creditcarddefault.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import RobustScaler\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn import datasets\n",
    "from io import StringIO\n",
    "from sklearn.tree import export_graphviz\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn import tree\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import precision_score, recall_score, f1_score, roc_auc_score, accuracy_score, classification_report,confusion_matrix\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#load data file\n",
    "data = pd.read_excel(r\"C:\\Users\\Lt col Haider\\OneDrive\\IBA\\semester 4\\Foundations to Data Science\\project\\default of credit card clients.xls\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30000, 25)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ID is unique variable so drop it\n",
    "data =  data.drop(columns = ['ID'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#rename target column for ease \n",
    "data = data.rename(columns={'default payment next month': 'Default'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#categorical variables have already been dummified"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Default</th>\n",
       "      <th>PAY_AMT6</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_AMT3</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>LIMIT_BAL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Percent</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Default  PAY_AMT6  SEX  EDUCATION  MARRIAGE  AGE  PAY_0  PAY_2  \\\n",
       "Total        0.0       0.0  0.0        0.0       0.0  0.0    0.0    0.0   \n",
       "Percent      0.0       0.0  0.0        0.0       0.0  0.0    0.0    0.0   \n",
       "\n",
       "         PAY_3  PAY_4  ...  BILL_AMT3  BILL_AMT4  BILL_AMT5  BILL_AMT6  \\\n",
       "Total      0.0    0.0  ...        0.0        0.0        0.0        0.0   \n",
       "Percent    0.0    0.0  ...        0.0        0.0        0.0        0.0   \n",
       "\n",
       "         PAY_AMT1  PAY_AMT2  PAY_AMT3  PAY_AMT4  PAY_AMT5  LIMIT_BAL  \n",
       "Total         0.0       0.0       0.0       0.0       0.0        0.0  \n",
       "Percent       0.0       0.0       0.0       0.0       0.0        0.0  \n",
       "\n",
       "[2 rows x 24 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for missing values \n",
    "total = data.isnull().sum().sort_values(ascending = False)\n",
    "percent = (data.isnull().sum()/data.isnull().count()*100).sort_values(ascending = False)\n",
    "pd.concat([total, percent], axis=1, keys=['Total', 'Percent']).transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No missing data found."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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_0</th>\n",
       "      <th>PAY_2</th>\n",
       "      <th>PAY_3</th>\n",
       "      <th>PAY_4</th>\n",
       "      <th>PAY_5</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</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>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>-0.266200</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>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>1.133187</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>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>-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>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>-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>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>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>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>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>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>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 × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            LIMIT_BAL           SEX     EDUCATION      MARRIAGE           AGE  \\\n",
       "count    30000.000000  30000.000000  30000.000000  30000.000000  30000.000000   \n",
       "mean    167484.322667      1.603733      1.853133      1.551867     35.485500   \n",
       "std     129747.661567      0.489129      0.790349      0.521970      9.217904   \n",
       "min      10000.000000      1.000000      0.000000      0.000000     21.000000   \n",
       "25%      50000.000000      1.000000      1.000000      1.000000     28.000000   \n",
       "50%     140000.000000      2.000000      2.000000      2.000000     34.000000   \n",
       "75%     240000.000000      2.000000      2.000000      2.000000     41.000000   \n",
       "max    1000000.000000      2.000000      6.000000      3.000000     79.000000   \n",
       "\n",
       "              PAY_0         PAY_2         PAY_3         PAY_4         PAY_5  \\\n",
       "count  30000.000000  30000.000000  30000.000000  30000.000000  30000.000000   \n",
       "mean      -0.016700     -0.133767     -0.166200     -0.220667     -0.266200   \n",
       "std        1.123802      1.197186      1.196868      1.169139      1.133187   \n",
       "min       -2.000000     -2.000000     -2.000000     -2.000000     -2.000000   \n",
       "25%       -1.000000     -1.000000     -1.000000     -1.000000     -1.000000   \n",
       "50%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "75%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "max        8.000000      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  \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 24 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp = data[\"Default\"].value_counts()\n",
    "df = pd.DataFrame({'Default': temp.index,'values': temp.values})\n",
    "plt.figure(figsize = (6,6))\n",
    "plt.title('Default Credit Card Clients - target value - data unbalance\\n (Not Default = 0, Default = 1)')\n",
    "sns.set_color_codes(\"pastel\")\n",
    "sns.barplot(x = 'Default', y=\"values\", data=df)\n",
    "locs, labels = plt.xticks()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "data is imbalanced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "numerical = ['LIMIT_BAL','AGE','PAY_0','PAY_2','PAY_3','PAY_4','PAY_5','PAY_6','BILL_AMT1','BILL_AMT2','BILL_AMT3',\n",
    "             'BILL_AMT4','BILL_AMT5','BILL_AMT6','PAY_AMT1','PAY_AMT2','PAY_AMT3','PAY_AMT4','PAY_AMT5','PAY_AMT6']\n",
    "categorical = ['SEX','EDUCATION','MARRIAGE']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#T Test for numerical columns\n",
    "p=[]\n",
    "from scipy.stats import ttest_ind\n",
    "\n",
    "for i in numerical:\n",
    "    df1=data.groupby('Default').get_group(0)\n",
    "    df2=data.groupby('Default').get_group(1)\n",
    "    t,pvalue=ttest_ind(df1[i],df2[i])\n",
    "    p.append(1-pvalue)\n",
    "plt.figure(figsize=(7,7))\n",
    "sns.barplot(x=p, y=numerical)\n",
    "plt.title('Best Numerical Features')\n",
    "plt.axvline(x=(1-0.05),color='r')\n",
    "plt.xlabel('1-p value')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All features show importance, and almost all of them show statistical significance so won't drop any."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Chi Square test for Categorical Columns\n",
    "from scipy.stats import chi2_contingency\n",
    "l=[]\n",
    "for i in categorical:\n",
    "    pvalue  = chi2_contingency(pd.crosstab(data['Default'],data[i]))[1]\n",
    "    l.append(1-pvalue)\n",
    "plt.figure(figsize=(7,7))\n",
    "sns.barplot(x=l, y=categorical)\n",
    "plt.title('Best Categorical Features')\n",
    "plt.axvline(x=(1-0.05),color='r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All categorical features show statistical significance so won't drop any."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop target variable: 'Default'\n",
    "features =  data.drop(columns = ['Default'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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_0</th>\n",
       "      <th>PAY_2</th>\n",
       "      <th>PAY_3</th>\n",
       "      <th>PAY_4</th>\n",
       "      <th>PAY_5</th>\n",
       "      <th>...</th>\n",
       "      <th>BILL_AMT3</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LIMIT_BAL</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.024755</td>\n",
       "      <td>0.219161</td>\n",
       "      <td>0.108139</td>\n",
       "      <td>0.144713</td>\n",
       "      <td>0.271214</td>\n",
       "      <td>0.296382</td>\n",
       "      <td>0.286123</td>\n",
       "      <td>0.267460</td>\n",
       "      <td>0.249411</td>\n",
       "      <td>...</td>\n",
       "      <td>0.283236</td>\n",
       "      <td>0.293988</td>\n",
       "      <td>0.295562</td>\n",
       "      <td>0.290389</td>\n",
       "      <td>0.195236</td>\n",
       "      <td>0.178408</td>\n",
       "      <td>0.210167</td>\n",
       "      <td>0.203242</td>\n",
       "      <td>0.217202</td>\n",
       "      <td>0.219595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEX</th>\n",
       "      <td>0.024755</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.014232</td>\n",
       "      <td>0.031389</td>\n",
       "      <td>0.090874</td>\n",
       "      <td>0.057643</td>\n",
       "      <td>0.070771</td>\n",
       "      <td>0.066096</td>\n",
       "      <td>0.060173</td>\n",
       "      <td>0.055064</td>\n",
       "      <td>...</td>\n",
       "      <td>0.024563</td>\n",
       "      <td>0.021880</td>\n",
       "      <td>0.017005</td>\n",
       "      <td>0.016733</td>\n",
       "      <td>0.000242</td>\n",
       "      <td>0.001391</td>\n",
       "      <td>0.008597</td>\n",
       "      <td>0.002229</td>\n",
       "      <td>0.001667</td>\n",
       "      <td>0.002766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDUCATION</th>\n",
       "      <td>0.219161</td>\n",
       "      <td>0.014232</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.143464</td>\n",
       "      <td>0.175061</td>\n",
       "      <td>0.105364</td>\n",
       "      <td>0.121566</td>\n",
       "      <td>0.114025</td>\n",
       "      <td>0.108793</td>\n",
       "      <td>0.097520</td>\n",
       "      <td>...</td>\n",
       "      <td>0.013002</td>\n",
       "      <td>0.000451</td>\n",
       "      <td>0.007567</td>\n",
       "      <td>0.009099</td>\n",
       "      <td>0.037456</td>\n",
       "      <td>0.030038</td>\n",
       "      <td>0.039943</td>\n",
       "      <td>0.038218</td>\n",
       "      <td>0.040358</td>\n",
       "      <td>0.037200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MARRIAGE</th>\n",
       "      <td>0.108139</td>\n",
       "      <td>0.031389</td>\n",
       "      <td>0.143464</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.414170</td>\n",
       "      <td>0.019917</td>\n",
       "      <td>0.024199</td>\n",
       "      <td>0.032688</td>\n",
       "      <td>0.033122</td>\n",
       "      <td>0.035629</td>\n",
       "      <td>...</td>\n",
       "      <td>0.024909</td>\n",
       "      <td>0.023344</td>\n",
       "      <td>0.025393</td>\n",
       "      <td>0.021207</td>\n",
       "      <td>0.005979</td>\n",
       "      <td>0.008093</td>\n",
       "      <td>0.003541</td>\n",
       "      <td>0.012659</td>\n",
       "      <td>0.001205</td>\n",
       "      <td>0.006641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGE</th>\n",
       "      <td>0.144713</td>\n",
       "      <td>0.090874</td>\n",
       "      <td>0.175061</td>\n",
       "      <td>0.414170</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.039447</td>\n",
       "      <td>0.050148</td>\n",
       "      <td>0.053048</td>\n",
       "      <td>0.049722</td>\n",
       "      <td>0.053826</td>\n",
       "      <td>...</td>\n",
       "      <td>0.053710</td>\n",
       "      <td>0.051353</td>\n",
       "      <td>0.049345</td>\n",
       "      <td>0.047613</td>\n",
       "      <td>0.026147</td>\n",
       "      <td>0.021785</td>\n",
       "      <td>0.029247</td>\n",
       "      <td>0.021379</td>\n",
       "      <td>0.022850</td>\n",
       "      <td>0.019478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAY_0</th>\n",
       "      <td>0.271214</td>\n",
       "      <td>0.057643</td>\n",
       "      <td>0.105364</td>\n",
       "      <td>0.019917</td>\n",
       "      <td>0.039447</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.672164</td>\n",
       "      <td>0.574245</td>\n",
       "      <td>0.538841</td>\n",
       "      <td>0.509426</td>\n",
       "      <td>...</td>\n",
       "      <td>0.179785</td>\n",
       "      <td>0.179125</td>\n",
       "      <td>0.180635</td>\n",
       "      <td>0.176980</td>\n",
       "      <td>0.079269</td>\n",
       "      <td>0.070101</td>\n",
       "      <td>0.070561</td>\n",
       "      <td>0.064005</td>\n",
       "      <td>0.058190</td>\n",
       "      <td>0.058673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAY_2</th>\n",
       "      <td>0.296382</td>\n",
       "      <td>0.070771</td>\n",
       "      <td>0.121566</td>\n",
       "      <td>0.024199</td>\n",
       "      <td>0.050148</td>\n",
       "      <td>0.672164</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.766552</td>\n",
       "      <td>0.662067</td>\n",
       "      <td>0.622780</td>\n",
       "      <td>...</td>\n",
       "      <td>0.224146</td>\n",
       "      <td>0.222237</td>\n",
       "      <td>0.221348</td>\n",
       "      <td>0.219403</td>\n",
       "      <td>0.080701</td>\n",
       "      <td>0.058990</td>\n",
       "      <td>0.055901</td>\n",
       "      <td>0.046858</td>\n",
       "      <td>0.037093</td>\n",
       "      <td>0.036500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAY_3</th>\n",
       "      <td>0.286123</td>\n",
       "      <td>0.066096</td>\n",
       "      <td>0.114025</td>\n",
       "      <td>0.032688</td>\n",
       "      <td>0.053048</td>\n",
       "      <td>0.574245</td>\n",
       "      <td>0.766552</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.777359</td>\n",
       "      <td>0.686775</td>\n",
       "      <td>...</td>\n",
       "      <td>0.227494</td>\n",
       "      <td>0.227202</td>\n",
       "      <td>0.225145</td>\n",
       "      <td>0.222327</td>\n",
       "      <td>0.001295</td>\n",
       "      <td>0.066793</td>\n",
       "      <td>0.053311</td>\n",
       "      <td>0.046067</td>\n",
       "      <td>0.035863</td>\n",
       "      <td>0.035861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAY_4</th>\n",
       "      <td>0.267460</td>\n",
       "      <td>0.060173</td>\n",
       "      <td>0.108793</td>\n",
       "      <td>0.033122</td>\n",
       "      <td>0.049722</td>\n",
       "      <td>0.538841</td>\n",
       "      <td>0.662067</td>\n",
       "      <td>0.777359</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.819835</td>\n",
       "      <td>...</td>\n",
       "      <td>0.244983</td>\n",
       "      <td>0.245917</td>\n",
       "      <td>0.242902</td>\n",
       "      <td>0.239154</td>\n",
       "      <td>0.009362</td>\n",
       "      <td>0.001944</td>\n",
       "      <td>0.069235</td>\n",
       "      <td>0.043461</td>\n",
       "      <td>0.033590</td>\n",
       "      <td>0.026565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAY_5</th>\n",
       "      <td>0.249411</td>\n",
       "      <td>0.055064</td>\n",
       "      <td>0.097520</td>\n",
       "      <td>0.035629</td>\n",
       "      <td>0.053826</td>\n",
       "      <td>0.509426</td>\n",
       "      <td>0.622780</td>\n",
       "      <td>0.686775</td>\n",
       "      <td>0.819835</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.243335</td>\n",
       "      <td>0.271915</td>\n",
       "      <td>0.269783</td>\n",
       "      <td>0.262509</td>\n",
       "      <td>0.006089</td>\n",
       "      <td>0.003191</td>\n",
       "      <td>0.009062</td>\n",
       "      <td>0.058299</td>\n",
       "      <td>0.033337</td>\n",
       "      <td>0.023027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAY_6</th>\n",
       "      <td>0.235195</td>\n",
       "      <td>0.044008</td>\n",
       "      <td>0.082316</td>\n",
       "      <td>0.034345</td>\n",
       "      <td>0.048773</td>\n",
       "      <td>0.474553</td>\n",
       "      <td>0.575501</td>\n",
       "      <td>0.632684</td>\n",
       "      <td>0.716449</td>\n",
       "      <td>0.816900</td>\n",
       "      <td>...</td>\n",
       "      <td>0.241181</td>\n",
       "      <td>0.266356</td>\n",
       "      <td>0.290894</td>\n",
       "      <td>0.285091</td>\n",
       "      <td>0.001496</td>\n",
       "      <td>0.005223</td>\n",
       "      <td>0.005834</td>\n",
       "      <td>0.019018</td>\n",
       "      <td>0.046434</td>\n",
       "      <td>0.025299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BILL_AMT1</th>\n",
       "      <td>0.285430</td>\n",
       "      <td>0.033642</td>\n",
       "      <td>0.023581</td>\n",
       "      <td>0.023472</td>\n",
       "      <td>0.056239</td>\n",
       "      <td>0.187068</td>\n",
       "      <td>0.234887</td>\n",
       "      <td>0.208473</td>\n",
       "      <td>0.202812</td>\n",
       "      <td>0.206684</td>\n",
       "      <td>...</td>\n",
       "      <td>0.892279</td>\n",
       "      <td>0.860272</td>\n",
       "      <td>0.829779</td>\n",
       "      <td>0.802650</td>\n",
       "      <td>0.140277</td>\n",
       "      <td>0.099355</td>\n",
       "      <td>0.156887</td>\n",
       "      <td>0.158303</td>\n",
       "      <td>0.167026</td>\n",
       "      <td>0.179341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BILL_AMT2</th>\n",
       "      <td>0.278314</td>\n",
       "      <td>0.031183</td>\n",
       "      <td>0.018749</td>\n",
       "      <td>0.021602</td>\n",
       "      <td>0.054283</td>\n",
       "      <td>0.189859</td>\n",
       "      <td>0.235257</td>\n",
       "      <td>0.237295</td>\n",
       "      <td>0.225816</td>\n",
       "      <td>0.226913</td>\n",
       "      <td>...</td>\n",
       "      <td>0.928326</td>\n",
       "      <td>0.892482</td>\n",
       "      <td>0.859778</td>\n",
       "      <td>0.831594</td>\n",
       "      <td>0.280365</td>\n",
       "      <td>0.100851</td>\n",
       "      <td>0.150718</td>\n",
       "      <td>0.147398</td>\n",
       "      <td>0.157957</td>\n",
       "      <td>0.174256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BILL_AMT3</th>\n",
       "      <td>0.283236</td>\n",
       "      <td>0.024563</td>\n",
       "      <td>0.013002</td>\n",
       "      <td>0.024909</td>\n",
       "      <td>0.053710</td>\n",
       "      <td>0.179785</td>\n",
       "      <td>0.224146</td>\n",
       "      <td>0.227494</td>\n",
       "      <td>0.244983</td>\n",
       "      <td>0.243335</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.923969</td>\n",
       "      <td>0.883910</td>\n",
       "      <td>0.853320</td>\n",
       "      <td>0.244335</td>\n",
       "      <td>0.316936</td>\n",
       "      <td>0.130011</td>\n",
       "      <td>0.143405</td>\n",
       "      <td>0.179712</td>\n",
       "      <td>0.182326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BILL_AMT4</th>\n",
       "      <td>0.293988</td>\n",
       "      <td>0.021880</td>\n",
       "      <td>0.000451</td>\n",
       "      <td>0.023344</td>\n",
       "      <td>0.051353</td>\n",
       "      <td>0.179125</td>\n",
       "      <td>0.222237</td>\n",
       "      <td>0.227202</td>\n",
       "      <td>0.245917</td>\n",
       "      <td>0.271915</td>\n",
       "      <td>...</td>\n",
       "      <td>0.923969</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.940134</td>\n",
       "      <td>0.900941</td>\n",
       "      <td>0.233012</td>\n",
       "      <td>0.207564</td>\n",
       "      <td>0.300023</td>\n",
       "      <td>0.130191</td>\n",
       "      <td>0.160433</td>\n",
       "      <td>0.177637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BILL_AMT5</th>\n",
       "      <td>0.295562</td>\n",
       "      <td>0.017005</td>\n",
       "      <td>0.007567</td>\n",
       "      <td>0.025393</td>\n",
       "      <td>0.049345</td>\n",
       "      <td>0.180635</td>\n",
       "      <td>0.221348</td>\n",
       "      <td>0.225145</td>\n",
       "      <td>0.242902</td>\n",
       "      <td>0.269783</td>\n",
       "      <td>...</td>\n",
       "      <td>0.883910</td>\n",
       "      <td>0.940134</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.946197</td>\n",
       "      <td>0.217031</td>\n",
       "      <td>0.181246</td>\n",
       "      <td>0.252305</td>\n",
       "      <td>0.293118</td>\n",
       "      <td>0.141574</td>\n",
       "      <td>0.164184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BILL_AMT6</th>\n",
       "      <td>0.290389</td>\n",
       "      <td>0.016733</td>\n",
       "      <td>0.009099</td>\n",
       "      <td>0.021207</td>\n",
       "      <td>0.047613</td>\n",
       "      <td>0.176980</td>\n",
       "      <td>0.219403</td>\n",
       "      <td>0.222327</td>\n",
       "      <td>0.239154</td>\n",
       "      <td>0.262509</td>\n",
       "      <td>...</td>\n",
       "      <td>0.853320</td>\n",
       "      <td>0.900941</td>\n",
       "      <td>0.946197</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.199965</td>\n",
       "      <td>0.172663</td>\n",
       "      <td>0.233770</td>\n",
       "      <td>0.250237</td>\n",
       "      <td>0.307729</td>\n",
       "      <td>0.115494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAY_AMT1</th>\n",
       "      <td>0.195236</td>\n",
       "      <td>0.000242</td>\n",
       "      <td>0.037456</td>\n",
       "      <td>0.005979</td>\n",
       "      <td>0.026147</td>\n",
       "      <td>0.079269</td>\n",
       "      <td>0.080701</td>\n",
       "      <td>0.001295</td>\n",
       "      <td>0.009362</td>\n",
       "      <td>0.006089</td>\n",
       "      <td>...</td>\n",
       "      <td>0.244335</td>\n",
       "      <td>0.233012</td>\n",
       "      <td>0.217031</td>\n",
       "      <td>0.199965</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.285576</td>\n",
       "      <td>0.252191</td>\n",
       "      <td>0.199558</td>\n",
       "      <td>0.148459</td>\n",
       "      <td>0.185735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAY_AMT2</th>\n",
       "      <td>0.178408</td>\n",
       "      <td>0.001391</td>\n",
       "      <td>0.030038</td>\n",
       "      <td>0.008093</td>\n",
       "      <td>0.021785</td>\n",
       "      <td>0.070101</td>\n",
       "      <td>0.058990</td>\n",
       "      <td>0.066793</td>\n",
       "      <td>0.001944</td>\n",
       "      <td>0.003191</td>\n",
       "      <td>...</td>\n",
       "      <td>0.316936</td>\n",
       "      <td>0.207564</td>\n",
       "      <td>0.181246</td>\n",
       "      <td>0.172663</td>\n",
       "      <td>0.285576</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.244770</td>\n",
       "      <td>0.180107</td>\n",
       "      <td>0.180908</td>\n",
       "      <td>0.157634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAY_AMT3</th>\n",
       "      <td>0.210167</td>\n",
       "      <td>0.008597</td>\n",
       "      <td>0.039943</td>\n",
       "      <td>0.003541</td>\n",
       "      <td>0.029247</td>\n",
       "      <td>0.070561</td>\n",
       "      <td>0.055901</td>\n",
       "      <td>0.053311</td>\n",
       "      <td>0.069235</td>\n",
       "      <td>0.009062</td>\n",
       "      <td>...</td>\n",
       "      <td>0.130011</td>\n",
       "      <td>0.300023</td>\n",
       "      <td>0.252305</td>\n",
       "      <td>0.233770</td>\n",
       "      <td>0.252191</td>\n",
       "      <td>0.244770</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.216325</td>\n",
       "      <td>0.159214</td>\n",
       "      <td>0.162740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAY_AMT4</th>\n",
       "      <td>0.203242</td>\n",
       "      <td>0.002229</td>\n",
       "      <td>0.038218</td>\n",
       "      <td>0.012659</td>\n",
       "      <td>0.021379</td>\n",
       "      <td>0.064005</td>\n",
       "      <td>0.046858</td>\n",
       "      <td>0.046067</td>\n",
       "      <td>0.043461</td>\n",
       "      <td>0.058299</td>\n",
       "      <td>...</td>\n",
       "      <td>0.143405</td>\n",
       "      <td>0.130191</td>\n",
       "      <td>0.293118</td>\n",
       "      <td>0.250237</td>\n",
       "      <td>0.199558</td>\n",
       "      <td>0.180107</td>\n",
       "      <td>0.216325</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.151830</td>\n",
       "      <td>0.157834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAY_AMT5</th>\n",
       "      <td>0.217202</td>\n",
       "      <td>0.001667</td>\n",
       "      <td>0.040358</td>\n",
       "      <td>0.001205</td>\n",
       "      <td>0.022850</td>\n",
       "      <td>0.058190</td>\n",
       "      <td>0.037093</td>\n",
       "      <td>0.035863</td>\n",
       "      <td>0.033590</td>\n",
       "      <td>0.033337</td>\n",
       "      <td>...</td>\n",
       "      <td>0.179712</td>\n",
       "      <td>0.160433</td>\n",
       "      <td>0.141574</td>\n",
       "      <td>0.307729</td>\n",
       "      <td>0.148459</td>\n",
       "      <td>0.180908</td>\n",
       "      <td>0.159214</td>\n",
       "      <td>0.151830</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.154896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAY_AMT6</th>\n",
       "      <td>0.219595</td>\n",
       "      <td>0.002766</td>\n",
       "      <td>0.037200</td>\n",
       "      <td>0.006641</td>\n",
       "      <td>0.019478</td>\n",
       "      <td>0.058673</td>\n",
       "      <td>0.036500</td>\n",
       "      <td>0.035861</td>\n",
       "      <td>0.026565</td>\n",
       "      <td>0.023027</td>\n",
       "      <td>...</td>\n",
       "      <td>0.182326</td>\n",
       "      <td>0.177637</td>\n",
       "      <td>0.164184</td>\n",
       "      <td>0.115494</td>\n",
       "      <td>0.185735</td>\n",
       "      <td>0.157634</td>\n",
       "      <td>0.162740</td>\n",
       "      <td>0.157834</td>\n",
       "      <td>0.154896</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           LIMIT_BAL       SEX  EDUCATION  MARRIAGE       AGE     PAY_0  \\\n",
       "LIMIT_BAL   1.000000  0.024755   0.219161  0.108139  0.144713  0.271214   \n",
       "SEX         0.024755  1.000000   0.014232  0.031389  0.090874  0.057643   \n",
       "EDUCATION   0.219161  0.014232   1.000000  0.143464  0.175061  0.105364   \n",
       "MARRIAGE    0.108139  0.031389   0.143464  1.000000  0.414170  0.019917   \n",
       "AGE         0.144713  0.090874   0.175061  0.414170  1.000000  0.039447   \n",
       "PAY_0       0.271214  0.057643   0.105364  0.019917  0.039447  1.000000   \n",
       "PAY_2       0.296382  0.070771   0.121566  0.024199  0.050148  0.672164   \n",
       "PAY_3       0.286123  0.066096   0.114025  0.032688  0.053048  0.574245   \n",
       "PAY_4       0.267460  0.060173   0.108793  0.033122  0.049722  0.538841   \n",
       "PAY_5       0.249411  0.055064   0.097520  0.035629  0.053826  0.509426   \n",
       "PAY_6       0.235195  0.044008   0.082316  0.034345  0.048773  0.474553   \n",
       "BILL_AMT1   0.285430  0.033642   0.023581  0.023472  0.056239  0.187068   \n",
       "BILL_AMT2   0.278314  0.031183   0.018749  0.021602  0.054283  0.189859   \n",
       "BILL_AMT3   0.283236  0.024563   0.013002  0.024909  0.053710  0.179785   \n",
       "BILL_AMT4   0.293988  0.021880   0.000451  0.023344  0.051353  0.179125   \n",
       "BILL_AMT5   0.295562  0.017005   0.007567  0.025393  0.049345  0.180635   \n",
       "BILL_AMT6   0.290389  0.016733   0.009099  0.021207  0.047613  0.176980   \n",
       "PAY_AMT1    0.195236  0.000242   0.037456  0.005979  0.026147  0.079269   \n",
       "PAY_AMT2    0.178408  0.001391   0.030038  0.008093  0.021785  0.070101   \n",
       "PAY_AMT3    0.210167  0.008597   0.039943  0.003541  0.029247  0.070561   \n",
       "PAY_AMT4    0.203242  0.002229   0.038218  0.012659  0.021379  0.064005   \n",
       "PAY_AMT5    0.217202  0.001667   0.040358  0.001205  0.022850  0.058190   \n",
       "PAY_AMT6    0.219595  0.002766   0.037200  0.006641  0.019478  0.058673   \n",
       "\n",
       "              PAY_2     PAY_3     PAY_4     PAY_5  ...  BILL_AMT3  BILL_AMT4  \\\n",
       "LIMIT_BAL  0.296382  0.286123  0.267460  0.249411  ...   0.283236   0.293988   \n",
       "SEX        0.070771  0.066096  0.060173  0.055064  ...   0.024563   0.021880   \n",
       "EDUCATION  0.121566  0.114025  0.108793  0.097520  ...   0.013002   0.000451   \n",
       "MARRIAGE   0.024199  0.032688  0.033122  0.035629  ...   0.024909   0.023344   \n",
       "AGE        0.050148  0.053048  0.049722  0.053826  ...   0.053710   0.051353   \n",
       "PAY_0      0.672164  0.574245  0.538841  0.509426  ...   0.179785   0.179125   \n",
       "PAY_2      1.000000  0.766552  0.662067  0.622780  ...   0.224146   0.222237   \n",
       "PAY_3      0.766552  1.000000  0.777359  0.686775  ...   0.227494   0.227202   \n",
       "PAY_4      0.662067  0.777359  1.000000  0.819835  ...   0.244983   0.245917   \n",
       "PAY_5      0.622780  0.686775  0.819835  1.000000  ...   0.243335   0.271915   \n",
       "PAY_6      0.575501  0.632684  0.716449  0.816900  ...   0.241181   0.266356   \n",
       "BILL_AMT1  0.234887  0.208473  0.202812  0.206684  ...   0.892279   0.860272   \n",
       "BILL_AMT2  0.235257  0.237295  0.225816  0.226913  ...   0.928326   0.892482   \n",
       "BILL_AMT3  0.224146  0.227494  0.244983  0.243335  ...   1.000000   0.923969   \n",
       "BILL_AMT4  0.222237  0.227202  0.245917  0.271915  ...   0.923969   1.000000   \n",
       "BILL_AMT5  0.221348  0.225145  0.242902  0.269783  ...   0.883910   0.940134   \n",
       "BILL_AMT6  0.219403  0.222327  0.239154  0.262509  ...   0.853320   0.900941   \n",
       "PAY_AMT1   0.080701  0.001295  0.009362  0.006089  ...   0.244335   0.233012   \n",
       "PAY_AMT2   0.058990  0.066793  0.001944  0.003191  ...   0.316936   0.207564   \n",
       "PAY_AMT3   0.055901  0.053311  0.069235  0.009062  ...   0.130011   0.300023   \n",
       "PAY_AMT4   0.046858  0.046067  0.043461  0.058299  ...   0.143405   0.130191   \n",
       "PAY_AMT5   0.037093  0.035863  0.033590  0.033337  ...   0.179712   0.160433   \n",
       "PAY_AMT6   0.036500  0.035861  0.026565  0.023027  ...   0.182326   0.177637   \n",
       "\n",
       "           BILL_AMT5  BILL_AMT6  PAY_AMT1  PAY_AMT2  PAY_AMT3  PAY_AMT4  \\\n",
       "LIMIT_BAL   0.295562   0.290389  0.195236  0.178408  0.210167  0.203242   \n",
       "SEX         0.017005   0.016733  0.000242  0.001391  0.008597  0.002229   \n",
       "EDUCATION   0.007567   0.009099  0.037456  0.030038  0.039943  0.038218   \n",
       "MARRIAGE    0.025393   0.021207  0.005979  0.008093  0.003541  0.012659   \n",
       "AGE         0.049345   0.047613  0.026147  0.021785  0.029247  0.021379   \n",
       "PAY_0       0.180635   0.176980  0.079269  0.070101  0.070561  0.064005   \n",
       "PAY_2       0.221348   0.219403  0.080701  0.058990  0.055901  0.046858   \n",
       "PAY_3       0.225145   0.222327  0.001295  0.066793  0.053311  0.046067   \n",
       "PAY_4       0.242902   0.239154  0.009362  0.001944  0.069235  0.043461   \n",
       "PAY_5       0.269783   0.262509  0.006089  0.003191  0.009062  0.058299   \n",
       "PAY_6       0.290894   0.285091  0.001496  0.005223  0.005834  0.019018   \n",
       "BILL_AMT1   0.829779   0.802650  0.140277  0.099355  0.156887  0.158303   \n",
       "BILL_AMT2   0.859778   0.831594  0.280365  0.100851  0.150718  0.147398   \n",
       "BILL_AMT3   0.883910   0.853320  0.244335  0.316936  0.130011  0.143405   \n",
       "BILL_AMT4   0.940134   0.900941  0.233012  0.207564  0.300023  0.130191   \n",
       "BILL_AMT5   1.000000   0.946197  0.217031  0.181246  0.252305  0.293118   \n",
       "BILL_AMT6   0.946197   1.000000  0.199965  0.172663  0.233770  0.250237   \n",
       "PAY_AMT1    0.217031   0.199965  1.000000  0.285576  0.252191  0.199558   \n",
       "PAY_AMT2    0.181246   0.172663  0.285576  1.000000  0.244770  0.180107   \n",
       "PAY_AMT3    0.252305   0.233770  0.252191  0.244770  1.000000  0.216325   \n",
       "PAY_AMT4    0.293118   0.250237  0.199558  0.180107  0.216325  1.000000   \n",
       "PAY_AMT5    0.141574   0.307729  0.148459  0.180908  0.159214  0.151830   \n",
       "PAY_AMT6    0.164184   0.115494  0.185735  0.157634  0.162740  0.157834   \n",
       "\n",
       "           PAY_AMT5  PAY_AMT6  \n",
       "LIMIT_BAL  0.217202  0.219595  \n",
       "SEX        0.001667  0.002766  \n",
       "EDUCATION  0.040358  0.037200  \n",
       "MARRIAGE   0.001205  0.006641  \n",
       "AGE        0.022850  0.019478  \n",
       "PAY_0      0.058190  0.058673  \n",
       "PAY_2      0.037093  0.036500  \n",
       "PAY_3      0.035863  0.035861  \n",
       "PAY_4      0.033590  0.026565  \n",
       "PAY_5      0.033337  0.023027  \n",
       "PAY_6      0.046434  0.025299  \n",
       "BILL_AMT1  0.167026  0.179341  \n",
       "BILL_AMT2  0.157957  0.174256  \n",
       "BILL_AMT3  0.179712  0.182326  \n",
       "BILL_AMT4  0.160433  0.177637  \n",
       "BILL_AMT5  0.141574  0.164184  \n",
       "BILL_AMT6  0.307729  0.115494  \n",
       "PAY_AMT1   0.148459  0.185735  \n",
       "PAY_AMT2   0.180908  0.157634  \n",
       "PAY_AMT3   0.159214  0.162740  \n",
       "PAY_AMT4   0.151830  0.157834  \n",
       "PAY_AMT5   1.000000  0.154896  \n",
       "PAY_AMT6   0.154896  1.000000  \n",
       "\n",
       "[23 rows x 23 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#correlation analysis\n",
    "corr_matrix = features.corr().abs()\n",
    "corr_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Feature Selection \n",
    "\n",
    "#Select upper triangle of correlation matrix\n",
    "upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(np.bool))\n",
    "to_drop = [column for column in upper.columns if any(upper[column] > 0.95)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Drop highly correlated attributes\n",
    "data = data.drop(data[to_drop], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30000, 23)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['LIMIT_BAL', 'SEX', 'EDUCATION', 'MARRIAGE', 'AGE', 'PAY_0', 'PAY_2',\n",
       "       'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6', 'BILL_AMT1', 'BILL_AMT3',\n",
       "       'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1', 'PAY_AMT2',\n",
       "       'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6', 'Default'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26a800a9248>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "data.SEX.value_counts().plot(kind = 'bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>Default</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEX</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9015</td>\n",
       "      <td>2873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14349</td>\n",
       "      <td>3763</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Default      0     1\n",
       "SEX                 \n",
       "1         9015  2873\n",
       "2        14349  3763"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(data.SEX, data.Default)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Female population is in majority and is more likely to default on the loan. This could be explained by their high percentage in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26a80c06d08>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MA0kShoEkCcNAkoRhIEnCMJAkYRhIkjAMJEkYBpIkDANJEoaBJAnDQJKEYSBJwjCQJGEYSJIwDCRJGAaSJAwDSRKGgSQJw0CShGEgScIwkCQxQBgkWZXkS0kOJnk8yQdb/dwk+5Mcas/LWz1JbkoykeSRJBd37Wtzm38oyebBX5YkaT4GOTI4BvybqvoZ4FJgW5KLgO3APVW1BrinrQNcDqxpj63AzdAJD2AHcAmwFthxPEAkSYuj7zCoquer6sG2/G3gILAC2AjsbtN2A1e15Y3ArdVxH7AsyQXAZcD+qjpSVUeB/cCGfvuSJM3fSblmkGQUeAdwP3B+VT0PncAA3tSmrQAOd2022Woz1Xv9nK1JxpOMT01NnYzWJUmchDBI8qPAHwK/VVXfOtHUHrU6Qf3VxaqdVTVWVWMjIyPzb1aS1NNAYZDk79EJgs9W1R+18ovt9A/t+aVWnwRWdW2+EnjuBHVJ0iIZ5G6iALcAB6vqv3QN7QWO3xG0Gbizq35tu6voUuDldhppH7A+yfJ24Xh9q0mSFsnSAbZ9F/BrwKNJHm613wFuAG5PsgV4Fri6jd0NXAFMAK8A7weoqiNJPgocaPM+UlVHBuhLkjRPfYdBVf0Zvc/3A6zrMb+AbTPsaxewq99eJEmD8RvIkiTDQJJkGEiSMAwkSRgGkiQMA0kShoEkCcNAkoRhIEnCMJAkYRhIkjAMJEkYBpIkDANJEoaBJAnDQJKEYSBJwjCQJGEYSJIwDCRJGAaSJAwDSRKGgSQJw0CShGEgScIwkCRhGEiSMAwkSRgGkiQMA0kShoEkCcNAkgQsHXYD0qludPtdC7r/Z264ckH3L82FRwaSJMNAknQKhUGSDUmeTDKRZPuw+5GkM8kpcc0gyRLg94FfAiaBA0n2VtUTw+1MOv15zUNzcUqEAbAWmKiqpwCS7AE2AoaBdIYzzBZHqmrYPZDkfcCGqvr1tv5rwCVV9YFp87YCW9vqW4AnF7Ct84BvLOD+F9Lp3DvY/7DZ/3AtdP8/WVUj04unypFBetRelVJVtRPYufDtQJLxqhpbjJ91sp3OvYP9D5v9D9ew+j9VLiBPAqu61lcCzw2pF0k645wqYXAAWJNkdZKzgE3A3iH3JElnjFPiNFFVHUvyAWAfsATYVVWPD7mtRTkdtUBO597B/ofN/odrKP2fEheQJUnDdaqcJpIkDZFhIEkyDCRJhoEkiVPkbqJhS/LTwArg/qr66676hqr6wvA6OzMkWQtUVR1IchGwAfh6Vd095NbmLck/pvPnVR6rqj8ddj/zleTWqrp22H3MVZJLgINV9a0krwW2AxfT+VM2/7GqXh5qg7No7z0b6bz/FJ3vV+2tqoOL3suZfjdRkt8EtgEHgbcDH6yqO9vYg1V18TD7G1SS91fVp4bdx0yS7AAup/PBZD9wCfBl4BeBfVV1/fC6m12Sr1bV2rb8z+n8Lv0xsB74k6q6YZj9nUiS6d/lCfBPgS8CVNV7F72peUryOPBz7fb0ncArwB3Aulb/5aE2eAJJPgRcA+yh88Vb6HzhdhOwZ7F/dwyD5FHg56vqr5OM0vlF+kxVfTzJQ1X1jqE2OKAkz1bVhcPuYybtv//bgbOBF4CVXZ/y7q+qnx1qg7Po/h1JcgC4oqqmkrwOuK+q/uFwO5xZkgfpfIL+JJ1PpQFuo/NmRFX9r+F1NzdJDlbVz7TlH/rwluThqnr78Lo7sSR/Dry1qr43rX4W8HhVrVnMfjxNBEuOnxqqqmeS/AJwR5KfpPffTDrlJHlkpiHg/MXspQ/Hqur7wCtJ/qKqvgVQVX+b5AdD7m0uXpNkOZ3rb6mqKYCq+pskx4bb2qzGgA8CHwb+XVU9nORvT4cQ6PJY19Hv15KMVdV4kn8AfG+2jYfsB8BPAH85rX5BG1tUhgG8kOTtVfUwQDtCeA+wCzhlP9VNcz5wGXB0Wj3A/1n8dublu0nOqapXgHceLyZ5A0P4H6IPbwAeoPPfupL8eFW9kORHOcU/TFTVD4Abk/xBe36R0+894deBjyf593T+0ue9SQ4Dh9vYqey3gHuSHKLTL8CFwJuBD8y41QLxNFGyks6n0xd6jL2rqv73ENqalyS3AJ+qqj/rMfY/qupXhtDWnCQ5u6q+06N+HnBBVT06hLYGluQc4PyqenrYvcxVkiuBd1XV7wy7l/lK8mPAT9EJs8mqenHILc1JktfQueFgBZ0PD5PAgXa0vLi9nOlhIEnyewaSJAwDSRKGgSQJw0CSBPxfSv77CNlzWjkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "data.EDUCATION.value_counts().plot(kind = 'bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>Default</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDUCATION</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8549</td>\n",
       "      <td>2036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10700</td>\n",
       "      <td>3330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3680</td>\n",
       "      <td>1237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>116</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>262</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>43</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Default        0     1\n",
       "EDUCATION             \n",
       "0             14     0\n",
       "1           8549  2036\n",
       "2          10700  3330\n",
       "3           3680  1237\n",
       "4            116     7\n",
       "5            262    18\n",
       "6             43     8"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(data.EDUCATION, data.Default)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "University education level in highest in the dataset, and is most likely to default. This could be explained by their high percentage in the dataset. Next are Graduates, and then Highschools. This should be the opposite, but again could be explained by the difference in their numbers in the dataset. Though, those least likely to default are unknown labels [0,4,5,6]. Logically speaking, they need to be above graduate level atleast as education level and likeliness of default has an inverse relationship. I tried removing these unknowns but they reduced the accuracy and AUC score. Thus, I believe that they carry important information. So I won't remove them.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26a80c80788>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5kuRLSVa0+lltfaKNr+t6jeta/bEklw7akyRpfhbiyOA3gUe71j8N3FhVo8AJYFurbwNOVNXbgBvbPJJsALYA5wObgM8mWbYAfUmS5migMEiyBvgA8Lm2HuB9wO1tyh7g8ra8ua3Txi9u8zcDe6vqxap6ApgANg7SlyRpfgY9MvgPwG8DP2zrbwGeq6qTbX0SWN2WVwNHAdr4823+/6v32OZHJNmeZDzJ+NTU1ICtS5JO6TsMknwQOFZV93eXe0ytWcZOt82PFqt2VdVYVY2NjIzMq19J0syWD7Dte4APJXk/8OPAm+gcKaxMsrz99r8GeLrNnwTWApNJlgNvBo531U/p3kaStAj6PjKoquuqak1VraNzAfjrVfUPgbuBK9q0rcD+tnygrdPGv15V1epb2t1G64FR4L5++5Ikzd8gRwYz+RiwN8mngAeAW1r9FuALSSboHBFsAaiqw0n2AY8AJ4FrqurlV6EvSdIMFiQMquoPgT9sy4/T426gqvpL4MoZtr8euH4hepEkzZ/fQJYkGQaSJMNAkoRhIEnCMJAkYRhIkjAMJEkYBpIkDANJEoaBJAnDQJKEYSBJwjCQJGEYSJIwDCRJGAaSJAwDSRKGgSQJw0CShGEgScIwkCRhGEiSMAwkSRgGkiQMA0kShoEkCcNAkoRhIEnCMJAkMUAYJFmb5O4kjyY5nOQ3W/3sJAeTHGnPq1o9SW5KMpHkwSQXdL3W1jb/SJKtg++WJGk+BjkyOAn886r6BeAi4JokG4AdwF1VNQrc1dYBLgNG22M7cDN0wgPYCVwIbAR2ngoQSdLi6DsMquqZqvpWW/4B8CiwGtgM7GnT9gCXt+XNwK3VcQ+wMsl5wKXAwao6XlUngIPApn77kiTN34JcM0iyDngXcC9wblU9A53AAN7apq0GjnZtNtlqM9UlSYtk4DBI8pPAfwF+q6q+f7qpPWp1mnqvn7U9yXiS8ampqfk3K0nqaaAwSPLX6ATBF6vqy6383Xb6h/Z8rNUngbVdm68Bnj5N/RWqaldVjVXV2MjIyCCtS5K6DHI3UYBbgEer6t93DR0ATt0RtBXY31W/ut1VdBHwfDuNdCdwSZJV7cLxJa0mSVokywfY9j3ArwMPJfl2q/1L4AZgX5JtwFPAlW3sDuD9wATwAvBhgKo6nuSTwKE27xNVdXyAviRJ89R3GFTVH9H7fD/AxT3mF3DNDK+1G9jdby+SpMH4DWRJkmEgSTIMJEkYBpIkDANJEoaBJAnDQJKEYSBJwjCQJGEYSJIwDCRJGAaSJAwDSRKGgSQJw0CShGEgScIwkCRhGEiSMAwkSRgGkiQMA0kShoEkCcNAkoRhIEnCMJAkYRhIkjAMJEnA8mE3IGl263Z8ZdgtzMmTN3xg2C2oTx4ZSJIMA0nSGRQGSTYleSzJRJIdw+5Hkl5PzogwSLIM+AxwGbABuCrJhuF2JUmvH2fKBeSNwERVPQ6QZC+wGXhkqF1Jek3ygvwrpaoW7YfN2ERyBbCpqv5xW/914MKqunbavO3A9rb6duCxRW20P+cA3xt2E68RvpcLy/dzYS2V9/NnqmpkevFMOTJIj9orUqqqdgG7Xv12Fk6S8aoaG3YfrwW+lwvL93NhLfX384y4ZgBMAmu71tcATw+pF0l63TlTwuAQMJpkfZIVwBbgwJB7kqTXjTPiNFFVnUxyLXAnsAzYXVWHh9zWQllSp7XOcL6XC8v3c2Et6ffzjLiALEkarjPlNJEkaYgMA0mSYSBJMgwkSZwhdxO9ViT5eWA1cG9V/UVXfVNVfW14nUmQZCNQVXWo/e2vTcB3quqOIbe25LR/65vp/HsvOt+LOlBVjw61sQF4ZLBAkvwGsB/4KPBwks1dw/96OF29NiX58LB7WGqS7ARuAm5O8m+A3wN+EtiR5ONDbW6JSfIxYC+dv5xwH53vSQW4bSn/xWVvLV0gSR4Cfrmq/iLJOuB24AtV9btJHqiqdw21wdeQJE9V1U8Pu4+lpP33+YvAWcCzwJqq+n6SN9A5kn3nUBtcQpL8CXB+Vf3VtPoK4HBVjQ6ns8F4mmjhLDt1aqiqnkzyXuD2JD9D77+9pNNI8uBMQ8C5i9nLa8TJqnoZeCHJn1bV9wGq6v8k+eGQe1tqfgj8FPBn0+rntbElyTBYOM8m+cWq+jZAO0L4ILAb+FvDbW1JOhe4FDgxrR7gfy1+O0veS0neWFUvAO8+VUzyZpbwB9iQ/BZwV5IjwNFW+2ngbcC1M251hvM00QJJsobOb1/P9hh7T1X9zyG0tWQluQX4/ar6ox5j/7mqfnUIbS1ZSc6qqhd71M8Bzquqh4bQ1pKV5Mfo/H9YVtP5BWUSONSOvpYkw0CS5N1EkiTDQJKEYSBJwjCQJAH/F1Wh4waC+OWnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "data.MARRIAGE.value_counts().plot(kind = 'bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>Default</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MARRIAGE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>49</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10453</td>\n",
       "      <td>3206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12623</td>\n",
       "      <td>3341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>239</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Default       0     1\n",
       "MARRIAGE             \n",
       "0            49     5\n",
       "1         10453  3206\n",
       "2         12623  3341\n",
       "3           239    84"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(data.MARRIAGE, data.Default)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Single people are highest in number and are most likely to default. This makes sense. Type '3' may be people in a relationship. The '0' column is unknown. I tried removing them but this reduce model accuracy. Thus i infer that they carry important information and won't remove them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#density plot for amount of credit limit (LIMIT_BAL) grouped by default payment next month.\n",
    "class_0 = data.loc[data['Default'] == 0][\"LIMIT_BAL\"]\n",
    "class_1 = data.loc[data['Default'] == 1][\"LIMIT_BAL\"]\n",
    "plt.figure(figsize = (14,6))\n",
    "plt.title('Default amount of credit limit  - grouped by Payment Next Month (Density Plot)')\n",
    "sns.set_color_codes(\"pastel\")\n",
    "sns.distplot(class_1,kde=True,bins=200, color=\"red\")\n",
    "sns.distplot(class_0,kde=True,bins=200, color=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This shows that higher the credit limit, lower is the chance of default. This is sensible as richer people tend to have higher credit limit and are so less likely to default on loans. The highest defaulters are for credit limit 0 to 100,000, with the highest being for credit limit 50,000, and the density for this interval is larger for defaulters than for non-defaulters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>Default</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>perc</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAY_0</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>-2</th>\n",
       "      <td>2394</td>\n",
       "      <td>365</td>\n",
       "      <td>0.132294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-1</th>\n",
       "      <td>4732</td>\n",
       "      <td>954</td>\n",
       "      <td>0.167781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12849</td>\n",
       "      <td>1888</td>\n",
       "      <td>0.128113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2436</td>\n",
       "      <td>1252</td>\n",
       "      <td>0.339479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>823</td>\n",
       "      <td>1844</td>\n",
       "      <td>0.691414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>78</td>\n",
       "      <td>244</td>\n",
       "      <td>0.757764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24</td>\n",
       "      <td>52</td>\n",
       "      <td>0.684211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0.545455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>0.777778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>0.578947</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Default      0     1      perc\n",
       "PAY_0                         \n",
       "-2        2394   365  0.132294\n",
       "-1        4732   954  0.167781\n",
       " 0       12849  1888  0.128113\n",
       " 1        2436  1252  0.339479\n",
       " 2         823  1844  0.691414\n",
       " 3          78   244  0.757764\n",
       " 4          24    52  0.684211\n",
       " 5          13    13  0.500000\n",
       " 6           5     6  0.545455\n",
       " 7           2     7  0.777778\n",
       " 8           8    11  0.578947"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def corr_2_cols(Col1, Col2):\n",
    "    per = data.groupby([Col1, Col2]).size().unstack()\n",
    "    per['perc'] = (per[per.columns[1]]/(per[per.columns[0]] + per[per.columns[1]]))\n",
    "    return per\n",
    "\n",
    "corr_2_cols('PAY_0', 'Default')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No '-2' value qouted in data description. I am not able to infer it on inspection of the dataset too. Will keep it as I am not able to find an explanation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pre-Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Split test and train data\n",
    "x = data.drop(columns = 'Default')\n",
    "y = data['Default']\n",
    "\n",
    "#will train on 70% of the data, test on the remaining 30%\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.30, random_state=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#feature scaling\n",
    "#To avoid data Leakage, scale the x_train and x_test separately\n",
    "scaler = RobustScaler(copy=True)\n",
    "x_train = scaler.fit_transform(x_train)\n",
    "x_train = pd.DataFrame(x_train, columns = x.columns)\n",
    "\n",
    "x_test = scaler.fit_transform(x_test)\n",
    "x_test = pd.DataFrame(x_test, columns = x.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.7788\n",
       "1    0.2212\n",
       "Name: Default, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#balancing the dataset\n",
    "y.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Majority class (not default) has 77% samples. Minority class (default) has 22% samples.\n",
    "Data is highly imbalanced. Thus, will apply SMOTE and Undersampling techniques."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "#so as not to lose any important information, apply SMOTE (Synthetic Minority Oversampling Technique) to fix class imbalance\n",
    "sm = SMOTE(random_state=10)\n",
    "x_smote, y_smote = sm.fit_sample(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Undersampling data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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_0</th>\n",
       "      <th>PAY_2</th>\n",
       "      <th>PAY_3</th>\n",
       "      <th>PAY_4</th>\n",
       "      <th>PAY_5</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</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16405</th>\n",
       "      <td>0.473684</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.615385</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.272480</td>\n",
       "      <td>0.420493</td>\n",
       "      <td>0.531837</td>\n",
       "      <td>-0.164547</td>\n",
       "      <td>-0.120476</td>\n",
       "      <td>0.994893</td>\n",
       "      <td>1.208054</td>\n",
       "      <td>0.904101</td>\n",
       "      <td>0.014384</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6101</th>\n",
       "      <td>0.105263</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.692308</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.334495</td>\n",
       "      <td>-0.354917</td>\n",
       "      <td>-0.358133</td>\n",
       "      <td>0.613812</td>\n",
       "      <td>-0.477619</td>\n",
       "      <td>0.174703</td>\n",
       "      <td>-0.402685</td>\n",
       "      <td>-0.387472</td>\n",
       "      <td>-0.385282</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7731</th>\n",
       "      <td>0.684211</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.615385</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.268863</td>\n",
       "      <td>-0.218991</td>\n",
       "      <td>0.147701</td>\n",
       "      <td>0.454500</td>\n",
       "      <td>1.555000</td>\n",
       "      <td>0.770665</td>\n",
       "      <td>1.612617</td>\n",
       "      <td>5.855990</td>\n",
       "      <td>0.601040</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14417</th>\n",
       "      <td>0.842105</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.461538</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.231979</td>\n",
       "      <td>-0.180790</td>\n",
       "      <td>-0.280762</td>\n",
       "      <td>-0.070307</td>\n",
       "      <td>1.240714</td>\n",
       "      <td>1.237886</td>\n",
       "      <td>2.116242</td>\n",
       "      <td>0.571908</td>\n",
       "      <td>1.296089</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19220</th>\n",
       "      <td>-0.631579</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.923077</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.365483</td>\n",
       "      <td>-0.374090</td>\n",
       "      <td>0.043499</td>\n",
       "      <td>-0.242084</td>\n",
       "      <td>-0.239048</td>\n",
       "      <td>-0.430285</td>\n",
       "      <td>-0.402685</td>\n",
       "      <td>4.569842</td>\n",
       "      <td>-0.385282</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7338</th>\n",
       "      <td>-0.578947</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.538462</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.148452</td>\n",
       "      <td>0.179777</td>\n",
       "      <td>0.219463</td>\n",
       "      <td>-0.174520</td>\n",
       "      <td>-0.072857</td>\n",
       "      <td>0.021021</td>\n",
       "      <td>-0.253154</td>\n",
       "      <td>-0.129157</td>\n",
       "      <td>0.128427</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16399</th>\n",
       "      <td>0.315789</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.384615</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.062742</td>\n",
       "      <td>-0.246924</td>\n",
       "      <td>-0.149793</td>\n",
       "      <td>3.042633</td>\n",
       "      <td>6.523333</td>\n",
       "      <td>3.328622</td>\n",
       "      <td>1.249664</td>\n",
       "      <td>2.184049</td>\n",
       "      <td>4.693508</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5429</th>\n",
       "      <td>0.421053</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.461538</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.872296</td>\n",
       "      <td>2.022964</td>\n",
       "      <td>2.027698</td>\n",
       "      <td>1.963351</td>\n",
       "      <td>4.106667</td>\n",
       "      <td>-0.430285</td>\n",
       "      <td>1.879195</td>\n",
       "      <td>-0.387472</td>\n",
       "      <td>1.669556</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15603</th>\n",
       "      <td>-0.473684</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.329518</td>\n",
       "      <td>0.388655</td>\n",
       "      <td>0.399278</td>\n",
       "      <td>-0.049863</td>\n",
       "      <td>-0.477619</td>\n",
       "      <td>0.329810</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.387472</td>\n",
       "      <td>0.385282</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20911</th>\n",
       "      <td>-0.631579</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.923077</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.014815</td>\n",
       "      <td>0.032509</td>\n",
       "      <td>0.024685</td>\n",
       "      <td>-0.099726</td>\n",
       "      <td>-0.168095</td>\n",
       "      <td>-0.264014</td>\n",
       "      <td>0.038389</td>\n",
       "      <td>-0.387472</td>\n",
       "      <td>-0.128427</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       LIMIT_BAL  SEX  EDUCATION  MARRIAGE       AGE  PAY_0  PAY_2  PAY_3  \\\n",
       "16405   0.473684 -1.0        0.0      -1.0  0.615385    0.0    0.0    0.0   \n",
       "6101    0.105263  0.0        1.0      -1.0  1.692308   -1.0   -1.0   -1.0   \n",
       "7731    0.684211 -1.0       -1.0      -1.0  0.615385   -1.0   -1.0   -1.0   \n",
       "14417   0.842105  0.0       -1.0       0.0 -0.461538    0.0    0.0    0.0   \n",
       "19220  -0.631579  0.0        0.0       0.0 -0.923077    0.0    0.0    0.0   \n",
       "7338   -0.578947  0.0       -1.0       0.0 -0.538462    2.0    0.0    0.0   \n",
       "16399   0.315789 -1.0        0.0       0.0 -0.384615   -2.0   -2.0   -2.0   \n",
       "5429    0.421053 -1.0       -1.0       0.0 -0.461538    2.0    0.0    0.0   \n",
       "15603  -0.473684 -1.0        1.0       0.0  0.000000    1.0    2.0    2.0   \n",
       "20911  -0.631579  0.0        0.0       0.0 -0.923077    0.0    0.0    0.0   \n",
       "\n",
       "       PAY_4  PAY_5  ...  BILL_AMT4  BILL_AMT5  BILL_AMT6  PAY_AMT1  PAY_AMT2  \\\n",
       "16405    0.0    0.0  ...   0.272480   0.420493   0.531837 -0.164547 -0.120476   \n",
       "6101    -1.0    0.0  ...  -0.334495  -0.354917  -0.358133  0.613812 -0.477619   \n",
       "7731    -1.0   -1.0  ...  -0.268863  -0.218991   0.147701  0.454500  1.555000   \n",
       "14417   -1.0   -1.0  ...  -0.231979  -0.180790  -0.280762 -0.070307  1.240714   \n",
       "19220    0.0   -2.0  ...  -0.365483  -0.374090   0.043499 -0.242084 -0.239048   \n",
       "7338     0.0    0.0  ...   0.148452   0.179777   0.219463 -0.174520 -0.072857   \n",
       "16399   -2.0   -2.0  ...  -0.062742  -0.246924  -0.149793  3.042633  6.523333   \n",
       "5429     2.0    2.0  ...   1.872296   2.022964   2.027698  1.963351  4.106667   \n",
       "15603    2.0    2.0  ...   0.329518   0.388655   0.399278 -0.049863 -0.477619   \n",
       "20911    0.0    0.0  ...  -0.014815   0.032509   0.024685 -0.099726 -0.168095   \n",
       "\n",
       "       PAY_AMT3  PAY_AMT4  PAY_AMT5  PAY_AMT6  Default  \n",
       "16405  0.994893  1.208054  0.904101  0.014384      1.0  \n",
       "6101   0.174703 -0.402685 -0.387472 -0.385282      NaN  \n",
       "7731   0.770665  1.612617  5.855990  0.601040      NaN  \n",
       "14417  1.237886  2.116242  0.571908  1.296089      NaN  \n",
       "19220 -0.430285 -0.402685  4.569842 -0.385282      1.0  \n",
       "7338   0.021021 -0.253154 -0.129157  0.128427      NaN  \n",
       "16399  3.328622  1.249664  2.184049  4.693508      0.0  \n",
       "5429  -0.430285  1.879195 -0.387472  1.669556      NaN  \n",
       "15603  0.329810  0.000000 -0.387472  0.385282      NaN  \n",
       "20911 -0.264014  0.038389 -0.387472 -0.128427      NaN  \n",
       "\n",
       "[10 rows x 23 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create the training df by remerging x_train and y_train\n",
    "df_train = x_train.join(y_train)\n",
    "df_train.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11305\n",
      "-----------\n",
      "3402\n",
      "-----------\n",
      "0.0    11305\n",
      "1.0     3402\n",
      "Name: Default, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "from sklearn.utils import resample\n",
    "# Separate majority and minority classes\n",
    "df_majority = df_train[df_train.Default==0]\n",
    "df_minority = df_train[df_train.Default==1]\n",
    "\n",
    "print(df_majority.Default.count())\n",
    "print(\"-----------\")\n",
    "print(df_minority.Default.count())\n",
    "print(\"-----------\")\n",
    "print(df_train.Default.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    3402\n",
       "0.0    3402\n",
       "Name: Default, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Downsample majority class\n",
    "df_majority_downsampled = resample(df_majority, \n",
    "                                 replace=False,    # sample without replacement\n",
    "                                 n_samples=3402,     # to match minority class\n",
    "                                 random_state=587) # reproducible results\n",
    "# Combine minority class with downsampled majority class\n",
    "df_downsampled = pd.concat([df_majority_downsampled, df_minority])\n",
    "# Display new class counts\n",
    "df_downsampled.Default.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Downsampled data\n",
    "y_downsampled = df_downsampled.Default\n",
    "x_downsampled = df_downsampled.drop(['Default'], axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[4966 2108]\n",
      " [ 671 1255]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.70      0.78      7074\n",
      "           1       0.37      0.65      0.47      1926\n",
      "\n",
      "    accuracy                           0.69      9000\n",
      "   macro avg       0.63      0.68      0.63      9000\n",
      "weighted avg       0.77      0.69      0.72      9000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "param_grid = {'C': [0.001, 0.01, 1, 10]}\n",
    "\n",
    "lr = LogisticRegression(penalty='l2', class_weight='balanced')\n",
    "lr = GridSearchCV(estimator = lr, param_grid = param_grid, n_jobs = -1)\n",
    "lr.fit(x_train, y_train)\n",
    "y_pred = lr.predict(x_test)\n",
    "\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'False Positive Rate')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, auc\n",
    "y_prob = lr.predict_proba(x_test)[:,1]\n",
    "false_positive_rate, true_positive_rate, thresholds = roc_curve(y_test, y_prob)\n",
    "roc_auc = auc(false_positive_rate, true_positive_rate)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(6,6))\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.plot(false_positive_rate,true_positive_rate, color='red',label = 'AUC = %0.2f' % roc_auc)\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.plot([0, 1], [0, 1],linestyle='--')\n",
    "plt.axis('tight')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[4839 2235]\n",
      " [ 663 1263]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.68      0.77      7074\n",
      "           1       0.36      0.66      0.47      1926\n",
      "\n",
      "    accuracy                           0.68      9000\n",
      "   macro avg       0.62      0.67      0.62      9000\n",
      "weighted avg       0.77      0.68      0.70      9000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "lr.fit(x_smote, y_smote)\n",
    "y_pred = lr.predict(x_test)\n",
    "\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[3423 3651]\n",
      " [ 626 1300]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.48      0.62      7074\n",
      "           1       0.26      0.67      0.38      1926\n",
      "\n",
      "    accuracy                           0.52      9000\n",
      "   macro avg       0.55      0.58      0.50      9000\n",
      "weighted avg       0.72      0.52      0.56      9000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "lr.fit(x_downsampled, y_downsampled)\n",
    "y_pred = lr.predict(x_test)\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Best result on original data = 0.69, AUC = 0.73"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training score:  0.8170952380952381\n",
      "Testing score:  0.8254444444444444\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "# Decision tree with depth = 2\n",
    "dt2 = tree.DecisionTreeClassifier(random_state=42, max_depth=2)\n",
    "dt2.fit(x_train, y_train)\n",
    "dt2_score_train = dt2.score(x_train, y_train)\n",
    "print(\"Training score: \",dt2_score_train)\n",
    "dt2_score_test = dt2.score(x_test, y_test)\n",
    "print(\"Testing score: \",dt2_score_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training score:  0.8199047619047619\n",
      "Testing score:  0.8257777777777778\n"
     ]
    }
   ],
   "source": [
    "# Decision tree with depth = 3\n",
    "dt3 = tree.DecisionTreeClassifier(random_state=42, max_depth=3)\n",
    "dt3.fit(x_train, y_train)\n",
    "dt3_score_train = dt3.score(x_train, y_train)\n",
    "print(\"Training score: \",dt3_score_train)\n",
    "dt3_score_test = dt3.score(x_test, y_test)\n",
    "print(\"Testing score: \",dt3_score_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training score:  0.8209047619047619\n",
      "Testing score:  0.8263333333333334\n"
     ]
    }
   ],
   "source": [
    "# Decision tree with depth = 4\n",
    "dt4 = tree.DecisionTreeClassifier(random_state=42, max_depth=4)\n",
    "dt4.fit(x_train, y_train)\n",
    "dt4_score_train = dt4.score(x_train, y_train)\n",
    "print(\"Training score: \",dt4_score_train)\n",
    "dt4_score_test = dt4.score(x_test, y_test)\n",
    "print(\"Testing score: \",dt4_score_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training score:  0.9994285714285714\n",
      "Testing score:  0.7181111111111111\n"
     ]
    }
   ],
   "source": [
    "# Decision tree: To the full depth\n",
    "dt1 = tree.DecisionTreeClassifier()\n",
    "dt1.fit(x_train, y_train)\n",
    "dt1_score_train = dt1.score(x_train, y_train)\n",
    "print(\"Training score: \", dt1_score_train)\n",
    "dt1_score_test = dt1.score(x_test, y_test)\n",
    "print(\"Testing score: \", dt1_score_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "depth      Training score       Testing score       \n",
      "-----      --------------       -------------       \n",
      "2        0.8170952380952381   0.8254444444444444\n",
      "3        0.8199047619047619   0.8257777777777778\n",
      "4        0.8209047619047619   0.8263333333333334\n",
      "max      0.9994285714285714   0.7181111111111111\n"
     ]
    }
   ],
   "source": [
    "#Compare Training and Testing scores for various tree depths used\n",
    "print('{:10} {:20} {:20}'.format('depth', 'Training score','Testing score'))\n",
    "print('{:10} {:20} {:20}'.format('-----', '--------------','-------------'))\n",
    "print('{:1} {:>25} {:>20}'.format(2, dt2_score_train, dt2_score_test))\n",
    "print('{:1} {:>25} {:>20}'.format(3, dt3_score_train, dt3_score_test))\n",
    "print('{:1} {:>25} {:>20}'.format(4, dt4_score_train, dt4_score_test))\n",
    "print('{:1} {:>23} {:>20}'.format(\"max\", dt1_score_train, dt1_score_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Highest testing accuracy reached at depth 4. At this depth, training accuracy is good too so will go with this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[6731  343]\n",
      " [1220  706]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.95      0.90      7074\n",
      "           1       0.67      0.37      0.47      1926\n",
      "\n",
      "    accuracy                           0.83      9000\n",
      "   macro avg       0.76      0.66      0.69      9000\n",
      "weighted avg       0.81      0.83      0.81      9000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = dt4.predict(x_test)\n",
    "\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'False Positive Rate')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aCJRS/ujECdvbJzbW9v7JzYWePeGDD2DgQNsIXMq2HEhlyPjFZGTn+Fz30FPRRKCU8g/GwG+/2ZP/tGlw7Bg0bAiPPw5Dh0KLFo6Ftml/CjdOWIwxhrjRPWhVp5JjsZwOTQRKKd+2detfo30TE+0Ar+uus1U/F1xQIqN9z8TxzGyGTFgMwJTRPWhWK9LReE6HJgKllO85dgxmzLBX/z//bPv39+oFTz8N11xjp37wEeXDQnnmqja0rBNJk5q+E1dxaCJQSvmGnBz48Ud78v/yS9sO0Lw5PP+8rfpp6Pwsne5W7jxC0rF0LmlTh8va1XU6nDOiiUAp5az16221z6efwu7dUKWKrfYZNgx69Cjx0b4lIWHbIYZ/spTalcLp1aqW3/QOKowmAqVU6UtOtit7xcbalb5CQqBvXzvg68or7YIvPmrRlmRujV1KnUoRfDayh98nAdBEoJQqLVlZdk3f2Fj4+mt7v317eO01O9q3Th2nIzylXzcdZOSkpTSoWp7PRnWnVqTvJqzi0ESglPIeY2DlSnvy//xzOHAAataEO++01T8dOzodYbH8sukA0dUrMHlkd2pU9M4cRU7QRKCUKnl799oTf2ws/PEHhIXBVVfZev++faFsWacjLJb0rBwiyobw2GWtuLt3cyo6vMZwSfP/yi2llG9IT4epU+Hyy+18/g89BOXLw3vv2cQwfbqt//ezJDD3j730fm0hO5KPIyIBlwRASwRKqTNhDCxaZK/8p061q31FRcGjj9qr/1atnI7wjHy1cjcPTFtFxwZVqFrBvxJYcWgiUEoV3/bttrvnpEmwaZO98r/mGlvv36uX7QXk52Ys28XDM1bRLboaHw/vSoUALAnkCdx3ppQqWamp8MUX9up/wQL72IUX2rl+rr0WIv1vaoXCfL9uPw/PWMU5TWswflgM5cL8P7EVRROBUqpwubn2pB8ba5PA8ePQtCk8+6wd7Rsd7XSEXnF20+rcfkFT7u3dnIiygZ0EQBOBUqogGzfak/+nn8LOnVCpEgwZYqt+zj7bJ0f7loTZq/bQu1UtKoSH8mhf/27fKA5NBEop6/Bh2+AbGwvx8XZWz0sugTFj7EIv5co5HaFXvffTZsZ8u4EHL27B3b2bOx1OqdJEoFQwy86GefPsyX/2bMjIgLZt4ZVX7GjfevWcjtDrjDG89cNmxs7fyFUd6nHHhU2dDqnUaSJQKhitWmVP/p99BklJUKMG3Habrfrp1Clgq37yM8bw6ncbeHfBFq7tHMWY69oTUiY43rs7TQRKBYv9+/8a7btqlR3Y1a+fPflfdpkd/RtkDqVlMj1hF4O7NeCFAe0oE4RJADQRKBXYMjLsBG+xsXbCt5wciImBt9+GQYNsSSAIGWMAqF4xnK/vPpeaFcODNgmAJgKlAo8xsHjxX6N9Dx+2df0PPWRH+7Zu7XSEjsrNNfznqzVEhIbwZL+zqF0pMGYQPROaCJQKFDt3/jXad8MG28vn6qtt1U/v3gEx2vdM5eQaHvtiNdOX7QrKRuHCaCJQyp+lpdllHWNj7TKPxsB558HDD8P119v+/wqA7JxcHp6xmpkrdnNv7+bc16c5EiSN4qeiiUApf5Obaxd0j421C7ynpkLjxvDUU7bqp0kTpyP0SY+4ksDDl7bkzl7NnA7Hp2giUMpfbN5sq30mTbKTvkVGwg032Kqfc86xA8BUoS5tW4ez6lZi1PmaKPPTRKCULztyBKZNs1f/v/9u+/dffDH8978wYICd9VMVKj0rh+U7DnN20xpc2sb3l8J0iiYCpXxNdjZ8/709+c+aZbuAnnUWvPQS3HQT1K/vdIR+IT0rh1GTEohPTObHBy+kQTVNmoXRRKCUr1izxp78J0+GffugWjUYOdJW/cTEBM1o35JwPDObWycmEL81mZevaa9J4BQ0ESjlpAMHIC7OJoDlyyE0FK64wp78L78cwgNngfTSkpqRzS2fLCVh+yFeH9iBqztFOR2Sz9NEoFRpy8yEOXPsyX/uXFsV1LkzvPkmDB4MNWs6HaFfm7ViN8t2HObNQZ24skPgT5pXEjQRKFUajIGEBHvyj4uDQ4egTh247z579d+2rdMRBowh3RvSpVFVzqqrYyg8pYlAKW/avdvW+cfGwvr1EBFhe/sMG2Z7/4Tqv2BJOJSWyb1TVvBUv9Y0rx2pSaCY9FuoVEk7fhxmzrT9/efPtwPAzjkHxo2zo32rVHE6woByICWDIRPi2Z58nH3H0mleO3DWTi4tXk0EItIXeBMIASYYY17Kt70hEAtUce3zmDFmrjdjUsorjIFffrFX/tOnQ0oKNGoETzxhr/6b6UhWb9h/LJ0bx8ez50g6nwzvytnNgnM21TPltUQgIiHAu8DFwC5gqYjMNsasc9vtP8A0Y8z7ItIamAtEeysmpUpcYuJfo323boWKFeG662y9//nn62hfL9p/LJ0bPlzEgZQMYm/pRrfG1ZwOyW95s0TQDdhsjEkEEJEpQH/APREYIK8yrzKwx4vxKFUy0tJgyhR79f/LL7Z/f+/e8MwzcM01UKGC0xEGhUoRZWlRO5LXBnakS6OqTofj17yZCOoDO93u7wK659vn/4DvRORuoALQx4vxKFUybrnFTvvQsqWd6uGmm6BBA6ejCho7ko9TpUJZKkWUZdywGKfDCQjeLLcWNAzS5Ls/GJhojIkCLgc+FZF/xCQio0UkQUQSDhw44IVQlfKAMfDBB3bah1GjbC+gf/9bk0Ap2pyUyvUf/s4DU1c6HUpA8WYi2AW4/4dE8c+qn1uBaQDGmEVABPCP1h5jzDhjTIwxJqamDrZRTti+HS65BO64w9b9P/ecTvlQyjbsS2HQuHhycuHhS1s5HU5A8WYiWAo0F5HGIhIGDAJm59tnB9AbQETOwiYCveRXvsMYGD8e2rWD+Hj48EP47juoXdvpyILKuj3HGDw+njICU0b3oGUd7SJakrzWRmCMyRaRu4B52K6hHxtj1orIs0CCMWY28CAwXkTux1YbDTd5q0or5bSdO20V0Lx5cNFF8NFHEB3tdFRBxxjDQ9NXER5ahs9H9aBxDW2ML2nib+fdmJgYk5CQ4HQYKpAZAxMn2ukfcnJgzBi4/XbtCuqgbQfTCCkjOovoGRCRZcaYAlvX9ZutlLvdu6FfP9szqFMnWL0a/vUvTQIOWLrtEM/PWYcxhugaFTQJeJF+u5UCWwqIjYU2bWDBAnjrLbsYvK7/64hFW5K5+eMl/PhnEkdPZDkdTsDTuYaU2rsXRo+2U0Ofey588olOCeGgXzYdYNSkBBpULc9no7pTpXyY0yEFPC0RqOBlDHz2mS0FzJ8PY8fCTz9pEnDQgj+TuDU2gcY1KjJldA9qRUY4HVJQ0BKBCk7799sG4FmzoGdP2zjcooXTUSmgXf3KfHRzjJYESpEmAhVcjIGpU+GuuyA1FV55Be6/H0JCnI4sqO05coJ6VcrRq1UtLmxZE9HBeqVKq4ZU8EhKsusBDB5sq39WroSHHtIk4LBZK3ZzwSsL+GlDEoAmAQdoIlDBYfp02xbw9dfw8svw66/QSqcpcNr0hJ3cP20lMY2q0TVap5F2iiYCFdgOHoQbboCBA+2o4OXL4ZFHdIlIH/D54h08PGM15zarwcfDu1IhXP8mTtFEoALXzJm2FDBzJrzwAixaZO8rx63aeYTHZ/5Br5Y1GT8shnJhWj3nJE3BKvAkJ8Pdd0NcHHTubLuGtmvndFTKTYcGVXhzUEf6tq1DeKgmAadpiUAFltmz7VX/9Onw7LN2xlBNAj5jwi+JrN1zFID+HetrEvARmghUYDh82C4S378/1KkDCQnw5JNQtqzTkSnsDKJvzN/I8/9bz9SlO0/9BFWqNBEo/zdnji0FxMXB00/DkiXQoYPTUSkXYwyvzNvAG/M3cV2XKJ6+UttpfI22ESj/deSIHQw2caKt/pkzx7YJKJ9hjOGF/61nwq9bGdytIS8MaEuZMjpOwNdoiUD5p2+/hbZt4dNP4YknYOlSTQI+KDvXsPlAKjf3bMR/r9Yk4Ku0RKD8y9Gj8OCDdrWw1q3tXEExBa61oRyUm2tIy8wmMqIs44bGUDZEdMSwD9MSgfIf339vq4A++QQeewyWLdMk4INycg2PfLGawePjSc/KISy0jCYBH6eJQPm+lBS47Ta45BKoUAF+/x1efBEidIpiX5Odk8sD01YyY9ku+pxVm/BQPcX4A/0rKd/2ww+2FDB+PDz8MKxYAd27Ox2VKkBWTi73TlnJVyv38PClLbmvTwstCfgJTQTKN6Wmwp13Qp8+EB5uJ4kbM0ZLAT7s+Tnr+N8fe/nPFWdxZy9d3MefaGOx8j0//WQXj9+2DR54AJ5/HsqVczoqdQqjzm9Cm3qVGdi1gdOhqGLSEoHyHWlpcM890KuXXSPg55/htdc0CfiwE5k5TPglkdxcQ1TV8poE/JSWCJRv+OUXGDECtmyBe++F//4Xypd3OipVhLSMbG6NXcrirYfo2KAKMbqegN/SEoFy1vHjdnTwBRfYZSR/+gneeEOTgI9LSc/i5o+XsGTrId64oaMmAT+nJQLlnN9/h+HDYdMm2zD80ktQsaLTUalTOHrCJoE1u4/y9uDOXNG+rtMhqTOkJQJV+k6csGsFn3suZGXBjz/CO+9oEvATm/ansCUplfeGaBIIFFoiUKUrPt6WAjZsgNtvt11CIyOdjkp5IDM7l7DQMsREV+PXRy+icnmd4jtQaIlAlY70dHj0UTjnHNsu8N138P77mgT8RFJKOle+/SvTXGsJaBIILFoiUN63Zg0MGgRr18LIkbZLaKVKTkelPLTvaDo3jo9n37F0GlTTRvxApCUC5T3GwLhx0LUrHDwI33xjp4rQJOA3dh85wQ3jFpGUkkHsLd3o2bS60yEpL9ASgfKOI0dg9Gi7dvAll8CkSVC7ttNRqWJISc/ihg8XcfREFpNu7UbnhlWdDkl5iSYCVfIWL7ZVQTt32i6hDz8MZbTw6W8iI8oy/OxoujWuRvuoKk6Ho7xIE4EqObm5tv7/8cehfn07WrhnT6ejUsW0OSmV45nZtI+qwsjzmjgdjioFepmmSkZSElxxBTzyCFx1lZ0uWpOA39mwL4VB4xZx/9SV5OQap8NRpUQTgTpzP/4IHTvCggXw3nswYwZU1fpkf7N2z1EGjVtESBlh3LAYQnR94aChiUCdvuxsePJJu2ZA5cqwZAnccQfoYiR+Z/WuI9w4fjHlyoYwdXRPmtbUUd7BRNsI1OnZuRNuvNEuGDNiBLz9tl1GUvmlib9vIzIilLhRPXSsQBDSRKCK76uv7Mk/KwsmT4YhQ5yOSJ0mYwwiwovXtOPo8SxqVdIV4IKRVg0pz2Vk2IVjBgyAxo1h+XJNAn7s980Hueb93zmUlkl4aIgmgSDmUSIQkTAR0UVIg9nGjbYX0Ntvw3332Smkmzd3Oip1mhZuPMCIiUtJy8jW3kHq1IlARK4A/gC+d93vKCIzvR2Y8iGTJ0OXLrB9O8yeDWPH2gXllV/6Yf1+RsUm0KRmReJG9aBmpP4tg50nJYJnge7AEQBjzEpASwfBIDXVThk9dCh06gSrVsGVVzodlToDCzYkcfvkZbSqG0ncqO5Ur6hJQHmWCLKMMUfyPaZlyUC3ahXExNg5gp56yo4ViIpyOip1hlrXrcQV7eoyeWR3qpQPczoc5SM8SQTrRWQgUEZEGovIG0C8l+NSTjEG3n0XuneHY8fghx/gmWcgVDuY+bP4xGSyc3KpXSmCNwZ1olKErieg/uJJIrgL6ALkAl8C6cC93gxKOeTwYbj2WrjrLrjoIlsq6NXL6ajUGZq2dCeDx8fz0a9bnQ5F+ShPEsGlxphHjTGdXD+PAZd5OzBVyn7/3U4TMWeOnThuzhyoWdPpqNQZmhy/nUe+WM25zWowrGe00+EoH+VJIvhPAY89UdKBKIfk5sKLL8L550NICPz2GzzwgE4bHQA++W0r/5m1hota1WL8sBjKhYU4HZLyUYVW/IrIpUBfoL6IvO62qRK2mkj5u337bI+g+fNh4EC7mljlyk5HpUrA/mPpvDJvA5e2qc3bgzsTFqqJXRWuqBbAJGANtk1grdvjKcBj3gxKlYLvvrNJICXFLh956606WVwAqV0pglJQfxUAACAASURBVC/uOJtmtSpSNkSTgCpaoYnAGLMCWCEinxlj0ksxJuVNWVm2O+hLL0GbNrZbaJs2TkelSoAxhrHzN1GjYhjDekZzVl1dG1p5xpNLhfoiMkVEVovIxrwfT15cRPqKyAYR2SwiBZYiRGSgiKwTkbUi8nmxolfFs22bbQt46SW7nvCSJZoEAoQxhpe/3cBbP2xi7e5jGKNDfZTnPOkcPhF4HngV21toBB60EYhICPAucDGwC1gqIrONMevc9mkO/Bs4xxhzWERqFfsdKM988QWMHGkbh6dMgRtucDoiVUKMMTw3Zz0f/7aVId0b8lz/tohW86li8KREUN4YMw/AGLPFGPMfwJPO5d2AzcaYRGNMJjAF6J9vn1HAu8aYw67XT/I8dOWREyfgX/+C666zk8StWKFJIIAYY3h69lo+/m0rw8+O5vkBbSmjK4upYvIkEWSIvbzYIiK3i8iVgCdX7vWBnW73d7kec9cCaCEiv4lIvIj0LeiFRGS0iCSISMKBAwc8OLQCYP166NED3n8fHnrILiLTRBcjDyQiQsNq5bnt/CY8fWVrLQmo0+JJ1dD9QEXgHuAFoDJwiwfPK+gbmb/iMhRoDlwIRAG/iEjb/HMbGWPGAeMAYmJitPLzVIyBiRPtCOHy5eF//4PLL3c6KlWCcnINWw+m0qxWJCPP0+SuzswpSwTGmMXGmBRjzA5jzFBjzFXAdg9eexfQwO1+FLCngH2+MsZkGWO2AhuwiUGdrpQUuOkmuOUWO1/QqlWaBAJMdk4u909dyYB3f2ffUe3Qp85ckYlARLqKyAARqeG630ZEJuHZpHNLgeauierCgEHA7Hz7zMLV3uA6RgsgsZjvQeVZvhw6d7aNwc8+C99/D/XqOR2VKkFZObncHbeC2av2cGevZtSprKuKqTNXaCIQkReBz4AhwLci8gSwAFiFPWEXyRiTjZ2wbh6wHphmjFkrIs+KyFWu3eYBySKyzvXaDxtjks/kDQUlY+DNN217QHo6/PQTPPmknTJCBYyM7BzumLycb9bs4z9XnMUdFzZ1OiQVIIpqI+gPdDDGnBCRathqnQ7GmA2evrgxZi4wN99jT7ndNsADrh91OrKzYdAg2z30qqvg44+henWno1JeEPv7Nuav38+z/dvoBHKqRBWVCNKNMScAjDGHROTP4iQBVUqefNImgZdegkce0WkiAtiIcxrTsk4lLmihs8KqklVUImgiIl+6bgsQ7XYfY8w1Xo1MndqsWTYBjBoFjz7qdDTKC9Iysnn263U8eGkLakVGaBJQXlFUIrg23/13vBmIKqZNm+Dmm+1ykm+95XQ0ygtS0rMY8clSVuw8wsWta9OntTYMK+8oatK5H0ozEFUMaWlwzTV2+cgZMyBCTxCB5ujxLIZ9soS1u4/yzuBO9Gld2+mQVADThWj9jTFw222wdi18+y00auR0RKqEHU7LZOjHi9mwL4X3b+rCxZoElJdpIvA3770Hn30Gzz0Hl1zidDTKC3JcM4eOGxZDr5Y6D6PyPvF0uloRCTfGZHg5nlOKiYkxCQkJTofhjEWL4IIL4NJL4auvdDnJAJOcmkGlcmUpG1KG3Fyjk8epEiUiy4wxMQVtO+WZRES6icgfwCbX/Q4i8nYJx6hOJSkJrr8eGjSASZM0CQSYfUfTuf6DRfz7yz8ANAmoUuXJ2eQtoB+QDGCMWYVn01CrkpI3aCw52Y4ZqFrV6YhUCdp1+DgDP1xEUkoGg7o2OPUTlCphnrQRlDHGbM83vW2Ol+JRBfnPf2DBAjujaMeOTkejStCO5OMMHh/PsfQsPr21G50aapJXpc+TRLBTRLoBxrXq2N2AR0tVqhIwcya8/LLtKXTzzU5Ho0pQTq5h5KSlpGVmEzeqB23rV3Y6JBWkPEkEd2CrhxoC+4H5rseUt23caE/+XbvaSeVUQAkpI7x4TXvKh4XoQvPKUZ4kgmxjzCCvR6L+Lm/QWFiYHTQWHu50RKqE/LnvGEu2HmJYz2i6NNKqIOU8TxLBUhHZAEwFvjTGpHg5JmWMnT9o3TqYNw8aNnQ6IlVC1uw+ytCPFhMeGsKATvWpFFHW6ZCU8miFsqbA80AX4A8RmSUiWkLwpnfegbg4O2js4oudjkaVkJU7j3Dj+HjKh4Uy9bYemgSUz/CoM7ox5ndjzD1AZ+AYdsEa5Q2//w4PPABXXgn//rfT0agSsmz7IW6asJjK5csy9bYeNKpewemQlDrJkwFlFUVkiIh8DSwBDgBnez2yYLR/vx001rChDhoLMBv3p1IrMpxpt/Ukqmp5p8NR6m88aSNYA3wNjDHG/OLleIJX3qCxQ4cgPh6qVHE6IlUCUjOyqRgeyuBuDbm6U30iyuryocr3eHLJ2cQYc7cmAS97/HG71vCHH0KHDk5Ho0rATxuSOO/lH1m2/TCAJgHlswotEYjIa8aYB4EvROQfM9PpCmUl6Msv4ZVX4PbbYdgwp6NRJWD+uv3867PlNK9dkSY1tD1A+baiqoamun7rymTetGEDDB8O3brBG284HY0qAd/8sZe741bQpl4lJt3SncrltXeQ8m1FrVC2xHXzLGPM35KBiNwF6ApmZyo11Q4aCw/XQWMBYtn2w9wVt4KODarwyYiu2kVU+QVP2ghuKeCxW0s6kKCTN2jszz/tmIEGOutkIOjYoAoPXNyC2Fu6aRJQfqOoNoIbgEFAYxH50m1TJHDE24EFvLffhilT4IUXoE8fp6NRZ+irlbvp1rgadSuX485ezZwOR6liKaqNYAl2DYIo4F23x1OAFd4MKuD99hs8+CBcdRU89pjT0agz9Gn8dp6ctYabejTk+QHtnA5HqWIrqo1gK7AVO9uoKin79tlBY40aQWysDhrzcx//upVn56yjz1m1eLJfa6fDUeq0FFU1tNAYc4GIHAbcu48KYIwx1bweXaDJGzR25Ah8+60OGvNzHy7cwovf/EnfNnV4a3AnwkI1qSv/VFTVUN5ylDVKI5Cg8O9/w8KFdvqI9u2djkadgfSsHGau2M2VHerx+sAOlA3RJKD8V1FVQ7mumw2APcaYTBE5F2gPTMZOPqc8NWMGvPoq/OtfMHSo09Go02SMISfXEFE2hKmje1IhPIRQTQLKz3nyDZ6FXaayKTAJOAv43KtRBZo//4QRI6B7d3j9daejUafJGMNL3/7JbZ8uIysnl8rly2oSUAHBk29xrjEmC7gGeMMYczdQ37thBZC8QWMRETB9ug4a81PGGJ6ds44PFyZSt0oEISJOh6RUifFoqUoRuR4YCgxwPaYjZTxhDIwcaaeR+O47HTTmp3JzDU/NXsPk+B2MOCeap/q1RjQRqADi6cjiXthpqBNFpDEQ592wAsSHH8LUqXbQWO/eTkejTtN/565ncvwObrugiSYBFZDEmH9MLPrPnURCgbzhkpuNMdlejaoIMTExJiEhwanDey49HRo3hpYt4ccfdbyAH/tj11EWbkzizl7NNAkovyUiy4wxMQVtO2XVkIicB3wK7MaOIagjIkONMb+VbJgB5pNP7OCxzz7TJOCHsnJy+WH9fvq2rUu7qMq0i6rsdEhKeY0nZ6ixwOXGmHOMMWcDVwBvejcsP5eVBWPGQI8e0KvXqfdXPiUzO5e7P1/B7ZOXs2LHYafDUcrrPGksDjPGrMu7Y4xZLyJhXozJ/8XFwbZtdmI5rUrwKxnZOdz52XLmr0/iqX6t6dSwqtMhKeV1niSC5SLyIbZ6CGAIOulc4XJz4cUX7cjhK65wOhpVDOlZOYz+dBk/bzzAcwPaMrRHI6dDUqpUeJIIbgfuAR7BthH8DLztzaD82syZdgDZlClaGvAzixKT+W3zQV6+th03dG3odDhKlZoiew2JSDugKbDWGLOp1KIqgk/3GjIGYmIgJQXWr4cQXazcHxhjTvYG2nYwjWhdY1gFoKJ6DRXaWCwij2OnlxgCfC8iBa1UptzNmwfLl9s1BjQJ+IVj6VkMmbCYXzcdBNAkoIJSUb2GhgDtjTHXA12BO0onJD/23//a0cM33eR0JMoDR49nMXTCYpZsPURqRpbT4SjlmKLaCDKMMWkAxpgDIqKd4Ysycyb88gu89RaEaacqX3coLZObJixmc1IqH9zUhT6tazsdklKOKSoRNHFbq1iApu5rFxtjrvFqZP4kPR3uuAOaNoVbb3U6GnUKR09kMXhcPNuS0xg3rAsXtqzldEhKOaqoRHBtvvvveDMQv/bpp7B/vx0/UL6809GoU4gMD6V7k2o8dWVrzmmm6y4p5dFcQ77E53oN5eTAWWdB5cqwZIl2GfVhe4+eIDvH0KCaJmsVfM5oriF1Cl99BZs2wbRpmgR82M5Dx7lxQjyR4WWZc/e5lCmjfyul8mgiOBPGwMsv27aBa7TJxFdtT07jxvGLSUnP4p3BnTUJKJWPx4lARMKNMRneDMbv/PyzrQ56/30dN+CjthxI5cbx8WRm5/L5qB60ra+ziCqV3ym7hIpINxH5A9jkut9BRHSKCbClgVq14OabnY5EFeLFuevJyTXEjdYkoFRhPBkb8BbQD0gGMMaswq5Ydkoi0ldENojIZhF5rIj9rhMRIyIFNmT4pNWr4Ztv4J57oFw5p6NRhXjt+o5Mva0nrepUcjoUpXyWJ4mgjDFme77Hck71JBEJAd4FLgNaA4NFpHUB+0ViJ7Vb7EEsvuPVV6FCBTt+QPmUNbuPcnfcCtKzcqhcvixNa1Z0OiSlfJoniWCniHQDjIiEiMh9wEYPntcNu6xlojEmE5gC9C9gv+eAMUC6p0E7bscOO2Zg9GioVs3paJSbFTsOM3h8PMu3H+ZQWqbT4SjlFzxJBHcADwANgf1ADzybd6g+sNPt/i7XYyeJSCeggTFmTlEvJCKjRSRBRBIOHDjgwaG9bOxY+/u++5yNQ/1NwrZDDP1oCVXLhzH1th7Uq6JVdkp54pS9howxScCg03jtgvronRy95pq7aCww3IMYxgHjwA4oO41YSs6hQzB+PAweDA11znpfsTgxmRETl1KnUgSfj+pBncoRToeklN/wZPH68bidwPMYY0af4qm7gAZu96OAPW73I4G2wE+uueDrALNF5CpjjA8NHc7nvfcgLQ0eftjpSJSbyuXL0rZ+Zd65sRO1IjUJKFUcnowjmO92OwK4mr9X+RRmKdBcRBoDu7GlihvzNhpjjgInJ3oRkZ+Ah3w6CZw4YWcXvfxyaNfO6WgUdpxAkxoVaFWnElNH9zi5wIxSynOnbCMwxkx1+4kFrsH2AjrV87KBu4B5wHpgmjFmrYg8KyJXnWngjpg4EQ4cgEcecToSBXy/bj+XvfELny/ZAaBJQKnTdDpTTDQGPFrV2xgzF5ib77GnCtn3wtOIpfTk5Nguo927w/nnOx1N0Jv7x17uiVtBm/qV6de+ntPhKOXXPGkjOMxfbQRlgENAoYPDAtYXX0BiIrzyik4u57CvVu7mgWmr6NigChNHdCUyoqzTISnl14pMBGLL2h2wdfwAucbf5q0uCcbAmDHQvDn0L2gohCotu4+c4KHpq4hpVJWPh3elQrjOm6jUmSryv8gYY0RkpjGmS2kF5JPWrYNly+Dtt3VyOYfVr1KO8cNi6N64OuXC9G+hVEnwZEDZEhHp7PVIfNmXX9rqoGvzL9qmSsun8dv5Yf1+AC5sWUuTgFIlqNBEICJ5pYVzsclgg4gsF5EVIrK8dMLzEV9/DT16QN26TkcSlCb8ksiTs9bw5Yrdp95ZKVVsRVUNLQE6AwNKKRbfdPAgJCTAM884HUlQeu+nzYz5dgOXt6vDGzd0dDocpQJSUYlAAIwxW0opFt/0/fe2sfjSS52OJOi8OX8TY+dv5KoO9Xh9YAdCQzypyVRKFVdRiaCmiDxQ2EZjzOteiMf3fPstVK8OXYK7vby0GWM4mJrBtZ2jGHNde0J0eUmlvKaoRBACVKTgyeOCQ24uzJsHF1+svYVKiTGG5LRMalQM55mr2gDoGsNKeVlRiWCvMebZUovEF61eDfv3Q9++TkcSFIwxPPP1Ouat3cfXd59LjYrhToekVFAoqtJVL8O+/db+vuQSZ+MIArm5hidmrWHi79u4vF1dqlcIczokpYJGUSWC3qUWha+aNw86dNBuo16Wk2t47IvVTF+2izsubMojl7bUCeSUKkWFlgiMMYdKMxCfk5ICv/6qvYVKwQcLtzB92S7u7d1ck4BSDtCJWgqzYAFkZ2v7QCkY1rMRNSPDGRjT4NQ7K6VKnHbMLsy330KFCnDOOU5HEpAys3N5c/4mTmTmEBlRVpOAUg7SRFAQY2wiuOgiCNNGy5KWnpXDHZOXMXb+RhZuTHI6HKWCniaCgmzeDFu3arWQF6Rn5TBqUgI//JnEC1e3pW9bbYhXymnaRlCQb76xv7WhuEQdz8zm1okJxG9NZsx17bU6SCkfoYmgIJMmQceO0LSp05EElKRjGSQeTOX1gR24ulOU0+EopVw0EeRnjF2I5vbbnY4kYJzIzCGibBmia1RgwUMXUj5Mv3ZK+RJtI8hv/344cQKaNHE6koBw5HgmAz9cxKvfbQDQJKCUD9JEkN8W16zbWi10xg6lZTJ4/GI27Euhc8OqToejlCqEXp7ll5hof2uJ4IwcSMlgyIR4ticfZ8LNMZzfoqbTISmlCqGJIL8tW+z6xNHRTkfit7Jzchn60WJ2HjrBJ8O7cnazGk6HpJQqgiaC/BITISoKwnUK5NMVGlKGe3o3p0bFcLo1ruZ0OEqpU9BEkN+WLVotdJp2HjrOhn0p9Gldm8vb6UAxpfyFNhbnl5ioDcWnYdvBNG74cBGPfbmatIxsp8NRShWDlgjcpaXBvn1aIiimzUmpDJkQT2Z2LpNHdqdCuH6tlPIn+h/rbutW+1tLBB7bsC+FIRMWA4Ypo3vSsk6k0yEppYpJE4E77TpabN+s2UsZgc9H9aRZrYpOh6OUOg2aCNzpYDKP5eQaQsoI9/ZuzpDudmEZpZR/0sZid4mJUKkSVNMuj0VZvuMwF49dyJYDqYiIJgGl/JwmAnd5XUd1zdxCLd12iKETFpOTa4goG+J0OEqpEqCJwJ12HS3Soi3JDPtoCbUrRzB1dE/qVynndEhKqRKgiSBPTo7tNaQNxQVavuMwIyYuIapqOaaM7kGdyhFOh6SUKiHaWJxnzx7IzNQSQSFa1Ynkms5RPHhxC6pX1DYBpQKJlgjy5PUY0hLB3/y++SCpGdmUDwvlv1e30ySgVADSRJAnbwyBlghO+t/qvQz7eAmvztvgdChKKS/SRJAnMRFCQqCBLqgOMGvFbu6OW06nhlV48JIWToejlPIibSPIs2ULNGoEZcs6HYnjpifs5JEvVtOjcXUm3ByjcwcpFeD0PzxPYqK2DwDHM7N5/fuNnNusBuOGxlAuTMcKKBXoNBHk2bIFrr3W6SgcZYyhfFgo027rSc3IcB0wplSQ0DYCgKNHITk5qBuKx/+cyDNfr8MYQ4Nq5TUJKBVENBFA0M86+u6Czbwwdz0HUjLIyTVOh6OUKmVaNQRB23XUGMObP2zijfmb6N+xHq9d34HQEL02UCrYaCKAoC0RjJ2/ibd+2MR1XaJ4+dr2hJTRyfaUCkZ6+Qe2obh6dahc2elISlX7+pUZ2qMRYzQJKBXUtEQAQdV1NDfX8Mfuo3RoUIU+rWvTp3Vtp0NSSjlMSwRgSwRB0D6Qm2t4YtYarn7vN9buOep0OEopH6GJIDsbtm8P+BJBTq7hkS9WE7dkB3dc2JTWdSs5HZJSykd4NRGISF8R2SAim0XksQK2PyAi60RktYj8ICKNvBlPgXbssGsRBHAiyM7J5YFpK5mxbBf392nBQ5e0RHQVNqWUi9cSgYiEAO8ClwGtgcEi0jrfbiuAGGNMe2AGMMZb8RQqCLqOfrt2H1+t3MPDl7bk3j7NNQkopf7Gm43F3YDNxphEABGZAvQH1uXtYIxZ4LZ/PHCTF+MpWBCsQ3BFu7rUui2Cbo2rOR2KUsoHebNqqD6w0+3+LtdjhbkV+MaL8RQsMRHCwqB+UaH5n/SsHB6YupIN+1IQEU0CSqlCeTMRFFT/UOD8BSJyExADvFLI9tEikiAiCQcOHCjBELGJIDrarkUQIE5k5jBqUgJfrtjN6l1HnA5HKeXjvJkIdgHuq7xEAXvy7yQifYAngKuMMRkFvZAxZpwxJsYYE1OzZs2SjTLAuo6mZWQzYuISft18kDHXtef6GF1oRylVNG8mgqVAcxFpLCJhwCBgtvsOItIJ+BCbBJK8GEvBjLGJIEDaB1Izsrn54yUs2XqIsQM7MlCTgFLKA15LBMaYbOAuYB6wHphmjFkrIs+KyFWu3V4BKgLTRWSliMwu5OW849AhOHYsYEoEoWWEihGhvD24MwM6BVabh1LKe7w6xYQxZi4wN99jT7nd7uPN459SgEw2d+R4JiJC5XJl+WR4V+0eqpQqluAeWRwAXUeTUzMYNC6eUZMSMMZoElBKFVtwJwI/LxEkpaQzaFw8Ww+mcfdFzTQJKKVOS3DPPpqYCLVrQ4UKTkdSbPuOpnPj+Hj2Hk3nkxFdObtpDadDUkr5qeBOBH7cdfTB6StJSslg0q3d6Bqtg8WUUqcvuBNBYiKcf77TUZyWF69uz6HjmXRsUMXpUJRSfi542wgyMmDnTr8qEWw9mMaYb/8kN9fQsHp5TQJKqRIRvIlg+3Y7oMxPGoo3J6Vww4eLmLJ0J3uPpTsdjlIqgARv1VBe11E/KBFs2JfCkAnxgDBldA/qVynndEhKqQASvCUCP+k6unbPUQaNW0RIGWHqbT1oUTvS6ZCUUgEmuEsE5cpBnTpOR1Kk5NRMqpQP45PhXYmu4X/dXJVSvi94E0Fioi0N+OggrMNpmVStEMb5LWry3f3nUzYkeAtvSinvCt6zS14i8EGLE5M5f8wC5v6xF0CTgFLKq4LzDGOMTQQ+2FD8++aDDP9kKbUqhdOlUVWnw1FKBYHgTASHDkFaGjRq5HQkf7Nw4wFGTFxKw2rlmTK6J7UrRTgdklIqCARnG0GSaw2c2rWdjcPN9uQ0RsUm0KxWRSaP7E61CmFOh6SUChLBmQjy1j0u6WUvz0Cj6hV4pn8bLmtbhyrlNQkopUpPcFYN5ZUIfCARzP1jL2t2HwVgcLeGmgSUUqUuOBOBj5QIZq7YxV2fL+etHzY5GodSKrgFdyKo4dwc/tOW7uSBaavo0aQ6bwzq6FgcSikVvG0EVapAmDPVMJ8t3s4TM9dwXvMajB8WQ0TZEEfiUEopCOYSgUPVQsYYflyfxEWtamkSUEr5hOAtETiQCNKzcogoG8K7QzpTRoSw0ODMw0op3xKcZyIHEsE7P27imvd+51h6FhFlQzQJKKV8RnCejUoxERhjeP37jbz63UZa1omkvFYFKaV8TPBVDeXm2kRQq5bXD2WM4eVvN/DBwi1c3yWKl65tT0gZ35ztVCkVvIIvERw5Ajk5pVIi+GBhIh8s3MKQ7g15rn9bymgSUEr5oOBLBKU4mKx/x3pk5+Ry10XNEB9d90AppYKvjcDLiSA31zAtYSc5uYZ6Vcpxd+/mmgSUUj5NSwQlKCfX8MiM1XyxfBeVIsrSt61vL4OplFKgiaDEZOfk8sC0VcxetYcHLm6hSUAp5Tc0EZSAzOxc7p2ygm/W7OPRvq2440LfW/lMKaUKE5yJoFIlCA8vsZfclJTCTxsO8J8rzmLkeb65DrJSShUm+BJBUlKJlQZycw1lyght6lVmwUMXUqeyLi2plPI/wdlrqAQSwYnMHG7+ZAmfL94BoElAKeW3NBGchrSMbIZ/soTfNh8kXOcMUkr5ueA7i51hIjiWnsWwj5eQsP0wY2/oyLVdokowOKWUKn3B1UZgzBklgszsXIZ+tIS1u4/yzuBOXNaubgkHqJRSpS+4EsGxY5CVddqJICy0DFe0q8NdvZpxcevaJRycUko5I7gSQd4YgmLOPHowNYO9R9JpF1WZ0efrGAGlVGAJrjaC0xhMlnQsnUHj4rk1dinpWTleCkwppZwTXCWCpCT728NEsO9oOjeOj2ffsXQ+Ht5V1xdWSgWk4EoExSgR7Dp8nBvHL+ZQWiaTbulGTHQ1LwenlFLO0ERQiPE/J3L4eCaf3tqNTg2rejkwpZRyTvAlggoVoFy5U+76xBWtGdozmma1KpZCYEop5ZzgaywuojSwOSmFmyYsJjk1g7DQMpoElFJBIfhKBIV0Hf1z3zGGjF9MmTLC4eNZVK9YcrOTKqWULwu+RFD3n6OB1+w+ytCPFhMeGsLno7rTpKaWBJQqCVlZWezatYv09HSnQwkaERERREVFUbZsWY+fE3yJoH37vz20ZvdRbhwfT2REWT4f1Z1G1Ss4FJxSgWfXrl1ERkYSHR2ta3eXAmMMycnJ7Nq1i8aNG3v8vOBpIzCmwLUIakWG06lhVabe1kOTgFIlLD09nerVq2sSKCUiQvXq1YtdAgueRJCaChkZJxPBn/uOkZ2TS61KEcTe0o2oquUdDlCpwKRJoHSdzucdPInAbQzBb5sPMuDd33jt+43OxqSUKhUzZ85ERPjzzz9PPvbTTz/Rr1+/v+03fPhwZsyYAdj2jccee4zmzZvTtm1bunXrxjfffHPGsbz44os0a9aMli1bMm/evAL3Oe+88+jYsSMdO3akXr16DBgw4GTMlStXPrnt2WefPeN4wMttBCLSF3gTCAEmGGNeyrc9HJgEdAGSgRuMMdu8EowrESwMrcnoiUuJrl6BW87xvA5NKeW/4uLiOPfcc5kyZQr/93//59FznnzySfbu3cuaNWsIDw9n//79LFy48IziWLduHVOmTGHt2rXs2bOHPn36sHHjRkJC/j59zS+//HLy9rXXXkv//v1P3j/vvPOYM2fOGcWRn9dKBCISArwLXAa0BgaLSOt8u90KHDbGNAPGAi97Kx4OHGB+026MWgdNa1YkbnQPakZqF1GlAl1qaiq//fYbH330EVOmjiwIPgAAC/NJREFUTPHoOcePH2f8+PG8/fbbhIfb80Tt2rUZOHDgGcXy1VdfMWjQIMLDw2ncuDHNmjVjyZIlhe6fkpLCjz/+eLJE4C3eLBF0AzYbYxIBRGQK0B9Y57ZPf+D/XLdnAO+IiBhjTEkHk/LlVzx0xX2cVb0ck0b1oHJ5z7tWKaVKwH33wcqVJfuaHTvCG28UucusWbPo27cvLVq0oFq1aixfvpzOnTsX+ZzNmzfTsGFDKlWqdMoQ7r//fhYsWPCPxwcNGsRjjz32t8d2795Njx49Tt6Piopi9+7dhb72zJkz6d2799/iWLRoER06dKBevXq8+uqrtGnT5pQxnoo3E0F9YKfb/V1A98L2McZki8hRoDpw0H0nERkNjAZo2LDhaQUTedklxH63iMa3/5dKmgSUChpxcXHcd999gD05x8XF0blz50IbVYvb2Dp27FiP9y3oGreo48XFxTFy5MiT9zt37sz27dupWLEic+fOZcCAAWzatKlY8RbEm4mgoHeX/1PwZB+MMeOAcQAxMTGnV1q4/no6XH/9aT1VKVUCTnHl7g3Jycn8+OOPrFmzBhEhJycHEWHMmDFUr16dw4cP/23/Q4cOUaNGDZo1a8aOHTtISUkhMjKyyGMUp0QQFRXFzp1/XR/v2rWLevXqFRr7kiVLmDlz5snH3EsGl19+Of/61784ePAgNWrUKDLGU/Fmr6FdQAO3+1HAnsL2EZFQoDJwyIsxKaWCyIwZMxg2bBjbt29n27Zt7Ny5k8aNG/Prr7/SvHlz9uzZw/r16wHYvn07q1atomPHjpQvX55bb72Ve+65h8zMTAD27t3L5MmT/3GMsWPHsnLlyn/85E8CAFdddRVTpkwhIyODrVu3smnTJrp161Zg7NOnT6dfv35EREScfGzfvn0nSxVLliwhNzeX6tWrn/Hn5M1EsBRoLiKNRSQMGATMzrfPbOBm1+3rgB+90T6glApOcXFxXH311X977Nprr+Xzzz8nPDycyZMnM2LECDp27Mh1113HhAkT/r+9+w+Suq7jOP58leBB6pWBjYZxOqJxXIB0OZQzGWEOXROQwxwwoNJojZQxavRHQ1NmNONoTkViSMaAjnrEjdaN4ZBjKA7DIVfKb01CshucYBhiGoVSfPfH53OyLnvsd29/sft9P2Z2Zve7393v+717t+/9fr7ffX9obGwEYPHixQwfPpzm5mZaWlqYPn06wwc433mfMWPG0N7eTnNzM1OmTGHp0qXvnTHU1tbG/v0nvit3dHQwe/bs9z2+s7OTlpYWxo0bx4IFC+jo6CjJ7zRUzs9dSW3ALwinj64ws59KuhPoMbMuSQ3Aw8DlhD2BWX0Hl/vT2tpqPT09ZYvZOVc6u3fvZvTo0dUOI3Vyve6S/mJmrbnWL+vvCMxsLbA2a9kPM64fA3zg3jnnqig9vyx2zjmXkxcC55xLOS8Ezrmy8vM/Kmsgr7cXAudc2TQ0NHDo0CEvBhXSNx9B5imnSaRrYhrnXEWNGDGC3t5eDvZ1/3Vl1zdDWSG8EDjnymbQoEEFzZTlqsOHhpxzLuW8EDjnXMp5IXDOuZQra4uJcpB0EPjHAB8+jKwW1yngOaeD55wOxeQ80sxyNkuquUJQDEk9/fXaqFeeczp4zulQrpx9aMg551LOC4FzzqVc2grB8moHUAWeczp4zulQlpxTdYzAOefcydK2R+Cccy5LXRYCSVMkvSJpj6STJg6VdKak1fH+zZKaKh9laSXI+XZJuyRtk/SMpJHViLOU8uWcsd4MSSap5s8wSZKzpPb4Xu+U9GilYyy1BH/bn5C0XtKL8e+7rRpxloqkFZIOSNrRz/2StCS+HtskTSh6o2ZWVxfCtJh/By4GBgNbgeasdb4FLIvXZwGrqx13BXKeBAyN1+enIee43tnABqAbaK123BV4n0cBLwIfibfPq3bcFch5OTA/Xm8G9lU77iJz/jwwAdjRz/1twFOAgInA5mK3WY97BFcAe8xsr5n9D+gApmWtMw1YFa93ApNVihmgqydvzma23szeije7gcLaE55+krzPAD8B7gaOVTK4MkmS8zeApWZ2GMDMDlQ4xlJLkrMB58TrjcB+apiZbSDM4d6facBDFnQDH5Z0fjHbrMdC8HHgnxm3e+OynOuY2TvAEeCjFYmuPJLknOlGwjeKWpY3Z0mXAxea2ZOVDKyMkrzPlwKXStooqVvSlIpFVx5Jcr4DmCuplzBH+ncqE1rVFPr/nlc9tqHO9c0++9SoJOvUksT5SJoLtAJXlTWi8jtlzpI+APwcmFepgCogyft8BmF46AuEvb7nJbWY2b/LHFu5JMl5NrDSzO6V9Fng4Zjzu+UPrypK/vlVj3sEvcCFGbdHcPKu4nvrSDqDsDt5ql2x012SnJF0NbAImGpm/61QbOWSL+ezgRbgWUn7CGOpXTV+wDjp3/YfzOxtM3sNeIVQGGpVkpxvBH4HYGabgAZCT556lej/vRD1WAi2AKMkXSRpMOFgcFfWOl3ADfH6DODPFo/C1Ki8OcdhkgcIRaDWx40hT85mdsTMhplZk5k1EY6LTDWznuqEWxJJ/rZ/TzgxAEnDCENFeysaZWklyfl1YDKApNGEQlDPU6J1AdfHs4cmAkfM7I1inrDuhobM7B1JtwDrCGccrDCznZLuBHrMrAv4LWH3cQ9hT2BW9SIuXsKc7wHOAtbE4+Kvm9nUqgVdpIQ515WEOa8DrpG0CzgOfM/MDlUv6uIkzPm7wG8k3UYYIplXy1/sJD1GGNobFo97/AgYBGBmywjHQdqAPcBbwNeL3mYNv17OOedKoB6HhpxzzhXAC4FzzqWcFwLnnEs5LwTOOZdyXgiccy7lvBC4046k45Jeyrg0nWLdpv66NBa4zWdjh8utsT3DZQN4jpslXR+vz5N0QcZ9D0pqLnGcWySNT/CYWyUNLXbbrn55IXCno6NmNj7jsq9C251jZuMIDQnvKfTBZrbMzB6KN+cBF2Tcd5OZ7SpJlCfivJ9kcd4KeCFw/fJC4GpC/Ob/vKS/xsvncqwzRtILcS9im6RRcfncjOUPSPpgns1tAC6Jj50c+9xvj33iz4zL79KJ+R1+FpfdIWmhpBmEfk6PxG0Oid/kWyXNl3R3RszzJP1qgHFuIqPZmKRfS+pRmIfgx3HZAkJBWi9pfVx2jaRN8XVcI+msPNtxdc4LgTsdDckYFnoiLjsAfMnMJgAzgSU5Hncz8EszG0/4IO6NLQdmAlfG5ceBOXm2/1Vgu6QGYCUw08w+Rfgl/nxJ5wJfA8aY2VhgceaDzawT6CF8cx9vZkcz7u4Ers24PRNYPcA4pxBaSvRZZGatwFjgKkljzWwJoQ/NJDObFNtO/AC4Or6WPcDtebbj6lzdtZhwdeFo/DDMNAi4L46JHyf00Mm2CVgkaQTwuJm9Kmky8GlgS2ytMYRQVHJ5RNJRYB+hlfFlwGtm9rd4/yrg28B9hPkNHpT0RyBxm2szOyhpb+wR82rcxsb4vIXE+SFCy4XM2anaJX2T8H99PmGSlm1Zj50Yl2+M2xlMeN1cinkhcLXiNuBfwDjCnuxJE82Y2aOSNgNfAdZJuonQsneVmX0/wTbmZDalk5RzjorY/+YKQqOzWcAtwBcLyGU10A68DDxhZqbwqZw4TsJMXXcBS4FrJV0ELAQ+Y2aHJa0kNF/LJuBpM5tdQLyuzvnQkKsVjcAbscf8dYRvw+8j6WJgbxwO6SIMkTwDzJB0XlznXCWfr/lloEnSJfH2dcBzcUy90czWEg7E5jpz5z+EVti5PA5MJ/TRXx2XFRSnmb1NGOKZGIeVzgHeBI5I+hjw5X5i6Qau7MtJ0lBJufauXIp4IXC14n7gBkndhGGhN3OsMxPYIekl4JOE6fx2ET4w/yRpG/A0YdgkLzM7RujsuEbSduBdYBnhQ/XJ+HzPEfZWsq0ElvUdLM563sPALmCkmb0QlxUcZzz2cC+w0My2EuYq3gmsIAw39VkOPCVpvZkdJJzR9FjcTjfhtXIp5t1HnXMu5XyPwDnnUs4LgXPOpZwXAuecSzkvBM45l3JeCJxzLuW8EDjnXMp5IXDOuZTzQuCccyn3f7iIG8fKL7lLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_prob = dt4.predict_proba(x_test)[:,1]\n",
    "false_positive_rate, true_positive_rate, thresholds = roc_curve(y_test, y_prob)\n",
    "roc_auc = auc(false_positive_rate, true_positive_rate)\n",
    "\n",
    "plt.figure(figsize=(6,6))\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.plot(false_positive_rate,true_positive_rate, color='red',label = 'AUC = %0.2f' % roc_auc)\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.plot([0, 1], [0, 1],linestyle='--')\n",
    "plt.axis('tight')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training score:  0.7147329650092081\n",
      "Testing score:  0.7863333333333333\n",
      "[[6043 1031]\n",
      " [ 892 1034]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.85      0.86      7074\n",
      "           1       0.50      0.54      0.52      1926\n",
      "\n",
      "    accuracy                           0.79      9000\n",
      "   macro avg       0.69      0.70      0.69      9000\n",
      "weighted avg       0.79      0.79      0.79      9000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "dt4.fit(x_smote, y_smote)\n",
    "dt4_score_train = dt4.score(x_smote, y_smote)\n",
    "print(\"Training score: \",dt4_score_train)\n",
    "dt4_score_test = dt4.score(x_test, y_test)\n",
    "print(\"Testing score: \",dt4_score_test)\n",
    "\n",
    "y_pred = dt4.predict(x_test)\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training score:  0.5177836566725456\n",
      "Testing score:  0.23133333333333334\n",
      "[[ 201 6873]\n",
      " [  45 1881]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.03      0.05      7074\n",
      "           1       0.21      0.98      0.35      1926\n",
      "\n",
      "    accuracy                           0.23      9000\n",
      "   macro avg       0.52      0.50      0.20      9000\n",
      "weighted avg       0.69      0.23      0.12      9000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "dt4.fit(x_downsampled, y_downsampled)\n",
    "dt4_score_train = dt4.score(x_downsampled, y_downsampled)\n",
    "print(\"Training score: \",dt4_score_train)\n",
    "dt4_score_test = dt4.score(x_test, y_test)\n",
    "print(\"Testing score: \",dt4_score_test)\n",
    "\n",
    "y_pred = dt4.predict(x_test)\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Best result on original data = 0.83, AUC = 0.75"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Random Forest Classifier "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "rf = RandomForestClassifier(n_estimators=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[6720  354]\n",
      " [1206  720]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.95      0.90      7074\n",
      "           1       0.67      0.37      0.48      1926\n",
      "\n",
      "    accuracy                           0.83      9000\n",
      "   macro avg       0.76      0.66      0.69      9000\n",
      "weighted avg       0.81      0.83      0.81      9000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "rf.fit(x_train, y_train)\n",
    "y_pred = rf.predict(x_test)\n",
    "\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'False Positive Rate')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_prob = rf.predict_proba(x_test)[:,1]\n",
    "false_positive_rate, true_positive_rate, thresholds = roc_curve(y_test, y_prob)\n",
    "roc_auc = auc(false_positive_rate, true_positive_rate)\n",
    "\n",
    "plt.figure(figsize=(6,6))\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.plot(false_positive_rate,true_positive_rate, color='red',label = 'AUC = %0.2f' % roc_auc)\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.plot([0, 1], [0, 1],linestyle='--')\n",
    "plt.axis('tight')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[6304  770]\n",
      " [ 989  937]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.89      0.88      7074\n",
      "           1       0.55      0.49      0.52      1926\n",
      "\n",
      "    accuracy                           0.80      9000\n",
      "   macro avg       0.71      0.69      0.70      9000\n",
      "weighted avg       0.80      0.80      0.80      9000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "rf.fit(x_smote, y_smote)\n",
    "y_pred = rf.predict(x_test)\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[3481 3593]\n",
      " [ 867 1059]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.49      0.61      7074\n",
      "           1       0.23      0.55      0.32      1926\n",
      "\n",
      "    accuracy                           0.50      9000\n",
      "   macro avg       0.51      0.52      0.47      9000\n",
      "weighted avg       0.68      0.50      0.55      9000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "rf.fit(x_downsampled, y_downsampled)\n",
    "y_pred = rf.predict(x_test)\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Best result on original data = 0.83, AUC = 0.77"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Support Vector Machine (SVM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import svm\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8246666666666667\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.96      0.90      7074\n",
      "           1       0.70      0.32      0.44      1926\n",
      "\n",
      "    accuracy                           0.82      9000\n",
      "   macro avg       0.77      0.64      0.67      9000\n",
      "weighted avg       0.81      0.82      0.80      9000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Radial Basis Function Kernel with low gamma 0.01\n",
    "\n",
    "clf = svm.SVC(kernel='rbf', random_state=0, gamma=.01, C=1) \n",
    "\n",
    "#Train the model using the training sets\n",
    "clf.fit(x_train, y_train)\n",
    "\n",
    "#Predict the response for test dataset\n",
    "y_pred = clf.predict(x_test)\n",
    "\n",
    "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))\n",
    "confusion_matrix(y_test, y_pred)\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'False Positive Rate')"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "false_positive_rate, true_positive_rate, thresholds = roc_curve(y_test, y_pred)\n",
    "roc_auc = auc(false_positive_rate, true_positive_rate)\n",
    "\n",
    "plt.figure(figsize=(6,6))\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.plot(false_positive_rate,true_positive_rate, color='red',label = 'AUC = %0.2f' % roc_auc)\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.plot([0, 1], [0, 1],linestyle='--')\n",
    "plt.axis('tight')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7883333333333333\n",
      "[[5982 1092]\n",
      " [ 813 1113]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.85      0.86      7074\n",
      "           1       0.50      0.58      0.54      1926\n",
      "\n",
      "    accuracy                           0.79      9000\n",
      "   macro avg       0.69      0.71      0.70      9000\n",
      "weighted avg       0.80      0.79      0.79      9000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "clf = svm.SVC(kernel='rbf', random_state=0, gamma=.01, C=1) \n",
    "\n",
    "#Train the model using the training sets\n",
    "clf.fit(x_smote, y_smote)\n",
    "\n",
    "#Predict the response for test dataset\n",
    "y_pred = clf.predict(x_test)\n",
    "\n",
    "# model accuracy:\n",
    "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.5201111111111111\n",
      "[[3466 3608]\n",
      " [ 711 1215]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.49      0.62      7074\n",
      "           1       0.25      0.63      0.36      1926\n",
      "\n",
      "    accuracy                           0.52      9000\n",
      "   macro avg       0.54      0.56      0.49      9000\n",
      "weighted avg       0.71      0.52      0.56      9000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "clf = svm.SVC(kernel='rbf', random_state=0, gamma=.01, C=1) \n",
    "\n",
    "#Train the model using the training sets\n",
    "clf.fit(x_downsampled, y_downsampled)\n",
    "\n",
    "#Predict the response for test dataset\n",
    "y_pred = clf.predict(x_test)\n",
    "\n",
    "# model accuracy:\n",
    "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Best result on original data = 0.82, AUC = 0.64"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.5765555555555556\n",
      "[[3710 3364]\n",
      " [ 447 1479]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.52      0.66      7074\n",
      "           1       0.31      0.77      0.44      1926\n",
      "\n",
      "    accuracy                           0.58      9000\n",
      "   macro avg       0.60      0.65      0.55      9000\n",
      "weighted avg       0.77      0.58      0.61      9000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "naive = GaussianNB()\n",
    "naive.fit(x_train, y_train)\n",
    "    \n",
    "# Make predictions and evalute\n",
    "y_pred = naive.predict(x_test)\n",
    "\n",
    "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))\n",
    "print(confusion_matrix(y_test, y_pred))    \n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'False Positive Rate')"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_prob = naive.predict_proba(x_test)[:,1]\n",
    "false_positive_rate, true_positive_rate, thresholds = roc_curve(y_test, y_prob)\n",
    "roc_auc = auc(false_positive_rate, true_positive_rate)\n",
    "\n",
    "plt.figure(figsize=(6,6))\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.plot(false_positive_rate,true_positive_rate, color='red',label = 'AUC = %0.2f' % roc_auc)\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.plot([0, 1], [0, 1],linestyle='--')\n",
    "plt.axis('tight')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.39444444444444443\n",
      "[[1846 5228]\n",
      " [ 222 1704]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.26      0.40      7074\n",
      "           1       0.25      0.88      0.38      1926\n",
      "\n",
      "    accuracy                           0.39      9000\n",
      "   macro avg       0.57      0.57      0.39      9000\n",
      "weighted avg       0.75      0.39      0.40      9000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "naive.fit(x_smote, y_smote)\n",
    "    \n",
    "# Make predictions and evalute\n",
    "y_pred = naive.predict(x_test)\n",
    "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))\n",
    "print(confusion_matrix(y_test, y_pred))    \n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1089 5985]\n",
      " [ 189 1737]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.15      0.26      7074\n",
      "           1       0.22      0.90      0.36      1926\n",
      "\n",
      "    accuracy                           0.31      9000\n",
      "   macro avg       0.54      0.53      0.31      9000\n",
      "weighted avg       0.72      0.31      0.28      9000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "naive.fit(x_downsampled, y_downsampled)\n",
    "y_pred = naive.predict(x_test)\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Best result on original data = 0.58, AUC = 0.74"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. XGBoost Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBClassifier\n",
    "\n",
    "model = XGBClassifier(silent=False, \n",
    "                      scale_pos_weight=1,\n",
    "                      learning_rate=0.01,  \n",
    "                      colsample_bytree = 0.8,\n",
    "                      subsample = 0.8,\n",
    "                      objective='binary:logistic', \n",
    "                      n_estimators=100, \n",
    "                      reg_alpha = 0.3,\n",
    "                      max_depth=4, \n",
    "                      gamma=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8281111111111111\n",
      "[[6758  316]\n",
      " [1231  695]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.96      0.90      7074\n",
      "           1       0.69      0.36      0.47      1926\n",
      "\n",
      "    accuracy                           0.83      9000\n",
      "   macro avg       0.77      0.66      0.69      9000\n",
      "weighted avg       0.81      0.83      0.81      9000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model.fit(x_train, y_train)\n",
    "    \n",
    "# Make predictions and evalute\n",
    "y_pred = model.predict(x_test)\n",
    "\n",
    "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))\n",
    "print(confusion_matrix(y_test, y_pred)) \n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'False Positive Rate')"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_prob = model.predict_proba(x_test)[:,1]\n",
    "false_positive_rate, true_positive_rate, thresholds = roc_curve(y_test, y_prob)\n",
    "roc_auc = auc(false_positive_rate, true_positive_rate)\n",
    "\n",
    "plt.figure(figsize=(6,6))\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.plot(false_positive_rate,true_positive_rate, color='red',label = 'AUC = %0.2f' % roc_auc)\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.plot([0, 1], [0, 1],linestyle='--')\n",
    "plt.axis('tight')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7912222222222223\n",
      "[[6049 1025]\n",
      " [ 854 1072]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.86      0.87      7074\n",
      "           1       0.51      0.56      0.53      1926\n",
      "\n",
      "    accuracy                           0.79      9000\n",
      "   macro avg       0.69      0.71      0.70      9000\n",
      "weighted avg       0.80      0.79      0.79      9000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model.fit(x_smote, y_smote)\n",
    "    \n",
    "# Make predictions and evalute\n",
    "y_pred = model.predict(x_test)\n",
    "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))\n",
    "print(confusion_matrix(y_test, y_pred)) \n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2947 4127]\n",
      " [ 631 1295]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.42      0.55      7074\n",
      "           1       0.24      0.67      0.35      1926\n",
      "\n",
      "    accuracy                           0.47      9000\n",
      "   macro avg       0.53      0.54      0.45      9000\n",
      "weighted avg       0.70      0.47      0.51      9000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model.fit(x_downsampled, y_downsampled)\n",
    "y_pred = model.predict(x_test)\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Best result on original data = 0.83, AUC = 0.78"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Best Accuracy = 0.83, DT, RF, XGB |  Best AUC = 0.78 XGB | Best Model = XGBoost Classifier"
   ]
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}