Loan Prediction Final.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Data mining A **\n",
    "\n",
    "**Username** : onde2anget"
   ]
  },
   {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dream Housing Finance company wants to automate the loan eligibility process (real time) based on customer detail provided while filling online application form. These details are Gender, Marital Status, Education, Number of Dependents, Income, Loan Amount, Credit History and others. To automate this process, they have given a problem to identify the customers segments, those are eligible for loan amount so that they can specifically target these customers. Here they have provided a partial data set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "my result ranked 12th in the preliminary leaderboard."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import Imputer, StandardScaler, LabelEncoder\n",
    "from sklearn.model_selection import StratifiedKFold, cross_val_score,GridSearchCV\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import RandomForestClassifier, BaggingClassifier, AdaBoostClassifier, GradientBoostingClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neural_network import MLPClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "scrolled": true
   },
   "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>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",
       "    <tr>\n",
       "      <th>Loan_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LP001002</th>\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",
       "      <th>LP001003</th>\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",
       "      <th>LP001005</th>\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",
       "      <th>LP001006</th>\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",
       "      <th>LP001008</th>\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": [
       "         Gender Married Dependents     Education Self_Employed  \\\n",
       "Loan_ID                                                          \n",
       "LP001002   Male      No          0      Graduate            No   \n",
       "LP001003   Male     Yes          1      Graduate            No   \n",
       "LP001005   Male     Yes          0      Graduate           Yes   \n",
       "LP001006   Male     Yes          0  Not Graduate            No   \n",
       "LP001008   Male      No          0      Graduate            No   \n",
       "\n",
       "          ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "Loan_ID                                                                      \n",
       "LP001002             5849                0.0         NaN             360.0   \n",
       "LP001003             4583             1508.0       128.0             360.0   \n",
       "LP001005             3000                0.0        66.0             360.0   \n",
       "LP001006             2583             2358.0       120.0             360.0   \n",
       "LP001008             6000                0.0       141.0             360.0   \n",
       "\n",
       "          Credit_History Property_Area Loan_Status  \n",
       "Loan_ID                                             \n",
       "LP001002             1.0         Urban           Y  \n",
       "LP001003             1.0         Rural           N  \n",
       "LP001005             1.0         Urban           Y  \n",
       "LP001006             1.0         Urban           Y  \n",
       "LP001008             1.0         Urban           Y  "
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('train.csv', index_col='Loan_ID')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 614 entries, LP001002 to LP002990\n",
      "Data columns (total 12 columns):\n",
      "Gender               601 non-null object\n",
      "Married              611 non-null object\n",
      "Dependents           599 non-null object\n",
      "Education            614 non-null object\n",
      "Self_Employed        582 non-null object\n",
      "ApplicantIncome      614 non-null int64\n",
      "CoapplicantIncome    614 non-null float64\n",
      "LoanAmount           592 non-null float64\n",
      "Loan_Amount_Term     600 non-null float64\n",
      "Credit_History       564 non-null float64\n",
      "Property_Area        614 non-null object\n",
      "Loan_Status          614 non-null object\n",
      "dtypes: float64(4), int64(1), object(7)\n",
      "memory usage: 62.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "terdapat 12 kolom dan 614 data. Hanya variabel ApplicantIncome, CoapplicantIncome, Education, Propoerty Area, dan Loan_Status yang tidak memiliki missing value."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Missing Value Imputation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Married</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>70</td>\n",
       "      <td>143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>270</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "CoapplicantIncome  False  True \n",
       "Married                        \n",
       "No                    70    143\n",
       "Yes                  270    128"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df['Married'], (df['CoapplicantIncome']==0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Imputasi variabel married berdasarkan coappilant income. diasumsikan hubungan Applicant dan Coapplicant adalah suami istri. Dari crosstable diatas dapat disimpulkan applicant yang belum menikah cenderung tidak memiliki coapplicant income dan sebaliknya. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.loc[(df['Married'].isnull()) & (df['CoapplicantIncome']==0), 'Married'] = 'No'\n",
    "df.loc[(df['Married'].isnull()), 'Married'] = 'Yes'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>col_0</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>86</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>180</td>\n",
       "      <td>309</td>\n",
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      "text/plain": [
       "col_0   False  True \n",
       "Gender              \n",
       "Female     86     26\n",
       "Male      180    309"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df['Gender'], (df['Married']=='Yes') & (df['ApplicantIncome']>df['CoapplicantIncome']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Diasumsikan jika applicant telah menikah dan pendapatannya lebih besar dari coapplicant maka gendernya adala pria. seperti pada crosstable diatas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "a=((df['Gender'].isnull()) & (df['Married']=='Yes') & (df['ApplicantIncome']>df['CoapplicantIncome']))\n",
    "df.loc[a, 'Gender'] = 'Male'\n",
    "df.loc[df['Gender'].isnull(),'Gender'] = 'Female'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Variabel lainnya dapat diimputasi dengan modus untuk variabel kualitatif dan mean untuk variabel kuantitatif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mmodus = ['Gender','Married','Self_Employed','Dependents','Credit_History']\n",
    "mmean = ['LoanAmount','ApplicantIncome','CoapplicantIncome','Loan_Amount_Term']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for feature in mmean:\n",
    "        if feature in df.columns.values:\n",
    "            df[feature] = df[feature].fillna(df[feature].mean())\n",
    "            \n",
    "for feature in mmodus:\n",
    "        if feature in df.columns.values:\n",
    "            df[feature] = df[feature].fillna(df[feature].value_counts().index[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Encoding Categorical Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "kategori = ['Gender','Married','Education','Self_Employed','Dependents','Credit_History', 'Property_Area','Loan_Status']\n",
    "for feature in kategori:\n",
    "        if feature in df.columns.values:\n",
    "            df[feature] = LabelEncoder().fit_transform(df[feature])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <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>Loan_Status</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Loan_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>LP001002</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>146.412162</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP001003</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP001005</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP001006</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP001008</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Gender  Married  Dependents  Education  Self_Employed  \\\n",
       "Loan_ID                                                           \n",
       "LP001002       1        0           0          0              0   \n",
       "LP001003       1        1           1          0              0   \n",
       "LP001005       1        1           0          0              1   \n",
       "LP001006       1        1           0          1              0   \n",
       "LP001008       1        0           0          0              0   \n",
       "\n",
       "          ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "Loan_ID                                                                      \n",
       "LP001002             5849                0.0  146.412162             360.0   \n",
       "LP001003             4583             1508.0  128.000000             360.0   \n",
       "LP001005             3000                0.0   66.000000             360.0   \n",
       "LP001006             2583             2358.0  120.000000             360.0   \n",
       "LP001008             6000                0.0  141.000000             360.0   \n",
       "\n",
       "          Credit_History  Property_Area  Loan_Status  \n",
       "Loan_ID                                               \n",
       "LP001002               1              2            1  \n",
       "LP001003               1              0            0  \n",
       "LP001005               1              2            1  \n",
       "LP001006               1              2            1  \n",
       "LP001008               1              2            1  "
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 614 entries, LP001002 to LP002990\n",
      "Data columns (total 12 columns):\n",
      "Gender               614 non-null int64\n",
      "Married              614 non-null int64\n",
      "Dependents           614 non-null int64\n",
      "Education            614 non-null int64\n",
      "Self_Employed        614 non-null int64\n",
      "ApplicantIncome      614 non-null int64\n",
      "CoapplicantIncome    614 non-null float64\n",
      "LoanAmount           614 non-null float64\n",
      "Loan_Amount_Term     614 non-null float64\n",
      "Credit_History       614 non-null int64\n",
      "Property_Area        614 non-null int64\n",
      "Loan_Status          614 non-null int64\n",
      "dtypes: float64(3), int64(9)\n",
      "memory usage: 62.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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S1GVASJK6DAhJUpcBIUnqMiAkSV0GhCSpy4CQJHUZEJKkrsFeGLSQJPcBjwNPAvuqajbJ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18e359c4f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=df.Loan_Status)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dari diagram batang diatas diketahui kelas data tidak seimbang"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IIYQQdZTMsAkhhBBCCCHqFZlhE0IIIYQQQgjhdzLDJoQQQgghhKhXjHo0LSUD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ESiCo/j/TusMAeVtR9J7FBxUxLNK5pcdwWJV5r7Zt7vmwlIiOdwE9k/IZ6S1Nrtm4dF9u9Ny7sm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o994oeyHJT0+kaO/uCmWCz25HwqRnkGAbBT8kkjprBgDRd9xHeK9+oBSOzHSSn5qIMy0VgJDz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\naKWa7b4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18e357e9c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr(),linewidths=.5,annot=True,cmap='RdYlGn')\n",
    "fig=plt.gcf()\n",
    "fig.set_size_inches(15,8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hanya variabel Credit_history yang memiliki korelasi cukup besar terhadap Loan_Status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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psHz5ciqVStGlSJImKT+LJE1UhkU1tM7OTjZt2sTKlSuLLkWSNEn5WSRpojIs\nqmFVKhW6urpIKdHV1eU3upKkQ87PIkkTmWFRDauzs5Pdu3cDMDg46De6kqRDzs8iSROZYVENq6en\nh4GBAQAGBgbo7u4uuCJJ0mTjZ5GkicywqIbV0tJCqVQCoFQqsXDhwoIrkiRNNn4WSZrIDItqWO3t\n7UyZUn2LNzU10dbWVnBFkqTJxs8iSROZYVENq1wu09raSkTQ2tpKuVwuuiRJ0iTjZ5GkiaxUdAFS\nLbW3t7Nlyxa/yZUkFcbPIkkTVaSUiq7hkGpubk69vb1FlyFJkiRJhYiIB1JKzWON8zBUSZIkSVKO\nYVGSJEmSlGNYlCRJkiTlGBYlSZIkSTmGRUmSJElSjmFRkiRJkpRjWJQkSZIk5RgWJUmSJEk5hkVJ\nkiRJUk7NwmJEHBER90XEjyLikYi4Ies/JSLujYjNEfGvEXFY1n941u7LHp87ZF8fy/p/EhEXD+lv\nzfr6IuK6Wr0WSZIkSZpsajmz+BLwnpTSW4EzgNaIOBf4NPCPKaX5wC+Aq7PxVwO/SCnNA/4xG0dE\nnAZcDiwAWoH/ERFNEdEE/DOwGDgNuCIbK0mSJEk6QDULi6nqhaw5Nbsl4D3At7P+TuB92f3LsjbZ\n4xdFRGT9t6SUXkopPQn0AWdnt76U0hMppZeBW7KxkiRJkqQDVNNzFrMZwIeAZ4Bu4HHglymlgWxI\nPzA7uz8b2AqQPf4cUB7av882++uXJEmSJB2gmobFlNJgSukMYA7VmcDfH2lY9jP289hr7c+JiKUR\n0RsRvTt37hy7cEmSJEma5A7JaqgppV8CdwDnAsdFRCl7aA6wPbvfD5wEkD3+RuDZof37bLO//pGe\nf0VKqTml1Dx9+vSD8ZIkSZIkqaHVcjXU6RFxXHb/SKAFeAzYAPxRNqwduC27vyprkz3+7ymllPVf\nnq2WegowH7gPuB+Yn62uehjVRXBW1er1SJIkSdJkUhp7yOt2ItCZrVo6Bbg1pfS9iHgUuCUi/g74\nIfCVbPxXgP8ZEX1UZxQvB0gpPRIRtwKPAgPAh1JKgwARcQ2wDmgCvppSeqSGr0eSJEmSJo2oTt5N\nHs3Nzam3t7foMiRJkiSpEBHxQEqpeaxxh+ScRUmSJEnSxGJYlCRJkiTlGBYlSZIkSTmGRUmSJElS\njmFRkiRJkpRjWJQkSZIk5RgWJUmSJEk5hkVJkiRJUo5hUZIkSZKUY1iUJEmSJOUYFiVJkiRJOYZF\nSZIkSVKOYVGSJEmSlGNYlCRJkiTlGBYlSZIkSTmGRUmSJElSjmFRkiRJkpRjWJQkSZIk5RgWJUmS\nJEk5hkVJkiRJUo5hUZIkSZL9BkHCAAAcx0lEQVSUY1iUpIJUKhWWL19OpVIpuhRJkqQcw6IkFaSz\ns5NNmzaxcuXKokuRJEnKMSxKUgEqlQpdXV2klOjq6nJ2UZIk1R3DoiQVoLOzk927dwMwODjo7KIk\nSao7hkVJKkBPTw8DAwMADAwM0N3dXXBFkiRJwxkWJakALS0tlEolAEqlEgsXLiy4IkmSpOEMi5JU\ngPb2dqZMqf4Kbmpqoq2treCKJEmShjMsSlIByuUyra2tRAStra2Uy+WiS5IkSRqmVHQBkjRZtbe3\ns2XLFmcVJUlSXTIsSlJByuUyHR0dRZchSZI0Ig9DlSRJkiTlGBYlSZIkSTmGRUmSJElSjmFRkiRJ\nkpRjWJSkglQqFZYvX06lUim6FEmSpBzDohqaf4yrnnV2drJp0yZWrlxZdCmSJEk5hkU1NP8YV72q\nVCp0dXWRUqKrq8svNCRJUt0xLKphVSoV1q5dS0qJtWvX+se46kpnZye7d+8GYHBw0C80JElS3TEs\nqmF1dnYyMDAAwCuvvOIf46orPT09e9+fAwMDdHd3F1yRpFrxlAjVK9+bGkvNwmJEnBQRGyLisYh4\nJCKuzfpPiIjuiNic/Tw+64+I6IiIvojYGBFnDdlXezZ+c0S0D+l/W0RsyrbpiIio1evRxNPd3U1K\nCYCUErfffnvBFUmvamlpYc+vrIhg4cKFBVckqVY8JUL1yvemxlLLmcUB4L+mlH4fOBf4UEScBlwH\nrE8pzQfWZ22AxcD87LYU+AJUwyVwPXAOcDZw/Z6AmY1ZOmS71hq+Hk0wM2fOHLUtFWnJkiXDvsy4\n9NJLC65IUi14frLqle9NjUfNwmJK6emU0oPZ/eeBx4DZwGVAZzasE3hfdv8yYGWq+gFwXEScCFwM\ndKeUnk0p/QLoBlqzx45NKd2Tqn9xrRyyL4kdO3aM2paKtGrVqmHt1atXF1SJpFry/GTVK9+bGo9D\ncs5iRMwFzgTuBWamlJ6GaqAEZmTDZgNbh2zWn/WN1t8/Qv9Iz780Inojonfnzp0H+nI0Qbzzne8c\n1r7gggsKqkTK6+npGdb2nEWpMXl+suqV702NR83DYkQcA3wH+HBK6VejDR2hL72O/nxnSitSSs0p\npebp06ePVbIahKewqp6df/75w9r7frkhqTG0tLRQKpUAKJVKnp+suuF7U+NR07AYEVOpBsVvpJT+\nLevekR1CSvbzmay/HzhpyOZzgO1j9M8ZoV8C4K677hq1LRXJLzOkyaG9vZ0pU6p/bjU1NdHW1lZw\nRVKV702NRy1XQw3gK8BjKaWbhjy0Ctizomk7cNuQ/rZsVdRzgeeyw1TXAYsi4vhsYZtFwLrssecj\n4tzsudqG7EvyGzPVNb/MkCaHcrlMa2srEUFrayvlcrnokiTA96bGp5Yzi+cBfwq8JyIeym6XAJ8C\nFkbEZmBh1gZYAzwB9AFfBv4CIKX0LPBx4P7sdmPWB/BB4F+ybR4H1tbw9WiC8Rsz1TO/zJAmj/b2\ndk4//XQ/h1R3fG9qLLFn6fbJorm5OfX29hZdhg6RT37yk9x+++1cfPHFfOxjHyu6HGmvSqXCFVdc\nwcsvv8xhhx3GN7/5Tb/VlSRJh0REPJBSah5r3CFZDVUqiueFqV6Vy2VmzZoFwKxZswyKkiSp7hgW\n1bAqlQobNmwA4I477vBis6orlUqFbdu2AbB9+3bfn5Ikqe4YFtWwvNis6llnZyd7TgPYvXu3709J\nklR3DItqWF5sVvXM96ckSap3hkU1LFebVD1raWnZe05tRPj+lCRJdcewqIblpTNUz5YsWbL3MNSU\nEpdeemnBFUmSJA1nWFTD8mKzqmff+ta3Rm1LkiQVzbCohubFZlWv1q9fP2pbkiSpaKWiC5BqqVwu\n09HRUXQZUs6eQ1D315YkSSqaM4tqaJVKheXLl3sNO9WdBQsWDGu/+c1vLqgSSZKkkRkW1dA6OzvZ\ntGmT17BT3XnkkUeGtR9++OGCKpEkSRqZYVENq1Kp0NXVRUqJrq4uZxdVVwYHB0dtS5IkFc2wqIbV\n2dnJ7t27geof4s4uqp7sucbi/tqSJElFMyyqYfX09DAwMADAwMAA3d3dBVckvWrq1KmjtiVJkopm\nWFTDamlpoVSqLvhbKpVYuHBhwRVJr5o1a9aobUmSpKIZFtWw2tvbmTKl+hafMmWK11pUXdmxY8eo\nbUmSpKIZFtWwyuUyM2bMAGDGjBmUy+WCK5Jete9M96JFiwqqRJIkaWSGRTWsSqXCtm3bANi+fbur\noaqunHHGGaO2JUmSimZYVMP60pe+REoJgN27d7NixYqCK5JeddNNNw1rf/azny2oEkmSpJEZFtWw\n1q9fP6zd09NTUCVS3gsvvDBqW5IkqWiGRTUsr2Oneub7U5Ik1TvDohrWRRddNGpbKtKeQ6T315Yk\nSSqaYVENa+nSpcMunbF06dKCK5JeNXfu3FHbkiRJRTMsqmGVy+W9lydYuHChl85QXbnmmmuGtZct\nW1ZQJZIkSSMzLKqhLV26lLe85S3OKqrufP/73x+1LUmSVDTDoiQVoLu7e1j79ttvL6gSSZKkkRkW\n1dA6OzvZtGkTK1euLLoUaZiZM2eO2pYkSSqaYVENq1KpsHbtWlJKrF27lkqlUnRJ0l4/+9nPRm1L\nkiQVzbCohtXZ2cnAwAAAr7zyirOLqiu/9Vu/NWpbkiSpaIZFNazu7u69165LKXlOmOrKjh07Rm1L\nkiQVzbCohuU5Yapney7rsseiRYsKqkSSJGlkhkU1LM8JUz1bsmTJsPall15aUCWSJEkjMyyqYXlO\nmOrZqlWrhrVXr15dUCWSJEkjMyyqYXlOmOqZ11mUJo9KpcLy5ctdlVvShGNYVMO64IILRm1LRZo2\nbdqobUmNw2v+SpqoDItqWC+++OKw9ksvvVRQJVLe9u3bR21LagyVSoWuri6v+StpQjIsqmHdfffd\nw9p33XVXQZVIeXsu67K/tqTG0NnZySuvvAJ4zV9JE49hUQ1r9+7do7alIu17KRcXYJIak9f8lTSR\nGRbVsKZMmTJqWyrSM888M6ztAkxSY/Kav5ImMv96VsM6++yzR21LRXLmW5ocXJlb0kRWs7AYEV+N\niGci4uEhfSdERHdEbM5+Hp/1R0R0RERfRGyMiLOGbNOejd8cEe1D+t8WEZuybToiImr1WjQxbd26\ndVi7v7+/oEqkPM9ZlCaHhQsXsudPlIhg0aJFBVckSeNXy5nFrwOt+/RdB6xPKc0H1mdtgMXA/Oy2\nFPgCVMMlcD1wDnA2cP2egJmNWTpku32fS5PcvuFw3/AoFcnDpKXJob29nVKpBMDUqVNpa2sruCJJ\nGr9SrXacUvp+RMzdp/sy4MLsfidwB/BXWf/KVP1q/QcRcVxEnJiN7U4pPQsQEd1Aa0TcARybUron\n618JvA9YW6vXo7ybb76Zvr6+osvYr8MPP3zY5TIOP/xwrr322gIr2r958+axbNmyostoKPX+/iyV\nSrz88svD2r4/pcZTLpdZvHgxq1evZvHixZTL5aJLkqRxO9RfZc9MKT0NkP2ckfXPBoZO+/RnfaP1\n94/QP6KIWBoRvRHRu3PnzgN+EZoYTj755GHtN73pTQVVIuUNDAyM2pbUONrb2zn99NOdVZQ04dRs\nZvE1Gul8w/Q6+keUUloBrABobm72xKCDZCLMNFx88cW89NJLzJ07lxUrVhRdjg6hen9/3nTTTaxa\ntQqonsd06aWX8pGPfKTgqiTVQrlcpqOjo+gypJxKpcINN9zA9ddf76y3RnSoZxZ3ZIeXkv3cs3Z8\nP3DSkHFzgO1j9M8ZoV8a5uSTT2bKlCn8zd/8TdGlSMO0t7fvXfTC85gkSUXo7Oxk06ZNrFy5suhS\nVKcOdVhcBexZ0bQduG1If1u2Kuq5wHPZYarrgEURcXy2sM0iYF322PMRcW62CmrbkH1Jex111FGc\nfvrpzJs3r+hSpGHK5TInnHACgOcxSZIOuUqlQldXFyklurq6qFQqRZekOlTLS2d8E7gH+N2I6I+I\nq4FPAQsjYjOwMGsDrAGeAPqALwN/AZAtbPNx4P7sduOexW6ADwL/km3zOC5uI2mCmTlzJkcffbSz\nipKkQ66zs5PBwUGget68s4saSS1XQ71iPw9dNMLYBHxoP/v5KvDVEfp7gTcfSI2SVKSpU6cyb948\nZxUlSYdcT0/P3rA4ODhId3e3584rxwt7SZIkSZPM+eefP6z9zne+s6BKVM8Mi5IkSTVUqVRYvny5\n54Sprgy91i8w7NrU0h6GRUmSpBpyxUnVo7vvvnvUtgSGRUmSpJpxxUnVq+qSIftvS2BYlCRJqhlX\nnFS9OvHEE0dtS2BYlCRJqpmRVpyU6sG+s9zOemskhkVJkqQaccVJ1aszzjhj1LYEhkVJkqSaiYii\nS5BGtHHjxlHbEhgWJUmSaub73//+qG2pKL/+9a9HbUtgWJQkSaqZmTNnjtqWilIqlUZtS2BYlCRJ\nqpkdO3aM2paK0tTUNGpbAsOiJElSzSxcuHDveYsRwaJFiwquSKq68MILR21LYFiUJEmqmfb2dqZO\nnQrA1KlTaWtrK7giqSqlVHQJmgA8OLlO3XzzzfT19RVdxoS359/w2muvLbiSiW/evHksW7as6DIk\naUIpl8u0trayevVqFi9eTLlcLrokCYC77757WPuuu+7iYx/7WEHVqF4ZFutUX18fDz38GINHnVB0\nKRPalJer35o98ITniByIpl3P7r3vFxkHj19mHDx+maF61t7ezpYtW5xVnGTq/fPyyCOPZNeuXcPa\n9fp55O/44hgW69jgUSfwm9+7pOgyJI788Zq99/v6+tj8yA85+ZjBAitqDIe9Uj0T4KWf9hZcycT2\n1AsuyqD6Vi6X6ejoKLoMaZiZM2dSqVSA6vm0rtSrkRgWJb1mJx8zyF+f9auiy5AA+OSDxxZdggpU\n77M3ANu2bQNg9uzZBVcyOmdvDq6J8G/5h3/4h1QqFZYsWcJHPvKRostRHTIsSpIk1dBvfvObokuQ\nRjRz5kxefPFFD5HWfhkWJUnShDURZm/2nAf2uc99ruBKpOGmTp3KvHnzXHhJ++WlMyRJkiRJOYZF\nSZIkSVKOYVGSJEmSlGNYlCRJkiTluMBNndq2bRtNu54bdn07qShNuyps2zYAVN+bv36+ycsVqG78\n9Pkmjs4uTSBJkg4eZxYlSZIkSTnOLNap2bNn87OXSvzm9y4puhSJI3+8htmzZwLV9+ZLA0/z12f9\nquCqpKpPPngsh9f5xc4lSZqInFmUJEmSJOUYFiVJkiRJOR6GWseadj3rAjcHaMqL1UMldx/hYiwH\nomnXs8DMve2nXnCBm4Nhx67q93Uzj9pdcCUT21MvNDG/6CIkSWpAhsU6NW/evKJLaAh9fc8DMO/U\nmWOM1Ohm7n1P+t48eF7u6wPg8Df5b3og5uP7UpKkWjAs1qlly5YVXUJDuPbaawH43Oc+V3AljcP3\n5sHj+1OSJNUzw6IkSRrRzTffTF82A67Xb8+/4Z4viPT6zZs3j2XLlvnePEh8bx48e96bjcawKEmS\nRtTX18dDDz/G4FEnFF3KhDbl5QTAA0/sKLiSia16/nxVX18fmx/5IScfM1hgRRPfYa9Uz51/6ae9\nBVcysT31QlPRJdSMYVGSJO3X4FEneM1f1YV9F/07+ZhBr/mrutDIi/556QxJkiRJUo5hUZIkSZKU\n42GokiRJmlC2bdvGr5/3mr+qDz99vomjt20ruoyacGZRkiRJkpTjzKIkSZImlNmzZ/PSwNMucKO6\n8MkHj+Xw2bOLLqMmnFmUJEmSJOVM+LAYEa0R8ZOI6IuI64quR5IkSZIawYQ+DDUimoB/BhYC/cD9\nEbEqpfRosZVNDjfffDN9fX1FlzGqPfVde+21BVcyunnz5rFs2bKiy2govj8PHt+fk9e2bdto2vVc\n7vp2UhGadlXYtm2g6DKkSWVCh0XgbKAvpfQEQETcAlwGGBYFwJFHHll0CdJ++f7UhDA4QNOuStFV\n7N/uQUip6CoaQwRMaSq6iv0bHB4Un3qhvldD3bFrCi8ORtFlNIQjmhIzj9pddBn79dQLTcwvuoga\nmehhcTawdUi7Hzhn30ERsRRYCnDyyScfmsomAWcaVM98f0oH7l3velfdz9Bv27aN3/zmN0WX0RCO\nPPJIZtf5Ih3z5s0b9rOeNW3bxhTfmwdF05FH1vUCMvOZGO/J1yPSBP42LiLeD1ycUvrzrP2nwNkp\npf3+ldjc3Jx6e3sPVYmSJEmSVFci4oGUUvNY4yb6Ajf9wElD2nOA7QXVIkmSJEkNY6KHxfuB+RFx\nSkQcBlwOrCq4JkmSJEma8Cb0OYsppYGIuAZYBzQBX00pPVJwWZIkSZI04U3osAiQUloDuKa3JEmS\nJB1EE/0wVEmSJElSDRgWJUmSJEk5hkVJkiRJUo5hUZIkSZKUY1iUJEmSJOUYFiVJkiRJOYZFSZIk\nSVKOYVGSJEmSlGNYlCRJkiTlGBYlSZIkSTmGRUmSJElSjmFRkiRJkpRjWJQkSZIk5RgWJUmSJEk5\nkVIquoZDKiJ2Aj8tug5pApsG/LzoIiRJk5afQ9KBe1NKafpYgyZdWJR0YCKiN6XUXHQdkqTJyc8h\n6dDxMFRJkiRJUo5hUZIkSZKUY1iU9FqtKLoASdKk5ueQdIh4zqIkSZIkKceZRUmSJElSjmFRkiRJ\nkpRjWJTqTET8nxGRIuL3DmAfX4+IP8ru/0tEnHbwKoSI+Ot92i8czP1LkmovIn4rIm6JiMcj4tGI\nWBMRv3MInvdvI+L/ye7fGBEtB3n/H46Io4a0t0TEtIP5HNJkYViU6s8VwN3A5QdjZymlP08pPXow\n9jXEX489RJJUryIigO8Cd6SUfjuldBrV3+0zD2UdKaX/llLqOci7/TBw1JijJI3JsCjVkYg4BjgP\nuJosLEbEhRHx/Yj4bvbN7xcjYkr22AsR8dmIeDAi1kfE9BH2eUdENGf3W7OxP4qI9Vnf2RHxvyPi\nh9nP3836r4yIf4uIrojYHBF/n/V/CjgyIh6KiG/s81wXZs/37Yj4cUR8I/uDhIh4e7b/H0XEfRHx\nhog4IiK+FhGbsud/95Dn/v8iYnVEPBkR10TER7MxP4iIE7Jxv53V90BE3HUgs7GSNMm8G3glpfTF\nPR0ppYeAuyPiMxHxcPa7+Y+h+vmUfc48mPVflvXPzX7fd0bExuz3/1HZY1si4tPZ7/z7ImLevkXs\ncyTMSJ8Tc7Pf7w9mt3dkY0f8vImI5cAsYENEbNjnueZGxGMR8eWIeCQibo+II7PH5kVET/bcD2af\nL7Gff4sLI+LOiLg1Iv4zIj4VER/Iat4UEb+djZseEd+JiPuz23kH+z+iVHMpJW/evNXJDfi/gK9k\n9/83cBZwIfAicCrQBHQDf5SNScAHsvv/Dfh8dv/rQ8bcATQD04GtwClZ/wnZz2OBUna/BfhOdv9K\n+P/bu7cYq646juPfHxdB63SMBW2LwPSB0AixKKBFCYLRF2NSSVFssZZobIgxo43GFx9aNV5jAxFT\nkz60taGFKqJJa6ttqEJaaSlgOzhCEy1DLcHWC50C2haZvw/rP87uuc0ZLmWQ3yfZOWvvtddln0n2\nmv9ee+/D00AnMBHYD0zNvCM1/T6Sn4uBfuBtlItR24CFwOuyrvnVNoEvAbfltkuBZ7KtlcCfgI7s\ndz+wKvdbDXwx05uBGZl+D/DQmf4bevHixcvZsADdwOoG26/McWYsZZbxGeCiPGefn/tMynO0gK4c\ni96XebcCX850H/DVTH8KuDfTN1b2uR1Y1mKceAMwMbfNAHZkuuF4U2l3UuWY+rLPXcB/gDm5/SfA\nJzP9GLA00xOz3WbfxWLghUxPAA4AX8uyXwDWZPquSp+mAXvO9N/di5eRLuMws9HkKmBNpjfk+i+B\n7RHxNICk9ZQAbCMwANyd+68DNrWo+3Jga0TsA4iIf+b2TuDHkmZQBvzxlTKbI6I/2/0jMJ0ScLay\nPSKezTJPUAbnfuBgRDyebb+Y+QuBtbltr6T9wODzMr+JiMPAYUn9wD25fTfwDpVZ2PcCP83JSyiD\ntpmZnbiFwPqIOA48J2kLMB+4H/iWpEWUsWcKQ7es/iUiHsn0Okog+v1cX1/5XN2i3Zk0HifOA34o\naQ5wnKExAhqPNw8Pc3z7osygAuwEuiR1AFMi4ufZ9ktZZ7Pv4kXg8Yg4mPv9GXgg69xNmbWFcgH2\n7ZUx6nxJHTm2mZ0VHCyajRKSLgA+AMyWFJQrmQHcl59VzX4gtdUPp6pJ/jcogdlSSV2UmchBL1fS\nx2nvnNGoTLO21WBbo3oGKusDWecY4IWImNNGn8zM7NV6KTN6tZqdl1dQ7vSYGxHHJPVRZuCg9RjV\nLN2o3Ub51wPPAZdRzvsvVfJOxRj1epof88mMUVD6uyAi/t1Gv8xGJT+zaDZ6LAPuiIjpEdEVEVOB\nfZSrvO+WdInKs4rLGbpyOoahwf5qWl9R3Qa8X9IlAIPP/VFmFg9kemWbfT0mafzwu/3PXuBiSfOz\n7Q5J44CtlH9AUHkD3zTgqXYqzKvO+yR9LMtL0mUj6JOZ2bnsIWCCpM8Obshz9CFguaSxKs/BLwK2\nU8aK5zNQXEK502TQNEkLMj34krZByyuf21r0p9k40UmZcRwArqFcSB3OYcpjDG3J8eRZSR/Ntifk\nc5dbafxdtOsB4PODKzk7anZWcbBoNnpcRXkzXdXPKEHgNuA7wB8oAeTgfkeBWZJ2UmYlv96s8oj4\nG3AdsEnSkwzdvvo94NuSHqG9QRjgFqBHNS+4adH2K5R/FNZm2w9SrkjfDIyVtDv7szIiXm5eU50V\nwGeyzl7gihGUNTM7Z0VEAEuBD6n8dEYv5VnCu4Ae4ElKQPmViPgrcCcwT9IOyrl3b6W6PcC1knqA\nNwM/quRNkPQY5Vm+61v0p9U4ca2kRym3oB5t4/BuAe6vfcHNMK4BuvMYfgdcSBlrG30X7eqmfGc9\n+SjHqhGUNRsVVM4VZjZaSVpMeRHARxrkHYmIN772vTIzMytvGKW8uGZ2g7w+YF5E/P017paZnSKe\nWTQzMzMzM7M6nlk0MzMzMzOzOp5ZNDMzMzMzszoOFs3MzMzMzKyOg0UzMzMzMzOr42DRzMzsNJJ0\nn6Q3DbPPkSbbb5fU6IfTzczMTrtxZ7oDZmZm/48kifIiuQ+f6b6YmZmdCM8smpmZtSDpu5I+V1m/\nUdINkjZL2iVpt6QrMq9L0h5JNwO7gKmS+iRNyvxfSNopqVfSdTXt3JT1bZY0uUE/5krakuV/Lemi\n03vkZmZ2rnOwaGZm1toGYHll/ePAbcDSiHgXsAS4KWcSAWYCd0TEOyNif01dn46IucA8oFvSBbn9\nPGBX1rcFuKFaSNJ4YC2wLMvfCnzzlB2hmZlZA74N1czMrIWI+L2kt0i6GJgMHAIOAqslLQIGgCnA\nW7PI/oh4tEl13ZKWZnoqMAP4R9Zxd25fB2yqKTcTmA08mDHp2OyDmZnZaeNg0czMbHgbgWXAhZSZ\nxhWUwHFuRByT1AdMzH2PNqpA0mLgg8CCiPiXpN9WytSK2uJAb0QsOIljMDMzGxHfhmpmZja8DcAn\nKAHjRqATeD4DxSXA9Dbq6AQOZaB4KXB5JW9M1g1wNfBwTdmngMmSFkC5LVXSrBM+GjMzszZ4ZtHM\nzGwYEdErqQM4EBEHJd0J3CNpB/AEsLeNan4FrJLUQwn+qreqHgVmSdoJ9PPqZySJiFfyJzR+IKmT\nMn6vAXpP9tjMzMyaUUTtnS5mZmZmZmZ2rvNtqGZmZmZmZlbHwaKZmZmZmZnVcbBoZmZmZmZmdRws\nmpmZmZmZWR0Hi2ZmZmZmZlbHwaKZmZmZmZnVcbBoZmZmZmZmdf4LmJnPpQVl8n8AAAAASUVORK5C\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18e35b0d208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dff=pd.melt(df[['ApplicantIncome','CoapplicantIncome','Loan_Status']],id_vars='Loan_Status')\n",
    "sns.boxplot(y=\"value\", x=\"variable\", hue='Loan_Status', data=dff)\n",
    "fig=plt.gcf()\n",
    "fig.set_size_inches(15,8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tampak variabel Applicant Income dan Coapplicant Income tidak memiliki perbedaan karakteristik terhadap Loan Status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kAXgIeAJoCfy15hamJEmStoFNBrIY4+UxxoHAWUBX4MkQwqNNXpkkSdKnRGOu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\nf1BsZLJNsS0tAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18e371e1518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dff2=pd.melt(df[['Gender','Married','Education','Self_Employed','Dependents','Credit_History', 'Property_Area','Loan_Status']],id_vars='Loan_Status')\n",
    "sns.swarmplot(x=\"variable\", y=\"value\", hue=\"Loan_Status\", data=dff2)\n",
    "plt.xticks(rotation=90)\n",
    "fig=plt.gcf()\n",
    "fig.set_size_inches(10,10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hanya variabel Credit_History yang memiliki perbedaan karakteristik terhadap Loan Status."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['TotalIncome']=df['ApplicantIncome']+df['CoapplicantIncome']\n",
    "df['Loan/Income']=df['LoanAmount']/df['TotalIncome']\n",
    "df['Loan/Term']=df['LoanAmount']/df['Loan_Amount_Term']\n",
    "df['RepaymentRatio']=(df['Loan/Term']*1000)/df['TotalIncome']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "transformation=StandardScaler().fit(df[['ApplicantIncome','CoapplicantIncome','LoanAmount','TotalIncome','Loan_Amount_Term']])\n",
    "df[['ApplicantIncome','CoapplicantIncome','LoanAmount','TotalIncome','Loan_Amount_Term']]=transformation.transform(df[['ApplicantIncome','CoapplicantIncome','LoanAmount','TotalIncome','Loan_Amount_Term']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>Loan_Status</th>\n",
       "      <th>TotalIncome</th>\n",
       "      <th>Loan/Income</th>\n",
       "      <th>Loan/Term</th>\n",
       "      <th>RepaymentRatio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Loan_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LP001002</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.072991</td>\n",
       "      <td>-0.554487</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.279851</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.182184</td>\n",
       "      <td>0.025032</td>\n",
       "      <td>0.406700</td>\n",
       "      <td>0.069533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP001003</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.134412</td>\n",
       "      <td>-0.038732</td>\n",
       "      <td>-0.219273</td>\n",
       "      <td>0.279851</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.144684</td>\n",
       "      <td>0.021015</td>\n",
       "      <td>0.355556</td>\n",
       "      <td>0.058374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP001005</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.393747</td>\n",
       "      <td>-0.554487</td>\n",
       "      <td>-0.957641</td>\n",
       "      <td>0.279851</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.623656</td>\n",
       "      <td>0.022000</td>\n",
       "      <td>0.183333</td>\n",
       "      <td>0.061111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP001006</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.462062</td>\n",
       "      <td>0.251980</td>\n",
       "      <td>-0.314547</td>\n",
       "      <td>0.279851</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.322885</td>\n",
       "      <td>0.024287</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.067463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP001008</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.097728</td>\n",
       "      <td>-0.554487</td>\n",
       "      <td>-0.064454</td>\n",
       "      <td>0.279851</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.158785</td>\n",
       "      <td>0.023500</td>\n",
       "      <td>0.391667</td>\n",
       "      <td>0.065278</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Gender  Married  Dependents  Education  Self_Employed  \\\n",
       "Loan_ID                                                           \n",
       "LP001002       1        0           0          0              0   \n",
       "LP001003       1        1           1          0              0   \n",
       "LP001005       1        1           0          0              1   \n",
       "LP001006       1        1           0          1              0   \n",
       "LP001008       1        0           0          0              0   \n",
       "\n",
       "          ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "Loan_ID                                                                      \n",
       "LP001002         0.072991          -0.554487    0.000000          0.279851   \n",
       "LP001003        -0.134412          -0.038732   -0.219273          0.279851   \n",
       "LP001005        -0.393747          -0.554487   -0.957641          0.279851   \n",
       "LP001006        -0.462062           0.251980   -0.314547          0.279851   \n",
       "LP001008         0.097728          -0.554487   -0.064454          0.279851   \n",
       "\n",
       "          Credit_History  Property_Area  Loan_Status  TotalIncome  \\\n",
       "Loan_ID                                                             \n",
       "LP001002               1              2            1    -0.182184   \n",
       "LP001003               1              0            0    -0.144684   \n",
       "LP001005               1              2            1    -0.623656   \n",
       "LP001006               1              2            1    -0.322885   \n",
       "LP001008               1              2            1    -0.158785   \n",
       "\n",
       "          Loan/Income  Loan/Term  RepaymentRatio  \n",
       "Loan_ID                                           \n",
       "LP001002     0.025032   0.406700        0.069533  \n",
       "LP001003     0.021015   0.355556        0.058374  \n",
       "LP001005     0.022000   0.183333        0.061111  \n",
       "LP001006     0.024287   0.333333        0.067463  \n",
       "LP001008     0.023500   0.391667        0.065278  "
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x=df.drop('Loan_Status', axis=1)\n",
    "y=df['Loan_Status']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classification Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cv=StratifiedKFold(n_splits=5,random_state=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['CART: 0.708778',\n",
       " 'KNN: 0.744474',\n",
       " 'Naive Bayes: 0.794897',\n",
       " 'SVM: 0.801401',\n",
       " 'Random Forest: 0.767187',\n",
       " 'Bagging: 0.778583',\n",
       " 'AdaBoost: 0.793178',\n",
       " 'Gradient Boosting: 0.788498',\n",
       " 'Logistic Regression: 0.804680',\n",
       " 'MLP: 0.778556']"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models = []\n",
    "models.append(('CART', DecisionTreeClassifier()))\n",
    "models.append(('KNN', KNeighborsClassifier()))\n",
    "models.append(('Naive Bayes', GaussianNB()))\n",
    "models.append(('SVM', SVC()))\n",
    "models.append(('Random Forest', RandomForestClassifier()))\n",
    "models.append(('Bagging', BaggingClassifier()))\n",
    "models.append(('AdaBoost', AdaBoostClassifier()))\n",
    "models.append(('Gradient Boosting', GradientBoostingClassifier()))\n",
    "models.append(('Logistic Regression', LogisticRegression()))\n",
    "models.append(('MLP', MLPClassifier ( max_iter=1000)))\n",
    "\n",
    "results = []\n",
    "names = []\n",
    "tabel=[]\n",
    "for name, model in models:\n",
    "    akurasi=cross_val_score(model, x, y,cv=cv)\n",
    "    results.append(akurasi)\n",
    "    names.append(name)\n",
    "    hasil = \"%s: %f\" % (name, akurasi.mean())\n",
    "    tabel.append(hasil)\n",
    "tabel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Loan_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LP001015</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5720</td>\n",
       "      <td>0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP001022</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3076</td>\n",
       "      <td>1500</td>\n",
       "      <td>126.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP001031</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5000</td>\n",
       "      <td>1800</td>\n",
       "      <td>208.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP001035</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2340</td>\n",
       "      <td>2546</td>\n",
       "      <td>100.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP001051</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3276</td>\n",
       "      <td>0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Gender Married Dependents     Education Self_Employed  \\\n",
       "Loan_ID                                                          \n",
       "LP001015   Male     Yes          0      Graduate            No   \n",
       "LP001022   Male     Yes          1      Graduate            No   \n",
       "LP001031   Male     Yes          2      Graduate            No   \n",
       "LP001035   Male     Yes          2      Graduate            No   \n",
       "LP001051   Male      No          0  Not Graduate            No   \n",
       "\n",
       "          ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "Loan_ID                                                                      \n",
       "LP001015             5720                  0       110.0             360.0   \n",
       "LP001022             3076               1500       126.0             360.0   \n",
       "LP001031             5000               1800       208.0             360.0   \n",
       "LP001035             2340               2546       100.0             360.0   \n",
       "LP001051             3276                  0        78.0             360.0   \n",
       "\n",
       "          Credit_History Property_Area  \n",
       "Loan_ID                                 \n",
       "LP001015             1.0         Urban  \n",
       "LP001022             1.0         Urban  \n",
       "LP001031             1.0         Urban  \n",
       "LP001035             NaN         Urban  \n",
       "LP001051             1.0         Urban  "
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test=pd.read_csv('test.csv',index_col='Loan_ID')\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Imputasi missing value\n",
    "df_test.loc[(df_test['Married'].isnull()) & (df_test['CoapplicantIncome']==0), 'Married'] = 'No'\n",
    "df_test.loc[(df_test['Married'].isnull()), 'Married'] = 'Yes'\n",
    "\n",
    "a=((df_test['Gender'].isnull()) & (df_test['Married']=='Yes') & (df_test['ApplicantIncome']>df_test['CoapplicantIncome']))\n",
    "df_test.loc[a, 'Gender'] = 'Male'\n",
    "df_test.loc[df_test['Gender'].isnull(),'Gender'] = 'Female'\n",
    "\n",
    "mmodus = ['Gender','Married','Self_Employed','Dependents','Credit_History']\n",
    "mmedian = ['LoanAmount','ApplicantIncome','CoapplicantIncome','Loan_Amount_Term']\n",
    "\n",
    "for feature in mmedian:\n",
    "        if feature in df_test.columns.values:\n",
    "            df_test[feature] = df_test[feature].fillna(df_test[feature].mean())\n",
    "            \n",
    "for feature in mmodus:\n",
    "        if feature in df_test.columns.values:\n",
    "            df_test[feature] = df_test[feature].fillna(df_test[feature].value_counts().index[0])\n",
    "#Variabel Kategori\n",
    "kategori = ['Gender','Married','Education','Self_Employed','Dependents','Credit_History', 'Property_Area','Loan_Status']\n",
    "\n",
    "for feature in kategori:\n",
    "        if feature in df_test.columns.values:\n",
    "            df_test[feature] = LabelEncoder().fit_transform(df_test[feature])\n",
    "\n",
    "#Feature Engineering\n",
    "df_test['TotalIncome']=df_test['ApplicantIncome']+df_test['CoapplicantIncome']\n",
    "df_test['Loan/Income']=df_test['LoanAmount']/df_test['TotalIncome']\n",
    "df_test['Loan/Term']=df_test['LoanAmount']/df_test['Loan_Amount_Term']\n",
    "df_test['RepaymentRatio']=(df_test['Loan/Term']*1000)/df_test['TotalIncome']\n",
    "\n",
    "#transformasi\n",
    "transformation=StandardScaler().fit(df_test[['ApplicantIncome','CoapplicantIncome','LoanAmount','TotalIncome','Loan_Amount_Term']])\n",
    "df_test[['ApplicantIncome','CoapplicantIncome','LoanAmount','TotalIncome','Loan_Amount_Term']]=transformation.transform(df_test[['ApplicantIncome','CoapplicantIncome','LoanAmount','TotalIncome','Loan_Amount_Term']])"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.77527763088313073"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=LogisticRegression(class_weight='balanced')\n",
    "cross_val_score(model, x, y,cv=cv).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1,\n",
       "       0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0,\n",
       "       0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0,\n",
       "       0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1,\n",
       "       1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1,\n",
       "       1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1,\n",
       "       1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,\n",
       "       1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1,\n",
       "       1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x,y)\n",
    "y_predict=model.predict(df_test)\n",
    "y_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style>\n",
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       "    .dataframe thead th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Loan_ID</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LP001015</th>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP001022</th>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP001031</th>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP001035</th>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP001051</th>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Loan_Status\n",
       "Loan_ID             \n",
       "LP001015           Y\n",
       "LP001022           Y\n",
       "LP001031           Y\n",
       "LP001035           Y\n",
       "LP001051           Y"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predict=pd.DataFrame(y_predict,columns =['Loan_Status'],index=df_test.index)\n",
    "y_predict['Loan_Status']= y_predict['Loan_Status'].map({1: 'Y', 0: 'N'})\n",
    "y_predict.to_csv(\"Submission.csv\")\n",
    "y_predict.head()"
   ]
  }
 ],
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