Visualizations.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Geers up\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from scipy.stats import norm\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from scipy import stats\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_train = pd.read_csv('train_u6lujuX_CVtuZ9i.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total</th>\n",
       "      <th>Percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Credit_History</th>\n",
       "      <td>50</td>\n",
       "      <td>0.081433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Self_Employed</th>\n",
       "      <td>32</td>\n",
       "      <td>0.052117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LoanAmount</th>\n",
       "      <td>22</td>\n",
       "      <td>0.035831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dependents</th>\n",
       "      <td>15</td>\n",
       "      <td>0.024430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <td>14</td>\n",
       "      <td>0.022801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <td>13</td>\n",
       "      <td>0.021173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Married</th>\n",
       "      <td>3</td>\n",
       "      <td>0.004886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Loan_Status</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Property_Area</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Education</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Loan_ID</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Total   Percent\n",
       "Credit_History        50  0.081433\n",
       "Self_Employed         32  0.052117\n",
       "LoanAmount            22  0.035831\n",
       "Dependents            15  0.024430\n",
       "Loan_Amount_Term      14  0.022801\n",
       "Gender                13  0.021173\n",
       "Married                3  0.004886\n",
       "Loan_Status            0  0.000000\n",
       "Property_Area          0  0.000000\n",
       "CoapplicantIncome      0  0.000000\n",
       "ApplicantIncome        0  0.000000\n",
       "Education              0  0.000000\n",
       "Loan_ID                0  0.000000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#missing data\n",
    "total = df_train.isnull().sum().sort_values(ascending=False)\n",
    "percent = (df_train.isnull().sum()/df_train.isnull().count()).sort_values(ascending=False)\n",
    "missing_data = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])\n",
    "missing_data.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_train['Gender'] = df_train['Gender'].fillna( \n",
    "                               df_train['Gender'].dropna().mode().values[0] )\n",
    "df_train['Married'] = df_train['Married'].fillna( \n",
    "                               df_train['Married'].dropna().mode().values[0] )\n",
    "df_train['Dependents'] = df_train['Dependents'].fillna( \n",
    "                               df_train['Dependents'].dropna().mode().values[0] )\n",
    "df_train['Self_Employed'] = df_train['Self_Employed'].fillna( \n",
    "                               df_train['Self_Employed'].dropna().mode().values[0] )\n",
    "df_train['LoanAmount'] = df_train['LoanAmount'].fillna( \n",
    "                               df_train['LoanAmount'].dropna().mean() )\n",
    "df_train['Loan_Amount_Term'] = df_train['Loan_Amount_Term'].fillna( \n",
    "                               df_train['Loan_Amount_Term'].dropna().mode().values[0] )\n",
    "df_train['Credit_History'] = df_train['Credit_History'].fillna(\n",
    "                               df_train['Credit_History'].dropna().mode().values[0] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    525\n",
       "0.0     89\n",
       "Name: Credit_History, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train['Credit_History'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x18500043278>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VkAFAkqQaMgBIklRDBgBJkmrIACBJUg219VsAETEJuBTYB1gDnJaZS9q5jnbymwKSoPXr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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1850007b2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.FacetGrid(df_train, row='Gender', col='Married', size=2.2, aspect=1.6)\n",
    "grid.map(plt.hist, 'ApplicantIncome', alpha=.5, bins=10)\n",
    "grid.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x185046cc6d8>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0ShwweLH0NAKApG2AW4ErI+IGSd8oe7sdeAlYll9XlndryZJXa27oK6+srHnaWixevLyu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\nA2b3H62rCxADAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x185046659b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.FacetGrid(df_train, row='Gender', col='Education', size=2.2, aspect=1.6)\n",
    "grid.map(plt.hist, 'ApplicantIncome', alpha=.5, bins=10)\n",
    "grid.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x185049a1e80>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9iNgY2IYyQVCSJPWh8fQAnADMj4h1gJuBizJzRUScSQkG1gLekZkP9LCckiSph0YVAGTm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\nortSt7HPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x185049a1438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.FacetGrid(df_train, row='Married', col='Education', size=2.2, aspect=1.6)\n",
    "grid.map(plt.hist, 'ApplicantIncome', alpha=.5, bins=10)\n",
    "grid.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x18504d7e6d8>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/hDI5bDplctYd9YqPegVxCPDZiNiUchV2SNNrvwbslpmNYaPLI2I7yjDSREom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\nwDAsqXhdbJYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18504d24358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.FacetGrid(df_train, row='Self_Employed', col='Education', size=2.2, aspect=1.6)\n",
    "grid.map(plt.hist, 'ApplicantIncome', alpha=.5, bins=10)\n",
    "grid.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x18501732860>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AAACezgA5AAAc4xZxUQYAAGxF5iAHAAAAAGCSXEE+AYde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\nAGCSDJADAAAAADBJ/x8hq8YP847J1AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x185017325c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.FacetGrid(df_train, row='Married', col='Dependents', size=3.2, aspect=1.6)\n",
    "grid.map(plt.hist, 'ApplicantIncome', alpha=.5, bins=10)\n",
    "grid.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x18504e42be0>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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I57SrvhI4of0bPADYWd9AIe1M1mBr8E6twdOxBi8+Q2NjYwudQZp3Mc137C5l\n7YzTcpqZrldm5i0LHEnSALIGT84arMl4zrm0k0XERTTfxDDRRzLzLTs5zmNovk3iHF8UJC0F1mBV\n58y5JEmSVITnnEuSJElF2JxLkiRJRSzoOecbNmzu+ZyaVat2Y+PG+/oZpy+q5oK62armgrrZquaC\nutmq5oL+ZhsdHZnpB10eYA3euapmq5oL6marmgvqZlsquWZTg6eyaGfOh4dn89WwO0/VXFA3W9Vc\nUDdb1VxQN1vVXFA721SqZq6aC+pmq5oL6marmgvqZjNX9xZtcy5JkiQNGptzSZIkqQibc0mSJKkI\nm3NJkiSpCJtzSZIkqQibc0mSJKkIm3NJkiSpiAX9ESJJ0uJ04ZVfZ8uWrfO6jyPW7DWv40tSRc6c\nS5IkSUXYnEuSJElF2JxLkiRJRdicS5IkSUXYnEuSJElF2JxLkiRJRdicS5IkSUXYnEuSJElF2JxL\nkiRJRdicS5IkSUXYnEuSJElF2JxLkiRJRdicS5IkSUXYnEuSJElFDE93Y0Q8BDgPWA0sB9YB/wZc\nDnyjXe3dmXlRRBwLHA9sA9Zl5uXzFVqSJEkaRNM258BRwN2Z+dKIeDhwM/AW4MzMPGN8pYjYEzgR\n2B9YAVwfEZ/MzK3zlFuSJEkaODM15x8GLm4vD9HMiu8HREQcTjN7/lrgqcANbTO+NSLuAPYBvjgv\nqSVJkqQBNDQ2NjbjShExAlwGrKc5veWWzLwxIk4FVtHMqD8pM09u178AuCAzr55u3G3bto8NDy+b\n412QJHUY6nbFudTgC6/8ek/bzcaRhzxh3vchSX3WdQ2eykwz50TEY4FLgbMz88KIeFhm3tPefCnw\nTuAzwEjHZiPAPcxg48b7Zp+4NTo6woYNm3vefr5UzQV1s1XNBXWzVc0FdbNVzQX9zTY6OjLzSq25\n1GCALVvm98zFXh6TpfI891PVXFA3W9VcUDfbUsk1mxo8lWm/rSUiHgVcBZycmee1i6+MiKe2l58B\n3Ah8AVgTESsiYg9gb+DWOaeTJEmSlpCZZs5PoTlt5fSIOL1d9qfAOyLix8D3gOMyc1NEnAVcR9Pw\nn5qZ989XaEmSJGkQTducZ+ZrgNdMctNBk6y7nuacdEmSJEk98EeIJEmSpCJsziVJkqQibM4lSZKk\nImzOJUmSpCJsziVJkqQibM4lSZKkImzOJUmSpCJsziVJkqQibM4lSZKkImzOJUmSpCJsziVJkqQi\nbM4lSZIRrgjFAAATI0lEQVSkImzOJUmSpCJsziVJkqQibM4lSZKkImzOJUmSpCJsziVJkqQibM4l\nSZKkImzOJUmSpCJsziVJkqQibM4lSZKkImzOJUmSpCJsziVJkqQihqe7MSIeApwHrAaWA+uArwHn\nA2PArcAJmbkjIo4Fjge2Aesy8/L5iy1JkiQNnplmzo8C7s7MNcChwLuAM4HT2mVDwOERsSdwInAQ\ncAjwtohYPn+xJUmSpMEz7cw58GHg4vbyEM2s+H7Ate2yK4BnA9uBGzJzK7A1Iu4A9gG+2PfEkiRJ\n0oCatjnPzB8ARMQITZN+GvDXmTnWrrIZ2APYHbi3Y9Px5dNatWo3hoeX9RC7MTo60vO286lqLqib\nrWouqJutai6om61qLliYbHOtwStXzu8B0l4fE5/n2auaC+pmq5oL6mYzV3dmmjknIh4LXAqcnZkX\nRsRfdtw8AtwDbGovT1w+rY0b75td2g6joyNs2LC55+3nS9VcUDdb1VxQN1vVXFA3W9Vc0N9ss3mR\nmUsNBtiyZeuctp9JL4/JUnme+6lqLqibrWouqJttqeTqR6M/7TnnEfEo4Crg5Mw8r118U0SsbS8f\nBlwHfAFYExErImIPYG+aD4tKkiRJ6tJMM+enAKuA0yPi9HbZa4CzImJX4Dbg4szcHhFn0TTquwCn\nZub98xVakiRJGkQznXP+GppmfKKDJ1l3PbC+T7kkSZKkJccfIZIkSZKKsDmXJEmSirA5lyRJkoqw\nOZckSZKKsDmXJEmSirA5lyRJkoqwOZckSZKKsDmXJEmSirA5lyRJkoqwOZckSZKKsDmXJEmSirA5\nlyRJkoqwOZckSZKKsDmXJEmSirA5lyRJkoqwOZckSZKKsDmXJEmSirA5lyRJkoqwOZckSZKKsDmX\nJEmSirA5lyRJkoqwOZckSZKKsDmXJEmSihjuZqWIOAB4e2aujYh9gcuBb7Q3vzszL4qIY4HjgW3A\nusy8fF4SS5IkSQNqxuY8Ik4CXgpsaRftB5yZmWd0rLMncCKwP7ACuD4iPpmZW/sfWZIkSRpM3cyc\nfxN4PvCB9vp+QETE4TSz568Fngrc0DbjWyPiDmAf4Iv9jyxJkiQNphmb88y8JCJWdyz6AnBuZt4Y\nEacCbwRuBu7tWGczsMdMY69atRvDw8tml7jD6OhIz9vOp6q5oG62qrmgbraquaButqq5YGGyzbUG\nr1y5vI9pHqzXx8Tnefaq5oK62armgrrZzNWdrs45n+DSzLxn/DLwTuAzQOc9GwHumbjhRBs33tfD\n7hujoyNs2LC55+3nS9VcUDdb1VxQN1vVXFA3W9Vc0N9ss3mRmUsNBtiyZX7PXOzlMVkqz3M/Vc0F\ndbNVzQV1sy2VXP1o9Hv5tpYrI+Kp7eVnADfSzKaviYgVEbEHsDdw65zTSZIkSUtILzPnrwLeGRE/\nBr4HHJeZmyLiLOA6mob/1My8v485JUmSpIHXVXOemXcBB7aXvwwcNMk664H1/QwnSZIkLSX+CJEk\nSZJUhM25JEmSVITNuSRJklSEzbkkSZJUhM25JEmSVITNuSRJklSEzbkkSZJUhM25JEmSVITNuSRJ\nklSEzbkkSZJUhM25JEmSVITNuSRJklSEzbkkSZJUhM25JEmSVITNuSRJklSEzbkkSZJUhM25JEmS\nVITNuSRJklSEzbkkSZJUhM25JEmSVITNuSRJklSEzbkkSZJUhM25JEmSVITNuSRJklTEcDcrRcQB\nwNszc21E/BJwPjAG3AqckJk7IuJY4HhgG7AuMy+fp8ySJEnSQJpx5jwiTgLOBVa0i84ETsvMNcAQ\ncHhE7AmcCBwEHAK8LSKWz09kSZIkaTB1M3P+TeD5wAfa6/sB17aXrwCeDWwHbsjMrcDWiLgD2Af4\n4nQDr1q1G8PDy3rJDcDo6EjP286nqrmgbraquaButqq5oG62qrlgYbLNtQavXDm/czC9PiY+z7NX\nNRfUzVY1F9TNZq7uzNicZ+YlEbG6Y9FQZo61lzcDewC7A/d2rDO+fFobN97XfdIJRkdH2LBhc8/b\nz5equaButqq5oG62qrmgbraquaC/2WbzIjOXGgywZcvWOW0/k14ek6XyPPdT1VxQN1vVXFA321LJ\n1Y9Gv5cPhO7ouDwC3ANsai9PXC5JkiSpS7005zdFxNr28mHAdcAXgDURsSIi9gD2pvmwqCRJkqQu\ndfVtLRO8DlgfEbsCtwEXZ+b2iDiLplHfBTg1M+/vY05JkiRp4HXVnGfmXcCB7eXbgYMnWWc9sL6f\n4SRJkqSlxB8hkiRJkoqwOZckSZKKsDmXJEmSirA5lyRJkoqwOZckSZKKsDmXJEmSirA5lyRJkoqw\nOZckSZKKsDmXJEmSirA5lyRJkoqwOZckSZKKsDmXJEmSirA5lyRJkoqwOZckSZKKsDmXJEmSirA5\nlyRJkoqwOZckSZKKsDmXJEmSirA5lyRJkoqwOZckSZKKsDmXJEmSirA5lyRJkoqwOZckSZKKGO51\nw4j4MrCpvfot4K3A+cAYcCtwQmbumGtASZIkaanoqTmPiBXAUGau7Vh2GXBaZl4TEecAhwOX9iXl\nLHz0ujt39i4fcMSavRZs35IkSVr8ep05fzKwW0Rc1Y5xCrAfcG17+xXAs1mA5lySJElarHptzu8D\n/ho4F/hlmmZ8KDPH2ts3A3vMNMiqVbsxPLysxwgwOjryoGUrVy7veby5Gs8zWa4qqmarmgvqZqua\nC+pmq5oLFibbXGvwfNfbXh8Tn+fZq5oL6marmgvqZjNXd3ptzm8H7mib8dsj4m6amfNxI8A9Mw2y\nceN9Pe6+eSA3bNj8oOVbtmztecy52rBh85S5KqiarWouqJutai6om61qLuhvttm8yMylBsP819te\nHpOl8jz3U9VcUDdb1VxQN9tSydWPRr/Xb2s5BjgDICIeDewOXBURa9vbDwOum3M6SZIkaQnpdeb8\nvcD5EXE9zbezHAN8H1gfEbsCtwEX9yeiJEmStDT01Jxn5o+AIye56eC5xZEkSZKWrp6/51ySpPnU\ny1fjrly5vOtz4f36W0kV+QuhkiRJUhE255IkSVIRNueSJElSETbnkiRJUhE255IkSVIRNueSJElS\nETbnkiRJUhE255IkSVIRNueSJElSETbnkiRJUhE255IkSVIRNueSJElSETbnkiRJUhE255IkSVIR\nNueSJElSEcMLHUCSpIXw0evunNfxj1iz17yOL2kwOXMuSZIkFWFzLkmSJBVhcy5JkiQVYXMuSZIk\nFWFzLkmSJBVhcy5JkiQV0devUoyIXYCzgScDW4FXZOYd/dyHJEmLwcSvaly5cjlbtmzt6z78ukZp\n8PR75vwIYEVm/gbweuCMPo8vSZIkDax+/wjR04BPAGTm5yJi/z6PX9pHr7tzXmZGZrIUZ07m+8dD\nxk32fC7Fx3sp2ll/Y/DTf2f+famSnfnvYDL9eE2d739TC/0YTTTbx8yaU8/Q2NhY3waLiHOBSzLz\nivb6vwJ7Zea2vu1EkiRJGlD9Pq1lEzDSOb6NuSRJktSdfjfnNwDPAYiIA4Gv9nl8SZIkaWD1+5zz\nS4FnRcRngSHg6D6PL0mSJA2svp5zLkmSJKl3/giRJEmSVITNuSRJklREv885n3cL8SukEXEA8PbM\nXBsRvwScD4wBtwInZOaOiDgWOB7YBqzLzMsj4qHAB4FHApuBl2XmhvbDsn/brntVZr55lnkeApwH\nrAaWA+uAry10rjbbMmA9EG2WVwL3V8jW5nskcCPwrHasKrm+TPNtRwDfAt5aIVtE/P/A84Bdaf7d\nXVsk18uBl7dXVwBPofmdhb9ZyGztv8330/zb3A4cS6G/s7my/j6QyRo8QDW4av1ts5WrwVXrb5tt\nIGrwYpw536m/QhoRJwHn0vwBApwJnJaZa2g+9Hp4ROwJnAgcBBwCvC0ilgOvAr7arnsBcFo7xjnA\nkTR/zAdExL6zjHUUcHc77qHAu4rkAvhtgMw8qB33rVWytf9o/w74YbuoSq4VwFBmrm3/O7pCtohY\nC/xmu7+DgcdWyAWQmeePP140L/QnAm8okO05wHBm/ibwFgr9/feJ9bdhDR6QGly1/rbZ1lKwBheu\nvzAgNXgxNuc/9SukwHz/Cuk3ged3XN+P5p0rwBXAM4GnAjdk5tbMvBe4A9inM+v4uhGxO7A8M7+Z\nmWPAle0Ys/Fh4PT28hDNO7kKucjMjwLHtVd/HrinSjbgr2n+gX2nvV4l15OB3SLiqoj4VPsOvUK2\nQ2i+DvVS4GPA5UVyPSCaXyH+1cx8T5FstwPD7Qzz7sCPi+TqF+tvwxo8ODW4av2F4jW4YP2FAanB\ni7E53x24t+P69oiYt9NzMvMSmid33FD75EBzyGOPSTJNtrxz2aZJ1p1Nph9k5uaIGAEupnlnt+C5\nOvJti4j3A+8E/r5CtvYw3IbMvLJj8YLnat1H86J1CM0h6BKPGfAImubrhR25dimQq9MpwPjhxQqP\n2Q9oDqd+nebUgrOK5OqXJV9/21zW4MGpwVXrL9SvwdXqLwxIDV6MzflC/wrpjo7LIzSzEhMzTbZ8\npnVnJSIeC3wa+EBmXlgl17jMfBnweJp/HA8tkO0Ymu/gv4bm/LgLaM4pW+hc0LzT/2BmjmXm7cDd\nwKMKZLsbuDIzf5SZSXPeamdBWtC/s4h4GBCZ+el2UYV/A39C85g9nmZG7v0054oudK5+sf62rMED\nU4Or1l8oXIOL1l8YkBq8GJvzhf4V0pva88AADgOuA74ArImIFRGxB7A3zYcOHsg6vm5mbgJ+FBG/\nGBFDNO/Wr5tNgIh4FHAVcHJmnlclV5vtpdF8gAWaGYkdwJcWOltm/lZmHpzNOXI3A38AXLHQuVrH\n0J67GxGPpnmXflWBbNcDh0bEUJtrJfDPBXKN+y3gnzuuV/g3sJGfzLr8F/CQIrn6ZcnXX7AG95Kt\ncA2uWn+hdg2uWH9hQGrwovu2Fhb+V0hfB6yPiF2B24CLM3N7RJxF82TtApyamfdHxLuB90fE9cCP\naD5MAD85PLWM5lO/n59lhlOAVcDpETF+3uNrgLMWOBfAR4D3RcRnaP5RvLbNs9CP2WQqPJcA7wXO\nb8ceo3mx+P5CZ8vmk+u/RVPEdgFOoPkmgwqPGTTfRnFnx/UKz+c7gPMi4jqa2ZpTgC8VyNUv1t+G\nNXhwanDJ+gvla3DF+gsDUoP9hVBJkiSpiMV4WoskSZI0kGzOJUmSpCJsziVJkqQibM4lSZKkImzO\nJUmSpCIW41cpaoBFxBNpvjv5Be2vA/YyxvnANTTfQ3xuZj5n2g1mN/abgasz87qIeBNAZr6pX+NL\n0kKyBksLz5lzVXM0zc9hv3KuA2Xmd/r5otA6mOZ7TiVpEFmDpQXmzLnKiIhh4ChgDfDZiPjFzPxm\nRNwF/BPNL5IBHJOZN0XzM9C3AQcAK4DXZuZVHeOtBq7JzNUR8fPA+2h+Lvo+4BWZeUtEvBV4BvBw\nmh+eeH5mfi8ivkvzAvU0YBvwe22u/YFzI+J3JmS/hvYXx4BR4I8z84pp9ns0zY82jAE3An+UmT+I\niO8BH2vH+S5wNnAi8Bjg5Zl5bUT8EvBu4GfbMf84M2/q9XGXJLAGW4NVhTPnquS5wLcz83bgo8Dx\nHbf9V2buC7wBeH/H8uWZ+Ws0v+L1/vYXwCZzNnBJZj4ReBNwWltgnwD8ZmY+HrgD+P12/T2Bf273\n+Rmawn0BzS+NvSIzJ/vZ8l0z8zeAPwHWTbPfJwGnAgdn5pOALcAb2/UfBVyemU9or/9OZq5pt31t\nu+z9wEnt/T4O+Icp7rMkzYY12BqsAmzOVcnRwIfayxcBL+8o9O8ByMyPAY+JiEe0y9e3y2+mmeXY\nZ4qxDwY+0K778cz8vcy8g2bm5BURcQbwG8DPdGzzifb/t9LM6sxksvUftN922ccy8+6O+/aMjnGu\naP//beBTHZdXRcTPAL9O8/PcNwMXAj8TET/bRT5Jmo41uGEN1oLytBaVEBGPBJ4D7B8RrwGGgFXA\n77arbOtYfRdg+xTLO693+nHHvoaAvYGH0rwQnUlz+HR7u18AMvP+9uJY5/JpTLb+ZPud+KZ4iI5/\ni5n5o47bJt6fZcD9mfmUjnEfA/xXF/kkaVLWYGuw6nDmXFUcRXMI8zGZuTozfx54Kz85rPpigPY8\nw9syc+OE5fvTvJBMdqgTmsOiL24vP5NmpuRgmvMhzwG+BjybmT9otI3ZvamdbL/XAM+LiPGZnWOB\nT3czWGbeC3wjIo4CiIhntfuQpLmwBnfBGqydwZlzVXE0cMqEZWcDJwGbgIMi4g9pzg18Wcc6e0XE\nl9vLL8rM7REx2fh/RPMholfTfigIuBf4SETcQjO7cgvwCzPk/ARwTkT8QZf360H7zcyvRcTbgGsj\n4iE0H0aazTcj/H6b4STgRzT3e2wW20vSRNbg7lmDNa+Gxsb8e1Jt7TcFrM3MuyYsvwZ4U2Zes9ND\nSdISYQ2Wdi5Pa5EkSZKKcOZckiRJKsKZc0mSJKkIm3NJkiSpCJtzSZIkqQibc0mSJKkIm3NJkiSp\nCJtzSZIkqYj/B09/F+VvTojzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1850548b748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.FacetGrid(df_train, row='Married', col='Credit_History', size=3.2, aspect=1.6)\n",
    "grid.map(plt.hist, 'ApplicantIncome', alpha=.5, bins=10)\n",
    "grid.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x185050f6b00>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EPLZl/ZyYjz4S7jeUg96PgAsy85LMvIAyDH41ZdtdR+enHNwMPCYivgiQmb8A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\n+EuSJEkNYOIvSZIkNcD/B8C6/hf9mHVzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x185050f6550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.FacetGrid(df_train, row='Credit_History', col='Property_Area', size=2.2, aspect=1.6)\n",
    "grid.map(plt.hist, 'ApplicantIncome', alpha=.5, bins=10)\n",
    "grid.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1850548b668>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KzAXAAHBARGwJHA/sAewLnBoRm0xMkyVJ0nh1cwrgl8ArWl7vDFxZpy8D9gZ2Ba7KzNWZ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18505319160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.FacetGrid(df_train, row='Education', col='Credit_History', size=2.2, aspect=1.6)\n",
    "grid.map(plt.hist, 'ApplicantIncome', alpha=.5, bins=10)\n",
    "grid.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x18501739a20>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2vpsa363TXB8RXwLOjIgtM3Mpy8fkKm2TPFbVa7z4rotxapw2Lk4jYj3gE5n5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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18501739ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.FacetGrid(df_train, row='Credit_History', col='Property_Area', size=2.2, aspect=1.6)\n",
    "grid.map(plt.hist, 'LoanAmount', alpha=.5, bins=10)\n",
    "grid.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1850276dfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Twl5E9lDT3NMmsuaufEuDlyJBZEqz225w3XVaBqTaKXGIFEls81s7h5ri3Ws2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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1850276d860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#histogram and normal probability plot\n",
    "sns.distplot(df_train['ApplicantIncome'], fit=norm);\n",
    "fig = plt.figure()\n",
    "res = stats.probplot(df_train['ApplicantIncome'], plot=plt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IErBlIqx41NPeW/E1O5SUuIvI/ClAT1jFEnd1cRcRERGRJWDz5GYAetrryaAr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\nZyMt7jOooDuOQzQc0hl0ESkrTspL0Gd7Bh0g29KiNWsiItPkui7dQ10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HOGHFAvZ2DdDV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h3irb1LqaypoKKmovF/VW0Vp2tPMSh7MGP6jeXBiQ9zxcArz8mnrd+j8HIt3rWIR5d+mcOnSkgL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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18502fafd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1850482a4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "flg, axes = plt.subplots(nrows = 1, ncols = 3, figsize = (14,6))\n",
    "\n",
    "sns.distplot(df_train['ApplicantIncome'], ax = axes[0]).set_title('ApplicantIncome Distribution')\n",
    "axes[0].set_ylabel('ApplicantIncomee Count')\n",
    "\n",
    "sns.distplot(df_train['CoapplicantIncome'], color = \"r\", ax = axes[1]).set_title('CoapplicantIncome Distribution')\n",
    "axes[1].set_ylabel('CoapplicantIncome Count')\n",
    "\n",
    "sns.distplot(df_train['LoanAmount'],color = \"g\", ax = axes[2]).set_title('LoanAmount Distribution')\n",
    "axes[2].set_ylabel('LoanAmount Count')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "plt.gcf().clear()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x18502d97ac8>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jO4FhY8z9wC+A0Fo7VOdxHzHG3Ad8DCgbY/JV2/8b8GtxxX43sLWei1EzeIt8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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18502d97358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.FacetGrid(df_train, hue=\"Gender\", size=6) \\\n",
    "   .map(sns.kdeplot, \"ApplicantIncome\") \\\n",
    "   .add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x18502e08f98>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bHo1nlPJL0QHbZhKEIVFS6fYP7ZnTsQebqdpNaqO2q4/arT4L0W6tFvK1dC/f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\nMzOzjvwFByE9EslT0NAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18502d7d780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.FacetGrid(df_train, hue=\"Married\", size=6) \\\n",
    "   .map(sns.kdeplot, \"ApplicantIncome\") \\\n",
    "   .add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x18502dba978>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bHo1nlPJL0QHbZhKEIVFS6fYP7ZnTsQebqdpNaqO2q4/arT4L0W6tFvK1dC/f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\nMzOzjvwFByE9EslT0NAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18502efc7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.FacetGrid(df_train, hue=\"Married\", size=6) \\\n",
    "   .map(sns.kdeplot, \"ApplicantIncome\") \\\n",
    "   .add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Loan_ID', 'Gender', 'Married', 'Dependents', 'Education',\n",
       "       'Self_Employed', 'ApplicantIncome', 'CoapplicantIncome', 'LoanAmount',\n",
       "       'Loan_Amount_Term', 'Credit_History', 'Property_Area', 'Loan_Status'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NMW/GHbv/hPu3rtGJpMgDmnrGRR543gQcSv+7HvdL61PAs+oGw82yblaJ38f1YP4SN0XY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\nEREZkv8H2xjcSqDS6G0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18505d38e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "var_name = \"CoapplicantIncome\"\n",
    "plt.figure(figsize=(12,6))\n",
    "sns.regplot(x=var_name, y='ApplicantIncome', data=df_train, scatter_kws={'alpha':0.5, 's':30})\n",
    "plt.xlabel(var_name, fontsize=12)\n",
    "plt.ylabel('ApplicantIncome', fontsize=12)\n",
    "plt.title(\"Distribution of y variable with \"+var_name, fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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A54EYbveSm5sYh+wAswu5cuAuKRQ9ZhdyDA+k2jQqERHZyQ6M9rFvOEMynSS3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\nkCmVpZyYyfLI2Nyqeup6Njv7Xb1PdiI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apBgGJHHCSr1DvR1Cque9F/N5gjAmilPiBGyL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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18505e32080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "var_name = \"LoanAmount\"\n",
    "plt.figure(figsize=(12,6))\n",
    "sns.regplot(x=var_name, y='ApplicantIncome', data=df_train, scatter_kws={'alpha':0.5, 's':30})\n",
    "plt.xlabel(var_name, fontsize=12)\n",
    "plt.ylabel('ApplicantIncome', fontsize=12)\n",
    "plt.title(\"Distribution of y variable with \"+var_name, fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JkqTCplvh8pCiJEmaMttwdYmIfcaMv6W++8meRyRJkjRgJqxwRcRrgV2A+RGx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\ncEmSJBVmwiVJklTY/wdvkDSUxZYEKgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x185049a1ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#correlation matrix\n",
    "corrmat = df_train.corr()\n",
    "f, ax = plt.subplots(figsize=(12, 9))\n",
    "sns.heatmap(corrmat, vmax=.8, square=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kwJI041XVzwGSLKAJWitphgpeWFW3JlkBvIemZ2vDhLduAhbu7rMXLTqAefPmDqRuSbu3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\nSZIkSVJHDFiSJEmS1BEDliRJkiR15P8HeAb6kg+wOisAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18502fb50f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18502fc7fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=3,figsize=(12,6))\n",
    "\n",
    "g = sns.countplot(df_train[\"Gender\"], ax=axes[0])\n",
    "plt.setp(g.get_xticklabels(), rotation=90)\n",
    "g = sns.countplot(df_train[\"Married\"], ax=axes[1])\n",
    "plt.setp(g.get_xticklabels(), rotation=90)\n",
    "g = sns.countplot(df_train[\"Dependents\"], ax=axes[2])\n",
    "plt.setp(g.get_xticklabels(), rotation=90)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "plt.gcf().clear()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SiPPzjaRB2toQwROAPYHTgDfNOL6R5gOLJHVlm9ubqroDeM5mTh24mceuopnSXdLC5ecb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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18502fbcef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x185041419b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=3,figsize=(12,6))\n",
    "\n",
    "g = sns.countplot(df_train[\"Education\"], ax=axes[0])\n",
    "plt.setp(g.get_xticklabels(), rotation=90)\n",
    "g = sns.countplot(df_train[\"Self_Employed\"], ax=axes[1])\n",
    "plt.setp(g.get_xticklabels(), rotation=90)\n",
    "g = sns.countplot(df_train[\"Property_Area\"], ax=axes[2])\n",
    "plt.setp(g.get_xticklabels(), rotation=90)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "plt.gcf().clear()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
      "count       614.000000         614.000000  614.000000        614.000000   \n",
      "mean       5403.459283        1621.245798  146.412162        342.410423   \n",
      "std        6109.041673        2926.248369   84.037468         64.428629   \n",
      "min         150.000000           0.000000    9.000000         12.000000   \n",
      "25%        2877.500000           0.000000  100.250000        360.000000   \n",
      "50%        3812.500000        1188.500000  129.000000        360.000000   \n",
      "75%        5795.000000        2297.250000  164.750000        360.000000   \n",
      "max       81000.000000       41667.000000  700.000000        480.000000   \n",
      "\n",
      "       Credit_History  \n",
      "count      614.000000  \n",
      "mean         0.855049  \n",
      "std          0.352339  \n",
      "min          0.000000  \n",
      "25%          1.000000  \n",
      "50%          1.000000  \n",
      "75%          1.000000  \n",
      "max          1.000000  \n"
     ]
    }
   ],
   "source": [
    "print(df_train.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(614, 13)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Loan_ID', 'Gender', 'Married', 'Dependents', 'Education',\n",
       "       'Self_Employed', 'ApplicantIncome', 'CoapplicantIncome', 'LoanAmount',\n",
       "       'Loan_Amount_Term', 'Credit_History', 'Property_Area', 'Loan_Status'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>5573.017928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Female</td>\n",
       "      <td>4643.473214</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender  ApplicantIncome\n",
       "1    Male      5573.017928\n",
       "0  Female      4643.473214"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train[['Gender', 'ApplicantIncome']].groupby(['Gender'],\n",
    "                                               as_index = False).mean().sort_values(by = 'Gender', ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Married</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Yes</td>\n",
       "      <td>5633.496259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No</td>\n",
       "      <td>4970.384977</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Married  ApplicantIncome\n",
       "1     Yes      5633.496259\n",
       "0      No      4970.384977"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train[['Married', 'ApplicantIncome']].groupby(['Married'],\n",
    "                                               as_index = False).mean().sort_values(by = 'Married', ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>Dependents</th>\n",
       "      <th>ApplicantIncome</th>\n",
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       "      <th>2</th>\n",
       "      <td>2</td>\n",
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      "text/plain": [
       "  Dependents  ApplicantIncome\n",
       "3         3+      8581.215686\n",
       "2          2      4926.782178\n",
       "1          1      5962.274510\n",
       "0          0      4928.680556"
      ]
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     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train[['Dependents', 'ApplicantIncome']].groupby(['Dependents'],\n",
    "                                               as_index = False).mean().sort_values(by = 'Dependents', ascending = False)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Education</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>3777.283582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Graduate</td>\n",
       "      <td>5857.433333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Education  ApplicantIncome\n",
       "1  Not Graduate      3777.283582\n",
       "0      Graduate      5857.433333"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train[['Education', 'ApplicantIncome']].groupby(['Education'],\n",
    "                                               as_index = False).mean().sort_values(by = 'Education', ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
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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>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Yes</td>\n",
       "      <td>7380.817073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No</td>\n",
       "      <td>5098.678571</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Self_Employed  ApplicantIncome\n",
       "1           Yes      7380.817073\n",
       "0            No      5098.678571"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train[['Self_Employed', 'ApplicantIncome']].groupby(['Self_Employed'],\n",
    "                                               as_index = False).mean().sort_values(by = 'Self_Employed', ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>5251.045113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>5689.067751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>4503.787500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4992.687500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender Married  ApplicantIncome\n",
       "2    Male      No      5251.045113\n",
       "3    Male     Yes      5689.067751\n",
       "0  Female      No      4503.787500\n",
       "1  Female     Yes      4992.687500"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train[['Gender', 'Married', 'ApplicantIncome']].groupby(['Gender', 'Married'],\n",
    "                                               as_index = False).mean().sort_values(by = 'Gender', \n",
    "                                                                                          ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "0.18241042345276873"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.groupby(['Gender']).size()[0]/(df_train.groupby(['Gender']).size()[0] + \n",
    "                                        df_train.groupby(['Gender']).size()[1])    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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