predict loan status  (Feature engg. and varrious algorithms comparision).ipynb

{
  "cells": [
    {
      "metadata": {
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        "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "import numpy as np\nimport pandas as pd\nimport seaborn as sns\nfrom matplotlib import pyplot as plt\n\nfrom sklearn.preprocessing import Normalizer\nfrom sklearn.model_selection import train_test_split\nfrom pylab import plot, show, subplot, specgram, imshow, savefig\nfrom sklearn import preprocessing\nfrom sklearn import cross_validation, metrics\nfrom sklearn.preprocessing import Normalizer\nfrom sklearn.cross_validation import cross_val_score\n%matplotlib inline",
      "execution_count": 34,
      "outputs": []
    },
    {
      "metadata": {
        "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
        "collapsed": true,
        "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
        "trusted": true
      },
      "cell_type": "code",
      "source": "data_train = pd.read_csv(\"../input/train_u6lujuX_CVtuZ9i.csv\") \n\ndata_test = pd.read_csv(\"../input/test_Y3wMUE5_7gLdaTN.csv\") ",
      "execution_count": 35,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "eba1ec43497753394387a127a777083b8d35f103"
      },
      "cell_type": "code",
      "source": "data_train.shape",
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 36,
          "data": {
            "text/plain": "(614, 13)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "56b349d06a7db574ee40ae4886eb3198218507ea"
      },
      "cell_type": "code",
      "source": "data_test.shape",
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 37,
          "data": {
            "text/plain": "(367, 12)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "bff4b9ff4e27dbccfaf63c073fbcf05bfd807bce"
      },
      "cell_type": "code",
      "source": "data_train.head(5)",
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 38,
          "data": {
            "text/plain": "    Loan_ID Gender Married     ...     Credit_History Property_Area Loan_Status\n0  LP001002   Male      No     ...                1.0         Urban           Y\n1  LP001003   Male     Yes     ...                1.0         Rural           N\n2  LP001005   Male     Yes     ...                1.0         Urban           Y\n3  LP001006   Male     Yes     ...                1.0         Urban           Y\n4  LP001008   Male      No     ...                1.0         Urban           Y\n\n[5 rows x 13 columns]",
            "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>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "68fc91a044bc396215b0afb4e60fa15e0c31b5f1"
      },
      "cell_type": "code",
      "source": "data_train.describe()",
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 39,
          "data": {
            "text/plain": "       ApplicantIncome       ...        Credit_History\ncount       614.000000       ...            564.000000\nmean       5403.459283       ...              0.842199\nstd        6109.041673       ...              0.364878\nmin         150.000000       ...              0.000000\n25%        2877.500000       ...              1.000000\n50%        3812.500000       ...              1.000000\n75%        5795.000000       ...              1.000000\nmax       81000.000000       ...              1.000000\n\n[8 rows x 5 columns]",
            "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>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "11f4505b8bf091f8fa3080fbb35d57151d322d99"
      },
      "cell_type": "code",
      "source": "data_test.head()",
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 40,
          "data": {
            "text/plain": "    Loan_ID Gender      ...      Credit_History Property_Area\n0  LP001015   Male      ...                 1.0         Urban\n1  LP001022   Male      ...                 1.0         Urban\n2  LP001031   Male      ...                 1.0         Urban\n3  LP001035   Male      ...                 NaN         Urban\n4  LP001051   Male      ...                 1.0         Urban\n\n[5 rows x 12 columns]",
            "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    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>LP001015</td>\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>1</th>\n      <td>LP001022</td>\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>2</th>\n      <td>LP001031</td>\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>3</th>\n      <td>LP001035</td>\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>4</th>\n      <td>LP001051</td>\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>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "adadcd99aed19d1a61ecde93a5847776a647964d"
      },
      "cell_type": "code",
      "source": "data_test.describe()",
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 41,
          "data": {
            "text/plain": "       ApplicantIncome       ...        Credit_History\ncount       367.000000       ...            338.000000\nmean       4805.599455       ...              0.825444\nstd        4910.685399       ...              0.380150\nmin           0.000000       ...              0.000000\n25%        2864.000000       ...              1.000000\n50%        3786.000000       ...              1.000000\n75%        5060.000000       ...              1.000000\nmax       72529.000000       ...              1.000000\n\n[8 rows x 5 columns]",
            "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>367.000000</td>\n      <td>367.000000</td>\n      <td>362.000000</td>\n      <td>361.000000</td>\n      <td>338.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>4805.599455</td>\n      <td>1569.577657</td>\n      <td>136.132597</td>\n      <td>342.537396</td>\n      <td>0.825444</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>4910.685399</td>\n      <td>2334.232099</td>\n      <td>61.366652</td>\n      <td>65.156643</td>\n      <td>0.380150</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>28.000000</td>\n      <td>6.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>2864.000000</td>\n      <td>0.000000</td>\n      <td>100.250000</td>\n      <td>360.000000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>3786.000000</td>\n      <td>1025.000000</td>\n      <td>125.000000</td>\n      <td>360.000000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>5060.000000</td>\n      <td>2430.500000</td>\n      <td>158.000000</td>\n      <td>360.000000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>72529.000000</td>\n      <td>24000.000000</td>\n      <td>550.000000</td>\n      <td>480.000000</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "ae17ca56e35078c039e5dbdaf06865a34cf6b1d4"
      },
      "cell_type": "code",
      "source": "data_train.isnull().sum()",
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 42,
          "data": {
            "text/plain": "Loan_ID               0\nGender               13\nMarried               3\nDependents           15\nEducation             0\nSelf_Employed        32\nApplicantIncome       0\nCoapplicantIncome     0\nLoanAmount           22\nLoan_Amount_Term     14\nCredit_History       50\nProperty_Area         0\nLoan_Status           0\ndtype: int64"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "a95fe111c68c53367b0782a66239ea895a8830b7",
        "collapsed": true
      },
      "cell_type": "code",
      "source": "#test.isnull().sum()",
      "execution_count": 43,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "f65c00a7042ba96e80cfbc12ed343564ed869ee7",
        "collapsed": true
      },
      "cell_type": "code",
      "source": "train = pd.read_csv(\"../input/train_u6lujuX_CVtuZ9i.csv\") \ntest = pd.read_csv(\"../input/test_Y3wMUE5_7gLdaTN.csv\") \ntargets = train.Loan_Status\n\ntrain.drop('Loan_Status', 1, inplace=True)\ncombined = train.append(test)\ncombined.reset_index(inplace=True)\ncombined.drop(['index', 'Loan_ID'], inplace=True, axis=1)",
      "execution_count": 44,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "2ccf4f73c961c7eeacf85cece9190e09e244d93f"
      },
      "cell_type": "code",
      "source": "combined.head(5)",
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 45,
          "data": {
            "text/plain": "  Gender Married      ...      Credit_History Property_Area\n0   Male      No      ...                 1.0         Urban\n1   Male     Yes      ...                 1.0         Rural\n2   Male     Yes      ...                 1.0         Urban\n3   Male     Yes      ...                 1.0         Urban\n4   Male      No      ...                 1.0         Urban\n\n[5 rows x 11 columns]",
            "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>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "4b2c034b69bad0b0ff13b52c86b448ce41a9dddc"
      },
      "cell_type": "code",
      "source": "combined.shape",
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 46,
          "data": {
            "text/plain": "(981, 11)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "167d1be020d698f39abc160b6b12f37dba1187c0"
      },
      "cell_type": "code",
      "source": "combined.describe()",
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 47,
          "data": {
            "text/plain": "       ApplicantIncome       ...        Credit_History\ncount       981.000000       ...            902.000000\nmean       5179.795107       ...              0.835920\nstd        5695.104533       ...              0.370553\nmin           0.000000       ...              0.000000\n25%        2875.000000       ...              1.000000\n50%        3800.000000       ...              1.000000\n75%        5516.000000       ...              1.000000\nmax       81000.000000       ...              1.000000\n\n[8 rows x 5 columns]",
            "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>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true,
        "_uuid": "b151cccc211fd6c938de559719f26ddad45c7056"
      },
      "cell_type": "code",
      "source": "# To check how many columns have missing values - this can be repeated to see the progress made\ndef show_missing():\n    missing = combined.columns[combined.isnull().any()].tolist()\n    return missing\n",
      "execution_count": 48,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "fad204d4a41b170fdd94942e96af592d00fe28f1"
      },
      "cell_type": "code",
      "source": "#from this we can find the total missing data in each columns\n\ncombined[show_missing()].isnull().sum()",
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 49,
          "data": {
            "text/plain": "Gender              24\nMarried              3\nDependents          25\nSelf_Employed       55\nLoanAmount          27\nLoan_Amount_Term    20\nCredit_History      79\ndtype: int64"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "b3cb5e1cd4efb373fcbc610d956b5980cb3c0354"
      },
      "cell_type": "code",
      "source": "print (combined['Property_Area'].value_counts())\nprint (combined['Education'].value_counts())\nprint (combined['Gender'].value_counts())\nprint (combined['Dependents'].value_counts())\nprint (combined['Married'].value_counts())\nprint (combined['Self_Employed'].value_counts())\nprint (combined['Credit_History'].value_counts())",
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Semiurban    349\nUrban        342\nRural        290\nName: Property_Area, dtype: int64\nGraduate        763\nNot Graduate    218\nName: Education, dtype: int64\nMale      775\nFemale    182\nName: Gender, dtype: int64\n0     545\n2     160\n1     160\n3+     91\nName: Dependents, dtype: int64\nYes    631\nNo     347\nName: Married, dtype: int64\nNo     807\nYes    119\nName: Self_Employed, dtype: int64\n1.0    754\n0.0    148\nName: Credit_History, dtype: int64\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true,
        "_uuid": "f799e9dba68e42781e3ca30dec3563fc949773a3"
      },
      "cell_type": "code",
      "source": "#filling data with approperiate measure of central tendency\ncombined['Gender'].fillna('Male', inplace=True)\ncombined['Married'].fillna('Yes', inplace=True)\n\ncombined['Self_Employed'].fillna('Yes', inplace=True)\n\ncombined['Credit_History'].fillna(1, inplace=True)\n\ncombined['LoanAmount'].fillna(combined['LoanAmount'].median(), inplace=True)\n#combined['Loan_Amount_Term'].fillna(combined['Loan_Amount_Term'].mean(), inplace=True)\n",
      "execution_count": 51,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "050cf724427eef37df468f4734782255bfdfcb99"
      },
      "cell_type": "code",
      "source": "combined.isnull().sum()",
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 52,
          "data": {
            "text/plain": "Gender                0\nMarried               0\nDependents           25\nEducation             0\nSelf_Employed         0\nApplicantIncome       0\nCoapplicantIncome     0\nLoanAmount            0\nLoan_Amount_Term     20\nCredit_History        0\nProperty_Area         0\ndtype: int64"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true,
        "_uuid": "c0578b3f6b08ae73a58d1d6f5c5752bf91b8fba7"
      },
      "cell_type": "markdown",
      "source": "## Visualization"
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "91ec07eb7728325bd38e0d28486f4ed6e82a8299"
      },
      "cell_type": "code",
      "source": "combined['ApplicantIncome'].hist()",
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 53,
          "data": {
            "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7f122460af60>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7f122460a358>",
            "image/png": 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BH1uT1jqblQd5o/nlfQg4BTiaJmBO6/E+3gScwcFB/8n9P3RgLfCJMr0S+Go50M4CtpT2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\nekmq3P8BFGRcUTKJABYAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "cdcc2e89f92fc4fd3a8a3bfb9d639667bf85affa"
      },
      "cell_type": "code",
      "source": "combined['LoanAmount'].hist()",
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 54,
          "data": {
            "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7f122460a940>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7f12245697b8>",
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "e6f82c565b79a3cf8285401782fa37d8f8e04dfe"
      },
      "cell_type": "code",
      "source": "combined['Loan_Amount_Term'].hist()",
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 55,
          "data": {
            "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7f12244e0470>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7f12244b6da0>",
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "50c96dd4c99219c2f7cd2b7227bbf6ed4349bbbf"
      },
      "cell_type": "code",
      "source": "ax = combined.groupby('Gender').ApplicantIncome.mean().plot(kind='bar')\nax.set_xlabel(\"Gender\")\nax.set_ylabel(\"mean ApplicantIncom\")",
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 56,
          "data": {
            "text/plain": "Text(0,0.5,'mean ApplicantIncom')"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7f12244226a0>",
            "image/png": 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q9hVwIPD+zalJkvT4dZlVtT9wCvAbwJOBJcDPqmrbTZz6UuBtwPeSXNXaPsAgMM5OchTw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\nRK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "01b6191e25c2a45f4fd76c7e682519a0022e2d88"
      },
      "cell_type": "code",
      "source": "ax = combined.groupby('Education').ApplicantIncome.mean().plot(kind='bar')\nax.set_xlabel(\"Education(1=Graduate)\")\nax.set_ylabel(\"mean ApplicantIncom\")",
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 57,
          "data": {
            "text/plain": "Text(0,0.5,'mean ApplicantIncom')"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7f1224395240>",
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "a35303c89ab7a7c4f659c0ed22f2710b6cb2edbd"
      },
      "cell_type": "code",
      "source": "ax = combined.groupby('Married').ApplicantIncome.mean().plot(kind='bar')\nax.set_xlabel(\"Married(1=yes)\")\nax.set_ylabel(\"mean ApplicantIncom\")",
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 58,
          "data": {
            "text/plain": "Text(0,0.5,'mean ApplicantIncom')"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7f12244b6240>",
            "image/png": 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KOAB43YbUJEm66/pcVbUv8M/Aw4B7AJsBN1fV1nPs+jjgz4BLklzU2v6WLjBOSXIE8CPg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\nUwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "6e232c8db4904ee29560c68440963857ebffccdf"
      },
      "cell_type": "code",
      "source": "\ntemp = pd.crosstab(data_train['Credit_History'], data_train['Loan_Status'])\ntemp.plot(kind='bar', stacked=True, color=['red','blue'], grid=False)",
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 59,
          "data": {
            "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7f122431c6a0>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7f1224383fd0>",
            "image/png": 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qn8dX7WOBbW36tVRtB4iIBRGxJiLWtLa2drMMSVJnevrD2OigLTvqmJnLMrM5M5vHjBnT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\nCetSELIksE8AAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "6350da5f997649c756b52794dcc4efa19b2ddf48"
      },
      "cell_type": "code",
      "source": "temp3 = pd.crosstab(data_train['Dependents'], data_train['Loan_Status'])\ntemp3.plot(kind='bar', stacked=True, color=['red','blue'], grid=False)",
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 60,
          "data": {
            "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7f12242a8ba8>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7f1224339e80>",
            "image/png": 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MEuegNzNLXIdBL+leSVslvZrXNlvSv0tamT0uyZt3i6T1kl6TdFGpCjczs8IUskd/PzCp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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "93fdfa799edd95ebb17b13cb9bc9572a7dcfa563"
      },
      "cell_type": "code",
      "source": "temp3 = pd.crosstab(data_train['Gender'], data_train['Loan_Status'])\ntemp3.plot(kind='bar', stacked=True, color=['red','blue'], grid=False)",
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 61,
          "data": {
            "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7f1224288710>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7f122453ba20>",
            "image/png": 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AamAD8PWWVixJ6pNegz4zH6bncXeAWT30T+D8naxLktQinhkrSYUz6CWpcAa9JBXOoJek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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "dfb8c27020f4cc5f471b47aff94225c6fa42a3d0"
      },
      "cell_type": "code",
      "source": "temp3 = pd.crosstab(data_train['Education'], data_train['Loan_Status'])\ntemp3.plot(kind='bar', stacked=True, color=['red','blue'], grid=False)",
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 62,
          "data": {
            "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7f122423f470>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7f1224331160>",
            "image/png": 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3gA8Ay4A1wCdaWrEkaYv0GPSZeQ/dz7sDTOtmfALnbmNdkqQW8cxYSSqcQS9JhTPoJalw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\nAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "1a4a88bdb6b533a91c4e2e184610bdcffff09ac9"
      },
      "cell_type": "code",
      "source": "temp3 = pd.crosstab(data_train['Property_Area'], data_train['Loan_Status'])\ntemp3.plot(kind='bar', stacked=True, color=['red','blue'], grid=False)",
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 63,
          "data": {
            "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7f12241b0e48>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7f12241a5208>",
            "image/png": 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fHmyRkqSBG3DQR8S4iNi76zXwB8D9wM3AWVW3s4CbBlukJGngBjN0cwDwnYjoms//zsx/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\nlQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "b54c5a07af77774e2fdfabcaa470873ec4068e1d"
      },
      "cell_type": "markdown",
      "source": "# feature engineering"
    },
    {
      "metadata": {
        "_uuid": "991a81e902223119219b5d6d36ce635888a81323"
      },
      "cell_type": "markdown",
      "source": "## one hot encoding"
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "dd4fdac24feadc62cd25a8e16c4d9aeaa377c8d2",
        "collapsed": true
      },
      "cell_type": "code",
      "source": "combined['Gender'] = combined['Gender'].map({'Male':1,'Female':0})\ncombined['Married'] = combined['Married'].map({'Yes':1,'No':0})\ncombined['Education'] = combined['Education'].map({'Graduate':1,'Not Graduate':0})\ncombined['Self_Employed'] = combined['Self_Employed'].map({'Yes':1,'No':0})",
      "execution_count": 64,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "63bd355fb04fcb5ef88553ad37a9f5ec0a387a46",
        "collapsed": true
      },
      "cell_type": "code",
      "source": "combined['Singleton'] = combined['Dependents'].map(lambda d: 1 if d=='1' else 0)\ncombined['Small_Family'] = combined['Dependents'].map(lambda d: 1 if d=='2' else 0)\ncombined['Large_Family'] = combined['Dependents'].map(lambda d: 1 if d=='3+' else 0)\ncombined.drop(['Dependents'], axis=1, inplace=True)",
      "execution_count": 65,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "fc01971cc1cbb4ed88bbf1b8acf0d3ecdf4cdde4",
        "collapsed": true
      },
      "cell_type": "code",
      "source": "combined['Total_Income'] = combined['ApplicantIncome'] + combined['CoapplicantIncome']\ncombined.drop(['ApplicantIncome','CoapplicantIncome'], axis=1, inplace=True)\n",
      "execution_count": 66,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "480b178d39595991f018f8a16136a5b1f7d4784a",
        "collapsed": true
      },
      "cell_type": "code",
      "source": "combined['Income_Ratio'] = combined['Total_Income'] / combined['LoanAmount']\n",
      "execution_count": 67,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "1762c1142a498a5c5b08b5b6c2db129afbe17153"
      },
      "cell_type": "code",
      "source": "combined['Loan_Amount_Term'].value_counts()",
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 68,
          "data": {
            "text/plain": "360.0    823\n180.0     66\n480.0     23\n300.0     20\n240.0      8\n84.0       7\n120.0      4\n36.0       3\n60.0       3\n12.0       2\n350.0      1\n6.0        1\nName: Loan_Amount_Term, dtype: int64"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "032d6da3f44c7e9e341e48f1f6bcbf91edaf25d9"
      },
      "cell_type": "code",
      "source": "approved_term = data_train[data_train['Loan_Status']=='Y']['Loan_Amount_Term'].value_counts()\nunapproved_term = data_train[data_train['Loan_Status']=='N']['Loan_Amount_Term'].value_counts()\ndf = pd.DataFrame([approved_term,unapproved_term])\ndf.index = ['Approved','Unapproved']\ndf.plot(kind='bar', stacked=True, figsize=(15,8))",
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 69,
          "data": {
            "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7f123729de80>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7f123729d630>",
            "image/png": 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af/urco9fU0q5tJTSXUrp3rx5877WCwAAMGCGDx+ed7zjHVmyZEk6Oztz3nnn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\nkAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "7f4a5237fabb933cba637c70d9eec855bebb4d92",
        "collapsed": true
      },
      "cell_type": "code",
      "source": "combined['Very_Short_Term'] = combined['Loan_Amount_Term'].map(lambda t: 1 if t<=60 else 0)\ncombined['Short_Term'] = combined['Loan_Amount_Term'].map(lambda t: 1 if t>60 and t<180 else 0)\ncombined['Long_Term'] = combined['Loan_Amount_Term'].map(lambda t: 1 if t>=180 and t<=300  else 0)\ncombined['Very_Long_Term'] = combined['Loan_Amount_Term'].map(lambda t: 1 if t>300 else 0)\ncombined.drop('Loan_Amount_Term', axis=1, inplace=True)\n",
      "execution_count": 70,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true,
        "_uuid": "5fb09bd6a076bbec5ae7b0df60c2756760d14305"
      },
      "cell_type": "code",
      "source": "combined['Credit_History_Bad'] = combined['Credit_History'].map(lambda c: 1 if c==0 else 0)\ncombined['Credit_History_Good'] = combined['Credit_History'].map(lambda c: 1 if c==1 else 0)\ncombined['Credit_History_Unknown'] = combined['Credit_History'].map(lambda c: 1 if c==2 else 0)\ncombined.drop('Credit_History', axis=1, inplace=True)",
      "execution_count": 71,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true,
        "_uuid": "7220b5eef26b80e5126f18e8682479a958c772a9"
      },
      "cell_type": "code",
      "source": "property_dummies = pd.get_dummies(combined['Property_Area'], prefix='Property')\ncombined = pd.concat([combined, property_dummies], axis=1)\ncombined.drop('Property_Area', axis=1, inplace=True)",
      "execution_count": 72,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "78f894b7546aad4000a9594e1e2090e753fdcdba"
      },
      "cell_type": "code",
      "source": "combined[60:70]",
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 73,
          "data": {
            "text/plain": "    Gender  Married       ...        Property_Semiurban  Property_Urban\n60       1        1       ...                         0               1\n61       1        1       ...                         0               1\n62       1        1       ...                         0               0\n63       1        1       ...                         0               0\n64       0        0       ...                         1               0\n65       1        1       ...                         1               0\n66       1        0       ...                         0               1\n67       1        1       ...                         0               1\n68       1        1       ...                         0               1\n69       0        0       ...                         1               0\n\n[10 rows x 20 columns]",
            "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>LoanAmount</th>\n      <th>Singleton</th>\n      <th>Small_Family</th>\n      <th>Large_Family</th>\n      <th>Total_Income</th>\n      <th>Income_Ratio</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>Credit_History_Unknown</th>\n      <th>Property_Rural</th>\n      <th>Property_Semiurban</th>\n      <th>Property_Urban</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>60</th>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>120.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>6296.0</td>\n      <td>52.466667</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>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>61</th>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>99.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>3029.0</td>\n      <td>30.595960</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>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>62</th>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>165.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>6058.0</td>\n      <td>36.715152</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>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>63</th>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>126.0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>4945.0</td>\n      <td>39.246032</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</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    </tr>\n    <tr>\n      <th>64</th>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>116.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>4166.0</td>\n      <td>35.913793</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>65</th>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>258.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>10321.0</td>\n      <td>40.003876</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>0</td>\n    </tr>\n    <tr>\n      <th>66</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>126.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>5454.0</td>\n      <td>43.285714</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>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>67</th>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>312.0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>10750.0</td>\n      <td>34.455128</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>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>68</th>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>125.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>7100.0</td>\n      <td>56.800000</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>69</th>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>136.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>4300.0</td>\n      <td>31.617647</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "9a26555bcc3e8235a8a9e6183434322c5e890178"
      },
      "cell_type": "markdown",
      "source": "## feature scaling"
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "a4a89a086ac7f9243c7337a35a6b654bbd1920ea",
        "collapsed": true
      },
      "cell_type": "code",
      "source": "def feature_scaling(dataframe):\n    dataframe -= dataframe.min()\n    dataframe /= dataframe.max()\n    return dataframe",
      "execution_count": 74,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "7e770ccf82890b79135a6b2ec8a6ff6d0b6ed2a4",
        "collapsed": true
      },
      "cell_type": "code",
      "source": "combined['LoanAmount'] = feature_scaling(combined['LoanAmount'])\ncombined['Total_Income'] = feature_scaling(combined['Total_Income'])\ncombined['Income_Ratio'] = feature_scaling(combined['Income_Ratio'])",
      "execution_count": 75,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "c205d38a9b3296a7691c670007383fd5c49a6de1"
      },
      "cell_type": "code",
      "source": "combined.head()",
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 76,
          "data": {
            "text/plain": "   Gender  Married       ...        Property_Semiurban  Property_Urban\n0       1        0       ...                         0               1\n1       1        1       ...                         0               0\n2       1        1       ...                         0               1\n3       1        1       ...                         0               1\n4       1        0       ...                         0               1\n\n[5 rows x 20 columns]",
            "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>LoanAmount</th>\n      <th>Singleton</th>\n      <th>Small_Family</th>\n      <th>Large_Family</th>\n      <th>Total_Income</th>\n      <th>Income_Ratio</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>Credit_History_Unknown</th>\n      <th>Property_Rural</th>\n      <th>Property_Semiurban</th>\n      <th>Property_Urban</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0.169320</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.055394</td>\n      <td>0.071120</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>0</td>\n      <td>1</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>0.172214</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.058435</td>\n      <td>0.073382</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>1</td>\n      <td>0</td>\n      <td>0</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>0.082489</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.019583</td>\n      <td>0.069245</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>0</td>\n      <td>1</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>0.160637</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.043980</td>\n      <td>0.060939</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>0</td>\n      <td>1</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>0.191027</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.057292</td>\n      <td>0.063614</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>0</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true,
        "_uuid": "5c220c8f392163cbf30201710e958790121a8cb5"
      },
      "cell_type": "markdown",
      "source": "## prediction model"
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "0c4a6fa2a47884a4df592c6f6581a4c8a9d0f5eb",
        "collapsed": true
      },
      "cell_type": "code",
      "source": "from sklearn.model_selection import cross_val_score\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.feature_selection import SelectFromModel",
      "execution_count": 77,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true,
        "_uuid": "1e8253cb2870dbacfac1cf6089a6e075f5f1260e"
      },
      "cell_type": "code",
      "source": "#function for computing score\ndef compute_score(clf, X, y, scoring='accuracy'):\n    xval = cross_val_score(clf, X, y, cv = 5, scoring=scoring)\n    return np.mean(xval)",
      "execution_count": 78,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "3666701d8faedf67a389afa953948ad643dcac03",
        "collapsed": true
      },
      "cell_type": "code",
      "source": "#recovering train test &target\nglobal combined, data_train\ntargets = data_train['Loan_Status'].map({'Y':1,'N':0})\ntrain = combined.head(614)\ntest = combined.iloc[614:]\n",
      "execution_count": 79,
      "outputs": []
    },
    {
      "metadata": {
        "_uuid": "55e79eed7a23368fc29890f9e584abf6ada17071"
      },
      "cell_type": "markdown",
      "source": "# Random forest algorithm"
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "447b7daf189961c1649f66fa04bbd36f684c79b0"
      },
      "cell_type": "markdown",
      "source": "## feature imortance"
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "5a5af6f77b6de27adfa6eb12106452197a96aab2",
        "collapsed": true
      },
      "cell_type": "code",
      "source": "clf = RandomForestClassifier(n_estimators=50, max_features='sqrt')\nclf = clf.fit(train, targets)",
      "execution_count": 80,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "29451a67ff33f0a5b24705be2c27a0b17ede4666",
        "collapsed": true
      },
      "cell_type": "code",
      "source": "features = pd.DataFrame()\nfeatures['Feature'] = train.columns\nfeatures['Importance'] = clf.feature_importances_\nfeatures.sort_values(by=['Importance'], ascending=False, inplace=True)\nfeatures.set_index('Feature', inplace=True)",
      "execution_count": 81,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "a1be8cd10ce73637a794745318f4610451e8540d"
      },
      "cell_type": "code",
      "source": "features.plot(kind='bar', figsize=(20, 10))",
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 82,
          "data": {
            "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7f1224551cf8>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7f1235238ac8>",
            "image/png": 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kbUgXmgJutf3YKD/WJm8h1bM6JG9fSVqKWAVJ3wVWB25i8PrYWpJI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\nAFMXQD7bW8L/SE8AAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "d0ae8ab77d17704e3b7a30dea8809711e5bdcb88"
      },
      "cell_type": "code",
      "source": "model = SelectFromModel(clf, prefit=True)\ntrain_reduced = model.transform(train)\ntrain_reduced.shape",
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 83,
          "data": {
            "text/plain": "(614, 5)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "5f5e75c3e8fcd07b2d32287147a98a9e88655df4"
      },
      "cell_type": "code",
      "source": "test_reduced = model.transform(test)\ntest_reduced.shape",
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 84,
          "data": {
            "text/plain": "(367, 5)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "a3d8ee13f92539998043700bdffb02918127466f"
      },
      "cell_type": "code",
      "source": "parameters  = {'bootstrap': False,\n              'min_samples_leaf': 3,\n              'n_estimators': 50,\n              'min_samples_split': 10,\n              'max_features': 'sqrt',\n              'max_depth': 6}\n\nmodel = RandomForestClassifier(**parameters)\nmodel.fit(train, targets)",
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 85,
          "data": {
            "text/plain": "RandomForestClassifier(bootstrap=False, class_weight=None, criterion='gini',\n            max_depth=6, max_features='sqrt', max_leaf_nodes=None,\n            min_impurity_decrease=0.0, min_impurity_split=None,\n            min_samples_leaf=3, min_samples_split=10,\n            min_weight_fraction_leaf=0.0, n_estimators=50, n_jobs=1,\n            oob_score=False, random_state=None, verbose=0,\n            warm_start=False)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "b9965370a6ae289ed97083126fd7e92d458d5a59"
      },
      "cell_type": "code",
      "source": "compute_score(model, train, targets, scoring='accuracy')",
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 86,
          "data": {
            "text/plain": "0.799788471708091"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "e6e51cf82241b07177f79fad425e4a939773dc5e",
        "collapsed": true
      },
      "cell_type": "code",
      "source": "#saving output as output.csv\noutput = model.predict(test).astype(int)\ndf_output = pd.DataFrame()\naux = pd.read_csv('../input/test_Y3wMUE5_7gLdaTN.csv')\ndf_output['Loan_ID'] = aux['Loan_ID']\ndf_output['Loan_Status'] = np.vectorize(lambda s: 'Y' if s==1 else 'N')(output)\ndf_output[['Loan_ID','Loan_Status']].to_csv('output.csv',index=False)",
      "execution_count": 87,
      "outputs": []
    },
    {
      "metadata": {
        "_uuid": "e9e1a3db4a9cf09b6e6b468002149046276345e9"
      },
      "cell_type": "markdown",
      "source": "# other algorithms"
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "8f7cae943eb27492ea4602abb6b72fcbaa731bf5"
      },
      "cell_type": "code",
      "source": "####Prediction model########\n#Train-Test split\nfrom sklearn.model_selection import train_test_split\ndatatrain, datatest, labeltrain, labeltest = train_test_split(train, targets, test_size = 0.2, random_state = 42)\nlabeltrain.shape",
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 88,
          "data": {
            "text/plain": "(491,)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "f71f5fb787743ed5556cf8eb28ddfdb7144ad7a1"
      },
      "cell_type": "code",
      "source": "#Logistic Regression\nfrom sklearn.linear_model import LogisticRegression\nlogis = LogisticRegression()\nlogis.fit(datatrain, labeltrain)\nlogis_score_train = logis.score(datatrain, labeltrain)\nprint(\"Training score: \",logis_score_train)\nlogis_score_test = logis.score(datatest, labeltest)\nprint(\"Testing score: \",logis_score_test)",
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Training score:  0.814663951120163\nTesting score:  0.7886178861788617\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true,
        "_uuid": "f099e2cc63d900ad03f79c7df58ca3d1f738c65e"
      },
      "cell_type": "code",
      "source": "#saving output as output.csv of decision tree\noutput2 = logis.predict(test).astype(int)\ndf_output2 = pd.DataFrame()\naux = pd.read_csv('../input/test_Y3wMUE5_7gLdaTN.csv')\ndf_output2['Loan_ID'] = aux['Loan_ID']\ndf_output2['Loan_Status'] = np.vectorize(lambda s: 'Y' if s==1 else 'N')(output2)\ndf_output2[['Loan_ID','Loan_Status']].to_csv('output2.csv',index=False)",
      "execution_count": 90,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true,
        "_uuid": "da1222d32b477a999be043b8e35066e63c2080d2"
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "362dab0fe79c1bb7c28b257784c45775d8268228"
      },
      "cell_type": "code",
      "source": "#decision tree\nfrom sklearn.ensemble import RandomForestClassifier\ndt = RandomForestClassifier()\ndt.fit(datatrain, labeltrain)\ndt_score_train = dt.score(datatrain, labeltrain)\nprint(\"Training score: \",dt_score_train)\ndt_score_test = dt.score(datatest, labeltest)\nprint(\"Testing score: \",dt_score_test)",
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Training score:  0.9938900203665988\nTesting score:  0.7317073170731707\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "7eef243816437ad67463ac72e1953a72ae1b20be",
        "collapsed": true
      },
      "cell_type": "code",
      "source": "#saving output as output.csv of decision tree\n#output2 = dt.predict(test).astype(int)\n#df_output2 = pd.DataFrame()\n#aux = pd.read_csv('../input/test_Y3wMUE5_7gLdaTN.csv')\n#df_output2['Loan_ID'] = aux['Loan_ID']\n#df_output2['Loan_Status'] = np.vectorize(lambda s: 'Y' if s==1 else 'N')(output2)\n#df_output2[['Loan_ID','Loan_Status']].to_csv('output2.csv',index=False)",
      "execution_count": 92,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true,
        "_uuid": "9e09324d6175e933ed966a4ae982d5941ac65ba5"
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "f8d4a84effe79d4db51f89cf2ca480241f831b72"
      },
      "cell_type": "code",
      "source": "#random forest\nfrom sklearn.ensemble import RandomForestClassifier\nrfc = RandomForestClassifier()\nrfc.fit(datatrain, labeltrain)\nrfc_score_train = rfc.score(datatrain, labeltrain)\nprint(\"Training score: \",rfc_score_train)\nrfc_score_test = rfc.score(datatest, labeltest)\nprint(\"Testing score: \",rfc_score_test)",
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Training score:  0.9857433808553971\nTesting score:  0.7073170731707317\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "894ffcb3bce21d7fafbd8f09659e33a399f8d00b"
      },
      "cell_type": "code",
      "source": "#Model comparison\nmodels = pd.DataFrame({\n        'Model'          : ['Logistic Regression',  'Decision Tree', 'Random Forest'],\n        'Training_Score' : [logis_score_train,  dt_score_train, rfc_score_train],\n        'Testing_Score'  : [logis_score_test, dt_score_test, rfc_score_test]\n    })\nmodels.sort_values(by='Testing_Score', ascending=False)",
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 94,
          "data": {
            "text/plain": "                 Model  Training_Score  Testing_Score\n0  Logistic Regression        0.814664       0.788618\n1        Decision Tree        0.993890       0.731707\n2        Random Forest        0.985743       0.707317",
            "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>Model</th>\n      <th>Training_Score</th>\n      <th>Testing_Score</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Logistic Regression</td>\n      <td>0.814664</td>\n      <td>0.788618</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Decision Tree</td>\n      <td>0.993890</td>\n      <td>0.731707</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Random Forest</td>\n      <td>0.985743</td>\n      <td>0.707317</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
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      "execution_count": null,
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