BIG_DATA_PROJECT_LOAN.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.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = pd.read_csv(\"train.csv\")\n",
    "test_data = pd.read_csv(\"test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "Loan_ID=test_data.Loan_ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      LP001015\n",
       "1      LP001022\n",
       "2      LP001031\n",
       "3      LP001035\n",
       "4      LP001051\n",
       "         ...   \n",
       "362    LP002971\n",
       "363    LP002975\n",
       "364    LP002980\n",
       "365    LP002986\n",
       "366    LP002989\n",
       "Name: Loan_ID, Length: 367, dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Loan_ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Loan_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>LP001002</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>LP001003</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>LP001005</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>LP001006</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>LP001008</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Loan_ID Gender Married Dependents     Education Self_Employed  \\\n",
       "0  LP001002   Male      No          0      Graduate            No   \n",
       "1  LP001003   Male     Yes          1      Graduate            No   \n",
       "2  LP001005   Male     Yes          0      Graduate           Yes   \n",
       "3  LP001006   Male     Yes          0  Not Graduate            No   \n",
       "4  LP001008   Male      No          0      Graduate            No   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             5849                0.0         NaN             360.0   \n",
       "1             4583             1508.0       128.0             360.0   \n",
       "2             3000                0.0        66.0             360.0   \n",
       "3             2583             2358.0       120.0             360.0   \n",
       "4             6000                0.0       141.0             360.0   \n",
       "\n",
       "   Credit_History Property_Area Loan_Status  \n",
       "0             1.0         Urban           Y  \n",
       "1             1.0         Rural           N  \n",
       "2             1.0         Urban           Y  \n",
       "3             1.0         Urban           Y  \n",
       "4             1.0         Urban           Y  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>592.000000</td>\n",
       "      <td>600.00000</td>\n",
       "      <td>564.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>5403.459283</td>\n",
       "      <td>1621.245798</td>\n",
       "      <td>146.412162</td>\n",
       "      <td>342.00000</td>\n",
       "      <td>0.842199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>6109.041673</td>\n",
       "      <td>2926.248369</td>\n",
       "      <td>85.587325</td>\n",
       "      <td>65.12041</td>\n",
       "      <td>0.364878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>12.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>2877.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>360.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>3812.500000</td>\n",
       "      <td>1188.500000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>360.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>5795.000000</td>\n",
       "      <td>2297.250000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>360.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>81000.000000</td>\n",
       "      <td>41667.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>480.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "count       614.000000         614.000000  592.000000         600.00000   \n",
       "mean       5403.459283        1621.245798  146.412162         342.00000   \n",
       "std        6109.041673        2926.248369   85.587325          65.12041   \n",
       "min         150.000000           0.000000    9.000000          12.00000   \n",
       "25%        2877.500000           0.000000  100.000000         360.00000   \n",
       "50%        3812.500000        1188.500000  128.000000         360.00000   \n",
       "75%        5795.000000        2297.250000  168.000000         360.00000   \n",
       "max       81000.000000       41667.000000  700.000000         480.00000   \n",
       "\n",
       "       Credit_History  \n",
       "count      564.000000  \n",
       "mean         0.842199  \n",
       "std          0.364878  \n",
       "min          0.000000  \n",
       "25%          1.000000  \n",
       "50%          1.000000  \n",
       "75%          1.000000  \n",
       "max          1.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Loan_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>609</td>\n",
       "      <td>LP002978</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>610</td>\n",
       "      <td>LP002979</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3+</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4106</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>611</td>\n",
       "      <td>LP002983</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>8072</td>\n",
       "      <td>240.0</td>\n",
       "      <td>253.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>612</td>\n",
       "      <td>LP002984</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>7583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>613</td>\n",
       "      <td>LP002990</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Loan_ID  Gender Married Dependents Education Self_Employed  \\\n",
       "609  LP002978  Female      No          0  Graduate            No   \n",
       "610  LP002979    Male     Yes         3+  Graduate            No   \n",
       "611  LP002983    Male     Yes          1  Graduate            No   \n",
       "612  LP002984    Male     Yes          2  Graduate            No   \n",
       "613  LP002990  Female      No          0  Graduate           Yes   \n",
       "\n",
       "     ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "609             2900                0.0        71.0             360.0   \n",
       "610             4106                0.0        40.0             180.0   \n",
       "611             8072              240.0       253.0             360.0   \n",
       "612             7583                0.0       187.0             360.0   \n",
       "613             4583                0.0       133.0             360.0   \n",
       "\n",
       "     Credit_History Property_Area Loan_Status  \n",
       "609             1.0         Rural           Y  \n",
       "610             1.0         Rural           Y  \n",
       "611             1.0         Urban           Y  \n",
       "612             1.0         Urban           Y  \n",
       "613             0.0     Semiurban           N  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>367.000000</td>\n",
       "      <td>367.000000</td>\n",
       "      <td>362.000000</td>\n",
       "      <td>361.000000</td>\n",
       "      <td>338.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>4805.599455</td>\n",
       "      <td>1569.577657</td>\n",
       "      <td>136.132597</td>\n",
       "      <td>342.537396</td>\n",
       "      <td>0.825444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>4910.685399</td>\n",
       "      <td>2334.232099</td>\n",
       "      <td>61.366652</td>\n",
       "      <td>65.156643</td>\n",
       "      <td>0.380150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>2864.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.250000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>3786.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>5060.000000</td>\n",
       "      <td>2430.500000</td>\n",
       "      <td>158.000000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>72529.000000</td>\n",
       "      <td>24000.000000</td>\n",
       "      <td>550.000000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "count       367.000000         367.000000  362.000000        361.000000   \n",
       "mean       4805.599455        1569.577657  136.132597        342.537396   \n",
       "std        4910.685399        2334.232099   61.366652         65.156643   \n",
       "min           0.000000           0.000000   28.000000          6.000000   \n",
       "25%        2864.000000           0.000000  100.250000        360.000000   \n",
       "50%        3786.000000        1025.000000  125.000000        360.000000   \n",
       "75%        5060.000000        2430.500000  158.000000        360.000000   \n",
       "max       72529.000000       24000.000000  550.000000        480.000000   \n",
       "\n",
       "       Credit_History  \n",
       "count      338.000000  \n",
       "mean         0.825444  \n",
       "std          0.380150  \n",
       "min          0.000000  \n",
       "25%          1.000000  \n",
       "50%          1.000000  \n",
       "75%          1.000000  \n",
       "max          1.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "# STATISTICS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Graduate        480\n",
       "Not Graduate    134\n",
       "Name: Education, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['Education'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     345\n",
       "1     102\n",
       "2     101\n",
       "3+     51\n",
       "Name: Dependents, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['Dependents'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Yes    398\n",
       "No     213\n",
       "Name: Married, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['Married'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Male      489\n",
       "Female    112\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['Gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No     500\n",
       "Yes     82\n",
       "Name: Self_Employed, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['Self_Employed'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Y    422\n",
       "N    192\n",
       "Name: Loan_Status, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['Loan_Status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Semiurban    233\n",
       "Urban        202\n",
       "Rural        179\n",
       "Name: Property_Area, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['Property_Area'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "x = train_data['ApplicantIncome']\n",
    "plt.hist(x, bins=60)\n",
    "plt.ylabel('Number of applicants')\n",
    "plt.xlabel('Income value')\n",
    "plt.title('Applicant income histogram')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "x = train_data['CoapplicantIncome']\n",
    "plt.hist(x, bins=60)\n",
    "plt.ylabel('Number of coapplicants')\n",
    "plt.xlabel('Income value')\n",
    "plt.title('Coapplicant income histogram')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bibarys.mussagaliye\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\numpy\\lib\\histograms.py:824: RuntimeWarning: invalid value encountered in greater_equal\n",
      "  keep = (tmp_a >= first_edge)\n",
      "C:\\Users\\bibarys.mussagaliye\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\numpy\\lib\\histograms.py:825: RuntimeWarning: invalid value encountered in less_equal\n",
      "  keep &= (tmp_a <= last_edge)\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": [
    "x = train_data['LoanAmount']\n",
    "plt.hist(x, bins=60)\n",
    "plt.xlabel('Loan amount')\n",
    "plt.ylabel('Number of applicants')\n",
    "plt.title('Loan amount histogram')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    475\n",
       "0.0     89\n",
       "Name: Credit_History, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['Credit_History'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x1080 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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": [
    "plt.figure(figsize = (15, 15))\n",
    "dfq = pd.crosstab(train_data['Credit_History'], train_data['Loan_Status'])\n",
    "dfq.plot(kind = 'bar', stacked = True, color = ['red','green'], grid = False)\n",
    "#plt.title('Loan status based on credit history bar-chart')\n",
    "plt.ylabel('Number of applicants')\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DATA PREPROCESSING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID               0\n",
       "Gender               13\n",
       "Married               3\n",
       "Dependents           15\n",
       "Education             0\n",
       "Self_Employed        32\n",
       "ApplicantIncome       0\n",
       "CoapplicantIncome     0\n",
       "LoanAmount           22\n",
       "Loan_Amount_Term     14\n",
       "Credit_History       50\n",
       "Property_Area         0\n",
       "Loan_Status           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID               0\n",
       "Gender               11\n",
       "Married               0\n",
       "Dependents           10\n",
       "Education             0\n",
       "Self_Employed        23\n",
       "ApplicantIncome       0\n",
       "CoapplicantIncome     0\n",
       "LoanAmount            5\n",
       "Loan_Amount_Term      6\n",
       "Credit_History       29\n",
       "Property_Area         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deleting the \"+\" sign"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <td>1</td>\n",
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       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <td>609</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>610</td>\n",
       "      <td>Male</td>\n",
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       "      <td>3+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>611</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>612</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>613</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>614 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Gender Married Dependents\n",
       "0      Male      No          0\n",
       "1      Male     Yes          1\n",
       "2      Male     Yes          0\n",
       "3      Male     Yes          0\n",
       "4      Male      No          0\n",
       "..      ...     ...        ...\n",
       "609  Female      No          0\n",
       "610    Male     Yes         3+\n",
       "611    Male     Yes          1\n",
       "612    Male     Yes          2\n",
       "613  Female      No          0\n",
       "\n",
       "[614 rows x 3 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "td=train_data.iloc[:,1:4]\n",
    "td"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
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       "      <td>3</td>\n",
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       "      <td>611</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>612</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>613</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>614 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Gender Married Dependents\n",
       "0      Male      No          0\n",
       "1      Male     Yes          1\n",
       "2      Male     Yes          0\n",
       "3      Male     Yes          0\n",
       "4      Male      No          0\n",
       "..      ...     ...        ...\n",
       "609  Female      No          0\n",
       "610    Male     Yes          3\n",
       "611    Male     Yes          1\n",
       "612    Male     Yes          2\n",
       "613  Female      No          0\n",
       "\n",
       "[614 rows x 3 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['Dependents'] = train_data['Dependents'].str.replace(\"+\", \"\")\n",
    "td=train_data.iloc[:,1:4]\n",
    "td"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>3</td>\n",
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       "      <td>4</td>\n",
       "      <td>Male</td>\n",
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       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>362</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>363</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>364</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>365</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>366</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>367 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Gender Married Dependents\n",
       "0     Male     Yes          0\n",
       "1     Male     Yes          1\n",
       "2     Male     Yes          2\n",
       "3     Male     Yes          2\n",
       "4     Male      No          0\n",
       "..     ...     ...        ...\n",
       "362   Male     Yes          3\n",
       "363   Male     Yes          0\n",
       "364   Male      No          0\n",
       "365   Male     Yes          0\n",
       "366   Male      No          0\n",
       "\n",
       "[367 rows x 3 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data['Dependents'] = test_data['Dependents'].str.replace(\"+\", \"\")\n",
    "ts=test_data.iloc[:,1:4]\n",
    "ts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# creating combined dataset (train + test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def combine_data():\n",
    "    train = train_data.copy() #pd.read_csv(r'C:\\Users\\bibarys.mussagaliye\\Loan-Prediction-Classification\\Dataset\\train.csv')\n",
    "    test = test_data.copy() #pd.read_csv(r'C:\\Users\\bibarys.mussagaliye\\Loan-Prediction-Classification\\Dataset\\test.csv')\n",
    "    tar = train.Loan_Status\n",
    "    train.drop('Loan_Status', 1, inplace=True)\n",
    "    comb = train.append(test)\n",
    "    comb.reset_index(inplace = True)\n",
    "    comb.drop(['index', 'Loan_ID'], inplace=True, axis=1)\n",
    "    return comb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>981.000000</td>\n",
       "      <td>981.000000</td>\n",
       "      <td>954.000000</td>\n",
       "      <td>961.000000</td>\n",
       "      <td>902.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>5179.795107</td>\n",
       "      <td>1601.916330</td>\n",
       "      <td>142.511530</td>\n",
       "      <td>342.201873</td>\n",
       "      <td>0.835920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>5695.104533</td>\n",
       "      <td>2718.772806</td>\n",
       "      <td>77.421743</td>\n",
       "      <td>65.100602</td>\n",
       "      <td>0.370553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>2875.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>3800.000000</td>\n",
       "      <td>1110.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>5516.000000</td>\n",
       "      <td>2365.000000</td>\n",
       "      <td>162.000000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>81000.000000</td>\n",
       "      <td>41667.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "count       981.000000         981.000000  954.000000        961.000000   \n",
       "mean       5179.795107        1601.916330  142.511530        342.201873   \n",
       "std        5695.104533        2718.772806   77.421743         65.100602   \n",
       "min           0.000000           0.000000    9.000000          6.000000   \n",
       "25%        2875.000000           0.000000  100.000000        360.000000   \n",
       "50%        3800.000000        1110.000000  126.000000        360.000000   \n",
       "75%        5516.000000        2365.000000  162.000000        360.000000   \n",
       "max       81000.000000       41667.000000  700.000000        480.000000   \n",
       "\n",
       "       Credit_History  \n",
       "count      902.000000  \n",
       "mean         0.835920  \n",
       "std          0.370553  \n",
       "min          0.000000  \n",
       "25%          1.000000  \n",
       "50%          1.000000  \n",
       "75%          1.000000  \n",
       "max          1.000000  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comb = combine_data()\n",
    "comb.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "comb['Gender'].fillna('Male', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "comb['Married'].fillna('Yes', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "comb['Self_Employed'].fillna('No', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "comb['LoanAmount'].fillna(comb['LoanAmount'].mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "comb['Credit_History'].fillna(1, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "comb['Dependents'].fillna(str(comb.Dependents.mode()),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "comb['Loan_Amount_Term'].fillna(comb.Loan_Amount_Term.mode()[0],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "comb['LoanAmount_log'] = np.log(comb['LoanAmount'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "comb['TotalIncome'] = comb['ApplicantIncome'] + comb['CoapplicantIncome']\n",
    "comb['TotalIncome_log'] = np.log(comb['TotalIncome'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender               0\n",
       "Married              0\n",
       "Dependents           0\n",
       "Education            0\n",
       "Self_Employed        0\n",
       "ApplicantIncome      0\n",
       "CoapplicantIncome    0\n",
       "LoanAmount           0\n",
       "Loan_Amount_Term     0\n",
       "Credit_History       0\n",
       "Property_Area        0\n",
       "LoanAmount_log       0\n",
       "TotalIncome          0\n",
       "TotalIncome_log      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comb.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "x = comb['Loan_Amount_Term']\n",
    "plt.hist(x, bins=60)\n",
    "plt.xlabel('Loan amount term')\n",
    "plt.ylabel('Number of applicants')\n",
    "#plt.title('Loan amount term histogram')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "comb['Loan_Amount_Term'] = np.log(comb['Loan_Amount_Term'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "x=comb['Loan_Amount_Term']\n",
    "plt.hist(x, bins=60)\n",
    "plt.xlabel('Loan amount term')\n",
    "plt.ylabel('Number of applicants')\n",
    "#plt.title('Loan amount term histogram (log)')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>LoanAmount_log</th>\n",
       "      <th>TotalIncome</th>\n",
       "      <th>TotalIncome_log</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>142.51153</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>4.959423</td>\n",
       "      <td>5849.0</td>\n",
       "      <td>8.674026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>128.00000</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>4.852030</td>\n",
       "      <td>6091.0</td>\n",
       "      <td>8.714568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.00000</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>4.189655</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>8.006368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120.00000</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>4.787492</td>\n",
       "      <td>4941.0</td>\n",
       "      <td>8.505323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.00000</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>4.948760</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>8.699515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>5417</td>\n",
       "      <td>4196.0</td>\n",
       "      <td>267.00000</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>5.587249</td>\n",
       "      <td>9613.0</td>\n",
       "      <td>9.170872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2333</td>\n",
       "      <td>1516.0</td>\n",
       "      <td>95.00000</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>4.553877</td>\n",
       "      <td>3849.0</td>\n",
       "      <td>8.255569</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Gender Married Dependents     Education Self_Employed  ApplicantIncome  \\\n",
       "0   Male      No          0      Graduate            No             5849   \n",
       "1   Male     Yes          1      Graduate            No             4583   \n",
       "2   Male     Yes          0      Graduate           Yes             3000   \n",
       "3   Male     Yes          0  Not Graduate            No             2583   \n",
       "4   Male      No          0      Graduate            No             6000   \n",
       "5   Male     Yes          2      Graduate           Yes             5417   \n",
       "6   Male     Yes          0  Not Graduate            No             2333   \n",
       "\n",
       "   CoapplicantIncome  LoanAmount  Loan_Amount_Term  Credit_History  \\\n",
       "0                0.0   142.51153          5.886104             1.0   \n",
       "1             1508.0   128.00000          5.886104             1.0   \n",
       "2                0.0    66.00000          5.886104             1.0   \n",
       "3             2358.0   120.00000          5.886104             1.0   \n",
       "4                0.0   141.00000          5.886104             1.0   \n",
       "5             4196.0   267.00000          5.886104             1.0   \n",
       "6             1516.0    95.00000          5.886104             1.0   \n",
       "\n",
       "  Property_Area  LoanAmount_log  TotalIncome  TotalIncome_log  \n",
       "0         Urban        4.959423       5849.0         8.674026  \n",
       "1         Rural        4.852030       6091.0         8.714568  \n",
       "2         Urban        4.189655       3000.0         8.006368  \n",
       "3         Urban        4.787492       4941.0         8.505323  \n",
       "4         Urban        4.948760       6000.0         8.699515  \n",
       "5         Urban        5.587249       9613.0         9.170872  \n",
       "6         Urban        4.553877       3849.0         8.255569  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comb.head(7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Missing Data is Fixed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender                object\n",
       "Married               object\n",
       "Dependents            object\n",
       "Education             object\n",
       "Self_Employed         object\n",
       "ApplicantIncome        int64\n",
       "CoapplicantIncome    float64\n",
       "LoanAmount           float64\n",
       "Loan_Amount_Term     float64\n",
       "Credit_History       float64\n",
       "Property_Area         object\n",
       "LoanAmount_log       float64\n",
       "TotalIncome          float64\n",
       "TotalIncome_log      float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comb.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "comb['Gender'] = comb['Gender'].map({'Male':1,'Female':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "comb['Married'] = comb['Married'].map({'Yes':1,'No':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "comb['Singleton'] = comb['Dependents'].map(lambda d: 1 if d=='1' else 0)\n",
    "comb['Small_Family'] = comb['Dependents'].map(lambda d: 1 if d=='2' else 0)\n",
    "comb['Large_Family'] = comb['Dependents'].map(lambda d: 1 if d=='3' else 0)\n",
    "comb.drop(['Dependents'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "comb['Education'] = comb['Education'].map({'Graduate':1,'Not Graduate':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "comb['Self_Employed'] = comb['Self_Employed'].map({'Yes':1,'No':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "property_dummies = pd.get_dummies(comb['Property_Area'], prefix='Property')\n",
    "comb = pd.concat([comb, property_dummies], axis=1)\n",
    "comb.drop('Property_Area', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender                  int64\n",
       "Married                 int64\n",
       "Education               int64\n",
       "Self_Employed           int64\n",
       "ApplicantIncome         int64\n",
       "CoapplicantIncome     float64\n",
       "LoanAmount            float64\n",
       "Loan_Amount_Term      float64\n",
       "Credit_History        float64\n",
       "LoanAmount_log        float64\n",
       "TotalIncome           float64\n",
       "TotalIncome_log       float64\n",
       "Singleton               int64\n",
       "Small_Family            int64\n",
       "Large_Family            int64\n",
       "Property_Rural          uint8\n",
       "Property_Semiurban      uint8\n",
       "Property_Urban          uint8\n",
       "dtype: object"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comb.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>LoanAmount_log</th>\n",
       "      <th>TotalIncome</th>\n",
       "      <th>TotalIncome_log</th>\n",
       "      <th>Singleton</th>\n",
       "      <th>Small_Family</th>\n",
       "      <th>Large_Family</th>\n",
       "      <th>Property_Rural</th>\n",
       "      <th>Property_Semiurban</th>\n",
       "      <th>Property_Urban</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>142.51153</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.959423</td>\n",
       "      <td>5849.0</td>\n",
       "      <td>8.674026</td>\n",
       "      <td>0</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",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>128.00000</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.852030</td>\n",
       "      <td>6091.0</td>\n",
       "      <td>8.714568</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.00000</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.189655</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>8.006368</td>\n",
       "      <td>0</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",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120.00000</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.787492</td>\n",
       "      <td>4941.0</td>\n",
       "      <td>8.505323</td>\n",
       "      <td>0</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",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.00000</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.948760</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>8.699515</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender  Married  Education  Self_Employed  ApplicantIncome  \\\n",
       "0       1        0          1              0             5849   \n",
       "1       1        1          1              0             4583   \n",
       "2       1        1          1              1             3000   \n",
       "3       1        1          0              0             2583   \n",
       "4       1        0          1              0             6000   \n",
       "\n",
       "   CoapplicantIncome  LoanAmount  Loan_Amount_Term  Credit_History  \\\n",
       "0                0.0   142.51153          5.886104             1.0   \n",
       "1             1508.0   128.00000          5.886104             1.0   \n",
       "2                0.0    66.00000          5.886104             1.0   \n",
       "3             2358.0   120.00000          5.886104             1.0   \n",
       "4                0.0   141.00000          5.886104             1.0   \n",
       "\n",
       "   LoanAmount_log  TotalIncome  TotalIncome_log  Singleton  Small_Family  \\\n",
       "0        4.959423       5849.0         8.674026          0             0   \n",
       "1        4.852030       6091.0         8.714568          1             0   \n",
       "2        4.189655       3000.0         8.006368          0             0   \n",
       "3        4.787492       4941.0         8.505323          0             0   \n",
       "4        4.948760       6000.0         8.699515          0             0   \n",
       "\n",
       "   Large_Family  Property_Rural  Property_Semiurban  Property_Urban  \n",
       "0             0               0                   0               1  \n",
       "1             0               1                   0               0  \n",
       "2             0               0                   0               1  \n",
       "3             0               0                   0               1  \n",
       "4             0               0                   0               1  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comb.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender                0\n",
       "Married               0\n",
       "Education             0\n",
       "Self_Employed         0\n",
       "ApplicantIncome       0\n",
       "CoapplicantIncome     0\n",
       "LoanAmount            0\n",
       "Loan_Amount_Term      0\n",
       "Credit_History        0\n",
       "LoanAmount_log        0\n",
       "TotalIncome           0\n",
       "TotalIncome_log       0\n",
       "Singleton             0\n",
       "Small_Family          0\n",
       "Large_Family          0\n",
       "Property_Rural        0\n",
       "Property_Semiurban    0\n",
       "Property_Urban        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comb.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "targets = train_data['Loan_Status'].map({'Y':1,'N':0})\n",
    "train_final = comb.head(614)\n",
    "test_final = comb.iloc[614:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>LoanAmount_log</th>\n",
       "      <th>TotalIncome</th>\n",
       "      <th>TotalIncome_log</th>\n",
       "      <th>Singleton</th>\n",
       "      <th>Small_Family</th>\n",
       "      <th>Large_Family</th>\n",
       "      <th>Property_Rural</th>\n",
       "      <th>Property_Semiurban</th>\n",
       "      <th>Property_Urban</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>5849</td>\n",
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       "   Gender  Married  Education  Self_Employed  ApplicantIncome  \\\n",
       "0       1        0          1              0             5849   \n",
       "1       1        1          1              0             4583   \n",
       "2       1        1          1              1             3000   \n",
       "3       1        1          0              0             2583   \n",
       "4       1        0          1              0             6000   \n",
       "\n",
       "   CoapplicantIncome  LoanAmount  Loan_Amount_Term  Credit_History  \\\n",
       "0                0.0   142.51153          5.886104             1.0   \n",
       "1             1508.0   128.00000          5.886104             1.0   \n",
       "2                0.0    66.00000          5.886104             1.0   \n",
       "3             2358.0   120.00000          5.886104             1.0   \n",
       "4                0.0   141.00000          5.886104             1.0   \n",
       "\n",
       "   LoanAmount_log  TotalIncome  TotalIncome_log  Singleton  Small_Family  \\\n",
       "0        4.959423       5849.0         8.674026          0             0   \n",
       "1        4.852030       6091.0         8.714568          1             0   \n",
       "2        4.189655       3000.0         8.006368          0             0   \n",
       "3        4.787492       4941.0         8.505323          0             0   \n",
       "4        4.948760       6000.0         8.699515          0             0   \n",
       "\n",
       "   Large_Family  Property_Rural  Property_Semiurban  Property_Urban  \n",
       "0             0               0                   0               1  \n",
       "1             0               1                   0               0  \n",
       "2             0               0                   0               1  \n",
       "3             0               0                   0               1  \n",
       "4             0               0                   0               1  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "comb.head()"
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  },
  {
   "cell_type": "code",
   "execution_count": 54,
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      "text/plain": [
       "   Gender  Married  Education  Self_Employed  ApplicantIncome  \\\n",
       "0       1        0          1              0             5849   \n",
       "1       1        1          1              0             4583   \n",
       "2       1        1          1              1             3000   \n",
       "3       1        1          0              0             2583   \n",
       "4       1        0          1              0             6000   \n",
       "\n",
       "   CoapplicantIncome  LoanAmount  Loan_Amount_Term  Credit_History  \\\n",
       "0                0.0   142.51153          5.886104             1.0   \n",
       "1             1508.0   128.00000          5.886104             1.0   \n",
       "2                0.0    66.00000          5.886104             1.0   \n",
       "3             2358.0   120.00000          5.886104             1.0   \n",
       "4                0.0   141.00000          5.886104             1.0   \n",
       "\n",
       "   LoanAmount_log  TotalIncome  TotalIncome_log  Singleton  Small_Family  \\\n",
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       "4        4.948760       6000.0         8.699515          0             0   \n",
       "\n",
       "   Large_Family  Property_Rural  Property_Semiurban  Property_Urban  \n",
       "0             0               0                   0               1  \n",
       "1             0               1                   0               0  \n",
       "2             0               0                   0               1  \n",
       "3             0               0                   0               1  \n",
       "4             0               0                   0               1  "
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     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "train_final.head()"
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  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
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       "      <td>280.0</td>\n",
       "      <td>5.480639</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.634790</td>\n",
       "      <td>13633.0</td>\n",
       "      <td>9.520249</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>623</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2400</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.812184</td>\n",
       "      <td>4800.0</td>\n",
       "      <td>8.476371</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>624</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3091</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.499810</td>\n",
       "      <td>3091.0</td>\n",
       "      <td>8.036250</td>\n",
       "      <td>0</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",
       "      <td>625</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2185</td>\n",
       "      <td>1516.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.087596</td>\n",
       "      <td>3701.0</td>\n",
       "      <td>8.216358</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>626</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4166</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>5.192957</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.688879</td>\n",
       "      <td>4166.0</td>\n",
       "      <td>8.334712</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>627</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>12173</td>\n",
       "      <td>0.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.111988</td>\n",
       "      <td>12173.0</td>\n",
       "      <td>9.406976</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>628</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4666</td>\n",
       "      <td>0.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.820282</td>\n",
       "      <td>4666.0</td>\n",
       "      <td>8.448057</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>629</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.875197</td>\n",
       "      <td>5667.0</td>\n",
       "      <td>8.642415</td>\n",
       "      <td>1</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",
       "      <td>630</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4583</td>\n",
       "      <td>2916.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.298317</td>\n",
       "      <td>7499.0</td>\n",
       "      <td>8.922525</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>631</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3786</td>\n",
       "      <td>333.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.836282</td>\n",
       "      <td>4119.0</td>\n",
       "      <td>8.323366</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>632</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>9226</td>\n",
       "      <td>7916.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.703782</td>\n",
       "      <td>17142.0</td>\n",
       "      <td>9.749287</td>\n",
       "      <td>0</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",
       "      <td>633</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1300</td>\n",
       "      <td>3470.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.192957</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.605170</td>\n",
       "      <td>4770.0</td>\n",
       "      <td>8.470102</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Gender  Married  Education  Self_Employed  ApplicantIncome  \\\n",
       "614       1        1          1              0             5720   \n",
       "615       1        1          1              0             3076   \n",
       "616       1        1          1              0             5000   \n",
       "617       1        1          1              0             2340   \n",
       "618       1        0          0              0             3276   \n",
       "619       1        1          0              1             2165   \n",
       "620       0        0          0              0             2226   \n",
       "621       1        1          0              0             3881   \n",
       "622       1        1          1              0            13633   \n",
       "623       1        0          0              0             2400   \n",
       "624       1        0          0              0             3091   \n",
       "625       1        1          1              0             2185   \n",
       "626       1        0          1              0             4166   \n",
       "627       1        1          1              0            12173   \n",
       "628       0        0          1              0             4666   \n",
       "629       1        0          1              0             5667   \n",
       "630       1        1          1              0             4583   \n",
       "631       1        1          1              0             3786   \n",
       "632       1        1          1              0             9226   \n",
       "633       1        0          1              0             1300   \n",
       "\n",
       "     CoapplicantIncome  LoanAmount  Loan_Amount_Term  Credit_History  \\\n",
       "614                0.0       110.0          5.886104             1.0   \n",
       "615             1500.0       126.0          5.886104             1.0   \n",
       "616             1800.0       208.0          5.886104             1.0   \n",
       "617             2546.0       100.0          5.886104             1.0   \n",
       "618                0.0        78.0          5.886104             1.0   \n",
       "619             3422.0       152.0          5.886104             1.0   \n",
       "620                0.0        59.0          5.886104             1.0   \n",
       "621                0.0       147.0          5.886104             0.0   \n",
       "622                0.0       280.0          5.480639             1.0   \n",
       "623             2400.0       123.0          5.886104             1.0   \n",
       "624                0.0        90.0          5.886104             1.0   \n",
       "625             1516.0       162.0          5.886104             1.0   \n",
       "626                0.0        40.0          5.192957             1.0   \n",
       "627                0.0       166.0          5.886104             0.0   \n",
       "628                0.0       124.0          5.886104             1.0   \n",
       "629                0.0       131.0          5.886104             1.0   \n",
       "630             2916.0       200.0          5.886104             1.0   \n",
       "631              333.0       126.0          5.886104             1.0   \n",
       "632             7916.0       300.0          5.886104             1.0   \n",
       "633             3470.0       100.0          5.192957             1.0   \n",
       "\n",
       "     LoanAmount_log  TotalIncome  TotalIncome_log  Singleton  Small_Family  \\\n",
       "614        4.700480       5720.0         8.651724          0             0   \n",
       "615        4.836282       4576.0         8.428581          1             0   \n",
       "616        5.337538       6800.0         8.824678          0             1   \n",
       "617        4.605170       4886.0         8.494129          0             1   \n",
       "618        4.356709       3276.0         8.094378          0             0   \n",
       "619        5.023881       5587.0         8.628198          0             0   \n",
       "620        4.077537       2226.0         7.707962          1             0   \n",
       "621        4.990433       3881.0         8.263848          0             1   \n",
       "622        5.634790      13633.0         9.520249          0             1   \n",
       "623        4.812184       4800.0         8.476371          0             0   \n",
       "624        4.499810       3091.0         8.036250          0             0   \n",
       "625        5.087596       3701.0         8.216358          1             0   \n",
       "626        3.688879       4166.0         8.334712          0             0   \n",
       "627        5.111988      12173.0         9.406976          0             1   \n",
       "628        4.820282       4666.0         8.448057          0             0   \n",
       "629        4.875197       5667.0         8.642415          1             0   \n",
       "630        5.298317       7499.0         8.922525          0             1   \n",
       "631        4.836282       4119.0         8.323366          0             0   \n",
       "632        5.703782      17142.0         9.749287          0             0   \n",
       "633        4.605170       4770.0         8.470102          0             0   \n",
       "\n",
       "     Large_Family  Property_Rural  Property_Semiurban  Property_Urban  \n",
       "614             0               0                   0               1  \n",
       "615             0               0                   0               1  \n",
       "616             0               0                   0               1  \n",
       "617             0               0                   0               1  \n",
       "618             0               0                   0               1  \n",
       "619             0               0                   0               1  \n",
       "620             0               0                   1               0  \n",
       "621             0               1                   0               0  \n",
       "622             0               0                   0               1  \n",
       "623             0               0                   1               0  \n",
       "624             0               0                   0               1  \n",
       "625             0               0                   1               0  \n",
       "626             1               0                   0               1  \n",
       "627             0               0                   1               0  \n",
       "628             0               0                   1               0  \n",
       "629             0               0                   0               1  \n",
       "630             0               0                   0               1  \n",
       "631             1               0                   1               0  \n",
       "632             0               0                   0               1  \n",
       "633             0               0                   1               0  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_final.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    0\n",
       "2    1\n",
       "3    1\n",
       "4    1\n",
       "Name: Loan_Status, dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "targets.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CLASSIFIERS DECLARATIONS AND SIMULATIONS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Full feature set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_val, y_train, y_val = train_test_split(train_final, targets, test_size = 0.20, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "#feature scaling\n",
    "st_sc = StandardScaler()\n",
    "X_train = st_sc.fit_transform(X_train)\n",
    "X_val = st_sc.transform(X_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "logreg = LogisticRegression(solver='lbfgs')\n",
    "logreg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.45      0.60        33\n",
      "           1       0.83      0.98      0.90        90\n",
      "\n",
      "    accuracy                           0.84       123\n",
      "   macro avg       0.86      0.72      0.75       123\n",
      "weighted avg       0.84      0.84      0.82       123\n",
      "\n",
      "Accuracy\n",
      "83.73983739837398 %\n",
      "\n",
      "\n",
      "Cross-Validation\n",
      "80.44761904761906 %\n",
      "\n",
      "\n",
      "Score on overfitting\n",
      "80.65173116089613 %\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1152x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.model_selection import cross_val_score\n",
    "y_pred = logreg.predict(X_val)\n",
    "\n",
    "print(\"Classification Report\")\n",
    "print(classification_report(y_val, y_pred))\n",
    "print(\"Accuracy\")\n",
    "print(accuracy_score(y_val, y_pred)*100, '%')\n",
    "print('\\n')\n",
    "print(\"Cross-Validation\")\n",
    "print(cross_val_score(estimator = logreg, X = X_train, y = y_train, cv = 10).mean()*100, '%')\n",
    "print('\\n')\n",
    "print(\"Score on overfitting\")\n",
    "print(logreg.score(X_train, y_train)*100, '%')\n",
    "\n",
    "#second_stack_probs = second_stack.predict_proba(first_stack.transform(X_test))\n",
    "conf_mat = confusion_matrix(y_val, y_pred)\n",
    "\n",
    "plt.figure(figsize=(16, 8))\n",
    "plt.matshow(conf_mat, cmap=plt.cm.Reds, alpha=0.2)\n",
    "for i in range(2):\n",
    "  for j in range(2):\n",
    "    plt.text(x=j, y=i, s=conf_mat[i, j], ha=\"center\", va=\"center\")\n",
    "plt.title(\"Confusion matrix\", y=1.1, fontdict={\"fontsize\": 20})\n",
    "plt.xlabel(\"Predicted\", fontdict={\"fontsize\": 14})\n",
    "plt.ylabel(\"Actual\", fontdict={\"fontsize\": 14});"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC: 0.821886\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import roc_auc_score\n",
    "import scikitplot as skplt\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "rf_probs = logreg.predict_proba(X_val)[:, 1]\n",
    "roc_value = roc_auc_score(y_val, rf_probs)\n",
    "\n",
    "print('AUC: %f' % roc_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_roc_curve(fpr, tpr):\n",
    "    plt.plot(fpr, tpr, color='orange', label='ROC Curve (area = %0.3f)' % roc_value)\n",
    "    plt.plot([0, 1], [0, 1], color='darkblue', linestyle='--')\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    #plt.title('Receiver Operating Characteristic (ROC) Curve')\n",
    "    plt.legend()\n",
    "    plt.grid()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "fpr, tpr, thresholds = roc_curve(y_val, rf_probs)\n",
    "plot_roc_curve(fpr, tpr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature selection set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 20 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   9 out of  18 | elapsed:    2.3s remaining:    2.3s\n",
      "[Parallel(n_jobs=-1)]: Done  18 out of  18 | elapsed:    2.3s finished\n",
      "\n",
      "[2019-11-29 07:51:21] Features: 1/5 -- score: 0.8043928571428574[Parallel(n_jobs=-1)]: Using backend LokyBackend with 20 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   5 out of  17 | elapsed:    0.0s remaining:    0.2s\n",
      "[Parallel(n_jobs=-1)]: Done  14 out of  17 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done  17 out of  17 | elapsed:    1.3s finished\n",
      "\n",
      "[2019-11-29 07:51:23] Features: 2/5 -- score: 0.8064761904761906[Parallel(n_jobs=-1)]: Using backend LokyBackend with 20 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   4 out of  16 | elapsed:    0.0s remaining:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done  13 out of  16 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done  16 out of  16 | elapsed:    0.0s finished\n",
      "\n",
      "[2019-11-29 07:51:23] Features: 3/5 -- score: 0.808517006802721[Parallel(n_jobs=-1)]: Using backend LokyBackend with 20 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   8 out of  15 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done  15 out of  15 | elapsed:    0.0s finished\n",
      "\n",
      "[2019-11-29 07:51:23] Features: 4/5 -- score: 0.808517006802721[Parallel(n_jobs=-1)]: Using backend LokyBackend with 20 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   7 out of  14 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done  14 out of  14 | elapsed:    0.0s finished\n",
      "\n",
      "[2019-11-29 07:51:23] Features: 5/5 -- score: 0.808517006802721"
     ]
    }
   ],
   "source": [
    "from mlxtend.feature_selection import SequentialFeatureSelector as sfs\n",
    "logreg_FS = LogisticRegression(solver='lbfgs')\n",
    "sfs_FS = sfs(logreg_FS, k_features=5, forward=True, floating=False, verbose=2, scoring='accuracy', cv=10, n_jobs=-1)\n",
    "sfs_FS = sfs_FS.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 2, 5, 8, 16]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['Gender',\n",
       " 'Married',\n",
       " 'Education',\n",
       " 'Self_Employed',\n",
       " 'ApplicantIncome',\n",
       " 'CoapplicantIncome',\n",
       " 'LoanAmount',\n",
       " 'Loan_Amount_Term',\n",
       " 'Credit_History',\n",
       " 'LoanAmount_log',\n",
       " 'TotalIncome',\n",
       " 'TotalIncome_log',\n",
       " 'Singleton',\n",
       " 'Small_Family',\n",
       " 'Large_Family',\n",
       " 'Property_Rural',\n",
       " 'Property_Semiurban',\n",
       " 'Property_Urban']"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_cols_1 = list(sfs_FS.k_feature_idx_)\n",
    "print(feat_cols_1)\n",
    "list(train_final.columns.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.45      0.60        33\n",
      "           1       0.83      0.98      0.90        90\n",
      "\n",
      "    accuracy                           0.84       123\n",
      "   macro avg       0.86      0.72      0.75       123\n",
      "weighted avg       0.84      0.84      0.82       123\n",
      "\n",
      "Accuracy\n",
      "83.73983739837398 %\n",
      "\n",
      "\n",
      "Cross-Validation\n",
      "80.8517006802721 %\n",
      "\n",
      "\n",
      "Score on overfitting\n",
      "80.85539714867616 %\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1152x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "logreg_FS = LogisticRegression(solver='lbfgs')\n",
    "logreg_FS.fit(X_train[:, feat_cols_1], y_train)\n",
    "y_pred = logreg_FS.predict(X_val[:, feat_cols_1])\n",
    "\n",
    "print(\"Classification Report\")\n",
    "print(classification_report(y_val, y_pred))\n",
    "\n",
    "print(\"Accuracy\")\n",
    "print(accuracy_score(y_val, y_pred)*100, '%')\n",
    "print('\\n')\n",
    "\n",
    "print(\"Cross-Validation\")\n",
    "print(cross_val_score(estimator = logreg_FS, X = X_train[:, feat_cols_1], y = y_train, cv = 10).mean()*100, '%')\n",
    "print('\\n')\n",
    "\n",
    "print(\"Score on overfitting\")\n",
    "y_pred = logreg_FS.predict(X_train[:, feat_cols_1])\n",
    "print(accuracy_score(y_train, y_pred)*100, '%')\n",
    "y_pred = logreg_FS.predict(X_val[:, feat_cols_1])\n",
    "\n",
    "conf_mat = confusion_matrix(y_val, y_pred)\n",
    "\n",
    "plt.figure(figsize=(16, 8))\n",
    "plt.matshow(conf_mat, cmap=plt.cm.Reds, alpha=0.2)\n",
    "for i in range(2):\n",
    "  for j in range(2):\n",
    "    plt.text(x=j, y=i, s=conf_mat[i, j], ha=\"center\", va=\"center\")\n",
    "plt.title(\"Confusion matrix\", y=1.1, fontdict={\"fontsize\": 20})\n",
    "plt.xlabel(\"Predicted\", fontdict={\"fontsize\": 14})\n",
    "plt.ylabel(\"Actual\", fontdict={\"fontsize\": 14});"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC: 0.768855\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import roc_auc_score\n",
    "import scikitplot as skplt\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "rf_probs = logreg_FS.predict_proba(X_val[:, feat_cols_1])[:, 1]\n",
    "roc_value = roc_auc_score(y_val, rf_probs)\n",
    "\n",
    "print('AUC: %f' % roc_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "fpr, tpr, thresholds = roc_curve(y_val, rf_probs)\n",
    "plot_roc_curve(fpr, tpr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random forest classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Full feature set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "                       max_depth=5, max_features='auto', max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                       n_jobs=-1, oob_score=False, random_state=0, verbose=0,\n",
       "                       warm_start=False)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rfr = RandomForestClassifier(n_estimators=100, max_depth=5, random_state=0, n_jobs=-1)\n",
    "rfr.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.42      0.56        33\n",
      "           1       0.82      0.97      0.89        90\n",
      "\n",
      "    accuracy                           0.82       123\n",
      "   macro avg       0.82      0.70      0.72       123\n",
      "weighted avg       0.82      0.82      0.80       123\n",
      "\n",
      "Accuracy\n",
      "82.11382113821138 %\n",
      "\n",
      "\n",
      "Cross-Validation\n",
      "80.4391156462585 %\n",
      "\n",
      "\n",
      "Score on overfitting\n",
      "82.68839103869654 %\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1152x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
    "\n",
    "y_pred = rfr.predict(X_val)\n",
    "\n",
    "print(\"Classification Report\")\n",
    "print(classification_report(y_val, y_pred))\n",
    "print(\"Accuracy\")\n",
    "print(accuracy_score(y_val, y_pred)*100, '%')\n",
    "print('\\n')\n",
    "print(\"Cross-Validation\")\n",
    "print(cross_val_score(estimator = rfr, X = X_train, y = y_train, cv = 10).mean()*100, '%')\n",
    "print('\\n')\n",
    "print(\"Score on overfitting\")\n",
    "print(rfr.score(X_train, y_train)*100, '%')\n",
    "\n",
    "conf_mat = confusion_matrix(y_val, y_pred)\n",
    "\n",
    "plt.figure(figsize=(16, 8))\n",
    "plt.matshow(conf_mat, cmap=plt.cm.Reds, alpha=0.2)\n",
    "for i in range(2):\n",
    "  for j in range(2):\n",
    "    plt.text(x=j, y=i, s=conf_mat[i, j], ha=\"center\", va=\"center\")\n",
    "plt.title(\"Confusion matrix\", y=1.1, fontdict={\"fontsize\": 20})\n",
    "plt.xlabel(\"Predicted\", fontdict={\"fontsize\": 14})\n",
    "plt.ylabel(\"Actual\", fontdict={\"fontsize\": 14});"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC: 0.787879\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import roc_auc_score\n",
    "import scikitplot as skplt\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "rf_probs = rfr.predict_proba(X_val)[:, 1]\n",
    "roc_value = roc_auc_score(y_val, rf_probs)\n",
    "\n",
    "print('AUC: %f' % roc_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "fpr, tpr, thresholds = roc_curve(y_val, rf_probs)\n",
    "plot_roc_curve(fpr, tpr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# above for checking the overfitting (not overfitting since not 100%) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature selection set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 20 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   9 out of  18 | elapsed:    2.5s remaining:    2.5s\n",
      "[Parallel(n_jobs=-1)]: Done  18 out of  18 | elapsed:    2.6s finished\n",
      "\n",
      "[2019-11-29 07:51:30] Features: 1/5 -- score: 0.8043928571428574[Parallel(n_jobs=-1)]: Using backend LokyBackend with 20 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   5 out of  17 | elapsed:    2.5s remaining:    6.1s\n",
      "[Parallel(n_jobs=-1)]: Done  14 out of  17 | elapsed:    2.6s remaining:    0.5s\n",
      "[Parallel(n_jobs=-1)]: Done  17 out of  17 | elapsed:    2.6s finished\n",
      "\n",
      "[2019-11-29 07:51:33] Features: 2/5 -- score: 0.8084744897959183[Parallel(n_jobs=-1)]: Using backend LokyBackend with 20 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   4 out of  16 | elapsed:    2.4s remaining:    7.3s\n",
      "[Parallel(n_jobs=-1)]: Done  13 out of  16 | elapsed:    2.5s remaining:    0.5s\n",
      "[Parallel(n_jobs=-1)]: Done  16 out of  16 | elapsed:    2.6s finished\n",
      "\n",
      "[2019-11-29 07:51:35] Features: 3/5 -- score: 0.8084744897959183[Parallel(n_jobs=-1)]: Using backend LokyBackend with 20 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   8 out of  15 | elapsed:    2.5s remaining:    2.2s\n",
      "[Parallel(n_jobs=-1)]: Done  15 out of  15 | elapsed:    2.6s finished\n",
      "\n",
      "[2019-11-29 07:51:38] Features: 4/5 -- score: 0.8084744897959183[Parallel(n_jobs=-1)]: Using backend LokyBackend with 20 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   7 out of  14 | elapsed:    2.5s remaining:    2.5s\n",
      "[Parallel(n_jobs=-1)]: Done  14 out of  14 | elapsed:    2.6s finished\n",
      "\n",
      "[2019-11-29 07:51:41] Features: 5/5 -- score: 0.8084744897959183"
     ]
    }
   ],
   "source": [
    "from mlxtend.feature_selection import SequentialFeatureSelector as sfs\n",
    "rfr_FS = RandomForestClassifier(n_estimators=100, max_depth=5, random_state=0, n_jobs=-1)\n",
    "sfs_FS = sfs(rfr_FS, k_features=5, forward=True, floating=False, verbose=2, scoring='accuracy', cv=10, n_jobs=-1)\n",
    "sfs_FS = sfs_FS.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3, 7, 8, 15, 16]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['Gender',\n",
       " 'Married',\n",
       " 'Education',\n",
       " 'Self_Employed',\n",
       " 'ApplicantIncome',\n",
       " 'CoapplicantIncome',\n",
       " 'LoanAmount',\n",
       " 'Loan_Amount_Term',\n",
       " 'Credit_History',\n",
       " 'LoanAmount_log',\n",
       " 'TotalIncome',\n",
       " 'TotalIncome_log',\n",
       " 'Singleton',\n",
       " 'Small_Family',\n",
       " 'Large_Family',\n",
       " 'Property_Rural',\n",
       " 'Property_Semiurban',\n",
       " 'Property_Urban']"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_cols_2 = list(sfs_FS.k_feature_idx_)\n",
    "print(feat_cols_2)\n",
    "list(train_final.columns.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.42      0.56        33\n",
      "           1       0.82      0.97      0.89        90\n",
      "\n",
      "    accuracy                           0.82       123\n",
      "   macro avg       0.82      0.70      0.72       123\n",
      "weighted avg       0.82      0.82      0.80       123\n",
      "\n",
      "Accuracy\n",
      "82.11382113821138 %\n",
      "\n",
      "\n",
      "Cross-Validation\n",
      "80.64336734693877 %\n",
      "\n",
      "\n",
      "Score on overfitting\n",
      "81.4663951120163 %\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1152x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rfr_FS = RandomForestClassifier(n_estimators=100, max_depth=5, random_state=0, n_jobs=-1)\n",
    "rfr_FS.fit(X_train[:, feat_cols_2], y_train)\n",
    "y_pred = rfr_FS.predict(X_val[:, feat_cols_2])\n",
    "\n",
    "print(\"Classification Report\")\n",
    "print(classification_report(y_val, y_pred))\n",
    "print(\"Accuracy\")\n",
    "print(accuracy_score(y_val, y_pred)*100, '%')\n",
    "print('\\n')\n",
    "print(\"Cross-Validation\")\n",
    "print(cross_val_score(estimator = rfr_FS, X = X_train[:, feat_cols_2], y = y_train, cv = 10).mean()*100, '%')\n",
    "print('\\n')\n",
    "print(\"Score on overfitting\")\n",
    "y_pred = rfr_FS.predict(X_train[:, feat_cols_2])\n",
    "print(accuracy_score(y_train, y_pred)*100, '%')\n",
    "\n",
    "y_pred = rfr_FS.predict(X_val[:, feat_cols_2])\n",
    "\n",
    "conf_mat = confusion_matrix(y_val, y_pred)\n",
    "\n",
    "plt.figure(figsize=(16, 8))\n",
    "plt.matshow(conf_mat, cmap=plt.cm.Reds, alpha=0.2)\n",
    "for i in range(2):\n",
    "  for j in range(2):\n",
    "    plt.text(x=j, y=i, s=conf_mat[i, j], ha=\"center\", va=\"center\")\n",
    "plt.title(\"Confusion matrix\", y=1.1, fontdict={\"fontsize\": 20})\n",
    "plt.xlabel(\"Predicted\", fontdict={\"fontsize\": 14})\n",
    "plt.ylabel(\"Actual\", fontdict={\"fontsize\": 14});"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC: 0.742761\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import roc_auc_score\n",
    "import scikitplot as skplt\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "rf_probs = rfr_FS.predict_proba(X_val[:, feat_cols_2])[:, 1]\n",
    "roc_value = roc_auc_score(y_val, rf_probs)\n",
    "\n",
    "print('AUC: %f' % roc_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "fpr, tpr, thresholds = roc_curve(y_val, rf_probs)\n",
    "plot_roc_curve(fpr, tpr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gradient boosting classifier XGBoost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Full feature set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Learning rate:  0.05\n",
      "Accuracy score (training): 0.811\n",
      "Accuracy score (validation): 0.821\n",
      "Learning rate:  0.075\n",
      "Accuracy score (training): 0.807\n",
      "Accuracy score (validation): 0.829\n",
      "Learning rate:  0.1\n",
      "Accuracy score (training): 0.807\n",
      "Accuracy score (validation): 0.829\n",
      "Learning rate:  0.25\n",
      "Accuracy score (training): 0.819\n",
      "Accuracy score (validation): 0.821\n",
      "Learning rate:  0.5\n",
      "Accuracy score (training): 0.841\n",
      "Accuracy score (validation): 0.837\n",
      "Learning rate:  0.75\n",
      "Accuracy score (training): 0.859\n",
      "Accuracy score (validation): 0.805\n",
      "Learning rate:  1\n",
      "Accuracy score (training): 0.872\n",
      "Accuracy score (validation): 0.821\n"
     ]
    }
   ],
   "source": [
    "lr_list = [0.05, 0.075, 0.1, 0.25, 0.5, 0.75, 1]\n",
    "for learning_rate in lr_list:\n",
    "    gb_clf = XGBClassifier(n_estimators=20, learning_rate=learning_rate, max_features=0.1, max_depth=2, random_state=0)\n",
    "    gb_clf.fit(X_train, y_train)\n",
    "    \n",
    "    print(\"Learning rate: \", learning_rate)\n",
    "    print(\"Accuracy score (training): {0:.3f}\".format(gb_clf.score(X_train, y_train)))\n",
    "    print(\"Accuracy score (validation): {0:.3f}\".format(gb_clf.score(X_val, y_val)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.70      0.58      0.63        33\n",
      "           1       0.85      0.91      0.88        90\n",
      "\n",
      "    accuracy                           0.82       123\n",
      "   macro avg       0.78      0.74      0.76       123\n",
      "weighted avg       0.81      0.82      0.82       123\n",
      "\n",
      "Accuracy\n",
      "82.11382113821138 %\n",
      "\n",
      "\n",
      "Cross-Validation\n",
      "77.38180272108843 %\n",
      "\n",
      "\n",
      "Score on overfitting\n",
      "87.16904276985743 %\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1152x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gb_clf2 = XGBClassifier(n_estimators=20, learning_rate=1, max_features=0.1, max_depth=2, random_state=0)\n",
    "gb_clf2.fit(X_train, y_train)\n",
    "y_pred = gb_clf2.predict(X_val)\n",
    "\n",
    "print(\"Classification Report\")\n",
    "print(classification_report(y_val, y_pred))\n",
    "\n",
    "print(\"Accuracy\")\n",
    "print(accuracy_score(y_val, y_pred)*100, '%')\n",
    "print('\\n')\n",
    "\n",
    "print(\"Cross-Validation\")\n",
    "print(cross_val_score(estimator = gb_clf, X = X_train, y = y_train, cv = 10).mean()*100, '%')\n",
    "print('\\n')\n",
    "\n",
    "print(\"Score on overfitting\")\n",
    "y_pred = gb_clf.predict(X_train)\n",
    "print(accuracy_score(y_train, y_pred)*100, '%')\n",
    "\n",
    "y_pred = gb_clf2.predict(X_val)\n",
    "conf_mat = confusion_matrix(y_val, y_pred)\n",
    "\n",
    "plt.figure(figsize=(16, 8))\n",
    "plt.matshow(conf_mat, cmap=plt.cm.Reds, alpha=0.2)\n",
    "for i in range(2):\n",
    "  for j in range(2):\n",
    "    plt.text(x=j, y=i, s=conf_mat[i, j], ha=\"center\", va=\"center\")\n",
    "plt.title(\"Confusion matrix\", y=1.1, fontdict={\"fontsize\": 20})\n",
    "plt.xlabel(\"Predicted\", fontdict={\"fontsize\": 14})\n",
    "plt.ylabel(\"Actual\", fontdict={\"fontsize\": 14});"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC: 0.776431\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import roc_auc_score\n",
    "import scikitplot as skplt\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "rf_probs = gb_clf2.predict_proba(X_val)[:, 1]\n",
    "roc_value = roc_auc_score(y_val, rf_probs)\n",
    "\n",
    "print('AUC: %f' % roc_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "fpr, tpr, thresholds = roc_curve(y_val, rf_probs)\n",
    "plot_roc_curve(fpr, tpr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature selection set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: xgboost in c:\\users\\bibarys.mussagaliye\\appdata\\local\\continuum\\anaconda3\\lib\\site-packages (0.90)\n",
      "Requirement already satisfied: numpy in c:\\users\\bibarys.mussagaliye\\appdata\\local\\continuum\\anaconda3\\lib\\site-packages (from xgboost) (1.16.5)\n",
      "Requirement already satisfied: scipy in c:\\users\\bibarys.mussagaliye\\appdata\\local\\continuum\\anaconda3\\lib\\site-packages (from xgboost) (1.3.1)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    1.3s finished\n",
      "\n",
      "[2019-11-29 07:51:48] Features: 1/5 -- score: 0.8043928571428574[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    1.4s finished\n",
      "\n",
      "[2019-11-29 07:51:50] Features: 2/5 -- score: 0.8043928571428574[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    1.4s finished\n",
      "\n",
      "[2019-11-29 07:51:51] Features: 3/5 -- score: 0.8043928571428574[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    1.4s finished\n",
      "\n",
      "[2019-11-29 07:51:53] Features: 4/5 -- score: 0.8043928571428574[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    1.5s finished\n",
      "\n",
      "[2019-11-29 07:51:54] Features: 5/5 -- score: 0.8084744897959183"
     ]
    }
   ],
   "source": [
    "from mlxtend.feature_selection import SequentialFeatureSelector as sfs\n",
    "xgb_FS = XGBClassifier(n_estimators=20, learning_rate=1, max_features=0.1, max_depth=8, random_state=0, n_jobs=-1)\n",
    "sfs_FS = sfs(xgb_FS, k_features=5, forward=True, floating=False, verbose=2, scoring='accuracy', cv=10)\n",
    "sfs_FS = sfs_FS.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 2, 8, 16]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['Gender',\n",
       " 'Married',\n",
       " 'Education',\n",
       " 'Self_Employed',\n",
       " 'ApplicantIncome',\n",
       " 'CoapplicantIncome',\n",
       " 'LoanAmount',\n",
       " 'Loan_Amount_Term',\n",
       " 'Credit_History',\n",
       " 'LoanAmount_log',\n",
       " 'TotalIncome',\n",
       " 'TotalIncome_log',\n",
       " 'Singleton',\n",
       " 'Small_Family',\n",
       " 'Large_Family',\n",
       " 'Property_Rural',\n",
       " 'Property_Semiurban',\n",
       " 'Property_Urban']"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_cols_3 = list(sfs_FS.k_feature_idx_)\n",
    "print(feat_cols_3)\n",
    "list(train_final.columns.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.42      0.57        33\n",
      "           1       0.82      0.98      0.89        90\n",
      "\n",
      "    accuracy                           0.83       123\n",
      "   macro avg       0.85      0.70      0.73       123\n",
      "weighted avg       0.84      0.83      0.81       123\n",
      "\n",
      "Accuracy\n",
      "82.92682926829268 %\n",
      "\n",
      "\n",
      "Cross-Validation\n",
      "80.84744897959183 %\n",
      "\n",
      "\n",
      "Score on overfitting\n",
      "80.85539714867616 %\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1152x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xgb_FS = XGBClassifier(n_estimators=20, learning_rate=1, max_features=0.1, max_depth=8, random_state=0)\n",
    "xgb_FS.fit(X_train[:, feat_cols_3], y_train)\n",
    "y_pred = xgb_FS.predict(X_val[:, feat_cols_3])\n",
    "\n",
    "print(\"Classification Report\")\n",
    "print(classification_report(y_val, y_pred))\n",
    "print(\"Accuracy\")\n",
    "print(accuracy_score(y_val, y_pred)*100, '%')\n",
    "print('\\n')\n",
    "print(\"Cross-Validation\")\n",
    "print(cross_val_score(estimator = xgb_FS, X = X_train[:, feat_cols_3], y = y_train, cv = 10).mean()*100, '%')\n",
    "print('\\n')\n",
    "print(\"Score on overfitting\")\n",
    "y_pred = xgb_FS.predict(X_train[:, feat_cols_3])\n",
    "print(accuracy_score(y_train, y_pred)*100, '%')\n",
    "\n",
    "y_pred = xgb_FS.predict(X_val[:, feat_cols_3])\n",
    "conf_mat = confusion_matrix(y_val, y_pred)\n",
    "\n",
    "plt.figure(figsize=(16, 8))\n",
    "plt.matshow(conf_mat, cmap=plt.cm.Reds, alpha=0.2)\n",
    "for i in range(2):\n",
    "  for j in range(2):\n",
    "    plt.text(x=j, y=i, s=conf_mat[i, j], ha=\"center\", va=\"center\")\n",
    "plt.title(\"Confusion matrix\", y=1.1, fontdict={\"fontsize\": 20})\n",
    "plt.xlabel(\"Predicted\", fontdict={\"fontsize\": 14})\n",
    "plt.ylabel(\"Actual\", fontdict={\"fontsize\": 14});"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC: 0.782660\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import roc_auc_score\n",
    "import scikitplot as skplt\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "rf_probs = xgb_FS.predict_proba(X_val[:, feat_cols_3])[:, 1]\n",
    "roc_value = roc_auc_score(y_val, rf_probs)\n",
    "\n",
    "print('AUC: %f' % roc_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "fpr, tpr, thresholds = roc_curve(y_val, rf_probs)\n",
    "plot_roc_curve(fpr, tpr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Credit_History        0.443567\n",
      "TotalIncome           0.100334\n",
      "TotalIncome_log       0.081893\n",
      "ApplicantIncome       0.074278\n",
      "LoanAmount_log        0.066737\n",
      "LoanAmount            0.064869\n",
      "CoapplicantIncome     0.039166\n",
      "Property_Rural        0.023717\n",
      "Loan_Amount_Term      0.021562\n",
      "Property_Semiurban    0.018388\n",
      "Education             0.015515\n",
      "Large_Family          0.011198\n",
      "Married               0.008586\n",
      "Singleton             0.008533\n",
      "Property_Urban        0.006833\n",
      "Gender                0.006407\n",
      "Self_Employed         0.004919\n",
      "Small_Family          0.003497\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "predictor_var = ['Gender', 'Married', 'Education', 'Self_Employed', 'ApplicantIncome', 'CoapplicantIncome', 'LoanAmount', 'Loan_Amount_Term', 'Credit_History', 'LoanAmount_log', 'TotalIncome', 'TotalIncome_log', 'Singleton', 'Small_Family', 'Large_Family', 'Property_Rural', 'Property_Semiurban', 'Property_Urban']\n",
    "featimp = pd.Series(rfr.feature_importances_, index=predictor_var).sort_values(ascending=False)\n",
    "print (featimp)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# KNN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Full feature set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                     metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
       "                     weights='uniform')"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn = KNeighborsClassifier(n_neighbors=5,metric='minkowski',p=2)\n",
    "knn.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.45      0.58        33\n",
      "           1       0.83      0.96      0.89        90\n",
      "\n",
      "    accuracy                           0.82       123\n",
      "   macro avg       0.81      0.71      0.73       123\n",
      "weighted avg       0.82      0.82      0.80       123\n",
      "\n",
      "Accuracy\n",
      "82.11382113821138 %\n",
      "\n",
      "\n",
      "Cross-Validation\n",
      "76.37380952380953 %\n",
      "\n",
      "\n",
      "Score on overfitting\n",
      "82.28105906313645 %\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1152x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "\n",
    "y_pred = knn.predict(X_val)\n",
    "\n",
    "print(\"Classification Report\")\n",
    "print(classification_report(y_val, y_pred))\n",
    "\n",
    "print(\"Accuracy\")\n",
    "print(accuracy_score(y_val, y_pred)*100, '%')\n",
    "print('\\n')\n",
    "\n",
    "print(\"Cross-Validation\")\n",
    "print(cross_val_score(estimator = knn, X = X_train, y = y_train, cv = 10).mean()*100, '%')\n",
    "print('\\n')\n",
    "\n",
    "print(\"Score on overfitting\")\n",
    "y_pred = knn.predict(X_train)\n",
    "print(accuracy_score(y_train, y_pred)*100, '%')\n",
    "\n",
    "y_pred = knn.predict(X_val)\n",
    "conf_mat = confusion_matrix(y_val, y_pred)\n",
    "\n",
    "plt.figure(figsize=(16, 8))\n",
    "plt.matshow(conf_mat, cmap=plt.cm.Reds, alpha=0.2)\n",
    "for i in range(2):\n",
    "  for j in range(2):\n",
    "    plt.text(x=j, y=i, s=conf_mat[i, j], ha=\"center\", va=\"center\")\n",
    "plt.title(\"Confusion matrix\", y=1.1, fontdict={\"fontsize\": 20})\n",
    "plt.xlabel(\"Predicted\", fontdict={\"fontsize\": 14})\n",
    "plt.ylabel(\"Actual\", fontdict={\"fontsize\": 14});"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC: 0.744949\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import roc_auc_score\n",
    "import scikitplot as skplt\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "rf_probs = knn.predict_proba(X_val)[:, 1]\n",
    "roc_value = roc_auc_score(y_val, rf_probs)\n",
    "\n",
    "print('AUC: %f' % roc_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "fpr, tpr, thresholds = roc_curve(y_val, rf_probs)\n",
    "plot_roc_curve(fpr, tpr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 20 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   9 out of  18 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done  18 out of  18 | elapsed:    0.0s finished\n",
      "\n",
      "[2019-11-29 07:51:55] Features: 1/5 -- score: 0.8043928571428574[Parallel(n_jobs=-1)]: Using backend LokyBackend with 20 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   5 out of  17 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done  14 out of  17 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done  17 out of  17 | elapsed:    0.0s finished\n",
      "\n",
      "[2019-11-29 07:51:55] Features: 2/5 -- score: 0.8043928571428574[Parallel(n_jobs=-1)]: Using backend LokyBackend with 20 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   4 out of  16 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done  13 out of  16 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done  16 out of  16 | elapsed:    0.0s finished\n",
      "\n",
      "[2019-11-29 07:51:55] Features: 3/5 -- score: 0.8043928571428574[Parallel(n_jobs=-1)]: Using backend LokyBackend with 20 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   8 out of  15 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done  15 out of  15 | elapsed:    0.0s finished\n",
      "\n",
      "[2019-11-29 07:51:55] Features: 4/5 -- score: 0.8043928571428574[Parallel(n_jobs=-1)]: Using backend LokyBackend with 20 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   7 out of  14 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done  14 out of  14 | elapsed:    0.0s finished\n",
      "\n",
      "[2019-11-29 07:51:56] Features: 5/5 -- score: 0.8043928571428574"
     ]
    }
   ],
   "source": [
    "from mlxtend.feature_selection import SequentialFeatureSelector as sfs\n",
    "knn_FS = KNeighborsClassifier(n_neighbors=5,metric='minkowski',p=2)\n",
    "sfs_FS = sfs(knn_FS, k_features=5, forward=True, floating=False, verbose=2, scoring='accuracy', cv=10, n_jobs=-1)\n",
    "sfs_FS = sfs_FS.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 2, 8, 13, 16]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['Gender',\n",
       " 'Married',\n",
       " 'Education',\n",
       " 'Self_Employed',\n",
       " 'ApplicantIncome',\n",
       " 'CoapplicantIncome',\n",
       " 'LoanAmount',\n",
       " 'Loan_Amount_Term',\n",
       " 'Credit_History',\n",
       " 'LoanAmount_log',\n",
       " 'TotalIncome',\n",
       " 'TotalIncome_log',\n",
       " 'Singleton',\n",
       " 'Small_Family',\n",
       " 'Large_Family',\n",
       " 'Property_Rural',\n",
       " 'Property_Semiurban',\n",
       " 'Property_Urban']"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_cols_4 = list(sfs_FS.k_feature_idx_)\n",
    "print(feat_cols_4)\n",
    "list(train_final.columns.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.42      0.57        33\n",
      "           1       0.82      0.98      0.89        90\n",
      "\n",
      "    accuracy                           0.83       123\n",
      "   macro avg       0.85      0.70      0.73       123\n",
      "weighted avg       0.84      0.83      0.81       123\n",
      "\n",
      "Accuracy\n",
      "82.92682926829268 %\n",
      "\n",
      "\n",
      "Cross-Validation\n",
      "80.43928571428573 %\n",
      "\n",
      "\n",
      "Score on overfitting\n",
      "80.44806517311609 %\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1152x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "knn_FS = KNeighborsClassifier(n_neighbors=5,metric='minkowski',p=2)\n",
    "knn_FS.fit(X_train[:, feat_cols_4], y_train)\n",
    "y_pred = knn_FS.predict(X_val[:, feat_cols_4])\n",
    "\n",
    "print(\"Classification Report\")\n",
    "print(classification_report(y_val, y_pred))\n",
    "print(\"Accuracy\")\n",
    "print(accuracy_score(y_val, y_pred)*100, '%')\n",
    "print('\\n')\n",
    "print(\"Cross-Validation\")\n",
    "print(cross_val_score(estimator = knn_FS, X = X_train[:, feat_cols_4], y = y_train, cv = 10).mean()*100, '%')\n",
    "print('\\n')\n",
    "print(\"Score on overfitting\")\n",
    "y_pred = knn_FS.predict(X_train[:, feat_cols_4])\n",
    "print(accuracy_score(y_train, y_pred)*100, '%')\n",
    "\n",
    "y_pred = knn_FS.predict(X_val[:, feat_cols_4])\n",
    "conf_mat = confusion_matrix(y_val, y_pred)\n",
    "\n",
    "plt.figure(figsize=(16, 8))\n",
    "plt.matshow(conf_mat, cmap=plt.cm.Reds, alpha=0.2)\n",
    "for i in range(2):\n",
    "  for j in range(2):\n",
    "    plt.text(x=j, y=i, s=conf_mat[i, j], ha=\"center\", va=\"center\")\n",
    "plt.title(\"Confusion matrix\", y=1.1, fontdict={\"fontsize\": 20})\n",
    "plt.xlabel(\"Predicted\", fontdict={\"fontsize\": 14})\n",
    "plt.ylabel(\"Actual\", fontdict={\"fontsize\": 14});"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC: 0.796128\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import roc_auc_score\n",
    "import scikitplot as skplt\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "rf_probs = knn_FS.predict_proba(X_val[:, feat_cols_4])[:, 1]\n",
    "roc_value = roc_auc_score(y_val, rf_probs)\n",
    "\n",
    "print('AUC: %f' % roc_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "fpr, tpr, thresholds = roc_curve(y_val, rf_probs)\n",
    "plot_roc_curve(fpr, tpr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1\n",
       "1      0\n",
       "2      1\n",
       "3      1\n",
       "4      1\n",
       "      ..\n",
       "609    1\n",
       "610    1\n",
       "611    1\n",
       "612    1\n",
       "613    0\n",
       "Name: Loan_Status, Length: 614, dtype: int64"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "targets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "                       max_depth=5, max_features='auto', max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                       n_jobs=-1, oob_score=False, random_state=0, verbose=0,\n",
       "                       warm_start=False)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfr_FS = RandomForestClassifier(n_estimators=100, max_depth=5, random_state=0, n_jobs=-1)\n",
    "rfr_FS.fit(X_train[:, feat_cols_2], y_train)\n",
    "#y_pred = rfr_FS.predict(X_val[:, feat_cols_2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[3, 7, 8, 15, 16]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output_file=pd.DataFrame()\n",
    "feat_cols_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "output=rfr_FS.predict(test_final.iloc[:, feat_cols_2]).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1,\n",
       "       1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1,\n",
       "       1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1,\n",
       "       1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0,\n",
       "       0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1,\n",
       "       0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0,\n",
       "       1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0,\n",
       "       1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1,\n",
       "       1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1,\n",
       "       1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0,\n",
       "       0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1,\n",
       "       0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0,\n",
       "       0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1,\n",
       "       0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0,\n",
       "       1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0])"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "output_file['Loan_Status'] = output\n",
    "output_file['Loan_ID'] = Loan_ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "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>Loan_Status</th>\n",
       "      <th>Loan_ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>LP001015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>LP001022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>LP001031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>LP001035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>LP001051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>LP001054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>LP001055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>LP001056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>LP001059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>LP001067</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Loan_Status   Loan_ID\n",
       "0            1  LP001015\n",
       "1            1  LP001022\n",
       "2            1  LP001031\n",
       "3            1  LP001035\n",
       "4            1  LP001051\n",
       "5            1  LP001054\n",
       "6            1  LP001055\n",
       "7            0  LP001056\n",
       "8            1  LP001059\n",
       "9            1  LP001067"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output_file.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "output_file[['Loan_ID', 'Loan_Status']].to_csv('output.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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