Basic Model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3355052e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "import sklearn\n",
    "#import xgboost as xgb\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "88dd6e5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# Going to use these 5 base models for the stacking\n",
    "from sklearn.ensemble import (RandomForestClassifier, AdaBoostClassifier, \n",
    "                              GradientBoostingClassifier, ExtraTreesClassifier)\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import KFold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6737ae13",
   "metadata": {},
   "outputs": [],
   "source": [
    "#reading in the two tables\n",
    "app = pd.read_csv('application_record.csv')\n",
    "credit = pd.read_csv('credit_record.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d7a223ed",
   "metadata": {},
   "outputs": [
    {
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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>CODE_GENDER</th>\n",
       "      <th>FLAG_OWN_CAR</th>\n",
       "      <th>FLAG_OWN_REALTY</th>\n",
       "      <th>CNT_CHILDREN</th>\n",
       "      <th>AMT_INCOME_TOTAL</th>\n",
       "      <th>NAME_INCOME_TYPE</th>\n",
       "      <th>NAME_EDUCATION_TYPE</th>\n",
       "      <th>NAME_FAMILY_STATUS</th>\n",
       "      <th>NAME_HOUSING_TYPE</th>\n",
       "      <th>DAYS_BIRTH</th>\n",
       "      <th>DAYS_EMPLOYED</th>\n",
       "      <th>FLAG_MOBIL</th>\n",
       "      <th>FLAG_WORK_PHONE</th>\n",
       "      <th>FLAG_PHONE</th>\n",
       "      <th>FLAG_EMAIL</th>\n",
       "      <th>OCCUPATION_TYPE</th>\n",
       "      <th>CNT_FAM_MEMBERS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5008804</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>427500.0</td>\n",
       "      <td>Working</td>\n",
       "      <td>Higher education</td>\n",
       "      <td>Civil marriage</td>\n",
       "      <td>Rented apartment</td>\n",
       "      <td>-12005</td>\n",
       "      <td>-4542</td>\n",
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       "      <td>427500.0</td>\n",
       "      <td>Working</td>\n",
       "      <td>Higher education</td>\n",
       "      <td>Civil marriage</td>\n",
       "      <td>Rented apartment</td>\n",
       "      <td>-12005</td>\n",
       "      <td>-4542</td>\n",
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       "      <td>0</td>\n",
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       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>112500.0</td>\n",
       "      <td>Working</td>\n",
       "      <td>Secondary / secondary special</td>\n",
       "      <td>Married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>-21474</td>\n",
       "      <td>-1134</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Security staff</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5008808</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>270000.0</td>\n",
       "      <td>Commercial associate</td>\n",
       "      <td>Secondary / secondary special</td>\n",
       "      <td>Single / not married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>-19110</td>\n",
       "      <td>-3051</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sales staff</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5008809</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>270000.0</td>\n",
       "      <td>Commercial associate</td>\n",
       "      <td>Secondary / secondary special</td>\n",
       "      <td>Single / not married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>-19110</td>\n",
       "      <td>-3051</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sales staff</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID CODE_GENDER FLAG_OWN_CAR FLAG_OWN_REALTY  CNT_CHILDREN  \\\n",
       "0  5008804           M            Y               Y             0   \n",
       "1  5008805           M            Y               Y             0   \n",
       "2  5008806           M            Y               Y             0   \n",
       "3  5008808           F            N               Y             0   \n",
       "4  5008809           F            N               Y             0   \n",
       "\n",
       "   AMT_INCOME_TOTAL      NAME_INCOME_TYPE            NAME_EDUCATION_TYPE  \\\n",
       "0          427500.0               Working               Higher education   \n",
       "1          427500.0               Working               Higher education   \n",
       "2          112500.0               Working  Secondary / secondary special   \n",
       "3          270000.0  Commercial associate  Secondary / secondary special   \n",
       "4          270000.0  Commercial associate  Secondary / secondary special   \n",
       "\n",
       "     NAME_FAMILY_STATUS  NAME_HOUSING_TYPE  DAYS_BIRTH  DAYS_EMPLOYED  \\\n",
       "0        Civil marriage   Rented apartment      -12005          -4542   \n",
       "1        Civil marriage   Rented apartment      -12005          -4542   \n",
       "2               Married  House / apartment      -21474          -1134   \n",
       "3  Single / not married  House / apartment      -19110          -3051   \n",
       "4  Single / not married  House / apartment      -19110          -3051   \n",
       "\n",
       "   FLAG_MOBIL  FLAG_WORK_PHONE  FLAG_PHONE  FLAG_EMAIL OCCUPATION_TYPE  \\\n",
       "0           1                1           0           0             NaN   \n",
       "1           1                1           0           0             NaN   \n",
       "2           1                0           0           0  Security staff   \n",
       "3           1                0           1           1     Sales staff   \n",
       "4           1                0           1           1     Sales staff   \n",
       "\n",
       "   CNT_FAM_MEMBERS  \n",
       "0              2.0  \n",
       "1              2.0  \n",
       "2              2.0  \n",
       "3              1.0  \n",
       "4              1.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "app.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "eaac1893",
   "metadata": {},
   "outputs": [
    {
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       "      <th>MONTHS_BALANCE</th>\n",
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      "text/plain": [
       "        ID  MONTHS_BALANCE STATUS\n",
       "0  5001711               0      X\n",
       "1  5001711              -1      0\n",
       "2  5001711              -2      0\n",
       "3  5001711              -3      0\n",
       "4  5001712               0      C"
      ]
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     "execution_count": 5,
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   "source": [
    "credit.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1ac1c20c",
   "metadata": {},
   "outputs": [
    {
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       "      <td>Rented apartment</td>\n",
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       "      <td>0</td>\n",
       "      <td>427500.0</td>\n",
       "      <td>Working</td>\n",
       "      <td>Higher education</td>\n",
       "      <td>Civil marriage</td>\n",
       "      <td>Rented apartment</td>\n",
       "      <td>-12005</td>\n",
       "      <td>-4542</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>5008804</td>\n",
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       "      <td>427500.0</td>\n",
       "      <td>Working</td>\n",
       "      <td>Higher education</td>\n",
       "      <td>Civil marriage</td>\n",
       "      <td>Rented apartment</td>\n",
       "      <td>-12005</td>\n",
       "      <td>-4542</td>\n",
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       "      <th>3</th>\n",
       "      <td>5008804</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>427500.0</td>\n",
       "      <td>Working</td>\n",
       "      <td>Higher education</td>\n",
       "      <td>Civil marriage</td>\n",
       "      <td>Rented apartment</td>\n",
       "      <td>-12005</td>\n",
       "      <td>-4542</td>\n",
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      "text/plain": [
       "        ID CODE_GENDER FLAG_OWN_CAR FLAG_OWN_REALTY  CNT_CHILDREN  \\\n",
       "0  5008804           M            Y               Y             0   \n",
       "1  5008804           M            Y               Y             0   \n",
       "2  5008804           M            Y               Y             0   \n",
       "3  5008804           M            Y               Y             0   \n",
       "4  5008804           M            Y               Y             0   \n",
       "\n",
       "   AMT_INCOME_TOTAL NAME_INCOME_TYPE NAME_EDUCATION_TYPE NAME_FAMILY_STATUS  \\\n",
       "0          427500.0          Working    Higher education     Civil marriage   \n",
       "1          427500.0          Working    Higher education     Civil marriage   \n",
       "2          427500.0          Working    Higher education     Civil marriage   \n",
       "3          427500.0          Working    Higher education     Civil marriage   \n",
       "4          427500.0          Working    Higher education     Civil marriage   \n",
       "\n",
       "  NAME_HOUSING_TYPE  DAYS_BIRTH  DAYS_EMPLOYED  FLAG_MOBIL  FLAG_WORK_PHONE  \\\n",
       "0  Rented apartment      -12005          -4542           1                1   \n",
       "1  Rented apartment      -12005          -4542           1                1   \n",
       "2  Rented apartment      -12005          -4542           1                1   \n",
       "3  Rented apartment      -12005          -4542           1                1   \n",
       "4  Rented apartment      -12005          -4542           1                1   \n",
       "\n",
       "   FLAG_PHONE  FLAG_EMAIL OCCUPATION_TYPE  CNT_FAM_MEMBERS  MONTHS_BALANCE  \\\n",
       "0           0           0             NaN              2.0               0   \n",
       "1           0           0             NaN              2.0              -1   \n",
       "2           0           0             NaN              2.0              -2   \n",
       "3           0           0             NaN              2.0              -3   \n",
       "4           0           0             NaN              2.0              -4   \n",
       "\n",
       "  STATUS  \n",
       "0      C  \n",
       "1      C  \n",
       "2      C  \n",
       "3      C  \n",
       "4      C  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#merging the two tables by customer ID\n",
    "total = pd.merge(app, credit, on='ID')\n",
    "total.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "46497f64",
   "metadata": {},
   "outputs": [
    {
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       "      <th>FLAG_EMAIL</th>\n",
       "      <th>OCCUPATION_TYPE</th>\n",
       "      <th>CNT_FAM_MEMBERS</th>\n",
       "      <th>MONTHS_BALANCE</th>\n",
       "      <th>STATUS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5008804</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>427500.0</td>\n",
       "      <td>Working</td>\n",
       "      <td>Higher education</td>\n",
       "      <td>Civil marriage</td>\n",
       "      <td>Rented apartment</td>\n",
       "      <td>-12005</td>\n",
       "      <td>-4542</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5008804</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>427500.0</td>\n",
       "      <td>Working</td>\n",
       "      <td>Higher education</td>\n",
       "      <td>Civil marriage</td>\n",
       "      <td>Rented apartment</td>\n",
       "      <td>-12005</td>\n",
       "      <td>-4542</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5008804</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>427500.0</td>\n",
       "      <td>Working</td>\n",
       "      <td>Higher education</td>\n",
       "      <td>Civil marriage</td>\n",
       "      <td>Rented apartment</td>\n",
       "      <td>-12005</td>\n",
       "      <td>-4542</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5008804</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>427500.0</td>\n",
       "      <td>Working</td>\n",
       "      <td>Higher education</td>\n",
       "      <td>Civil marriage</td>\n",
       "      <td>Rented apartment</td>\n",
       "      <td>-12005</td>\n",
       "      <td>-4542</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5008804</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>427500.0</td>\n",
       "      <td>Working</td>\n",
       "      <td>Higher education</td>\n",
       "      <td>Civil marriage</td>\n",
       "      <td>Rented apartment</td>\n",
       "      <td>-12005</td>\n",
       "      <td>-4542</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID CODE_GENDER FLAG_OWN_CAR FLAG_OWN_REALTY  CNT_CHILDREN  \\\n",
       "0  5008804           M            Y               Y             0   \n",
       "1  5008804           M            Y               Y             0   \n",
       "2  5008804           M            Y               Y             0   \n",
       "3  5008804           M            Y               Y             0   \n",
       "4  5008804           M            Y               Y             0   \n",
       "\n",
       "   AMT_INCOME_TOTAL NAME_INCOME_TYPE NAME_EDUCATION_TYPE NAME_FAMILY_STATUS  \\\n",
       "0          427500.0          Working    Higher education     Civil marriage   \n",
       "1          427500.0          Working    Higher education     Civil marriage   \n",
       "2          427500.0          Working    Higher education     Civil marriage   \n",
       "3          427500.0          Working    Higher education     Civil marriage   \n",
       "4          427500.0          Working    Higher education     Civil marriage   \n",
       "\n",
       "  NAME_HOUSING_TYPE  DAYS_BIRTH  DAYS_EMPLOYED  FLAG_MOBIL  FLAG_WORK_PHONE  \\\n",
       "0  Rented apartment      -12005          -4542           1                1   \n",
       "1  Rented apartment      -12005          -4542           1                1   \n",
       "2  Rented apartment      -12005          -4542           1                1   \n",
       "3  Rented apartment      -12005          -4542           1                1   \n",
       "4  Rented apartment      -12005          -4542           1                1   \n",
       "\n",
       "   FLAG_PHONE  FLAG_EMAIL OCCUPATION_TYPE  CNT_FAM_MEMBERS  MONTHS_BALANCE  \\\n",
       "0           0           0             NaN              2.0               0   \n",
       "1           0           0             NaN              2.0              -1   \n",
       "2           0           0             NaN              2.0              -2   \n",
       "3           0           0             NaN              2.0              -3   \n",
       "4           0           0             NaN              2.0              -4   \n",
       "\n",
       "   STATUS  \n",
       "0       0  \n",
       "1       0  \n",
       "2       0  \n",
       "3       0  \n",
       "4       0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#change the status labels to good or bad (0|1)\n",
    "#Here we will define that any late payment is bad\n",
    "total['STATUS'] = total['STATUS'].replace(['C'],0)\n",
    "total['STATUS'] = total['STATUS'].replace(['X'],0)\n",
    "total['STATUS'] = total['STATUS'].apply(pd.to_numeric)\n",
    "total['STATUS'] = np.where(total['STATUS'] <1, 0, 1)\n",
    "\n",
    "total.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "30367660",
   "metadata": {},
   "outputs": [],
   "source": [
    "group = total.groupby('ID')\n",
    "series = group.apply(lambda x: x['STATUS'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "29e1bc09",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "temp = series.apply(pd.Series)\n",
    "temp = temp.drop(0,axis=1)\n",
    "# temp.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9bf1f408",
   "metadata": {},
   "outputs": [],
   "source": [
    "temp[1] = temp[1].fillna(0)\n",
    "temp[1] = temp[1].astype(int)\n",
    "temp.head(5)\n",
    "temp2 = total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "731ac5ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>CODE_GENDER</th>\n",
       "      <th>FLAG_OWN_CAR</th>\n",
       "      <th>FLAG_OWN_REALTY</th>\n",
       "      <th>CNT_CHILDREN</th>\n",
       "      <th>AMT_INCOME_TOTAL</th>\n",
       "      <th>NAME_INCOME_TYPE</th>\n",
       "      <th>NAME_EDUCATION_TYPE</th>\n",
       "      <th>NAME_FAMILY_STATUS</th>\n",
       "      <th>NAME_HOUSING_TYPE</th>\n",
       "      <th>...</th>\n",
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       "      <th>FLAG_PHONE</th>\n",
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       "      <th>CNT_FAM_MEMBERS</th>\n",
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       "      <td>5008804</td>\n",
       "      <td>M</td>\n",
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       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>427500.0</td>\n",
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       "      <td>Rented apartment</td>\n",
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       "      <td>Rented apartment</td>\n",
       "      <td>...</td>\n",
       "      <td>-4542</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5008804</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>427500.0</td>\n",
       "      <td>Working</td>\n",
       "      <td>Higher education</td>\n",
       "      <td>Civil marriage</td>\n",
       "      <td>Rented apartment</td>\n",
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       "      <td>-4542</td>\n",
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       "      <td>5008804</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>427500.0</td>\n",
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       "      <td>Higher education</td>\n",
       "      <td>Civil marriage</td>\n",
       "      <td>Rented apartment</td>\n",
       "      <td>...</td>\n",
       "      <td>-4542</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID CODE_GENDER FLAG_OWN_CAR FLAG_OWN_REALTY  CNT_CHILDREN  \\\n",
       "0  5008804           M            Y               Y             0   \n",
       "1  5008804           M            Y               Y             0   \n",
       "2  5008804           M            Y               Y             0   \n",
       "3  5008804           M            Y               Y             0   \n",
       "4  5008804           M            Y               Y             0   \n",
       "\n",
       "   AMT_INCOME_TOTAL NAME_INCOME_TYPE NAME_EDUCATION_TYPE NAME_FAMILY_STATUS  \\\n",
       "0          427500.0          Working    Higher education     Civil marriage   \n",
       "1          427500.0          Working    Higher education     Civil marriage   \n",
       "2          427500.0          Working    Higher education     Civil marriage   \n",
       "3          427500.0          Working    Higher education     Civil marriage   \n",
       "4          427500.0          Working    Higher education     Civil marriage   \n",
       "\n",
       "  NAME_HOUSING_TYPE  ...  DAYS_EMPLOYED  FLAG_MOBIL  FLAG_WORK_PHONE  \\\n",
       "0  Rented apartment  ...          -4542           1                1   \n",
       "1  Rented apartment  ...          -4542           1                1   \n",
       "2  Rented apartment  ...          -4542           1                1   \n",
       "3  Rented apartment  ...          -4542           1                1   \n",
       "4  Rented apartment  ...          -4542           1                1   \n",
       "\n",
       "   FLAG_PHONE  FLAG_EMAIL  OCCUPATION_TYPE CNT_FAM_MEMBERS  MONTHS_BALANCE  \\\n",
       "0           0           0              NaN             2.0               0   \n",
       "1           0           0              NaN             2.0              -1   \n",
       "2           0           0              NaN             2.0              -2   \n",
       "3           0           0              NaN             2.0              -3   \n",
       "4           0           0              NaN             2.0              -4   \n",
       "\n",
       "   STATUS  1  \n",
       "0       0  1  \n",
       "1       0  1  \n",
       "2       0  1  \n",
       "3       0  1  \n",
       "4       0  1  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged = pd.merge(temp2, temp, on='ID')\n",
    "merged.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5f7f9102",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3909\n"
     ]
    },
    {
     "data": {
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       "      <td>Married</td>\n",
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       "      <td>-21474</td>\n",
       "      <td>-1134</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Security staff</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
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       "      <td>F</td>\n",
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       "      <td>0</td>\n",
       "      <td>270000.0</td>\n",
       "      <td>Commercial associate</td>\n",
       "      <td>Secondary / secondary special</td>\n",
       "      <td>Single / not married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>-19110</td>\n",
       "      <td>-3051</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sales staff</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>5008809</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>270000.0</td>\n",
       "      <td>Commercial associate</td>\n",
       "      <td>Secondary / secondary special</td>\n",
       "      <td>Single / not married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>-19110</td>\n",
       "      <td>-3051</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sales staff</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-22</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         ID CODE_GENDER FLAG_OWN_CAR FLAG_OWN_REALTY  CNT_CHILDREN  \\\n",
       "0   5008804           M            Y               Y             0   \n",
       "16  5008805           M            Y               Y             0   \n",
       "31  5008806           M            Y               Y             0   \n",
       "61  5008808           F            N               Y             0   \n",
       "66  5008809           F            N               Y             0   \n",
       "\n",
       "    AMT_INCOME_TOTAL      NAME_INCOME_TYPE            NAME_EDUCATION_TYPE  \\\n",
       "0           427500.0               Working               Higher education   \n",
       "16          427500.0               Working               Higher education   \n",
       "31          112500.0               Working  Secondary / secondary special   \n",
       "61          270000.0  Commercial associate  Secondary / secondary special   \n",
       "66          270000.0  Commercial associate  Secondary / secondary special   \n",
       "\n",
       "      NAME_FAMILY_STATUS  NAME_HOUSING_TYPE  DAYS_BIRTH  DAYS_EMPLOYED  \\\n",
       "0         Civil marriage   Rented apartment      -12005          -4542   \n",
       "16        Civil marriage   Rented apartment      -12005          -4542   \n",
       "31               Married  House / apartment      -21474          -1134   \n",
       "61  Single / not married  House / apartment      -19110          -3051   \n",
       "66  Single / not married  House / apartment      -19110          -3051   \n",
       "\n",
       "    FLAG_MOBIL  FLAG_WORK_PHONE  FLAG_PHONE  FLAG_EMAIL OCCUPATION_TYPE  \\\n",
       "0            1                1           0           0             NaN   \n",
       "16           1                1           0           0             NaN   \n",
       "31           1                0           0           0  Security staff   \n",
       "61           1                0           1           1     Sales staff   \n",
       "66           1                0           1           1     Sales staff   \n",
       "\n",
       "    CNT_FAM_MEMBERS  MONTHS_BALANCE  STATUS  \n",
       "0               2.0               0       1  \n",
       "16              2.0               0       1  \n",
       "31              2.0               0       0  \n",
       "61              1.0               0       0  \n",
       "66              1.0             -22       0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dropping any duplicate rows\n",
    "merged = merged.drop_duplicates('ID')\n",
    "print(len(merged[(merged[1]>0)]))\n",
    "merged = merged.drop(['STATUS'],axis=1)\n",
    "merged = merged.rename(columns={1:'STATUS'})\n",
    "merged.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1571ee03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "36457\n",
      "11575\n"
     ]
    }
   ],
   "source": [
    "print(len(merged[(merged['STATUS']>-1)]))\n",
    "print(len(total[(total['STATUS']>0)]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2f9d9f46",
   "metadata": {},
   "outputs": [],
   "source": [
    "total = merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "73f5720b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#change flag to numnerical 0,1\n",
    "d1 = pd.get_dummies(total.CODE_GENDER)\n",
    "total['CODE_GENDER'] = d1['M']\n",
    "\n",
    "d2 = pd.get_dummies(total.FLAG_OWN_CAR)\n",
    "total['FLAG_OWN_CAR'] = d2['Y']\n",
    "\n",
    "d3 = pd.get_dummies(total.FLAG_OWN_REALTY)\n",
    "total['FLAG_OWN_REALTY'] = d3['Y']\n",
    "\n",
    "#total.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e9ed914b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#converting professions into numeric values\n",
    "#Here we will group similar occupations\n",
    "total['OCCUPATION_TYPE'] = total['OCCUPATION_TYPE'].replace(['Managers','Realty agents','High skill tech staff'],5)\n",
    "total['OCCUPATION_TYPE'] = total['OCCUPATION_TYPE'].replace(['Accountants','IT staff','Private service staff'],4)\n",
    "total['OCCUPATION_TYPE'] = total['OCCUPATION_TYPE'].replace(['HR staff','Core staff','Laborers'],3)\n",
    "total['OCCUPATION_TYPE'] = total['OCCUPATION_TYPE'].replace(['Security staff','Sales staff','Secretaries','Medicine staff','Drivers'],2)\n",
    "total['OCCUPATION_TYPE'] = total['OCCUPATION_TYPE'].replace(['Waiters/barmen staff','Cleaning staff','Cooking staff','Low-skill Laborers'],1)\n",
    "total['OCCUPATION_TYPE'] = total['OCCUPATION_TYPE'].apply(pd.to_numeric)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1c42b948",
   "metadata": {},
   "outputs": [],
   "source": [
    "#converting education to numeric values\n",
    "total['NAME_EDUCATION_TYPE'] = total['NAME_EDUCATION_TYPE'].replace(['Academic degree'],5)\n",
    "total['NAME_EDUCATION_TYPE'] = total['NAME_EDUCATION_TYPE'].replace(['Higher education'],4)\n",
    "total['NAME_EDUCATION_TYPE'] = total['NAME_EDUCATION_TYPE'].replace(['Incomplete higher'],3)\n",
    "total['NAME_EDUCATION_TYPE'] = total['NAME_EDUCATION_TYPE'].replace(['Secondary / secondary special'],2)\n",
    "total['NAME_EDUCATION_TYPE'] = total['NAME_EDUCATION_TYPE'].replace(['Lower secondary'],1)\n",
    "total['NAME_EDUCATION_TYPE'] = total['NAME_EDUCATION_TYPE'].apply(pd.to_numeric)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "95af667c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#converting income type to numeric values\n",
    "total['NAME_INCOME_TYPE'] = total['NAME_INCOME_TYPE'].replace(['Pensioner'],5)\n",
    "total['NAME_INCOME_TYPE'] = total['NAME_INCOME_TYPE'].replace(['Commercial associate'],4)\n",
    "total['NAME_INCOME_TYPE'] = total['NAME_INCOME_TYPE'].replace(['State servant'],3)\n",
    "total['NAME_INCOME_TYPE'] = total['NAME_INCOME_TYPE'].replace(['Working'],2)\n",
    "total['NAME_INCOME_TYPE'] = total['NAME_INCOME_TYPE'].replace(['Student'],1)\n",
    "total['NAME_INCOME_TYPE'] = total['NAME_INCOME_TYPE'].apply(pd.to_numeric)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f2786839",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#convert housing to numeric values\n",
    "total['NAME_HOUSING_TYPE'] = total['NAME_HOUSING_TYPE'].replace(['Office apartment'],6)\n",
    "total['NAME_HOUSING_TYPE'] = total['NAME_HOUSING_TYPE'].replace(['Co-op apartment'],5)\n",
    "total['NAME_HOUSING_TYPE'] = total['NAME_HOUSING_TYPE'].replace(['Rented apartment'],4)\n",
    "total['NAME_HOUSING_TYPE'] = total['NAME_HOUSING_TYPE'].replace(['House / apartment'],3)\n",
    "total['NAME_HOUSING_TYPE'] = total['NAME_HOUSING_TYPE'].replace(['Municipal apartment'],2)\n",
    "total['NAME_HOUSING_TYPE'] = total['NAME_HOUSING_TYPE'].replace(['With parents'],1)\n",
    "total['NAME_HOUSING_TYPE'] = total['NAME_HOUSING_TYPE'].apply(pd.to_numeric)\n",
    "\n",
    "#total.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9a050cf8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# convert family status\n",
    "total['NAME_FAMILY_STATUS'] = total['NAME_FAMILY_STATUS'].replace(['Married'],1)\n",
    "total['NAME_FAMILY_STATUS'] = total['NAME_FAMILY_STATUS'].replace(['Single / not married'],2)\n",
    "total['NAME_FAMILY_STATUS'] = total['NAME_FAMILY_STATUS'].replace(['Civil marriage'],3)\n",
    "total['NAME_FAMILY_STATUS'] = total['NAME_FAMILY_STATUS'].replace(['Separated'],4)\n",
    "total['NAME_FAMILY_STATUS'] = total['NAME_FAMILY_STATUS'].replace(['Widow'],5)\n",
    "#total.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f2ed49de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3909\n",
      "32548\n"
     ]
    }
   ],
   "source": [
    "bad = total.loc[total['STATUS'] == 1].count()[0]\n",
    "good = total.loc[total['STATUS'] == 0].count()[0]\n",
    "plt.pie([bad,good],  autopct = '%.2f %%')\n",
    "plt.show()\n",
    "print(bad)\n",
    "print(good)\n",
    "#We can see that over 98% of data is good, will lead to overfitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "dd340009",
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop id since we dont want ml model to use that\n",
    "total = total.drop(columns=['ID'])\n",
    "#also drop na rows\n",
    "total = total.dropna()\n",
    "#total.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "84d6407b",
   "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>CODE_GENDER</th>\n",
       "      <th>FLAG_OWN_CAR</th>\n",
       "      <th>FLAG_OWN_REALTY</th>\n",
       "      <th>CNT_CHILDREN</th>\n",
       "      <th>AMT_INCOME_TOTAL</th>\n",
       "      <th>NAME_INCOME_TYPE</th>\n",
       "      <th>NAME_EDUCATION_TYPE</th>\n",
       "      <th>NAME_FAMILY_STATUS</th>\n",
       "      <th>NAME_HOUSING_TYPE</th>\n",
       "      <th>DAYS_BIRTH</th>\n",
       "      <th>DAYS_EMPLOYED</th>\n",
       "      <th>FLAG_MOBIL</th>\n",
       "      <th>FLAG_WORK_PHONE</th>\n",
       "      <th>FLAG_PHONE</th>\n",
       "      <th>FLAG_EMAIL</th>\n",
       "      <th>OCCUPATION_TYPE</th>\n",
       "      <th>CNT_FAM_MEMBERS</th>\n",
       "      <th>MONTHS_BALANCE</th>\n",
       "      <th>STATUS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>112500.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>-21474</td>\n",
       "      <td>-1134</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>270000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>-19110</td>\n",
       "      <td>-3051</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>270000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>-19110</td>\n",
       "      <td>-3051</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-22</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>270000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>-19110</td>\n",
       "      <td>-3051</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>270000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>-19110</td>\n",
       "      <td>-3051</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    CODE_GENDER  FLAG_OWN_CAR  FLAG_OWN_REALTY  CNT_CHILDREN  \\\n",
       "31            1             1                1             0   \n",
       "61            0             0                1             0   \n",
       "66            0             0                1             0   \n",
       "71            0             0                1             0   \n",
       "98            0             0                1             0   \n",
       "\n",
       "    AMT_INCOME_TOTAL  NAME_INCOME_TYPE  NAME_EDUCATION_TYPE  \\\n",
       "31          112500.0                 2                    2   \n",
       "61          270000.0                 4                    2   \n",
       "66          270000.0                 4                    2   \n",
       "71          270000.0                 4                    2   \n",
       "98          270000.0                 4                    2   \n",
       "\n",
       "    NAME_FAMILY_STATUS  NAME_HOUSING_TYPE  DAYS_BIRTH  DAYS_EMPLOYED  \\\n",
       "31                   1                  3      -21474          -1134   \n",
       "61                   2                  3      -19110          -3051   \n",
       "66                   2                  3      -19110          -3051   \n",
       "71                   2                  3      -19110          -3051   \n",
       "98                   2                  3      -19110          -3051   \n",
       "\n",
       "    FLAG_MOBIL  FLAG_WORK_PHONE  FLAG_PHONE  FLAG_EMAIL  OCCUPATION_TYPE  \\\n",
       "31           1                0           0           0              2.0   \n",
       "61           1                0           1           1              2.0   \n",
       "66           1                0           1           1              2.0   \n",
       "71           1                0           1           1              2.0   \n",
       "98           1                0           1           1              2.0   \n",
       "\n",
       "    CNT_FAM_MEMBERS  MONTHS_BALANCE  STATUS  \n",
       "31              2.0               0       0  \n",
       "61              1.0               0       0  \n",
       "66              1.0             -22       0  \n",
       "71              1.0               0       0  \n",
       "98              1.0               0       0  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a6e80ccb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "import pandas as pd\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import (AdaBoostClassifier, GradientBoostingClassifier)\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import roc_auc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1145d8fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import imblearn\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "\n",
    "x,y = total.loc[:,total.columns != 'STATUS'], total.loc[:,'STATUS']\n",
    "rus = RandomUnderSampler(sampling_strategy=.35)\n",
    "X_res, y_res = rus.fit_resample(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "23e29759",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test = train_test_split(X_res,y_res,test_size = 0.20,random_state = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e0d6f257",
   "metadata": {},
   "outputs": [],
   "source": [
    "models = {}\n",
    "models['KNN'] = KNeighborsClassifier()\n",
    "models['DecisionTree'] = DecisionTreeClassifier()\n",
    "# models['svm'] = SVC()\n",
    "models['bayes'] = GaussianNB()\n",
    "models['RandomForest'] = RandomForestClassifier()\n",
    "models['LogisticRegression'] = LogisticRegression(max_iter=1000)\n",
    "models['ada'] = AdaBoostClassifier()\n",
    "models['gda'] = GradientBoostingClassifier()\n",
    "models['bca'] = BaggingClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ed778bac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.78      0.86      0.82      1581\n",
      "           1       0.48      0.35      0.41       585\n",
      "\n",
      "    accuracy                           0.72      2166\n",
      "   macro avg       0.63      0.61      0.61      2166\n",
      "weighted avg       0.70      0.72      0.71      2166\n",
      "\n",
      "No Skill: ROC AUC=0.500\n",
      "Logistic: ROC AUC=0.675\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTree               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.82      0.81      1581\n",
      "           1       0.49      0.46      0.48       585\n",
      "\n",
      "    accuracy                           0.72      2166\n",
      "   macro avg       0.65      0.64      0.64      2166\n",
      "weighted avg       0.72      0.72      0.72      2166\n",
      "\n",
      "No Skill: ROC AUC=0.500\n",
      "Logistic: ROC AUC=0.664\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bayes               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.73      0.98      0.84      1581\n",
      "           1       0.28      0.03      0.05       585\n",
      "\n",
      "    accuracy                           0.72      2166\n",
      "   macro avg       0.50      0.50      0.44      2166\n",
      "weighted avg       0.61      0.72      0.62      2166\n",
      "\n",
      "No Skill: ROC AUC=0.500\n",
      "Logistic: ROC AUC=0.499\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForest               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.92      0.86      1581\n",
      "           1       0.66      0.42      0.51       585\n",
      "\n",
      "    accuracy                           0.79      2166\n",
      "   macro avg       0.74      0.67      0.69      2166\n",
      "weighted avg       0.77      0.79      0.77      2166\n",
      "\n",
      "No Skill: ROC AUC=0.500\n",
      "Logistic: ROC AUC=0.741\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.73      1.00      0.84      1581\n",
      "           1       0.00      0.00      0.00       585\n",
      "\n",
      "    accuracy                           0.73      2166\n",
      "   macro avg       0.36      0.50      0.42      2166\n",
      "weighted avg       0.53      0.73      0.62      2166\n",
      "\n",
      "No Skill: ROC AUC=0.500\n",
      "Logistic: ROC AUC=0.514\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ada               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.73      1.00      0.85      1581\n",
      "           1       0.88      0.01      0.02       585\n",
      "\n",
      "    accuracy                           0.73      2166\n",
      "   macro avg       0.80      0.51      0.43      2166\n",
      "weighted avg       0.77      0.73      0.62      2166\n",
      "\n",
      "No Skill: ROC AUC=0.500\n",
      "Logistic: ROC AUC=0.562\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gda               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.73      0.99      0.84      1581\n",
      "           1       0.58      0.02      0.05       585\n",
      "\n",
      "    accuracy                           0.73      2166\n",
      "   macro avg       0.66      0.51      0.44      2166\n",
      "weighted avg       0.69      0.73      0.63      2166\n",
      "\n",
      "No Skill: ROC AUC=0.500\n",
      "Logistic: ROC AUC=0.601\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bca               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.90      0.85      1581\n",
      "           1       0.60      0.41      0.49       585\n",
      "\n",
      "    accuracy                           0.77      2166\n",
      "   macro avg       0.70      0.65      0.67      2166\n",
      "weighted avg       0.75      0.77      0.75      2166\n",
      "\n",
      "No Skill: ROC AUC=0.500\n",
      "Logistic: ROC AUC=0.716\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for name, model in models.items():\n",
    "    model.fit(x_train, y_train)\n",
    "    y_pred = model.predict(x_test)\n",
    "    classification = classification_report(y_test,y_pred)\n",
    "    print(name, classification)\n",
    "    # generate no skill prediction \n",
    "    ns_probs = [0 for _ in range(len(y_test))]\n",
    "    #generate model prediction\n",
    "    model_probs = model.predict_proba(x_test)\n",
    "    # keep probabilities for the positive outcome only\n",
    "    model_probs = model_probs[:, 1]\n",
    "    # calculate scores\n",
    "    ns_auc = roc_auc_score(y_test, ns_probs)\n",
    "    lr_auc = roc_auc_score(y_test, model_probs)\n",
    "    # summarize scores\n",
    "    print('No Skill: ROC AUC=%.3f' % (ns_auc))\n",
    "    print('Logistic: ROC AUC=%.3f' % (lr_auc))\n",
    "    # calculate roc curves\n",
    "    ns_fpr, ns_tpr, _ = roc_curve(y_test, ns_probs)\n",
    "    lr_fpr, lr_tpr, _ = roc_curve(y_test, model_probs)\n",
    "    # plot the roc curve for the model\n",
    "    plt.plot(ns_fpr, ns_tpr, linestyle='--', label='No Skill')\n",
    "    plt.plot(lr_fpr, lr_tpr, marker='.', label='Logistic')\n",
    "    # axis labels\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    # show the legend\n",
    "    plt.legend()\n",
    "    # show the plot\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07d9062d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "describe = merged.dropna()\n",
    "MALE = total.loc[total['NAME_INCOME_TYPE'] == 1].count()[0]\n",
    "FEMALE = total.loc[total['NAME_INCOME_TYPE'] == 0].count()[0]\n",
    "plt.pie([MALE,FEMALE],  autopct = '%.2f %%')\n",
    "plt.show()\n",
    "print(len(merged[(merged['CODE_GENDER'] == 'M')]))\n",
    "print(MALE)\n",
    "len(total.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d23a32c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "count = describe['OCCUPATION_TYPE'].value_counts()\n",
    "print(count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5130bc59",
   "metadata": {},
   "outputs": [],
   "source": [
    "describe['CNT_FAM_MEMBERS'].describe()"
   ]
  }
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