Module-02b-Explore-Logistic.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic - Explore the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Raw Data\n",
    "\n",
    "You are provided with the following data: **loan_data.csv**  \n",
    "This is the historical data that the bank has provided. It has the following columns\n",
    "\n",
    "**Application Attributes**:\n",
    "- `years`: Number of years the applicant has been employed  \n",
    "- `ownership`: Whether the applicant owns a house or not  \n",
    "- `income`:  Annual income of the applicant  \n",
    "- `age`: Age of the applicant  \n",
    "\n",
    "**Behavioural Attributes**:\n",
    "- `grade`:  Credit grade of the applicant\n",
    "\n",
    "**Outcome Variable**:\n",
    "- `amount` : Amount of Loan provided to the applicant  \n",
    "- `interest`: Interest rate charged for the applicant  \n",
    "- `default` : Whether the applicant has defaulted or not "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us build some intuition around the Loan Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Frame the Problem\n",
    "\n",
    "- What are the features\n",
    "- What are the target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the Refine Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Load the libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Default Variables\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (16,9)\n",
    "plt.rcParams['font.size'] = 18\n",
    "plt.style.use('fivethirtyeight')\n",
    "pd.set_option('display.float_format', lambda x: '%.2f' % x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Load the dataset\n",
    "df = pd.read_csv(\"data/loan_data_clean.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>default</th>\n",
       "      <th>amount</th>\n",
       "      <th>interest</th>\n",
       "      <th>grade</th>\n",
       "      <th>years</th>\n",
       "      <th>ownership</th>\n",
       "      <th>income</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>5000</td>\n",
       "      <td>10.65</td>\n",
       "      <td>B</td>\n",
       "      <td>10.00</td>\n",
       "      <td>RENT</td>\n",
       "      <td>24000.00</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>2400</td>\n",
       "      <td>10.99</td>\n",
       "      <td>C</td>\n",
       "      <td>25.00</td>\n",
       "      <td>RENT</td>\n",
       "      <td>12252.00</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>10000</td>\n",
       "      <td>13.49</td>\n",
       "      <td>C</td>\n",
       "      <td>13.00</td>\n",
       "      <td>RENT</td>\n",
       "      <td>49200.00</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>5000</td>\n",
       "      <td>10.99</td>\n",
       "      <td>A</td>\n",
       "      <td>3.00</td>\n",
       "      <td>RENT</td>\n",
       "      <td>36000.00</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3000</td>\n",
       "      <td>10.99</td>\n",
       "      <td>E</td>\n",
       "      <td>9.00</td>\n",
       "      <td>RENT</td>\n",
       "      <td>48000.00</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   default  amount  interest grade  years ownership   income  age\n",
       "0        0    5000     10.65     B  10.00      RENT 24000.00   33\n",
       "1        0    2400     10.99     C  25.00      RENT 12252.00   31\n",
       "2        0   10000     13.49     C  13.00      RENT 49200.00   24\n",
       "3        0    5000     10.99     A   3.00      RENT 36000.00   39\n",
       "4        0    3000     10.99     E   9.00      RENT 48000.00   24"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Dual Variable Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a crosstab of default and grade\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a crosstab of default and grade - percentage by default type\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a crosstab of default and grade - percentage by all type\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a crosstab of default and grade - percentage by default type\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Explore the impact of `ownership`  with `default`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore the impact of `age` with `defualt`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explore the impact of  `income` with `default`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create the transformed income variable\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explore the impact of  `years` with `default`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Three Variable Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Plot age, years and default\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explore the relationship of `age`, `income` and `default`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explore the relationshiop of `age`, `grade` and `default`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}