Predicting loan deferral using ANN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting Loan Defaulting using ANN and Deep Learning\n",
    "\n",
    "## The Data\n",
    "\n",
    "We will be using a subset of the LendingClub DataSet obtained from Kaggle: https://www.kaggle.com/wordsforthewise/lending-club\n",
    "\n",
    "## NOTE: Do not download the full zip from the link! We provide a special version of this file that has some extra feature engineering for you to do. You won't be able to follow along with the original file!\n",
    "\n",
    "LendingClub is a US peer-to-peer lending company, headquartered in San Francisco, California.[3] It was the first peer-to-peer lender to register its offerings as securities with the Securities and Exchange Commission (SEC), and to offer loan trading on a secondary market. LendingClub is the world's largest peer-to-peer lending platform.\n",
    "\n",
    "### Our Goal\n",
    "\n",
    "Given historical data on loans given out with information on whether or not the borrower defaulted (charge-off), can we build a model thatcan predict wether or nor a borrower will pay back their loan? This way in the future when we get a new potential customer we can assess whether or not they are likely to pay back the loan. Keep in mind classification metrics when evaluating the performance of your model!\n",
    "\n",
    "The \"loan_status\" column contains our label.\n",
    "\n",
    "### Data Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "-----\n",
    "There are many LendingClub data sets on Kaggle. Here is the information on this particular data set:\n",
    "\n",
    "<table border=\"1\" class=\"dataframe\">\n",
    "  <thead>\n",
    "    <tr style=\"text-align: right;\">\n",
    "      <th></th>\n",
    "      <th>LoanStatNew</th>\n",
    "      <th>Description</th>\n",
    "    </tr>\n",
    "  </thead>\n",
    "  <tbody>\n",
    "    <tr>\n",
    "      <th>0</th>\n",
    "      <td>loan_amnt</td>\n",
    "      <td>The listed amount of the loan applied for by the borrower. If at some point in time, the credit department reduces the loan amount, then it will be reflected in this value.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>1</th>\n",
    "      <td>term</td>\n",
    "      <td>The number of payments on the loan. Values are in months and can be either 36 or 60.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>2</th>\n",
    "      <td>int_rate</td>\n",
    "      <td>Interest Rate on the loan</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>3</th>\n",
    "      <td>installment</td>\n",
    "      <td>The monthly payment owed by the borrower if the loan originates.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>4</th>\n",
    "      <td>grade</td>\n",
    "      <td>LC assigned loan grade</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>5</th>\n",
    "      <td>sub_grade</td>\n",
    "      <td>LC assigned loan subgrade</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>6</th>\n",
    "      <td>emp_title</td>\n",
    "      <td>The job title supplied by the Borrower when applying for the loan.*</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>7</th>\n",
    "      <td>emp_length</td>\n",
    "      <td>Employment length in years. Possible values are between 0 and 10 where 0 means less than one year and 10 means ten or more years.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>8</th>\n",
    "      <td>home_ownership</td>\n",
    "      <td>The home ownership status provided by the borrower during registration or obtained from the credit report. Our values are: RENT, OWN, MORTGAGE, OTHER</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>9</th>\n",
    "      <td>annual_inc</td>\n",
    "      <td>The self-reported annual income provided by the borrower during registration.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>10</th>\n",
    "      <td>verification_status</td>\n",
    "      <td>Indicates if income was verified by LC, not verified, or if the income source was verified</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>11</th>\n",
    "      <td>issue_d</td>\n",
    "      <td>The month which the loan was funded</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>12</th>\n",
    "      <td>loan_status</td>\n",
    "      <td>Current status of the loan</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>13</th>\n",
    "      <td>purpose</td>\n",
    "      <td>A category provided by the borrower for the loan request.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>14</th>\n",
    "      <td>title</td>\n",
    "      <td>The loan title provided by the borrower</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>15</th>\n",
    "      <td>zip_code</td>\n",
    "      <td>The first 3 numbers of the zip code provided by the borrower in the loan application.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>16</th>\n",
    "      <td>addr_state</td>\n",
    "      <td>The state provided by the borrower in the loan application</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>17</th>\n",
    "      <td>dti</td>\n",
    "      <td>A ratio calculated using the borrower’s total monthly debt payments on the total debt obligations, excluding mortgage and the requested LC loan, divided by the borrower’s self-reported monthly income.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>18</th>\n",
    "      <td>earliest_cr_line</td>\n",
    "      <td>The month the borrower's earliest reported credit line was opened</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>19</th>\n",
    "      <td>open_acc</td>\n",
    "      <td>The number of open credit lines in the borrower's credit file.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>20</th>\n",
    "      <td>pub_rec</td>\n",
    "      <td>Number of derogatory public records</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>21</th>\n",
    "      <td>revol_bal</td>\n",
    "      <td>Total credit revolving balance</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>22</th>\n",
    "      <td>revol_util</td>\n",
    "      <td>Revolving line utilization rate, or the amount of credit the borrower is using relative to all available revolving credit.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>23</th>\n",
    "      <td>total_acc</td>\n",
    "      <td>The total number of credit lines currently in the borrower's credit file</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>24</th>\n",
    "      <td>initial_list_status</td>\n",
    "      <td>The initial listing status of the loan. Possible values are – W, F</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>25</th>\n",
    "      <td>application_type</td>\n",
    "      <td>Indicates whether the loan is an individual application or a joint application with two co-borrowers</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>26</th>\n",
    "      <td>mort_acc</td>\n",
    "      <td>Number of mortgage accounts.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>27</th>\n",
    "      <td>pub_rec_bankruptcies</td>\n",
    "      <td>Number of public record bankruptcies</td>\n",
    "    </tr>\n",
    "  </tbody>\n",
    "</table>\n",
    "\n",
    "---\n",
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Starter Code\n",
    "\n",
    "#### Note: We also provide feature information on the data as a .csv file for easy lookup throughout the notebook:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_info = pd.read_csv('../DATA/lending_club_info.csv',index_col='LoanStatNew')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Revolving line utilization rate, or the amount of credit the borrower is using relative to all available revolving credit.\n"
     ]
    }
   ],
   "source": [
    "print(data_info.loc['revol_util']['Description'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Description</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LoanStatNew</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>loan_amnt</th>\n",
       "      <td>The listed amount of the loan applied for by t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>term</th>\n",
       "      <td>The number of payments on the loan. Values are...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>int_rate</th>\n",
       "      <td>Interest Rate on the loan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>installment</th>\n",
       "      <td>The monthly payment owed by the borrower if th...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grade</th>\n",
       "      <td>LC assigned loan grade</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   Description\n",
       "LoanStatNew                                                   \n",
       "loan_amnt    The listed amount of the loan applied for by t...\n",
       "term         The number of payments on the loan. Values are...\n",
       "int_rate                             Interest Rate on the loan\n",
       "installment  The monthly payment owed by the borrower if th...\n",
       "grade                                   LC assigned loan grade"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_info.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def feat_info(col_name):\n",
    "    print(data_info.loc[col_name]['Description'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of mortgage accounts.\n"
     ]
    }
   ],
   "source": [
    "feat_info('mort_acc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the data and other imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# might be needed depending on your version of Jupyter\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('../DATA/lending_club_loan_two.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 396030 entries, 0 to 396029\n",
      "Data columns (total 27 columns):\n",
      " #   Column                Non-Null Count   Dtype  \n",
      "---  ------                --------------   -----  \n",
      " 0   loan_amnt             396030 non-null  float64\n",
      " 1   term                  396030 non-null  object \n",
      " 2   int_rate              396030 non-null  float64\n",
      " 3   installment           396030 non-null  float64\n",
      " 4   grade                 396030 non-null  object \n",
      " 5   sub_grade             396030 non-null  object \n",
      " 6   emp_title             373103 non-null  object \n",
      " 7   emp_length            377729 non-null  object \n",
      " 8   home_ownership        396030 non-null  object \n",
      " 9   annual_inc            396030 non-null  float64\n",
      " 10  verification_status   396030 non-null  object \n",
      " 11  issue_d               396030 non-null  object \n",
      " 12  loan_status           396030 non-null  object \n",
      " 13  purpose               396030 non-null  object \n",
      " 14  title                 394275 non-null  object \n",
      " 15  dti                   396030 non-null  float64\n",
      " 16  earliest_cr_line      396030 non-null  object \n",
      " 17  open_acc              396030 non-null  float64\n",
      " 18  pub_rec               396030 non-null  float64\n",
      " 19  revol_bal             396030 non-null  float64\n",
      " 20  revol_util            395754 non-null  float64\n",
      " 21  total_acc             396030 non-null  float64\n",
      " 22  initial_list_status   396030 non-null  object \n",
      " 23  application_type      396030 non-null  object \n",
      " 24  mort_acc              358235 non-null  float64\n",
      " 25  pub_rec_bankruptcies  395495 non-null  float64\n",
      " 26  address               396030 non-null  object \n",
      "dtypes: float64(12), object(15)\n",
      "memory usage: 81.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project Tasks\n",
    "\n",
    "-----\n",
    "------\n",
    "\n",
    "# Section 1: Exploratory Data Analysis\n",
    "\n",
    "**OVERALL GOAL: Get an understanding for which variables are important, view summary statistics, and visualize the data**\n",
    "\n",
    "\n",
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Since we will be attempting to predict loan_status, create a countplot as shown below.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b3bf9cab88>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='loan_status',data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Create a histogram of the loan_amnt column.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 45000)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4))\n",
    "sns.distplot(df['loan_amnt'],kde=False,bins=40)\n",
    "plt.xlim(0,45000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Let's explore correlation between the continuous feature variables. Calculate the correlation between all continuous numeric variables using .corr() method.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>dti</th>\n",
       "      <th>open_acc</th>\n",
       "      <th>pub_rec</th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>revol_util</th>\n",
       "      <th>total_acc</th>\n",
       "      <th>mort_acc</th>\n",
       "      <th>pub_rec_bankruptcies</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>loan_amnt</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.168921</td>\n",
       "      <td>0.953929</td>\n",
       "      <td>0.336887</td>\n",
       "      <td>0.016636</td>\n",
       "      <td>0.198556</td>\n",
       "      <td>-0.077779</td>\n",
       "      <td>0.328320</td>\n",
       "      <td>0.099911</td>\n",
       "      <td>0.223886</td>\n",
       "      <td>0.222315</td>\n",
       "      <td>-0.106539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>int_rate</th>\n",
       "      <td>0.168921</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.162758</td>\n",
       "      <td>-0.056771</td>\n",
       "      <td>0.079038</td>\n",
       "      <td>0.011649</td>\n",
       "      <td>0.060986</td>\n",
       "      <td>-0.011280</td>\n",
       "      <td>0.293659</td>\n",
       "      <td>-0.036404</td>\n",
       "      <td>-0.082583</td>\n",
       "      <td>0.057450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>installment</th>\n",
       "      <td>0.953929</td>\n",
       "      <td>0.162758</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.330381</td>\n",
       "      <td>0.015786</td>\n",
       "      <td>0.188973</td>\n",
       "      <td>-0.067892</td>\n",
       "      <td>0.316455</td>\n",
       "      <td>0.123915</td>\n",
       "      <td>0.202430</td>\n",
       "      <td>0.193694</td>\n",
       "      <td>-0.098628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>annual_inc</th>\n",
       "      <td>0.336887</td>\n",
       "      <td>-0.056771</td>\n",
       "      <td>0.330381</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.081685</td>\n",
       "      <td>0.136150</td>\n",
       "      <td>-0.013720</td>\n",
       "      <td>0.299773</td>\n",
       "      <td>0.027871</td>\n",
       "      <td>0.193023</td>\n",
       "      <td>0.236320</td>\n",
       "      <td>-0.050162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dti</th>\n",
       "      <td>0.016636</td>\n",
       "      <td>0.079038</td>\n",
       "      <td>0.015786</td>\n",
       "      <td>-0.081685</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.136181</td>\n",
       "      <td>-0.017639</td>\n",
       "      <td>0.063571</td>\n",
       "      <td>0.088375</td>\n",
       "      <td>0.102128</td>\n",
       "      <td>-0.025439</td>\n",
       "      <td>-0.014558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>open_acc</th>\n",
       "      <td>0.198556</td>\n",
       "      <td>0.011649</td>\n",
       "      <td>0.188973</td>\n",
       "      <td>0.136150</td>\n",
       "      <td>0.136181</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.018392</td>\n",
       "      <td>0.221192</td>\n",
       "      <td>-0.131420</td>\n",
       "      <td>0.680728</td>\n",
       "      <td>0.109205</td>\n",
       "      <td>-0.027732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pub_rec</th>\n",
       "      <td>-0.077779</td>\n",
       "      <td>0.060986</td>\n",
       "      <td>-0.067892</td>\n",
       "      <td>-0.013720</td>\n",
       "      <td>-0.017639</td>\n",
       "      <td>-0.018392</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.101664</td>\n",
       "      <td>-0.075910</td>\n",
       "      <td>0.019723</td>\n",
       "      <td>0.011552</td>\n",
       "      <td>0.699408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>revol_bal</th>\n",
       "      <td>0.328320</td>\n",
       "      <td>-0.011280</td>\n",
       "      <td>0.316455</td>\n",
       "      <td>0.299773</td>\n",
       "      <td>0.063571</td>\n",
       "      <td>0.221192</td>\n",
       "      <td>-0.101664</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.226346</td>\n",
       "      <td>0.191616</td>\n",
       "      <td>0.194925</td>\n",
       "      <td>-0.124532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>revol_util</th>\n",
       "      <td>0.099911</td>\n",
       "      <td>0.293659</td>\n",
       "      <td>0.123915</td>\n",
       "      <td>0.027871</td>\n",
       "      <td>0.088375</td>\n",
       "      <td>-0.131420</td>\n",
       "      <td>-0.075910</td>\n",
       "      <td>0.226346</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.104273</td>\n",
       "      <td>0.007514</td>\n",
       "      <td>-0.086751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_acc</th>\n",
       "      <td>0.223886</td>\n",
       "      <td>-0.036404</td>\n",
       "      <td>0.202430</td>\n",
       "      <td>0.193023</td>\n",
       "      <td>0.102128</td>\n",
       "      <td>0.680728</td>\n",
       "      <td>0.019723</td>\n",
       "      <td>0.191616</td>\n",
       "      <td>-0.104273</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.381072</td>\n",
       "      <td>0.042035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mort_acc</th>\n",
       "      <td>0.222315</td>\n",
       "      <td>-0.082583</td>\n",
       "      <td>0.193694</td>\n",
       "      <td>0.236320</td>\n",
       "      <td>-0.025439</td>\n",
       "      <td>0.109205</td>\n",
       "      <td>0.011552</td>\n",
       "      <td>0.194925</td>\n",
       "      <td>0.007514</td>\n",
       "      <td>0.381072</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.027239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pub_rec_bankruptcies</th>\n",
       "      <td>-0.106539</td>\n",
       "      <td>0.057450</td>\n",
       "      <td>-0.098628</td>\n",
       "      <td>-0.050162</td>\n",
       "      <td>-0.014558</td>\n",
       "      <td>-0.027732</td>\n",
       "      <td>0.699408</td>\n",
       "      <td>-0.124532</td>\n",
       "      <td>-0.086751</td>\n",
       "      <td>0.042035</td>\n",
       "      <td>0.027239</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      loan_amnt  int_rate  installment  annual_inc       dti  \\\n",
       "loan_amnt              1.000000  0.168921     0.953929    0.336887  0.016636   \n",
       "int_rate               0.168921  1.000000     0.162758   -0.056771  0.079038   \n",
       "installment            0.953929  0.162758     1.000000    0.330381  0.015786   \n",
       "annual_inc             0.336887 -0.056771     0.330381    1.000000 -0.081685   \n",
       "dti                    0.016636  0.079038     0.015786   -0.081685  1.000000   \n",
       "open_acc               0.198556  0.011649     0.188973    0.136150  0.136181   \n",
       "pub_rec               -0.077779  0.060986    -0.067892   -0.013720 -0.017639   \n",
       "revol_bal              0.328320 -0.011280     0.316455    0.299773  0.063571   \n",
       "revol_util             0.099911  0.293659     0.123915    0.027871  0.088375   \n",
       "total_acc              0.223886 -0.036404     0.202430    0.193023  0.102128   \n",
       "mort_acc               0.222315 -0.082583     0.193694    0.236320 -0.025439   \n",
       "pub_rec_bankruptcies  -0.106539  0.057450    -0.098628   -0.050162 -0.014558   \n",
       "\n",
       "                      open_acc   pub_rec  revol_bal  revol_util  total_acc  \\\n",
       "loan_amnt             0.198556 -0.077779   0.328320    0.099911   0.223886   \n",
       "int_rate              0.011649  0.060986  -0.011280    0.293659  -0.036404   \n",
       "installment           0.188973 -0.067892   0.316455    0.123915   0.202430   \n",
       "annual_inc            0.136150 -0.013720   0.299773    0.027871   0.193023   \n",
       "dti                   0.136181 -0.017639   0.063571    0.088375   0.102128   \n",
       "open_acc              1.000000 -0.018392   0.221192   -0.131420   0.680728   \n",
       "pub_rec              -0.018392  1.000000  -0.101664   -0.075910   0.019723   \n",
       "revol_bal             0.221192 -0.101664   1.000000    0.226346   0.191616   \n",
       "revol_util           -0.131420 -0.075910   0.226346    1.000000  -0.104273   \n",
       "total_acc             0.680728  0.019723   0.191616   -0.104273   1.000000   \n",
       "mort_acc              0.109205  0.011552   0.194925    0.007514   0.381072   \n",
       "pub_rec_bankruptcies -0.027732  0.699408  -0.124532   -0.086751   0.042035   \n",
       "\n",
       "                      mort_acc  pub_rec_bankruptcies  \n",
       "loan_amnt             0.222315             -0.106539  \n",
       "int_rate             -0.082583              0.057450  \n",
       "installment           0.193694             -0.098628  \n",
       "annual_inc            0.236320             -0.050162  \n",
       "dti                  -0.025439             -0.014558  \n",
       "open_acc              0.109205             -0.027732  \n",
       "pub_rec               0.011552              0.699408  \n",
       "revol_bal             0.194925             -0.124532  \n",
       "revol_util            0.007514             -0.086751  \n",
       "total_acc             0.381072              0.042035  \n",
       "mort_acc              1.000000              0.027239  \n",
       "pub_rec_bankruptcies  0.027239              1.000000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Visualize this using a heatmap. Depending on your version of matplotlib, you may need to manually adjust the heatmap.**\n",
    "\n",
    "* [Heatmap info](https://seaborn.pydata.org/generated/seaborn.heatmap.html#seaborn.heatmap)\n",
    "* [Help with resizing](https://stackoverflow.com/questions/56942670/matplotlib-seaborn-first-and-last-row-cut-in-half-of-heatmap-plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 0)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,7))\n",
    "sns.heatmap(df.corr(),annot=True,cmap='viridis')\n",
    "plt.ylim(10, 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: You should have noticed almost perfect correlation with the \"installment\" feature. Explore this feature further. Print out their descriptions and perform a scatterplot between them. Does this relationship make sense to you? Do you think there is duplicate information here?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The monthly payment owed by the borrower if the loan originates.\n"
     ]
    }
   ],
   "source": [
    "feat_info('installment')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The listed amount of the loan applied for by the borrower. If at some point in time, the credit department reduces the loan amount, then it will be reflected in this value.\n"
     ]
    }
   ],
   "source": [
    "feat_info('loan_amnt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b3c5e6a708>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x='installment',y='loan_amnt',data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Create a boxplot showing the relationship between the loan_status and the Loan Amount.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b3c638ee88>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='loan_status',y='loan_amnt',data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Calculate the summary statistics for the loan amount, grouped by the loan_status.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loan_status</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Charged Off</th>\n",
       "      <td>77673.0</td>\n",
       "      <td>15126.300967</td>\n",
       "      <td>8505.090557</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>8525.0</td>\n",
       "      <td>14000.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>40000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fully Paid</th>\n",
       "      <td>318357.0</td>\n",
       "      <td>13866.878771</td>\n",
       "      <td>8302.319699</td>\n",
       "      <td>500.0</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>19225.0</td>\n",
       "      <td>40000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                count          mean          std     min     25%      50%  \\\n",
       "loan_status                                                                 \n",
       "Charged Off   77673.0  15126.300967  8505.090557  1000.0  8525.0  14000.0   \n",
       "Fully Paid   318357.0  13866.878771  8302.319699   500.0  7500.0  12000.0   \n",
       "\n",
       "                 75%      max  \n",
       "loan_status                    \n",
       "Charged Off  20000.0  40000.0  \n",
       "Fully Paid   19225.0  40000.0  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('loan_status')['loan_amnt'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Let's explore the Grade and SubGrade columns that LendingClub attributes to the loans. What are the unique possible grades and subgrades?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LC assigned loan grade\n",
      "LC assigned loan subgrade\n"
     ]
    }
   ],
   "source": [
    "feat_info('grade')\n",
    "feat_info('sub_grade')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['A', 'B', 'C', 'D', 'E', 'F', 'G']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(df['grade'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['A1',\n",
       " 'A2',\n",
       " 'A3',\n",
       " 'A4',\n",
       " 'A5',\n",
       " 'B1',\n",
       " 'B2',\n",
       " 'B3',\n",
       " 'B4',\n",
       " 'B5',\n",
       " 'C1',\n",
       " 'C2',\n",
       " 'C3',\n",
       " 'C4',\n",
       " 'C5',\n",
       " 'D1',\n",
       " 'D2',\n",
       " 'D3',\n",
       " 'D4',\n",
       " 'D5',\n",
       " 'E1',\n",
       " 'E2',\n",
       " 'E3',\n",
       " 'E4',\n",
       " 'E5',\n",
       " 'F1',\n",
       " 'F2',\n",
       " 'F3',\n",
       " 'F4',\n",
       " 'F5',\n",
       " 'G1',\n",
       " 'G2',\n",
       " 'G3',\n",
       " 'G4',\n",
       " 'G5']"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(df['sub_grade'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Create a countplot per grade. Set the hue to the loan_status label.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b3c6309348>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='grade',data=df,hue='loan_status')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Display a count plot per subgrade. You may need to resize for this plot and reorder the x axis. Feel free to edit the color palette. Explore both all loans made per subgrade as well being separated based on the loan_status**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b3c60e9ec8>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4))\n",
    "subgrade_order = sorted(df['sub_grade'].unique())\n",
    "sns.countplot(x='sub_grade',data=df,order = subgrade_order,palette='coolwarm',hue='loan_status')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b3c60f9dc8>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4))\n",
    "subgrade_order = sorted(df['sub_grade'].unique())\n",
    "sns.countplot(x='sub_grade',data=df,order = subgrade_order,palette='coolwarm' ,hue='loan_status')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: It looks like F and G subgrades don't get paid back that often. Isloate those and recreate the countplot just for those subgrades.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b3c55f3648>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f_and_g = df[(df['grade']=='G') | (df['grade']=='F')]\n",
    "\n",
    "plt.figure(figsize=(12,4))\n",
    "subgrade_order = sorted(f_and_g['sub_grade'].unique())\n",
    "sns.countplot(x='sub_grade',data=f_and_g,order = subgrade_order,hue='loan_status')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Create a new column called 'load_repaid' which will contain a 1 if the loan status was \"Fully Paid\" and a 0 if it was \"Charged Off\".**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Fully Paid', 'Charged Off'], dtype=object)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['loan_status'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['loan_repaid'] = df['loan_status'].map({'Fully Paid':1,'Charged Off':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_repaid</th>\n",
       "      <th>loan_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>Charged Off</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396025</th>\n",
       "      <td>1</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396026</th>\n",
       "      <td>1</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396027</th>\n",
       "      <td>1</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396028</th>\n",
       "      <td>1</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396029</th>\n",
       "      <td>1</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>396030 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        loan_repaid  loan_status\n",
       "0                 1   Fully Paid\n",
       "1                 1   Fully Paid\n",
       "2                 1   Fully Paid\n",
       "3                 1   Fully Paid\n",
       "4                 0  Charged Off\n",
       "...             ...          ...\n",
       "396025            1   Fully Paid\n",
       "396026            1   Fully Paid\n",
       "396027            1   Fully Paid\n",
       "396028            1   Fully Paid\n",
       "396029            1   Fully Paid\n",
       "\n",
       "[396030 rows x 2 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['loan_repaid','loan_status']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**CHALLENGE TASK: (Note this is hard, but can be done in one line!) Create a bar plot showing the correlation of the numeric features to the new loan_repaid column. [Helpful Link](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.bar.html)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b3c57bef08>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.corr()['loan_repaid'].sort_values().drop('loan_repaid').plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "---\n",
    "# Section 2: Data PreProcessing\n",
    "\n",
    "**Section Goals: Remove or fill any missing data. Remove unnecessary or repetitive features. Convert categorical string features to dummy variables.**\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_amnt</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>emp_title</th>\n",
       "      <th>emp_length</th>\n",
       "      <th>home_ownership</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>...</th>\n",
       "      <th>pub_rec</th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>revol_util</th>\n",
       "      <th>total_acc</th>\n",
       "      <th>initial_list_status</th>\n",
       "      <th>application_type</th>\n",
       "      <th>mort_acc</th>\n",
       "      <th>pub_rec_bankruptcies</th>\n",
       "      <th>address</th>\n",
       "      <th>loan_repaid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>11.44</td>\n",
       "      <td>329.48</td>\n",
       "      <td>B</td>\n",
       "      <td>B4</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>117000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36369.0</td>\n",
       "      <td>41.8</td>\n",
       "      <td>25.0</td>\n",
       "      <td>w</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0174 Michelle Gateway\\nMendozaberg, OK 22690</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>11.99</td>\n",
       "      <td>265.68</td>\n",
       "      <td>B</td>\n",
       "      <td>B5</td>\n",
       "      <td>Credit analyst</td>\n",
       "      <td>4 years</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>65000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20131.0</td>\n",
       "      <td>53.3</td>\n",
       "      <td>27.0</td>\n",
       "      <td>f</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1076 Carney Fort Apt. 347\\nLoganmouth, SD 05113</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15600.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>10.49</td>\n",
       "      <td>506.97</td>\n",
       "      <td>B</td>\n",
       "      <td>B3</td>\n",
       "      <td>Statistician</td>\n",
       "      <td>&lt; 1 year</td>\n",
       "      <td>RENT</td>\n",
       "      <td>43057.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11987.0</td>\n",
       "      <td>92.2</td>\n",
       "      <td>26.0</td>\n",
       "      <td>f</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>87025 Mark Dale Apt. 269\\nNew Sabrina, WV 05113</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7200.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>6.49</td>\n",
       "      <td>220.65</td>\n",
       "      <td>A</td>\n",
       "      <td>A2</td>\n",
       "      <td>Client Advocate</td>\n",
       "      <td>6 years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>54000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5472.0</td>\n",
       "      <td>21.5</td>\n",
       "      <td>13.0</td>\n",
       "      <td>f</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>823 Reid Ford\\nDelacruzside, MA 00813</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24375.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>17.27</td>\n",
       "      <td>609.33</td>\n",
       "      <td>C</td>\n",
       "      <td>C5</td>\n",
       "      <td>Destiny Management Inc.</td>\n",
       "      <td>9 years</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>55000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24584.0</td>\n",
       "      <td>69.8</td>\n",
       "      <td>43.0</td>\n",
       "      <td>f</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>679 Luna Roads\\nGreggshire, VA 11650</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   loan_amnt        term  int_rate  installment grade sub_grade  \\\n",
       "0    10000.0   36 months     11.44       329.48     B        B4   \n",
       "1     8000.0   36 months     11.99       265.68     B        B5   \n",
       "2    15600.0   36 months     10.49       506.97     B        B3   \n",
       "3     7200.0   36 months      6.49       220.65     A        A2   \n",
       "4    24375.0   60 months     17.27       609.33     C        C5   \n",
       "\n",
       "                 emp_title emp_length home_ownership  annual_inc  ... pub_rec  \\\n",
       "0                Marketing  10+ years           RENT    117000.0  ...     0.0   \n",
       "1          Credit analyst     4 years       MORTGAGE     65000.0  ...     0.0   \n",
       "2             Statistician   < 1 year           RENT     43057.0  ...     0.0   \n",
       "3          Client Advocate    6 years           RENT     54000.0  ...     0.0   \n",
       "4  Destiny Management Inc.    9 years       MORTGAGE     55000.0  ...     0.0   \n",
       "\n",
       "  revol_bal revol_util total_acc initial_list_status  application_type  \\\n",
       "0   36369.0       41.8      25.0                   w        INDIVIDUAL   \n",
       "1   20131.0       53.3      27.0                   f        INDIVIDUAL   \n",
       "2   11987.0       92.2      26.0                   f        INDIVIDUAL   \n",
       "3    5472.0       21.5      13.0                   f        INDIVIDUAL   \n",
       "4   24584.0       69.8      43.0                   f        INDIVIDUAL   \n",
       "\n",
       "  mort_acc  pub_rec_bankruptcies  \\\n",
       "0      0.0                   0.0   \n",
       "1      3.0                   0.0   \n",
       "2      0.0                   0.0   \n",
       "3      0.0                   0.0   \n",
       "4      1.0                   0.0   \n",
       "\n",
       "                                           address  loan_repaid  \n",
       "0     0174 Michelle Gateway\\nMendozaberg, OK 22690            1  \n",
       "1  1076 Carney Fort Apt. 347\\nLoganmouth, SD 05113            1  \n",
       "2  87025 Mark Dale Apt. 269\\nNew Sabrina, WV 05113            1  \n",
       "3            823 Reid Ford\\nDelacruzside, MA 00813            1  \n",
       "4             679 Luna Roads\\nGreggshire, VA 11650            0  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Missing Data\n",
    "\n",
    "**Let's explore this missing data columns. We use a variety of factors to decide whether or not they would be useful, to see if we should keep, discard, or fill in the missing data.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: What is the length of the dataframe?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "396030"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Create a Series that displays the total count of missing values per column.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loan_amnt                   0\n",
       "term                        0\n",
       "int_rate                    0\n",
       "installment                 0\n",
       "grade                       0\n",
       "sub_grade                   0\n",
       "emp_title               22927\n",
       "emp_length              18301\n",
       "home_ownership              0\n",
       "annual_inc                  0\n",
       "verification_status         0\n",
       "issue_d                     0\n",
       "loan_status                 0\n",
       "purpose                     0\n",
       "title                    1755\n",
       "dti                         0\n",
       "earliest_cr_line            0\n",
       "open_acc                    0\n",
       "pub_rec                     0\n",
       "revol_bal                   0\n",
       "revol_util                276\n",
       "total_acc                   0\n",
       "initial_list_status         0\n",
       "application_type            0\n",
       "mort_acc                37795\n",
       "pub_rec_bankruptcies      535\n",
       "address                     0\n",
       "loan_repaid                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Convert this Series to be in term of percentage of the total DataFrame**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loan_amnt               0.000000\n",
       "term                    0.000000\n",
       "int_rate                0.000000\n",
       "installment             0.000000\n",
       "grade                   0.000000\n",
       "sub_grade               0.000000\n",
       "emp_title               5.789208\n",
       "emp_length              4.621115\n",
       "home_ownership          0.000000\n",
       "annual_inc              0.000000\n",
       "verification_status     0.000000\n",
       "issue_d                 0.000000\n",
       "loan_status             0.000000\n",
       "purpose                 0.000000\n",
       "title                   0.443148\n",
       "dti                     0.000000\n",
       "earliest_cr_line        0.000000\n",
       "open_acc                0.000000\n",
       "pub_rec                 0.000000\n",
       "revol_bal               0.000000\n",
       "revol_util              0.069692\n",
       "total_acc               0.000000\n",
       "initial_list_status     0.000000\n",
       "application_type        0.000000\n",
       "mort_acc                9.543469\n",
       "pub_rec_bankruptcies    0.135091\n",
       "address                 0.000000\n",
       "loan_repaid             0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()*100/len(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Let's examine emp_title and emp_length to see whether it will be okay to drop them. Print out their feature information using the feat_info() function from the top of this notebook.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job title supplied by the Borrower when applying for the loan.*\n",
      "\n",
      "\n",
      "Employment length in years. Possible values are between 0 and 10 where 0 means less than one year and 10 means ten or more years. \n"
     ]
    }
   ],
   "source": [
    "feat_info('emp_title')\n",
    "print('\\n')\n",
    "feat_info('emp_length')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: How many unique employment job titles are there?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "173105"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['emp_title'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Teacher                                    4389\n",
       "Manager                                    4250\n",
       "Registered Nurse                           1856\n",
       "RN                                         1846\n",
       "Supervisor                                 1830\n",
       "                                           ... \n",
       "Bombardier Flexjet                            1\n",
       "Keller Williams Realty/Schiff Home Team       1\n",
       "publicworks                                   1\n",
       "State farm ins agency                         1\n",
       "precision measurements                        1\n",
       "Name: emp_title, Length: 173105, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['emp_title'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Realistically there are too many unique job titles to try to convert this to a dummy variable feature. Let's remove that emp_title column.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop('emp_title',axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Create a count plot of the emp_length feature column. Challenge: Sort the order of the values.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['1 year',\n",
       " '10+ years',\n",
       " '2 years',\n",
       " '3 years',\n",
       " '4 years',\n",
       " '5 years',\n",
       " '6 years',\n",
       " '7 years',\n",
       " '8 years',\n",
       " '9 years',\n",
       " '< 1 year']"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(df['emp_length'].dropna().unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "emp_length_order = [ '< 1 year',\n",
    "                      '1 year',\n",
    "                     '2 years',\n",
    "                     '3 years',\n",
    "                     '4 years',\n",
    "                     '5 years',\n",
    "                     '6 years',\n",
    "                     '7 years',\n",
    "                     '8 years',\n",
    "                     '9 years',\n",
    "                     '10+ years']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b3c5923c48>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4))\n",
    "\n",
    "sns.countplot(x='emp_length',data=df,order=emp_length_order)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Plot out the countplot with a hue separating Fully Paid vs Charged Off**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b3c5923ac8>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4))\n",
    "sns.countplot(x='emp_length',data=df,order=emp_length_order,hue='loan_status')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**This still doesn't really inform us if there is a strong relationship between employment length and being charged off, what we want is the percentage of charge offs per category. Essentially informing us what percent of people per employment category didn't pay back their loan. There are a multitude of ways to create this Series. Once you've created it, see if visualize it with a [bar plot](https://pandas.pydata.org/pandas-docs/version/0.23.4/generated/pandas.DataFrame.plot.html). This may be tricky, refer to solutions if you get stuck on creating this Series.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "emp_length\n",
       "1 year        5154\n",
       "10+ years    23215\n",
       "2 years       6924\n",
       "3 years       6182\n",
       "4 years       4608\n",
       "5 years       5092\n",
       "6 years       3943\n",
       "7 years       4055\n",
       "8 years       3829\n",
       "9 years       3070\n",
       "< 1 year      6563\n",
       "Name: loan_status, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emp_co = df[df['loan_status']==\"Charged Off\"].groupby(\"emp_length\").count()['loan_status']\n",
    "emp_co"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "emp_fp = df[df['loan_status']==\"Fully Paid\"].groupby(\"emp_length\").count()['loan_status']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "emp_len = emp_co/emp_fp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "emp_length\n",
       "1 year       0.248649\n",
       "10+ years    0.225770\n",
       "2 years      0.239560\n",
       "3 years      0.242593\n",
       "4 years      0.238213\n",
       "5 years      0.237911\n",
       "6 years      0.233341\n",
       "7 years      0.241887\n",
       "8 years      0.249625\n",
       "9 years      0.250735\n",
       "< 1 year     0.260830\n",
       "Name: loan_status, dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emp_len"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b3c6256888>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "emp_len.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Charge off rates are extremely similar across all employment lengths. Go ahead and drop the emp_length column.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop('emp_length',axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Revisit the DataFrame to see what feature columns still have missing data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loan_amnt                   0\n",
       "term                        0\n",
       "int_rate                    0\n",
       "installment                 0\n",
       "grade                       0\n",
       "sub_grade                   0\n",
       "home_ownership              0\n",
       "annual_inc                  0\n",
       "verification_status         0\n",
       "issue_d                     0\n",
       "loan_status                 0\n",
       "purpose                     0\n",
       "title                    1755\n",
       "dti                         0\n",
       "earliest_cr_line            0\n",
       "open_acc                    0\n",
       "pub_rec                     0\n",
       "revol_bal                   0\n",
       "revol_util                276\n",
       "total_acc                   0\n",
       "initial_list_status         0\n",
       "application_type            0\n",
       "mort_acc                37795\n",
       "pub_rec_bankruptcies      535\n",
       "address                     0\n",
       "loan_repaid                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Review the title column vs the purpose column. Is this repeated information?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0              vacation\n",
       "1    debt_consolidation\n",
       "2           credit_card\n",
       "3           credit_card\n",
       "4           credit_card\n",
       "5    debt_consolidation\n",
       "6      home_improvement\n",
       "7           credit_card\n",
       "8    debt_consolidation\n",
       "9    debt_consolidation\n",
       "Name: purpose, dtype: object"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['purpose'].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                   Vacation\n",
       "1         Debt consolidation\n",
       "2    Credit card refinancing\n",
       "3    Credit card refinancing\n",
       "4      Credit Card Refinance\n",
       "5         Debt consolidation\n",
       "6           Home improvement\n",
       "7       No More Credit Cards\n",
       "8         Debt consolidation\n",
       "9         Debt Consolidation\n",
       "Name: title, dtype: object"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['title'].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: The title column is simply a string subcategory/description of the purpose column. Go ahead and drop the title column.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop('title',axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Find out what the mort_acc feature represents**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of mortgage accounts.\n"
     ]
    }
   ],
   "source": [
    "feat_info('mort_acc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Create a value_counts of the mort_acc column.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0     139777\n",
       "1.0      60416\n",
       "2.0      49948\n",
       "3.0      38049\n",
       "4.0      27887\n",
       "5.0      18194\n",
       "6.0      11069\n",
       "7.0       6052\n",
       "8.0       3121\n",
       "9.0       1656\n",
       "10.0       865\n",
       "11.0       479\n",
       "12.0       264\n",
       "13.0       146\n",
       "14.0       107\n",
       "15.0        61\n",
       "16.0        37\n",
       "17.0        22\n",
       "18.0        18\n",
       "19.0        15\n",
       "20.0        13\n",
       "24.0        10\n",
       "22.0         7\n",
       "21.0         4\n",
       "25.0         4\n",
       "27.0         3\n",
       "23.0         2\n",
       "32.0         2\n",
       "26.0         2\n",
       "31.0         2\n",
       "30.0         1\n",
       "28.0         1\n",
       "34.0         1\n",
       "Name: mort_acc, dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['mort_acc'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: There are many ways we could deal with this missing data. We could attempt to build a simple model to fill it in, such as a linear model, we could just fill it in based on the mean of the other columns, or you could even bin the columns into categories and then set NaN as its own category. There is no 100% correct approach! Let's review the other columsn to see which most highly correlates to mort_acc**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation with the mort_acc column\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "int_rate               -0.082583\n",
       "dti                    -0.025439\n",
       "revol_util              0.007514\n",
       "pub_rec                 0.011552\n",
       "pub_rec_bankruptcies    0.027239\n",
       "loan_repaid             0.073111\n",
       "open_acc                0.109205\n",
       "installment             0.193694\n",
       "revol_bal               0.194925\n",
       "loan_amnt               0.222315\n",
       "annual_inc              0.236320\n",
       "total_acc               0.381072\n",
       "mort_acc                1.000000\n",
       "Name: mort_acc, dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Correlation with the mort_acc column\")\n",
    "df.corr()['mort_acc'].sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Looks like the total_acc feature correlates with the mort_acc , this makes sense! Let's try this fillna() approach. We will group the dataframe by the total_acc and calculate the mean value for the mort_acc per total_acc entry. To get the result below:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean of mort_acc column per total_acc\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "total_acc\n",
       "2.0      0.000000\n",
       "3.0      0.052023\n",
       "4.0      0.066743\n",
       "5.0      0.103289\n",
       "6.0      0.151293\n",
       "           ...   \n",
       "124.0    1.000000\n",
       "129.0    1.000000\n",
       "135.0    3.000000\n",
       "150.0    2.000000\n",
       "151.0    0.000000\n",
       "Name: mort_acc, Length: 118, dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Mean of mort_acc column per total_acc\")\n",
    "df.groupby('total_acc').mean()['mort_acc']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**CHALLENGE TASK: Let's fill in the missing mort_acc values based on their total_acc value. If the mort_acc is missing, then we will fill in that missing value with the mean value corresponding to its total_acc value from the Series we created above. This involves using an .apply() method with two columns. Check out the link below for more info, or review the solutions video/notebook.**\n",
    "\n",
    "[Helpful Link](https://stackoverflow.com/questions/13331698/how-to-apply-a-function-to-two-columns-of-pandas-dataframe) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_acc_avg = df.groupby('total_acc').mean()['mort_acc']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_acc_avg[2.0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fill_mort_acc(total_acc,mort_acc):\n",
    "    '''\n",
    "    Accepts the total_acc and mort_acc values for the row.\n",
    "    Checks if the mort_acc is NaN , if so, it returns the avg mort_acc value\n",
    "    for the corresponding total_acc value for that row.\n",
    "    \n",
    "    total_acc_avg here should be a Series or dictionary containing the mapping of the\n",
    "    groupby averages of mort_acc per total_acc values.\n",
    "    '''\n",
    "    if np.isnan(mort_acc):\n",
    "        return total_acc_avg[total_acc]\n",
    "    else:\n",
    "        return mort_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['mort_acc'] = df.apply(lambda x: fill_mort_acc(x['total_acc'], x['mort_acc']), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loan_amnt                 0\n",
       "term                      0\n",
       "int_rate                  0\n",
       "installment               0\n",
       "grade                     0\n",
       "sub_grade                 0\n",
       "home_ownership            0\n",
       "annual_inc                0\n",
       "verification_status       0\n",
       "issue_d                   0\n",
       "loan_status               0\n",
       "purpose                   0\n",
       "dti                       0\n",
       "earliest_cr_line          0\n",
       "open_acc                  0\n",
       "pub_rec                   0\n",
       "revol_bal                 0\n",
       "revol_util              276\n",
       "total_acc                 0\n",
       "initial_list_status       0\n",
       "application_type          0\n",
       "mort_acc                  0\n",
       "pub_rec_bankruptcies    535\n",
       "address                   0\n",
       "loan_repaid               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: revol_util and the pub_rec_bankruptcies have missing data points, but they account for less than 0.5% of the total data. Go ahead and remove the rows that are missing those values in those columns with dropna().**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loan_amnt               0\n",
       "term                    0\n",
       "int_rate                0\n",
       "installment             0\n",
       "grade                   0\n",
       "sub_grade               0\n",
       "home_ownership          0\n",
       "annual_inc              0\n",
       "verification_status     0\n",
       "issue_d                 0\n",
       "loan_status             0\n",
       "purpose                 0\n",
       "dti                     0\n",
       "earliest_cr_line        0\n",
       "open_acc                0\n",
       "pub_rec                 0\n",
       "revol_bal               0\n",
       "revol_util              0\n",
       "total_acc               0\n",
       "initial_list_status     0\n",
       "application_type        0\n",
       "mort_acc                0\n",
       "pub_rec_bankruptcies    0\n",
       "address                 0\n",
       "loan_repaid             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Categorical Variables and Dummy Variables\n",
    "\n",
    "**We're done working with the missing data! Now we just need to deal with the string values due to the categorical columns.**\n",
    "\n",
    "**List all the columns that are currently non-numeric**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['term', 'grade', 'sub_grade', 'home_ownership', 'verification_status',\n",
       "       'issue_d', 'loan_status', 'purpose', 'earliest_cr_line',\n",
       "       'initial_list_status', 'application_type', 'address'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.select_dtypes(['object']).columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "**Let's now go through all the string features to see what we should do with them.**\n",
    "\n",
    "---\n",
    "\n",
    "\n",
    "### term feature\n",
    "\n",
    "**Convert the term feature into either a 36 or 60 integer numeric data type using .apply() or .map().**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 36 months    301247\n",
       " 60 months     93972\n",
       "Name: term, dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['term'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Or just use .map()\n",
    "df['term'] = df['term'].apply(lambda term: int(term[:3]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### grade feature\n",
    "\n",
    "**We already know grade is part of sub_grade, so just drop the grade feature.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop('grade',axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Convert the subgrade into dummy variables. Then concatenate these new columns to the original dataframe. Remember to drop the original subgrade column and to add drop_first=True to your get_dummies call.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CODE HERE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "subgrade_dummies = pd.get_dummies(df['sub_grade'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([df.drop('sub_grade',axis=1),subgrade_dummies],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['loan_amnt', 'term', 'int_rate', 'installment', 'home_ownership',\n",
       "       'annual_inc', 'verification_status', 'issue_d', 'loan_status',\n",
       "       'purpose', 'dti', 'earliest_cr_line', 'open_acc', 'pub_rec',\n",
       "       'revol_bal', 'revol_util', 'total_acc', 'initial_list_status',\n",
       "       'application_type', 'mort_acc', 'pub_rec_bankruptcies', 'address',\n",
       "       'loan_repaid', 'A2', 'A3', 'A4', 'A5', 'B1', 'B2', 'B3', 'B4', 'B5',\n",
       "       'C1', 'C2', 'C3', 'C4', 'C5', 'D1', 'D2', 'D3', 'D4', 'D5', 'E1', 'E2',\n",
       "       'E3', 'E4', 'E5', 'F1', 'F2', 'F3', 'F4', 'F5', 'G1', 'G2', 'G3', 'G4',\n",
       "       'G5'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['home_ownership', 'verification_status', 'issue_d', 'loan_status',\n",
       "       'purpose', 'earliest_cr_line', 'initial_list_status',\n",
       "       'application_type', 'address'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.select_dtypes(['object']).columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### verification_status, application_type,initial_list_status,purpose \n",
    "**Convert these columns: ['verification_status', 'application_type','initial_list_status','purpose'] into dummy variables and concatenate them with the original dataframe. Remember to set drop_first=True and to drop the original columns.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "dummies = pd.get_dummies(df[['verification_status', 'application_type','initial_list_status','purpose' ]],drop_first=True)\n",
    "df = df.drop(['verification_status', 'application_type','initial_list_status','purpose'],axis=1)\n",
    "df = pd.concat([df,dummies],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### home_ownership\n",
    "**Review the value_counts for the home_ownership column.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MORTGAGE    198022\n",
       "RENT        159395\n",
       "OWN          37660\n",
       "OTHER          110\n",
       "NONE            29\n",
       "ANY              3\n",
       "Name: home_ownership, dtype: int64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['home_ownership'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Convert these to dummy variables, but [replace](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.replace.html) NONE and ANY with OTHER, so that we end up with just 4 categories, MORTGAGE, RENT, OWN, OTHER. Then concatenate them with the original dataframe. Remember to set drop_first=True and to drop the original columns.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['home_ownership']=df['home_ownership'].replace(['NONE', 'ANY'], 'OTHER')\n",
    "\n",
    "dummies = pd.get_dummies(df['home_ownership'],drop_first=True)\n",
    "df = df.drop('home_ownership',axis=1)\n",
    "df = pd.concat([df,dummies],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### address\n",
    "**Let's feature engineer a zip code column from the address in the data set. Create a column called 'zip_code' that extracts the zip code from the address column.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['zip_code'] = df['address'].apply(lambda address:address[-5:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Now make this zip_code column into dummy variables using pandas. Concatenate the result and drop the original zip_code column along with dropping the address column.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "dummies = pd.get_dummies(df['zip_code'],drop_first=True)\n",
    "df = df.drop(['zip_code','address'],axis=1)\n",
    "df = pd.concat([df,dummies],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### issue_d \n",
    "\n",
    "**TASK: This would be data leakage, we wouldn't know beforehand whether or not a loan would be issued when using our model, so in theory we wouldn't have an issue_date, drop this feature.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The month which the loan was funded\n"
     ]
    }
   ],
   "source": [
    "feat_info('issue_d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop('issue_d',axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### earliest_cr_line\n",
    "**This appears to be a historical time stamp feature. Extract the year from this feature using a .apply function, then convert it to a numeric feature. Set this new data to a feature column called 'earliest_cr_year'.Then drop the earliest_cr_line feature.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['earliest_cr_year'] = df['earliest_cr_line'].apply(lambda date:int(date[-4:]))\n",
    "df = df.drop('earliest_cr_line',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['loan_status'], dtype='object')"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.select_dtypes(['object']).columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train Test Split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Import train_test_split from sklearn.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**drop the load_status column we created earlier, since its a duplicate of the loan_repaid column. We'll use the loan_repaid column since its already in 0s and 1s.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop('loan_status',axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "----\n",
    "\n",
    "# OPTIONAL\n",
    "\n",
    "## Grabbing a Sample for Training Time\n",
    "\n",
    "### We use .sample() to grab a sample of the 490k+ entries to save time on training. Highly recommended for lower RAM computers or if you are not using GPU.\n",
    "\n",
    "----\n",
    "----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "395219\n"
     ]
    }
   ],
   "source": [
    "#df = df.sample(frac=0.1,random_state=101)\n",
    "print(len(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(395219, 79)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Set X and y variables to the .values of the features and label.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop('loan_repaid',axis=1).values\n",
    "y = df['loan_repaid'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Perform a train/test split with test_size=0.2 and a random_state of 101.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=101)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Normalizing the Data\n",
    "\n",
    "**TASK: Use a MinMaxScaler to normalize the feature data X_train and X_test. Recall we don't want data leakge from the test set so we only fit on the X_train data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = scaler.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.22307692, 0.        , 0.46552396, ..., 0.        , 0.        ,\n",
       "        0.89855072],\n",
       "       [0.48717949, 0.        , 0.32450331, ..., 0.        , 0.        ,\n",
       "        0.85507246],\n",
       "       [0.31666667, 1.        , 0.46552396, ..., 0.        , 0.        ,\n",
       "        0.85507246],\n",
       "       ...,\n",
       "       [0.19358974, 0.        , 0.16945851, ..., 0.        , 0.        ,\n",
       "        0.8115942 ],\n",
       "       [0.28205128, 1.        , 0.4253993 , ..., 0.        , 0.        ,\n",
       "        0.79710145],\n",
       "       [0.87179487, 0.        , 0.57148422, ..., 0.        , 0.        ,\n",
       "        0.76811594]])"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating the Model\n",
    "\n",
    "**Run the cell below to import the necessary Keras functions.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Activation,Dropout\n",
    "from tensorflow.keras.constraints import max_norm\n",
    "from tensorflow.keras.callbacks import EarlyStopping"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**We build a sequential model to will be trained on the data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CODE HERE\n",
    "model = Sequential()\n",
    "\n",
    "# Choose whatever number of layers/neurons you want.\n",
    "\n",
    "# https://stats.stackexchange.com/questions/181/how-to-choose-the-number-of-hidden-layers-and-nodes-in-a-feedforward-neural-netw\n",
    "\n",
    "# Remember to compile()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "\n",
    "# https://stats.stackexchange.com/questions/181/how-to-choose-the-number-of-hidden-layers-and-nodes-in-a-feedforward-neural-netw\n",
    "\n",
    "\n",
    "# input layer\n",
    "model.add(Dense(78,  activation='relu'))\n",
    "#model.add(Dropout(0.6))\n",
    "\n",
    "# hidden layer\n",
    "model.add(Dense(50, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "# hidden layer\n",
    "model.add(Dense(30, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "# output layer\n",
    "model.add(Dense(units=1,activation='sigmoid'))\n",
    "\n",
    "# Compile model\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Adding in early stopping to prevent overfitting\n",
    "early_stop = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Fit the model to the training data for at least 25 epochs. Also add in the validation data for later plotting. Optional: add in a batch_size of 256.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 316175 samples, validate on 79044 samples\n",
      "Epoch 1/20\n",
      "316175/316175 [==============================] - 13s 43us/sample - loss: 0.3039 - val_loss: 0.2647\n",
      "Epoch 2/20\n",
      "316175/316175 [==============================] - 12s 39us/sample - loss: 0.2681 - val_loss: 0.2646\n",
      "Epoch 3/20\n",
      "316175/316175 [==============================] - 12s 37us/sample - loss: 0.2659 - val_loss: 0.2640\n",
      "Epoch 4/20\n",
      "316175/316175 [==============================] - 11s 35us/sample - loss: 0.2644 - val_loss: 0.2631\n",
      "Epoch 5/20\n",
      "316175/316175 [==============================] - 12s 39us/sample - loss: 0.2633 - val_loss: 0.2642\n",
      "Epoch 6/20\n",
      "316175/316175 [==============================] - 11s 35us/sample - loss: 0.2630 - val_loss: 0.2646\n",
      "Epoch 7/20\n",
      "316175/316175 [==============================] - 11s 34us/sample - loss: 0.2620 - val_loss: 0.2643\n",
      "Epoch 8/20\n",
      "316175/316175 [==============================] - 12s 38us/sample - loss: 0.2616 - val_loss: 0.2644\n",
      "Epoch 9/20\n",
      "316175/316175 [==============================] - 10s 30us/sample - loss: 0.2611 - val_loss: 0.2634\n",
      "Epoch 10/20\n",
      "316175/316175 [==============================] - 11s 35us/sample - loss: 0.2609 - val_loss: 0.2637\n",
      "Epoch 11/20\n",
      "316175/316175 [==============================] - 12s 37us/sample - loss: 0.2605 - val_loss: 0.2630\n",
      "Epoch 12/20\n",
      "316175/316175 [==============================] - 10s 31us/sample - loss: 0.2604 - val_loss: 0.2625\n",
      "Epoch 13/20\n",
      "316175/316175 [==============================] - 12s 37us/sample - loss: 0.2601 - val_loss: 0.2630\n",
      "Epoch 14/20\n",
      "316175/316175 [==============================] - 12s 37us/sample - loss: 0.2597 - val_loss: 0.2631\n",
      "Epoch 15/20\n",
      "316175/316175 [==============================] - 13s 42us/sample - loss: 0.2593 - val_loss: 0.2654\n",
      "Epoch 16/20\n",
      "316175/316175 [==============================] - 13s 42us/sample - loss: 0.2590 - val_loss: 0.2636\n",
      "Epoch 17/20\n",
      "316175/316175 [==============================] - 12s 38us/sample - loss: 0.2587 - val_loss: 0.2624\n",
      "Epoch 18/20\n",
      "316175/316175 [==============================] - 12s 37us/sample - loss: 0.2586 - val_loss: 0.2623\n",
      "Epoch 19/20\n",
      "316175/316175 [==============================] - 11s 36us/sample - loss: 0.2584 - val_loss: 0.2633\n",
      "Epoch 20/20\n",
      "316175/316175 [==============================] - 12s 37us/sample - loss: 0.2580 - val_loss: 0.2620\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1ae082c8048>"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x=X_train, \n",
    "          y=y_train, \n",
    "          epochs=20,\n",
    "          batch_size=256,\n",
    "          validation_data=(X_test, y_test)\n",
    "          #,callbacks=[early_stop]\n",
    "          )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Section 3: Evaluating Model Performance.\n",
    "\n",
    "**Plotting out the validation loss versus the training loss.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ae1150bb48>"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "losses = pd.DataFrame(model.history.history)\n",
    "losses[['loss','val_loss']].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The loss and val_loss both are decreasing and we are stopping training just as the graphs start to separate. We have thus ensured no over-fitting on the training data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Creating predictions from the X_test set and display a classification report and confusion matrix for the X_test set.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report,confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = model.predict_classes(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.43      0.60     15658\n",
      "           1       0.88      1.00      0.93     63386\n",
      "\n",
      "    accuracy                           0.89     79044\n",
      "   macro avg       0.94      0.72      0.77     79044\n",
      "weighted avg       0.90      0.89      0.87     79044\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 6740,  8918],\n",
       "       [   21, 63365]], dtype=int64)"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test,predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Though the accuracy is quite good at 89%, the recall for defaulted cases is quite low at 43%. This is to be expected due to the large offset in the samples available for loan repair vs loan defaulted. We already have ensured no over-fitting; we now need to play with the hyperparameters to make a better ANN."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GridSearchCV\n",
    "\n",
    "Let's use GridSearchCV to find the best set of hyperparameters for our ANN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.wrappers.scikit_learn import KerasClassifier\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating a basic model structure which will be used for iterations\n",
    "\n",
    "def create_model(layers, activation):\n",
    "    model = Sequential()\n",
    "    for i, nodes in enumerate(layers):\n",
    "        if i==0:\n",
    "            model.add(Dense(nodes, input_dim=X_train.shape[1]))\n",
    "            model.add(Activation(activation))\n",
    "        else:\n",
    "            model.add(Dense(nodes))\n",
    "            model.add(Activation(activation))\n",
    "    model.add(Dense(1))\n",
    "    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "    \n",
    "    return  model\n",
    "\n",
    "model=KerasClassifier(build_fn=create_model, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating iteration params\n",
    "layers = [50, (50,25), (50,25,10), (60,45,30,15)]\n",
    "#layers = [50, (50,25)]\n",
    "activations = ('sigmoid', 'relu')\n",
    "param_grid = dict(layers= layers, activation= activations, batch_size= [500,256,128], epochs= [20])\n",
    "#param_grid = dict(layers= layers, activation= activations, batch_size= [500,250], epochs= [25])\n",
    "grid = GridSearchCV(estimator= model, param_grid= param_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[78, (50, 25), (50, 25, 10), (60, 45, 30, 15)]"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lst=[]\n",
    "\n",
    "for i, nodes in enumerate(layers):\n",
    "    if i==0:\n",
    "        lst.append(78)\n",
    "    else:\n",
    "        lst.append(nodes)\n",
    "        \n",
    "lst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Pranav Tumkur\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "TypeError: 'int' object is not iterable\n",
      "\n",
      "  FitFailedWarning)\n"
     ]
    }
   ],
   "source": [
    "grid_result = grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.8883434772491455,\n",
       " {'activation': 'relu', 'batch_size': 128, 'epochs': 20, 'layers': (50, 25)}]"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[grid_result.best_score_, grid_result.best_params_]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This is what our Hyperparameter optimization has predicted is the best ANN configuration and training parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred= grid.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred= (y_pred>0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = y_pred.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.47      0.62     15658\n",
      "           1       0.88      0.99      0.93     63386\n",
      "\n",
      "    accuracy                           0.89     79044\n",
      "   macro avg       0.90      0.73      0.78     79044\n",
      "weighted avg       0.89      0.89      0.87     79044\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 7317,  8341],\n",
       "       [  579, 62807]], dtype=int64)"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Therefore, it seems that we have achieved a 4% increase in recall while maintaining the same 89% accuracy after using GridSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <ins>But this result of recall 47% for loan deferral cases is still not acceptable since it is quite low</ins>!\n",
    "\n",
    "#### We already know that the reason for low recall of cases where loan was deferred, is due to the imbalance in the dataset for samples with loan repaid (very high) vs loan deferred cases (very low).\n",
    "\n",
    "## So now, let us try to feed our ML model better and more balanced data by using technique- <ins>oversampling</ins>, for records where loan was deferred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.combine import SMOTETomek\n",
    "from imblearn.over_sampling import RandomOverSampler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original/Imbalanced dataset shape Counter({1: 317696, 0: 77523})\n"
     ]
    }
   ],
   "source": [
    "from collections import Counter\n",
    "print('Original/Imbalanced dataset shape {}'.format(Counter(y)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The original dataset has 317696 cases of loan paid off v/s just 77523 cases where loan was deferred. Let's use SMOTTomek class to reshape the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating resampled training datasets as X_res and y_res \n",
    "smk = SMOTETomek(random_state=42)\n",
    "X_res,y_res=smk.fit_resample(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(583648, 78) (583648,)\n"
     ]
    }
   ],
   "source": [
    "print(X_res.shape, y_res.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### As we can see, the reshaped dataset has 291824 cases of loan paid off as well as 291824 cases where loan was deferred. We now have 1:1 balanced dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original/Imbalanced dataset shape Counter({1: 317696, 0: 77523})\n",
      "Resampled dataset shape Counter({1: 291824, 0: 291824})\n"
     ]
    }
   ],
   "source": [
    "from collections import Counter\n",
    "print('Original/Imbalanced dataset shape {}'.format(Counter(y)))\n",
    "print('Resampled dataset shape {}'.format(Counter(y_res)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's split the newly formed dataset into training and testing datasets and fit it to our ANN which we deteermined was the best from hyperparameter optimization using GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_res, y_res, test_size=0.20, random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Activation,Dropout\n",
    "from tensorflow.keras.constraints import max_norm\n",
    "from tensorflow.keras.callbacks import EarlyStopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "\n",
    "# input layer\n",
    "model.add(Dense(78,  activation='relu'))\n",
    "#model.add(Dropout(0.6))\n",
    "\n",
    "# hidden layer\n",
    "model.add(Dense(50, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "# hidden layer\n",
    "model.add(Dense(30, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "# output layer\n",
    "model.add(Dense(units=1,activation='sigmoid'))\n",
    "\n",
    "# Compile model\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 466918 samples, validate on 116730 samples\n",
      "Epoch 1/20\n",
      "466918/466918 [==============================] - 7s 16us/sample - loss: 0.2772 - val_loss: 0.2087\n",
      "Epoch 2/20\n",
      "466918/466918 [==============================] - 5s 12us/sample - loss: 0.2117 - val_loss: 0.1937\n",
      "Epoch 3/20\n",
      "466918/466918 [==============================] - 5s 11us/sample - loss: 0.1996 - val_loss: 0.1894\n",
      "Epoch 4/20\n",
      "466918/466918 [==============================] - 5s 11us/sample - loss: 0.1922 - val_loss: 0.1846\n",
      "Epoch 5/20\n",
      "466918/466918 [==============================] - 5s 11us/sample - loss: 0.1871 - val_loss: 0.1804\n",
      "Epoch 6/20\n",
      "466918/466918 [==============================] - 5s 11us/sample - loss: 0.1835 - val_loss: 0.1819\n",
      "Epoch 7/20\n",
      "466918/466918 [==============================] - 5s 11us/sample - loss: 0.1808 - val_loss: 0.1745\n",
      "Epoch 8/20\n",
      "466918/466918 [==============================] - 6s 12us/sample - loss: 0.1785 - val_loss: 0.1749\n",
      "Epoch 9/20\n",
      "466918/466918 [==============================] - 5s 11us/sample - loss: 0.1761 - val_loss: 0.1751\n",
      "Epoch 10/20\n",
      "466918/466918 [==============================] - 5s 12us/sample - loss: 0.1751 - val_loss: 0.1722\n",
      "Epoch 11/20\n",
      "466918/466918 [==============================] - 5s 11us/sample - loss: 0.1739 - val_loss: 0.1684\n",
      "Epoch 12/20\n",
      "466918/466918 [==============================] - 6s 12us/sample - loss: 0.1730 - val_loss: 0.1692\n",
      "Epoch 13/20\n",
      "466918/466918 [==============================] - 6s 12us/sample - loss: 0.1712 - val_loss: 0.1687\n",
      "Epoch 14/20\n",
      "466918/466918 [==============================] - 6s 12us/sample - loss: 0.1709 - val_loss: 0.1692\n",
      "Epoch 15/20\n",
      "466918/466918 [==============================] - 6s 13us/sample - loss: 0.1701 - val_loss: 0.1661\n",
      "Epoch 16/20\n",
      "466918/466918 [==============================] - 7s 14us/sample - loss: 0.1693 - val_loss: 0.1694\n",
      "Epoch 17/20\n",
      "466918/466918 [==============================] - 7s 14us/sample - loss: 0.1690 - val_loss: 0.1683\n",
      "Epoch 18/20\n",
      "466918/466918 [==============================] - 7s 14us/sample - loss: 0.1689 - val_loss: 0.1666\n",
      "Epoch 19/20\n",
      "466918/466918 [==============================] - 6s 14us/sample - loss: 0.1684 - val_loss: 0.1673\n",
      "Epoch 20/20\n",
      "466918/466918 [==============================] - 7s 14us/sample - loss: 0.1675 - val_loss: 0.1688\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1b3e7659388>"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x=X_train, \n",
    "          y=y_train, \n",
    "          epochs=20,\n",
    "          batch_size=256,\n",
    "          validation_data=(X_test, y_test)\n",
    "          #,callbacks=[early_stop]\n",
    "          )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b3e8d4ad88>"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "losses = pd.DataFrame(model.history.history)\n",
    "losses[['loss','val_loss']].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We are stopping training at just the right time to avoid overfitting since loss and val_loss are intersecting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = model.predict_classes(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.87      0.92     58156\n",
      "           1       0.88      0.99      0.93     58574\n",
      "\n",
      "    accuracy                           0.93    116730\n",
      "   macro avg       0.93      0.93      0.93    116730\n",
      "weighted avg       0.93      0.93      0.93    116730\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[50358,  7798],\n",
       "       [  844, 57730]], dtype=int64)"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test,predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our model has improved by a lot!\n",
    "\n",
    "### Earlier we had 47% recall for loan deferred cases with 89% model accuracy\n",
    "\n",
    "## But now, we have <ins>87% recall for loan deferred cases with 93% model accuracy</ins>\n",
    "\n",
    "## This is a model good enough to deploy for all practical purposes!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Since this is a good model and took days to train and test, we save it as a h5 file so we can use it in the future without having to tun this Jupyter notebook\n",
    "\n",
    "from tensorflow.keras.models import load_model\n",
    "model.save('ANN loan predictor with oversampling & hyperparameter-optimization.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### And we thus come to the end for our project!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"giphy.gif\" width=\"350\" align=\"center\">"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
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 "nbformat": 4,
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