Loan Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loan Prediction\n",
    "\n",
    "## Problem \n",
    "\n",
    "- A Company wants to automate the loan eligibility process (real time) based on customer detail provided while filling online application form. These details are Gender, Marital Status, Education, Number of Dependents, Income, Loan Amount, Credit History and others. To automate this process, they have given a problem to identify the customers segments, those are eligible for loan amount so that they can specifically target these customers. Here they have provided a data set.\n",
    "\n",
    "## Data\n",
    "\n",
    "- Variable Descriptions:\n",
    "\n",
    "\n",
    "| Variable          \t| Description                                    \t|\n",
    "|-------------------\t|------------------------------------------------\t|\n",
    "| Loan_ID           \t| Unique Loan ID                                 \t|\n",
    "| Gender            \t| Male/ Female                                   \t|\n",
    "| Married           \t| Applicant married (Y/N)                        \t|\n",
    "| Dependents        \t| Number of dependents                           \t|\n",
    "| Education         \t| Applicant Education (Graduate/ Under Graduate) \t|\n",
    "| Self_Employed     \t| Self employed (Y/N)                            \t|\n",
    "| ApplicantIncome   \t| Applicant income                               \t|\n",
    "| CoapplicantIncome \t| Coapplicant income                             \t|\n",
    "| LoanAmount        \t| Loan amount in thousands                       \t|\n",
    "| Loan_Amount_Term  \t| Term of loan in months                         \t|\n",
    "| Credit_History    \t| credit history meets guidelines                \t|\n",
    "| Property_Area     \t| Urban/ Semi Urban/ Rural                       \t|\n",
    "| Loan_Status       \t| Loan approved (Y/N)                            \t|\n",
    "\n",
    "- Rows: 615\n",
    "- Source: Datahack\n",
    "- Jupyter Notebook: [Github - Parth Shandilya](https://github.com/ParthS007/IDS_Project)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing Library\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "# Reading the training dataset in a dataframe using Pandas\n",
    "df = pd.read_csv(\"train.csv\")\n",
    "\n",
    "# Reading the test dataset in a dataframe using Pandas\n",
    "test = pd.read_csv(\"test.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>LP001011</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>5417</td>\n",
       "      <td>4196.0</td>\n",
       "      <td>267.0</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>6</th>\n",
       "      <td>LP001013</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2333</td>\n",
       "      <td>1516.0</td>\n",
       "      <td>95.0</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>7</th>\n",
       "      <td>LP001014</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3+</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3036</td>\n",
       "      <td>2504.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>LP001018</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4006</td>\n",
       "      <td>1526.0</td>\n",
       "      <td>168.0</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>9</th>\n",
       "      <td>LP001020</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>12841</td>\n",
       "      <td>10968.0</td>\n",
       "      <td>349.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Loan_ID Gender Married Dependents     Education Self_Employed  \\\n",
       "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",
       "5  LP001011   Male     Yes          2      Graduate           Yes   \n",
       "6  LP001013   Male     Yes          0  Not Graduate            No   \n",
       "7  LP001014   Male     Yes         3+      Graduate            No   \n",
       "8  LP001018   Male     Yes          2      Graduate            No   \n",
       "9  LP001020   Male     Yes          1      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",
       "5             5417             4196.0       267.0             360.0   \n",
       "6             2333             1516.0        95.0             360.0   \n",
       "7             3036             2504.0       158.0             360.0   \n",
       "8             4006             1526.0       168.0             360.0   \n",
       "9            12841            10968.0       349.0             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  \n",
       "5             1.0         Urban           Y  \n",
       "6             1.0         Urban           Y  \n",
       "7             0.0     Semiurban           N  \n",
       "8             1.0         Urban           Y  \n",
       "9             1.0     Semiurban           N  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# First 10 Rows of training Dataset\n",
    "\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Store total number of observation in training dataset\n",
    "df_length =len(df)\n",
    "\n",
    "# Store total number of columns in testing data set\n",
    "test_col = len(test.columns)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Understanding the various features (columns) of the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summary of numerical variables for training data set\n",
    "\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. For the non-numerical values (e.g. Property_Area, Credit_History etc.), we can look at frequency distribution to understand whether they make sense or not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Semiurban    233\n",
       "Urban        202\n",
       "Rural        179\n",
       "Name: Property_Area, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the unique values and their frequency of variable Property_Area\n",
    "\n",
    "df['Property_Area'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Understanding Distribution of Numerical Variables\n",
    "    - ApplicantIncome\n",
    "    - LoanAmount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11719e898>"
      ]
     },
     "execution_count": 6,
     "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": [
    "# Box Plot for understanding the distributions and to observe the outliers.\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "# Histogram of variable ApplicantIncome\n",
    "\n",
    "df['ApplicantIncome'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1172d2da0>"
      ]
     },
     "execution_count": 7,
     "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": [
    "# Box Plot for variable ApplicantIncome of training data set\n",
    "\n",
    "df.boxplot(column='ApplicantIncome')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. The above Box Plot confirms the presence of a lot of outliers/extreme values. This can be attributed to the income disparity in the society. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x117263e48>"
      ]
     },
     "execution_count": 8,
     "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": [
    "# Box Plot for variable ApplicantIncome by variable Education of training data set\n",
    "\n",
    "df.boxplot(column='ApplicantIncome', by = 'Education')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. We can see that there is no substantial different between the mean income of graduate and non-graduates. But there are a higher number of graduates with very high incomes, which are appearing to be the outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1173a1128>"
      ]
     },
     "execution_count": 9,
     "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": [
    "# Histogram of variable LoanAmount\n",
    "\n",
    "df['LoanAmount'].hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x117693940>"
      ]
     },
     "execution_count": 10,
     "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": [
    "# Box Plot for variable LoanAmount of training data set\n",
    "\n",
    "df.boxplot(column='LoanAmount')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11763cb38>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3t+cU0djmpGBmuaqsrKStrW2Hsra2NiorK3OKaGxzUjCzXPniteHFA81mlitfvDa8OCmYWe5qa2upra31xWvDgLuPzMyswEnBzMwKnBTMzKzAScHMzAqcFMzMrMBJwczMCpwUzMyswEnBzMwKnBTMzKzAScHMzAqcFMzMrMBJwczMCpwUzMysoF9JQdLjkn4t6V5JK1PZIZJ+LOnh9O/BqVyS/k3SI5Lul/SmofwAZmY2eAbSUqiJiGMiojpNXwisiIgZwIo0DXASMCO95gFXDFawZjY6NTc3U1VVxZw5c6iqqqK5uTnvkMasPek+OgVYmt4vBU4tKr8mMr8ADpI0ZQ+2YwPk/2A2kjQ3NzN//nw6OzuJCDo7O5k/f77325z09yE7AdwhKYBvRsRVwOSIWA8QEeslvSrVPRxYV7RsRypbP0gx2y40NzfT0NBAU1MTXV1dlJWVUVdXB+AnWdmwtGDBAsrKyliyZElhnz3rrLNYsGCB99kc9DcpvDMinkwH/h9LenAXdVWiLHaqJM0j615i8uTJtLa29jMU25WLLrqIT37yk0hiy5Yt7LffftTX13PRRRcxZYobbDb8dHR0cNZZZ3HuuefyxBNPcOSRR3L88cezbNkyHxdyoIidjte7XkC6GNgEnAfMTq2EKUBrRLxe0jfT++ZU/zfd9XpbZ3V1daxcuXJ3P4MVKSsrY8uWLYwfP77waMOXXnqJiooKurq68g7PbCeSePWrX82yZct2aCk89dRTDPT4ZL2TdE/RmHCv+hxTkDRJ0v7d74ETgNXALcDcVG0ucHN6fwvw0XQW0nHAxl0lBBtclZWVtLW17VDW1tZGZWVlThGZ7Vp5eTlbt27doWzr1q2Ul/sR8nnoz7c+GfiBpO76yyLiR5J+CdwgqQ54Ajg91b8NOBl4BNgMfGzQo7ZeNTQ0UFdXVxhTaGlpoa6ujsbGxrxDMyupq6uLF154geOPP75QNn78eLdsc9JnUoiIR4GjS5T/AZhTojyA8wclOhuw7oG5+vp62tvbqayspLGx0QN2Nmztu+++dHZ2cvDBB7Nhw4bCv5MmTco7tDFpwGMKQ8FjCkOje0zBbDiTxP7778/NN99cGFM45ZRTeOGFFzymMIgGbUzBzGyoXXbZZdTX13PiiSdSX1/PZZddlndIY5aTgpnlShKrVq1i9erVrFixgtWrV7Nq1SrSOKbtZR7eN7Ncvec97+GKK7K74Zx88sn87d/+LVdccQUnnHBCzpGNTU4KZpar5cuXc+KJJ3LllVdyxRVXIIkTTjiB5cuX5x3amOSkYGa5604APjkifx5TMDOzAicFMzMrcFIwM7MCJwUzMytwUjAzswInBTPLnZ8WOHz4lFQzy5WfFji8uKVgZrlqbGykqamJmpoaysvLqampoampybd7z4mTgpnlqr29nVmzZu1QNmvWLNrb23OKaGxzUjCzXPlpgcOLk4KZ5ar7aYEtLS1s27at8LTAhoaGvEMbkzzQbGa58tMChxcnBTPLXW1tLbW1tb4h3jDg7iMzMytwUjAzswInBTPLna9oHj76nRQklUlaJemHafo1ku6S9LCk6yVNSOUT0/Qjaf70oQndzEaD5uZm5s+fT2dnJwCdnZ3Mnz/fiSEnA2kpzAeKryZZBHwtImYAG4C6VF4HbIiI1wFfS/XMzEpasGAB5eXlLFmyhOXLl7NkyRLKy8tZsGBB3qGNSf1KCpKmAu8DvpWmBRwP3JiqLAVOTe9PSdOk+XNSfTOznXR0dLB06dIdbnOxdOlSOjo68g5tTOrvKan/CiwA9k/TrwCei4htaboDODy9PxxYBxAR2yRtTPWfKV6hpHnAPIDJkyfT2tq6mx/BerNp0yZ/rzYi3HfffYwfP76wz953330A3n9z0GdSkPR+4HcRcY+k2d3FJapGP+ZtL4i4CrgKoLq6Onxu8uDzOd82EkydOpXLLruMZcuWUVFRQURw2WWXMXXqVO+/OehPS+GdwAcknQxUAAeQtRwOklSeWgtTgSdT/Q7gCKBDUjlwIPDsoEduZqPCl7/8ZebPn8+5557L2rVrmTZtGl1dXXz1q1/NO7Qxqc8xhYj4bERMjYjpwJnAnRFxNtACnJaqzQVuTu9vSdOk+XdGxE4tBTMzyK5mPuOMM1i/fj0Rwfr16znjjDN8m4uc7MltLhYC35X0RWAV0JTKm4BrJT1C1kI4c89CNLPRrLm5mVtvvZXbb799h4fsvOMd73BiyIGGw4/46urqWLlyZd5hjDoeU7CRoKqqilNPPZWbbrqpcEO87unVq1fnHd6oIemeiKjuq55viGdmuVqzZg2dnZ0sWbKk0FLoHl+wvc+3uTCzXE2YMIH6+vodrlOor69nwoQJeYc2JrmlYGa52rp1K5dffjnHHnssXV1dtLS0cPnll7N169a8QxuTnBTMLFczZ85kxowZnHTSSbz44otMnDiRk046iX333Tfv0MYkJwUzy1VNTQ1XXnklixYtYubMmaxZs4aFCxfyiU98Iu/QxiQnBTPLVUtLCwsXLmTJkiWFs48WLlzITTfdlHdoY5JPSR3FfEqqjQRlZWVs2bKF8ePHF/bZl156iYqKCrq6uvIOb9To7ympPvtoFPIDS2wkqays5JJLLtlhn73kkkuorKzMO7Qxyd1Ho0xzczMNDQ00NTXtcHUo4KtDbViqqalh0aJFHlMYJtx9NMpUVVWxePFiampqCk3xlpYW6uvrfXWoDUu+onnv6G/3kZPCKOP+WRtpvM/uHR5TGKMqKytpa2vboaytrc39szZseZ8dXpwURpmGhgbq6upoaWlh27ZttLS0UFdXR0NDQ96hmZXkfXZ48UDzKNM9mFxfX1/on21sbPQgsw1btbW1/OxnP9vhiubzzjvP+2xO3FIYhWpra1m9ejUrVqxg9erV/s9lw1pzczPXX389U6ZMYdy4cUyZMoXrr7/ep1LnxEnBzHK1YMECysvLWbJkCcuXL2fJkiWUl5ezYMGCvEMbk5wUzCxXHR0dzJ07l/r6ek488UTq6+uZO3cuHR0deYc2JjkpmFnuvvGNb9DZ2UlE0NnZyTe+8Y28QxqzPNBsZrkqKyvj+eefLySFdevWFa7Gt73PLQUzy1X3BWrdF9J2/+sL1/LhpGBmuauoqODII49k3LhxHHnkkVRUVOQd0pjVZ1KQVCHpbkn3SXpA0iWp/DWS7pL0sKTrJU1I5RPT9CNp/vSh/QhmNtKNGzdul9O29/Tnm38ROD4ijgaOAd4r6ThgEfC1iJgBbADqUv06YENEvA74WqpnZtarzZs3s27dOl5++WXWrVvH5s2b8w5pzOozKURmU5ocn14BHA/cmMqXAqem96ekadL8OZI0aBGb2ajSfXjoOabgw0Y++tVGk1Qm6V7gd8CPgd8Cz0XEtlSlAzg8vT8cWAeQ5m8EXjGYQZvZ6NEzCfRMErZ39euU1IjoAo6RdBDwA6DU7Qu7/4Kl0vtOf11J84B5AJMnT6a1tbU/odgAbNq0yd+rjQhHHXUUDz30EF1dXYwbN443vOENPPDAA95/czCg6xQi4jlJrcBxwEGSylNrYCrwZKrWARwBdEgqBw4Eni2xrquAqyB7noKfJTx4mpubaWxsLNwQr6Ghwfc/smFtzZo1fOUrXyk8ee0zn/kMgJ8xnoM+k4KkVwIvpYSwD/BussHjFuA04LvAXODmtMgtafrnaf6d4XbgXuPHcdpIU15ezsSJE1m8eDFr165l2rRp7Lvvvrz44ot5hzYm9fnkNUlvJBs4LiMbg7ghIj4v6bVkCeEQYBXw4Yh4UVIFcC1wLFkL4cyIeHRX2/CT1waPH21oI824ceM49NBDmTRpUiEpdHZ28swzz/Dyyy/nHd6o0d8nr/XZUoiI+8kO8D3LHwXeWqJ8C3B6P+O0QbZmzRo2b968U0vh8ccfzzs0s5JmzpxZ+OEiiUmTJnH22Wdz00035R3amOQrREaZCRMmcMEFF1BTU0N5eTk1NTVccMEFTJgwIe/QzEpqaGhg2bJlLF68mOXLl7N48WKWLVvmJ6/lpM/uo73B3UeDZ9y4cUybNo0lS5YUWgrnnnsua9eudVPcho3dvQZhOByvRqpB6z6ykaW7KV78OE43xW246e3gPv3CW3n80vft5WismJPCKNPQ0FDy7KPGxsa8QzOzEcBJYZTxQ9DNbE84KYwyzc3N3Hrrrdx+++07tBTe8Y53ODGYWZ+cFEaZxsZGzjrrrB3GFM466ywaGxudFMysT04Ko4yvUzCzPeHrFEYZX6dgZnvCLYVRZuvWrSxevJhjjz2Wrq4uWlpaWLx4MVu3bs07NDMbAZwURhlfp2Bme8JJYZTxdQpmtiecFEaZ7jOMilsKPvPIzPrLSWEUqq2tpba2ltbWVj+kxMwGxGcfmZlZgZPCKFRfX09FRQU1NTVUVFRQX1+fd0hmNkK4+2iUqa+v58orr2TRokWF590uXLgQgMWLF+ccnZkNd24pjDJXX301ixYt4lOf+hQVFRV86lOfYtGiRVx99dV5h2ZmI4CTwijz4osvcvDBB1NVVcWcOXOoqqri4IMP9kPQzaxf3H00ypSXl/OZz3yGG2+8sXCdwmmnnUZ5uf/UZtY3txRGmQMOOICNGzeyatUqtm3bxqpVq9i4cSMHHHBA3qGZ2Qjgn4+jzHPPPcfHP/5xLrroosJDdubNm8c3v/nNvEMzsxGgz5aCpCMktUhql/SApPmp/BBJP5b0cPr34FQuSf8m6RFJ90t601B/CNuusrKS008/nS1bttDS0sKWLVs4/fTTqayszDs0MxsB+tN9tA34dERUAscB50uaCVwIrIiIGcCKNA1wEjAjveYBVwx61NarhoYG6urqaGlpYdu2bbS0tFBXV0dDQ0PeoZnZCNBn91FErAfWp/cvSGoHDgdOAWanakuBVmBhKr8mIgL4haSDJE1J67Eh5nsfmdmeGNCYgqTpwLHAXcDk7gN9RKyX9KpU7XBgXdFiHalsh6QgaR5ZS4LJkyfT2to68OitpClTpnD55ZezadMm9ttvPwB/vzZieF/NV7+TgqT9gO8DfxcRz0vqtWqJstipIOIq4CqA6urq8I3bBp9viGcjzo9u9T6bs34lBUnjyRLCdRHxn6n46e5uIUlTgN+l8g7giKLFpwJPDlbAtrNdJOhdynr4zMy268/ZRwKagPaI+GrRrFuAuen9XODmovKPprOQjgM2ejxhaEVEyde0hT/sdZ4TgpmV0p+WwjuBjwC/lnRvKrsIuBS4QVId8ARwepp3G3Ay8AiwGfjYoEZsZmZDpj9nH7VRepwAYE6J+gGcv4dxmZlZDnybCzMzK3BSMDOzAt/7yMyGxNGX3MHGP7404OWmX3jrgOofuM947vunEwa8HSvNScHMhsTGP77E45e+b0DL7M61NQNNIrZr7j4yM7MCJwUzMytwUjAzswInBTMzK3BSMDOzAicFMzMrcFIwM7MCJwUzMytwUjAzswJf0TyC7M5tA3zLADMbCCeFEWSgtw3wLQPMbKDcfWRmZgVuKZjZkNi/8kL+bOmFA19w6UC3AzCwG+9Z75wUzGxIvNB+qe+SOgK5+8jMzAqcFMzMrMBJwczMCjymMILs1sCdB+3MbAD6TAqSlgDvB34XEVWp7BDgemA68DjwoYjYIEnA14GTgc3AORHxq6EJfewZ6MCdB+3MbKD60330beC9PcouBFZExAxgRZoGOAmYkV7zgCsGJ0wzM9sb+mwpRMRPJE3vUXwKMDu9Xwq0AgtT+TUREcAvJB0kaUpErB+sgM1s5NitluePBn5rFhs8uzumMLn7QB8R6yW9KpUfDqwrqteRynZKCpLmkbUmmDx5Mq2trbsZytgykO9p06ZNu/W9+m9hg+Hb75004GXO+VHnbi3nfXbwDPZAs0qURamKEXEVcBVAdXV1DLTve0z60a0DGiPYnTGFgW7DbFB5/8vd7p6S+rSkKQDp39+l8g7giKJ6U4Endz88MzPbm3Y3KdwCzE3v5wI3F5V/VJnjgI0eTzAzGzn6c0pqM9mg8qGSOoB/Ai4FbpBUBzwBnJ6q30Z2OuojZKekfmwIYh7TBjxw50E7MxuA/px9VNvLrDkl6gZw/p4GZaUN9OZi0y+8dcDLmNlbPjfSAAAGnElEQVTY5ttcmJlZgZOCmZkVOCmYmVmBk4KZmRU4KZiZWYGTgpmZFTgpmJlZgZOCmZkVOCmYmVmBk4KZmRU4KZiZWYGTgpmZFTgpmJlZgZOCmZkVOCmYmVmBk4KZmRX0+ZAdG/4k9T5vUe/LZc9EMjPbzi2FUSAiSr5aWlp6neeEYGalOCmYmVmBk8Io1NzcTFVVFXPmzKGqqorm5ua8QzKzEcJjCqNMc3MzDQ0NNDU10dXVRVlZGXV1dQDU1tbmHJ1ZxuNgw9eQtBQkvVfSbyQ9IunCodiGldbY2EhTUxM1NTWUl5dTU1NDU1MTjY2NeYdmVuBxsOFr0JOCpDLg34GTgJlAraSZg70dK629vZ1Zs2btUDZr1iza29tzisjMRpKhaCm8FXgkIh6NiK3Ad4FThmA7VkJlZSVtbW07lLW1tVFZWZlTRGY2kgxFUjgcWFc03ZHKbC9oaGigrq6OlpYWtm3bRktLC3V1dTQ0NOQdmpmNAEMx0FxqBGmnzkBJ84B5AJMnT6a1tXUIQhl7pkyZwtlnn825557LE088wZFHHsmHP/xhpkyZ4u/Yhr1NmzZ5P82ZBnvwRtLbgYsj4sQ0/VmAiPhSb8tUV1fHypUrBzUOg9bWVmbPnp13GGb95n126Ei6JyKq+6o3FN1HvwRmSHqNpAnAmcAtQ7AdMzMbZIPefRQR2yRdACwHyoAlEfHAYG/HzMwG35BcvBYRtwG3DcW6zcxs6Pg2F2ZmVuCkYGZmBYN+9tFuBSH9Hlibdxyj0KHAM3kHYTYA3meHzrSIeGVflYZFUrChIWllf05BMxsuvM/mz91HZmZW4KRgZmYFTgqj21V5B2A2QN5nc+YxBTMzK3BLwczMCpwUhilJXZLuLXpNH8JtnSPp8qFav41tkkLStUXT5ZJ+L+mHfSw3u686Nvj8jObh648RcUzeQZgNgk6gStI+EfFH4D3A/+Uck/XCLYURRFKZpH+R9EtJ90v6eCqfLel/JN0g6SFJl0o6W9Ldkn4t6U9Svb+UdJekVZL+W9LkEtt4paTvp238UtI79/bntFHpduB96X0t0Nw9Q9JbJf0s7Zc/k/T6ngtLmiRpSdonV0ny0xyHiJPC8LVPUdfRD1JZHbAxIt4CvAU4T9Jr0ryjgfnAnwEfAf40It4KfAuoT3XagOMi4liyx6QuKLHdrwNfS9v467S82Z76LnCmpArgjcBdRfMeBP487ZefA/65xPINwJ1pv6wB/kXSpCGOeUxy99HwVar76ATgjZJOS9MHAjOArcAvI2I9gKTfAnekOr8m+08EMBW4XtIUYALwWIntvhuYKRUeoHeApP0j4oVB+Ew2RkXE/WlcrJad76B8ILBU0gyypzSOL7GKE4APSPpMmq4AjgTahyTgMcxJYWQRUB8Ry3colGYDLxYVvVw0/TLb/86Lga9GxC1pmYtLbGMc8PbU92s2mG4BvgLMBl5RVP4FoCUi/ioljtYSywr464j4zdCGaO4+GlmWA38jaTyApD8dYBP6QLYP8M3tpc4dwAXdE5I82G2DZQnw+Yj4dY/y4v3ynF6WXQ7UKzVhJR07JBGak8II8y1gDfArSauBbzKw1t7FwPck/S+934nyk0B1GsheA3xiD+I1K4iIjoj4eolZXwa+JOmnZE9rLOULZN1K96d9/wtDFOaY5yuazcyswC0FMzMrcFIwM7MCJwUzMytwUjAzswInBTMzK3BSsDFH0mRJyyQ9KukeST+X9FeDsF7f1dNGPCcFG1PSxU83AT+JiNdGxJuBM8luAbK3Y/EdBWzYcVKwseZ4YGtEXNldEBFrI2JxH3ehbZV0o6QHJV1XdGXte1NZG/DB7nX2dlfP9OyK70n6L7bfn8ps2PAvFRtrjgJ+1cu8wl1oJU0Efiqp+8B9bFr2SeCnwDslrQSuJks0jwDXF62r+66e50o6CLhb0n+neW8H3hgRzw7mBzMbDE4KNqZJ+ndgFtmdZtfS+11o746IjrTMvcB0YBPwWEQ8nMq/A8xLy/Z2V0+AHzsh2HDlpGBjzQNkz4kAICLOl3QosBJ4gv7dhbaL7f93ertPTMm7ekp6G9mTyMyGJY8p2FhzJ1Ah6W+KyvZN/w70LrQPAq/pfrId2bMCuvmunjYiOSnYmBLZHSBPBf5C0mOS7gaWAgsZ4F1oI2ILWXfRrWmgeW3RbN/V00Yk3yXVzMwK3FIwM7MCJwUzMytwUjAzswInBTMzK3BSMDOzAicFMzMrcFIwM7MCJwUzMyv4/01EDsocSwcEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Box Plot for variable LoanAmount by variable Gender of training data set\n",
    "\n",
    "df.boxplot(column='LoanAmount', by = 'Gender')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5. LoanAmount has missing as well as extreme values, while ApplicantIncome has a few extreme values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Understanding Distribution of Categorical Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "422\n"
     ]
    }
   ],
   "source": [
    "# Loan approval rates in absolute numbers\n",
    "loan_approval = df['Loan_Status'].value_counts()['Y']\n",
    "print(loan_approval)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 422 number of loans were approved."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Loan_Status</th>\n",
       "      <th>N</th>\n",
       "      <th>Y</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Credit_History</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>82</td>\n",
       "      <td>7</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>97</td>\n",
       "      <td>378</td>\n",
       "      <td>475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>179</td>\n",
       "      <td>385</td>\n",
       "      <td>564</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Loan_Status       N    Y  All\n",
       "Credit_History               \n",
       "0.0              82    7   89\n",
       "1.0              97  378  475\n",
       "All             179  385  564"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Credit History and Loan Status\n",
    "pd.crosstab(df ['Credit_History'], df ['Loan_Status'], margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Function to output percentage row wise in a cross table\n",
    "def percentageConvert(ser):\n",
    "    return ser/float(ser[-1])\n",
    "\n",
    "# # Loan approval rate for customers having Credit_History (1)\n",
    "#df['Y'] = pd.crosstab(df [\"Credit_History\"], df [\"Loan_Status\"], margins=True).apply(percentageConvert, axis=1)\n",
    "#loan_approval_with_Credit_1 = df['Y'][1]\n",
    "#print(loan_approval_with_Credit_1*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 79.58 % of the applicants whose loans were approved have Credit_History equals to 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace missing value of Self_Employed with more frequent category\n",
    "df['Self_Employed'].fillna('No',inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Outliers of LoanAmount and Applicant Income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x117cd6e48>"
      ]
     },
     "execution_count": 19,
     "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": [
    "# Add both ApplicantIncome and CoapplicantIncome to TotalIncome\n",
    "df['TotalIncome'] = df['ApplicantIncome'] + df['CoapplicantIncome']\n",
    "\n",
    "# Looking at the distribtion of TotalIncome\n",
    "df['LoanAmount'].hist(bins=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The extreme values are practically possible, i.e. some people might apply for high value loans due to specific needs. So instead of treating them as outliers, let’s try a log transformation to nullify their effect:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x117dd07b8>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAEcJJREFUeJzt3X+MZeV93/H3J6wdYzb2OsGeol3UIfWKNGXbGo+QW1TrrskPHCzDH3aD5TqLQ7WqSl232SheJ3+g/mGVqCWOI7WWtuB4o7heU2wLZIgTRHxrRSoku9jxgjH1imxhYQ2ODCRDrESbfvvHHKQpc83s3nPv3DvPvF/SaO557nPP+erR3M8889xzzqSqkCS164dmXYAkaboMeklqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1Ljts26AIALL7ywFhcXefHFF7ngggtmXc7ccVzWckxGc1xGa3Vcjh079udV9cb1+s1F0C8uLnL06FGGwyGDwWDW5cwdx2Utx2Q0x2W0Vsclyf85m34u3UhS49YN+iSfSvJskodHPPfLSSrJhd12kvxWkhNJvpHk8mkULUk6e2czo/80cPXLG5NcDPw08MSq5ncCu7uv/cAn+5coSepj3aCvqq8C3xvx1MeBXwFW3+f4WuB3asUDwI4kF02kUknSWMb6MDbJu4GnqupPk6x+aifw5KrtU13b6RH72M/KrJ+FhQWGwyHLy8sMh8NxSmqa47KWYzKa4zLaVh+Xcw76JK8Ffg34mVFPj2gb+Z9NquoQcAhgaWmpBoNBs5+M9+W4rOWYjOa4jLbVx2WcGf3fAy4BXprN7wIeSnIFKzP4i1f13QU83bdISdL4zvn0yqo6XlVvqqrFqlpkJdwvr6rvAHcDv9CdffM24IWqWrNsI0naOGdzeuVngf8FXJrkVJIbX6H7vcDjwAngvwH/eiJVSpLGtu7STVW9b53nF1c9LuCm/mVJbVg8eM/Yrz15yzUTrERbmVfGSlLjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjRvrH49Imr5x7pNzYM8Zbjh4j/fJ0f/HGb0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDVu3aBP8qkkzyZ5eFXbf0ryrSTfSPLFJDtWPffRJCeSPJbkZ6dVuCTp7JzNjP7TwNUva7sPuKyq/iHwv4GPAiT5SeB64B90r/mvSc6bWLWSpHO2btBX1VeB772s7Q+q6ky3+QCwq3t8LXCkqv66qv4MOAFcMcF6JUnnaBI3NftF4HPd452sBP9LTnVtayTZD+wHWFhYYDgcsry8zHA4nEBJbXFc1tosY3Jgz5n1O03Qwvkrx9wMY7ORNsvPy7T0CvokvwacAT7zUtOIbjXqtVV1CDgEsLS0VIPBgOFwyGAw6FNSkxyXtTbLmNwwxh0o+ziw5wy3Ht/GyfcPNvS4826z/LxMy9hBn2Qf8C7gqqp6KcxPARev6rYLeHr88iRJfY11emWSq4GPAO+uqr9a9dTdwPVJfjjJJcBu4I/7lylJGte6M/oknwUGwIVJTgE3s3KWzQ8D9yUBeKCq/lVVPZLkDuCbrCzp3FRVfzut4iVJ61s36KvqfSOab3+F/h8DPtanKEnS5HhlrCQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1Lj1g36JJ9K8mySh1e1/WiS+5J8u/v+hq49SX4ryYkk30hy+TSLlySt72xm9J8Grn5Z20Hg/qraDdzfbQO8E9jdfe0HPjmZMiVJ41o36Kvqq8D3XtZ8LXC4e3wYuG5V++/UigeAHUkumlSxkqRzN+4a/UJVnQbovr+pa98JPLmq36muTZI0I9smvL+MaKuRHZP9rCzvsLCwwHA4ZHl5meFwOOGSNj/HZa3NMiYH9pzZ0OMtnL9yzM0wNhtps/y8TMu4Qf9Mkouq6nS3NPNs134KuHhVv13A06N2UFWHgEMAS0tLNRgMGA6HDAaDMUtql+Oy1mYZkxsO3rOhxzuw5wy3Ht8Gx18cex8nb7lmghXNh83y8zIt4y7d3A3s6x7vA+5a1f4L3dk3bwNeeGmJR5I0G+vO6JN8FhgAFyY5BdwM3ALckeRG4AngvV33e4GfA04AfwV8cAo1S5LOwbpBX1Xv+wFPXTWibwE39S1KkjQ5XhkrSY0z6CWpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1LheQZ/k3yd5JMnDST6b5DVJLknyYJJvJ/lckldPqlhJ0rkbO+iT7AT+LbBUVZcB5wHXA78OfLyqdgPPATdOolBJ0nj6Lt1sA85Psg14LXAaeAdwZ/f8YeC6nseQJPUwdtBX1VPAfwaeYCXgXwCOAc9X1Zmu2ylgZ98iJUnjS1WN98LkDcDngZ8Hngf+R7d9c1W9uetzMXBvVe0Z8fr9wH6AhYWFtx45coTl5WW2b98+Vj0tc1zW2ixjcvypFzb0eAvnwzPf77ePPTtfP5li5shm+Xk5V3v37j1WVUvr9dvW4xg/BfxZVX0XIMkXgH8K7EiyrZvV7wKeHvXiqjoEHAJYWlqqwWDAcDhkMBj0KKlNjstam2VMbjh4z4Ye78CeM9x6vM/bGk6+fzCZYubIZvl5mZY+a/RPAG9L8tokAa4Cvgl8BXhP12cfcFe/EiVJffRZo3+QlQ9dHwKOd/s6BHwE+KUkJ4AfA26fQJ2SpDH1+huvqm4Gbn5Z8+PAFX32K0maHK+MlaTGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWrctj4vTrIDuA24DCjgF4HHgM8Bi8BJ4J9X1XO9qpRmaPHgPbMuQeql74z+E8CXq+ongH8EPAocBO6vqt3A/d22JGlGxg76JK8D3g7cDlBVf1NVzwPXAoe7boeB6/oWKUkaX58Z/Y8D3wV+O8nXktyW5AJgoapOA3Tf3zSBOiVJY0pVjffCZAl4ALiyqh5M8gngL4APVdWOVf2eq6o3jHj9fmA/wMLCwluPHDnC8vIy27dvH6ueljkua23kmBx/6oUNOc4kLJwPz3y/3z727Hz9ZIqZI62+h/bu3XusqpbW69cn6P8O8EBVLXbb/4yV9fg3A4OqOp3kImBYVZe+0r6Wlpbq6NGjDIdDBoPBWPW0zHFZayPHZDN9GHtgzxluPd7rHAtO3nLNhKqZH62+h5KcVdCPvXRTVd8BnkzyUohfBXwTuBvY17XtA+4a9xiSpP76/eqHDwGfSfJq4HHgg6z88rgjyY3AE8B7ex5DktRDr6Cvqq8Do/5suKrPfiVJk+OVsZLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mNM+glqXG9gz7JeUm+luRL3fYlSR5M8u0kn0vy6v5lSpLGNYkZ/YeBR1dt/zrw8araDTwH3DiBY0iSxtQr6JPsAq4Bbuu2A7wDuLPrchi4rs8xJEn9pKrGf3FyJ/AfgR8Bfhm4AXigqt7cPX8x8HtVddmI1+4H9gMsLCy89ciRIywvL7N9+/ax62mV47LWRo7J8ade2JDjTMLC+fDM9/vtY8/O10+mmDnS6nto7969x6pqab1+28Y9QJJ3Ac9W1bEkg5eaR3Qd+Zukqg4BhwCWlpZqMBgwHA4ZDAajum9pjstaGzkmNxy8Z0OOMwkH9pzh1uNjv60BOPn+wWSKmSNb/T3U5yfiSuDdSX4OeA3wOuA3gR1JtlXVGWAX8HT/MiVJ4xp7jb6qPlpVu6pqEbge+MOqej/wFeA9Xbd9wF29q5QkjW0a59F/BPilJCeAHwNun8IxJElnqd9iXqeqhsCwe/w4cMUk9itJ6s8rYyWpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1LjJnJ6pTTPFjfRLQzmQd/xOnnLNROqRJPijF6SGmfQS1LjDHpJapxBL0mN88NYSRPV58NcP8idDmf0ktQ4g16SGmfQS1LjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUuLGDPsnFSb6S5NEkjyT5cNf+o0nuS/Lt7vsbJleuJOlc9ZnRnwEOVNXfB94G3JTkJ4GDwP1VtRu4v9uWJM3I2EFfVaer6qHu8V8CjwI7gWuBw123w8B1fYuUJI1vImv0SRaBtwAPAgtVdRpWfhkAb5rEMSRJ40lV9dtBsh34n8DHquoLSZ6vqh2rnn+uqtas0yfZD+wHWFhYeOuRI0dYXl5m+/btveppkeOy1rmMyfGnXphyNfNj4Xx45vuzrmJ8e3a+fir7bfU9tHfv3mNVtbRev15Bn+RVwJeA36+q3+jaHgMGVXU6yUXAsKoufaX9LC0t1dGjRxkOhwwGg7HraZXjsta5jMlW+p+xB/ac4dbjm/fu49O6TXGr76EkZxX0fc66CXA78OhLId+5G9jXPd4H3DXuMSRJ/fX51X8l8AHgeJKvd22/CtwC3JHkRuAJ4L39SpQk9TF20FfVHwH5AU9fNe5+pVFevvxyYM8ZbthCSzJSH14ZK0mNM+glqXEGvSQ1zqCXpMYZ9JLUuM17ZYWk5vS5uG1aF1u1wBm9JDXOGb02zFa6FYE0T5zRS1LjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMZ5eqWkLa/vqb/zfrGWM3pJapwzeklNeKVZ+Vb/RzUGvST1NO/36HHpRpIa54xe58T71Uibj0G/CbV+hoCkyZra0k2Sq5M8luREkoPTOo4k6ZVNZUaf5DzgvwA/DZwC/iTJ3VX1zWkcT+fG5Rdpa5nW0s0VwImqehwgyRHgWmDiQT/LZYx5/6RdkmB6Szc7gSdXbZ/q2iRJGyxVNfmdJu8Ffraq/mW3/QHgiqr60Ko++4H93ealwGPAhcCfT7ygzc9xWcsxGc1xGa3Vcfm7VfXG9TpNa+nmFHDxqu1dwNOrO1TVIeDQ6rYkR6tqaUo1bVqOy1qOyWiOy2hbfVymtXTzJ8DuJJckeTVwPXD3lI4lSXoFU5nRV9WZJP8G+H3gPOBTVfXINI4lSXplU7tgqqruBe49x5cdWr/LluS4rOWYjOa4jLalx2UqH8ZKkuaHNzWTpMbNPOiTXJzkK0keTfJIkg/PuqZ5kOQ1Sf44yZ924/IfZl3TPElyXpKvJfnSrGuZF0lOJjme5OtJjs66nnmRZEeSO5N8q8uZfzLrmjbaPNzU7AxwoKoeSvIjwLEk93m7BP4aeEdVLSd5FfBHSX6vqh6YdWFz4sPAo8DrZl3InNlbVS2eL97HJ4AvV9V7urMAXzvrgjbazGf0VXW6qh7qHv8lK2/eLX8Vba1Y7jZf1X35gQqQZBdwDXDbrGvRfEvyOuDtwO0AVfU3VfX8bKvaeDMP+tWSLAJvAR6cbSXzoVue+DrwLHBfVTkuK34T+BXg/866kDlTwB8kOdZdeS74ceC7wG93S323Jblg1kVttLkJ+iTbgc8D/66q/mLW9cyDqvrbqvrHrFxZfEWSy2Zd06wleRfwbFUdm3Utc+jKqroceCdwU5K3z7qgObANuBz4ZFW9BXgR2HK3TZ+LoO/WoD8PfKaqvjDreuZN96fmELh6xqXMgyuBdyc5CRwB3pHkd2db0nyoqqe7788CX2TlLrJb3Sng1Kq/hu9kJfi3lJkHfZKwsn72aFX9xqzrmRdJ3phkR/f4fOCngG/NtqrZq6qPVtWuqlpk5dYaf1hV/2LGZc1ckgu6kxnoliZ+Bnh4tlXNXlV9B3gyyaVd01VM4Xbp824ezrq5EvgAcLxbjwb41e7K2q3sIuBw909cfgi4o6o8lVA/yALwxZV5E9uA/15VX55tSXPjQ8BnujNuHgc+OON6NpxXxkpS42a+dCNJmi6DXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxv0/NkUbNNwFepMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Perform log transformation of TotalIncome to make it closer to normal\n",
    "df['LoanAmount_log'] = np.log(df['LoanAmount'])\n",
    "\n",
    "# Looking at the distribtion of TotalIncome_log\n",
    "df['LoanAmount_log'].hist(bins=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Preparation for Model Building"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- sklearn requires all inputs to be numeric, we should convert all our categorical variables into numeric by encoding the categories. Before that we will fill all the missing values in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID               object\n",
       "Gender                 int64\n",
       "Married                int64\n",
       "Dependents             int64\n",
       "Education              int64\n",
       "Self_Employed          int64\n",
       "ApplicantIncome        int64\n",
       "CoapplicantIncome    float64\n",
       "LoanAmount           float64\n",
       "Loan_Amount_Term     float64\n",
       "Credit_History         int64\n",
       "Property_Area          int64\n",
       "Loan_Status           object\n",
       "TotalIncome          float64\n",
       "LoanAmount_log       float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Impute missing values for Gender\n",
    "df['Gender'].fillna(df['Gender'].mode()[0],inplace=True)\n",
    "\n",
    "# Impute missing values for Married\n",
    "df['Married'].fillna(df['Married'].mode()[0],inplace=True)\n",
    "\n",
    "# Impute missing values for Dependents\n",
    "df['Dependents'].fillna(df['Dependents'].mode()[0],inplace=True)\n",
    "\n",
    "# Impute missing values for Credit_History\n",
    "df['Credit_History'].fillna(df['Credit_History'].mode()[0],inplace=True)\n",
    "\n",
    "# Convert all non-numeric values to number\n",
    "cat=['Gender','Married','Dependents','Education','Self_Employed','Credit_History','Property_Area']\n",
    "\n",
    "for var in cat:\n",
    "    le = preprocessing.LabelEncoder()\n",
    "    df[var]=le.fit_transform(df[var].astype('str'))\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generic Classification Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Technocrat/anaconda3/lib/python3.6/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "#Import models from scikit learn module:\n",
    "from sklearn import metrics\n",
    "from sklearn.cross_validation import KFold\n",
    "\n",
    "#Generic function for making a classification model and accessing performance:\n",
    "\n",
    "def classification_model(model, data, predictors, outcome):\n",
    "    #Fit the model:\n",
    "    model.fit(data[predictors],data[outcome])\n",
    "  \n",
    "    #Make predictions on training set:\n",
    "    predictions = model.predict(data[predictors])\n",
    "  \n",
    "    #Print accuracy\n",
    "    accuracy = metrics.accuracy_score(predictions,data[outcome])\n",
    "    print (\"Accuracy : %s\" % \"{0:.3%}\".format(accuracy))\n",
    "\n",
    "    #Perform k-fold cross-validation with 5 folds\n",
    "    kf = KFold(data.shape[0], n_folds=5)\n",
    "    error = []\n",
    "    for train, test in kf:\n",
    "        # Filter training data\n",
    "        train_predictors = (data[predictors].iloc[train,:])\n",
    "    \n",
    "        # The target we're using to train the algorithm.\n",
    "        train_target = data[outcome].iloc[train]\n",
    "    \n",
    "        # Training the algorithm using the predictors and target.\n",
    "        model.fit(train_predictors, train_target)\n",
    "    \n",
    "        #Record error from each cross-validation run\n",
    "        error.append(model.score(data[predictors].iloc[test,:], data[outcome].iloc[test]))\n",
    " \n",
    "    print (\"Cross-Validation Score : %s\" % \"{0:.3%}\".format(np.mean(error)))\n",
    "\n",
    "    #Fit the model again so that it can be refered outside the function:\n",
    "    model.fit(data[predictors],data[outcome])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ApplicantIncome        0\n",
       "CoapplicantIncome      0\n",
       "Credit_History        29\n",
       "Dependents            10\n",
       "Education              0\n",
       "Gender                11\n",
       "LoanAmount            27\n",
       "LoanAmount_log       389\n",
       "Loan_Amount_Term      20\n",
       "Loan_ID                0\n",
       "Loan_Status          367\n",
       "Married                0\n",
       "Property_Area          0\n",
       "Self_Employed         23\n",
       "TotalIncome          367\n",
       "Type                   0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Combining both train and test dataset\n",
    "\n",
    "#Create a flag for Train and Test Data set\n",
    "df['Type']='Train' \n",
    "test['Type']='Test'\n",
    "fullData = pd.concat([df,test], axis=0)\n",
    "\n",
    "#Look at the available missing values in the dataset\n",
    "fullData.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Identify categorical and continuous variables\n",
    "ID_col = ['Loan_ID']\n",
    "target_col = [\"Loan_Status\"]\n",
    "cat_cols = ['Credit_History','Dependents','Gender','Married','Education','Property_Area','Self_Employed']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Imputing Missing values with mean for continuous variable\n",
    "fullData['LoanAmount'].fillna(fullData['LoanAmount'].mean(), inplace=True)\n",
    "fullData['LoanAmount_log'].fillna(fullData['LoanAmount_log'].mean(), inplace=True)\n",
    "fullData['Loan_Amount_Term'].fillna(fullData['Loan_Amount_Term'].mean(), inplace=True)\n",
    "fullData['ApplicantIncome'].fillna(fullData['ApplicantIncome'].mean(), inplace=True)\n",
    "fullData['CoapplicantIncome'].fillna(fullData['CoapplicantIncome'].mean(), inplace=True)\n",
    "\n",
    "#Imputing Missing values with mode for categorical variables\n",
    "fullData['Gender'].fillna(fullData['Gender'].mode()[0], inplace=True)\n",
    "fullData['Married'].fillna(fullData['Married'].mode()[0], inplace=True)\n",
    "fullData['Dependents'].fillna(fullData['Dependents'].mode()[0], inplace=True)\n",
    "fullData['Loan_Amount_Term'].fillna(fullData['Loan_Amount_Term'].mode()[0], inplace=True)\n",
    "fullData['Credit_History'].fillna(fullData['Credit_History'].mode()[0], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x117f1cd30>"
      ]
     },
     "execution_count": 26,
     "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": [
    "#Create a new column as Total Income\n",
    "\n",
    "fullData['TotalIncome']=fullData['ApplicantIncome'] + fullData['CoapplicantIncome']\n",
    "\n",
    "fullData['TotalIncome_log'] = np.log(fullData['TotalIncome'])\n",
    "\n",
    "#Histogram for Total Income\n",
    "fullData['TotalIncome_log'].hist(bins=20) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Technocrat/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "#create label encoders for categorical features\n",
    "for var in cat_cols:\n",
    "    number = LabelEncoder()\n",
    "    fullData[var] = number.fit_transform(fullData[var].astype('str'))\n",
    "\n",
    "train_modified=fullData[fullData['Type']=='Train']\n",
    "test_modified=fullData[fullData['Type']=='Test']\n",
    "train_modified[\"Loan_Status\"] = number.fit_transform(train_modified[\"Loan_Status\"].astype('str'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. The chances of getting a loan will be higher for:\n",
    "\n",
    "    - Applicants having a credit history (we observed this in exploration.)\n",
    "    - Applicants with higher applicant and co-applicant incomes\n",
    "    - Applicants with higher education level\n",
    "    - Properties in urban areas with high growth perspectives\n",
    "\n",
    "So let’s make our model with ‘Credit_History’, 'Education' & 'Gender'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "\n",
    "predictors_Logistic=['Credit_History','Education','Gender']\n",
    "\n",
    "x_train = train_modified[list(predictors_Logistic)].values\n",
    "y_train = train_modified[\"Loan_Status\"].values\n",
    "\n",
    "x_test=test_modified[list(predictors_Logistic)].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy : 80.945%\n",
      "Cross-Validation Score : 80.946%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Technocrat/anaconda3/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "/Users/Technocrat/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "# Create logistic regression object\n",
    "model = LogisticRegression()\n",
    "\n",
    "# Train the model using the training sets\n",
    "model.fit(x_train, y_train)\n",
    "\n",
    "#Predict Output\n",
    "predicted= model.predict(x_test)\n",
    "\n",
    "#Reverse encoding for predicted outcome\n",
    "predicted = number.inverse_transform(predicted)\n",
    "\n",
    "#Store it to test dataset\n",
    "test_modified['Loan_Status']=predicted\n",
    "\n",
    "outcome_var = 'Loan_Status'\n",
    "\n",
    "classification_model(model, df,predictors_Logistic,outcome_var)\n",
    "\n",
    "test_modified.to_csv(\"Logistic_Prediction.csv\",columns=['Loan_ID','Loan_Status'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
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  "language_info": {
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   "file_extension": ".py",
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