First Place EDA done.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd \n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (307511, 122)\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th>TARGET</th>\n",
       "      <th>NAME_CONTRACT_TYPE</th>\n",
       "      <th>CODE_GENDER</th>\n",
       "      <th>FLAG_OWN_CAR</th>\n",
       "      <th>FLAG_OWN_REALTY</th>\n",
       "      <th>CNT_CHILDREN</th>\n",
       "      <th>AMT_INCOME_TOTAL</th>\n",
       "      <th>AMT_CREDIT</th>\n",
       "      <th>AMT_ANNUITY</th>\n",
       "      <th>...</th>\n",
       "      <th>FLAG_DOCUMENT_18</th>\n",
       "      <th>FLAG_DOCUMENT_19</th>\n",
       "      <th>FLAG_DOCUMENT_20</th>\n",
       "      <th>FLAG_DOCUMENT_21</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_HOUR</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_DAY</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_WEEK</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_MON</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_QRT</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_YEAR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100002</td>\n",
       "      <td>1</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>M</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>202500.0</td>\n",
       "      <td>406597.5</td>\n",
       "      <td>24700.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100003</td>\n",
       "      <td>0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>270000.0</td>\n",
       "      <td>1293502.5</td>\n",
       "      <td>35698.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100004</td>\n",
       "      <td>0</td>\n",
       "      <td>Revolving loans</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>67500.0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>6750.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100006</td>\n",
       "      <td>0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>312682.5</td>\n",
       "      <td>29686.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100007</td>\n",
       "      <td>0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>M</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>121500.0</td>\n",
       "      <td>513000.0</td>\n",
       "      <td>21865.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 122 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_CURR  TARGET NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR  \\\n",
       "0      100002       1         Cash loans           M            N   \n",
       "1      100003       0         Cash loans           F            N   \n",
       "2      100004       0    Revolving loans           M            Y   \n",
       "3      100006       0         Cash loans           F            N   \n",
       "4      100007       0         Cash loans           M            N   \n",
       "\n",
       "  FLAG_OWN_REALTY  CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  AMT_ANNUITY  \\\n",
       "0               Y             0          202500.0    406597.5      24700.5   \n",
       "1               N             0          270000.0   1293502.5      35698.5   \n",
       "2               Y             0           67500.0    135000.0       6750.0   \n",
       "3               Y             0          135000.0    312682.5      29686.5   \n",
       "4               Y             0          121500.0    513000.0      21865.5   \n",
       "\n",
       "              ...              FLAG_DOCUMENT_18 FLAG_DOCUMENT_19  \\\n",
       "0             ...                             0                0   \n",
       "1             ...                             0                0   \n",
       "2             ...                             0                0   \n",
       "3             ...                             0                0   \n",
       "4             ...                             0                0   \n",
       "\n",
       "  FLAG_DOCUMENT_20 FLAG_DOCUMENT_21 AMT_REQ_CREDIT_BUREAU_HOUR  \\\n",
       "0                0                0                        0.0   \n",
       "1                0                0                        0.0   \n",
       "2                0                0                        0.0   \n",
       "3                0                0                        NaN   \n",
       "4                0                0                        0.0   \n",
       "\n",
       "  AMT_REQ_CREDIT_BUREAU_DAY  AMT_REQ_CREDIT_BUREAU_WEEK  \\\n",
       "0                       0.0                         0.0   \n",
       "1                       0.0                         0.0   \n",
       "2                       0.0                         0.0   \n",
       "3                       NaN                         NaN   \n",
       "4                       0.0                         0.0   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_MON  AMT_REQ_CREDIT_BUREAU_QRT  \\\n",
       "0                        0.0                        0.0   \n",
       "1                        0.0                        0.0   \n",
       "2                        0.0                        0.0   \n",
       "3                        NaN                        NaN   \n",
       "4                        0.0                        0.0   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_YEAR  \n",
       "0                         1.0  \n",
       "1                         0.0  \n",
       "2                         0.0  \n",
       "3                         NaN  \n",
       "4                         0.0  \n",
       "\n",
       "[5 rows x 122 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "app_train = pd.read_csv('application_train.csv')\n",
    "print('Training data shape: ', app_train.shape)\n",
    "app_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Classification: The label is a binary variable, 0 (will repay loan on time), 1 (will have difficulty repaying loan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing data shape:  (48744, 121)\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th>NAME_CONTRACT_TYPE</th>\n",
       "      <th>CODE_GENDER</th>\n",
       "      <th>FLAG_OWN_CAR</th>\n",
       "      <th>FLAG_OWN_REALTY</th>\n",
       "      <th>CNT_CHILDREN</th>\n",
       "      <th>AMT_INCOME_TOTAL</th>\n",
       "      <th>AMT_CREDIT</th>\n",
       "      <th>AMT_ANNUITY</th>\n",
       "      <th>AMT_GOODS_PRICE</th>\n",
       "      <th>...</th>\n",
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       "      <th>FLAG_DOCUMENT_19</th>\n",
       "      <th>FLAG_DOCUMENT_20</th>\n",
       "      <th>FLAG_DOCUMENT_21</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_HOUR</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_DAY</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_WEEK</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_MON</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100001</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>568800.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100005</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>M</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>99000.0</td>\n",
       "      <td>222768.0</td>\n",
       "      <td>17370.0</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100013</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>202500.0</td>\n",
       "      <td>663264.0</td>\n",
       "      <td>69777.0</td>\n",
       "      <td>630000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100028</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>2</td>\n",
       "      <td>315000.0</td>\n",
       "      <td>1575000.0</td>\n",
       "      <td>49018.5</td>\n",
       "      <td>1575000.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100038</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>625500.0</td>\n",
       "      <td>32067.0</td>\n",
       "      <td>625500.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 121 columns</p>\n",
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      ],
      "text/plain": [
       "   SK_ID_CURR NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR FLAG_OWN_REALTY  \\\n",
       "0      100001         Cash loans           F            N               Y   \n",
       "1      100005         Cash loans           M            N               Y   \n",
       "2      100013         Cash loans           M            Y               Y   \n",
       "3      100028         Cash loans           F            N               Y   \n",
       "4      100038         Cash loans           M            Y               N   \n",
       "\n",
       "   CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  AMT_ANNUITY  AMT_GOODS_PRICE  \\\n",
       "0             0          135000.0    568800.0      20560.5         450000.0   \n",
       "1             0           99000.0    222768.0      17370.0         180000.0   \n",
       "2             0          202500.0    663264.0      69777.0         630000.0   \n",
       "3             2          315000.0   1575000.0      49018.5        1575000.0   \n",
       "4             1          180000.0    625500.0      32067.0         625500.0   \n",
       "\n",
       "              ...             FLAG_DOCUMENT_18 FLAG_DOCUMENT_19  \\\n",
       "0             ...                            0                0   \n",
       "1             ...                            0                0   \n",
       "2             ...                            0                0   \n",
       "3             ...                            0                0   \n",
       "4             ...                            0                0   \n",
       "\n",
       "  FLAG_DOCUMENT_20 FLAG_DOCUMENT_21 AMT_REQ_CREDIT_BUREAU_HOUR  \\\n",
       "0                0                0                        0.0   \n",
       "1                0                0                        0.0   \n",
       "2                0                0                        0.0   \n",
       "3                0                0                        0.0   \n",
       "4                0                0                        NaN   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_DAY  AMT_REQ_CREDIT_BUREAU_WEEK  \\\n",
       "0                        0.0                         0.0   \n",
       "1                        0.0                         0.0   \n",
       "2                        0.0                         0.0   \n",
       "3                        0.0                         0.0   \n",
       "4                        NaN                         NaN   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_MON  AMT_REQ_CREDIT_BUREAU_QRT  \\\n",
       "0                        0.0                        0.0   \n",
       "1                        0.0                        0.0   \n",
       "2                        0.0                        1.0   \n",
       "3                        0.0                        0.0   \n",
       "4                        NaN                        NaN   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_YEAR  \n",
       "0                         0.0  \n",
       "1                         3.0  \n",
       "2                         4.0  \n",
       "3                         3.0  \n",
       "4                         NaN  \n",
       "\n",
       "[5 rows x 121 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Testing data features\n",
    "app_test = pd.read_csv('application_test.csv')\n",
    "print('Testing data shape: ', app_test.shape)\n",
    "app_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    282686\n",
       "1     24825\n",
       "Name: TARGET, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Examine the Distribution of the Target Column\n",
    "# The target is what we are asked to predict: either a 0 for the loan was repaid on time, \n",
    "# or a 1 indicating the client had payment difficulties. We can first examine the number of loans falling into each category.\n",
    "\n",
    "app_train['TARGET'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x283bb12f940>"
      ]
     },
     "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": [
    "app_train['TARGET'].astype(int).plot.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Examine Missing Values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Function to calculate missing values by column# Funct \n",
    "def missing_values_table(df):\n",
    "        # Total missing values\n",
    "        mis_val = df.isnull().sum()\n",
    "        \n",
    "        # Percentage of missing values\n",
    "        mis_val_percent = 100 * df.isnull().sum() / len(df)\n",
    "        \n",
    "        # Make a table with the results\n",
    "        mis_val_table = pd.concat([mis_val, mis_val_percent], axis=1)\n",
    "        # Rename the columns\n",
    "        mis_val_table_ren_columns = mis_val_table.rename(\n",
    "        columns = {0 : 'Missing Values', 1 : '% of Total Values'})\n",
    "        \n",
    "        # Sort the table by percentage of missing descending\n",
    "        mis_val_table_ren_columns = mis_val_table_ren_columns[\n",
    "            mis_val_table_ren_columns.iloc[:,1] != 0].sort_values(\n",
    "        '% of Total Values', ascending=False).round(1)\n",
    "        \n",
    "        # Print some summary information\n",
    "        print (\"Your selected dataframe has \" + str(df.shape[1]) + \" columns.\\n\"      \n",
    "            \"There are \" + str(mis_val_table_ren_columns.shape[0]) +\n",
    "              \" columns that have missing values.\")\n",
    "        \n",
    "        # Return the dataframe with missing information\n",
    "        return mis_val_table_ren_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your selected dataframe has 122 columns.\n",
      "There are 67 columns that have missing values.\n"
     ]
    },
    {
     "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>Missing Values</th>\n",
       "      <th>% of Total Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>COMMONAREA_MEDI</th>\n",
       "      <td>214865</td>\n",
       "      <td>69.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COMMONAREA_AVG</th>\n",
       "      <td>214865</td>\n",
       "      <td>69.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COMMONAREA_MODE</th>\n",
       "      <td>214865</td>\n",
       "      <td>69.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NONLIVINGAPARTMENTS_MEDI</th>\n",
       "      <td>213514</td>\n",
       "      <td>69.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NONLIVINGAPARTMENTS_MODE</th>\n",
       "      <td>213514</td>\n",
       "      <td>69.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NONLIVINGAPARTMENTS_AVG</th>\n",
       "      <td>213514</td>\n",
       "      <td>69.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FONDKAPREMONT_MODE</th>\n",
       "      <td>210295</td>\n",
       "      <td>68.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIVINGAPARTMENTS_MODE</th>\n",
       "      <td>210199</td>\n",
       "      <td>68.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIVINGAPARTMENTS_MEDI</th>\n",
       "      <td>210199</td>\n",
       "      <td>68.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIVINGAPARTMENTS_AVG</th>\n",
       "      <td>210199</td>\n",
       "      <td>68.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FLOORSMIN_MODE</th>\n",
       "      <td>208642</td>\n",
       "      <td>67.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FLOORSMIN_MEDI</th>\n",
       "      <td>208642</td>\n",
       "      <td>67.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FLOORSMIN_AVG</th>\n",
       "      <td>208642</td>\n",
       "      <td>67.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YEARS_BUILD_MODE</th>\n",
       "      <td>204488</td>\n",
       "      <td>66.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YEARS_BUILD_MEDI</th>\n",
       "      <td>204488</td>\n",
       "      <td>66.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YEARS_BUILD_AVG</th>\n",
       "      <td>204488</td>\n",
       "      <td>66.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OWN_CAR_AGE</th>\n",
       "      <td>202929</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LANDAREA_AVG</th>\n",
       "      <td>182590</td>\n",
       "      <td>59.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LANDAREA_MEDI</th>\n",
       "      <td>182590</td>\n",
       "      <td>59.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LANDAREA_MODE</th>\n",
       "      <td>182590</td>\n",
       "      <td>59.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BASEMENTAREA_MEDI</th>\n",
       "      <td>179943</td>\n",
       "      <td>58.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BASEMENTAREA_AVG</th>\n",
       "      <td>179943</td>\n",
       "      <td>58.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BASEMENTAREA_MODE</th>\n",
       "      <td>179943</td>\n",
       "      <td>58.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EXT_SOURCE_1</th>\n",
       "      <td>173378</td>\n",
       "      <td>56.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NONLIVINGAREA_MEDI</th>\n",
       "      <td>169682</td>\n",
       "      <td>55.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NONLIVINGAREA_MODE</th>\n",
       "      <td>169682</td>\n",
       "      <td>55.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NONLIVINGAREA_AVG</th>\n",
       "      <td>169682</td>\n",
       "      <td>55.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ELEVATORS_MEDI</th>\n",
       "      <td>163891</td>\n",
       "      <td>53.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ELEVATORS_MODE</th>\n",
       "      <td>163891</td>\n",
       "      <td>53.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ELEVATORS_AVG</th>\n",
       "      <td>163891</td>\n",
       "      <td>53.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIVINGAREA_MEDI</th>\n",
       "      <td>154350</td>\n",
       "      <td>50.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIVINGAREA_MODE</th>\n",
       "      <td>154350</td>\n",
       "      <td>50.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIVINGAREA_AVG</th>\n",
       "      <td>154350</td>\n",
       "      <td>50.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOUSETYPE_MODE</th>\n",
       "      <td>154297</td>\n",
       "      <td>50.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FLOORSMAX_MEDI</th>\n",
       "      <td>153020</td>\n",
       "      <td>49.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FLOORSMAX_AVG</th>\n",
       "      <td>153020</td>\n",
       "      <td>49.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FLOORSMAX_MODE</th>\n",
       "      <td>153020</td>\n",
       "      <td>49.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YEARS_BEGINEXPLUATATION_AVG</th>\n",
       "      <td>150007</td>\n",
       "      <td>48.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YEARS_BEGINEXPLUATATION_MEDI</th>\n",
       "      <td>150007</td>\n",
       "      <td>48.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YEARS_BEGINEXPLUATATION_MODE</th>\n",
       "      <td>150007</td>\n",
       "      <td>48.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOTALAREA_MODE</th>\n",
       "      <td>148431</td>\n",
       "      <td>48.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EMERGENCYSTATE_MODE</th>\n",
       "      <td>145755</td>\n",
       "      <td>47.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OCCUPATION_TYPE</th>\n",
       "      <td>96391</td>\n",
       "      <td>31.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EXT_SOURCE_3</th>\n",
       "      <td>60965</td>\n",
       "      <td>19.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_WEEK</th>\n",
       "      <td>41519</td>\n",
       "      <td>13.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_DAY</th>\n",
       "      <td>41519</td>\n",
       "      <td>13.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_MON</th>\n",
       "      <td>41519</td>\n",
       "      <td>13.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_QRT</th>\n",
       "      <td>41519</td>\n",
       "      <td>13.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_HOUR</th>\n",
       "      <td>41519</td>\n",
       "      <td>13.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_YEAR</th>\n",
       "      <td>41519</td>\n",
       "      <td>13.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAME_TYPE_SUITE</th>\n",
       "      <td>1292</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEF_30_CNT_SOCIAL_CIRCLE</th>\n",
       "      <td>1021</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OBS_60_CNT_SOCIAL_CIRCLE</th>\n",
       "      <td>1021</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEF_60_CNT_SOCIAL_CIRCLE</th>\n",
       "      <td>1021</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OBS_30_CNT_SOCIAL_CIRCLE</th>\n",
       "      <td>1021</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EXT_SOURCE_2</th>\n",
       "      <td>660</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_GOODS_PRICE</th>\n",
       "      <td>278</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_ANNUITY</th>\n",
       "      <td>12</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CNT_FAM_MEMBERS</th>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAYS_LAST_PHONE_CHANGE</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>67 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Missing Values  % of Total Values\n",
       "COMMONAREA_MEDI                       214865               69.9\n",
       "COMMONAREA_AVG                        214865               69.9\n",
       "COMMONAREA_MODE                       214865               69.9\n",
       "NONLIVINGAPARTMENTS_MEDI              213514               69.4\n",
       "NONLIVINGAPARTMENTS_MODE              213514               69.4\n",
       "NONLIVINGAPARTMENTS_AVG               213514               69.4\n",
       "FONDKAPREMONT_MODE                    210295               68.4\n",
       "LIVINGAPARTMENTS_MODE                 210199               68.4\n",
       "LIVINGAPARTMENTS_MEDI                 210199               68.4\n",
       "LIVINGAPARTMENTS_AVG                  210199               68.4\n",
       "FLOORSMIN_MODE                        208642               67.8\n",
       "FLOORSMIN_MEDI                        208642               67.8\n",
       "FLOORSMIN_AVG                         208642               67.8\n",
       "YEARS_BUILD_MODE                      204488               66.5\n",
       "YEARS_BUILD_MEDI                      204488               66.5\n",
       "YEARS_BUILD_AVG                       204488               66.5\n",
       "OWN_CAR_AGE                           202929               66.0\n",
       "LANDAREA_AVG                          182590               59.4\n",
       "LANDAREA_MEDI                         182590               59.4\n",
       "LANDAREA_MODE                         182590               59.4\n",
       "BASEMENTAREA_MEDI                     179943               58.5\n",
       "BASEMENTAREA_AVG                      179943               58.5\n",
       "BASEMENTAREA_MODE                     179943               58.5\n",
       "EXT_SOURCE_1                          173378               56.4\n",
       "NONLIVINGAREA_MEDI                    169682               55.2\n",
       "NONLIVINGAREA_MODE                    169682               55.2\n",
       "NONLIVINGAREA_AVG                     169682               55.2\n",
       "ELEVATORS_MEDI                        163891               53.3\n",
       "ELEVATORS_MODE                        163891               53.3\n",
       "ELEVATORS_AVG                         163891               53.3\n",
       "...                                      ...                ...\n",
       "LIVINGAREA_MEDI                       154350               50.2\n",
       "LIVINGAREA_MODE                       154350               50.2\n",
       "LIVINGAREA_AVG                        154350               50.2\n",
       "HOUSETYPE_MODE                        154297               50.2\n",
       "FLOORSMAX_MEDI                        153020               49.8\n",
       "FLOORSMAX_AVG                         153020               49.8\n",
       "FLOORSMAX_MODE                        153020               49.8\n",
       "YEARS_BEGINEXPLUATATION_AVG           150007               48.8\n",
       "YEARS_BEGINEXPLUATATION_MEDI          150007               48.8\n",
       "YEARS_BEGINEXPLUATATION_MODE          150007               48.8\n",
       "TOTALAREA_MODE                        148431               48.3\n",
       "EMERGENCYSTATE_MODE                   145755               47.4\n",
       "OCCUPATION_TYPE                        96391               31.3\n",
       "EXT_SOURCE_3                           60965               19.8\n",
       "AMT_REQ_CREDIT_BUREAU_WEEK             41519               13.5\n",
       "AMT_REQ_CREDIT_BUREAU_DAY              41519               13.5\n",
       "AMT_REQ_CREDIT_BUREAU_MON              41519               13.5\n",
       "AMT_REQ_CREDIT_BUREAU_QRT              41519               13.5\n",
       "AMT_REQ_CREDIT_BUREAU_HOUR             41519               13.5\n",
       "AMT_REQ_CREDIT_BUREAU_YEAR             41519               13.5\n",
       "NAME_TYPE_SUITE                         1292                0.4\n",
       "DEF_30_CNT_SOCIAL_CIRCLE                1021                0.3\n",
       "OBS_60_CNT_SOCIAL_CIRCLE                1021                0.3\n",
       "DEF_60_CNT_SOCIAL_CIRCLE                1021                0.3\n",
       "OBS_30_CNT_SOCIAL_CIRCLE                1021                0.3\n",
       "EXT_SOURCE_2                             660                0.2\n",
       "AMT_GOODS_PRICE                          278                0.1\n",
       "AMT_ANNUITY                               12                0.0\n",
       "CNT_FAM_MEMBERS                            2                0.0\n",
       "DAYS_LAST_PHONE_CHANGE                     1                0.0\n",
       "\n",
       "[67 rows x 2 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Missing values statistics\n",
    "missing_values = missing_values_table(app_train)\n",
    "missing_values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Column Types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float64    65\n",
       "int64      41\n",
       "object     16\n",
       "dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of each type of column\n",
    "app_train.dtypes.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Thus, we get the intuition that there are nearly 65 float , 41 int and 16 categorical/strings columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NAME_CONTRACT_TYPE             2\n",
       "CODE_GENDER                    3\n",
       "FLAG_OWN_CAR                   2\n",
       "FLAG_OWN_REALTY                2\n",
       "NAME_TYPE_SUITE                7\n",
       "NAME_INCOME_TYPE               8\n",
       "NAME_EDUCATION_TYPE            5\n",
       "NAME_FAMILY_STATUS             6\n",
       "NAME_HOUSING_TYPE              6\n",
       "OCCUPATION_TYPE               18\n",
       "WEEKDAY_APPR_PROCESS_START     7\n",
       "ORGANIZATION_TYPE             58\n",
       "FONDKAPREMONT_MODE             4\n",
       "HOUSETYPE_MODE                 3\n",
       "WALLSMATERIAL_MODE             7\n",
       "EMERGENCYSTATE_MODE            2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "app_train.select_dtypes('object').apply(pd.Series.nunique, axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 columns were label encoded.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "le = LabelEncoder()\n",
    "le_count = 0\n",
    "\n",
    "# Iterate through the columns\n",
    "for col in app_train:\n",
    "    if app_train[col].dtype == 'object':\n",
    "        # If 2 or fewer unique categories\n",
    "        if len(list(app_train[col].unique())) <= 2:\n",
    "            # Train on the training data\n",
    "            le.fit(app_train[col])\n",
    "            # Transform both training and testing data\n",
    "            app_train[col] = le.transform(app_train[col])\n",
    "            app_test[col] = le.transform(app_test[col])\n",
    "            \n",
    "            # Keep track of how many columns were label encoded\n",
    "            le_count += 1\n",
    "            \n",
    "print('%d columns were label encoded.' % le_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Features shape:  (307511, 243)\n",
      "Testing Features shape:  (48744, 239)\n"
     ]
    }
   ],
   "source": [
    "# one-hot encoding of categorical variables\n",
    "app_train = pd.get_dummies(app_train)\n",
    "app_test = pd.get_dummies(app_test)\n",
    "list1=app_train.columns\n",
    "\n",
    "print('Training Features shape: ', app_train.shape)\n",
    "print('Testing Features shape: ', app_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['SK_ID_CURR', 'TARGET', 'NAME_CONTRACT_TYPE', 'FLAG_OWN_CAR',\n",
      "       'FLAG_OWN_REALTY', 'CNT_CHILDREN', 'AMT_INCOME_TOTAL', 'AMT_CREDIT',\n",
      "       'AMT_ANNUITY', 'AMT_GOODS_PRICE',\n",
      "       ...\n",
      "       'HOUSETYPE_MODE_terraced house', 'WALLSMATERIAL_MODE_Block',\n",
      "       'WALLSMATERIAL_MODE_Mixed', 'WALLSMATERIAL_MODE_Monolithic',\n",
      "       'WALLSMATERIAL_MODE_Others', 'WALLSMATERIAL_MODE_Panel',\n",
      "       'WALLSMATERIAL_MODE_Stone, brick', 'WALLSMATERIAL_MODE_Wooden',\n",
      "       'EMERGENCYSTATE_MODE_No', 'EMERGENCYSTATE_MODE_Yes'],\n",
      "      dtype='object', length=243)\n"
     ]
    }
   ],
   "source": [
    "print(list1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Features shape:  (307511, 240)\n",
      "Testing Features shape:  (48744, 239)\n"
     ]
    }
   ],
   "source": [
    "train_labels = app_train['TARGET']\n",
    "\n",
    "# Align the training and testing data, keep only columns present in both dataframes\n",
    "app_train, app_test = app_train.align(app_test, join = 'inner', axis = 1)\n",
    "\n",
    "# Add the target back in\n",
    "app_train['TARGET'] = train_labels\n",
    "\n",
    "print('Training Features shape: ', app_train.shape)\n",
    "print('Testing Features shape: ', app_test.shape)\n",
    "list2=app_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "columns dropped from train set were : \n",
      "CODE_GENDER_XNA\n",
      "NAME_INCOME_TYPE_Maternity leave\n",
      "NAME_FAMILY_STATUS_Unknown\n"
     ]
    }
   ],
   "source": [
    "print(\"columns dropped from train set were : \")\n",
    "for x in list1:\n",
    "    if(x not in list2):\n",
    "        print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Anomalies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    307511.000000\n",
       "mean         43.936973\n",
       "std          11.956133\n",
       "min          20.517808\n",
       "25%          34.008219\n",
       "50%          43.150685\n",
       "75%          53.923288\n",
       "max          69.120548\n",
       "Name: DAYS_BIRTH, dtype: float64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((app_train['DAYS_BIRTH']*-1)/365).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Those ages look reasonable. There are no outliers for the age on either the high or low end.\n",
    "# How about the days of employment?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    307511.000000\n",
       "mean      63815.045904\n",
       "std      141275.766519\n",
       "min      -17912.000000\n",
       "25%       -2760.000000\n",
       "50%       -1213.000000\n",
       "75%        -289.000000\n",
       "max      365243.000000\n",
       "Name: DAYS_EMPLOYED, dtype: float64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "app_train['DAYS_EMPLOYED'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "app_train['DAYS_EMPLOYED'].plot.hist(title = 'Days Employment Histogram');\n",
    "plt.xlabel('Days Employment')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([252137.,      0.,      0.,      0.,      0.,      0.,      0.,\n",
       "             0.,      0.,  55374.]),\n",
       " array([-17912. ,  20403.5,  58719. ,  97034.5, 135350. , 173665.5,\n",
       "        211981. , 250296.5, 288612. , 326927.5, 365243. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jrSZJGoPl425gFhlRq9cMSnYAO9rL/5Pk6bPa1WtdBLywkAny6UXqZLQF93eW2d/8LeXewP4WYs69LfDnyD/rGbTUQ+MYsGbo9Wrg+NRBVXU3cPe5amqqJAeqauO49j8b+1uYpdzfUu4N7G8hlmpvS/301OPAuiSXJTkP2AbsGXNPkvS6taQ/aVTVK0luBvYBy4BdVXV4zG1J0uvWkg4NgKraC+wddx+zGNupsU72tzBLub+l3BvY30Isyd5S9ZrrypIkjbTUr2lIkpYQQ2OBzuWfOUnybJKnkhxMcqDVLkyyP8nR9ryi1ZPkztbXk0kuH5pnext/NMn2ofp72/wTbdtRtzwP97Mryckkh4ZqZ72f6fbR2d9tSX7UjuHBJFcPrbul7evpJFcO1Ud+jdsNGo+2Pr7SbtYgyfnt9URbv3ZEb2uSPJLkSJLDST66lI7fDP0tleP3a0keS/Ld1t9/me+ci9V3R2/3JvnB0LHbMI6v7YJVlY95PhhcnP8+8A7gPOC7wPqzuL9ngYum1P4rsLMt7wQ+3ZavBh5i8Lsum4BHW/1C4Jn2vKItr2jrHgN+q23zEHDVLP38NnA5cOhc9jPdPjr7uw34TyPGrm9fv/OBy9rXddlMX2PgAWBbW/4z4CNt+T8Cf9aWtwFfGbG/VcDlbfnNwN+1HpbE8Zuhv6Vy/AK8qS2/AXi0HZc5zbmYfXf0di9w7Yj3cs6/Nxb0c+hs/YB7PTzaF23f0OtbgFvO4v6e5bWh8TSwqi2vAp5uy58Hrp86Drge+PxQ/fOttgr43lD9jHEz9LSWM38on/V+pttHZ3+3MfqH3hlfOwZ37P3WdF/j9s36ArB86r+FyW3b8vI2LrMcxweBf7fUjt+I/pbc8QP+KfA3wPvmOudi9t3R272MDo2xfm3n+vD01MKc6z9zUsBfJnkig9+CB7ikqk4AtOeLZ+ltpvqxEfW5Ohf9TLePXje30wC7hj6+z7W/twE/qapXRvT3/7dp619q40dqp0rew+C/SJfc8ZvSHyyR45dkWZKDwElgP4NPBnOdczH7nra3qpo8dre3Y3dHkvOn9tbZw9n83piVobEwXX/mZBG9v6ouZ/BXf29K8tszjJ2ut7nWF8tS6ecu4J3ABuAE8Jmz0F9370neBHwN+FhV/XT6tsdz/Eb0t2SOX1W9WlUbGPyliCuAd81jzrNyXKf2luTdDD6p/HPgXzE45fTxRe7tnDA0Fqbrz5wslqo63p5PAt9g8I3yfJJVAO355Cy9zVRfPaI+V+ein+n2Mauqer59Q/8C+AKDYzif/l4ALkiyfEr9jLna+rcCp6f2kuQNDH4gf6mqvj7Lezvnx29Uf0vp+E2qqp8A/4vB9YC5zrmYfc/U25aqOlEDPwf+G/M/dmfle6OXobEw5+zPnCT59SRvnlwGNgOH2v62t2HbGZx7ptVvaHdmbAJeah9X9wGbk6xopxY2MzgnewJ4OcmmdifGDUNzzcW56Ge6fcxq8huq+RCDYzg557Z2l81lwDoGFxtHfo1rcNL4EeDaad7rZH/XAt9s44f7CHAPcKSqPju0akkcv+n6W0LHb2WSC9ryG4F/CxyZx5yL2fdMvX1v6Id5gGumHLuxf290W+yLJK+3B4M7H/6OwfnUPzqL+3kHgzs4vgscntwXg3OsDwNH2/OFrR4G/wOr7wNPARuH5voPwER7fHiovpHBP+TvA3/C7Bdvv8zgFMX/ZfBfPzeei36m20dnf19s+3+SwTfYqqHxf9T29TRDd45N9zVuX5PHWt//Azi/1X+tvZ5o698xord/zeCUwpPAwfa4eqkcvxn6WyrH718A32l9HAL+83znXKy+O3r7Zjt2h4A/55d3WJ3z742FPPyNcElSN09PSZK6GRqSpG6GhiSpm6EhSepmaEiSuhkakqRuhoYkqZuhIUnq9v8AZpMgGhMaNfAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(app_train['DAYS_EMPLOYED'])\n",
    "# plt.xlabel('Days Employment')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Just out of curiousity, let's subset the anomalous clients and see if they tend to have higher or low rates of default than the rest of the clients.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The non-anomalies default on 8.66% of loans\n",
      "The anomalies default on 5.40% of loans\n",
      "There are 55374 anomalous days of employment\n",
      "There are 252137 anomalous days of employment\n"
     ]
    }
   ],
   "source": [
    "anom = app_train[app_train['DAYS_EMPLOYED'] == 365243]\n",
    "non_anom = app_train[app_train['DAYS_EMPLOYED'] != 365243]\n",
    "print('The non-anomalies default on %0.2f%% of loans' % (100 * non_anom['TARGET'].mean()))\n",
    "print('The anomalies default on %0.2f%% of loans' % (100 * anom['TARGET'].mean()))\n",
    "print('There are %d anomalous days of employment' % len(anom))\n",
    "print('There are %d anomalous days of employment' % len(non_anom))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Well that is extremely interesting! It turns out that the anomalies have a lower rate of default.\n",
    "\n",
    "# # Handling the anomalies depends on the exact situation, with no set rules. One of the safest approaches is just to set \n",
    "# the anomalies to a missing value and then have them filled in (using Imputation) before machine learning. In this case, \n",
    "# since all the anomalies have the exact same value, we want to fill them in with the same value in case all of these loans \n",
    "# share something in common. The anomalous values seem to have some importance, so we want to tell the machine learning model\n",
    "# if we did in fact fill in these values. As a solution, we will fill in the anomalous values with not a number (np.nan) and \n",
    "# then create a new boolean column indicating whether or not the value was anomalous."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'Days Employment')"
      ]
     },
     "execution_count": 101,
     "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 an anomalous flag column\n",
    "app_train['DAYS_EMPLOYED_ANOM'] = app_train[\"DAYS_EMPLOYED\"] == 365243\n",
    "\n",
    "# Replace the anomalous values with nan\n",
    "app_train['DAYS_EMPLOYED'].replace({365243: np.nan}, inplace = True)\n",
    "\n",
    "app_train['DAYS_EMPLOYED'].plot.hist(title = 'Days Employment Histogram');\n",
    "plt.xlabel('Days Employment')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2    False\n",
       "3    False\n",
       "4    False\n",
       "Name: DAYS_EMPLOYED_ANOM, dtype: bool"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "app_train['DAYS_EMPLOYED_ANOM'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now all the values which were outliers are nan values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The distribution looks to be much more in line with what we would expect, and we also have created a new column to \n",
    "# tell the model that these values were originally anomalous (becuase we will have to fill in the nans with some value,\n",
    "# probably the median of the column). The other columns with DAYS in the dataframe look to be about what we expect with no\n",
    "# obvious outliers.\n",
    "\n",
    "# As an extremely important note, anything we do to the training data we also have to do to the testing data.\n",
    "# Let's make sure to create the new column and fill in the existing column with np.nan in the testing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 9274 anomalies in the test data out of 48744 entries\n"
     ]
    }
   ],
   "source": [
    "# app_test['DAYS_EMPLOYED_ANOM'] = app_test[\"DAYS_EMPLOYED\"] == 365243\n",
    "app_test[\"DAYS_EMPLOYED\"].replace({365243: np.nan}, inplace = True)\n",
    "\n",
    "print('There are %d anomalies in the test data out of %d entries' % (app_test[\"DAYS_EMPLOYED_ANOM\"].sum(), len(app_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "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>SK_ID_CURR</th>\n",
       "      <th>NAME_CONTRACT_TYPE</th>\n",
       "      <th>FLAG_OWN_CAR</th>\n",
       "      <th>FLAG_OWN_REALTY</th>\n",
       "      <th>CNT_CHILDREN</th>\n",
       "      <th>AMT_INCOME_TOTAL</th>\n",
       "      <th>AMT_CREDIT</th>\n",
       "      <th>AMT_ANNUITY</th>\n",
       "      <th>AMT_GOODS_PRICE</th>\n",
       "      <th>REGION_POPULATION_RELATIVE</th>\n",
       "      <th>...</th>\n",
       "      <th>WALLSMATERIAL_MODE_Mixed</th>\n",
       "      <th>WALLSMATERIAL_MODE_Monolithic</th>\n",
       "      <th>WALLSMATERIAL_MODE_Others</th>\n",
       "      <th>WALLSMATERIAL_MODE_Panel</th>\n",
       "      <th>WALLSMATERIAL_MODE_Stone, brick</th>\n",
       "      <th>WALLSMATERIAL_MODE_Wooden</th>\n",
       "      <th>EMERGENCYSTATE_MODE_No</th>\n",
       "      <th>EMERGENCYSTATE_MODE_Yes</th>\n",
       "      <th>TARGET</th>\n",
       "      <th>DAYS_EMPLOYED_ANOM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001654</td>\n",
       "      <td>0.001216</td>\n",
       "      <td>0.000703</td>\n",
       "      <td>-0.001129</td>\n",
       "      <td>-0.001820</td>\n",
       "      <td>-0.000343</td>\n",
       "      <td>-0.000433</td>\n",
       "      <td>-0.000232</td>\n",
       "      <td>0.000849</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000976</td>\n",
       "      <td>0.000072</td>\n",
       "      <td>-0.001396</td>\n",
       "      <td>0.002300</td>\n",
       "      <td>-0.001281</td>\n",
       "      <td>-0.000270</td>\n",
       "      <td>0.000510</td>\n",
       "      <td>0.002549</td>\n",
       "      <td>-0.002108</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAME_CONTRACT_TYPE</th>\n",
       "      <td>0.001654</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.004022</td>\n",
       "      <td>0.067177</td>\n",
       "      <td>0.029998</td>\n",
       "      <td>-0.003531</td>\n",
       "      <td>-0.221648</td>\n",
       "      <td>-0.241544</td>\n",
       "      <td>-0.185850</td>\n",
       "      <td>0.026867</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003783</td>\n",
       "      <td>0.010609</td>\n",
       "      <td>0.001571</td>\n",
       "      <td>0.011120</td>\n",
       "      <td>0.015098</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>0.027372</td>\n",
       "      <td>-0.000339</td>\n",
       "      <td>-0.030896</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FLAG_OWN_CAR</th>\n",
       "      <td>0.001216</td>\n",
       "      <td>0.004022</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.002817</td>\n",
       "      <td>0.102023</td>\n",
       "      <td>0.083383</td>\n",
       "      <td>0.116225</td>\n",
       "      <td>0.141588</td>\n",
       "      <td>0.120310</td>\n",
       "      <td>0.041314</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000727</td>\n",
       "      <td>0.011128</td>\n",
       "      <td>-0.002715</td>\n",
       "      <td>-0.009758</td>\n",
       "      <td>-0.025584</td>\n",
       "      <td>-0.009212</td>\n",
       "      <td>-0.034754</td>\n",
       "      <td>-0.007584</td>\n",
       "      <td>-0.021851</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FLAG_OWN_REALTY</th>\n",
       "      <td>0.000703</td>\n",
       "      <td>0.067177</td>\n",
       "      <td>-0.002817</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.002366</td>\n",
       "      <td>0.002934</td>\n",
       "      <td>-0.039270</td>\n",
       "      <td>-0.005225</td>\n",
       "      <td>-0.045537</td>\n",
       "      <td>0.015175</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002567</td>\n",
       "      <td>0.000833</td>\n",
       "      <td>0.000757</td>\n",
       "      <td>0.014874</td>\n",
       "      <td>0.017445</td>\n",
       "      <td>-0.014577</td>\n",
       "      <td>0.022013</td>\n",
       "      <td>-0.008535</td>\n",
       "      <td>-0.006148</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CNT_CHILDREN</th>\n",
       "      <td>-0.001129</td>\n",
       "      <td>0.029998</td>\n",
       "      <td>0.102023</td>\n",
       "      <td>-0.002366</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.012882</td>\n",
       "      <td>0.002145</td>\n",
       "      <td>0.021374</td>\n",
       "      <td>-0.001827</td>\n",
       "      <td>-0.025573</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000709</td>\n",
       "      <td>0.001607</td>\n",
       "      <td>-0.002032</td>\n",
       "      <td>-0.020892</td>\n",
       "      <td>-0.025088</td>\n",
       "      <td>0.011036</td>\n",
       "      <td>-0.038644</td>\n",
       "      <td>0.004525</td>\n",
       "      <td>0.019187</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 241 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    SK_ID_CURR  NAME_CONTRACT_TYPE  FLAG_OWN_CAR  \\\n",
       "SK_ID_CURR            1.000000            0.001654      0.001216   \n",
       "NAME_CONTRACT_TYPE    0.001654            1.000000      0.004022   \n",
       "FLAG_OWN_CAR          0.001216            0.004022      1.000000   \n",
       "FLAG_OWN_REALTY       0.000703            0.067177     -0.002817   \n",
       "CNT_CHILDREN         -0.001129            0.029998      0.102023   \n",
       "\n",
       "                    FLAG_OWN_REALTY  CNT_CHILDREN  AMT_INCOME_TOTAL  \\\n",
       "SK_ID_CURR                 0.000703     -0.001129         -0.001820   \n",
       "NAME_CONTRACT_TYPE         0.067177      0.029998         -0.003531   \n",
       "FLAG_OWN_CAR              -0.002817      0.102023          0.083383   \n",
       "FLAG_OWN_REALTY            1.000000     -0.002366          0.002934   \n",
       "CNT_CHILDREN              -0.002366      1.000000          0.012882   \n",
       "\n",
       "                    AMT_CREDIT  AMT_ANNUITY  AMT_GOODS_PRICE  \\\n",
       "SK_ID_CURR           -0.000343    -0.000433        -0.000232   \n",
       "NAME_CONTRACT_TYPE   -0.221648    -0.241544        -0.185850   \n",
       "FLAG_OWN_CAR          0.116225     0.141588         0.120310   \n",
       "FLAG_OWN_REALTY      -0.039270    -0.005225        -0.045537   \n",
       "CNT_CHILDREN          0.002145     0.021374        -0.001827   \n",
       "\n",
       "                    REGION_POPULATION_RELATIVE         ...          \\\n",
       "SK_ID_CURR                            0.000849         ...           \n",
       "NAME_CONTRACT_TYPE                    0.026867         ...           \n",
       "FLAG_OWN_CAR                          0.041314         ...           \n",
       "FLAG_OWN_REALTY                       0.015175         ...           \n",
       "CNT_CHILDREN                         -0.025573         ...           \n",
       "\n",
       "                    WALLSMATERIAL_MODE_Mixed  WALLSMATERIAL_MODE_Monolithic  \\\n",
       "SK_ID_CURR                         -0.000976                       0.000072   \n",
       "NAME_CONTRACT_TYPE                  0.003783                       0.010609   \n",
       "FLAG_OWN_CAR                        0.000727                       0.011128   \n",
       "FLAG_OWN_REALTY                     0.002567                       0.000833   \n",
       "CNT_CHILDREN                       -0.000709                       0.001607   \n",
       "\n",
       "                    WALLSMATERIAL_MODE_Others  WALLSMATERIAL_MODE_Panel  \\\n",
       "SK_ID_CURR                          -0.001396                  0.002300   \n",
       "NAME_CONTRACT_TYPE                   0.001571                  0.011120   \n",
       "FLAG_OWN_CAR                        -0.002715                 -0.009758   \n",
       "FLAG_OWN_REALTY                      0.000757                  0.014874   \n",
       "CNT_CHILDREN                        -0.002032                 -0.020892   \n",
       "\n",
       "                    WALLSMATERIAL_MODE_Stone, brick  \\\n",
       "SK_ID_CURR                                -0.001281   \n",
       "NAME_CONTRACT_TYPE                         0.015098   \n",
       "FLAG_OWN_CAR                              -0.025584   \n",
       "FLAG_OWN_REALTY                            0.017445   \n",
       "CNT_CHILDREN                              -0.025088   \n",
       "\n",
       "                    WALLSMATERIAL_MODE_Wooden  EMERGENCYSTATE_MODE_No  \\\n",
       "SK_ID_CURR                          -0.000270                0.000510   \n",
       "NAME_CONTRACT_TYPE                   0.000040                0.027372   \n",
       "FLAG_OWN_CAR                        -0.009212               -0.034754   \n",
       "FLAG_OWN_REALTY                     -0.014577                0.022013   \n",
       "CNT_CHILDREN                         0.011036               -0.038644   \n",
       "\n",
       "                    EMERGENCYSTATE_MODE_Yes    TARGET  DAYS_EMPLOYED_ANOM  \n",
       "SK_ID_CURR                         0.002549 -0.002108                 NaN  \n",
       "NAME_CONTRACT_TYPE                -0.000339 -0.030896                 NaN  \n",
       "FLAG_OWN_CAR                      -0.007584 -0.021851                 NaN  \n",
       "FLAG_OWN_REALTY                   -0.008535 -0.006148                 NaN  \n",
       "CNT_CHILDREN                       0.004525  0.019187                 NaN  \n",
       "\n",
       "[5 rows x 241 columns]"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "app_train.corr().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most Positive Correlations:\n",
      " FLAG_DOCUMENT_3                                      0.044346\n",
      "REG_CITY_NOT_LIVE_CITY                               0.044395\n",
      "FLAG_EMP_PHONE                                       0.045982\n",
      "NAME_EDUCATION_TYPE_Secondary / secondary special    0.049824\n",
      "REG_CITY_NOT_WORK_CITY                               0.050994\n",
      "DAYS_ID_PUBLISH                                      0.051457\n",
      "CODE_GENDER_M                                        0.054713\n",
      "DAYS_LAST_PHONE_CHANGE                               0.055218\n",
      "NAME_INCOME_TYPE_Working                             0.057481\n",
      "REGION_RATING_CLIENT                                 0.058899\n",
      "REGION_RATING_CLIENT_W_CITY                          0.060893\n",
      "DAYS_EMPLOYED                                        0.074958\n",
      "DAYS_BIRTH                                           0.078239\n",
      "TARGET                                               1.000000\n",
      "DAYS_EMPLOYED_ANOM                                        NaN\n",
      "Name: TARGET, dtype: float64\n",
      "\n",
      "Most Negative Correlations:\n",
      " EXT_SOURCE_3                           -0.178919\n",
      "EXT_SOURCE_2                           -0.160472\n",
      "EXT_SOURCE_1                           -0.155317\n",
      "NAME_EDUCATION_TYPE_Higher education   -0.056593\n",
      "CODE_GENDER_F                          -0.054704\n",
      "NAME_INCOME_TYPE_Pensioner             -0.046209\n",
      "ORGANIZATION_TYPE_XNA                  -0.045987\n",
      "FLOORSMAX_AVG                          -0.044003\n",
      "FLOORSMAX_MEDI                         -0.043768\n",
      "FLOORSMAX_MODE                         -0.043226\n",
      "EMERGENCYSTATE_MODE_No                 -0.042201\n",
      "HOUSETYPE_MODE_block of flats          -0.040594\n",
      "AMT_GOODS_PRICE                        -0.039645\n",
      "REGION_POPULATION_RELATIVE             -0.037227\n",
      "ELEVATORS_AVG                          -0.034199\n",
      "Name: TARGET, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Find correlations with the target and sort\n",
    "correlations = app_train.corr()['TARGET'].sort_values()\n",
    "\n",
    "# Display correlations\n",
    "print('Most Positive Correlations:\\n', correlations.tail(15))\n",
    "print('\\nMost Negative Correlations:\\n', correlations.head(15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Looking at the documentation, DAYS_BIRTH is the age in days of the client at the time of the loan in negative days \n",
    "# (for whatever reason!). The correlation is positive, but the value of this feature is actually negative, meaning that \n",
    "# as the client gets older, they are less likely to default on their loan (ie the target == 0). \n",
    "# That's a little confusing, so we will take the absolute value of the feature and then the correlation will be negative."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.07823930830982712"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the correlation of the positive days since birth and target\n",
    "app_train['DAYS_BIRTH'] = abs(app_train['DAYS_BIRTH'])\n",
    "app_train['DAYS_BIRTH'].corr(app_train['TARGET'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# As the client gets older, there is a negative linear relationship with the target meaning that as clients get older,\n",
    "# they tend to repay their loans on time more often.\n",
    "\n",
    "# Let's start looking at this variable. First, we can make a histogram of the age. We will put the x axis in years \n",
    "# to make the plot a little more understandable.\n",
    "\n",
    "# Set the style of plots\n",
    "plt.style.use('fivethirtyeight')\n",
    "\n",
    "# Plot the distribution of ages in years\n",
    "plt.hist(app_train['DAYS_BIRTH'] / 365, edgecolor = 'k', bins = 25)\n",
    "plt.title('Age of Client')\n",
    "plt.xlabel('Age (years)')\n",
    "plt.ylabel('Count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "# By itself, the distribution of age does not tell us much other than that there are no outliers as all the ages \n",
    "# are reasonable. To visualize the effect of the age on the target, we will next make a kernel density estimation plot\n",
    "# (KDE) colored by the value of the target. A kernel density estimate plot shows the distribution of a single variable and \n",
    "# can be thought of as a smoothed histogram (it is created by computing a kernel, usually a Gaussian, at each data point and\n",
    "# then averaging all the individual kernels to develop a single smooth curve). We\n",
    "# will use the seaborn kdeplot for this graph.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Distribution of Ages')"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize = (10, 8))\n",
    "\n",
    "# KDE plot of loans that were repaid on time\n",
    "sns.kdeplot(app_train.loc[app_train['TARGET'] == 0, 'DAYS_BIRTH'] / 365, label = 'target == 0')\n",
    "\n",
    "# KDE plot of loans which were not repaid on time\n",
    "sns.kdeplot(app_train.loc[app_train['TARGET'] == 1, 'DAYS_BIRTH'] / 365, label = 'target == 1')\n",
    "\n",
    "# Labeling of plot\n",
    "plt.xlabel('Age (years)') \n",
    "plt.ylabel('Density') \n",
    "plt.title('Distribution of Ages')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The target == 1 curve skews towards the younger end of the range. Although this is not a significant correlation \n",
    "# (-0.07 correlation coefficient), this variable is likely going to be useful in a machine learning model because it \n",
    "# does affect the target. Let's look at this relationship in another way: average failure to repay loans by age bracket.\n",
    "\n",
    "# To make this graph, first we cut the age category into bins of 5 years each. Then, for each bin, \n",
    "# we calculate the average value of the target, which tells us the ratio of loans that were not repaid in each age category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "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>TARGET</th>\n",
       "      <th>DAYS_BIRTH</th>\n",
       "      <th>YEARS_BIRTH</th>\n",
       "      <th>YEARS_BINNED</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>9461</td>\n",
       "      <td>25.920548</td>\n",
       "      <td>(25.0, 30.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>16765</td>\n",
       "      <td>45.931507</td>\n",
       "      <td>(45.0, 50.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>19046</td>\n",
       "      <td>52.180822</td>\n",
       "      <td>(50.0, 55.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>19005</td>\n",
       "      <td>52.068493</td>\n",
       "      <td>(50.0, 55.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>19932</td>\n",
       "      <td>54.608219</td>\n",
       "      <td>(50.0, 55.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>16941</td>\n",
       "      <td>46.413699</td>\n",
       "      <td>(45.0, 50.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>13778</td>\n",
       "      <td>37.747945</td>\n",
       "      <td>(35.0, 40.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>18850</td>\n",
       "      <td>51.643836</td>\n",
       "      <td>(50.0, 55.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>20099</td>\n",
       "      <td>55.065753</td>\n",
       "      <td>(55.0, 60.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>14469</td>\n",
       "      <td>39.641096</td>\n",
       "      <td>(35.0, 40.0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   TARGET  DAYS_BIRTH  YEARS_BIRTH  YEARS_BINNED\n",
       "0       1        9461    25.920548  (25.0, 30.0]\n",
       "1       0       16765    45.931507  (45.0, 50.0]\n",
       "2       0       19046    52.180822  (50.0, 55.0]\n",
       "3       0       19005    52.068493  (50.0, 55.0]\n",
       "4       0       19932    54.608219  (50.0, 55.0]\n",
       "5       0       16941    46.413699  (45.0, 50.0]\n",
       "6       0       13778    37.747945  (35.0, 40.0]\n",
       "7       0       18850    51.643836  (50.0, 55.0]\n",
       "8       0       20099    55.065753  (55.0, 60.0]\n",
       "9       0       14469    39.641096  (35.0, 40.0]"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Age information into a separate dataframe\n",
    "age_data = app_train[['TARGET', 'DAYS_BIRTH']]\n",
    "age_data['YEARS_BIRTH'] = age_data['DAYS_BIRTH'] / 365\n",
    "\n",
    "# # Bin the age data\n",
    "age_data['YEARS_BINNED'] = pd.cut(age_data['YEARS_BIRTH'], bins = np.linspace(20, 70, num = 11))\n",
    "age_data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "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>TARGET</th>\n",
       "      <th>DAYS_BIRTH</th>\n",
       "      <th>YEARS_BIRTH</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YEARS_BINNED</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(20.0, 25.0]</th>\n",
       "      <td>0.123036</td>\n",
       "      <td>8532.795625</td>\n",
       "      <td>23.377522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(25.0, 30.0]</th>\n",
       "      <td>0.111436</td>\n",
       "      <td>10155.219250</td>\n",
       "      <td>27.822518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(30.0, 35.0]</th>\n",
       "      <td>0.102814</td>\n",
       "      <td>11854.848377</td>\n",
       "      <td>32.479037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(35.0, 40.0]</th>\n",
       "      <td>0.089414</td>\n",
       "      <td>13707.908253</td>\n",
       "      <td>37.555913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(40.0, 45.0]</th>\n",
       "      <td>0.078491</td>\n",
       "      <td>15497.661233</td>\n",
       "      <td>42.459346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(45.0, 50.0]</th>\n",
       "      <td>0.074171</td>\n",
       "      <td>17323.900441</td>\n",
       "      <td>47.462741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(50.0, 55.0]</th>\n",
       "      <td>0.066968</td>\n",
       "      <td>19196.494791</td>\n",
       "      <td>52.593136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(55.0, 60.0]</th>\n",
       "      <td>0.055314</td>\n",
       "      <td>20984.262742</td>\n",
       "      <td>57.491131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(60.0, 65.0]</th>\n",
       "      <td>0.052737</td>\n",
       "      <td>22780.547460</td>\n",
       "      <td>62.412459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(65.0, 70.0]</th>\n",
       "      <td>0.037270</td>\n",
       "      <td>24292.614340</td>\n",
       "      <td>66.555108</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                TARGET    DAYS_BIRTH  YEARS_BIRTH\n",
       "YEARS_BINNED                                     \n",
       "(20.0, 25.0]  0.123036   8532.795625    23.377522\n",
       "(25.0, 30.0]  0.111436  10155.219250    27.822518\n",
       "(30.0, 35.0]  0.102814  11854.848377    32.479037\n",
       "(35.0, 40.0]  0.089414  13707.908253    37.555913\n",
       "(40.0, 45.0]  0.078491  15497.661233    42.459346\n",
       "(45.0, 50.0]  0.074171  17323.900441    47.462741\n",
       "(50.0, 55.0]  0.066968  19196.494791    52.593136\n",
       "(55.0, 60.0]  0.055314  20984.262742    57.491131\n",
       "(60.0, 65.0]  0.052737  22780.547460    62.412459\n",
       "(65.0, 70.0]  0.037270  24292.614340    66.555108"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group by the bin and calculate averages\n",
    "age_groups  = age_data.groupby('YEARS_BINNED').mean()\n",
    "age_groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8, 8))\n",
    "\n",
    "# Graph the age bins and the average of the target as a bar plot\n",
    "plt.bar(age_groups.index.astype(str), 100 * age_groups['TARGET'])\n",
    "\n",
    "# Plot labeling\n",
    "plt.xticks(rotation = 75)\n",
    "plt.xlabel('Age Group (years)'); plt.ylabel('Failure to Repay (%)')\n",
    "plt.title('Failure to Repay by Age Group');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['(20.0, 25.0]', '(25.0, 30.0]', '(30.0, 35.0]', '(35.0, 40.0]',\n",
       "       '(40.0, 45.0]', '(45.0, 50.0]', '(50.0, 55.0]', '(55.0, 60.0]',\n",
       "       '(60.0, 65.0]', '(65.0, 70.0]'],\n",
       "      dtype='object', name='YEARS_BINNED')"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_groups.index.astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "# There is a clear trend: younger applicants are more likely to not repay the loan! The rate of failure to repay is above \n",
    "#     10% for the youngest three age groups and beolow 5% for the oldest age group.\n",
    "\n",
    "# This is information that could be directly used by the bank: because younger clients are less likely to repay the loan,\n",
    "#     maybe they should be provided with more guidance or financial planning tips. This does not mean the bank should \n",
    "#     discriminate \n",
    "#     against younger clients, but it would be smart to take precautionary measures to help younger clients pay on time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The 3 variables with the strongest negative correlations with the target are EXT_SOURCE_1, EXT_SOURCE_2, \n",
    "# and EXT_SOURCE_3. According to the documentation, these features represent a \"normalized score from external data source\".\n",
    "# I'm not sure what this exactly means, but it may be a cumulative sort of credit rating made using numerous sources of data.\n",
    "\n",
    "# Let's take a look at these variables.\n",
    "\n",
    "# First, we can show the correlations of the EXT_SOURCE features with the target and with each other.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "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>TARGET</th>\n",
       "      <th>EXT_SOURCE_1</th>\n",
       "      <th>EXT_SOURCE_2</th>\n",
       "      <th>EXT_SOURCE_3</th>\n",
       "      <th>DAYS_BIRTH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TARGET</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.155317</td>\n",
       "      <td>-0.160472</td>\n",
       "      <td>-0.178919</td>\n",
       "      <td>-0.078239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EXT_SOURCE_1</th>\n",
       "      <td>-0.155317</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.213982</td>\n",
       "      <td>0.186846</td>\n",
       "      <td>0.600610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EXT_SOURCE_2</th>\n",
       "      <td>-0.160472</td>\n",
       "      <td>0.213982</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.109167</td>\n",
       "      <td>0.091996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EXT_SOURCE_3</th>\n",
       "      <td>-0.178919</td>\n",
       "      <td>0.186846</td>\n",
       "      <td>0.109167</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.205478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAYS_BIRTH</th>\n",
       "      <td>-0.078239</td>\n",
       "      <td>0.600610</td>\n",
       "      <td>0.091996</td>\n",
       "      <td>0.205478</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                TARGET  EXT_SOURCE_1  EXT_SOURCE_2  EXT_SOURCE_3  DAYS_BIRTH\n",
       "TARGET        1.000000     -0.155317     -0.160472     -0.178919   -0.078239\n",
       "EXT_SOURCE_1 -0.155317      1.000000      0.213982      0.186846    0.600610\n",
       "EXT_SOURCE_2 -0.160472      0.213982      1.000000      0.109167    0.091996\n",
       "EXT_SOURCE_3 -0.178919      0.186846      0.109167      1.000000    0.205478\n",
       "DAYS_BIRTH   -0.078239      0.600610      0.091996      0.205478    1.000000"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract the EXT_SOURCE variables and show correlations\n",
    "ext_data = app_train[['TARGET', 'EXT_SOURCE_1', 'EXT_SOURCE_2', 'EXT_SOURCE_3', 'DAYS_BIRTH']]\n",
    "ext_data_corrs = ext_data.corr()\n",
    "ext_data_corrs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8, 6))\n",
    "\n",
    "# Heatmap of correlations\n",
    "sns.heatmap(ext_data_corrs, cmap = plt.cm.RdYlBu_r, vmin = -0.25, annot = True, vmax = 0.6)\n",
    "plt.title('Correlation Heatmap');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All three EXT_SOURCE features have negative correlations with the target, indicating that as the value of the EXT_SOURCE increases, the client is more likely to repay the loan. We can also see that DAYS_BIRTH is positively correlated with EXT_SOURCE_1 indicating that maybe one of the factors in this score is the client age.\n",
    "\n",
    "Next we can look at the distribution of each of these features colored by the value of the target. This will let us visualize the effect of this variable on the target."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x864 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 12))\n",
    "\n",
    "# iterate through the sources\n",
    "for i, source in enumerate(['EXT_SOURCE_1', 'EXT_SOURCE_2', 'EXT_SOURCE_3']):\n",
    "    \n",
    "    # create a new subplot for each source\n",
    "    plt.subplot(3, 1, i+1 )\n",
    "    # plot repaid loans\n",
    "    sns.kdeplot(app_train.loc[app_train['TARGET'] == 0, source], label = 'target == 0')\n",
    "    # plot loans that were not repaid\n",
    "    sns.kdeplot(app_train.loc[app_train['TARGET'] == 1, source], label = 'target == 1')\n",
    "    \n",
    "    # Label the plots\n",
    "    plt.title('Distribution of %s by Target Value' % source)\n",
    "    plt.ylabel('%s' % source) \n",
    "    plt.xlabel('Target')\n",
    "    \n",
    "plt.tight_layout(h_pad = 2.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}