Loan Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib as plt\n",
    "%matplotlib inline\n",
    "\n",
    "df = pd.read_csv(\"C:/Users/premanand.naik/OneDrive - Accenture/loan prediction/train.csv\") #Reading the dataset in a dataframe using Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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_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",
       "      <th>0</th>\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",
       "      <th>1</th>\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",
       "      <th>2</th>\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",
       "      <th>3</th>\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",
       "      <th>4</th>\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": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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>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",
       "      <th>count</th>\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",
       "      <th>mean</th>\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",
       "      <th>std</th>\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",
       "      <th>min</th>\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",
       "      <th>25%</th>\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",
       "      <th>50%</th>\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",
       "      <th>75%</th>\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",
       "      <th>max</th>\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": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Semiurban    233\n",
       "Urban        202\n",
       "Rural        179\n",
       "Name: Property_Area, dtype: int64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Property_Area'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x155144c27b8>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['ApplicantIncome'].hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1551453d978>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot(column='ApplicantIncome', by = 'Education')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x15514442be0>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['LoanAmount'].hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x15514687080>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot(column='LoanAmount')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0     89\n",
       "1.0    475\n",
       "Name: Credit_History, dtype: int64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Credit_History'].value_counts(ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Frequency Table for Credit History:\n",
      "0.0     89\n",
      "1.0    475\n",
      "Name: Credit_History, dtype: int64\n",
      "\n",
      "Probility of getting loan for each Credit History class:\n",
      "                Loan_Status\n",
      "Credit_History             \n",
      "0.0                0.078652\n",
      "1.0                0.795789\n"
     ]
    }
   ],
   "source": [
    "temp1 = df['Credit_History'].value_counts(ascending=True)\n",
    "temp2 = df.pivot_table(values='Loan_Status',index=['Credit_History'],aggfunc=lambda x: x.map({'Y':1,'N':0}).mean())\n",
    "print ('Frequency Table for Credit History:') \n",
    "print (temp1)\n",
    "\n",
    "print ('\\nProbility of getting loan for each Credit History class:')\n",
    "print (temp2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x155147271d0>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp1.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1551477def0>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp2.plot(kind = 'bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x155147e3b38>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp3 = pd.crosstab(df['Credit_History'], df['Loan_Status'])\n",
    "temp3.plot(kind='bar', stacked=True, color=['red','blue'], grid=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Frequency Table for Education:\n",
      "Not Graduate    134\n",
      "Graduate        480\n",
      "Name: Education, dtype: int64\n",
      "\n",
      "Probility of getting loan for each Education class:\n",
      "                             Loan_Status\n",
      "Education    Credit_History             \n",
      "Graduate     0.0                0.095238\n",
      "             1.0                0.807895\n",
      "Not Graduate 0.0                0.038462\n",
      "             1.0                0.747368\n"
     ]
    }
   ],
   "source": [
    "temp1 = df['Education'].value_counts(ascending=True)\n",
    "temp2 = df.pivot_table(values='Loan_Status',index=['Education','Credit_History'],aggfunc=lambda x: x.map({'Y':1,'N':0}).mean())\n",
    "print ('Frequency Table for Education:') \n",
    "print (temp1)\n",
    "\n",
    "print ('\\nProbility of getting loan for each Education class:')\n",
    "print (temp2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x15514844b38>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp2.plot(kind = 'bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getCombinedData():\n",
    "    train = pd.read_csv(\"C:/Users/premanand.naik/OneDrive - Accenture/loan prediction/train.csv\") \n",
    "    test = pd.read_csv(\"C:/Users/premanand.naik/OneDrive - Accenture/loan prediction/test.csv\") \n",
    "    target = train['Loan_Status']\n",
    "    train.drop('Loan_Status',1,inplace=True)\n",
    "    combined = train.append(test)\n",
    "    combined.reset_index(inplace=True)\n",
    "    combined.drop(['index', 'Loan_ID'], inplace=True, axis=1)\n",
    "    return combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</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",
       "    </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",
       "\n",
       "   CoapplicantIncome  LoanAmount  Loan_Amount_Term  Credit_History  \\\n",
       "0                0.0         NaN             360.0             1.0   \n",
       "1             1508.0       128.0             360.0             1.0   \n",
       "2                0.0        66.0             360.0             1.0   \n",
       "3             2358.0       120.0             360.0             1.0   \n",
       "4                0.0       141.0             360.0             1.0   \n",
       "\n",
       "  Property_Area  \n",
       "0         Urban  \n",
       "1         Rural  \n",
       "2         Urban  \n",
       "3         Urban  \n",
       "4         Urban  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined = getCombinedData()\n",
    "combined.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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>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",
       "      <th>count</th>\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",
       "      <th>mean</th>\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",
       "      <th>std</th>\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",
       "      <th>min</th>\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",
       "      <th>25%</th>\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",
       "      <th>50%</th>\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",
       "      <th>75%</th>\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",
       "      <th>max</th>\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": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined['LoanAmount'].fillna(combined['LoanAmount'].mean(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined['Loan_Amount_Term'].fillna(combined['Loan_Amount_Term'].mean(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined['Credit_History'].fillna(combined['Credit_History'].mode()[0],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined['Gender'].fillna(combined['Gender'].mode()[0],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined['Married'].fillna(combined['Married'].mode()[0],inplace=True)\n",
    "combined['Dependents'].fillna(combined['Dependents'].mode()[0],inplace=True)\n",
    "combined['Self_Employed'].fillna(combined['Self_Employed'].mode()[0],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined['Gender'] = combined['Gender'].map({'Male':1,'Female':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined['Married'] = combined['Married'].map({'Yes':1,'No':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined['NoDependents'] = combined['Dependents'].map(lambda d:1 if d=='0' else 0)\n",
    "combined['Singleton'] = combined['Dependents'].map(lambda d:1 if d=='1' else 0)\n",
    "combined['Small_Family'] = combined['Dependents'].map(lambda d:1 if d=='2' else 0)\n",
    "combined['Large_Family'] = combined['Dependents'].map(lambda d:1 if d=='3+' else 0)\n",
    "combined.drop('Dependents',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined['Education'] = combined['Education'].map({'Graduate':1,'Not Graduate':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined['Self_Employed'] = combined['Self_Employed'].map({'Yes':1,'No':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined['Total_Income'] = combined['ApplicantIncome'] + combined['CoapplicantIncome']\n",
    "combined.drop(['ApplicantIncome','CoapplicantIncome'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined['Debt_Income_Ratio'] = combined['Total_Income'] / combined['LoanAmount']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "    combined['Very_Short_Term'] = combined['Loan_Amount_Term'].map(lambda t: 1 if t<=60 else 0)\n",
    "    combined['Short_Term'] = combined['Loan_Amount_Term'].map(lambda t: 1 if t>60 and t<180 else 0)\n",
    "    combined['Long_Term'] = combined['Loan_Amount_Term'].map(lambda t: 1 if t>=180 and t<=300  else 0)\n",
    "    combined['Very_Long_Term'] = combined['Loan_Amount_Term'].map(lambda t: 1 if t>300 else 0)\n",
    "    combined.drop('Loan_Amount_Term', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined['Credit_History_Bad'] = combined['Credit_History'].map(lambda c: 1 if c==0 else 0)\n",
    "combined['Credit_History_Good'] = combined['Credit_History'].map(lambda c: 1 if c==1 else 0)\n",
    "combined.drop('Credit_History', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "property_dummies = pd.get_dummies(combined['Property_Area'], prefix='Property')\n",
    "combined = pd.concat([combined, property_dummies], axis=1)\n",
    "combined.drop('Property_Area', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined['LoanAmountLog'] = np.log(combined['LoanAmount'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x15515bdb0f0>"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "combined['LoanAmountLog'].hist(bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined.drop('LoanAmount', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined['Total_IncomeLog'] = np.log(combined['Total_Income'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined.drop('Total_Income', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x15515c44e48>"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "combined['Total_IncomeLog'].hist(bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x15515ceb240>"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "combined['Debt_Income_RatioLog'] = np.log(combined['Debt_Income_Ratio'])\n",
    "combined.drop('Debt_Income_Ratio', axis=1, inplace=True)\n",
    "combined['Debt_Income_RatioLog'].hist(bins=20)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier, export_graphviz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "    targets = df['Loan_Status'].map({'Y':1,'N':0})\n",
    "    train = combined.head(614)\n",
    "    test = combined.iloc[614:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf  =  RandomForestClassifier (n_estimators=50, max_features='sqrt')\n",
    "clf = clf.fit(train, targets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = pd.DataFrame()\n",
    "features['Feature'] = train.columns\n",
    "features['Importance'] = clf.feature_importances_\n",
    "features.sort_values(by=['Importance'], ascending=False, inplace=True)\n",
    "features.set_index('Feature', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x15515d71710>"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "features.plot(kind='bar', figsize=(20, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(614, 5)"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = SelectFromModel(clf, prefit=True)\n",
    "train_reduced = model.transform(train)\n",
    "train_reduced.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(367, 5)"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_reduced = model.transform(test)\n",
    "test_reduced.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "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>NoDependents</th>\n",
       "      <th>Singleton</th>\n",
       "      <th>Small_Family</th>\n",
       "      <th>Large_Family</th>\n",
       "      <th>Very_Short_Term</th>\n",
       "      <th>Short_Term</th>\n",
       "      <th>Long_Term</th>\n",
       "      <th>Very_Long_Term</th>\n",
       "      <th>Credit_History_Bad</th>\n",
       "      <th>Credit_History_Good</th>\n",
       "      <th>Property_Rural</th>\n",
       "      <th>Property_Semiurban</th>\n",
       "      <th>Property_Urban</th>\n",
       "      <th>LoanAmountLog</th>\n",
       "      <th>Total_IncomeLog</th>\n",
       "      <th>Debt_Income_RatioLog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.959423</td>\n",
       "      <td>8.674026</td>\n",
       "      <td>3.714603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.852030</td>\n",
       "      <td>8.714568</td>\n",
       "      <td>3.862537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.189655</td>\n",
       "      <td>8.006368</td>\n",
       "      <td>3.816713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</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",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.787492</td>\n",
       "      <td>8.505323</td>\n",
       "      <td>3.717831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.948760</td>\n",
       "      <td>8.699515</td>\n",
       "      <td>3.750755</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender  Married  Education  Self_Employed  NoDependents  Singleton  \\\n",
       "0       1        0          1              0             1          0   \n",
       "1       1        1          1              0             0          1   \n",
       "2       1        1          1              1             1          0   \n",
       "3       1        1          0              0             1          0   \n",
       "4       1        0          1              0             1          0   \n",
       "\n",
       "   Small_Family  Large_Family  Very_Short_Term  Short_Term  Long_Term  \\\n",
       "0             0             0                0           0          0   \n",
       "1             0             0                0           0          0   \n",
       "2             0             0                0           0          0   \n",
       "3             0             0                0           0          0   \n",
       "4             0             0                0           0          0   \n",
       "\n",
       "   Very_Long_Term  Credit_History_Bad  Credit_History_Good  Property_Rural  \\\n",
       "0               1                   0                    1               0   \n",
       "1               1                   0                    1               1   \n",
       "2               1                   0                    1               0   \n",
       "3               1                   0                    1               0   \n",
       "4               1                   0                    1               0   \n",
       "\n",
       "   Property_Semiurban  Property_Urban  LoanAmountLog  Total_IncomeLog  \\\n",
       "0                   0               1       4.959423         8.674026   \n",
       "1                   0               0       4.852030         8.714568   \n",
       "2                   0               1       4.189655         8.006368   \n",
       "3                   0               1       4.787492         8.505323   \n",
       "4                   0               1       4.948760         8.699515   \n",
       "\n",
       "   Debt_Income_RatioLog  \n",
       "0              3.714603  \n",
       "1              3.862537  \n",
       "2              3.816713  \n",
       "3              3.717831  \n",
       "4              3.750755  "
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=7, max_features=1, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=25,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=25, n_jobs=1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = RandomForestClassifier(n_estimators=25, min_samples_split=25, max_depth=7, max_features=1)\n",
    "#model.fit(train[['Total_IncomeLog','LoanAmountLog','Credit_History_Bad','Credit_History_Good','NoDependents','Singleton','Small_Family','Large_Family','Property_Rural','Property_Semiurban','Property_Urban']], targets)\n",
    "model.fit(train_reduced, targets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7964833421470121"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xval = cross_val_score(model, train_reduced, targets, cv = 5, scoring='accuracy')\n",
    "np.mean(xval)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "output =  model.predict(test_reduced).astype(int)\n",
    "df_output  = pd.DataFrame()\n",
    "aux = pd.read_csv('C:/Users/premanand.naik/OneDrive - Accenture/loan prediction/test.csv')\n",
    "df_output['Loan_ID'] = aux['Loan_ID']\n",
    "df_output['Loan_Status'] = np.vectorize(lambda s: 'Y' if s==1 else 'N')(output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "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_ID</th>\n",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001015</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001022</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001031</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001035</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001051</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Loan_ID Loan_Status\n",
       "0  LP001015           Y\n",
       "1  LP001022           Y\n",
       "2  LP001031           Y\n",
       "3  LP001035           Y\n",
       "4  LP001051           Y"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_output.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_output[['Loan_ID','Loan_Status']].to_csv('C:/Users/premanand.naik/OneDrive - Accenture/loan prediction/output.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}