6ACAAAsi6ACAAAsi6ACAAAsi6ACAAAsi6ACAAAsi6ACAAAsi6ACAAAsi6ACAAAsi6ACAAAsy6egC3CLGTOkwMCCrgJW0qdPQVcAAHADjqgAAADLIqgAAADLIqgAAADLIqgAAADLIqgAAADLIqgAAADLIqgAAADLIqgAAADLIqgAAADLIqgAAADLIqgAAADLIqgAAADLIqgAAADLIqgAAADLIqgAAADLIqgAAADLIqgAAADLIqgAAADLIqgAAADLIqgAAADLIqgAAADLIqgAAADLIqgAAADLIqgAAADLIqgAAADLIqgAAADLIqgAAADLIqgAAADLIqgAAADLIqigUNmwYYO6d++uihUryt/fX4GBgapevbpeeOEFpaSkFHR5AIC/Iaig0FiyZIkaN26sP/74Qy1bttRtt90mm82m1q1ba/369apTp47+/PPPgi4TAHAZggoKjRdeeEEjRozQmjVrNH36dH333XcaNGiQDh48qDVr1uiuu+7SkCFDCrpMAMBlfFzpnJWVpTVr1ui7777T/v37dfbsWYWFhalOnTqKi4tTRESEp+oErtmePXv0wAMPOLTdd999atmypSSpb9++atWqVUGUBgDIg1NHVDIyMvTKK68oIiJCbdu21dKlS5WSkiJvb2/t2bNHw4cPV6VKldS2bVv98MMPnq4ZyJdKlSrpxx9/dGj76aefVK5cOUlSyZIldfbs2YIoDQCQB6eOqFSuXFkNGzbU+++/r7vvvlu+vr45+uzfv19z5sxR165d9eKLL6p3795uLxa4FoMGDdLjjz+un3/+WVWqVFFiYqLef/99TZw4UZK0e/duxcTEFHCVAIDL2Ywx5mqdEhMTVbVqVadmeOHCBR04cEDR0dHXXNzVpKamKiQkRKffflvBgYEeXx5uIH365Nr86aefavLkydq/f78iIyP1xBNP6OGHH5YkJSUl6dSpU6pbt+71rBQACh379/fp0woODr5iX6eCilURVJCnPIIKAKDguRJUXL7rZ9myZVq7dq39+ZQpU3TLLbfowQcf1KlTp1yvFigAFy5c0OHDh3XkyBHdwFkdAP7xXA4qzz//vFJTUyVJW7du1aBBg9S2bVslJSVp4MCBbi8QcKcvv/xSjRs3VpEiRVSuXDmVLVtWISEh6tatm/bu3VvQ5QEA/sbloJKUlKRq1apJkubNm6d77rlHr732mqZMmaKlS5e6vUDAXWbOnKmuXbvq5ptv1ogRI9S9e3cVKVJEgwcPlp+fnxo0aKDExMSCLhMAcBmXxlGRJD8/P/stnCtXrlSPHj0kSSVKlLAfaQGs6LXXXtPkyZPVq1cve9vEiRO1ZMkSLVu2TEOHDtULL7yghQsXFmCVAIDLuXxEpUmTJho4cKBGjx6tn376Se3atZMk7dq1S+XLl3dpXt9++63at2+v8PBw2Ww2ffnll66WAzjt0KFDuvPOOx3amjdvro0bN0qSunXrpjVr1hREaQCAPLgcVCZPniwfHx998cUXmjZtmn2wrKVLl6p169YuzSs9PV21a9fWlClTXC0DcFmVKlVynJ5cunSpKlWqJEkqVqyYLl68WBClAQDy4PKpn8jISC1atChH+1tvveXywtu0aaM2bdq4/DogP0aNGqV//etf+uabb1S5cmUlJiZq0aJFmj9/viTpt99+U/Xq1Qu4SgDA5fL1o4R79+7VSy+9pG7duuno0aOSLv1l+ttvv7m1OMCd7rnnHq1Zs0ZBQUFat26dihYtqtWrV6tjx46SpNtvv13z5s0r4CoBAJdz+YjKmjVr1KZNGzVu3FjffvutXn31VZUqVUqbN2/Wf//7X33xxReeqFOSdO7cOZ07d87+nIt34aqGDRuqYcOGuU4rWbLkda4GAHA1Lh9ReeGFF/TKK69oxYoV8vPzs7e3aNHC4z9I+PrrryskJMT+4Nea4YrDhw8rKSmpoMsAALjA5aCydetWde7cOUd7qVKldPz4cbcUlZehQ4fq9OnT9sfBgwc9ujz8s/Tq1UufffZZntP/85//6IknnriOFQEArsbloBIaGqrDhw/naN+4caP9DiBP8ff3V3BwsMMDcNavv/6qli1b2p+npaVp2rRp9uexsbFauXJlQZQGAMiDy0Gla9euGjJkiJKTk2Wz2ZSVlaV169bpueeesw/+5qwzZ85o06ZN2rRpk6RLo95u2rRJBw4ccLUs4KpOnTqlsLAw+/OjR49q8ODB9ueRkZH6888/C6I0AEAenA4qU6dOlXRpdM/Y2FhFRETozJkzqlatmpo2bapGjRrppZdecmnhGzZsUJ06dVSnTh1J0sCBA1WnTh0NGzbMpfkAzihdurTD6cLDhw8rPT1d6enpkqSTJ0+qaNGiBVUeACAXNuPkT8eWKFFC9evX14wZMxQeHq4DBw5o27ZtOnPmjOrUqaOYmBhP15qD/Wei335bwYGB1335sLA+fXI0Pfzwwzp9+rTmzp0rLy8v3X///dq1a5datWqlRx99VC+99BIjJAPAdWD//j59+qqXcTh9e/K2bdvUu3dv1ahRQxMnTtRDDz2kyMjIay4WuF6GDRumRo0aqUSJErLZbIqNjdXixYvVsmVLTZw4UREREVq+fHlBlwkAuIzTQSU8PFyLFy/WzJkz1b9/fy1YsEAvvfSSvL29HfrVqlXL7UUC7hATE6PExEQtWbJE/v7+6ty5s/z8/JSYmKiDBw+qYsWKstlsBV0mAOAyTp/6udzKlSvVunVrGWN0+cttNpsyMzPdWuCVcOoHecrl1A8AwBo8cuon24QJE/Tyyy/roYce0ssvvywfH5dnARSo3bt3a926dTp8+LC8vLwUFRWlu+++W6GhoQVdGgDgb5xOGb///rvi4+O1e/duzZkzx/77KMCN4syZM3r44Yf11Vdf2dt8fHzk7e0tm82m0aNHa+DAgQVYIQDg75y+PblWrVoqXbq0tm3bRkjBDenZZ5/V3r17tXLlSh08eFBff/21oqOj9d5772nx4sV66623NHPmzIIuEwBwGaePqLz77rt66KGHPFkL4FHz58/XokWL7D9KGB4erokTJ6p///7avn27xo0bp9dee009e/Ys2EIBAHZOH1EhpOBG5+XlpWLFijm0BQcHKy0tTZJ02223adeuXQVRGgAgDy4PoQ/cqJo3b66hQ4fq5MmTkqTjx4/rxRdfVKNGjSRJ6enpCgkJKcgSAQB/Q1BBofH2229r//79KlWqlMqUKaMyZcrojz/+0Pjx4yVJ+/bt0/3331/AVQIALse9xSg0ypUrp40bN2rVqlXav3+/IiMjdeedd9pvsb/nnnt0zz33FHCVAIDLEVRQqHh7eysuLq6gywAAOClfp35mzZqlhQsXOrQtXLhQs2bNcktRAAAAUj6H0Pfy8lJsbKy2b99ub4uNjdXu3bsZQh/WwBD6AGBZHh1CX5KysrJytO3YsSM/swIAAMgTd/0AAADLcltQuXDhgg4cOOCu2QEAALgvqGzfvl2VKlVy1+wAAAA49QMAAKzL6Ytp69ate8XpGRkZ11wMAADA5ZwOKtu3b1fXrl3zPL1z+PBhftANAAC4ldNBpUaNGmrQoIH69u2b6/RNmzbp/fffd1thAAAATl+j0rhxY+3cuTPP6cWKFVPTpk3dUhQAAIDkwhGVd95554rTo6OjtWrVqmsuCAAAIBt3/QAAAMsiqAAAAMsiqAAAAMsiqAAAAMsiqAAAAMsiqAAAAMtya1Bp0aKFRo8erbNnz7pztgAAoJBya1CJjIxUQkKCYmNj3TlbAABQSDk94JszZs6cKUlKTU1152wBAEAh5ZFrVIKDgz0xWwAAUMjkK6isWbNG7du3180336ybb75ZHTp00Hfffefu2gAAQCHnclD56KOPFBcXp6CgIPXv31/9+/dXYGCg7rrrLs2ZM8cTNQIAgELKZowxrrygatWq6tOnj5599lmH9gkTJuj9999XYmKiWwu8ktTUVIWEhOj0228rODDwui0XN4A+fQq6AgBAHuzf36dPX/VyEZePqPz+++9q3759jvYOHTooKSnJ1dkBAADkyeWgEhERoYSEhBztK1euVEREhFuKAgAAkPJxe/KgQYPUv39/bdq0SY0aNZIkrVu3TjNnztQ777zj9gIBAEDh5fI1KpK0YMECjR8/3n49StWqVfX888+rY8eObi/wSlw5xwUAAKzBle/vfAUVqyCoAABw4/HoxbQAAADXi9PXqFSqVEk2m+2KfWw2m/bu3XvNRQEAAEguBJUBAwbkOW3fvn167733dO7cOXfUBAAAIMmFoPLMM8/kaDt58qRGjx6tadOmqUGDBhozZoxbiwMAAIVbvn49OSMjQxMmTNC4ceNUoUIFzZ8/X23btnV3bQAAoJBzKahkZmbq/fff18iRIxUQEKCJEyfqoYceuuq1KwAAAPnhdFD5/PPP9dJLLyklJUUvvvii+vbtKz8/P0/WBgAACjmnx1Hx8vJSYGCgunXrdsV7nidMmOC24q6GcVQAALjxuPL97fQRlaZNm1719mNOAQEAAHdyOqisXr3ag2UAAADkxMi0AADAsggqAADAsggqAADAsggqAADAsggqAADAsggqAADAsvIVVCpVqqS7777boS0uLk5RUVFuKQoAAEDK548SxsfHKywszKGtc+fOOn78uFuKAgAAkFwYQt+KGEIfAIAbjyvf31yjAgAALMuloLJ9+3Y9+eSTqlOnjsqWLauyZcuqTp06evLJJ7V9+3ZP1QgAAAopp69RWbp0qTp16qS6deuqY8eOKl26tCTpyJEjWrFiherWrauFCxeqVatWHisWAAAULk5fo1K7dm117NhRo0aNynX6iBEjNH/+fG3ZssWtBV4J16gAAHDj8cg1Krt27VL37t3znN6tWzft3r3b+SoBAACuwumgUrFiRS1evDjP6YsXL1aFChXcUhQAAIDkwjUqo0aN0oMPPqjVq1crLi7O4RqVhIQELVu2THPmzPFYoQAAoPBxOqjcf//9KleunCZOnKjx48crOTlZklSmTBk1bNhQq1evVsOGDT1WKAAAKHxcGpm2UaNGatSokadqAQAAcMCAbwAAwLKcCiqtW7fWDz/8cNV+aWlpGjNmjKZMmXLNhQEAADh16uf+++/Xvffeq5CQELVv31633nqrwsPDFRAQoFOnTmn79u1au3atlixZonbt2mns2LGerhsAABQCTg/4du7cOc2dO1efffaZ1q5dq9OnT1+agc2matWqqVWrVnrsscdUtWpVjxZ8OQZ8AwDgxuPK93e+fz359OnTysjI0E033SRfX998FXqtCCoAANx4XPn+dumun8uFhIQoJCQkvy8HAAC4Ku76AQAAlkVQAQAAlkVQAQAAlkVQAQAAluVyUImKitKJEydytKekpCgqKsotRQEAAEj5CCr79u1TZmZmjvZz587p0KFDbikKAABAcuH25K+++sr+7+XLlzvcmpyZmamEhARVrFjRrcUBAIDCzemg0qlTJ0mXRqKNj493mObr66uKFStq/Pjxbi0OAAAUbk4HlaysLElSpUqV9PPPP6tkyZIeKwoAAEDKx8i0SUlJnqgDAAAgh3wNoZ+QkKCEhAQdPXrUfqQl2wcffOCWwgAAAFwOKiNHjtSoUaN06623qmzZsrLZbJ6oCwAAwPWg8u6772rmzJl6+OGHPVEPAACAncvjqJw/f16NGjXyRC0AAAAOXA4qvXr10pw5czxRCwAAgAOXT/389ddfmj59ulauXKlatWrJ19fXYfqECRPcVhwAACjcXA4qW7Zs0S233CJJ2rZtm8M0LqwFAADu5HJQWbVqlSfqAAAAyMHla1Sy7dmzR8uXL1dGRoYkyRjjtqIAAACkfASVEydO6K677lLlypXVtm1bHT58WJL02GOPadCgQW4vEAAAFF4uB5Vnn31Wvr6+OnDggIKCguztXbp00bJly9xaHAAAKNxcvkbl66+/1vLly1W+fHmH9piYGO3fv99thQEAALh8RCU9Pd3hSEq2kydPyt/f3y1FAQAASPkIKnfccYdmzZplf26z2ZSVlaU333xTd955p1uLAwAAhZvLp37efPNN3XXXXdqwYYPOnz+vwYMH67ffftPJkye1bt06T9QIAAAKKZePqNSoUUO7du1SkyZN1LFjR6Wnp+tf//qXNm7cqOjoaE/UCAAACimbuYEHQElNTVVISIhOnz6t4ODggi4HAAA4wZXvb5dP/UiXfu9ny5YtOnr0qLKyshymdejQIT+zBAAAyMHloLJs2TL16NFDx48fzzHNZrMpMzPTLYUBAAC4fI3K008/rfvvv1+HDx9WVlaWw4OQAgAA3MnloHLkyBENHDhQpUuX9kQ9AAAAdi4Hlfvuu0+rV6/2QCkAAACOXL7r5+zZs7r//vsVFhammjVrytfX12F6//793VrglXDXDwAANx6P3vXzySef6Ouvv1ZAQIBWr14tm81mn2az2a5rUAEAAP9sLgeVF198USNHjtQLL7wgLy+XzxwBAAA4zeWkcf78eXXp0oWQAgAAPM7ltBEfH6/PPvvME7UAAAA4cPnUT2Zmpt58800tX75ctWrVynEx7YQJE9xWHAAAKNxcDipbt25VnTp1JEnbtm1zmHb5hbUAAADXyuWgsmrVKk/UAQAAkIPL16jMmDFDGRkZnqgFAADAgctB5YUXXlDp0qX12GOP6fvvv/dETQAAAJLyEVQOHTqkDz/8UMePH1fz5s0VGxurMWPGKDk52RP1AQCAQszloOLj46POnTtr4cKFOnjwoHr37q2PP/5YkZGR6tChgxYuXKisrCxP1AoAAAqZaxq1rXTp0mrSpIkaNmwoLy8vbd26VfHx8YqOjuaHCwEAwDXLV1A5cuSIxo0bp+rVq6t58+ZKTU3VokWLlJSUpEOHDumBBx5QfHy8u2sFAACFjMu/nty+fXstX75clStXVq9evdSjRw+VKFHCoc/Ro0dVpkwZj58C4teTAQC48Xj015NLlSqlNWvWqGHDhnn2CQsLU1JSkquzBgAAcODyERUr4YgKAAA3Ho8eUZGk9PR0rVmzRgcOHND58+cdpvXv3z8/swQAAMjB5aCyceNGtW3bVmfPnlV6erpKlCih48ePKygoSKVKlSKoAAAAt3H5rp9nn31W7du316lTpxQYGKgffvhB+/fvV7169TRu3DhP1AgAAAopl4PKpk2bNGjQIHl5ecnb21vnzp1TRESE3nzzTf373//2RI0AAKCQcjmo+Pr6ysvr0stKlSqlAwcOSJJCQkJ08OBB91YHAAAKNZevUalTp45+/vlnxcTEqFmzZho2bJiOHz+u2bNnq0aNGp6oEQAAFFIuH1F57bXXVLZsWUnSq6++quLFi6tv3746duyYpk+f7vYCAQBA4eXSERVjjEJCQhQYGKiLFy+qVKlSWrZsmadqc9ozyyS/oIKuAgCAf5b37inoClw4opKUlKRatWopNjZWtWrVUnR0tDZs2ODJ2gAAQCHndFB5/vnndfHiRX300Uf64osvVL58eT3++OOerA0AABRyTp/6Wbt2rb744gs1adJEknT77berfPnySk9PV5EiRTxWIAAAKLycPqJy9OhRxcTE2J+XLVtWgYGBOnr0qEcKAwAAcPqIis1m05kzZxQYGGhv8/LyUlpamlJTU+1t/DggAABwF6eDijFGlStXztFWp04d+79tNpsyMzPdWyEAACi0nA4qq1at8mQdAAAAOTgdVJo1a+bJOgAAAHJweWRaAACA64WgAgAALIugAgAALIugAgAALMvloPLoo48qLS0tR3t6eroeffRRtxQFAAAg5SOofPjhh8rIyMjRnpGRoVmzZrmlKAAAAMmF25NTU1NljJExRmlpaQoICLBPy8zM1JIlS1SqVCmPFAkAAAonp4NKaGiobDabbDZbjhFqpUtD7I8cOdKtxQEAgMLNpZFpjTFq0aKF5s2bpxIlStin+fn5qUKFCgoPD/dIkQAAoHByeWTapKQkRUZGymazeawoAAAAycmgsmXLFofnW7duzbNvrVq1rq0iAACA/+NUULnllltks9lkjLliP349GQAAuJNTQSUpKcnTdQAAAOTgVFCpUKGCp+sAAADIwemLabNdbVC3Hj165LsYAACAy7kcVJ555hmH5xcuXNDZs2fl5+enoKAgggoAAHAbl4fQP3XqlMPjzJkz2rlzp5o0aaJPPvnEEzUCAIBCyi2/nhwTE6M33ngjx9EWAADwz+Ll5aUnn3wyz+lt27bV66+/7r7luWtGPj4++vPPP901OwAAYEE2m02ffPKJnnrqqVynd+vWTQsWLHDb8ly+RuWrr75yeG6M0eHDhzV58mQ1btzYbYUBAABr+uqrr/Tggw9KkiZPnuww7dZbb9WOHTvctiyXg0qnTp0cnttsNoWFhalFixYaP368u+oCAAAWVblyZa1Zs0Z33nmnMjMzNXXqVPtP61y8eFGBgYFuW5bLQSUrK8ttCwcAADemqKgofffdd2revLnat2+vt99+W6GhoRo6dKgaNWrktuW4HFQAAAAkKTIyUuvWrVPXrl1VpUoVGWMUGRmpr7/+2m3LyFdQ+eOPP/TVV1/pwIEDOn/+vMO0CRMmuKUwAABgPTNmzFBISIj9edmyZbVmzRpt375dJ0+eVL169Qr21E9CQoI6dOigqKgo7dixQzVq1NC+fftkjFHdunXdVhgAALCevAZ2rVatmkeW5/LtyUOHDtVzzz2nrVu3KiAgQPPmzdPBgwfVrFkz3X///Z6oEQAAWMThw4ev648VuxxUEhMT7WnKx8dHGRkZKlq0qEaNGqUxY8a4vUAAAGAdvXr10meffZbn9P/85z964okn3LY8l4NKkSJF7NellC1bVnv37rVPO378uNsKAwAA1vPrr7+qZcuW9udpaWmaNm2a/XlsbKxWrlzptuW5HFRuv/12rV27VtKlYXIHDRqkV199VY8++qhuv/12txUGAACs59SpUwoLC7M/P3r0qAYPHmx/HhkZ6daR6l2+mHbChAk6c+aMJGnkyJE6c+aMPvvsM8XExHDHDwAA/3ClS5fWwYMHFRERIenSNSvp6elKT09XkSJFdPLkSRUtWtRty3PqiMrEiRP1119/Sbp0XUrNmjUlXToN9O6772rLli2aN2+eKlSo4LbCAACA9TRt2lRvvPGGzp07pwsXLmjcuHGKjY3VSy+9pK1bt2r48OFuHfDNqaAycOBApaamSpIqVaqkY8eOua0AAABw4xg2bJjWr1+vEiVKqHjx4vrjjz+0ePFiLVq0SLfccos2b97s1ptrnDr1Ex4ernnz5qlt27YyxuiPP/6wH2H5u8jISLcVBwAArCUmJkaJiYlasmSJ/P391blzZ/n5+SkxMVEHDx5UxYoV7b/74w42Y4y5Wqfp06fr6aef1sWLF/PsY4yRzWZTZmamy0VMmTJFY8eOVXJysmrXrq1Jkybptttuu+rrUlNTFRISop6fnZZfULDLywUAAHl77x7PzDf7+/v06dMKDr7y97dTR1T69Omjbt26af/+/apVq5ZWrlypm266yS3FfvbZZxo4cKDeffddNWjQQG+//bZatWqlnTt3qlSpUm5ZBgAAcI/Vq1crIyNDbdq0yXV6SkqKsrKyVKJECbcsz+nbk4sVK6YaNWpoxowZaty4sWrXrp3rw1UTJkxQ79699cgjj6hatWp69913FRQUpA8++MDleQEAAM968cUXdeTIkTynf/bZZ+revbvblufyOCrx8fHKyMjQf/7zHw0dOlQnT56UdGkAmEOHDrk0r/Pnz+uXX35RXFzc/wry8lJcXJzWr1+fo/+5c+eUmprq8AAAANdPYmKi6tevb39+9OhRPfjgg/bn9evX15YtW9y2PJeDypYtW1S5cmWNGTNG48aNU0pKiiRp/vz5Gjp0qEvzOn78uDIzM1W6dGmH9tKlSys5OTlH/9dff10hISH2R/Y93AAA4Po4d+6cwzgpKSkpWrhwof15WFiYTpw44bbluRxUnn32WfXs2VO7d+9WQECAvb1t27b69ttv3VZYboYOHarTp0/bHwcPHvTo8gAAgKOIiAht27bN/nzXrl3KyMiwj0a7Z88et15j6vLItBs2bND06dNztJcrVy7XoyBXUrJkSXl7e+c413XkyBGVKVMmR39/f3/5+/u7VjAAAHCbe++9V4MHD1axYsXk7e2toUOHqkOHDurevbv+9a9/aerUqerYsaPblufyERV/f/9crw3ZtWuXw9j/zvDz81O9evWUkJBgb8vKylJCQoIaNmzoamkAAMDDhgwZopIlS+rOO+/UHXfcoaZNm+rzzz9XZGSkxo0bp3r16um1115z2/JcPqLSoUMHjRo1Sp9//rkkyWaz6cCBAxoyZIjuvfdelwsYOHCg4uPjdeutt+q2227T22+/rfT0dD3yyCMuzwsAAHhWcHCw1qxZowMHDsjf399+nemHH37okeW5HFTGjx+v++67T6VKlVJGRoaaNWum5ORkNWzYUK+++qrLBXTp0kXHjh3TsGHDlJycrFtuuUXLli3LcYEtAACwjus1Er1TI9PmZu3atdqyZYvOnDmjunXrOtxifL0wMi0AAJ6T18i06enpmjNnjtatW6fDhw/Ly8tLUVFRat++vVq3bn3V+bp9ZNrcNGnSRE2aNMnvywEAwA1o165duvvuu3XixAmFh4fr0KFDysrKUmZmph588EHdcccdmjt3rvz8/NyyPJcups3KytIHH3yge+65RzVq1FDNmjXVoUMHzZo1S/k8MAMAAG4gTz/9tG6//XYdPXpUu3bt0pEjR3T33XerXr16+v3333Xs2DGNHDnSbctzOqgYY9ShQwf16tVLhw4dUs2aNVW9enXt379fPXv2VOfOnd1WFAAAsKZ169ZpxIgRCgoKkiQVLVpUw4YN0+zZsxUaGqpXX31VH3/8sduW5/Spn5kzZ+rbb79VQkKC7rzzTodp33zzjTp16qRZs2apR48ebisOAABYy0033aRjx46patWq9rbjx4+rSJEikqTo6GiXx1W7EqePqHzyySf697//nSOkSFKLFi30wgsvuDVBAQAA6+nSpYt69+6thQsXaseOHVqwYIH69u2rDh06SJIOHz6s8PBwty3P6SMqW7Zs0Ztvvpnn9DZt2mjixIluKQoAAFjT6NGjlZqaqvvuu09ZWVmy2WyKj4/X6NGjJV26nnXYsGFuW57Ttyf7+flp//79Klu2bK7T//zzT1WqVEnnzp1zW3FXw+3JAAB4Tl63J0uXfpzwjz/+UPny5V3+eRtXbk92+tRPZmamfHzyPgDj7e2tixcvOl8lAAC4oZ05c0YXLlzw6DKcPvVjjFHPnj3zTE3X80gKAAAoWHv37lXdunVVoUIFzZo1y2NjqzkdVOLj46/ahzt+AAAoPLy9vTVt2jQ9+OCDeuSRRzR8+HB5ebn8e8dX5HRQmTFjhlsXDAAAbmzGGLVq1UqbN29Wr1691LRpU3300UeqWLGi25bh3tgDAAAKneLFi2vevHl67LHHdMcdd+jTTz9127zz/Vs/AACg8HnkkUckSSkpKcrKyrI/zxYdHa0HH3xQXbt2dcvyCCoAAMBpp0+flnTpjp/Ln2crUaKEOnXq5LblEVQAAIDT5s+fL0lKTExU7dq17c89hWtUAACAy2JiYrRz506PL4egAgAAXObj46NKlSp5fDkEFQAAYFkEFQAAYFkEFQAAYFkEFQAAYFkEFQAAYFkEFQAAYFkEFQAAYFkEFQAAYFkEFQAAYFkEFQAAYFkEFQAAYFkEFQAAYFkEFQAAYFkEFQAAYFkEFQAAYFkEFQAAYFkEFQAAYFkEFQAAYFkEFQAAYFkEFQAAYFkEFQAAYFkEFQAAYFkEFQAAYFk+BV2AO7zTWgoOLugqAACAu3FEBQAAWBZBBQAAWBZBBQAAWBZBBQAAWBZBBQAAWBZBBQAAWBZBBQAAWBZBBQAAWBZBBQAAWBZBBQAAWBZBBQAAWBZBBQAAWBZBBQAAWBZBBQAAWBZBBQAAWBZBBQAAWBZBBQAAWBZBBQAAWBZBBQAAWBZBBQAAWBZBBQAAWBZBBQAAWBZBBQAAWBZBBQAAWBZBBQAAWBZBBQAAWBZBBQAAWBZBBQAAWBZBBQAAWBZBBQAAWBZBBQAAWBZBBQAAWBZBBQAAWBZBBQAAWBZBBQAAWBZBBQAAWBZBBQAAWBZBBQAAWBZBBQAAWBZBBQAAWBZBBQAAWBZBBQAAWBZBBQAAWBZBBQAAWJZPQRdwLYwxkqTU1NQCrgQAADgr+3s7+3v8Sm7ooHLixAlJUkRERAFXAgAAXJWWlqaQkJAr9rmhg0qJEiUkSQcOHLjqiuL6SE1NVUREhA4ePKjg4OCCLgdin1gR+8Sa2C/XjzFGaWlpCg8Pv2rfGzqoeHldusQmJCSEN5XFBAcHs08shn1iPewTa2K/XB/OHmDgYloAAGBZBBUAAGBZN3RQ8ff31/Dhw+Xv71/QpeD/sE+sh31iPewTa2K/WJPNOHNvEAAAQAG4oY+oAACAfzaCCgAAsCyCCgAAsCyCCgAAsKwbOqhMmTJFFStWVEBAgBo0aKCffvqpoEv6RxgxYoRsNpvDIzY21j79r7/+Ur9+/XTTTTepaNGiuvfee3XkyBGHeRw4cEDt2rVTUFCQSpUqpeeff14XL1506LN69WrVrVtX/v7+uvnmmzVz5szrsXo3hG+//Vbt27dXeHi4bDabvvzyS4fpxhgNGzZMZcuWVWBgoOLi4rR7926HPidPnlT37t0VHBys0NBQPfbYYzpz5oxDny1btuiOO+5QQECAIiIi9Oabb+aoZe7cuYqNjVVAQIBq1qypJUuWuH19bwRX2yc9e/bM8f+mdevWDn3YJ+71+uuvq379+ipWrJhKlSqlTp06aefOnQ59rufnFd9JHmJuUJ9++qnx8/MzH3zwgfntt99M7969TWhoqDly5EhBl3bDGz58uKlevbo5fPiw/XHs2DH79CeeeMJERESYhIQEs2HDBnP77bebRo0a2adfvHjR1KhRw8TFxZmNGzeaJUuWmJIlS5qhQ4fa+/z+++8mKCjIDBw40Gzfvt1MmjTJeHt7m2XLll3XdbWqJUuWmBdffNHMnz/fSDILFixwmP7GG2+YkJAQ8+WXX5rNmzebDh06mEqVKpmMjAx7n9atW5vatWubH374wXz33Xfm5ptvNt26dbNPP336tCldurTp3r272bZtm/nkk09MYGCgee+99+x91q1bZ7y9vc2bb75ptm/fbl566SXj6+trtm7d6vFtYDVX2yfx8fGmdevWDv9vTp486dCHfeJerVq1MjNmzDDbtm0zmzZtMm3btjWRkZHmzJkz9j7X6/OK7yTPuWGDym233Wb69etnf56ZmWnCw8PN66+/XoBV/TMMHz7c1K5dO9dpKSkpxtfX18ydO9felpiYaCSZ9evXG2MufaB7eXmZ5ORke59p06aZ4OBgc+7cOWOMMYMHDzbVq1d3mHeXLl1Mq1at3Lw2N76/fylmZWWZMmXKmLFjx9rbUlJSjL+/v/nkk0+MMcZs377dSDI///yzvc/SpUuNzWYzhw4dMsYYM3XqVFO8eHH7PjHGmCFDhpgqVarYnz/wwAOmXbt2DvU0aNDAPP74425dxxtNXkGlY8eOeb6GfeJ5R48eNZLMmjVrjDHX9/OK7yTPuSFP/Zw/f16//PKL4uLi7G1eXl6Ki4vT+vXrC7Cyf47du3crPDxcUVFR6t69uw4cOCBJ+uWXX3ThwgWHbR8bG6vIyEj7tl+/fr1q1qyp0qVL2/u0atVKqamp+u233+x9Lp9Hdh/239UlJSUpOTnZYfuFhISoQYMGDvsgNDRUt956q71PXFycvLy89OOPP9r7NG3aVH5+fvY+rVq10s6dO3Xq1Cl7H/aT81avXq1SpUqpSpUq6tu3r/0X3iX2yfVw+vRpSf/7wdrr9XnFd5Jn3ZBB5fjx48rMzHR4Y0lS6dKllZycXEBV/XM0aNBAM2fO1LJlyzRt2jQlJSXpjjvuUFpampKTk+Xn56fQ0FCH11y+7ZOTk3PdN9nTrtQnNTVVGRkZHlqzf4bsbXil939ycrJKlSrlMN3Hx0clSpRwy37i/1lOrVu31qxZs5SQkKAxY8ZozZo1atOmjTIzMyWxTzwtKytLAwYMUOPGjVWjRg1Jum6fV3wnedYN/evJ8Iw2bdrY/12rVi01aNBAFSpU0Oeff67AwMACrAywrq5du9r/XbNmTdWqVUvR0dFavXq17rrrrgKsrHDo16+ftm3bprVr1xZ0KXCzG/KISsmSJeXt7Z3jyu0jR46oTJkyBVTVP1doaKgqV66sPXv2qEyZMjp//rxSUlIc+ly+7cuUKZPrvsmedqU+wcHBhKGryN6GV3r/lylTRkePHnWYfvHiRZ08edIt+4n/Z1cXFRWlkiVLas+ePZLYJ5701FNPadGiRVq1apXKly9vb79en1d8J3nWDRlU/Pz8VK9ePSUkJNjbsrKylJCQoIYNGxZgZf9MZ86c0d69e1W2bFnVq1dPvr6+Dtt+586dOnDggH3bN2zYUFu3bnX4UF6xYoWCg4NVrVo1e5/L55Hdh/13dZUqVVKZMmUctl9qaqp+/PFHh32QkpKiX375xd7nm2++UVZWlho0aGDv8+233+rChQv2PitWrFCVKlVUvHhxex/2U/788ccfOnHihMqWLSuJfeIJxhg99dRTWrBggb755htVqlTJYfr1+rziO8nDCvpq3vz69NNPjb+/v5k5c6bZvn276dOnjwkNDXW4chv5M2jQILN69WqTlJRk1q1bZ+Li4kzJkiXN0aNHjTGXbveLjIw033zzjdmwYYNp2LChadiwof312bf7tWzZ0mzatMksW7bMhIWF5Xq73/PPP28SExPNlClTuD35MmlpaWbjxo1m48aNRpKZMGGC2bhxo9m/f78x5tLtyaGhoWbhwoVmy5YtpmPHjrnenlynTh3z448/mrVr15qYmBiHW2FTUlJM6dKlzcMPP2y2bdtmPv30UxMUFJTjVlgfHx8zbtw4k5iYaIYPH15ob4W90j5JS0szzz33nFm/fr1JSkoyK1euNHXr1jUxMTHmr7/+ss+DfeJeffv2NSEhIWb16tUOt4WfPXvW3ud6fV7xneQ5N2xQMcaYSZMmmcjISOPn52duu+0288MPPxR0Sf8IXbp0MWXLljV+fn6mXLlypkuXLmbPnj326RkZGebJJ580xYsXN0FBQaZz587m8OHDDvPYt2+fadOmjQkMDDQlS5Y0gwYNMhcuXHDos2rVKnPLLbcYPz8/ExUVZWbMmHE9Vu+GsGrVKiMpxyM+Pt4Yc+kW5ZdfftmULl3a+Pv7m7vuusvs3LnTYR4nTpww3bp1M0WLFjXBwcHmkUceMWlpaQ59Nm/ebJo0aWL8/f1NuXLlzBtvvJGjls8//9xUrlzZ+Pn5merVq5vFixd7bL2t7Er75OzZs6Zly5YmLCzM+Pr6mgoVKpjevXvn+JJin7hXbvtDksNnyfX8vOI7yTNsxhhzvY/iAAAAOOOGvEYFAAAUDgQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAG6XnJysp59+WlFRUfL391dERITat2+f4/dSPM1ms+nLL7+8rssE4F4+BV0AgH+Wffv2qXHjxgoNDdXYsWNVs2ZNXbhwQcuXL1e/fv20Y8eOgi4RwA2EIfQBuFXbtm21ZcsW7dy5U0WKFHGYlpKSotDQUB04cEBPP/20EhIS5OXlpdatW2vSpEkqXbq0JKlnz55KSUlxOBoyYMAAbdq0SatXr5YkNW/eXLVq1VJAQID+85//yM/PT0888YRGjBghSapYsaL2799vf32FChW0b98+T646AA/g1A8Atzl58qSWLVumfv365QgpkhQaGqqsrCx17NhRJ0+e1Jo1a7RixQr9/vvv6tKli8vL+/DDD1WkSBH9+OOPevPNNzVq1CitWLFCkvTzzz9LkmbMmKHDhw/bnwO4sXDqB4Db7NmzR8YYxcbG5tknISFBW7duVVJSkiIiIiRJs2bNUvXq1fXzzz+rfv36Ti+vVq1aGj58uCQpJiZGkydPVkJCgu6++26FhYVJuhSOypQpcw1rBaAgcUQFgNs4cyY5MTFRERER9pAiSdWqVVNoaKgSExNdWl6tWrUcnpctW1ZHjx51aR4ArI2gAsBtYmJiZLPZrvmCWS8vrxyh58KFCzn6+fr6Ojy32WzKysq6pmUDsBaCCgC3KVGihFq1aqUpU6YoPT09x/SUlBRVrVpVBw8e1MGDB+3t27dvV0pKiqpVqyZJCgsL0+HDhx1eu2nTJpfr8fX1VWZmpsuvA2AdBBUAbjVlyhRlZmbqtttu07x587R7924lJiZq4sSJatiwoeLi4lSzZk11795dv/76q3766Sf16NFDzZo106233ipJatGihTZs2KBZs2Zp9+7dGj58uLZt2+ZyLRUrVlRCQoKSk5N16tQpd68qgOuAoALAraKiovTrr7/qzjvv1KBBg1SjRg3dfffdSkhI0LRp02Sz2bRw4UIVL15cTZs2VVxcnKKiovTZZ5/Z59GqVSu9/PLLGjx4sOrXr6+0tDT16NHD5VrGjx+vFStWKCIiQnXq1HHnagK4ThhHBQAAWBZHVAAAgGURVAAAgGURVAAAgGURVAAAgGURVAAAgGURVAAAgGURVAAAgGURVAAAgGURVAAAgGURVAAAgGURVAAAgGURVAAAgGX9f89kvpfX9nGbAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Identify quantity of target (0 or 1), to validate whether it is balanced or unbalanced data\n",
    "\n",
    "colors = ['#66b3ff','#ff9999']\n",
    "ax = df['default payment next month'].value_counts().plot(kind='barh', color=colors)\n",
    "plt.title('Count of Default Payment (Next Month)')\n",
    "plt.xlabel('Count')\n",
    "plt.ylabel('Default Payment (0: No, 1: Yes)')\n",
    "ax.bar_label(ax.containers[0], label_type='edge', rotation=270)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Out of the 30k rows, it seems that the 23364 of them are considered non-default \"0\" , while 6636 of them are considered as default \"1\".\n",
    "\n",
    "Hence we can deduced that it is an unbalanced data, we need to take it into account, and modify our data set accordingly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 4: Handling imbalanced data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 2 ways to deal with imbalanced data, upsampling and downsampling. For this project, we will be using the upsampling method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create majority and minority class\n",
    "majority_class = df[df['default payment next month'] == 0]\n",
    "minority_class = df[df['default payment next month'] == 1]\n",
    "\n",
    "#Upsample the minority class\n",
    "minority_upsampled = resample(minority_class,\n",
    "                              replace=True,  # Sample with replacement\n",
    "                              n_samples=len(majority_class))  # Match number of majority class samples\n",
    "\n",
    "#Create new dataframe with majority class + upsampled minority class\n",
    "df_upsampled = pd.concat([majority_class, minority_upsampled])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df_upsampled['default payment next month'].value_counts().plot(kind='barh', color=colors)\n",
    "plt.title('Count of Default Payment (Next Month)')\n",
    "plt.xlabel('Count')\n",
    "plt.ylabel('Default Payment (0: No, 1: Yes)')\n",
    "ax.bar_label(ax.containers[0], label_type='edge', rotation=270)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have equal amount of '0' and '1' for out target variable, hence it is now a balanced data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 5: Split our data set to training sets and testing sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_upsampled = df_upsampled.drop(columns=['default payment next month'])\n",
    "y_upsampled = df_upsampled['default payment next month']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_upsampled, y_upsampled, test_size=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 6: Create our machine learning model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 7: Train our machine learning model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 8: Make prediction using the testing sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_target = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 9: Evaluate our model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(9346,)\n",
      "(9346,)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predicted</th>\n",
       "      <th>Actual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24793</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20411</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29204</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26332</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7452</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2753</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11305</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21314</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28718</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27653</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9346 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Predicted  Actual\n",
       "24793          1       1\n",
       "20411          0       0\n",
       "29204          1       0\n",
       "26332          1       0\n",
       "7452           1       1\n",
       "...          ...     ...\n",
       "2753           1       1\n",
       "11305          1       1\n",
       "21314          1       1\n",
       "28718          0       0\n",
       "27653          1       1\n",
       "\n",
       "[9346 rows x 2 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(y_test.shape)\n",
    "print(predicted_target.shape)\n",
    "\n",
    "d={'Predicted': predicted_target, 'Actual':y_test}\n",
    "comparison = pd.DataFrame(data = d)\n",
    "comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.80      0.87      4650\n",
      "           1       0.83      0.96      0.89      4696\n",
      "\n",
      "    accuracy                           0.88      9346\n",
      "   macro avg       0.89      0.88      0.88      9346\n",
      "weighted avg       0.89      0.88      0.88      9346\n",
      "\n",
      "[[3740  910]\n",
      " [ 180 4516]]\n"
     ]
    }
   ],
   "source": [
    "classi_report = classification_report(y_test, predicted_target)\n",
    "confus_matrix = confusion_matrix(y_test, predicted_target)\n",
    "\n",
    "print(classi_report)\n",
    "print(confus_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Confusion Matrix\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(confus_matrix, annot=True, fmt='d', cmap='Blues')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Actual')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ROC Curve\n",
    "fpr, tpr, thresholds = roc_curve(y_test, predicted_target) #False Positive Rate and True Positive Rate\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "# Plot\n",
    "plt.plot(fpr, tpr, color='blue', label=f'ROC Curve (AUC = {roc_auc:.2f})')\n",
    "plt.plot([0, 1], [0, 1], color='gray', linestyle='--') \n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver Operating Characteristic (ROC) Curve')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}