Module-01a-Frame-Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Regression - Frame, Acquire & Refine"
   ]
  },
  {
   "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",
    "- `default` : Whether the applicant has defaulted or not \n",
    "- `interest`: Interest rate charged for the applicant  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Frame the Problem"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- What are the features?\n",
    "- What is the target?\n",
    "\n",
    "Discuss?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Acquire the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load the libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Defualt Variables\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (16,9)\n",
    "plt.style.use('fivethirtyeight')\n",
    "pd.set_option('display.float_format', lambda x: '%.2f' % x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Load the dataset\n",
    "df = pd.read_csv('data/loan_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>nan</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>nan</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>nan</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       nan     C  25.00      RENT 12252.00   31\n",
       "2        0   10000     13.49     C  13.00      RENT 49200.00   24\n",
       "3        0    5000       nan     A   3.00      RENT 36000.00   39\n",
       "4        0    3000       nan     E   9.00      RENT 48000.00   24"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#View the first few rows of train\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['default', 'amount', 'interest', 'grade', 'years', 'ownership',\n",
       "       'income', 'age'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#View the columns of the train dataset\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "default        int64\n",
       "amount         int64\n",
       "interest     float64\n",
       "grade         object\n",
       "years        float64\n",
       "ownership     object\n",
       "income       float64\n",
       "age            int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#View the data types of the train dataset\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(29092, 8)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#View the number of records in the data\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>years</th>\n",
       "      <th>income</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>29092.00</td>\n",
       "      <td>29092.00</td>\n",
       "      <td>26316.00</td>\n",
       "      <td>28283.00</td>\n",
       "      <td>29092.00</td>\n",
       "      <td>29092.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.11</td>\n",
       "      <td>9593.51</td>\n",
       "      <td>11.00</td>\n",
       "      <td>6.15</td>\n",
       "      <td>67168.83</td>\n",
       "      <td>27.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.31</td>\n",
       "      <td>6323.42</td>\n",
       "      <td>3.24</td>\n",
       "      <td>6.68</td>\n",
       "      <td>63606.52</td>\n",
       "      <td>6.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00</td>\n",
       "      <td>500.00</td>\n",
       "      <td>5.42</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4000.00</td>\n",
       "      <td>20.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.00</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>7.90</td>\n",
       "      <td>2.00</td>\n",
       "      <td>40000.00</td>\n",
       "      <td>23.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.00</td>\n",
       "      <td>8000.00</td>\n",
       "      <td>10.99</td>\n",
       "      <td>4.00</td>\n",
       "      <td>56424.00</td>\n",
       "      <td>26.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.00</td>\n",
       "      <td>12250.00</td>\n",
       "      <td>13.47</td>\n",
       "      <td>8.00</td>\n",
       "      <td>80000.00</td>\n",
       "      <td>30.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.00</td>\n",
       "      <td>35000.00</td>\n",
       "      <td>23.22</td>\n",
       "      <td>62.00</td>\n",
       "      <td>6000000.00</td>\n",
       "      <td>144.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       default   amount  interest    years     income      age\n",
       "count 29092.00 29092.00  26316.00 28283.00   29092.00 29092.00\n",
       "mean      0.11  9593.51     11.00     6.15   67168.83    27.70\n",
       "std       0.31  6323.42      3.24     6.68   63606.52     6.23\n",
       "min       0.00   500.00      5.42     0.00    4000.00    20.00\n",
       "25%       0.00  5000.00      7.90     2.00   40000.00    23.00\n",
       "50%       0.00  8000.00     10.99     4.00   56424.00    26.00\n",
       "75%       0.00 12250.00     13.47     8.00   80000.00    30.00\n",
       "max       1.00 35000.00     23.22    62.00 6000000.00   144.00"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#View summary of raw data \n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Refine the Data\n",
    "\n",
    "Lets check the dataset for quality and compeleteness\n",
    "1. Missing Values\n",
    "2. Outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check for Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  default amount interest  grade  years ownership income    age\n",
       "0   False  False    False  False  False     False  False  False\n",
       "1   False  False     True  False  False     False  False  False\n",
       "2   False  False    False  False  False     False  False  False\n",
       "3   False  False     True  False  False     False  False  False\n",
       "4   False  False     True  False  False     False  False  False"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find if df has missing values. Hint: There is a isnull() function\n",
    "df.isnull().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One consideration we check here is the number of observations with missing values for those columns that have missing values. If a column has too many missing values, it might make sense to drop the column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "default         0\n",
       "amount          0\n",
       "interest     2776\n",
       "grade           0\n",
       "years         809\n",
       "ownership       0\n",
       "income          0\n",
       "age             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#let's see how many missing values are present\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
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       "      <th>9</th>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29062</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29063</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29064</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29065</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29066</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29067</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29068</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29069</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29070</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29071</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29072</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29073</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29074</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29075</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29076</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29077</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29078</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29079</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29080</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29081</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29082</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29083</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29084</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29085</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29086</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29087</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29088</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29089</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29090</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29091</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>29092 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      default amount interest  grade  years ownership income    age\n",
       "0       False  False    False  False  False     False  False  False\n",
       "1       False  False     True  False  False     False  False  False\n",
       "2       False  False    False  False  False     False  False  False\n",
       "3       False  False     True  False  False     False  False  False\n",
       "4       False  False     True  False  False     False  False  False\n",
       "5       False  False    False  False  False     False  False  False\n",
       "6       False  False    False  False  False     False  False  False\n",
       "7       False  False    False  False  False     False  False  False\n",
       "8       False  False    False  False  False     False  False  False\n",
       "9       False  False    False  False  False     False  False  False\n",
       "10      False  False     True  False  False     False  False  False\n",
       "11      False  False    False  False  False     False  False  False\n",
       "12      False  False     True  False  False     False  False  False\n",
       "13      False  False    False  False  False     False  False  False\n",
       "14      False  False    False  False  False     False  False  False\n",
       "15      False  False    False  False  False     False  False  False\n",
       "16      False  False    False  False  False     False  False  False\n",
       "17      False  False    False  False  False     False  False  False\n",
       "18      False  False    False  False  False     False  False  False\n",
       "19      False  False    False  False  False     False  False  False\n",
       "20      False  False    False  False  False     False  False  False\n",
       "21      False  False    False  False  False     False  False  False\n",
       "22      False  False    False  False  False     False  False  False\n",
       "23      False  False    False  False  False     False  False  False\n",
       "24      False  False    False  False  False     False  False  False\n",
       "25      False  False    False  False  False     False  False  False\n",
       "26      False  False    False  False  False     False  False  False\n",
       "27      False  False    False  False  False     False  False  False\n",
       "28      False  False    False  False  False     False  False  False\n",
       "29      False  False    False  False  False     False  False  False\n",
       "...       ...    ...      ...    ...    ...       ...    ...    ...\n",
       "29062   False  False    False  False  False     False  False  False\n",
       "29063   False  False     True  False  False     False  False  False\n",
       "29064   False  False    False  False  False     False  False  False\n",
       "29065   False  False    False  False  False     False  False  False\n",
       "29066   False  False    False  False  False     False  False  False\n",
       "29067   False  False    False  False  False     False  False  False\n",
       "29068   False  False    False  False  False     False  False  False\n",
       "29069   False  False     True  False  False     False  False  False\n",
       "29070   False  False    False  False  False     False  False  False\n",
       "29071   False  False    False  False  False     False  False  False\n",
       "29072   False  False    False  False  False     False  False  False\n",
       "29073   False  False    False  False  False     False  False  False\n",
       "29074   False  False    False  False  False     False  False  False\n",
       "29075   False  False    False  False  False     False  False  False\n",
       "29076   False  False    False  False  False     False  False  False\n",
       "29077   False  False     True  False  False     False  False  False\n",
       "29078   False  False    False  False  False     False  False  False\n",
       "29079   False  False    False  False  False     False  False  False\n",
       "29080   False  False    False  False  False     False  False  False\n",
       "29081   False  False    False  False  False     False  False  False\n",
       "29082   False  False    False  False  False     False  False  False\n",
       "29083   False  False     True  False  False     False  False  False\n",
       "29084   False  False    False  False  False     False  False  False\n",
       "29085   False  False    False  False  False     False  False  False\n",
       "29086   False  False    False  False  False     False  False  False\n",
       "29087   False  False    False  False  False     False  False  False\n",
       "29088   False  False    False  False  False     False  False  False\n",
       "29089   False  False    False  False  False     False  False  False\n",
       "29090   False  False    False  False  False     False  False  False\n",
       "29091   False  False     True  False  False     False  False  False\n",
       "\n",
       "[29092 rows x 8 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>1</th>\n",
       "      <td>0</td>\n",
       "      <td>2400</td>\n",
       "      <td>nan</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>3</th>\n",
       "      <td>0</td>\n",
       "      <td>5000</td>\n",
       "      <td>nan</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>nan</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",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>10000</td>\n",
       "      <td>nan</td>\n",
       "      <td>C</td>\n",
       "      <td>4.00</td>\n",
       "      <td>RENT</td>\n",
       "      <td>42000.00</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>6000</td>\n",
       "      <td>nan</td>\n",
       "      <td>B</td>\n",
       "      <td>1.00</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>84000.00</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    default  amount  interest grade  years ownership   income  age\n",
       "1         0    2400       nan     C  25.00      RENT 12252.00   31\n",
       "3         0    5000       nan     A   3.00      RENT 36000.00   39\n",
       "4         0    3000       nan     E   9.00      RENT 48000.00   24\n",
       "10        0   10000       nan     C   4.00      RENT 42000.00   23\n",
       "12        0    6000       nan     B   1.00  MORTGAGE 84000.00   30"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.isnull().any(axis=1)].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, we see that two columns have missing values: **interest and years**. Both the columns are numeric. We have three options for dealing with this missing values\n",
    "\n",
    "Options to treat Missing Values\n",
    "- **REMOVE** - NAN rows\n",
    "- **IMPUTATION** - Replace them with something??\n",
    "    - Mean \n",
    "    - Median\n",
    "    - Fixed Number - Domain Relevant\n",
    "    - High Number (999) - Issue with modelling\n",
    "- **BINNING** - Categorical variable and \"Missing becomes a number*\n",
    "- **DOMAIN SPECIFIC** - Entry error, pipeline, etc.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.99"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's replace missing values with the median of the column\n",
    "df.interest.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "?df.fillna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "      <td>84000.00</td>\n",
       "      <td>30</td>\n",
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       "    <tr>\n",
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       "    <tr>\n",
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       "      <td>62000.00</td>\n",
       "      <td>29</td>\n",
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       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>0</td>\n",
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       "      <td>nan</td>\n",
       "      <td>A</td>\n",
       "      <td>1.00</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>52000.00</td>\n",
       "      <td>37</td>\n",
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       "    <tr>\n",
       "      <th>76</th>\n",
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       "      <td>80000.00</td>\n",
       "      <td>32</td>\n",
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       "      <td>0</td>\n",
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       "      <td>50000.00</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>0</td>\n",
       "      <td>18000</td>\n",
       "      <td>nan</td>\n",
       "      <td>A</td>\n",
       "      <td>12.00</td>\n",
       "      <td>RENT</td>\n",
       "      <td>56964.00</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>0</td>\n",
       "      <td>6375</td>\n",
       "      <td>nan</td>\n",
       "      <td>A</td>\n",
       "      <td>4.00</td>\n",
       "      <td>RENT</td>\n",
       "      <td>44000.00</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>0</td>\n",
       "      <td>12000</td>\n",
       "      <td>nan</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>RENT</td>\n",
       "      <td>57000.00</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
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       "      <td>7.51</td>\n",
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       "      <td>12000.00</td>\n",
       "      <td>21</td>\n",
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       "    <tr>\n",
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       "      <td>0</td>\n",
       "      <td>10000</td>\n",
       "      <td>nan</td>\n",
       "      <td>C</td>\n",
       "      <td>2.00</td>\n",
       "      <td>RENT</td>\n",
       "      <td>45900.00</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>1</td>\n",
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       "      <td>8.00</td>\n",
       "      <td>RENT</td>\n",
       "      <td>75000.00</td>\n",
       "      <td>50</td>\n",
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       "    <tr>\n",
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       "      <td>0</td>\n",
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       "      <td>nan</td>\n",
       "      <td>B</td>\n",
       "      <td>9.00</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>74000.00</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>1</td>\n",
       "      <td>12000</td>\n",
       "      <td>nan</td>\n",
       "      <td>C</td>\n",
       "      <td>8.00</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>70000.00</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     default  amount  interest grade  years ownership    income  age\n",
       "1          0    2400       nan     C  25.00      RENT  12252.00   31\n",
       "3          0    5000       nan     A   3.00      RENT  36000.00   39\n",
       "4          0    3000       nan     E   9.00      RENT  48000.00   24\n",
       "10         0   10000       nan     C   4.00      RENT  42000.00   23\n",
       "12         0    6000       nan     B   1.00  MORTGAGE  84000.00   30\n",
       "35         0   14000       nan     B   6.00      RENT  80000.00   26\n",
       "38         0   10000       nan     A  11.00      RENT  60000.00   37\n",
       "41         0   25600       nan     B   9.00      RENT 110000.00   29\n",
       "48         0    8000       nan     D   0.00      RENT  62000.00   29\n",
       "58         0   15000       nan     A   1.00  MORTGAGE  52000.00   37\n",
       "76         0    6000       nan     C  30.00      RENT  80000.00   32\n",
       "87         0   12000       nan     B   1.00      RENT  50000.00   26\n",
       "91         0   18000       nan     A  12.00      RENT  56964.00   29\n",
       "100        0    6375       nan     A   4.00      RENT  44000.00   25\n",
       "115        0   12000       nan     C   0.00      RENT  57000.00   23\n",
       "125        1    2500      7.51     A    nan       OWN  12000.00   21\n",
       "128        0   10000       nan     C   2.00      RENT  45900.00   65\n",
       "138        1   15000       nan     D   8.00      RENT  75000.00   50\n",
       "140        0    9000       nan     B   9.00  MORTGAGE  74000.00   22\n",
       "146        1   12000       nan     C   8.00  MORTGAGE  70000.00   33"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.isnull().any(axis=1)].head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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",
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       "      <th>0</th>\n",
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       "      <th>1</th>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>A</td>\n",
       "      <td>3.00</td>\n",
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       "      <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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>12000</td>\n",
       "      <td>12.69</td>\n",
       "      <td>B</td>\n",
       "      <td>11.00</td>\n",
       "      <td>OWN</td>\n",
       "      <td>75000.00</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>9000</td>\n",
       "      <td>13.49</td>\n",
       "      <td>C</td>\n",
       "      <td>0.00</td>\n",
       "      <td>RENT</td>\n",
       "      <td>30000.00</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>3000</td>\n",
       "      <td>9.91</td>\n",
       "      <td>B</td>\n",
       "      <td>3.00</td>\n",
       "      <td>RENT</td>\n",
       "      <td>15000.00</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>10000</td>\n",
       "      <td>10.65</td>\n",
       "      <td>B</td>\n",
       "      <td>3.00</td>\n",
       "      <td>RENT</td>\n",
       "      <td>100000.00</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>1000</td>\n",
       "      <td>16.29</td>\n",
       "      <td>D</td>\n",
       "      <td>0.00</td>\n",
       "      <td>RENT</td>\n",
       "      <td>28000.00</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>10000</td>\n",
       "      <td>10.99</td>\n",
       "      <td>C</td>\n",
       "      <td>4.00</td>\n",
       "      <td>RENT</td>\n",
       "      <td>42000.00</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>3600</td>\n",
       "      <td>6.03</td>\n",
       "      <td>A</td>\n",
       "      <td>13.00</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>110000.00</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>6000</td>\n",
       "      <td>10.99</td>\n",
       "      <td>B</td>\n",
       "      <td>1.00</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>84000.00</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>9200</td>\n",
       "      <td>6.03</td>\n",
       "      <td>A</td>\n",
       "      <td>6.00</td>\n",
       "      <td>RENT</td>\n",
       "      <td>77385.19</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>21000</td>\n",
       "      <td>12.42</td>\n",
       "      <td>B</td>\n",
       "      <td>17.00</td>\n",
       "      <td>RENT</td>\n",
       "      <td>105000.00</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0</td>\n",
       "      <td>10000</td>\n",
       "      <td>11.71</td>\n",
       "      <td>B</td>\n",
       "      <td>13.00</td>\n",
       "      <td>OWN</td>\n",
       "      <td>50000.00</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0</td>\n",
       "      <td>10000</td>\n",
       "      <td>11.71</td>\n",
       "      <td>B</td>\n",
       "      <td>5.00</td>\n",
       "      <td>RENT</td>\n",
       "      <td>50000.00</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>6000</td>\n",
       "      <td>11.71</td>\n",
       "      <td>B</td>\n",
       "      <td>1.00</td>\n",
       "      <td>RENT</td>\n",
       "      <td>76000.00</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0</td>\n",
       "      <td>15000</td>\n",
       "      <td>9.91</td>\n",
       "      <td>B</td>\n",
       "      <td>2.00</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>92000.00</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</td>\n",
       "      <td>15000</td>\n",
       "      <td>14.27</td>\n",
       "      <td>C</td>\n",
       "      <td>9.00</td>\n",
       "      <td>RENT</td>\n",
       "      <td>60000.00</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0</td>\n",
       "      <td>4000</td>\n",
       "      <td>11.71</td>\n",
       "      <td>B</td>\n",
       "      <td>19.00</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>106000.00</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0</td>\n",
       "      <td>8500</td>\n",
       "      <td>11.71</td>\n",
       "      <td>B</td>\n",
       "      <td>0.00</td>\n",
       "      <td>RENT</td>\n",
       "      <td>25000.00</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0</td>\n",
       "      <td>4375</td>\n",
       "      <td>7.51</td>\n",
       "      <td>A</td>\n",
       "      <td>7.00</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>17108.00</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0</td>\n",
       "      <td>31825</td>\n",
       "      <td>7.90</td>\n",
       "      <td>A</td>\n",
       "      <td>5.00</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>75000.00</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0</td>\n",
       "      <td>5000</td>\n",
       "      <td>8.90</td>\n",
       "      <td>A</td>\n",
       "      <td>2.00</td>\n",
       "      <td>RENT</td>\n",
       "      <td>24044.00</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0</td>\n",
       "      <td>7000</td>\n",
       "      <td>15.96</td>\n",
       "      <td>C</td>\n",
       "      <td>7.00</td>\n",
       "      <td>RENT</td>\n",
       "      <td>34000.00</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0</td>\n",
       "      <td>12400</td>\n",
       "      <td>10.65</td>\n",
       "      <td>B</td>\n",
       "      <td>18.00</td>\n",
       "      <td>RENT</td>\n",
       "      <td>41000.00</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0</td>\n",
       "      <td>10800</td>\n",
       "      <td>9.91</td>\n",
       "      <td>B</td>\n",
       "      <td>2.00</td>\n",
       "      <td>RENT</td>\n",
       "      <td>55596.00</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0</td>\n",
       "      <td>15000</td>\n",
       "      <td>7.90</td>\n",
       "      <td>A</td>\n",
       "      <td>1.00</td>\n",
       "      <td>RENT</td>\n",
       "      <td>45000.00</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0</td>\n",
       "      <td>6000</td>\n",
       "      <td>12.42</td>\n",
       "      <td>B</td>\n",
       "      <td>1.00</td>\n",
       "      <td>RENT</td>\n",
       "      <td>36852.00</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29062</th>\n",
       "      <td>0</td>\n",
       "      <td>12000</td>\n",
       "      <td>12.49</td>\n",
       "      <td>D</td>\n",
       "      <td>9.00</td>\n",
       "      <td>OWN</td>\n",
       "      <td>75000.00</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29063</th>\n",
       "      <td>1</td>\n",
       "      <td>20000</td>\n",
       "      <td>10.99</td>\n",
       "      <td>E</td>\n",
       "      <td>6.00</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>145000.00</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29064</th>\n",
       "      <td>0</td>\n",
       "      <td>1200</td>\n",
       "      <td>11.54</td>\n",
       "      <td>C</td>\n",
       "      <td>0.00</td>\n",
       "      <td>RENT</td>\n",
       "      <td>20000.00</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29065</th>\n",
       "      <td>0</td>\n",
       "      <td>12250</td>\n",
       "      <td>10.59</td>\n",
       "      <td>C</td>\n",
       "      <td>3.00</td>\n",
       "      <td>RENT</td>\n",
       "      <td>60000.00</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29066</th>\n",
       "      <td>0</td>\n",
       "      <td>2700</td>\n",
       "      <td>15.96</td>\n",
       "      <td>F</td>\n",
       "      <td>1.00</td>\n",
       "      <td>RENT</td>\n",
       "      <td>52200.00</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29067</th>\n",
       "      <td>0</td>\n",
       "      <td>5000</td>\n",
       "      <td>9.01</td>\n",
       "      <td>B</td>\n",
       "      <td>1.00</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>250000.00</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29068</th>\n",
       "      <td>0</td>\n",
       "      <td>3500</td>\n",
       "      <td>9.96</td>\n",
       "      <td>B</td>\n",
       "      <td>0.00</td>\n",
       "      <td>RENT</td>\n",
       "      <td>13500.00</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29069</th>\n",
       "      <td>0</td>\n",
       "      <td>5000</td>\n",
       "      <td>10.99</td>\n",
       "      <td>A</td>\n",
       "      <td>8.00</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>150000.00</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29070</th>\n",
       "      <td>0</td>\n",
       "      <td>5000</td>\n",
       "      <td>7.43</td>\n",
       "      <td>A</td>\n",
       "      <td>7.00</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>95000.00</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29071</th>\n",
       "      <td>0</td>\n",
       "      <td>25000</td>\n",
       "      <td>10.59</td>\n",
       "      <td>C</td>\n",
       "      <td>2.00</td>\n",
       "      <td>RENT</td>\n",
       "      <td>48000.00</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29072</th>\n",
       "      <td>0</td>\n",
       "      <td>10000</td>\n",
       "      <td>10.28</td>\n",
       "      <td>C</td>\n",
       "      <td>0.00</td>\n",
       "      <td>RENT</td>\n",
       "      <td>27376.00</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29073</th>\n",
       "      <td>0</td>\n",
       "      <td>5000</td>\n",
       "      <td>9.01</td>\n",
       "      <td>B</td>\n",
       "      <td>1.00</td>\n",
       "      <td>RENT</td>\n",
       "      <td>73000.00</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29074</th>\n",
       "      <td>0</td>\n",
       "      <td>5100</td>\n",
       "      <td>8.38</td>\n",
       "      <td>A</td>\n",
       "      <td>4.00</td>\n",
       "      <td>RENT</td>\n",
       "      <td>32000.00</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29075</th>\n",
       "      <td>0</td>\n",
       "      <td>5400</td>\n",
       "      <td>7.75</td>\n",
       "      <td>A</td>\n",
       "      <td>0.00</td>\n",
       "      <td>RENT</td>\n",
       "      <td>8000.00</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29076</th>\n",
       "      <td>0</td>\n",
       "      <td>5300</td>\n",
       "      <td>8.38</td>\n",
       "      <td>A</td>\n",
       "      <td>7.00</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>74000.00</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29077</th>\n",
       "      <td>0</td>\n",
       "      <td>5000</td>\n",
       "      <td>10.99</td>\n",
       "      <td>C</td>\n",
       "      <td>1.00</td>\n",
       "      <td>OWN</td>\n",
       "      <td>30000.00</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29078</th>\n",
       "      <td>0</td>\n",
       "      <td>3000</td>\n",
       "      <td>10.28</td>\n",
       "      <td>C</td>\n",
       "      <td>2.00</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>200000.00</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29079</th>\n",
       "      <td>0</td>\n",
       "      <td>3000</td>\n",
       "      <td>8.70</td>\n",
       "      <td>B</td>\n",
       "      <td>0.00</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>25000.00</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29080</th>\n",
       "      <td>0</td>\n",
       "      <td>5000</td>\n",
       "      <td>7.43</td>\n",
       "      <td>A</td>\n",
       "      <td>8.00</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>120000.00</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29081</th>\n",
       "      <td>0</td>\n",
       "      <td>5000</td>\n",
       "      <td>8.07</td>\n",
       "      <td>A</td>\n",
       "      <td>0.00</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>180000.00</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29082</th>\n",
       "      <td>0</td>\n",
       "      <td>5000</td>\n",
       "      <td>9.33</td>\n",
       "      <td>B</td>\n",
       "      <td>2.00</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>180000.00</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29083</th>\n",
       "      <td>0</td>\n",
       "      <td>5000</td>\n",
       "      <td>10.99</td>\n",
       "      <td>B</td>\n",
       "      <td>4.00</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>48000.00</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29084</th>\n",
       "      <td>0</td>\n",
       "      <td>5000</td>\n",
       "      <td>11.22</td>\n",
       "      <td>C</td>\n",
       "      <td>0.00</td>\n",
       "      <td>OWN</td>\n",
       "      <td>80000.00</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29085</th>\n",
       "      <td>0</td>\n",
       "      <td>5000</td>\n",
       "      <td>7.43</td>\n",
       "      <td>A</td>\n",
       "      <td>1.00</td>\n",
       "      <td>OWN</td>\n",
       "      <td>85000.00</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29086</th>\n",
       "      <td>0</td>\n",
       "      <td>5000</td>\n",
       "      <td>8.70</td>\n",
       "      <td>B</td>\n",
       "      <td>5.00</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>75000.00</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29087</th>\n",
       "      <td>0</td>\n",
       "      <td>2500</td>\n",
       "      <td>8.07</td>\n",
       "      <td>A</td>\n",
       "      <td>4.00</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>110000.00</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29088</th>\n",
       "      <td>0</td>\n",
       "      <td>8500</td>\n",
       "      <td>10.28</td>\n",
       "      <td>C</td>\n",
       "      <td>3.00</td>\n",
       "      <td>RENT</td>\n",
       "      <td>18000.00</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29089</th>\n",
       "      <td>0</td>\n",
       "      <td>5000</td>\n",
       "      <td>8.07</td>\n",
       "      <td>A</td>\n",
       "      <td>0.00</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>100000.00</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29090</th>\n",
       "      <td>0</td>\n",
       "      <td>5000</td>\n",
       "      <td>7.43</td>\n",
       "      <td>A</td>\n",
       "      <td>0.00</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>200000.00</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29091</th>\n",
       "      <td>0</td>\n",
       "      <td>7500</td>\n",
       "      <td>10.99</td>\n",
       "      <td>E</td>\n",
       "      <td>0.00</td>\n",
       "      <td>OWN</td>\n",
       "      <td>22000.00</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>29092 rows × 8 columns</p>\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\n",
       "5            0   12000     12.69     B  11.00       OWN  75000.00   28\n",
       "6            1    9000     13.49     C   0.00      RENT  30000.00   22\n",
       "7            0    3000      9.91     B   3.00      RENT  15000.00   22\n",
       "8            1   10000     10.65     B   3.00      RENT 100000.00   28\n",
       "9            0    1000     16.29     D   0.00      RENT  28000.00   22\n",
       "10           0   10000     10.99     C   4.00      RENT  42000.00   23\n",
       "11           0    3600      6.03     A  13.00  MORTGAGE 110000.00   27\n",
       "12           0    6000     10.99     B   1.00  MORTGAGE  84000.00   30\n",
       "13           0    9200      6.03     A   6.00      RENT  77385.19   24\n",
       "14           1   21000     12.42     B  17.00      RENT 105000.00   29\n",
       "15           0   10000     11.71     B  13.00       OWN  50000.00   23\n",
       "16           0   10000     11.71     B   5.00      RENT  50000.00   22\n",
       "17           1    6000     11.71     B   1.00      RENT  76000.00   31\n",
       "18           0   15000      9.91     B   2.00  MORTGAGE  92000.00   23\n",
       "19           1   15000     14.27     C   9.00      RENT  60000.00   30\n",
       "20           0    4000     11.71     B  19.00  MORTGAGE 106000.00   27\n",
       "21           0    8500     11.71     B   0.00      RENT  25000.00   24\n",
       "22           0    4375      7.51     A   7.00  MORTGAGE  17108.00   38\n",
       "23           0   31825      7.90     A   5.00  MORTGAGE  75000.00   23\n",
       "24           0    5000      8.90     A   2.00      RENT  24044.00   22\n",
       "25           0    7000     15.96     C   7.00      RENT  34000.00   27\n",
       "26           0   12400     10.65     B  18.00      RENT  41000.00   36\n",
       "27           0   10800      9.91     B   2.00      RENT  55596.00   35\n",
       "28           0   15000      7.90     A   1.00      RENT  45000.00   23\n",
       "29           0    6000     12.42     B   1.00      RENT  36852.00   25\n",
       "...        ...     ...       ...   ...    ...       ...       ...  ...\n",
       "29062        0   12000     12.49     D   9.00       OWN  75000.00   23\n",
       "29063        1   20000     10.99     E   6.00  MORTGAGE 145000.00   28\n",
       "29064        0    1200     11.54     C   0.00      RENT  20000.00   29\n",
       "29065        0   12250     10.59     C   3.00      RENT  60000.00   23\n",
       "29066        0    2700     15.96     F   1.00      RENT  52200.00   25\n",
       "29067        0    5000      9.01     B   1.00  MORTGAGE 250000.00   30\n",
       "29068        0    3500      9.96     B   0.00      RENT  13500.00   21\n",
       "29069        0    5000     10.99     A   8.00  MORTGAGE 150000.00   30\n",
       "29070        0    5000      7.43     A   7.00  MORTGAGE  95000.00   25\n",
       "29071        0   25000     10.59     C   2.00      RENT  48000.00   27\n",
       "29072        0   10000     10.28     C   0.00      RENT  27376.00   22\n",
       "29073        0    5000      9.01     B   1.00      RENT  73000.00   24\n",
       "29074        0    5100      8.38     A   4.00      RENT  32000.00   25\n",
       "29075        0    5400      7.75     A   0.00      RENT   8000.00   22\n",
       "29076        0    5300      8.38     A   7.00  MORTGAGE  74000.00   42\n",
       "29077        0    5000     10.99     C   1.00       OWN  30000.00   25\n",
       "29078        0    3000     10.28     C   2.00  MORTGAGE 200000.00   67\n",
       "29079        0    3000      8.70     B   0.00  MORTGAGE  25000.00   36\n",
       "29080        0    5000      7.43     A   8.00  MORTGAGE 120000.00   29\n",
       "29081        0    5000      8.07     A   0.00  MORTGAGE 180000.00   21\n",
       "29082        0    5000      9.33     B   2.00  MORTGAGE 180000.00   23\n",
       "29083        0    5000     10.99     B   4.00  MORTGAGE  48000.00   33\n",
       "29084        0    5000     11.22     C   0.00       OWN  80000.00   39\n",
       "29085        0    5000      7.43     A   1.00       OWN  85000.00   46\n",
       "29086        0    5000      8.70     B   5.00  MORTGAGE  75000.00   35\n",
       "29087        0    2500      8.07     A   4.00  MORTGAGE 110000.00   27\n",
       "29088        0    8500     10.28     C   3.00      RENT  18000.00   25\n",
       "29089        0    5000      8.07     A   0.00  MORTGAGE 100000.00   27\n",
       "29090        0    5000      7.43     A   0.00  MORTGAGE 200000.00   23\n",
       "29091        0    7500     10.99     E   0.00       OWN  22000.00   25\n",
       "\n",
       "[29092 rows x 8 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#there's a fillna function\n",
    "df.fillna(df.median(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "default      0\n",
       "amount       0\n",
       "interest     0\n",
       "grade        0\n",
       "years        0\n",
       "ownership    0\n",
       "income       0\n",
       "age          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Now, let's check if train has missing values or not\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check for Outlier Values\n",
    "\n",
    "Let us check first the categorical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "default        int64\n",
       "amount         int64\n",
       "interest     float64\n",
       "grade         object\n",
       "years        float64\n",
       "ownership     object\n",
       "income       float64\n",
       "age            int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Which variables are Categorical?\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    9649\n",
       "B    9329\n",
       "C    5748\n",
       "D    3231\n",
       "E     868\n",
       "F     211\n",
       "G      56\n",
       "Name: grade, dtype: int64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.grade.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>default</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grade</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>9084</td>\n",
       "      <td>565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>8344</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>4904</td>\n",
       "      <td>844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>2651</td>\n",
       "      <td>580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>692</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>155</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>35</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "default     0    1\n",
       "grade             \n",
       "A        9084  565\n",
       "B        8344  985\n",
       "C        4904  844\n",
       "D        2651  580\n",
       "E         692  176\n",
       "F         155   56\n",
       "G          35   21"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a Crosstab of those variables with another variable\n",
    "pd.crosstab(df.grade, df.default)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RENT        14692\n",
       "MORTGAGE    12002\n",
       "OWN          2301\n",
       "OTHER          97\n",
       "Name: ownership, dtype: int64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a Crosstab of those variables with another variable\n",
    "df.ownership.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us check outliers in the continuous variable\n",
    "\n",
    "- Plotting\n",
    "    - Histogram\n",
    "    - Box-Plot \n",
    "- Measuring \n",
    "    - Z-score > 3\n",
    "    - Modified Z-score > 3.5\n",
    "    where modified Z-score = 0.6745 * (x - x_median) / MAD \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>years</th>\n",
       "      <th>income</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>29092.00</td>\n",
       "      <td>29092.00</td>\n",
       "      <td>29092.00</td>\n",
       "      <td>29092.00</td>\n",
       "      <td>29092.00</td>\n",
       "      <td>29092.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.11</td>\n",
       "      <td>9593.51</td>\n",
       "      <td>11.00</td>\n",
       "      <td>6.09</td>\n",
       "      <td>67168.83</td>\n",
       "      <td>27.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.31</td>\n",
       "      <td>6323.42</td>\n",
       "      <td>3.08</td>\n",
       "      <td>6.59</td>\n",
       "      <td>63606.52</td>\n",
       "      <td>6.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00</td>\n",
       "      <td>500.00</td>\n",
       "      <td>5.42</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4000.00</td>\n",
       "      <td>20.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.00</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>8.49</td>\n",
       "      <td>2.00</td>\n",
       "      <td>40000.00</td>\n",
       "      <td>23.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.00</td>\n",
       "      <td>8000.00</td>\n",
       "      <td>10.99</td>\n",
       "      <td>4.00</td>\n",
       "      <td>56424.00</td>\n",
       "      <td>26.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.00</td>\n",
       "      <td>12250.00</td>\n",
       "      <td>13.11</td>\n",
       "      <td>8.00</td>\n",
       "      <td>80000.00</td>\n",
       "      <td>30.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.00</td>\n",
       "      <td>35000.00</td>\n",
       "      <td>23.22</td>\n",
       "      <td>62.00</td>\n",
       "      <td>6000000.00</td>\n",
       "      <td>144.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       default   amount  interest    years     income      age\n",
       "count 29092.00 29092.00  29092.00 29092.00   29092.00 29092.00\n",
       "mean      0.11  9593.51     11.00     6.09   67168.83    27.70\n",
       "std       0.31  6323.42      3.08     6.59   63606.52     6.23\n",
       "min       0.00   500.00      5.42     0.00    4000.00    20.00\n",
       "25%       0.00  5000.00      8.49     2.00   40000.00    23.00\n",
       "50%       0.00  8000.00     10.99     4.00   56424.00    26.00\n",
       "75%       0.00 12250.00     13.11     8.00   80000.00    30.00\n",
       "max       1.00 35000.00     23.22    62.00 6000000.00   144.00"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Describe the data set continuous values\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clearly the `age` variable looks like it has an outlier - Age cannot be greater 100! \n",
    "Also the `income` variable looks like it may also have an outlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "?plt.yscale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1148f2160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make a histogram of age\n",
    "\n",
    "df.age.hist()\n",
    "#plt.xlim(60,80)\n",
    "#plt.ylim(0,100)\n",
    "plt.yscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/amitkaps/miniconda3/lib/python3.5/site-packages/statsmodels/nonparametric/kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1136b4828>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bQMkRj3GyqkWS3BUO+dwOHevqo84DAFAyCB4anzUekjSlzq1wxKDOAwBQMsbX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\nCB4AACBv/n/IUXFiPM1jGwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1136a1e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df.age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XYeyrcMXa14ViZtg/MltTUzP4NkrE/935mDZt2nAvBwCMccOOjjlz5sTBgwfj8OHD0d/f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\ndiQzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1148c84a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make a histogram of income\n",
    "df.income.hist()\n",
    "plt.yscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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42Mc+VuUJx4edO3fG/v374+zZs3HHHXfETTfdVFuvp2IC+vGPf1xs3LixKIqi+M1vflOs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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114b85198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make Histograms for all other \n",
    "df.years.hist()\n",
    "plt.yscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'boxes': [<matplotlib.lines.Line2D at 0x1147d0cc0>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x1147be780>,\n",
       "  <matplotlib.lines.Line2D at 0x1147be8d0>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x114bf1be0>],\n",
       " 'means': [],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x114793da0>],\n",
       " 'whiskers': [<matplotlib.lines.Line2D at 0x1147d07f0>,\n",
       "  <matplotlib.lines.Line2D at 0x11476c240>]}"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AAEUSMQBAkUQMAFAkEQMAFEnEAABFEjEAQJFEDABQJBEDABRJxAAARRIxAECRRAwAUCQR\nAwAUScQAAEUSMQBAkUQMAFAkEQMAFEnEAABFEjEAQJFEDABQJBEDABRJxAAARRIxAECRRAwAUCQR\nAwAUScQAAEUSMQBAkUQMAFAkEQMAFEnEAABFEjEAQJFEDABQJBEDABRJxAAARRIxAECRRAwAUCQR\nAwAUScQAAEV6RxHz/PPP584770ySHD9+POvWrcsdd9yR7du3p16vJ0n279+fNWvWZO3atXnhhRea\nOgsAMG/EPPLII3nggQdy9uzZJMmuXbuyYcOGPPbYY2k0Gjl48GDGx8dz5MiRHDhwIHv37s2OHTua\nOgsAMG/E9PX1Zd++fbOPx8fHMzg4mCQZHh7OM888k+eeey5DQ0OpVCrp7e3NzMxMTp061bRZAIB5\nI2b16tWpVquzjxuNRiqVSpJk8eLFmZqayvT0dDo6OmZn3jrerFkAgOr8Ixdra/vv7qnVaunq6kpH\nR0dqtdpFxzs7O5s2O5+lSxelWm1/ty8NKExPz/yfB8Dl611HzIoVKzI2NpZVq1ZldHQ0t9xyS/r6\n+rJnz56sX78+J0+eTL1eT3d3d9Nm5zM5efo9vRlAOXp6OjMxYWcWLndzXay864jZuHFjtm7dmr17\n96a/vz+rV69Oe3t7BgYGcvvtt6der2fbtm1NnQUAqDQajUarF3GpuTqDy5+dGFgY5tqJ8cfuAIAi\niRgAoEgiBgAokogBAIokYgCAIokYAKBIIgYAKJKIAQCKJGIAgCKJGACgSCIGACiSiAEAiiRiAIAi\niRgAoEgiBgAokogBAIokYgCAIokYAKBIIgYAKJKIAQCKJGIAgCKJGACgSCIGACiSiAEAiiRiAIAi\niRgAoEgiBgAokogBAIokYgCAIokYAKBIIgYAKJKIAQCKJGIAgCKJGACgSCIGACiSiAEAiiRiAIAi\niRgAoEjVVi8AWFiGh1flpZf+2OplXOSGGz6S0dGxVi8DeJcqjUaj0epFXGoTE1OtXgLQZNdc05XX\nX//PVi8DaLKens63Ped2EgBQpCJuJ9Xr9Tz44IN5+eWXc8UVV2RkZCTXXXddq5cFC8by5X158803\nW72Mf3LNNV2tXsI/WbJkSf7jP/7S6mXAglBExPzud7/LuXPn8vjjj+fo0aN56KGH8vDDD7d6WbBg\nDH7n3/L/Ptjd6mUUYfqV91/sweWqiIh57rnncuuttyZJbr755rz44ostXhEsLD/9///e6iX8E78T\nAxQRMdPT0+no6Jh93N7engsXLqRa/d+Xv3TpolSr7f9XywPehRtvvDHj4+OX5GddqttJH/3oR10c\nQYGKiJiOjo7UarXZx/V6/W0DJkkmJ0//XywLeA+eeurZS/Jzeno6L+k3EX2rEd6fiv920sqVKzM6\nOpokOXr0aJYvX97iFQEArVbETsxnP/vZHD58OGvXrk2j0cjOnTtbvSQAoMX8sTugSJf6dhLw/lT8\n7SQAgP9JxAAARRIxAECRRAwAUCQRAwAUScQAAEUSMQBAkUQMAFAkEQMAFEnEAABFuiz/7QAAcPmz\nEwMAFEnEAABFEjEAQJFEDABQJBEDABRJxAAARRIxQJGef/753Hnnna1eBtBC1VYvAODdeuSRR/Lk\nk0/m6quvbvVSgBayEwMUp6+vL/v27Wv1MoAWEzFAcVavXp1q1UYyLHQiBgAokogBAIokYgCAIvkv\n1gBAkezEAABFEjEAQJFEDABQJBEDABRJxAAARRIxAECRRAwAUCQRAwAU6b8Ajb0B4Yqev+EAAAAA\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11464d6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(df.income)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'boxes': [<matplotlib.lines.Line2D at 0x114da7ba8>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x114da1fd0>,\n",
       "  <matplotlib.lines.Line2D at 0x114d9aef0>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x1147d5fd0>],\n",
       " 'means': [],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x1147d50f0>],\n",
       " 'whiskers': [<matplotlib.lines.Line2D at 0x114da7e10>,\n",
       "  <matplotlib.lines.Line2D at 0x114da1e10>]}"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Pr4SVzfYHAFBCVAAAJUQFAFBCVAAAJc7rP2qeOXMm999/f37/+9/nsssuy+TkZK677rrq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\nNCveVHEDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1144193c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(df.years)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x114dec6d8>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QDKvK51VNtZ9VRAEAGCG2DBuxWFzP/u4tvfqnY6fMRJEkwzBy1CoAAEYnW4aNp3/ZqE27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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114769978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make a scatter of age and income\n",
    "plt.scatter(df.income, df.age)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find the observation which has age = 144 and remove it from the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "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>19485</th>\n",
       "      <td>0</td>\n",
       "      <td>5000</td>\n",
       "      <td>12.73</td>\n",
       "      <td>C</td>\n",
       "      <td>12.00</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>6000000.00</td>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       default  amount  interest grade  years ownership     income  age\n",
       "19485        0    5000     12.73     C  12.00  MORTGAGE 6000000.00  144"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the observation \n",
    "df[df.age == df.age.max()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([19485], dtype='int64')"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.age == df.age.max()].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.drop?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use drop to remove the observation inplace\n",
    "df.drop(19485, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(29091, 8)"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the shape of the df\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x113d20710>"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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78dRTT6GrqwuDBw/Giy++iNzcXPzud7/DO++8A6PRiEcffRR33HEHWltbEz6WiIiIKG4S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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1147f0198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check again for outliers\n",
    "plt.scatter(df.age, df.income)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the new file as cleaned data\n",
    "df.to_csv(\"data/loan_data_clean.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#We are good to go to the next step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}