2007-2015_pred.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem loan prediction 2017 - Lending Club\n",
    "## Testing model with 2007-2015 Data from Kaggle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as pl\n",
    "import datetime\n",
    "%matplotlib inline\n",
    "l_encoder = LabelEncoder()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Uploading and cleaning the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Uploading data from 2016\n",
    "data_old = pd.read_csv('../loan.csv', low_memory=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(887379, 74)\n",
      "HEAD\n"
     ]
    },
    {
     "data": {
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       "    }\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>id</th>\n",
       "      <th>member_id</th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>funded_amnt</th>\n",
       "      <th>funded_amnt_inv</th>\n",
       "      <th>term</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>grade</th>\n",
       "      <th>sub_grade</th>\n",
       "      <th>...</th>\n",
       "      <th>total_bal_il</th>\n",
       "      <th>il_util</th>\n",
       "      <th>open_rv_12m</th>\n",
       "      <th>open_rv_24m</th>\n",
       "      <th>max_bal_bc</th>\n",
       "      <th>all_util</th>\n",
       "      <th>total_rev_hi_lim</th>\n",
       "      <th>inq_fi</th>\n",
       "      <th>total_cu_tl</th>\n",
       "      <th>inq_last_12m</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1077501</td>\n",
       "      <td>1296599</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>4975.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>10.65</td>\n",
       "      <td>162.87</td>\n",
       "      <td>B</td>\n",
       "      <td>B2</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1077430</td>\n",
       "      <td>1314167</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>15.27</td>\n",
       "      <td>59.83</td>\n",
       "      <td>C</td>\n",
       "      <td>C4</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1077175</td>\n",
       "      <td>1313524</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>15.96</td>\n",
       "      <td>84.33</td>\n",
       "      <td>C</td>\n",
       "      <td>C5</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1076863</td>\n",
       "      <td>1277178</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>13.49</td>\n",
       "      <td>339.31</td>\n",
       "      <td>C</td>\n",
       "      <td>C1</td>\n",
       "      <td>...</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",
       "      <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>1075358</td>\n",
       "      <td>1311748</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>12.69</td>\n",
       "      <td>67.79</td>\n",
       "      <td>B</td>\n",
       "      <td>B5</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 74 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        id  member_id  loan_amnt  funded_amnt  funded_amnt_inv        term  \\\n",
       "0  1077501    1296599     5000.0       5000.0           4975.0   36 months   \n",
       "1  1077430    1314167     2500.0       2500.0           2500.0   60 months   \n",
       "2  1077175    1313524     2400.0       2400.0           2400.0   36 months   \n",
       "3  1076863    1277178    10000.0      10000.0          10000.0   36 months   \n",
       "4  1075358    1311748     3000.0       3000.0           3000.0   60 months   \n",
       "\n",
       "   int_rate  installment grade sub_grade     ...      total_bal_il il_util  \\\n",
       "0     10.65       162.87     B        B2     ...               NaN     NaN   \n",
       "1     15.27        59.83     C        C4     ...               NaN     NaN   \n",
       "2     15.96        84.33     C        C5     ...               NaN     NaN   \n",
       "3     13.49       339.31     C        C1     ...               NaN     NaN   \n",
       "4     12.69        67.79     B        B5     ...               NaN     NaN   \n",
       "\n",
       "  open_rv_12m  open_rv_24m max_bal_bc all_util total_rev_hi_lim inq_fi  \\\n",
       "0         NaN          NaN        NaN      NaN              NaN    NaN   \n",
       "1         NaN          NaN        NaN      NaN              NaN    NaN   \n",
       "2         NaN          NaN        NaN      NaN              NaN    NaN   \n",
       "3         NaN          NaN        NaN      NaN              NaN    NaN   \n",
       "4         NaN          NaN        NaN      NaN              NaN    NaN   \n",
       "\n",
       "  total_cu_tl inq_last_12m  \n",
       "0         NaN          NaN  \n",
       "1         NaN          NaN  \n",
       "2         NaN          NaN  \n",
       "3         NaN          NaN  \n",
       "4         NaN          NaN  \n",
       "\n",
       "[5 rows x 74 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " TAIL\n"
     ]
    },
    {
     "data": {
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       "    }\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>id</th>\n",
       "      <th>member_id</th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>funded_amnt</th>\n",
       "      <th>funded_amnt_inv</th>\n",
       "      <th>term</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>grade</th>\n",
       "      <th>sub_grade</th>\n",
       "      <th>...</th>\n",
       "      <th>total_bal_il</th>\n",
       "      <th>il_util</th>\n",
       "      <th>open_rv_12m</th>\n",
       "      <th>open_rv_24m</th>\n",
       "      <th>max_bal_bc</th>\n",
       "      <th>all_util</th>\n",
       "      <th>total_rev_hi_lim</th>\n",
       "      <th>inq_fi</th>\n",
       "      <th>total_cu_tl</th>\n",
       "      <th>inq_last_12m</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>887374</th>\n",
       "      <td>36371250</td>\n",
       "      <td>39102635</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>11.99</td>\n",
       "      <td>332.10</td>\n",
       "      <td>B</td>\n",
       "      <td>B5</td>\n",
       "      <td>...</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",
       "      <td>17100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887375</th>\n",
       "      <td>36441262</td>\n",
       "      <td>39152692</td>\n",
       "      <td>24000.0</td>\n",
       "      <td>24000.0</td>\n",
       "      <td>24000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>11.99</td>\n",
       "      <td>797.03</td>\n",
       "      <td>B</td>\n",
       "      <td>B5</td>\n",
       "      <td>...</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",
       "      <td>10200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887376</th>\n",
       "      <td>36271333</td>\n",
       "      <td>38982739</td>\n",
       "      <td>13000.0</td>\n",
       "      <td>13000.0</td>\n",
       "      <td>13000.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>15.99</td>\n",
       "      <td>316.07</td>\n",
       "      <td>D</td>\n",
       "      <td>D2</td>\n",
       "      <td>...</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",
       "      <td>18000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887377</th>\n",
       "      <td>36490806</td>\n",
       "      <td>39222577</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>19.99</td>\n",
       "      <td>317.86</td>\n",
       "      <td>E</td>\n",
       "      <td>E3</td>\n",
       "      <td>...</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",
       "      <td>27000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887378</th>\n",
       "      <td>36271262</td>\n",
       "      <td>38982659</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>11.99</td>\n",
       "      <td>664.20</td>\n",
       "      <td>B</td>\n",
       "      <td>B5</td>\n",
       "      <td>...</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",
       "      <td>41700.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 74 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              id  member_id  loan_amnt  funded_amnt  funded_amnt_inv  \\\n",
       "887374  36371250   39102635    10000.0      10000.0          10000.0   \n",
       "887375  36441262   39152692    24000.0      24000.0          24000.0   \n",
       "887376  36271333   38982739    13000.0      13000.0          13000.0   \n",
       "887377  36490806   39222577    12000.0      12000.0          12000.0   \n",
       "887378  36271262   38982659    20000.0      20000.0          20000.0   \n",
       "\n",
       "              term  int_rate  installment grade sub_grade     ...       \\\n",
       "887374   36 months     11.99       332.10     B        B5     ...        \n",
       "887375   36 months     11.99       797.03     B        B5     ...        \n",
       "887376   60 months     15.99       316.07     D        D2     ...        \n",
       "887377   60 months     19.99       317.86     E        E3     ...        \n",
       "887378   36 months     11.99       664.20     B        B5     ...        \n",
       "\n",
       "       total_bal_il il_util open_rv_12m  open_rv_24m max_bal_bc all_util  \\\n",
       "887374          NaN     NaN         NaN          NaN        NaN      NaN   \n",
       "887375          NaN     NaN         NaN          NaN        NaN      NaN   \n",
       "887376          NaN     NaN         NaN          NaN        NaN      NaN   \n",
       "887377          NaN     NaN         NaN          NaN        NaN      NaN   \n",
       "887378          NaN     NaN         NaN          NaN        NaN      NaN   \n",
       "\n",
       "       total_rev_hi_lim inq_fi total_cu_tl inq_last_12m  \n",
       "887374          17100.0    NaN         NaN          NaN  \n",
       "887375          10200.0    NaN         NaN          NaN  \n",
       "887376          18000.0    NaN         NaN          NaN  \n",
       "887377          27000.0    NaN         NaN          NaN  \n",
       "887378          41700.0    NaN         NaN          NaN  \n",
       "\n",
       "[5 rows x 74 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print (data_old.shape)\n",
    "print('HEAD')\n",
    "display(data_old.head())\n",
    "print('\\n TAIL')\n",
    "display(data_old.tail())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are a few data descriptors at the end of the files than need to be remove. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        text-align: right;\n",
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       "    .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>id</th>\n",
       "      <th>member_id</th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>funded_amnt</th>\n",
       "      <th>funded_amnt_inv</th>\n",
       "      <th>term</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>grade</th>\n",
       "      <th>sub_grade</th>\n",
       "      <th>...</th>\n",
       "      <th>total_bal_il</th>\n",
       "      <th>il_util</th>\n",
       "      <th>open_rv_12m</th>\n",
       "      <th>open_rv_24m</th>\n",
       "      <th>max_bal_bc</th>\n",
       "      <th>all_util</th>\n",
       "      <th>total_rev_hi_lim</th>\n",
       "      <th>inq_fi</th>\n",
       "      <th>total_cu_tl</th>\n",
       "      <th>inq_last_12m</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>466812</th>\n",
       "      <td>68616588</td>\n",
       "      <td>73519410</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>7.91</td>\n",
       "      <td>469.43</td>\n",
       "      <td>A</td>\n",
       "      <td>A5</td>\n",
       "      <td>...</td>\n",
       "      <td>14796.0</td>\n",
       "      <td>44.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9747.0</td>\n",
       "      <td>47.4</td>\n",
       "      <td>18500.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466709</th>\n",
       "      <td>68616757</td>\n",
       "      <td>73519581</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>6.99</td>\n",
       "      <td>308.73</td>\n",
       "      <td>A</td>\n",
       "      <td>A3</td>\n",
       "      <td>...</td>\n",
       "      <td>23084.0</td>\n",
       "      <td>55.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2882.0</td>\n",
       "      <td>47.6</td>\n",
       "      <td>21600.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466532</th>\n",
       "      <td>68616825</td>\n",
       "      <td>73519650</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>9.17</td>\n",
       "      <td>637.58</td>\n",
       "      <td>B</td>\n",
       "      <td>B2</td>\n",
       "      <td>...</td>\n",
       "      <td>25470.0</td>\n",
       "      <td>77.8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3043.0</td>\n",
       "      <td>56.9</td>\n",
       "      <td>33400.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466547</th>\n",
       "      <td>68616851</td>\n",
       "      <td>73519677</td>\n",
       "      <td>12500.0</td>\n",
       "      <td>12500.0</td>\n",
       "      <td>12500.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>7.49</td>\n",
       "      <td>388.78</td>\n",
       "      <td>A</td>\n",
       "      <td>A4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6158.0</td>\n",
       "      <td>80.7</td>\n",
       "      <td>12400.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466492</th>\n",
       "      <td>68616867</td>\n",
       "      <td>73519693</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>14.85</td>\n",
       "      <td>379.39</td>\n",
       "      <td>C</td>\n",
       "      <td>C5</td>\n",
       "      <td>...</td>\n",
       "      <td>52879.0</td>\n",
       "      <td>81.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>81.8</td>\n",
       "      <td>13300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466303</th>\n",
       "      <td>68616873</td>\n",
       "      <td>73519699</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>11.48</td>\n",
       "      <td>263.74</td>\n",
       "      <td>B</td>\n",
       "      <td>B5</td>\n",
       "      <td>...</td>\n",
       "      <td>106748.0</td>\n",
       "      <td>72.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4725.0</td>\n",
       "      <td>48.8</td>\n",
       "      <td>18000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466462</th>\n",
       "      <td>68616891</td>\n",
       "      <td>73519717</td>\n",
       "      <td>24000.0</td>\n",
       "      <td>24000.0</td>\n",
       "      <td>24000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>11.48</td>\n",
       "      <td>791.20</td>\n",
       "      <td>B</td>\n",
       "      <td>B5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8641.0</td>\n",
       "      <td>86.5</td>\n",
       "      <td>26400.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466454</th>\n",
       "      <td>68616919</td>\n",
       "      <td>73519746</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>5.32</td>\n",
       "      <td>451.73</td>\n",
       "      <td>A</td>\n",
       "      <td>A1</td>\n",
       "      <td>...</td>\n",
       "      <td>11138.0</td>\n",
       "      <td>44.6</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6299.0</td>\n",
       "      <td>22.6</td>\n",
       "      <td>78800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466388</th>\n",
       "      <td>68617034</td>\n",
       "      <td>73519870</td>\n",
       "      <td>14650.0</td>\n",
       "      <td>14650.0</td>\n",
       "      <td>14650.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>20.50</td>\n",
       "      <td>392.23</td>\n",
       "      <td>E</td>\n",
       "      <td>E4</td>\n",
       "      <td>...</td>\n",
       "      <td>51435.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10159.0</td>\n",
       "      <td>92.7</td>\n",
       "      <td>11200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466305</th>\n",
       "      <td>68617057</td>\n",
       "      <td>73519894</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>10.78</td>\n",
       "      <td>324.50</td>\n",
       "      <td>B</td>\n",
       "      <td>B4</td>\n",
       "      <td>...</td>\n",
       "      <td>71557.0</td>\n",
       "      <td>80.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8415.0</td>\n",
       "      <td>75.6</td>\n",
       "      <td>35400.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 74 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              id  member_id  loan_amnt  funded_amnt  funded_amnt_inv  \\\n",
       "466812  68616588   73519410    15000.0      15000.0          15000.0   \n",
       "466709  68616757   73519581    10000.0      10000.0          10000.0   \n",
       "466532  68616825   73519650    20000.0      20000.0          20000.0   \n",
       "466547  68616851   73519677    12500.0      12500.0          12500.0   \n",
       "466492  68616867   73519693    16000.0      16000.0          16000.0   \n",
       "466303  68616873   73519699     8000.0       8000.0           8000.0   \n",
       "466462  68616891   73519717    24000.0      24000.0          24000.0   \n",
       "466454  68616919   73519746    15000.0      15000.0          15000.0   \n",
       "466388  68617034   73519870    14650.0      14650.0          14650.0   \n",
       "466305  68617057   73519894    15000.0      15000.0          15000.0   \n",
       "\n",
       "              term  int_rate  installment grade sub_grade     ...       \\\n",
       "466812   36 months      7.91       469.43     A        A5     ...        \n",
       "466709   36 months      6.99       308.73     A        A3     ...        \n",
       "466532   36 months      9.17       637.58     B        B2     ...        \n",
       "466547   36 months      7.49       388.78     A        A4     ...        \n",
       "466492   60 months     14.85       379.39     C        C5     ...        \n",
       "466303   36 months     11.48       263.74     B        B5     ...        \n",
       "466462   36 months     11.48       791.20     B        B5     ...        \n",
       "466454   36 months      5.32       451.73     A        A1     ...        \n",
       "466388   60 months     20.50       392.23     E        E4     ...        \n",
       "466305   60 months     10.78       324.50     B        B4     ...        \n",
       "\n",
       "       total_bal_il il_util open_rv_12m  open_rv_24m max_bal_bc all_util  \\\n",
       "466812      14796.0    44.3         0.0          0.0     9747.0     47.4   \n",
       "466709      23084.0    55.3         0.0          1.0     2882.0     47.6   \n",
       "466532      25470.0    77.8         1.0          6.0     3043.0     56.9   \n",
       "466547          0.0     NaN         0.0          0.0     6158.0     80.7   \n",
       "466492      52879.0    81.5         0.0          1.0     2401.0     81.8   \n",
       "466303     106748.0    72.2         0.0          2.0     4725.0     48.8   \n",
       "466462          0.0     NaN         0.0          0.0     8641.0     86.5   \n",
       "466454      11138.0    44.6         2.0          3.0     6299.0     22.6   \n",
       "466388      51435.0    91.0         0.0          0.0    10159.0     92.7   \n",
       "466305      71557.0    80.6         1.0          2.0     8415.0     75.6   \n",
       "\n",
       "       total_rev_hi_lim inq_fi total_cu_tl inq_last_12m  \n",
       "466812          18500.0    4.0         0.0          2.0  \n",
       "466709          21600.0    0.0         0.0          1.0  \n",
       "466532          33400.0    6.0         0.0          5.0  \n",
       "466547          12400.0    0.0         0.0          0.0  \n",
       "466492          13300.0    0.0         5.0          0.0  \n",
       "466303          18000.0    0.0         0.0          1.0  \n",
       "466462          26400.0    0.0         0.0          0.0  \n",
       "466454          78800.0    0.0         2.0          0.0  \n",
       "466388          11200.0    0.0         0.0          0.0  \n",
       "466305          35400.0    0.0         1.0          1.0  \n",
       "\n",
       "[10 rows x 74 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_old = data_old.sort_values(by = 'id',na_position = 'first', ascending=True)\n",
    "display(data_old.tail(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The data frame has 887379 rows (loans) with 74 columns\n"
     ]
    },
    {
     "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>id</th>\n",
       "      <th>member_id</th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>funded_amnt</th>\n",
       "      <th>funded_amnt_inv</th>\n",
       "      <th>term</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>grade</th>\n",
       "      <th>sub_grade</th>\n",
       "      <th>...</th>\n",
       "      <th>total_bal_il</th>\n",
       "      <th>il_util</th>\n",
       "      <th>open_rv_12m</th>\n",
       "      <th>open_rv_24m</th>\n",
       "      <th>max_bal_bc</th>\n",
       "      <th>all_util</th>\n",
       "      <th>total_rev_hi_lim</th>\n",
       "      <th>inq_fi</th>\n",
       "      <th>total_cu_tl</th>\n",
       "      <th>inq_last_12m</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>466812</th>\n",
       "      <td>68616588</td>\n",
       "      <td>73519410</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>7.91</td>\n",
       "      <td>469.43</td>\n",
       "      <td>A</td>\n",
       "      <td>A5</td>\n",
       "      <td>...</td>\n",
       "      <td>14796.0</td>\n",
       "      <td>44.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9747.0</td>\n",
       "      <td>47.4</td>\n",
       "      <td>18500.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466709</th>\n",
       "      <td>68616757</td>\n",
       "      <td>73519581</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>6.99</td>\n",
       "      <td>308.73</td>\n",
       "      <td>A</td>\n",
       "      <td>A3</td>\n",
       "      <td>...</td>\n",
       "      <td>23084.0</td>\n",
       "      <td>55.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2882.0</td>\n",
       "      <td>47.6</td>\n",
       "      <td>21600.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466532</th>\n",
       "      <td>68616825</td>\n",
       "      <td>73519650</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>9.17</td>\n",
       "      <td>637.58</td>\n",
       "      <td>B</td>\n",
       "      <td>B2</td>\n",
       "      <td>...</td>\n",
       "      <td>25470.0</td>\n",
       "      <td>77.8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3043.0</td>\n",
       "      <td>56.9</td>\n",
       "      <td>33400.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466547</th>\n",
       "      <td>68616851</td>\n",
       "      <td>73519677</td>\n",
       "      <td>12500.0</td>\n",
       "      <td>12500.0</td>\n",
       "      <td>12500.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>7.49</td>\n",
       "      <td>388.78</td>\n",
       "      <td>A</td>\n",
       "      <td>A4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6158.0</td>\n",
       "      <td>80.7</td>\n",
       "      <td>12400.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466492</th>\n",
       "      <td>68616867</td>\n",
       "      <td>73519693</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>14.85</td>\n",
       "      <td>379.39</td>\n",
       "      <td>C</td>\n",
       "      <td>C5</td>\n",
       "      <td>...</td>\n",
       "      <td>52879.0</td>\n",
       "      <td>81.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>81.8</td>\n",
       "      <td>13300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 74 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              id  member_id  loan_amnt  funded_amnt  funded_amnt_inv  \\\n",
       "466812  68616588   73519410    15000.0      15000.0          15000.0   \n",
       "466709  68616757   73519581    10000.0      10000.0          10000.0   \n",
       "466532  68616825   73519650    20000.0      20000.0          20000.0   \n",
       "466547  68616851   73519677    12500.0      12500.0          12500.0   \n",
       "466492  68616867   73519693    16000.0      16000.0          16000.0   \n",
       "\n",
       "              term  int_rate  installment grade sub_grade     ...       \\\n",
       "466812   36 months      7.91       469.43     A        A5     ...        \n",
       "466709   36 months      6.99       308.73     A        A3     ...        \n",
       "466532   36 months      9.17       637.58     B        B2     ...        \n",
       "466547   36 months      7.49       388.78     A        A4     ...        \n",
       "466492   60 months     14.85       379.39     C        C5     ...        \n",
       "\n",
       "       total_bal_il il_util open_rv_12m  open_rv_24m max_bal_bc all_util  \\\n",
       "466812      14796.0    44.3         0.0          0.0     9747.0     47.4   \n",
       "466709      23084.0    55.3         0.0          1.0     2882.0     47.6   \n",
       "466532      25470.0    77.8         1.0          6.0     3043.0     56.9   \n",
       "466547          0.0     NaN         0.0          0.0     6158.0     80.7   \n",
       "466492      52879.0    81.5         0.0          1.0     2401.0     81.8   \n",
       "\n",
       "       total_rev_hi_lim inq_fi total_cu_tl inq_last_12m  \n",
       "466812          18500.0    4.0         0.0          2.0  \n",
       "466709          21600.0    0.0         0.0          1.0  \n",
       "466532          33400.0    6.0         0.0          5.0  \n",
       "466547          12400.0    0.0         0.0          0.0  \n",
       "466492          13300.0    0.0         5.0          0.0  \n",
       "\n",
       "[5 rows x 74 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print ('The data frame has %d rows (loans) with %d columns' % (data_old.shape[0], data_old.shape[1]))\n",
    "data_old.drop(data_old.tail(5).index,inplace=True)\n",
    "display(data_old.tail(5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove the columns with only NaN and or with large number of NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>addr_state</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dti</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>revol_bal</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>initial_list_status</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>out_prncp_inv</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_pymnt</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_pymnt_inv</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_rec_prncp</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_rec_int</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_rec_late_fee</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recoveries</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>collection_recovery_fee</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_pymnt_amnt</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>policy_code</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>application_type</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zip_code</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>purpose</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>out_prncp</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>funded_amnt</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member_id</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loan_amnt</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>url</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>funded_amnt_inv</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>term</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>int_rate</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>installment</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grade</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sub_grade</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>home_ownership</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>emp_length</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>verification_status</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pymnt_plan</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loan_status</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>issue_d</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>annual_inc</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         0\n",
       "id                       0\n",
       "addr_state               0\n",
       "dti                      0\n",
       "revol_bal                0\n",
       "initial_list_status      0\n",
       "out_prncp_inv            0\n",
       "total_pymnt              0\n",
       "total_pymnt_inv          0\n",
       "total_rec_prncp          0\n",
       "total_rec_int            0\n",
       "total_rec_late_fee       0\n",
       "recoveries               0\n",
       "collection_recovery_fee  0\n",
       "last_pymnt_amnt          0\n",
       "policy_code              0\n",
       "application_type         0\n",
       "zip_code                 0\n",
       "purpose                  0\n",
       "out_prncp                0\n",
       "funded_amnt              0\n",
       "member_id                0\n",
       "loan_amnt                0\n",
       "url                      0\n",
       "funded_amnt_inv          0\n",
       "term                     0\n",
       "int_rate                 0\n",
       "installment              0\n",
       "grade                    0\n",
       "sub_grade                0\n",
       "home_ownership           0\n",
       "emp_length               0\n",
       "verification_status      0\n",
       "pymnt_plan               0\n",
       "loan_status              0\n",
       "issue_d                  0\n",
       "annual_inc               4"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(data_old.isnull().sum().sort_values()[data_old.isnull().sum().sort_values()<10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(887374, 74)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_old = data_old.dropna(axis=1, how='all')\n",
    "data_old.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def df_col_low_nan(df_data, min_nan): \n",
    "    # Returns a df with columns with less nan than min_nan\n",
    "    use_col = pd.DataFrame(df_data.isnull().sum().sort_values()[df_data.isnull().sum().sort_values()<min_nan])\n",
    "    use_col.reset_index(drop = False, inplace=True)\n",
    "    use_col.columns = (['col_name','num_NaN'])\n",
    "    new_data = pd.DataFrame()\n",
    "    for i in range(0,len(use_col.col_name)):\n",
    "        df = df_data[use_col.col_name[i]]\n",
    "        new_data = pd.concat([new_data, df], axis=1)\n",
    "    return new_data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(887374, 36)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_old = df_col_low_nan(data_old, 10)\n",
    "data_old.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Index(['loan_amnt', 'hardship_flag', 'revol_bal', 'out_prncp', 'total_pymnt',\n",
       "       'disbursement_method', 'total_pymnt_inv', 'total_rec_late_fee',\n",
       "       'recoveries', 'collection_recovery_fee', 'last_pymnt_amnt',\n",
       "       'application_type', 'total_rec_int', 'addr_state', 'total_rec_prncp',\n",
       "       'verification_status', 'term', 'int_rate', 'grade', 'emp_length',\n",
       "       'home_ownership', 'purpose', 'issue_d', 'loan_status', 'pymnt_plan',\n",
       "       'debt_settlement_flag', 'annual_inc', 'last_credit_pull_d'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_loan_df = pd.read_csv('data_files/loans_2017_clean.csv')\n",
    "print(len(n_loan_df.columns))\n",
    "n_loan_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['loan_amnt',\n",
       " 'revol_bal',\n",
       " 'out_prncp',\n",
       " 'total_pymnt',\n",
       " 'total_pymnt_inv',\n",
       " 'total_rec_late_fee',\n",
       " 'recoveries',\n",
       " 'collection_recovery_fee',\n",
       " 'last_pymnt_amnt',\n",
       " 'application_type',\n",
       " 'total_rec_int',\n",
       " 'addr_state',\n",
       " 'total_rec_prncp',\n",
       " 'verification_status',\n",
       " 'term',\n",
       " 'int_rate',\n",
       " 'grade',\n",
       " 'emp_length',\n",
       " 'home_ownership',\n",
       " 'purpose',\n",
       " 'issue_d',\n",
       " 'loan_status',\n",
       " 'pymnt_plan',\n",
       " 'annual_inc']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l_error = list(n_loan_df.columns)\n",
    "l_error.remove('last_credit_pull_d')\n",
    "l_error.remove('hardship_flag')\n",
    "l_error.remove('disbursement_method') \n",
    "l_error.remove('debt_settlement_flag')# remove column names that might give you error here\n",
    "l_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(887374, 24)\n"
     ]
    }
   ],
   "source": [
    "new_df = pd.DataFrame()\n",
    "for i in range(0,len(l_error)):\n",
    "    col = l_error[i]\n",
    "    df = data_old[col]\n",
    "    new_df = pd.concat([new_df, df], axis=1)\n",
    "print(new_df.shape)\n",
    "n_loan_df = new_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaning target feature (loan_status)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n_loan_df = new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Fully Paid', 'Does not meet the credit policy. Status:Fully Paid',\n",
       "       'Does not meet the credit policy. Status:Charged Off', 'Current',\n",
       "       'Charged Off', 'Issued', 'In Grace Period', 'Late (31-120 days)',\n",
       "       'Default', 'Late (16-30 days)'], dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_loan_df.loan_status.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Fully Paid', 'Problem Loan', 'Current', 'Issued'], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Collapsing all but Fully Paid into Problem Loan\n",
    "n_loan_df.loan_status = n_loan_df.loan_status.str.replace(r'(^.*Paid$)', 'Fully Paid')\n",
    "n_loan_df.loan_status = n_loan_df.loan_status.str.replace(r'(^.*Charged Off$)', 'Charged Off')\n",
    "n_loan_df.loan_status = n_loan_df.loan_status.str.replace(r'(^Late.*$)', 'Late')\n",
    "n_loan_df.loan_status = n_loan_df.loan_status.str.replace(r'(Late)|(Charged Off)|(In Grace Period)|(Default)', \n",
    "                                                          'Problem Loan')\n",
    "n_loan_df.loan_status.unique()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Fully Paid', 'Problem Loan'], dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Removing fully paid loans\n",
    "n_loan_df = n_loan_df[n_loan_df.loan_status.str.contains('Current')==False]\n",
    "n_loan_df = n_loan_df[n_loan_df.loan_status.str.contains('Issued')==False]\n",
    "\n",
    "n_loan_df.loan_status.unique()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature cleaning and selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Custom functiones needed later\n",
    "def types_of_columns(df):\n",
    "    # creates two separate dataframes \n",
    "    # one with float column names and the other with object column names\n",
    "    non_num_cols = df.dtypes[df.dtypes == 'object']\n",
    "    float_cols   = df.dtypes[df.dtypes != 'object']\n",
    "    return non_num_cols, float_cols\n",
    "\n",
    "def column_checker (df1, df2, num):\n",
    "    # df1 dataframe whit the columns\n",
    "    # df2 dataframe with colum names\n",
    "    for col in range(0,len(df2)):\n",
    "        col = df2.index[col]\n",
    "        values = df1[col].unique()\n",
    "        if len(values) > num:\n",
    "           print('\\n', col, '\\n',len(values),'\\n',values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sorting the columns by type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "col_types_original = types_of_columns(n_loan_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Non-Numeric Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loan_amnt                  float64\n",
       "revol_bal                  float64\n",
       "out_prncp                  float64\n",
       "total_pymnt                float64\n",
       "total_pymnt_inv            float64\n",
       "total_rec_late_fee         float64\n",
       "recoveries                 float64\n",
       "collection_recovery_fee    float64\n",
       "last_pymnt_amnt            float64\n",
       "total_rec_int              float64\n",
       "total_rec_prncp            float64\n",
       "int_rate                   float64\n",
       "annual_inc                 float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nn_col_list = col_types_original[1]\n",
    "nn_col_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " application_type \n",
      " 2 \n",
      " ['INDIVIDUAL' 'JOINT']\n",
      "\n",
      " addr_state \n",
      " 51 \n",
      " ['CA' 'IL' 'NY' 'TX' 'CT' 'RI' 'MD' 'MA' 'SC' 'OH' 'MI' 'MO' 'KY' 'MS' 'KS'\n",
      " 'DC' 'GA' 'NC' 'NJ' 'WA' 'FL' 'ID' 'AZ' 'LA' 'CO' 'WI' 'WY' 'IN' 'VA' 'ME'\n",
      " 'NE' 'NH' 'VT' 'UT' 'NM' 'MT' 'AL' 'AR' 'TN' 'SD' 'DE' 'NV' 'OR' 'MN' 'HI'\n",
      " 'PA' 'IA' 'AK' 'OK' 'WV' 'ND']\n",
      "\n",
      " verification_status \n",
      " 3 \n",
      " ['Verified' 'Not Verified' 'Source Verified']\n",
      "\n",
      " term \n",
      " 2 \n",
      " [' 36 months' ' 60 months']\n",
      "\n",
      " grade \n",
      " 7 \n",
      " ['B' 'F' 'D' 'C' 'A' 'E' 'G']\n",
      "\n",
      " emp_length \n",
      " 12 \n",
      " ['< 1 year' '10+ years' '1 year' '6 years' '9 years' '3 years' '5 years'\n",
      " '7 years' '8 years' '2 years' '4 years' 'n/a']\n",
      "\n",
      " home_ownership \n",
      " 6 \n",
      " ['RENT' 'MORTGAGE' 'OWN' 'NONE' 'OTHER' 'ANY']\n",
      "\n",
      " purpose \n",
      " 14 \n",
      " ['debt_consolidation' 'credit_card' 'educational' 'car' 'home_improvement'\n",
      " 'other' 'small_business' 'major_purchase' 'moving' 'medical' 'house'\n",
      " 'vacation' 'wedding' 'renewable_energy']\n",
      "\n",
      " issue_d \n",
      " 103 \n",
      " ['Aug-2009' 'Jul-2008' 'May-2008' 'Apr-2008' 'Aug-2014' 'Mar-2010'\n",
      " 'Nov-2009' 'Aug-2012' 'Sep-2009' 'Feb-2010' 'Apr-2010' 'Jan-2010'\n",
      " 'Mar-2008' 'Jun-2008' 'Sep-2008' 'Feb-2009' 'Dec-2008' 'Aug-2010'\n",
      " 'Oct-2009' 'Aug-2008' 'May-2010' 'Apr-2015' 'Jun-2007' 'Aug-2007'\n",
      " 'Jul-2007' 'Sep-2007' 'Oct-2007' 'Nov-2007' 'Dec-2007' 'Jan-2008'\n",
      " 'Feb-2008' 'Nov-2008' 'May-2011' 'Jan-2015' 'Jan-2009' 'Jul-2010'\n",
      " 'Jul-2009' 'Oct-2008' 'Jun-2009' 'Jun-2010' 'Mar-2009' 'Feb-2011'\n",
      " 'Dec-2010' 'Nov-2010' 'Apr-2009' 'May-2009' 'Jan-2013' 'Dec-2009'\n",
      " 'Nov-2014' 'Jul-2014' 'Jun-2011' 'Jul-2011' 'Sep-2010' 'Jan-2012'\n",
      " 'May-2013' 'May-2015' 'Jun-2012' 'Mar-2011' 'Oct-2010' 'Nov-2011'\n",
      " 'Feb-2015' 'Jan-2014' 'Jan-2011' 'Apr-2012' 'Feb-2012' 'Mar-2012'\n",
      " 'Apr-2011' 'Jun-2013' 'Sep-2011' 'Aug-2015' 'Jul-2012' 'Dec-2015'\n",
      " 'Oct-2011' 'May-2012' 'Jun-2014' 'Aug-2013' 'Aug-2011' 'Oct-2013'\n",
      " 'Nov-2012' 'Sep-2012' 'Dec-2011' 'Oct-2012' 'Mar-2015' 'Dec-2012'\n",
      " 'Oct-2014' 'Mar-2014' 'Dec-2013' 'Apr-2014' 'Mar-2013' 'Dec-2014'\n",
      " 'Feb-2013' 'Nov-2013' 'Jul-2015' 'Apr-2013' 'May-2014' 'Sep-2013'\n",
      " 'Sep-2014' 'Jul-2013' 'Feb-2014' 'Jun-2015' 'Oct-2015' 'Nov-2015'\n",
      " 'Sep-2015']\n",
      "\n",
      " loan_status \n",
      " 2 \n",
      " ['Fully Paid' 'Problem Loan']\n",
      "\n",
      " pymnt_plan \n",
      " 2 \n",
      " ['n' 'y']\n"
     ]
    }
   ],
   "source": [
    "column_checker(n_loan_df, types_of_columns(n_loan_df)[0], 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dropping columns with repetitive or non-relevant information\n",
    "\n",
    "zip_code and addr_state have similar information - keeping addr_state\n",
    "\n",
    "grade and sub_grade also have similar information - keeping grade"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Converting dates and other numbers into actual numeric variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# emp_length\n",
    "# Removing 2110 rows with n/a in emp_length\n",
    "n_loan_df = n_loan_df[n_loan_df.emp_length.str.contains('n/a')==False]\n",
    "# less than one year is now 0\n",
    "n_loan_df.emp_length = n_loan_df.emp_length.str.replace(r'( years)|( year)|(\\+ years)', \"\").str.replace('< 1','0').astype(float)\n",
    "\n",
    "# Dates\n",
    "# issue_d is now delta time since issued\n",
    "n_loan_df.issue_d = ((max(pd.to_datetime(n_loan_df['issue_d'])) - pd.to_datetime(n_loan_df['issue_d'])).dt.days/365).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " application_type \n",
      " 2 \n",
      " ['INDIVIDUAL' 'JOINT']\n",
      "\n",
      " addr_state \n",
      " 51 \n",
      " ['CA' 'IL' 'NY' 'TX' 'CT' 'RI' 'MD' 'MA' 'SC' 'OH' 'MI' 'MO' 'KY' 'MS' 'KS'\n",
      " 'DC' 'GA' 'NC' 'NJ' 'WA' 'FL' 'ID' 'AZ' 'LA' 'CO' 'WI' 'WY' 'IN' 'VA' 'ME'\n",
      " 'NE' 'NH' 'VT' 'UT' 'NM' 'MT' 'AL' 'AR' 'TN' 'SD' 'DE' 'NV' 'OR' 'MN' 'HI'\n",
      " 'PA' 'IA' 'AK' 'OK' 'WV' 'ND']\n",
      "\n",
      " verification_status \n",
      " 3 \n",
      " ['Verified' 'Not Verified' 'Source Verified']\n",
      "\n",
      " term \n",
      " 2 \n",
      " [' 36 months' ' 60 months']\n",
      "\n",
      " grade \n",
      " 7 \n",
      " ['B' 'F' 'D' 'C' 'A' 'E' 'G']\n",
      "\n",
      " home_ownership \n",
      " 6 \n",
      " ['RENT' 'MORTGAGE' 'OWN' 'NONE' 'OTHER' 'ANY']\n",
      "\n",
      " purpose \n",
      " 14 \n",
      " ['debt_consolidation' 'credit_card' 'educational' 'car' 'home_improvement'\n",
      " 'other' 'small_business' 'major_purchase' 'moving' 'medical' 'house'\n",
      " 'vacation' 'wedding' 'renewable_energy']\n",
      "\n",
      " loan_status \n",
      " 2 \n",
      " ['Fully Paid' 'Problem Loan']\n",
      "\n",
      " pymnt_plan \n",
      " 2 \n",
      " ['n' 'y']\n"
     ]
    }
   ],
   "source": [
    "column_checker(n_loan_df, types_of_columns(n_loan_df)[0], 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(266039, 24)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#n_loan_df = pd.read_csv('loans_2017.csv')\n",
    "n_loan_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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AAKBjim4AAAAA0DFFNwAAAADomKIbAAAAAHRM0Q0AeJKqenZVXVNV362qJVX1+ar6xUHn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\nAAAAAOjZ/w/kfQOHRIVUdAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113d1e6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nn_col_list = types_of_columns(n_loan_df)[0].index.drop(['loan_status'])\n",
    "\n",
    "row = 4\n",
    "col = 3\n",
    "\n",
    "fig, axs = pl.subplots(row,col, figsize=(20,30))\n",
    "fig.subplots_adjust(wspace=0.5, hspace=0.25)\n",
    "\n",
    "for num, name in enumerate(nn_col_list):\n",
    "    #print (num, name)\n",
    "    i = num%row\n",
    "    j = num%col\n",
    "    graph = sns.countplot(y = n_loan_df[name], hue=n_loan_df['loan_status'], ax = axs[i,j], linewidth = 1.5)\n",
    "    graph.set(xlabel=name, ylabel='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " application_type\n",
      "INDIVIDUAL    266034\n",
      "JOINT              5\n",
      "Name: loan_status, dtype: int64\n",
      "\n",
      " addr_state\n",
      "AK      695\n",
      "AL     3321\n",
      "AR     1820\n",
      "AZ     6250\n",
      "CA    44813\n",
      "CO     5858\n",
      "CT     3910\n",
      "DC      856\n",
      "DE      712\n",
      "FL    18354\n",
      "GA     8448\n",
      "HI     1575\n",
      "IA       13\n",
      "ID        9\n",
      "IL     9745\n",
      "IN     2993\n",
      "KS     2155\n",
      "KY     2336\n",
      "LA     3130\n",
      "MA     6543\n",
      "MD     6423\n",
      "ME       13\n",
      "MI     6204\n",
      "MN     4706\n",
      "MO     4124\n",
      "MS      513\n",
      "MT      779\n",
      "NC     7246\n",
      "ND       12\n",
      "NE       58\n",
      "NH     1212\n",
      "NJ    10237\n",
      "NM     1434\n",
      "NV     3945\n",
      "NY    22718\n",
      "OH     8104\n",
      "OK     2271\n",
      "OR     3395\n",
      "PA     8989\n",
      "RI     1160\n",
      "SC     2910\n",
      "SD      575\n",
      "TN     2665\n",
      "TX    20552\n",
      "UT     2222\n",
      "VA     8473\n",
      "VT      440\n",
      "WA     6109\n",
      "WI     3231\n",
      "WV     1166\n",
      "WY      617\n",
      "Name: loan_status, dtype: int64\n",
      "\n",
      " verification_status\n",
      "Not Verified       91663\n",
      "Source Verified    80743\n",
      "Verified           93633\n",
      "Name: loan_status, dtype: int64\n",
      "\n",
      " term\n",
      " 36 months    202829\n",
      " 60 months     63210\n",
      "Name: loan_status, dtype: int64\n",
      "\n",
      " grade\n",
      "A    41771\n",
      "B    77075\n",
      "C    69281\n",
      "D    44363\n",
      "E    22160\n",
      "F     8932\n",
      "G     2457\n",
      "Name: loan_status, dtype: int64\n",
      "\n",
      " home_ownership\n",
      "ANY              1\n",
      "MORTGAGE    130484\n",
      "NONE            47\n",
      "OTHER          176\n",
      "OWN          22464\n",
      "RENT        112867\n",
      "Name: loan_status, dtype: int64\n",
      "\n",
      " purpose\n",
      "car                     3690\n",
      "credit_card            51850\n",
      "debt_consolidation    157227\n",
      "educational              413\n",
      "home_improvement       15561\n",
      "house                   1746\n",
      "major_purchase          6499\n",
      "medical                 2943\n",
      "moving                  2120\n",
      "other                  15005\n",
      "renewable_energy         271\n",
      "small_business          5111\n",
      "vacation                1607\n",
      "wedding                 1996\n",
      "Name: loan_status, dtype: int64\n",
      "\n",
      " pymnt_plan\n",
      "n    266032\n",
      "y         7\n",
      "Name: loan_status, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "for col in nn_col_list:\n",
    "    series = n_loan_df.groupby(col)['loan_status'].count()\n",
    "    print ('\\n',series)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Numeric Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(types_of_columns(n_loan_df)[1].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loan_amnt 1342\n",
      "revol_bal 48530\n",
      "out_prncp 18444\n",
      "total_pymnt 252624\n",
      "total_pymnt_inv 245933\n",
      "total_rec_late_fee 5338\n",
      "recoveries 22109\n",
      "collection_recovery_fee 19829\n",
      "last_pymnt_amnt 198581\n",
      "total_rec_int 191175\n",
      "total_rec_prncp 59804\n",
      "int_rate 535\n",
      "emp_length 11\n",
      "issue_d 9\n",
      "annual_inc 18954\n"
     ]
    }
   ],
   "source": [
    "num_col_list = types_of_columns(n_loan_df)[1].index\n",
    "\n",
    "for col in num_col_list:\n",
    "    length = len(n_loan_df[col].unique())\n",
    "    print (col, length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "num_col_list = types_of_columns(n_loan_df)[1].index\n",
    "\n",
    "cor_df = pd.DataFrame()\n",
    "for i in range(0,len(num_col_list)):\n",
    "    cor_df = pd.concat([cor_df, n_loan_df[num_col_list[i]]],axis=1)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11e82eb38>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ul0azlXHp4e0n/IPSLzmZ1Us/58v3lnDtZZcyf+FCR5xarWbZu++wZtlSsvft5+CRI12i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\n1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OzSVtfpYxF6dbTyyKx1PPjeOn53djcSo4NbXc/x6Az0//neO2+6kjqEsnB9Do/MWo+t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\n9FQFvp4eBPhq6BDkw9FTFdSZLFgkHMguo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ogorJN1/MvdY1GakhUUFBQUFBQUWsG5+CpZ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E+Bq4Gvgc+BiYI6WMFUL8H4CUslII8SwwTEo53xj3QqAPMAnwAFKEEO9IKa3GraSU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\nHwsa/Z1lAK0rAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e851518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = pl.subplots(figsize=(10, 8))\n",
    "corr = cor_df.corr()\n",
    "sns.heatmap(corr, mask=np.zeros_like(corr, dtype=np.bool), cmap=sns.diverging_palette(250, 15, as_cmap=True),\n",
    "            square=True, ax=ax, annot=True, vmin=0, vmax=1, linewidths=2, xticklabels=1, yticklabels=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gYeAv+1Q1qmplVS2pqiXz58/vVzW0FYODg5xyyikMDg72uyrS9vSlXbMNkyRJ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\nAWJNvAmgD5R6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1183582e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "row1 = 4\n",
    "col1 = 4\n",
    "\n",
    "fig, axs = pl.subplots(row1,col1, figsize=(20,15))\n",
    "fig.subplots_adjust(wspace=0.5, hspace=0.25)\n",
    "\n",
    "for num, name in enumerate(num_col_list):\n",
    "    i = num//row1\n",
    "    j = num%col1\n",
    "    graph = sns.boxplot(y = n_loan_df[name], ax = axs[i,j], linewidth = 1.5)\n",
    "    graph.set(xlabel=name, ylabel='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n_loan_df = n_loan_df[n_loan_df.annual_inc.isnull()==False]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Removing outgroups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "33385 outliers found in more than one feature will be remove\n",
      "(266035, 24) (232650, 24)\n"
     ]
    }
   ],
   "source": [
    "n_loans = n_loan_df.reset_index(drop=True)\n",
    "# ADAPTE FROM CUSTOMER SEGMENTATION PROJECT\n",
    "outliers_all = np.array([], dtype='int64')\n",
    "for column in types_of_columns(n_loans)[1].index:\n",
    "    Q1 = np.percentile(n_loans[column], 25)\n",
    "    Q3 = np.percentile(n_loans[column], 75)\n",
    "    step = (Q3 - Q1)*2\n",
    "    out_list_feat=list(n_loans[~((n_loans[column] >= Q1 - step) & (n_loans[column] <= Q3 + step))].index)\n",
    "    outliers_all = np.append(outliers_all, out_list_feat)\n",
    "\n",
    "from collections import Counter\n",
    "out_count = Counter(outliers_all)\n",
    "out_customer =[]\n",
    "for customer, count in out_count.items():\n",
    "    if count > 1:\n",
    "        out_customer.append(customer.astype(int))\n",
    "outliers  = list(sorted(out_customer))\n",
    "\n",
    "print(\"%d outliers found in more than one feature will be remove\" % len(outliers))\n",
    "\n",
    "n_loan_no_out = n_loans.drop(n_loans.index[outliers])\n",
    "    \n",
    "print(n_loan_df.shape, n_loan_no_out.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1zCSTJEnSNJTk0iSPJbm7q+zgJBuSPND+HtT13blJhpN8O8kpXeXHJbmrfXdh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\npIH9f/gwizKbdUa0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1147ffe48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "row1 = 4\n",
    "col1 = 4\n",
    "\n",
    "fig, axs = pl.subplots(row1,col1, figsize=(20,15))\n",
    "fig.subplots_adjust(wspace=0.5, hspace=0.25)\n",
    "\n",
    "for num, name in enumerate(num_col_list):\n",
    "    i = num//row1\n",
    "    j = num%col1\n",
    "    graph = sns.boxplot(y = n_loan_no_out[name], ax = axs[i,j], linewidth = 1.5)\n",
    "    graph.set(xlabel=name, ylabel='')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Encoding categorical columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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",
       "    }\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_amnt</th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>out_prncp</th>\n",
       "      <th>total_pymnt</th>\n",
       "      <th>total_pymnt_inv</th>\n",
       "      <th>total_rec_late_fee</th>\n",
       "      <th>recoveries</th>\n",
       "      <th>collection_recovery_fee</th>\n",
       "      <th>last_pymnt_amnt</th>\n",
       "      <th>total_rec_int</th>\n",
       "      <th>total_rec_prncp</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>emp_length</th>\n",
       "      <th>issue_d</th>\n",
       "      <th>annual_inc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>232650.000000</td>\n",
       "      <td>232650.000000</td>\n",
       "      <td>232650.000000</td>\n",
       "      <td>232650.000000</td>\n",
       "      <td>232650.000000</td>\n",
       "      <td>232650.000000</td>\n",
       "      <td>232650.000000</td>\n",
       "      <td>232650.0</td>\n",
       "      <td>232650.000000</td>\n",
       "      <td>232650.000000</td>\n",
       "      <td>232650.000000</td>\n",
       "      <td>232650.00000</td>\n",
       "      <td>232650.000000</td>\n",
       "      <td>232650.000000</td>\n",
       "      <td>2.326500e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>13143.957124</td>\n",
       "      <td>14160.155212</td>\n",
       "      <td>745.059624</td>\n",
       "      <td>13168.609075</td>\n",
       "      <td>13055.630472</td>\n",
       "      <td>0.397936</td>\n",
       "      <td>1.514911</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6367.716661</td>\n",
       "      <td>1798.150853</td>\n",
       "      <td>11368.545383</td>\n",
       "      <td>13.55390</td>\n",
       "      <td>5.806770</td>\n",
       "      <td>2.044444</td>\n",
       "      <td>7.068893e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7610.114282</td>\n",
       "      <td>14419.720687</td>\n",
       "      <td>3364.528120</td>\n",
       "      <td>8842.589509</td>\n",
       "      <td>8854.904457</td>\n",
       "      <td>3.814432</td>\n",
       "      <td>134.768893</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7032.455246</td>\n",
       "      <td>1715.128933</td>\n",
       "      <td>7841.298550</td>\n",
       "      <td>4.29026</td>\n",
       "      <td>3.629967</td>\n",
       "      <td>1.516609</td>\n",
       "      <td>5.345573e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>500.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.32000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.896000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7200.000000</td>\n",
       "      <td>5782.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6319.917015</td>\n",
       "      <td>6213.375000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>502.822500</td>\n",
       "      <td>626.320000</td>\n",
       "      <td>5000.010000</td>\n",
       "      <td>10.64000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.500000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>12000.000000</td>\n",
       "      <td>10716.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11352.635000</td>\n",
       "      <td>11249.205000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4089.520000</td>\n",
       "      <td>1288.510000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>13.33000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.200000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>18000.000000</td>\n",
       "      <td>18436.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18186.567500</td>\n",
       "      <td>18055.827500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9968.712500</td>\n",
       "      <td>2391.415000</td>\n",
       "      <td>16000.000000</td>\n",
       "      <td>16.29000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.500000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>35000.000000</td>\n",
       "      <td>814300.000000</td>\n",
       "      <td>35000.000000</td>\n",
       "      <td>42252.731954</td>\n",
       "      <td>42133.210000</td>\n",
       "      <td>294.680000</td>\n",
       "      <td>29623.350000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36475.590000</td>\n",
       "      <td>20295.760000</td>\n",
       "      <td>35000.030000</td>\n",
       "      <td>28.99000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.706582e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           loan_amnt      revol_bal      out_prncp    total_pymnt  \\\n",
       "count  232650.000000  232650.000000  232650.000000  232650.000000   \n",
       "mean    13143.957124   14160.155212     745.059624   13168.609075   \n",
       "std      7610.114282   14419.720687    3364.528120    8842.589509   \n",
       "min       500.000000       0.000000       0.000000       0.000000   \n",
       "25%      7200.000000    5782.000000       0.000000    6319.917015   \n",
       "50%     12000.000000   10716.000000       0.000000   11352.635000   \n",
       "75%     18000.000000   18436.750000       0.000000   18186.567500   \n",
       "max     35000.000000  814300.000000   35000.000000   42252.731954   \n",
       "\n",
       "       total_pymnt_inv  total_rec_late_fee     recoveries  \\\n",
       "count    232650.000000       232650.000000  232650.000000   \n",
       "mean      13055.630472            0.397936       1.514911   \n",
       "std        8854.904457            3.814432     134.768893   \n",
       "min           0.000000            0.000000       0.000000   \n",
       "25%        6213.375000            0.000000       0.000000   \n",
       "50%       11249.205000            0.000000       0.000000   \n",
       "75%       18055.827500            0.000000       0.000000   \n",
       "max       42133.210000          294.680000   29623.350000   \n",
       "\n",
       "       collection_recovery_fee  last_pymnt_amnt  total_rec_int  \\\n",
       "count                 232650.0    232650.000000  232650.000000   \n",
       "mean                       0.0      6367.716661    1798.150853   \n",
       "std                        0.0      7032.455246    1715.128933   \n",
       "min                        0.0         0.000000       0.000000   \n",
       "25%                        0.0       502.822500     626.320000   \n",
       "50%                        0.0      4089.520000    1288.510000   \n",
       "75%                        0.0      9968.712500    2391.415000   \n",
       "max                        0.0     36475.590000   20295.760000   \n",
       "\n",
       "       total_rec_prncp      int_rate     emp_length        issue_d  \\\n",
       "count    232650.000000  232650.00000  232650.000000  232650.000000   \n",
       "mean      11368.545383      13.55390       5.806770       2.044444   \n",
       "std        7841.298550       4.29026       3.629967       1.516609   \n",
       "min           0.000000       5.32000       0.000000       0.000000   \n",
       "25%        5000.010000      10.64000       2.000000       1.000000   \n",
       "50%       10000.000000      13.33000       6.000000       2.000000   \n",
       "75%       16000.000000      16.29000      10.000000       3.000000   \n",
       "max       35000.030000      28.99000      10.000000       8.000000   \n",
       "\n",
       "         annual_inc  \n",
       "count  2.326500e+05  \n",
       "mean   7.068893e+04  \n",
       "std    5.345573e+04  \n",
       "min    1.896000e+03  \n",
       "25%    4.500000e+04  \n",
       "50%    6.200000e+04  \n",
       "75%    8.500000e+04  \n",
       "max    8.706582e+06  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_loan_no_out.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "\n",
       "    .dataframe tbody tr th {\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>loan_amnt</th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>out_prncp</th>\n",
       "      <th>total_pymnt</th>\n",
       "      <th>total_pymnt_inv</th>\n",
       "      <th>total_rec_late_fee</th>\n",
       "      <th>recoveries</th>\n",
       "      <th>collection_recovery_fee</th>\n",
       "      <th>last_pymnt_amnt</th>\n",
       "      <th>application_type</th>\n",
       "      <th>...</th>\n",
       "      <th>term</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>grade</th>\n",
       "      <th>emp_length</th>\n",
       "      <th>home_ownership</th>\n",
       "      <th>purpose</th>\n",
       "      <th>issue_d</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>pymnt_plan</th>\n",
       "      <th>annual_inc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25000.0</td>\n",
       "      <td>28854.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29324.32</td>\n",
       "      <td>21811.70</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7392.08</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>11.89</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>0</td>\n",
       "      <td>85000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>10125.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1207.76</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>538.97</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>16.08</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>0</td>\n",
       "      <td>30000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7000.0</td>\n",
       "      <td>33623.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8215.45</td>\n",
       "      <td>684.85</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>228.48</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>10.71</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>0</td>\n",
       "      <td>65000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1200.0</td>\n",
       "      <td>2584.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1457.31</td>\n",
       "      <td>1457.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.73</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>13.11</td>\n",
       "      <td>2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>0</td>\n",
       "      <td>54000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10800.0</td>\n",
       "      <td>3511.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13195.27</td>\n",
       "      <td>13061.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>398.00</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>13.57</td>\n",
       "      <td>2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>0</td>\n",
       "      <td>32000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   loan_amnt  revol_bal  out_prncp  total_pymnt  total_pymnt_inv  \\\n",
       "0    25000.0    28854.0        0.0     29324.32         21811.70   \n",
       "1     1000.0    10125.0        0.0      1207.76             0.00   \n",
       "2     7000.0    33623.0        0.0      8215.45           684.85   \n",
       "5     1200.0     2584.0        0.0      1457.31          1457.31   \n",
       "6    10800.0     3511.0        0.0     13195.27         13061.31   \n",
       "\n",
       "   total_rec_late_fee  recoveries  collection_recovery_fee  last_pymnt_amnt  \\\n",
       "0                 0.0         0.0                      0.0          7392.08   \n",
       "1                 0.0         0.0                      0.0           538.97   \n",
       "2                 0.0         0.0                      0.0           228.48   \n",
       "5                 0.0         0.0                      0.0            42.73   \n",
       "6                 0.0         0.0                      0.0           398.00   \n",
       "\n",
       "   application_type     ...      term  int_rate  grade  emp_length  \\\n",
       "0                 0     ...         0     11.89      1         0.0   \n",
       "1                 0     ...         0     16.08      5         0.0   \n",
       "2                 0     ...         0     10.71      1         0.0   \n",
       "5                 0     ...         0     13.11      2        10.0   \n",
       "6                 0     ...         0     13.57      2         6.0   \n",
       "\n",
       "   home_ownership  purpose  issue_d  loan_status  pymnt_plan  annual_inc  \n",
       "0               5        2        6   Fully Paid           0     85000.0  \n",
       "1               5        2        7   Fully Paid           0     30000.0  \n",
       "2               5        1        7   Fully Paid           0     65000.0  \n",
       "5               4        2        5   Fully Paid           0     54000.0  \n",
       "6               5        2        6   Fully Paid           0     32000.0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for col in (nn_col_list):\n",
    "    n_loan_no_out[col]=l_encoder.fit_transform(n_loan_no_out[col])\n",
    "n_loan_no_out.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_loan_no_out.loan_status = l_encoder.fit_transform(n_loan_no_out.loan_status)\n",
    "n_loan_no_out.loan_status.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(232650, 24)\n"
     ]
    },
    {
     "data": {
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       "    }\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_amnt</th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>out_prncp</th>\n",
       "      <th>total_pymnt</th>\n",
       "      <th>total_pymnt_inv</th>\n",
       "      <th>total_rec_late_fee</th>\n",
       "      <th>recoveries</th>\n",
       "      <th>collection_recovery_fee</th>\n",
       "      <th>last_pymnt_amnt</th>\n",
       "      <th>application_type</th>\n",
       "      <th>...</th>\n",
       "      <th>term</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>grade</th>\n",
       "      <th>emp_length</th>\n",
       "      <th>home_ownership</th>\n",
       "      <th>purpose</th>\n",
       "      <th>issue_d</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>pymnt_plan</th>\n",
       "      <th>annual_inc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25000.0</td>\n",
       "      <td>28854.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29324.320000</td>\n",
       "      <td>21811.70</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7392.08</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>11.89</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>85000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>10125.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1207.760000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>538.97</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>16.08</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7000.0</td>\n",
       "      <td>33623.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8215.450000</td>\n",
       "      <td>684.85</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>228.48</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>10.71</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>65000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1200.0</td>\n",
       "      <td>2584.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1457.310000</td>\n",
       "      <td>1457.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.73</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>13.11</td>\n",
       "      <td>2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>54000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10800.0</td>\n",
       "      <td>3511.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13195.270000</td>\n",
       "      <td>13061.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>398.00</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>13.57</td>\n",
       "      <td>2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7200.0</td>\n",
       "      <td>3874.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9100.804663</td>\n",
       "      <td>9100.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3559.18</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>19.05</td>\n",
       "      <td>3</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>58000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7500.0</td>\n",
       "      <td>33667.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5843.800000</td>\n",
       "      <td>571.22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>162.63</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>10.08</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>85000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3000.0</td>\n",
       "      <td>4740.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3703.380000</td>\n",
       "      <td>3687.23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>111.23</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>14.26</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>80800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4000.0</td>\n",
       "      <td>50807.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4451.170000</td>\n",
       "      <td>4339.90</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1454.31</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>7.88</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>148000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3200.0</td>\n",
       "      <td>3198.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3413.920000</td>\n",
       "      <td>3413.92</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2592.23</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>9.88</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>54000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    loan_amnt  revol_bal  out_prncp   total_pymnt  total_pymnt_inv  \\\n",
       "0     25000.0    28854.0        0.0  29324.320000         21811.70   \n",
       "1      1000.0    10125.0        0.0   1207.760000             0.00   \n",
       "2      7000.0    33623.0        0.0   8215.450000           684.85   \n",
       "5      1200.0     2584.0        0.0   1457.310000          1457.31   \n",
       "6     10800.0     3511.0        0.0  13195.270000         13061.31   \n",
       "7      7200.0     3874.0        0.0   9100.804663          9100.80   \n",
       "8      7500.0    33667.0        0.0   5843.800000           571.22   \n",
       "9      3000.0     4740.0        0.0   3703.380000          3687.23   \n",
       "10     4000.0    50807.0        0.0   4451.170000          4339.90   \n",
       "12     3200.0     3198.0        0.0   3413.920000          3413.92   \n",
       "\n",
       "    total_rec_late_fee  recoveries  collection_recovery_fee  last_pymnt_amnt  \\\n",
       "0                  0.0         0.0                      0.0          7392.08   \n",
       "1                  0.0         0.0                      0.0           538.97   \n",
       "2                  0.0         0.0                      0.0           228.48   \n",
       "5                  0.0         0.0                      0.0            42.73   \n",
       "6                  0.0         0.0                      0.0           398.00   \n",
       "7                  0.0         0.0                      0.0          3559.18   \n",
       "8                  0.0         0.0                      0.0           162.63   \n",
       "9                  0.0         0.0                      0.0           111.23   \n",
       "10                 0.0         0.0                      0.0          1454.31   \n",
       "12                 0.0         0.0                      0.0          2592.23   \n",
       "\n",
       "    application_type     ...      term  int_rate  grade  emp_length  \\\n",
       "0                  0     ...         0     11.89      1         0.0   \n",
       "1                  0     ...         0     16.08      5         0.0   \n",
       "2                  0     ...         0     10.71      1         0.0   \n",
       "5                  0     ...         0     13.11      2        10.0   \n",
       "6                  0     ...         0     13.57      2         6.0   \n",
       "7                  0     ...         0     19.05      3         9.0   \n",
       "8                  0     ...         0     10.08      1         3.0   \n",
       "9                  0     ...         0     14.26      2         3.0   \n",
       "10                 0     ...         0      7.88      0         0.0   \n",
       "12                 0     ...         0      9.88      1         5.0   \n",
       "\n",
       "    home_ownership  purpose  issue_d  loan_status  pymnt_plan  annual_inc  \n",
       "0                5        2        6            0           0     85000.0  \n",
       "1                5        2        7            0           0     30000.0  \n",
       "2                5        1        7            0           0     65000.0  \n",
       "5                4        2        5            0           0     54000.0  \n",
       "6                5        2        6            0           0     32000.0  \n",
       "7                5        2        3            0           0     58000.0  \n",
       "8                5        2        7            0           0     85000.0  \n",
       "9                1        1        6            0           0     80800.0  \n",
       "10               1        1        5            0           0    148000.0  \n",
       "12               5        2        5            0           0     54000.0  \n",
       "\n",
       "[10 rows x 24 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(n_loan_no_out.shape)\n",
    "n_loan_no_out.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Spliting the data for the model\n",
    "\n",
    "Spliting into features and target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loans original data 232650, target: 232650, features: 232650\n"
     ]
    }
   ],
   "source": [
    "target = n_loan_no_out.loan_status.values\n",
    "features = n_loan_no_out.drop(['loan_status'], axis=1).values\n",
    "print ('Loans original data %d, target: %d, features: %d' % (n_loan_no_out.shape[0], target.shape[0], features.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building an initial model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import itertools\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=pl.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    #print(cm)\n",
    "\n",
    "    pl.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    pl.title(title)\n",
    "    pl.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    pl.xticks(tick_marks, classes, rotation=45)\n",
    "    pl.yticks(tick_marks, classes)\n",
    "\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        pl.text(j, i, format(cm[i, j], fmt),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    pl.tight_layout()\n",
    "    pl.ylabel('True label')\n",
    "    pl.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# USING TRAIN, VALIDATION, TEST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loans original data 232650, target: 232650, features: 232650\n"
     ]
    }
   ],
   "source": [
    "target = n_loan_no_out.loan_status.values\n",
    "features = n_loan_no_out.drop(['loan_status'], axis=1).values\n",
    "print ('Loans original data %d, target: %d, features: %d' % (n_loan_no_out.shape[0], target.shape[0], features.shape[0]))\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.40, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X_old = sc.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "classifier = Sequential()\n",
    "classifier.add(Dense(units = 14, kernel_initializer = 'uniform', activation = 'relu', input_dim = 23))\n",
    "classifier.add(Dense(units = 14, kernel_initializer = 'uniform', activation = 'relu'))\n",
    "classifier.add(Dense(units = 1, kernel_initializer = 'uniform', activation = 'sigmoid'))\n",
    "classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "139590/139590 [==============================] - 50s - loss: 0.0314 - acc: 0.9916    \n",
      "Epoch 2/10\n",
      "139590/139590 [==============================] - 49s - loss: 0.0169 - acc: 0.9960    \n",
      "Epoch 3/10\n",
      "139590/139590 [==============================] - 54s - loss: 0.0155 - acc: 0.9965    \n",
      "Epoch 4/10\n",
      "139590/139590 [==============================] - 63s - loss: 0.0145 - acc: 0.9969    \n",
      "Epoch 5/10\n",
      "139590/139590 [==============================] - 53s - loss: 0.0141 - acc: 0.9971    \n",
      "Epoch 6/10\n",
      "139590/139590 [==============================] - 51s - loss: 0.0136 - acc: 0.9972    \n",
      "Epoch 7/10\n",
      "139590/139590 [==============================] - 51s - loss: 0.0131 - acc: 0.9973    \n",
      "Epoch 8/10\n",
      "139590/139590 [==============================] - 51s - loss: 0.0127 - acc: 0.9974    \n",
      "Epoch 9/10\n",
      "139590/139590 [==============================] - 49s - loss: 0.0123 - acc: 0.9975    \n",
      "Epoch 10/10\n",
      "139590/139590 [==============================] - 47s - loss: 0.0119 - acc: 0.9977    \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x135adccc0>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Training with training set\n",
    "classifier.fit(X_old, y_train, batch_size = 10, epochs = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Predicting on the test set \n",
    "pred_old = classifier.predict(X_test)\n",
    "pred_old = (pred_old > 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[78495,    13],\n",
       "       [ 2494, 12058]])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "cnf_matrix = confusion_matrix(y_test, pred_old, labels=[0, 1])\n",
    "cnf_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n"
     ]
    },
    {
     "data": {
      "image/png": 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8j6QX0rbLlHpHklaTNCqVPyGpW1NnmFuClfRXSXMkvZjXMVqbjz+ez+HHHscl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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1361f78d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pl.figure()\n",
    "plot_confusion_matrix(cnf_matrix, classes=n_loan_no_out.loan_status.unique(),\n",
    "                      cmap=pl.cm.Reds, normalize=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.90583330203207746"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "f1_score(y_test, pred_old.round(), average='binary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy = 97.3060%, Loss = 0.4338\n"
     ]
    }
   ],
   "source": [
    "loss, accuracy = classifier.evaluate(X_test, y_test,batch_size=128, verbose=0)\n",
    "print(\"Accuracy = {:.4f}%, Loss = {:.4f}\".format(accuracy* 100, loss))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precission 0.9692 and Recall 0.9998, of the proposed ANN on the 2016 dataset\n"
     ]
    }
   ],
   "source": [
    "Precission = 78495/(78495+2494)\n",
    "Recall = 78495/(78495+13)\n",
    "\n",
    "print (\"Precission {:.4f} and Recall {:.4f}, of the proposed ANN on the 2016 dataset\".format(Precission, Recall))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}