project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np# numpy deals with number and larger size array\n",
    "import pandas as pd#pandas deals with the discriptive statistics\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\satwik\\\\Desktop\\\\Data Sets'"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\satwik\n"
     ]
    }
   ],
   "source": [
    "cd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\satwik\\Desktop\n"
     ]
    }
   ],
   "source": [
    "cd Desktop/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\satwik\\Desktop\\Data Sets\n"
     ]
    }
   ],
   "source": [
    "cd Data Sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Volume in drive C is Windows\n",
      " Volume Serial Number is DA76-C8A2\n",
      "\n",
      " Directory of C:\\Users\\satwik\\Desktop\\Data Sets\n",
      "\n",
      "30-05-2018  10:51    <DIR>          .\n",
      "30-05-2018  10:51    <DIR>          ..\n",
      "22-05-2018  11:35             2,438 50_startups.csv\n",
      "17-05-2018  23:44             9,739 assign1.xlsx\n",
      "23-05-2018  09:26           554,144 bikebuyer1.csv\n",
      "30-05-2018  21:21           616,875 bikebuyerne.csv\n",
      "30-05-2018  10:51           616,875 bikebuyernew.csv\n",
      "21-05-2018  11:45               388 challenger.csv\n",
      "29-05-2018  10:52    <DIR>          data\n",
      "23-05-2018  11:03           125,773 data (1).csv\n",
      "24-05-2018  12:09             2,798 data_collected.csv\n",
      "22-05-2018  09:29            54,288 insurance.csv\n",
      "25-05-2018  12:15             5,107 Iris.csv\n",
      "20-05-2018  20:57                57 New Text Document.txt\n",
      "18-05-2018  11:34               693 sales.csv\n",
      "20-05-2018  21:27                39 sdb.txt\n",
      "20-05-2018  21:47                56 sdb-1.txt\n",
      "              14 File(s)      1,989,270 bytes\n",
      "               3 Dir(s)  382,751,535,104 bytes free\n"
     ]
    }
   ],
   "source": [
    "ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "data1=pd.read_csv('bikebuyer1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Marital Status</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Yearly Income</th>\n",
       "      <th>Children</th>\n",
       "      <th>Education</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Home Owner</th>\n",
       "      <th>Cars</th>\n",
       "      <th>Commute Distance</th>\n",
       "      <th>Region</th>\n",
       "      <th>Age</th>\n",
       "      <th>Bike Buyer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22711.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>30000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Clerical</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>33</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13555.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>40000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Graduate Degree</td>\n",
       "      <td>Clerical</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>37</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>160000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>55</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>160000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Graduate Degree</td>\n",
       "      <td>Management</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>47</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25410.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>70000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Skilled Manual</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>North America</td>\n",
       "      <td>38</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID Marital Status  Gender  Yearly Income  Children        Education  \\\n",
       "0  22711.0         Single    Male          30000       0.0  Partial College   \n",
       "1  13555.0        Married  Female          40000       0.0  Graduate Degree   \n",
       "2      NaN        Married    Male         160000       5.0  Partial College   \n",
       "3      2.0         Single    Male         160000       0.0  Graduate Degree   \n",
       "4  25410.0            NaN  Female          70000       2.0        Bachelors   \n",
       "\n",
       "       Occupation Home Owner  Cars  Commute Distance         Region  Age  \\\n",
       "0        Clerical         No     1               1.0         Europe   33   \n",
       "1        Clerical        Yes     0               1.0         Europe   37   \n",
       "2    Professional         No     3               2.0         Europe   55   \n",
       "3      Management        Yes     2               5.0        Pacific   47   \n",
       "4  Skilled Manual         No     1               1.0  North America   38   \n",
       "\n",
       "  Bike Buyer  \n",
       "0        Yes  \n",
       "1        Yes  \n",
       "2         No  \n",
       "3         No  \n",
       "4        Yes  "
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.head()#5 upper entries of dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Marital Status</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Yearly Income</th>\n",
       "      <th>Children</th>\n",
       "      <th>Education</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Home Owner</th>\n",
       "      <th>Cars</th>\n",
       "      <th>Commute Distance</th>\n",
       "      <th>Region</th>\n",
       "      <th>Age</th>\n",
       "      <th>Bike Buyer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6992</th>\n",
       "      <td>22820.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>100000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>High School</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>52</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6993</th>\n",
       "      <td>22821.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>130000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>52</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6994</th>\n",
       "      <td>22823.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>160000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Management</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>53</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6995</th>\n",
       "      <td>22825.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>Female</td>\n",
       "      <td>120000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>54</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6996</th>\n",
       "      <td>22826.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>130000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>High School</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>54</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           ID Marital Status  Gender  Yearly Income  Children  \\\n",
       "6992  22820.0        Married    Male         100000       4.0   \n",
       "6993  22821.0        Married  Female         130000       4.0   \n",
       "6994  22823.0        Married  Female         160000       5.0   \n",
       "6995  22825.0         Single  Female         120000       5.0   \n",
       "6996  22826.0        Married    Male         130000       5.0   \n",
       "\n",
       "            Education    Occupation Home Owner  Cars  Commute Distance  \\\n",
       "6992      High School  Professional        Yes     3               1.0   \n",
       "6993  Partial College  Professional        Yes     4               2.0   \n",
       "6994        Bachelors    Management        Yes     2               1.0   \n",
       "6995  Partial College  Professional        Yes     3               1.0   \n",
       "6996      High School  Professional        Yes     3               2.0   \n",
       "\n",
       "      Region  Age Bike Buyer  \n",
       "6992  Europe   52         No  \n",
       "6993  Europe   52         No  \n",
       "6994  Europe   53         No  \n",
       "6995  Europe   54         No  \n",
       "6996  Europe   54         No  "
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.tail()#5 last entries of datset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Marital Status</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Yearly Income</th>\n",
       "      <th>Children</th>\n",
       "      <th>Education</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Home Owner</th>\n",
       "      <th>Cars</th>\n",
       "      <th>Commute Distance</th>\n",
       "      <th>Region</th>\n",
       "      <th>Age</th>\n",
       "      <th>Bike Buyer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22711.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>30000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Clerical</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>33</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13555.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>40000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Graduate Degree</td>\n",
       "      <td>Clerical</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>37</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>160000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>55</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>160000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Graduate Degree</td>\n",
       "      <td>Management</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>47</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25410.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>70000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Skilled Manual</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>North America</td>\n",
       "      <td>38</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>NaN</td>\n",
       "      <td>120000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Management</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>North America</td>\n",
       "      <td>40</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15756.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>Female</td>\n",
       "      <td>70000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>High School</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>49</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>11085.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>Female</td>\n",
       "      <td>60000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>High School</td>\n",
       "      <td>Professional</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>North America</td>\n",
       "      <td>49</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>17974.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Clerical</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>38</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>21008.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>Female</td>\n",
       "      <td>20000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Manual</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>46</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>28985.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>50000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Management</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>North America</td>\n",
       "      <td>64</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11087.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>70000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>North America</td>\n",
       "      <td>49</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>20434.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Skilled Manual</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>North America</td>\n",
       "      <td>31</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14902.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>40000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Clerical</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>North America</td>\n",
       "      <td>50</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>11091.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>90000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>North America</td>\n",
       "      <td>49</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>11094.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>70000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Skilled Manual</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>44</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>18568.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>Female</td>\n",
       "      <td>50000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Skilled Manual</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>North America</td>\n",
       "      <td>39</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>16366.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>40000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Clerical</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>North America</td>\n",
       "      <td>51</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>20768.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Clerical</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>North America</td>\n",
       "      <td>48</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>11098.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>60000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Skilled Manual</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>45</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>18269.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>Female</td>\n",
       "      <td>60000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>43</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>11102.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>41</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>11113.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>70000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>44</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>26529.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>70000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Graduate Degree</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>North America</td>\n",
       "      <td>37</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>11114.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>Female</td>\n",
       "      <td>70000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Skilled Manual</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>44</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>23612.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>Female</td>\n",
       "      <td>90000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>62</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>11115.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>70000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Skilled Manual</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>44</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>11116.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>70000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Skilled Manual</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>44</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>11118.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>80000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Skilled Manual</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>49</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>19441.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>40000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Graduate Degree</td>\n",
       "      <td>Clerical</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>37</td>\n",
       "      <td>Yes</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6967</th>\n",
       "      <td>22779.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>60000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>North America</td>\n",
       "      <td>54</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6968</th>\n",
       "      <td>22781.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>70000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>North America</td>\n",
       "      <td>55</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6969</th>\n",
       "      <td>22783.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>70000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Partial High School</td>\n",
       "      <td>Skilled Manual</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>North America</td>\n",
       "      <td>54</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6970</th>\n",
       "      <td>22786.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>70000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Partial High School</td>\n",
       "      <td>Skilled Manual</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>North America</td>\n",
       "      <td>54</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6971</th>\n",
       "      <td>22792.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>70000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>North America</td>\n",
       "      <td>53</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6972</th>\n",
       "      <td>22793.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>70000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>North America</td>\n",
       "      <td>53</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6973</th>\n",
       "      <td>22794.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>130000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>48</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6974</th>\n",
       "      <td>22795.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>110000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>49</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6975</th>\n",
       "      <td>22796.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>110000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>49</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6976</th>\n",
       "      <td>22798.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>150000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>High School</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>49</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6977</th>\n",
       "      <td>22799.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>120000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Partial High School</td>\n",
       "      <td>Professional</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>50</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6978</th>\n",
       "      <td>22801.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>110000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>51</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6979</th>\n",
       "      <td>22802.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>Female</td>\n",
       "      <td>120000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Partial High School</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>51</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6980</th>\n",
       "      <td>22804.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>80000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>51</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6981</th>\n",
       "      <td>22805.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>80000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Management</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>62</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6982</th>\n",
       "      <td>22806.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>90000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>62</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6983</th>\n",
       "      <td>22807.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>130000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Management</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>62</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6984</th>\n",
       "      <td>22809.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>80000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>61</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6985</th>\n",
       "      <td>22811.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>110000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Management</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>61</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6986</th>\n",
       "      <td>22812.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>120000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Management</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>61</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6987</th>\n",
       "      <td>22813.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>130000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Management</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>61</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6988</th>\n",
       "      <td>22814.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>100000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Management</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>60</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6989</th>\n",
       "      <td>22815.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>100000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Management</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>60</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6990</th>\n",
       "      <td>22818.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>150000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Graduate Degree</td>\n",
       "      <td>Management</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>60</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6991</th>\n",
       "      <td>22819.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>80000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>53</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6992</th>\n",
       "      <td>22820.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>100000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>High School</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>52</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6993</th>\n",
       "      <td>22821.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>130000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>52</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6994</th>\n",
       "      <td>22823.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>160000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Management</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>53</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6995</th>\n",
       "      <td>22825.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>Female</td>\n",
       "      <td>120000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>54</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6996</th>\n",
       "      <td>22826.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>130000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>High School</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>54</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6997 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           ID Marital Status  Gender  Yearly Income  Children  \\\n",
       "0     22711.0         Single    Male          30000       0.0   \n",
       "1     13555.0        Married  Female          40000       0.0   \n",
       "2         NaN        Married    Male         160000       5.0   \n",
       "3         2.0         Single    Male         160000       0.0   \n",
       "4     25410.0            NaN  Female          70000       2.0   \n",
       "5         4.0        Married     NaN         120000       2.0   \n",
       "6     15756.0         Single  Female          70000       0.0   \n",
       "7     11085.0         Single  Female          60000       0.0   \n",
       "8     17974.0            NaN  Female              0       NaN   \n",
       "9     21008.0         Single  Female          20000       1.0   \n",
       "10    28985.0         Single    Male          50000       0.0   \n",
       "11    11087.0        Married  Female          70000       2.0   \n",
       "12    20434.0         Single     NaN          60000       0.0   \n",
       "13    14902.0            NaN  Female          40000       0.0   \n",
       "14    11091.0        Married    Male          90000       0.0   \n",
       "15    11094.0         Single    Male          70000       0.0   \n",
       "16    18568.0         Single  Female          50000       2.0   \n",
       "17    16366.0        Married  Female          40000       0.0   \n",
       "18    20768.0         Single    Male              0       0.0   \n",
       "19    11098.0            NaN  Female          60000       0.0   \n",
       "20    18269.0         Single  Female          60000       2.0   \n",
       "21    11102.0         Single     NaN          80000       5.0   \n",
       "22    11113.0        Married    Male          70000       4.0   \n",
       "23    26529.0            NaN  Female          70000       0.0   \n",
       "24    11114.0         Single  Female          70000       4.0   \n",
       "25    23612.0         Single  Female          90000       0.0   \n",
       "26    11115.0        Married    Male          70000       4.0   \n",
       "27    11116.0        Married    Male          70000       4.0   \n",
       "28    11118.0         Single    Male          80000       NaN   \n",
       "29    19441.0         Single    Male          40000       0.0   \n",
       "...       ...            ...     ...            ...       ...   \n",
       "6967  22779.0        Married    Male          60000       1.0   \n",
       "6968  22781.0        Married    Male          70000       2.0   \n",
       "6969  22783.0        Married    Male          70000       1.0   \n",
       "6970  22786.0        Married    Male          70000       1.0   \n",
       "6971  22792.0        Married    Male          70000       3.0   \n",
       "6972  22793.0         Single    Male          70000       3.0   \n",
       "6973  22794.0        Married  Female         130000       4.0   \n",
       "6974  22795.0        Married  Female         110000       4.0   \n",
       "6975  22796.0        Married    Male         110000       4.0   \n",
       "6976  22798.0        Married  Female         150000       3.0   \n",
       "6977  22799.0        Married  Female         120000       4.0   \n",
       "6978  22801.0        Married  Female         110000       4.0   \n",
       "6979  22802.0         Single  Female         120000       4.0   \n",
       "6980  22804.0        Married  Female          80000       1.0   \n",
       "6981  22805.0        Married  Female          80000       0.0   \n",
       "6982  22806.0        Married  Female          90000       0.0   \n",
       "6983  22807.0        Married  Female         130000       4.0   \n",
       "6984  22809.0        Married    Male          80000       0.0   \n",
       "6985  22811.0        Married    Male         110000       4.0   \n",
       "6986  22812.0        Married  Female         120000       4.0   \n",
       "6987  22813.0        Married    Male         130000       4.0   \n",
       "6988  22814.0        Married    Male         100000       3.0   \n",
       "6989  22815.0        Married  Female         100000       4.0   \n",
       "6990  22818.0        Married  Female         150000       4.0   \n",
       "6991  22819.0        Married  Female          80000       2.0   \n",
       "6992  22820.0        Married    Male         100000       4.0   \n",
       "6993  22821.0        Married  Female         130000       4.0   \n",
       "6994  22823.0        Married  Female         160000       5.0   \n",
       "6995  22825.0         Single  Female         120000       5.0   \n",
       "6996  22826.0        Married    Male         130000       5.0   \n",
       "\n",
       "                Education      Occupation Home Owner  Cars  Commute Distance  \\\n",
       "0         Partial College        Clerical         No     1               1.0   \n",
       "1         Graduate Degree        Clerical        Yes     0               1.0   \n",
       "2         Partial College    Professional         No     3               2.0   \n",
       "3         Graduate Degree      Management        Yes     2               5.0   \n",
       "4               Bachelors  Skilled Manual         No     1               1.0   \n",
       "5               Bachelors      Management        Yes     3               1.0   \n",
       "6             High School    Professional        Yes     2               NaN   \n",
       "7             High School    Professional         No     2               1.0   \n",
       "8               Bachelors        Clerical        Yes     0              10.0   \n",
       "9         Partial College          Manual         No     0               1.0   \n",
       "10              Bachelors      Management         No     2               2.0   \n",
       "11        Partial College    Professional         No     0              11.0   \n",
       "12        Partial College  Skilled Manual         No     2               1.0   \n",
       "13        Partial College        Clerical        Yes     1               1.0   \n",
       "14        Partial College    Professional        Yes     1               1.0   \n",
       "15        Partial College  Skilled Manual         No     1               1.0   \n",
       "16              Bachelors  Skilled Manual        Yes     1               2.0   \n",
       "17        Partial College        Clerical        Yes     1               1.0   \n",
       "18        Partial College        Clerical        Yes     1               2.0   \n",
       "19        Partial College  Skilled Manual         No     1               6.0   \n",
       "20              Bachelors    Professional        Yes     2               1.0   \n",
       "21              Bachelors    Professional        Yes     4              12.0   \n",
       "22              Bachelors    Professional        Yes     2               1.0   \n",
       "23        Graduate Degree    Professional        Yes     0               NaN   \n",
       "24        Partial College  Skilled Manual        Yes     2               1.0   \n",
       "25        Partial College    Professional         No     2               1.0   \n",
       "26        Partial College  Skilled Manual        Yes     2               1.0   \n",
       "27        Partial College  Skilled Manual        Yes     2               9.0   \n",
       "28        Partial College  Skilled Manual         No     1               5.0   \n",
       "29        Graduate Degree        Clerical        Yes     0               1.0   \n",
       "...                   ...             ...        ...   ...               ...   \n",
       "6967      Partial College    Professional        Yes     2               2.0   \n",
       "6968      Partial College    Professional        Yes     1               1.0   \n",
       "6969  Partial High School  Skilled Manual        Yes     2               1.0   \n",
       "6970  Partial High School  Skilled Manual        Yes     3               2.0   \n",
       "6971      Partial College    Professional        Yes     1               7.0   \n",
       "6972      Partial College    Professional        Yes     1               8.0   \n",
       "6973      Partial College    Professional        Yes     1               9.0   \n",
       "6974      Partial College    Professional        Yes     3               2.0   \n",
       "6975      Partial College    Professional         No     3               3.0   \n",
       "6976          High School    Professional        Yes     4               5.0   \n",
       "6977  Partial High School    Professional         No     4               6.0   \n",
       "6978      Partial College    Professional        Yes     4               7.0   \n",
       "6979  Partial High School    Professional        Yes     4               1.0   \n",
       "6980      Partial College    Professional        Yes     2               2.0   \n",
       "6981            Bachelors      Management        Yes     2               1.0   \n",
       "6982      Partial College    Professional        Yes     2               1.0   \n",
       "6983            Bachelors      Management        Yes     4               2.0   \n",
       "6984      Partial College    Professional        Yes     2               7.0   \n",
       "6985            Bachelors      Management        Yes     4               8.0   \n",
       "6986            Bachelors      Management        Yes     4               9.0   \n",
       "6987            Bachelors      Management        Yes     4               2.0   \n",
       "6988            Bachelors      Management        Yes     4               3.0   \n",
       "6989            Bachelors      Management        Yes     1               5.0   \n",
       "6990      Graduate Degree      Management        Yes     4               6.0   \n",
       "6991      Partial College    Professional        Yes     2               7.0   \n",
       "6992          High School    Professional        Yes     3               1.0   \n",
       "6993      Partial College    Professional        Yes     4               2.0   \n",
       "6994            Bachelors      Management        Yes     2               1.0   \n",
       "6995      Partial College    Professional        Yes     3               1.0   \n",
       "6996          High School    Professional        Yes     3               2.0   \n",
       "\n",
       "             Region  Age Bike Buyer  \n",
       "0            Europe   33        Yes  \n",
       "1            Europe   37        Yes  \n",
       "2            Europe   55         No  \n",
       "3           Pacific   47         No  \n",
       "4     North America   38        Yes  \n",
       "5     North America   40         No  \n",
       "6           Pacific   49        Yes  \n",
       "7     North America   49         No  \n",
       "8            Europe   38        Yes  \n",
       "9            Europe   46        Yes  \n",
       "10    North America   64         No  \n",
       "11    North America   49         No  \n",
       "12    North America   31        Yes  \n",
       "13    North America   50        Yes  \n",
       "14    North America   49         No  \n",
       "15          Pacific   44         No  \n",
       "16    North America   39        Yes  \n",
       "17    North America   51        Yes  \n",
       "18    North America   48        Yes  \n",
       "19          Pacific   45         No  \n",
       "20          Pacific   43        Yes  \n",
       "21          Pacific   41         No  \n",
       "22          Pacific   44         No  \n",
       "23    North America   37        Yes  \n",
       "24          Pacific   44         No  \n",
       "25           Europe   62        Yes  \n",
       "26          Pacific   44         No  \n",
       "27          Pacific   44         No  \n",
       "28          Pacific   49         No  \n",
       "29           Europe   37        Yes  \n",
       "...             ...  ...        ...  \n",
       "6967  North America   54         No  \n",
       "6968  North America   55         No  \n",
       "6969  North America   54         No  \n",
       "6970  North America   54         No  \n",
       "6971  North America   53         No  \n",
       "6972  North America   53         No  \n",
       "6973         Europe   48         No  \n",
       "6974         Europe   49         No  \n",
       "6975         Europe   49         No  \n",
       "6976         Europe   49         No  \n",
       "6977         Europe   50         No  \n",
       "6978         Europe   51         No  \n",
       "6979         Europe   51         No  \n",
       "6980         Europe   51         No  \n",
       "6981         Europe   62         No  \n",
       "6982         Europe   62         No  \n",
       "6983         Europe   62         No  \n",
       "6984         Europe   61         No  \n",
       "6985         Europe   61         No  \n",
       "6986         Europe   61         No  \n",
       "6987         Europe   61         No  \n",
       "6988         Europe   60         No  \n",
       "6989         Europe   60         No  \n",
       "6990         Europe   60         No  \n",
       "6991         Europe   53         No  \n",
       "6992         Europe   52         No  \n",
       "6993         Europe   52         No  \n",
       "6994         Europe   53         No  \n",
       "6995         Europe   54         No  \n",
       "6996         Europe   54         No  \n",
       "\n",
       "[6997 rows x 13 columns]"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Yearly Income</th>\n",
       "      <th>Children</th>\n",
       "      <th>Cars</th>\n",
       "      <th>Commute Distance</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.006802</td>\n",
       "      <td>0.012046</td>\n",
       "      <td>0.068315</td>\n",
       "      <td>0.011511</td>\n",
       "      <td>0.021135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yearly Income</th>\n",
       "      <td>-0.006802</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.474575</td>\n",
       "      <td>0.472089</td>\n",
       "      <td>0.009467</td>\n",
       "      <td>0.195351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Children</th>\n",
       "      <td>0.012046</td>\n",
       "      <td>0.474575</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.448379</td>\n",
       "      <td>0.026377</td>\n",
       "      <td>-0.000520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cars</th>\n",
       "      <td>0.068315</td>\n",
       "      <td>0.472089</td>\n",
       "      <td>0.448379</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.018397</td>\n",
       "      <td>0.141093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Commute Distance</th>\n",
       "      <td>0.011511</td>\n",
       "      <td>0.009467</td>\n",
       "      <td>0.026377</td>\n",
       "      <td>0.018397</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.026119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.021135</td>\n",
       "      <td>0.195351</td>\n",
       "      <td>-0.000520</td>\n",
       "      <td>0.141093</td>\n",
       "      <td>-0.026119</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        ID  Yearly Income  Children      Cars  \\\n",
       "ID                1.000000      -0.006802  0.012046  0.068315   \n",
       "Yearly Income    -0.006802       1.000000  0.474575  0.472089   \n",
       "Children          0.012046       0.474575  1.000000  0.448379   \n",
       "Cars              0.068315       0.472089  0.448379  1.000000   \n",
       "Commute Distance  0.011511       0.009467  0.026377  0.018397   \n",
       "Age               0.021135       0.195351 -0.000520  0.141093   \n",
       "\n",
       "                  Commute Distance       Age  \n",
       "ID                        0.011511  0.021135  \n",
       "Yearly Income             0.009467  0.195351  \n",
       "Children                  0.026377 -0.000520  \n",
       "Cars                      0.018397  0.141093  \n",
       "Commute Distance          1.000000 -0.026119  \n",
       "Age                      -0.026119  1.000000  "
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.corr()#yearly incoms,car,age is the significant factors which is affecting the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                   4337.428859\n",
       "Yearly Income       32080.449720\n",
       "Children                1.600912\n",
       "Cars                    1.146782\n",
       "Commute Distance        2.926211\n",
       "Age                    11.916654\n",
       "dtype: float64"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.std()#standard deviation of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Yearly Income</th>\n",
       "      <th>Children</th>\n",
       "      <th>Cars</th>\n",
       "      <th>Commute Distance</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6996.000000</td>\n",
       "      <td>6997.000000</td>\n",
       "      <td>6979.000000</td>\n",
       "      <td>6997.000000</td>\n",
       "      <td>6968.000000</td>\n",
       "      <td>6997.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>17744.435249</td>\n",
       "      <td>57020.151493</td>\n",
       "      <td>1.111621</td>\n",
       "      <td>1.586823</td>\n",
       "      <td>4.210103</td>\n",
       "      <td>45.107332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4337.428859</td>\n",
       "      <td>32080.449720</td>\n",
       "      <td>1.600912</td>\n",
       "      <td>1.146782</td>\n",
       "      <td>2.926211</td>\n",
       "      <td>11.916654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>14249.750000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>36.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>17406.500000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>44.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>20609.500000</td>\n",
       "      <td>70000.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>53.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>29476.000000</td>\n",
       "      <td>170000.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>96.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ID  Yearly Income     Children         Cars  \\\n",
       "count   6996.000000    6997.000000  6979.000000  6997.000000   \n",
       "mean   17744.435249   57020.151493     1.111621     1.586823   \n",
       "std     4337.428859   32080.449720     1.600912     1.146782   \n",
       "min        2.000000       0.000000     0.000000     0.000000   \n",
       "25%    14249.750000   30000.000000     0.000000     1.000000   \n",
       "50%    17406.500000   60000.000000     0.000000     2.000000   \n",
       "75%    20609.500000   70000.000000     2.000000     2.000000   \n",
       "max    29476.000000  170000.000000     5.000000     4.000000   \n",
       "\n",
       "       Commute Distance          Age  \n",
       "count       6968.000000  6997.000000  \n",
       "mean           4.210103    45.107332  \n",
       "std            2.926211    11.916654  \n",
       "min            1.000000    25.000000  \n",
       "25%            1.000000    36.000000  \n",
       "50%            4.000000    44.000000  \n",
       "75%            6.000000    53.000000  \n",
       "max           13.000000    96.000000  "
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                  6996\n",
       "Marital Status      6981\n",
       "Gender              6968\n",
       "Yearly Income       6997\n",
       "Children            6979\n",
       "Education           6997\n",
       "Occupation          6997\n",
       "Home Owner          6997\n",
       "Cars                6997\n",
       "Commute Distance    6968\n",
       "Region              6997\n",
       "Age                 6997\n",
       "Bike Buyer          6997\n",
       "dtype: int64"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6997, 13)"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.shape #there are 6997 rows and 12 columns in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "#every dataset is the combination of feature and target or dependent and independent. In our dataset till age there \n",
    "# are features and bikebuyer is the target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'Marital Status', 'Gender', 'Yearly Income', 'Children',\n",
       "       'Education', 'Occupation', 'Home Owner', 'Cars', 'Commute Distance',\n",
       "       'Region', 'Age', 'Bike Buyer'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Marital Status</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Yearly Income</th>\n",
       "      <th>Children</th>\n",
       "      <th>Education</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Home Owner</th>\n",
       "      <th>Cars</th>\n",
       "      <th>Commute Distance</th>\n",
       "      <th>Region</th>\n",
       "      <th>Age</th>\n",
       "      <th>Bike Buyer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>30000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Clerical</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>33</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>40000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Graduate Degree</td>\n",
       "      <td>Clerical</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>37</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>160000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>55</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>160000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Graduate Degree</td>\n",
       "      <td>Management</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>47</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>70000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Skilled Manual</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>North America</td>\n",
       "      <td>38</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Married</td>\n",
       "      <td>NaN</td>\n",
       "      <td>120000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Management</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>North America</td>\n",
       "      <td>40</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Single</td>\n",
       "      <td>Female</td>\n",
       "      <td>70000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>High School</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>49</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Single</td>\n",
       "      <td>Female</td>\n",
       "      <td>60000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>High School</td>\n",
       "      <td>Professional</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>North America</td>\n",
       "      <td>49</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Clerical</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>38</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Single</td>\n",
       "      <td>Female</td>\n",
       "      <td>20000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Manual</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>46</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>50000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Management</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>North America</td>\n",
       "      <td>64</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>70000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>North America</td>\n",
       "      <td>49</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Single</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Skilled Manual</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>North America</td>\n",
       "      <td>31</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>40000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Clerical</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>North America</td>\n",
       "      <td>50</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>90000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>North America</td>\n",
       "      <td>49</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>70000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Skilled Manual</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>44</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Single</td>\n",
       "      <td>Female</td>\n",
       "      <td>50000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Skilled Manual</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>North America</td>\n",
       "      <td>39</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>40000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Clerical</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>North America</td>\n",
       "      <td>51</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Clerical</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>North America</td>\n",
       "      <td>48</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>60000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Skilled Manual</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>45</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Single</td>\n",
       "      <td>Female</td>\n",
       "      <td>60000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>43</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Single</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>41</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>70000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>44</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>70000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Graduate Degree</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>North America</td>\n",
       "      <td>37</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Single</td>\n",
       "      <td>Female</td>\n",
       "      <td>70000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Skilled Manual</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>44</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Single</td>\n",
       "      <td>Female</td>\n",
       "      <td>90000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>62</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>70000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Skilled Manual</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>44</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>70000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Skilled Manual</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>44</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>80000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Skilled Manual</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>49</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>40000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Graduate Degree</td>\n",
       "      <td>Clerical</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>37</td>\n",
       "      <td>Yes</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6967</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>60000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>North America</td>\n",
       "      <td>54</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6968</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>70000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>North America</td>\n",
       "      <td>55</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6969</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>70000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Partial High School</td>\n",
       "      <td>Skilled Manual</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>North America</td>\n",
       "      <td>54</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6970</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>70000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Partial High School</td>\n",
       "      <td>Skilled Manual</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>North America</td>\n",
       "      <td>54</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6971</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>70000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>North America</td>\n",
       "      <td>53</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6972</th>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>70000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>North America</td>\n",
       "      <td>53</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6973</th>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>130000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>48</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6974</th>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>110000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>49</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6975</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>110000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>49</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6976</th>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>150000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>High School</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>49</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6977</th>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>120000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Partial High School</td>\n",
       "      <td>Professional</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>50</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6978</th>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>110000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>51</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6979</th>\n",
       "      <td>Single</td>\n",
       "      <td>Female</td>\n",
       "      <td>120000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Partial High School</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>51</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6980</th>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>80000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>51</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6981</th>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>80000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Management</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>62</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6982</th>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>90000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>62</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6983</th>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>130000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Management</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>62</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6984</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>80000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>61</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6985</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>110000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Management</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>61</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6986</th>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>120000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Management</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>61</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6987</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>130000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Management</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>61</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6988</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>100000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Management</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>60</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6989</th>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>100000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Management</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>60</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6990</th>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>150000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Graduate Degree</td>\n",
       "      <td>Management</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>60</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6991</th>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>80000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>53</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6992</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>100000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>High School</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>52</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6993</th>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>130000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>52</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6994</th>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>160000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Management</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>53</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6995</th>\n",
       "      <td>Single</td>\n",
       "      <td>Female</td>\n",
       "      <td>120000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>54</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6996</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>130000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>High School</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>54</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6997 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Marital Status  Gender  Yearly Income  Children            Education  \\\n",
       "0            Single    Male          30000       0.0      Partial College   \n",
       "1           Married  Female          40000       0.0      Graduate Degree   \n",
       "2           Married    Male         160000       5.0      Partial College   \n",
       "3            Single    Male         160000       0.0      Graduate Degree   \n",
       "4               NaN  Female          70000       2.0            Bachelors   \n",
       "5           Married     NaN         120000       2.0            Bachelors   \n",
       "6            Single  Female          70000       0.0          High School   \n",
       "7            Single  Female          60000       0.0          High School   \n",
       "8               NaN  Female              0       NaN            Bachelors   \n",
       "9            Single  Female          20000       1.0      Partial College   \n",
       "10           Single    Male          50000       0.0            Bachelors   \n",
       "11          Married  Female          70000       2.0      Partial College   \n",
       "12           Single     NaN          60000       0.0      Partial College   \n",
       "13              NaN  Female          40000       0.0      Partial College   \n",
       "14          Married    Male          90000       0.0      Partial College   \n",
       "15           Single    Male          70000       0.0      Partial College   \n",
       "16           Single  Female          50000       2.0            Bachelors   \n",
       "17          Married  Female          40000       0.0      Partial College   \n",
       "18           Single    Male              0       0.0      Partial College   \n",
       "19              NaN  Female          60000       0.0      Partial College   \n",
       "20           Single  Female          60000       2.0            Bachelors   \n",
       "21           Single     NaN          80000       5.0            Bachelors   \n",
       "22          Married    Male          70000       4.0            Bachelors   \n",
       "23              NaN  Female          70000       0.0      Graduate Degree   \n",
       "24           Single  Female          70000       4.0      Partial College   \n",
       "25           Single  Female          90000       0.0      Partial College   \n",
       "26          Married    Male          70000       4.0      Partial College   \n",
       "27          Married    Male          70000       4.0      Partial College   \n",
       "28           Single    Male          80000       NaN      Partial College   \n",
       "29           Single    Male          40000       0.0      Graduate Degree   \n",
       "...             ...     ...            ...       ...                  ...   \n",
       "6967        Married    Male          60000       1.0      Partial College   \n",
       "6968        Married    Male          70000       2.0      Partial College   \n",
       "6969        Married    Male          70000       1.0  Partial High School   \n",
       "6970        Married    Male          70000       1.0  Partial High School   \n",
       "6971        Married    Male          70000       3.0      Partial College   \n",
       "6972         Single    Male          70000       3.0      Partial College   \n",
       "6973        Married  Female         130000       4.0      Partial College   \n",
       "6974        Married  Female         110000       4.0      Partial College   \n",
       "6975        Married    Male         110000       4.0      Partial College   \n",
       "6976        Married  Female         150000       3.0          High School   \n",
       "6977        Married  Female         120000       4.0  Partial High School   \n",
       "6978        Married  Female         110000       4.0      Partial College   \n",
       "6979         Single  Female         120000       4.0  Partial High School   \n",
       "6980        Married  Female          80000       1.0      Partial College   \n",
       "6981        Married  Female          80000       0.0            Bachelors   \n",
       "6982        Married  Female          90000       0.0      Partial College   \n",
       "6983        Married  Female         130000       4.0            Bachelors   \n",
       "6984        Married    Male          80000       0.0      Partial College   \n",
       "6985        Married    Male         110000       4.0            Bachelors   \n",
       "6986        Married  Female         120000       4.0            Bachelors   \n",
       "6987        Married    Male         130000       4.0            Bachelors   \n",
       "6988        Married    Male         100000       3.0            Bachelors   \n",
       "6989        Married  Female         100000       4.0            Bachelors   \n",
       "6990        Married  Female         150000       4.0      Graduate Degree   \n",
       "6991        Married  Female          80000       2.0      Partial College   \n",
       "6992        Married    Male         100000       4.0          High School   \n",
       "6993        Married  Female         130000       4.0      Partial College   \n",
       "6994        Married  Female         160000       5.0            Bachelors   \n",
       "6995         Single  Female         120000       5.0      Partial College   \n",
       "6996        Married    Male         130000       5.0          High School   \n",
       "\n",
       "          Occupation Home Owner  Cars  Commute Distance         Region  Age  \\\n",
       "0           Clerical         No     1               1.0         Europe   33   \n",
       "1           Clerical        Yes     0               1.0         Europe   37   \n",
       "2       Professional         No     3               2.0         Europe   55   \n",
       "3         Management        Yes     2               5.0        Pacific   47   \n",
       "4     Skilled Manual         No     1               1.0  North America   38   \n",
       "5         Management        Yes     3               1.0  North America   40   \n",
       "6       Professional        Yes     2               NaN        Pacific   49   \n",
       "7       Professional         No     2               1.0  North America   49   \n",
       "8           Clerical        Yes     0              10.0         Europe   38   \n",
       "9             Manual         No     0               1.0         Europe   46   \n",
       "10        Management         No     2               2.0  North America   64   \n",
       "11      Professional         No     0              11.0  North America   49   \n",
       "12    Skilled Manual         No     2               1.0  North America   31   \n",
       "13          Clerical        Yes     1               1.0  North America   50   \n",
       "14      Professional        Yes     1               1.0  North America   49   \n",
       "15    Skilled Manual         No     1               1.0        Pacific   44   \n",
       "16    Skilled Manual        Yes     1               2.0  North America   39   \n",
       "17          Clerical        Yes     1               1.0  North America   51   \n",
       "18          Clerical        Yes     1               2.0  North America   48   \n",
       "19    Skilled Manual         No     1               6.0        Pacific   45   \n",
       "20      Professional        Yes     2               1.0        Pacific   43   \n",
       "21      Professional        Yes     4              12.0        Pacific   41   \n",
       "22      Professional        Yes     2               1.0        Pacific   44   \n",
       "23      Professional        Yes     0               NaN  North America   37   \n",
       "24    Skilled Manual        Yes     2               1.0        Pacific   44   \n",
       "25      Professional         No     2               1.0         Europe   62   \n",
       "26    Skilled Manual        Yes     2               1.0        Pacific   44   \n",
       "27    Skilled Manual        Yes     2               9.0        Pacific   44   \n",
       "28    Skilled Manual         No     1               5.0        Pacific   49   \n",
       "29          Clerical        Yes     0               1.0         Europe   37   \n",
       "...              ...        ...   ...               ...            ...  ...   \n",
       "6967    Professional        Yes     2               2.0  North America   54   \n",
       "6968    Professional        Yes     1               1.0  North America   55   \n",
       "6969  Skilled Manual        Yes     2               1.0  North America   54   \n",
       "6970  Skilled Manual        Yes     3               2.0  North America   54   \n",
       "6971    Professional        Yes     1               7.0  North America   53   \n",
       "6972    Professional        Yes     1               8.0  North America   53   \n",
       "6973    Professional        Yes     1               9.0         Europe   48   \n",
       "6974    Professional        Yes     3               2.0         Europe   49   \n",
       "6975    Professional         No     3               3.0         Europe   49   \n",
       "6976    Professional        Yes     4               5.0         Europe   49   \n",
       "6977    Professional         No     4               6.0         Europe   50   \n",
       "6978    Professional        Yes     4               7.0         Europe   51   \n",
       "6979    Professional        Yes     4               1.0         Europe   51   \n",
       "6980    Professional        Yes     2               2.0         Europe   51   \n",
       "6981      Management        Yes     2               1.0         Europe   62   \n",
       "6982    Professional        Yes     2               1.0         Europe   62   \n",
       "6983      Management        Yes     4               2.0         Europe   62   \n",
       "6984    Professional        Yes     2               7.0         Europe   61   \n",
       "6985      Management        Yes     4               8.0         Europe   61   \n",
       "6986      Management        Yes     4               9.0         Europe   61   \n",
       "6987      Management        Yes     4               2.0         Europe   61   \n",
       "6988      Management        Yes     4               3.0         Europe   60   \n",
       "6989      Management        Yes     1               5.0         Europe   60   \n",
       "6990      Management        Yes     4               6.0         Europe   60   \n",
       "6991    Professional        Yes     2               7.0         Europe   53   \n",
       "6992    Professional        Yes     3               1.0         Europe   52   \n",
       "6993    Professional        Yes     4               2.0         Europe   52   \n",
       "6994      Management        Yes     2               1.0         Europe   53   \n",
       "6995    Professional        Yes     3               1.0         Europe   54   \n",
       "6996    Professional        Yes     3               2.0         Europe   54   \n",
       "\n",
       "     Bike Buyer  \n",
       "0           Yes  \n",
       "1           Yes  \n",
       "2            No  \n",
       "3            No  \n",
       "4           Yes  \n",
       "5            No  \n",
       "6           Yes  \n",
       "7            No  \n",
       "8           Yes  \n",
       "9           Yes  \n",
       "10           No  \n",
       "11           No  \n",
       "12          Yes  \n",
       "13          Yes  \n",
       "14           No  \n",
       "15           No  \n",
       "16          Yes  \n",
       "17          Yes  \n",
       "18          Yes  \n",
       "19           No  \n",
       "20          Yes  \n",
       "21           No  \n",
       "22           No  \n",
       "23          Yes  \n",
       "24           No  \n",
       "25          Yes  \n",
       "26           No  \n",
       "27           No  \n",
       "28           No  \n",
       "29          Yes  \n",
       "...         ...  \n",
       "6967         No  \n",
       "6968         No  \n",
       "6969         No  \n",
       "6970         No  \n",
       "6971         No  \n",
       "6972         No  \n",
       "6973         No  \n",
       "6974         No  \n",
       "6975         No  \n",
       "6976         No  \n",
       "6977         No  \n",
       "6978         No  \n",
       "6979         No  \n",
       "6980         No  \n",
       "6981         No  \n",
       "6982         No  \n",
       "6983         No  \n",
       "6984         No  \n",
       "6985         No  \n",
       "6986         No  \n",
       "6987         No  \n",
       "6988         No  \n",
       "6989         No  \n",
       "6990         No  \n",
       "6991         No  \n",
       "6992         No  \n",
       "6993         No  \n",
       "6994         No  \n",
       "6995         No  \n",
       "6996         No  \n",
       "\n",
       "[6997 rows x 12 columns]"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1=data1.iloc[:,1:]\n",
    "data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1['Yearly Income'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "170000"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1['Yearly Income'].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# data visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "#histogram plot is used to explor one variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20ac07acfd0>"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20ac07ace48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data1['Yearly Income'].hist() #among approax seven thousand people ,approax 1500 people's yearly income is in approax \n",
    "#twenty thousand to thirty thousand. this graph is trying to explain the for limited range of income there are limited\n",
    "#there are limited peoples so this is the distribution of yerly income.it is the list of people which satisfying this \n",
    "#criteria"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "#box plot is used to find the outliers we can calculate outliers by a formula \n",
    "#1.5(interquartile range)\n",
    "#thera are two type of outliers one is lower outliers and other is outer outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20ac07936a0>"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20ac1caa320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data1.boxplot(column='Yearly Income')#the green line in the middle of boxplot tells us the median\n",
    "# here median =62500(APPROAX)\n",
    "#it also talks about outliers \n",
    "#yearly income of range 150000,162500,175000 are the outliers bcz it is just disturbing the dataset\n",
    "#it also decreasing the accuracy of model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "median= data1.loc[data1[\"Yearly Income\"]<135000,'Yearly Income'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1.loc[data1['Yearly Income'] >135000,'Yearly Income']=np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1['Yearly Income'].fillna(median ,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "130000.0"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1['Yearly Income'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6997, 12)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20ac1caa470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data1=data1[np.abs(data1['Yearly Income']-data1['Yearly Income'].mean())<=(3*data1['Yearly Income'].std())]\n",
    "data1.boxplot(column='Yearly Income')\n",
    "print(data1.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.0"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1['Children'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20ac1d8b080>"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20ac1d51e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data1['Children'].hist(bins=10)#bins is basically to define the range or we also call it as a bucket 0 to 1\n",
    "# means it contain 10 quantities\n",
    "#this children histogram explaining the distribution of child in data \n",
    "#like how many children person have \n",
    "#approax 0-1 there are more than four thousand peoples have \n",
    "#1-2 approax 900 people hava\n",
    "#so this box plot is explaing such a distribution of child among children"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20ac1ddcb38>"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20ac1e35860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data1.boxplot(column='Children')#this box plot has no outliers median of 0 we can calculate outliers by this formula\n",
    "#1.5(IQR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1['Cars'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1[\"Cars\"].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20ac1e35eb8>"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20ac1e98438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data1['Cars'].hist(bins=10)#almost more than 2500 people have more than 2 cars in this dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20ac1eb1fd0>"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20ac1f01898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data1.boxplot(column='Cars')#those have 4 cars are the outliers in the dataset creating the disturbance in the dataset \n",
    "#median=2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "median= data1.loc[data1['Cars'] <3,'Cars'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1.loc[data1['Cars'] >3,'Cars']=np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Marital Status</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Yearly Income</th>\n",
       "      <th>Children</th>\n",
       "      <th>Education</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Home Owner</th>\n",
       "      <th>Cars</th>\n",
       "      <th>Commute Distance</th>\n",
       "      <th>Region</th>\n",
       "      <th>Age</th>\n",
       "      <th>Bike Buyer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Clerical</td>\n",
       "      <td>No</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>33</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Graduate Degree</td>\n",
       "      <td>Clerical</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>37</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>No</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>55</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Graduate Degree</td>\n",
       "      <td>Management</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>47</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Skilled Manual</td>\n",
       "      <td>No</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>North America</td>\n",
       "      <td>38</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Married</td>\n",
       "      <td>NaN</td>\n",
       "      <td>120000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Management</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>North America</td>\n",
       "      <td>40</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Single</td>\n",
       "      <td>Female</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>High School</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>49</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Single</td>\n",
       "      <td>Female</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>High School</td>\n",
       "      <td>Professional</td>\n",
       "      <td>No</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>North America</td>\n",
       "      <td>49</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Clerical</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>38</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Single</td>\n",
       "      <td>Female</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Manual</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>46</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Management</td>\n",
       "      <td>No</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>North America</td>\n",
       "      <td>64</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>North America</td>\n",
       "      <td>49</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Single</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Skilled Manual</td>\n",
       "      <td>No</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>North America</td>\n",
       "      <td>31</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Clerical</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>North America</td>\n",
       "      <td>50</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>90000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>North America</td>\n",
       "      <td>49</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Skilled Manual</td>\n",
       "      <td>No</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>44</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Single</td>\n",
       "      <td>Female</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Skilled Manual</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>North America</td>\n",
       "      <td>39</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Clerical</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>North America</td>\n",
       "      <td>51</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Clerical</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>North America</td>\n",
       "      <td>48</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Skilled Manual</td>\n",
       "      <td>No</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>45</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Single</td>\n",
       "      <td>Female</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>43</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Single</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>41</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>44</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Graduate Degree</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>North America</td>\n",
       "      <td>37</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Single</td>\n",
       "      <td>Female</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Skilled Manual</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>44</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Single</td>\n",
       "      <td>Female</td>\n",
       "      <td>90000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>No</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>62</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Skilled Manual</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>44</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Skilled Manual</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>44</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Skilled Manual</td>\n",
       "      <td>No</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>49</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Graduate Degree</td>\n",
       "      <td>Clerical</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>37</td>\n",
       "      <td>Yes</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6967</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>North America</td>\n",
       "      <td>54</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6968</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>North America</td>\n",
       "      <td>55</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6969</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Partial High School</td>\n",
       "      <td>Skilled Manual</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>North America</td>\n",
       "      <td>54</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6970</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Partial High School</td>\n",
       "      <td>Skilled Manual</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>North America</td>\n",
       "      <td>54</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6971</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>North America</td>\n",
       "      <td>53</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6972</th>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>North America</td>\n",
       "      <td>53</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6973</th>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>130000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>48</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6974</th>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>110000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>49</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6975</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>110000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>No</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>49</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6976</th>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>High School</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>49</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6977</th>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>120000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Partial High School</td>\n",
       "      <td>Professional</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>50</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6978</th>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>110000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>51</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6979</th>\n",
       "      <td>Single</td>\n",
       "      <td>Female</td>\n",
       "      <td>120000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Partial High School</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>51</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6980</th>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>51</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6981</th>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Management</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>62</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6982</th>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>90000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>62</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6983</th>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>130000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Management</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>62</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6984</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>61</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6985</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>110000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Management</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>61</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6986</th>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>120000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Management</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>61</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6987</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>130000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Management</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>61</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6988</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Management</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>60</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6989</th>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Management</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>60</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6990</th>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Graduate Degree</td>\n",
       "      <td>Management</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>60</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6991</th>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>53</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6992</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>High School</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>52</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6993</th>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>130000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>52</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6994</th>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Management</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>53</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6995</th>\n",
       "      <td>Single</td>\n",
       "      <td>Female</td>\n",
       "      <td>120000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>54</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6996</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>130000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>High School</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>54</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6997 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Marital Status  Gender  Yearly Income  Children            Education  \\\n",
       "0            Single    Male        30000.0       0.0      Partial College   \n",
       "1           Married  Female        40000.0       0.0      Graduate Degree   \n",
       "2           Married    Male        60000.0       5.0      Partial College   \n",
       "3            Single    Male        60000.0       0.0      Graduate Degree   \n",
       "4               NaN  Female        70000.0       2.0            Bachelors   \n",
       "5           Married     NaN       120000.0       2.0            Bachelors   \n",
       "6            Single  Female        70000.0       0.0          High School   \n",
       "7            Single  Female        60000.0       0.0          High School   \n",
       "8               NaN  Female            0.0       NaN            Bachelors   \n",
       "9            Single  Female        20000.0       1.0      Partial College   \n",
       "10           Single    Male        50000.0       0.0            Bachelors   \n",
       "11          Married  Female        70000.0       2.0      Partial College   \n",
       "12           Single     NaN        60000.0       0.0      Partial College   \n",
       "13              NaN  Female        40000.0       0.0      Partial College   \n",
       "14          Married    Male        90000.0       0.0      Partial College   \n",
       "15           Single    Male        70000.0       0.0      Partial College   \n",
       "16           Single  Female        50000.0       2.0            Bachelors   \n",
       "17          Married  Female        40000.0       0.0      Partial College   \n",
       "18           Single    Male            0.0       0.0      Partial College   \n",
       "19              NaN  Female        60000.0       0.0      Partial College   \n",
       "20           Single  Female        60000.0       2.0            Bachelors   \n",
       "21           Single     NaN        80000.0       5.0            Bachelors   \n",
       "22          Married    Male        70000.0       4.0            Bachelors   \n",
       "23              NaN  Female        70000.0       0.0      Graduate Degree   \n",
       "24           Single  Female        70000.0       4.0      Partial College   \n",
       "25           Single  Female        90000.0       0.0      Partial College   \n",
       "26          Married    Male        70000.0       4.0      Partial College   \n",
       "27          Married    Male        70000.0       4.0      Partial College   \n",
       "28           Single    Male        80000.0       NaN      Partial College   \n",
       "29           Single    Male        40000.0       0.0      Graduate Degree   \n",
       "...             ...     ...            ...       ...                  ...   \n",
       "6967        Married    Male        60000.0       1.0      Partial College   \n",
       "6968        Married    Male        70000.0       2.0      Partial College   \n",
       "6969        Married    Male        70000.0       1.0  Partial High School   \n",
       "6970        Married    Male        70000.0       1.0  Partial High School   \n",
       "6971        Married    Male        70000.0       3.0      Partial College   \n",
       "6972         Single    Male        70000.0       3.0      Partial College   \n",
       "6973        Married  Female       130000.0       4.0      Partial College   \n",
       "6974        Married  Female       110000.0       4.0      Partial College   \n",
       "6975        Married    Male       110000.0       4.0      Partial College   \n",
       "6976        Married  Female        60000.0       3.0          High School   \n",
       "6977        Married  Female       120000.0       4.0  Partial High School   \n",
       "6978        Married  Female       110000.0       4.0      Partial College   \n",
       "6979         Single  Female       120000.0       4.0  Partial High School   \n",
       "6980        Married  Female        80000.0       1.0      Partial College   \n",
       "6981        Married  Female        80000.0       0.0            Bachelors   \n",
       "6982        Married  Female        90000.0       0.0      Partial College   \n",
       "6983        Married  Female       130000.0       4.0            Bachelors   \n",
       "6984        Married    Male        80000.0       0.0      Partial College   \n",
       "6985        Married    Male       110000.0       4.0            Bachelors   \n",
       "6986        Married  Female       120000.0       4.0            Bachelors   \n",
       "6987        Married    Male       130000.0       4.0            Bachelors   \n",
       "6988        Married    Male       100000.0       3.0            Bachelors   \n",
       "6989        Married  Female       100000.0       4.0            Bachelors   \n",
       "6990        Married  Female        60000.0       4.0      Graduate Degree   \n",
       "6991        Married  Female        80000.0       2.0      Partial College   \n",
       "6992        Married    Male       100000.0       4.0          High School   \n",
       "6993        Married  Female       130000.0       4.0      Partial College   \n",
       "6994        Married  Female        60000.0       5.0            Bachelors   \n",
       "6995         Single  Female       120000.0       5.0      Partial College   \n",
       "6996        Married    Male       130000.0       5.0          High School   \n",
       "\n",
       "          Occupation Home Owner  Cars  Commute Distance         Region  Age  \\\n",
       "0           Clerical         No   1.0               1.0         Europe   33   \n",
       "1           Clerical        Yes   0.0               1.0         Europe   37   \n",
       "2       Professional         No   3.0               2.0         Europe   55   \n",
       "3         Management        Yes   2.0               5.0        Pacific   47   \n",
       "4     Skilled Manual         No   1.0               1.0  North America   38   \n",
       "5         Management        Yes   3.0               1.0  North America   40   \n",
       "6       Professional        Yes   2.0               NaN        Pacific   49   \n",
       "7       Professional         No   2.0               1.0  North America   49   \n",
       "8           Clerical        Yes   0.0              10.0         Europe   38   \n",
       "9             Manual         No   0.0               1.0         Europe   46   \n",
       "10        Management         No   2.0               2.0  North America   64   \n",
       "11      Professional         No   0.0              11.0  North America   49   \n",
       "12    Skilled Manual         No   2.0               1.0  North America   31   \n",
       "13          Clerical        Yes   1.0               1.0  North America   50   \n",
       "14      Professional        Yes   1.0               1.0  North America   49   \n",
       "15    Skilled Manual         No   1.0               1.0        Pacific   44   \n",
       "16    Skilled Manual        Yes   1.0               2.0  North America   39   \n",
       "17          Clerical        Yes   1.0               1.0  North America   51   \n",
       "18          Clerical        Yes   1.0               2.0  North America   48   \n",
       "19    Skilled Manual         No   1.0               6.0        Pacific   45   \n",
       "20      Professional        Yes   2.0               1.0        Pacific   43   \n",
       "21      Professional        Yes   NaN              12.0        Pacific   41   \n",
       "22      Professional        Yes   2.0               1.0        Pacific   44   \n",
       "23      Professional        Yes   0.0               NaN  North America   37   \n",
       "24    Skilled Manual        Yes   2.0               1.0        Pacific   44   \n",
       "25      Professional         No   2.0               1.0         Europe   62   \n",
       "26    Skilled Manual        Yes   2.0               1.0        Pacific   44   \n",
       "27    Skilled Manual        Yes   2.0               9.0        Pacific   44   \n",
       "28    Skilled Manual         No   1.0               5.0        Pacific   49   \n",
       "29          Clerical        Yes   0.0               1.0         Europe   37   \n",
       "...              ...        ...   ...               ...            ...  ...   \n",
       "6967    Professional        Yes   2.0               2.0  North America   54   \n",
       "6968    Professional        Yes   1.0               1.0  North America   55   \n",
       "6969  Skilled Manual        Yes   2.0               1.0  North America   54   \n",
       "6970  Skilled Manual        Yes   3.0               2.0  North America   54   \n",
       "6971    Professional        Yes   1.0               7.0  North America   53   \n",
       "6972    Professional        Yes   1.0               8.0  North America   53   \n",
       "6973    Professional        Yes   1.0               9.0         Europe   48   \n",
       "6974    Professional        Yes   3.0               2.0         Europe   49   \n",
       "6975    Professional         No   3.0               3.0         Europe   49   \n",
       "6976    Professional        Yes   NaN               5.0         Europe   49   \n",
       "6977    Professional         No   NaN               6.0         Europe   50   \n",
       "6978    Professional        Yes   NaN               7.0         Europe   51   \n",
       "6979    Professional        Yes   NaN               1.0         Europe   51   \n",
       "6980    Professional        Yes   2.0               2.0         Europe   51   \n",
       "6981      Management        Yes   2.0               1.0         Europe   62   \n",
       "6982    Professional        Yes   2.0               1.0         Europe   62   \n",
       "6983      Management        Yes   NaN               2.0         Europe   62   \n",
       "6984    Professional        Yes   2.0               7.0         Europe   61   \n",
       "6985      Management        Yes   NaN               8.0         Europe   61   \n",
       "6986      Management        Yes   NaN               9.0         Europe   61   \n",
       "6987      Management        Yes   NaN               2.0         Europe   61   \n",
       "6988      Management        Yes   NaN               3.0         Europe   60   \n",
       "6989      Management        Yes   1.0               5.0         Europe   60   \n",
       "6990      Management        Yes   NaN               6.0         Europe   60   \n",
       "6991    Professional        Yes   2.0               7.0         Europe   53   \n",
       "6992    Professional        Yes   3.0               1.0         Europe   52   \n",
       "6993    Professional        Yes   NaN               2.0         Europe   52   \n",
       "6994      Management        Yes   2.0               1.0         Europe   53   \n",
       "6995    Professional        Yes   3.0               1.0         Europe   54   \n",
       "6996    Professional        Yes   3.0               2.0         Europe   54   \n",
       "\n",
       "     Bike Buyer  \n",
       "0           Yes  \n",
       "1           Yes  \n",
       "2            No  \n",
       "3            No  \n",
       "4           Yes  \n",
       "5            No  \n",
       "6           Yes  \n",
       "7            No  \n",
       "8           Yes  \n",
       "9           Yes  \n",
       "10           No  \n",
       "11           No  \n",
       "12          Yes  \n",
       "13          Yes  \n",
       "14           No  \n",
       "15           No  \n",
       "16          Yes  \n",
       "17          Yes  \n",
       "18          Yes  \n",
       "19           No  \n",
       "20          Yes  \n",
       "21           No  \n",
       "22           No  \n",
       "23          Yes  \n",
       "24           No  \n",
       "25          Yes  \n",
       "26           No  \n",
       "27           No  \n",
       "28           No  \n",
       "29          Yes  \n",
       "...         ...  \n",
       "6967         No  \n",
       "6968         No  \n",
       "6969         No  \n",
       "6970         No  \n",
       "6971         No  \n",
       "6972         No  \n",
       "6973         No  \n",
       "6974         No  \n",
       "6975         No  \n",
       "6976         No  \n",
       "6977         No  \n",
       "6978         No  \n",
       "6979         No  \n",
       "6980         No  \n",
       "6981         No  \n",
       "6982         No  \n",
       "6983         No  \n",
       "6984         No  \n",
       "6985         No  \n",
       "6986         No  \n",
       "6987         No  \n",
       "6988         No  \n",
       "6989         No  \n",
       "6990         No  \n",
       "6991         No  \n",
       "6992         No  \n",
       "6993         No  \n",
       "6994         No  \n",
       "6995         No  \n",
       "6996         No  \n",
       "\n",
       "[6997 rows x 12 columns]"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1['Cars'].fillna( value = median,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method Series.max of 0       1.0\n",
       "1       0.0\n",
       "2       3.0\n",
       "3       2.0\n",
       "4       1.0\n",
       "5       3.0\n",
       "6       2.0\n",
       "7       2.0\n",
       "8       0.0\n",
       "9       0.0\n",
       "10      2.0\n",
       "11      0.0\n",
       "12      2.0\n",
       "13      1.0\n",
       "14      1.0\n",
       "15      1.0\n",
       "16      1.0\n",
       "17      1.0\n",
       "18      1.0\n",
       "19      1.0\n",
       "20      2.0\n",
       "21      1.0\n",
       "22      2.0\n",
       "23      0.0\n",
       "24      2.0\n",
       "25      2.0\n",
       "26      2.0\n",
       "27      2.0\n",
       "28      1.0\n",
       "29      0.0\n",
       "       ... \n",
       "6967    2.0\n",
       "6968    1.0\n",
       "6969    2.0\n",
       "6970    3.0\n",
       "6971    1.0\n",
       "6972    1.0\n",
       "6973    1.0\n",
       "6974    3.0\n",
       "6975    3.0\n",
       "6976    1.0\n",
       "6977    1.0\n",
       "6978    1.0\n",
       "6979    1.0\n",
       "6980    2.0\n",
       "6981    2.0\n",
       "6982    2.0\n",
       "6983    1.0\n",
       "6984    2.0\n",
       "6985    1.0\n",
       "6986    1.0\n",
       "6987    1.0\n",
       "6988    1.0\n",
       "6989    1.0\n",
       "6990    1.0\n",
       "6991    2.0\n",
       "6992    3.0\n",
       "6993    1.0\n",
       "6994    2.0\n",
       "6995    3.0\n",
       "6996    3.0\n",
       "Name: Cars, Length: 6997, dtype: float64>"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1[\"Cars\"].max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13.0"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1[\"Commute Distance\"].max()#13 time maxximum can go here and there or making any ride"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1['Commute Distance'].min()#1 time minimum ride"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20ac1f6f908>"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20ac1f8ac50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data1['Commute Distance'].hist(bins=10)#approax more 2800 people's commute distance is atleat one or two time\n",
    "#this the range of people \n",
    "#only few people making commute distance 13 times in dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20ac1f77358>"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAADU1JREFUeJzt3X+M5HV9x/HnC46qHFeBQLdEGrYlChhaoFm1BSNTDhIsRNDSgNEGKnXbpKVo2sLZxto0/eMaTFP706yIkEigFbFFzp5QYMBGi3Bw/DwCEcFST8AQChypSnn3j5kzy2WX3fm1x332+Ug2N/ud78znPWTyvO999ztsqgpJ0p5vr909gCRpPAy6JDXCoEtSIwy6JDXCoEtSIwy6JDXCoEtSIwy6JDXCoEtSI9as5GIHHXRQTU9Pr+SS0rLs2LGDtWvX7u4xpAVt2bLl+1V18FL7rWjQp6enufPOO1dySWlZut0unU5nd48hLSjJ48vZz1MuktQIgy5JjTDoktQIgy5JjTDoktQIgy5JjTDoktQIgy5JjVjRDxZJKyXJiqzj7+TVa4lH6GpSVQ30ddjF1w/8GGOu1xqDLkmNMOiS1AiDLkmNMOiS1AiDLkmNMOiS1AiDLkmNMOiS1AiDLkmNMOiS1AiDLkmNWDLoSS5L8lSS++dtuyTJQ0nuTfKlJPtPdkxJ0lKWc4R+OXDqLttuBI6uql8AHgY+Nua5JEkDWjLoVXUb8Mwu226oqpf63/4ncOgEZpMkDWAc59A/BPzbGJ5HkjSCkX7BRZI/AV4CrnyVfWaBWYCpqSm63e4oS0oT43tTe7qhg57kXOB0YH29yv/pv6rmgDmAmZmZ6nQ6wy4pTc7mTfje1J5uqKAnORW4GDixql4c70iSpGEs57LFq4BvAEckeSLJ+cDfAeuAG5NsTfLpCc8pSVrCkkfoVfX+BTZ/dgKzSJJG4CdFJakRBl2SGmHQJakRBl2SGmHQJakRBl2SGmHQJakRBl2SGmHQJakRBl2SGmHQJakRBl2SGmHQJakRBl2SGmHQJakRBl2SGmHQJakRBl2SGmHQJakRBl2SGmHQJakRBl2SGmHQJakRBl2SGrFk0JNcluSpJPfP23ZgkhuTPNL/84DJjilJWspyjtAvB07dZdsG4KaqejNwU/97SdJutGTQq+o24JldNp8BXNG/fQVw5pjnkiQNaNhz6FNVtR2g/+dPjW8kSdIw1kx6gSSzwCzA1NQU3W530ktKQ/G9qT3dsEF/MskhVbU9ySHAU4vtWFVzwBzAzMxMdTqdIZeUJmjzJnxvak837CmX64Bz+7fPBf51PONIkoa1nMsWrwK+ARyR5Ikk5wMbgVOSPAKc0v9ekrQbLXnKparev8hd68c8iyRpBH5SVJIaYdAlqREGXZIaYdAlqREGXZIaYdAlqREGXZIaYdAlqREGXZIaYdAlqREGXZIaYdAlqREGXZIaYdAlqREGXZIaYdAlqREGXZIaYdAlqREGXZIaYdAlqREGXZIaYdAlqREGXZIaYdAlqREjBT3JR5M8kOT+JFclef24BpMkDWbooCd5E/D7wExVHQ3sDZwzrsEkSYMZ9ZTLGuANSdYA+wLfHX0kSdIwhg56Vf038EngO8B24H+q6oZxDSZJGsyaYR+Y5ADgDOBngWeBLyT5YFV9fpf9ZoFZgKmpKbrd7vDTShPke1N7uqGDDpwMfLuqngZIci1wPPCKoFfVHDAHMDMzU51OZ4QlpQnZvAnfm9rTjXIO/TvALyXZN0mA9cC28YwlSRrUKOfQbweuAe4C7us/19yY5pIkDWiUUy5U1SeAT4xpFknSCPykqCQ1wqBLUiMMuiQ1wqBLUiMMuiQ1wqBLUiMMuiQ1wqBLUiMMuiQ1wqBLUiMMuiQ1wqBLUiMMuiQ1wqBLUiMMuiQ1wqBLUiMMuiQ1wqBLUiMMuiQ1wqBLUiMMuiQ1wqBLUiMMuiQ1wqBLUiNGCnqS/ZNck+ShJNuS/PK4BpMkDWbNiI//FLC5qs5K8hPAvmOYSZI0hKGDnuQngXcB5wFU1Q+BH45nLEnSoEY55fJzwNPA55LcneTSJGvHNJckaUCjnHJZA/wicEFV3Z7kU8AG4OPzd0oyC8wCTE1N0e12R1hSq9Hv3rSDHT+a/DrTGzZN9PnX7gN/v95jHk3OKEF/Aniiqm7vf38NvaC/QlXNAXMAMzMz1el0RlhSq9GOzZt4bONpE12j2+0y6ffm9IZNE19Dq9vQp1yq6nvAfyU5or9pPfDgWKaSJA1s1KtcLgCu7F/h8ijwm6OPJEkaxkhBr6qtwMyYZpEkjcBPikpSIwy6JDXCoEtSIwy6JDXCoEtSIwy6JDXCoEtSIwy6JDXCoEtSIwy6JDXCoEtSIwy6JDXCoEtSIwy6JDXCoEtSIwy6JDXCoEtSIwy6JDXCoEtSIwy6JDXCoEtSIwy6JDXCoEtSIwy6JDVi5KAn2TvJ3UmuH8dAkqThjOMI/UJg2xieR5I0gpGCnuRQ4DTg0vGMI0ka1poRH//XwEXAusV2SDILzAJMTU3R7XZHXFKrzbqjNvDzV2yY/EJXTPbp1x0F3e7ayS6iVW3ooCc5HXiqqrYk6Sy2X1XNAXMAMzMz1eksuqu0oOc3bOSxjadNdI1ut8uk35vTGzbROXeya2h1G+WUywnAe5I8BlwNnJTk82OZSpI0sKGDXlUfq6pDq2oaOAe4uao+OLbJJEkD8Tp0SWrEqD8UBaCqukB3HM8lSRqOR+iS1AiDLkmNMOiS1AiDLkmNMOiS1AiDLkmNMOiS1AiDLkmNMOiS1AiDLkmNMOiS1AiDLkmNMOiS1AiDLkmNMOiS1AiDLkmNMOiS1AiDLkmNMOiS1AiDLkmNMOiS1AiDLkmNMOiS1AiDLkmNGDroSX4myS1JtiV5IMmF4xxMkjSYNSM89iXgD6rqriTrgC1JbqyqB8c0myRpAEMfoVfV9qq6q3/7eWAb8KZxDSZJGswoR+g/lmQaOA64fYH7ZoFZgKmpKbrd7jiW1Coz6ffNCy+8sCLvTd//mqSRg55kP+CLwEeq6rld76+qOWAOYGZmpjqdzqhLarXZvIlJv2+63e7E11iJ16HVbaSrXJLsQy/mV1bVteMZSZI0jFGucgnwWWBbVf3V+EaSJA1jlCP0E4DfAE5KsrX/9atjmkuSNKChz6FX1X8AGeMskqQR+ElRSWqEQZekRhh0SWqEQZekRhh0SWqEQZekRhh0SWqEQZekRhh0SWqEQZekRhh0SWrEWH7BhTRp0xs2DbT/4395+oQmeaXDLr5+2fu+8Q37THASyaBrD/DYxtMGf9DGGmj3FfkFF9KEecpFkhph0CWpEQZdkhph0CWpEQZdkhph0CWpEQZdkhph0CWpEaka7AMYIy2WPA08vmILSst3EPD93T2EtIjDqurgpXZa0aBLr1VJ7qyqmd09hzQKT7lIUiMMuiQ1wqBLPXO7ewBpVJ5Dl6RGeIQuSY0w6BqrJD+d5Ook30ryYJKvJHnLa2CuM5O8dcDHnJfk6SR3J3kkyVeTHD/v/j9PcvI415RGYdA1NkkCfAnoVtXhVfVW4I+Bqd07GQBnAsPE9Z+q6riqejOwEbg2yVEAVfWnVfXvE1hTGopB1zj9CvCjqvr0zg1VtbWqvpaeS5Lcn+S+JGcDJOkkuTXJPyd5OMnGJB9I8s3+fof397s8yT8muSXJo0lOTHJZkm1JLt+5XpIX5t0+q/+444H3AJck2Zrk8P7X5iRbknwtyZFLvbiquoXeD09n5810Vv/2xv6/SO5N8slF1vxwkjuS3JPki0n2nfc8f5Pk6/3Xdta813BR/7/DPUk29rcNPLtWB38FncbpaGDLIve9DzgWOIbepzLvSHJb/75jgKOAZ4BHgUur6u1JLgQuAD7S3+8A4CR6ofwycALwW/3nOraqti60cFV9Pcl1wPVVdQ1AkpuA36mqR5K8A/iH/nMv5S7gt+dvSHIg8F7gyKqqJPtX1bMLrPlsVX2mf/svgPOBv+0/zSHAO4EjgeuAa5K8m95R/juq6sX+OtD7S2WY2dU4g66V8k7gqqr6P+DJJLcCbwOeA+6oqu0ASb4F3NB/zH30jvp3+nI/mPcBT1bVff3HPABMAwsGfVdJ9gOOB77QO0sEwOuW+TqywLbngP8FLk2yCVjsN0cf3Q/5/sB+wFfn3fcvVfUy8GCSnaeoTgY+V1UvAlTVMyPOrsYZdI3TA8BZi9y3UAh3+sG82y/P+/5lXvke/cEC++y63/zrcF+/yHp7Ac9W1bGvMtNijgO2zd9QVS8leTuwHjgH+D0WPmK+HDizqu5Jch7QmXff/NeTeX/uel3xKLOrcZ5D1zjdDLwuyYd3bkjytiQnArcBZyfZO8nBwLuAb05ghieTHJVkL3qnQXZ6HlgHUFXPAd9O8uv9GZPkmKWeuP86ZoHP7LJ9P+CNVfUVeqeHdsb2x2v2rQO2J9kH+MAyXssNwIfmnWs/cNjZtToYdI1N9T6l9l7glPQuW3wA+DPgu/SufrkXuIde+C+qqu9NYIwN9E553Axsn7f9auCP0rsE8XB6QT0/yT30/mVxxiLPd3b/h5oP07ti59eqatsu+6wDrk9yL3Ar8NFF1vw4cDtwI/DQUi+kqjbTO59+Z5KtwB/271ru7Fpl/KSoJDXCI3RJaoRBl6RGGHRJaoRBl6RGGHRJaoRBl6RGGHRJaoRBl6RG/D9aFDHfOHJAQwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20ac1fb8ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data1.boxplot(column='Commute Distance')#the middle green line tells us that median of column dataset \n",
    "#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "96"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1[\"Age\"].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1[\"Age\"].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20ac1fcce80>"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20ac205d1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data1['Age'].hist(bins=10)#this histogram plot is showing that 40-45 years people are approax 1600 in number in out datset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20ac1d5d5f8>"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20ac206fa90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data1.boxplot(column='Age')#upper outlier=77.5 approax so above this all are the outliers and creating disturbance in \n",
    "# dataset\n",
    "#meadian=45\n",
    "#lower outlier<30 are the lower outliers \n",
    "#outliers are basically which are not properly fitting in our dataset\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "median=data1.loc[data1['Age']<78,'Age'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43.0"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1.loc[data1['Age']>78,'Age']=np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1['Age'].fillna(value=median,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       33.0\n",
       "1       37.0\n",
       "2       55.0\n",
       "3       47.0\n",
       "4       38.0\n",
       "5       40.0\n",
       "6       49.0\n",
       "7       49.0\n",
       "8       38.0\n",
       "9       46.0\n",
       "10      64.0\n",
       "11      49.0\n",
       "12      31.0\n",
       "13      50.0\n",
       "14      49.0\n",
       "15      44.0\n",
       "16      39.0\n",
       "17      51.0\n",
       "18      48.0\n",
       "19      45.0\n",
       "20      43.0\n",
       "21      41.0\n",
       "22      44.0\n",
       "23      37.0\n",
       "24      44.0\n",
       "25      62.0\n",
       "26      44.0\n",
       "27      44.0\n",
       "28      49.0\n",
       "29      37.0\n",
       "        ... \n",
       "6967    54.0\n",
       "6968    55.0\n",
       "6969    54.0\n",
       "6970    54.0\n",
       "6971    53.0\n",
       "6972    53.0\n",
       "6973    48.0\n",
       "6974    49.0\n",
       "6975    49.0\n",
       "6976    49.0\n",
       "6977    50.0\n",
       "6978    51.0\n",
       "6979    51.0\n",
       "6980    51.0\n",
       "6981    62.0\n",
       "6982    62.0\n",
       "6983    62.0\n",
       "6984    61.0\n",
       "6985    61.0\n",
       "6986    61.0\n",
       "6987    61.0\n",
       "6988    60.0\n",
       "6989    60.0\n",
       "6990    60.0\n",
       "6991    53.0\n",
       "6992    52.0\n",
       "6993    52.0\n",
       "6994    53.0\n",
       "6995    54.0\n",
       "6996    54.0\n",
       "Name: Age, Length: 6997, dtype: float64"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1[\"Age\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20ac05da080>"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20ac068df28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data1.boxplot(column=\"Age\")#when outliers is replaced by median\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Married    4133\n",
       "Single     2848\n",
       "Name: Marital Status, dtype: int64"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1['Marital Status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Male      3527\n",
       "Female    3441\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1['Gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Partial College        1932\n",
       "Bachelors              1870\n",
       "High School            1313\n",
       "Graduate Degree        1272\n",
       "Partial High School     610\n",
       "Name: Education, dtype: int64"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1['Education'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Professional      2031\n",
       "Skilled Manual    1748\n",
       "Management        1265\n",
       "Manual             990\n",
       "Clerical           963\n",
       "Name: Occupation, dtype: int64"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1['Occupation'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Yes    4979\n",
       "No     2018\n",
       "Name: Home Owner, dtype: int64"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1['Home Owner'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "North America    3728\n",
       "Europe           2096\n",
       "Pacific          1173\n",
       "Name: Region, dtype: int64"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1['Region'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Yearly Income</th>\n",
       "      <th>Children</th>\n",
       "      <th>Cars</th>\n",
       "      <th>Commute Distance</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6997.000000</td>\n",
       "      <td>6979.000000</td>\n",
       "      <td>6997.000000</td>\n",
       "      <td>6968.000000</td>\n",
       "      <td>6997.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>55689.581249</td>\n",
       "      <td>1.111621</td>\n",
       "      <td>1.345863</td>\n",
       "      <td>4.210103</td>\n",
       "      <td>44.91725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>29778.095282</td>\n",
       "      <td>1.600912</td>\n",
       "      <td>0.903824</td>\n",
       "      <td>2.926211</td>\n",
       "      <td>11.59705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>25.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>30000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>36.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>60000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>43.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>70000.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>53.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>130000.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>78.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Yearly Income     Children         Cars  Commute Distance         Age\n",
       "count    6997.000000  6979.000000  6997.000000       6968.000000  6997.00000\n",
       "mean    55689.581249     1.111621     1.345863          4.210103    44.91725\n",
       "std     29778.095282     1.600912     0.903824          2.926211    11.59705\n",
       "min         0.000000     0.000000     0.000000          1.000000    25.00000\n",
       "25%     30000.000000     0.000000     1.000000          1.000000    36.00000\n",
       "50%     60000.000000     0.000000     1.000000          4.000000    43.00000\n",
       "75%     70000.000000     2.000000     2.000000          6.000000    53.00000\n",
       "max    130000.000000     5.000000     3.000000         13.000000    78.00000"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Yearly Income</th>\n",
       "      <th>Children</th>\n",
       "      <th>Cars</th>\n",
       "      <th>Commute Distance</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Yearly Income</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.451281</td>\n",
       "      <td>0.169690</td>\n",
       "      <td>0.008878</td>\n",
       "      <td>0.209482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Children</th>\n",
       "      <td>0.451281</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.243753</td>\n",
       "      <td>0.026377</td>\n",
       "      <td>0.009640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cars</th>\n",
       "      <td>0.169690</td>\n",
       "      <td>0.243753</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.006356</td>\n",
       "      <td>0.137914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Commute Distance</th>\n",
       "      <td>0.008878</td>\n",
       "      <td>0.026377</td>\n",
       "      <td>0.006356</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.023096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.209482</td>\n",
       "      <td>0.009640</td>\n",
       "      <td>0.137914</td>\n",
       "      <td>-0.023096</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Yearly Income  Children      Cars  Commute Distance  \\\n",
       "Yearly Income          1.000000  0.451281  0.169690          0.008878   \n",
       "Children               0.451281  1.000000  0.243753          0.026377   \n",
       "Cars                   0.169690  0.243753  1.000000          0.006356   \n",
       "Commute Distance       0.008878  0.026377  0.006356          1.000000   \n",
       "Age                    0.209482  0.009640  0.137914         -0.023096   \n",
       "\n",
       "                       Age  \n",
       "Yearly Income     0.209482  \n",
       "Children          0.009640  \n",
       "Cars              0.137914  \n",
       "Commute Distance -0.023096  \n",
       "Age               1.000000  "
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#examine Yearly income\n",
    "data1['Yearly Income'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "130000.0"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1['Yearly Income'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "55689.58124910676"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1['Yearly Income'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60000.0"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1['Yearly Income'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29775.967287050902"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.std(data1['Yearly Income'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "886608227.8795254"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.var(data1['Yearly Income'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Male      3527\n",
       "Female    3441\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#examine gender\n",
    "data1['Gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6968"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1['Gender'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Marital Status      16\n",
       "Gender              29\n",
       "Yearly Income        0\n",
       "Children            18\n",
       "Education            0\n",
       "Occupation           0\n",
       "Home Owner           0\n",
       "Cars                 0\n",
       "Commute Distance    29\n",
       "Region               0\n",
       "Age                  0\n",
       "Bike Buyer           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.apply(lambda x: sum(x.isnull()),axis=0)#these are the missing place in dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Married    4133\n",
       "Single     2848\n",
       "Name: Marital Status, dtype: int64"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#examine marital status\n",
    "data1['Marital Status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Male      3527\n",
       "Female    3441\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#data imputation \n",
    "data1['Marital Status'].fillna('Married' , inplace=True)\n",
    "data1['Gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Marital Status       0\n",
       "Gender              29\n",
       "Yearly Income        0\n",
       "Children            18\n",
       "Education            0\n",
       "Occupation           0\n",
       "Home Owner           0\n",
       "Cars                 0\n",
       "Commute Distance    29\n",
       "Region               0\n",
       "Age                  0\n",
       "Bike Buyer           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#After imputation\n",
    "data1.apply(lambda x: sum(x.isnull()),axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1['Gender'].fillna('Male' , inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Marital Status       0\n",
       "Gender               0\n",
       "Yearly Income        0\n",
       "Children            18\n",
       "Education            0\n",
       "Occupation           0\n",
       "Home Owner           0\n",
       "Cars                 0\n",
       "Commute Distance    29\n",
       "Region               0\n",
       "Age                  0\n",
       "Bike Buyer           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.apply(lambda x: sum(x.isnull()),axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.1116205760137556"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1['Children'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1['Children'].fillna(data1['Children'].mean() , inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Marital Status       0\n",
       "Gender               0\n",
       "Yearly Income        0\n",
       "Children             0\n",
       "Education            0\n",
       "Occupation           0\n",
       "Home Owner           0\n",
       "Cars                 0\n",
       "Commute Distance    29\n",
       "Region               0\n",
       "Age                  0\n",
       "Bike Buyer           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.apply(lambda x: sum(x.isnull()),axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.210103329506315"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1['Commute Distance'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1['Commute Distance'].fillna(data1['Commute Distance'].mean() , inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Marital Status      0\n",
       "Gender              0\n",
       "Yearly Income       0\n",
       "Children            0\n",
       "Education           0\n",
       "Occupation          0\n",
       "Home Owner          0\n",
       "Cars                0\n",
       "Commute Distance    0\n",
       "Region              0\n",
       "Age                 0\n",
       "Bike Buyer          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.apply(lambda x: sum(x.isnull()),axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Marital Status</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Yearly Income</th>\n",
       "      <th>Children</th>\n",
       "      <th>Education</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Home Owner</th>\n",
       "      <th>Cars</th>\n",
       "      <th>Commute Distance</th>\n",
       "      <th>Region</th>\n",
       "      <th>Age</th>\n",
       "      <th>Bike Buyer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Clerical</td>\n",
       "      <td>No</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Europe</td>\n",
       "      <td>33.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Graduate Degree</td>\n",
       "      <td>Clerical</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Europe</td>\n",
       "      <td>37.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>No</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>Europe</td>\n",
       "      <td>55.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Graduate Degree</td>\n",
       "      <td>Management</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>47.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Skilled Manual</td>\n",
       "      <td>No</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>North America</td>\n",
       "      <td>38.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>120000.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Management</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>North America</td>\n",
       "      <td>40.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Single</td>\n",
       "      <td>Female</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>High School</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.210103</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>49.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Single</td>\n",
       "      <td>Female</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>High School</td>\n",
       "      <td>Professional</td>\n",
       "      <td>No</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>North America</td>\n",
       "      <td>49.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.111621</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Clerical</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>Europe</td>\n",
       "      <td>38.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Single</td>\n",
       "      <td>Female</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Manual</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Europe</td>\n",
       "      <td>46.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Management</td>\n",
       "      <td>No</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>North America</td>\n",
       "      <td>64.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>North America</td>\n",
       "      <td>49.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Skilled Manual</td>\n",
       "      <td>No</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>North America</td>\n",
       "      <td>31.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Clerical</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>North America</td>\n",
       "      <td>50.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>90000.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>North America</td>\n",
       "      <td>49.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Skilled Manual</td>\n",
       "      <td>No</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>44.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Single</td>\n",
       "      <td>Female</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Skilled Manual</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>North America</td>\n",
       "      <td>39.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Clerical</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>North America</td>\n",
       "      <td>51.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Clerical</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>North America</td>\n",
       "      <td>48.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Skilled Manual</td>\n",
       "      <td>No</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>45.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Single</td>\n",
       "      <td>Female</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>43.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>41.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>44.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Graduate Degree</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.210103</td>\n",
       "      <td>North America</td>\n",
       "      <td>37.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Single</td>\n",
       "      <td>Female</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Skilled Manual</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>44.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Single</td>\n",
       "      <td>Female</td>\n",
       "      <td>90000.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>No</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Europe</td>\n",
       "      <td>62.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Skilled Manual</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>44.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Skilled Manual</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>44.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>1.111621</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Skilled Manual</td>\n",
       "      <td>No</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>49.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Graduate Degree</td>\n",
       "      <td>Clerical</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Europe</td>\n",
       "      <td>37.0</td>\n",
       "      <td>Yes</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6967</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>North America</td>\n",
       "      <td>54.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6968</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>North America</td>\n",
       "      <td>55.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6969</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Partial High School</td>\n",
       "      <td>Skilled Manual</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>North America</td>\n",
       "      <td>54.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6970</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Partial High School</td>\n",
       "      <td>Skilled Manual</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>North America</td>\n",
       "      <td>54.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6971</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>North America</td>\n",
       "      <td>53.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6972</th>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>North America</td>\n",
       "      <td>53.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6973</th>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>130000.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>Europe</td>\n",
       "      <td>48.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6974</th>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>110000.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>Europe</td>\n",
       "      <td>49.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6975</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>110000.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>No</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>Europe</td>\n",
       "      <td>49.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6976</th>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>High School</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>Europe</td>\n",
       "      <td>49.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6977</th>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>120000.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>Partial High School</td>\n",
       "      <td>Professional</td>\n",
       "      <td>No</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>Europe</td>\n",
       "      <td>50.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6978</th>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>110000.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>Europe</td>\n",
       "      <td>51.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6979</th>\n",
       "      <td>Single</td>\n",
       "      <td>Female</td>\n",
       "      <td>120000.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>Partial High School</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Europe</td>\n",
       "      <td>51.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6980</th>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>Europe</td>\n",
       "      <td>51.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6981</th>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Management</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Europe</td>\n",
       "      <td>62.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6982</th>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>90000.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Europe</td>\n",
       "      <td>62.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6983</th>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>130000.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Management</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>Europe</td>\n",
       "      <td>62.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6984</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>Europe</td>\n",
       "      <td>61.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6985</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>110000.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Management</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>Europe</td>\n",
       "      <td>61.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6986</th>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>120000.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Management</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>Europe</td>\n",
       "      <td>61.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6987</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>130000.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Management</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>Europe</td>\n",
       "      <td>61.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6988</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Management</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>Europe</td>\n",
       "      <td>60.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6989</th>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Management</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>Europe</td>\n",
       "      <td>60.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6990</th>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>Graduate Degree</td>\n",
       "      <td>Management</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>Europe</td>\n",
       "      <td>60.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6991</th>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>Europe</td>\n",
       "      <td>53.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6992</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>High School</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Europe</td>\n",
       "      <td>52.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6993</th>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>130000.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>Europe</td>\n",
       "      <td>52.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6994</th>\n",
       "      <td>Married</td>\n",
       "      <td>Female</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Management</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Europe</td>\n",
       "      <td>53.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6995</th>\n",
       "      <td>Single</td>\n",
       "      <td>Female</td>\n",
       "      <td>120000.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Europe</td>\n",
       "      <td>54.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6996</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>130000.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>High School</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>Europe</td>\n",
       "      <td>54.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6997 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Marital Status  Gender  Yearly Income  Children            Education  \\\n",
       "0            Single    Male        30000.0  0.000000      Partial College   \n",
       "1           Married  Female        40000.0  0.000000      Graduate Degree   \n",
       "2           Married    Male        60000.0  5.000000      Partial College   \n",
       "3            Single    Male        60000.0  0.000000      Graduate Degree   \n",
       "4           Married  Female        70000.0  2.000000            Bachelors   \n",
       "5           Married    Male       120000.0  2.000000            Bachelors   \n",
       "6            Single  Female        70000.0  0.000000          High School   \n",
       "7            Single  Female        60000.0  0.000000          High School   \n",
       "8           Married  Female            0.0  1.111621            Bachelors   \n",
       "9            Single  Female        20000.0  1.000000      Partial College   \n",
       "10           Single    Male        50000.0  0.000000            Bachelors   \n",
       "11          Married  Female        70000.0  2.000000      Partial College   \n",
       "12           Single    Male        60000.0  0.000000      Partial College   \n",
       "13          Married  Female        40000.0  0.000000      Partial College   \n",
       "14          Married    Male        90000.0  0.000000      Partial College   \n",
       "15           Single    Male        70000.0  0.000000      Partial College   \n",
       "16           Single  Female        50000.0  2.000000            Bachelors   \n",
       "17          Married  Female        40000.0  0.000000      Partial College   \n",
       "18           Single    Male            0.0  0.000000      Partial College   \n",
       "19          Married  Female        60000.0  0.000000      Partial College   \n",
       "20           Single  Female        60000.0  2.000000            Bachelors   \n",
       "21           Single    Male        80000.0  5.000000            Bachelors   \n",
       "22          Married    Male        70000.0  4.000000            Bachelors   \n",
       "23          Married  Female        70000.0  0.000000      Graduate Degree   \n",
       "24           Single  Female        70000.0  4.000000      Partial College   \n",
       "25           Single  Female        90000.0  0.000000      Partial College   \n",
       "26          Married    Male        70000.0  4.000000      Partial College   \n",
       "27          Married    Male        70000.0  4.000000      Partial College   \n",
       "28           Single    Male        80000.0  1.111621      Partial College   \n",
       "29           Single    Male        40000.0  0.000000      Graduate Degree   \n",
       "...             ...     ...            ...       ...                  ...   \n",
       "6967        Married    Male        60000.0  1.000000      Partial College   \n",
       "6968        Married    Male        70000.0  2.000000      Partial College   \n",
       "6969        Married    Male        70000.0  1.000000  Partial High School   \n",
       "6970        Married    Male        70000.0  1.000000  Partial High School   \n",
       "6971        Married    Male        70000.0  3.000000      Partial College   \n",
       "6972         Single    Male        70000.0  3.000000      Partial College   \n",
       "6973        Married  Female       130000.0  4.000000      Partial College   \n",
       "6974        Married  Female       110000.0  4.000000      Partial College   \n",
       "6975        Married    Male       110000.0  4.000000      Partial College   \n",
       "6976        Married  Female        60000.0  3.000000          High School   \n",
       "6977        Married  Female       120000.0  4.000000  Partial High School   \n",
       "6978        Married  Female       110000.0  4.000000      Partial College   \n",
       "6979         Single  Female       120000.0  4.000000  Partial High School   \n",
       "6980        Married  Female        80000.0  1.000000      Partial College   \n",
       "6981        Married  Female        80000.0  0.000000            Bachelors   \n",
       "6982        Married  Female        90000.0  0.000000      Partial College   \n",
       "6983        Married  Female       130000.0  4.000000            Bachelors   \n",
       "6984        Married    Male        80000.0  0.000000      Partial College   \n",
       "6985        Married    Male       110000.0  4.000000            Bachelors   \n",
       "6986        Married  Female       120000.0  4.000000            Bachelors   \n",
       "6987        Married    Male       130000.0  4.000000            Bachelors   \n",
       "6988        Married    Male       100000.0  3.000000            Bachelors   \n",
       "6989        Married  Female       100000.0  4.000000            Bachelors   \n",
       "6990        Married  Female        60000.0  4.000000      Graduate Degree   \n",
       "6991        Married  Female        80000.0  2.000000      Partial College   \n",
       "6992        Married    Male       100000.0  4.000000          High School   \n",
       "6993        Married  Female       130000.0  4.000000      Partial College   \n",
       "6994        Married  Female        60000.0  5.000000            Bachelors   \n",
       "6995         Single  Female       120000.0  5.000000      Partial College   \n",
       "6996        Married    Male       130000.0  5.000000          High School   \n",
       "\n",
       "          Occupation Home Owner  Cars  Commute Distance         Region   Age  \\\n",
       "0           Clerical         No   1.0          1.000000         Europe  33.0   \n",
       "1           Clerical        Yes   0.0          1.000000         Europe  37.0   \n",
       "2       Professional         No   3.0          2.000000         Europe  55.0   \n",
       "3         Management        Yes   2.0          5.000000        Pacific  47.0   \n",
       "4     Skilled Manual         No   1.0          1.000000  North America  38.0   \n",
       "5         Management        Yes   3.0          1.000000  North America  40.0   \n",
       "6       Professional        Yes   2.0          4.210103        Pacific  49.0   \n",
       "7       Professional         No   2.0          1.000000  North America  49.0   \n",
       "8           Clerical        Yes   0.0         10.000000         Europe  38.0   \n",
       "9             Manual         No   0.0          1.000000         Europe  46.0   \n",
       "10        Management         No   2.0          2.000000  North America  64.0   \n",
       "11      Professional         No   0.0         11.000000  North America  49.0   \n",
       "12    Skilled Manual         No   2.0          1.000000  North America  31.0   \n",
       "13          Clerical        Yes   1.0          1.000000  North America  50.0   \n",
       "14      Professional        Yes   1.0          1.000000  North America  49.0   \n",
       "15    Skilled Manual         No   1.0          1.000000        Pacific  44.0   \n",
       "16    Skilled Manual        Yes   1.0          2.000000  North America  39.0   \n",
       "17          Clerical        Yes   1.0          1.000000  North America  51.0   \n",
       "18          Clerical        Yes   1.0          2.000000  North America  48.0   \n",
       "19    Skilled Manual         No   1.0          6.000000        Pacific  45.0   \n",
       "20      Professional        Yes   2.0          1.000000        Pacific  43.0   \n",
       "21      Professional        Yes   1.0         12.000000        Pacific  41.0   \n",
       "22      Professional        Yes   2.0          1.000000        Pacific  44.0   \n",
       "23      Professional        Yes   0.0          4.210103  North America  37.0   \n",
       "24    Skilled Manual        Yes   2.0          1.000000        Pacific  44.0   \n",
       "25      Professional         No   2.0          1.000000         Europe  62.0   \n",
       "26    Skilled Manual        Yes   2.0          1.000000        Pacific  44.0   \n",
       "27    Skilled Manual        Yes   2.0          9.000000        Pacific  44.0   \n",
       "28    Skilled Manual         No   1.0          5.000000        Pacific  49.0   \n",
       "29          Clerical        Yes   0.0          1.000000         Europe  37.0   \n",
       "...              ...        ...   ...               ...            ...   ...   \n",
       "6967    Professional        Yes   2.0          2.000000  North America  54.0   \n",
       "6968    Professional        Yes   1.0          1.000000  North America  55.0   \n",
       "6969  Skilled Manual        Yes   2.0          1.000000  North America  54.0   \n",
       "6970  Skilled Manual        Yes   3.0          2.000000  North America  54.0   \n",
       "6971    Professional        Yes   1.0          7.000000  North America  53.0   \n",
       "6972    Professional        Yes   1.0          8.000000  North America  53.0   \n",
       "6973    Professional        Yes   1.0          9.000000         Europe  48.0   \n",
       "6974    Professional        Yes   3.0          2.000000         Europe  49.0   \n",
       "6975    Professional         No   3.0          3.000000         Europe  49.0   \n",
       "6976    Professional        Yes   1.0          5.000000         Europe  49.0   \n",
       "6977    Professional         No   1.0          6.000000         Europe  50.0   \n",
       "6978    Professional        Yes   1.0          7.000000         Europe  51.0   \n",
       "6979    Professional        Yes   1.0          1.000000         Europe  51.0   \n",
       "6980    Professional        Yes   2.0          2.000000         Europe  51.0   \n",
       "6981      Management        Yes   2.0          1.000000         Europe  62.0   \n",
       "6982    Professional        Yes   2.0          1.000000         Europe  62.0   \n",
       "6983      Management        Yes   1.0          2.000000         Europe  62.0   \n",
       "6984    Professional        Yes   2.0          7.000000         Europe  61.0   \n",
       "6985      Management        Yes   1.0          8.000000         Europe  61.0   \n",
       "6986      Management        Yes   1.0          9.000000         Europe  61.0   \n",
       "6987      Management        Yes   1.0          2.000000         Europe  61.0   \n",
       "6988      Management        Yes   1.0          3.000000         Europe  60.0   \n",
       "6989      Management        Yes   1.0          5.000000         Europe  60.0   \n",
       "6990      Management        Yes   1.0          6.000000         Europe  60.0   \n",
       "6991    Professional        Yes   2.0          7.000000         Europe  53.0   \n",
       "6992    Professional        Yes   3.0          1.000000         Europe  52.0   \n",
       "6993    Professional        Yes   1.0          2.000000         Europe  52.0   \n",
       "6994      Management        Yes   2.0          1.000000         Europe  53.0   \n",
       "6995    Professional        Yes   3.0          1.000000         Europe  54.0   \n",
       "6996    Professional        Yes   3.0          2.000000         Europe  54.0   \n",
       "\n",
       "     Bike Buyer  \n",
       "0           Yes  \n",
       "1           Yes  \n",
       "2            No  \n",
       "3            No  \n",
       "4           Yes  \n",
       "5            No  \n",
       "6           Yes  \n",
       "7            No  \n",
       "8           Yes  \n",
       "9           Yes  \n",
       "10           No  \n",
       "11           No  \n",
       "12          Yes  \n",
       "13          Yes  \n",
       "14           No  \n",
       "15           No  \n",
       "16          Yes  \n",
       "17          Yes  \n",
       "18          Yes  \n",
       "19           No  \n",
       "20          Yes  \n",
       "21           No  \n",
       "22           No  \n",
       "23          Yes  \n",
       "24           No  \n",
       "25          Yes  \n",
       "26           No  \n",
       "27           No  \n",
       "28           No  \n",
       "29          Yes  \n",
       "...         ...  \n",
       "6967         No  \n",
       "6968         No  \n",
       "6969         No  \n",
       "6970         No  \n",
       "6971         No  \n",
       "6972         No  \n",
       "6973         No  \n",
       "6974         No  \n",
       "6975         No  \n",
       "6976         No  \n",
       "6977         No  \n",
       "6978         No  \n",
       "6979         No  \n",
       "6980         No  \n",
       "6981         No  \n",
       "6982         No  \n",
       "6983         No  \n",
       "6984         No  \n",
       "6985         No  \n",
       "6986         No  \n",
       "6987         No  \n",
       "6988         No  \n",
       "6989         No  \n",
       "6990         No  \n",
       "6991         No  \n",
       "6992         No  \n",
       "6993         No  \n",
       "6994         No  \n",
       "6995         No  \n",
       "6996         No  \n",
       "\n",
       "[6997 rows x 12 columns]"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Marital Status</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Yearly Income</th>\n",
       "      <th>Children</th>\n",
       "      <th>Education</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Home Owner</th>\n",
       "      <th>Cars</th>\n",
       "      <th>Commute Distance</th>\n",
       "      <th>Region</th>\n",
       "      <th>Age</th>\n",
       "      <th>Bike Buyer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Clerical</td>\n",
       "      <td>No</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Europe</td>\n",
       "      <td>33.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Skilled Manual</td>\n",
       "      <td>No</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>North America</td>\n",
       "      <td>31.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Clerical</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>North America</td>\n",
       "      <td>48.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Graduate Degree</td>\n",
       "      <td>Clerical</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Europe</td>\n",
       "      <td>37.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Professional</td>\n",
       "      <td>No</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>42.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Clerical</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>Europe</td>\n",
       "      <td>63.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Graduate Degree</td>\n",
       "      <td>Management</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>63.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Graduate Degree</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>North America</td>\n",
       "      <td>47.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Partial High School</td>\n",
       "      <td>Clerical</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>58.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Graduate Degree</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>North America</td>\n",
       "      <td>39.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>110000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Partial High School</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.210103</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>35.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Clerical</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>North America</td>\n",
       "      <td>52.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>High School</td>\n",
       "      <td>Skilled Manual</td>\n",
       "      <td>No</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>North America</td>\n",
       "      <td>27.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>High School</td>\n",
       "      <td>Skilled Manual</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>North America</td>\n",
       "      <td>48.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>Europe</td>\n",
       "      <td>49.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Clerical</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.210103</td>\n",
       "      <td>Europe</td>\n",
       "      <td>37.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Skilled Manual</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>Europe</td>\n",
       "      <td>34.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Graduate Degree</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>North America</td>\n",
       "      <td>47.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>High School</td>\n",
       "      <td>Manual</td>\n",
       "      <td>No</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>Europe</td>\n",
       "      <td>35.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>High School</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>North America</td>\n",
       "      <td>60.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Management</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>63.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Skilled Manual</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>46.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Management</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>North America</td>\n",
       "      <td>58.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>North America</td>\n",
       "      <td>48.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Management</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>47.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Europe</td>\n",
       "      <td>47.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Graduate Degree</td>\n",
       "      <td>Management</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>North America</td>\n",
       "      <td>65.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>North America</td>\n",
       "      <td>43.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Graduate Degree</td>\n",
       "      <td>Clerical</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>Europe</td>\n",
       "      <td>38.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Graduate Degree</td>\n",
       "      <td>Clerical</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>Europe</td>\n",
       "      <td>45.0</td>\n",
       "      <td>Yes</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1834</th>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>High School</td>\n",
       "      <td>Skilled Manual</td>\n",
       "      <td>No</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>50.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1837</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Clerical</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>North America</td>\n",
       "      <td>51.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1839</th>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>North America</td>\n",
       "      <td>45.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1846</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Clerical</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>Europe</td>\n",
       "      <td>40.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1850</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>90000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>35.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1853</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Skilled Manual</td>\n",
       "      <td>No</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>North America</td>\n",
       "      <td>42.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1857</th>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Skilled Manual</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>North America</td>\n",
       "      <td>37.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1861</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Clerical</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Europe</td>\n",
       "      <td>47.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1863</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Clerical</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>27.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1870</th>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Clerical</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>North America</td>\n",
       "      <td>48.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1872</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Skilled Manual</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>North America</td>\n",
       "      <td>45.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1875</th>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>90000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>40.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1881</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>High School</td>\n",
       "      <td>Professional</td>\n",
       "      <td>No</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>North America</td>\n",
       "      <td>51.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1887</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Graduate Degree</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>North America</td>\n",
       "      <td>36.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1899</th>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>No</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>North America</td>\n",
       "      <td>32.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1905</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Graduate Degree</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>North America</td>\n",
       "      <td>34.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1909</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>130000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>Europe</td>\n",
       "      <td>49.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1921</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>High School</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>North America</td>\n",
       "      <td>53.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1922</th>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>110000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>High School</td>\n",
       "      <td>Management</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>34.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Graduate Degree</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>North America</td>\n",
       "      <td>37.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1925</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>High School</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>Europe</td>\n",
       "      <td>51.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1926</th>\n",
       "      <td>Single</td>\n",
       "      <td>Male</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Skilled Manual</td>\n",
       "      <td>No</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>North America</td>\n",
       "      <td>39.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1932</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Skilled Manual</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>North America</td>\n",
       "      <td>45.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1934</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Clerical</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>North America</td>\n",
       "      <td>48.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1935</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Partial College</td>\n",
       "      <td>Skilled Manual</td>\n",
       "      <td>No</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>North America</td>\n",
       "      <td>39.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1939</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Graduate Degree</td>\n",
       "      <td>Clerical</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Europe</td>\n",
       "      <td>44.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1941</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Skilled Manual</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>North America</td>\n",
       "      <td>36.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1944</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>High School</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>North America</td>\n",
       "      <td>49.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1946</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>High School</td>\n",
       "      <td>Skilled Manual</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>North America</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958</th>\n",
       "      <td>Married</td>\n",
       "      <td>Male</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Skilled Manual</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>Europe</td>\n",
       "      <td>44.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>478 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Marital Status Gender  Yearly Income  Children            Education  \\\n",
       "0            Single   Male        30000.0       0.0      Partial College   \n",
       "12           Single   Male        60000.0       0.0      Partial College   \n",
       "18           Single   Male            0.0       0.0      Partial College   \n",
       "29           Single   Male        40000.0       0.0      Graduate Degree   \n",
       "31          Married   Male        70000.0       0.0            Bachelors   \n",
       "33           Single   Male        30000.0       0.0            Bachelors   \n",
       "44          Married   Male        50000.0       0.0      Graduate Degree   \n",
       "45          Married   Male        80000.0       0.0      Graduate Degree   \n",
       "48           Single   Male        20000.0       1.0  Partial High School   \n",
       "50           Single   Male        60000.0       0.0      Graduate Degree   \n",
       "54          Married   Male       110000.0       5.0  Partial High School   \n",
       "58          Married   Male        40000.0       0.0      Partial College   \n",
       "66           Single   Male        40000.0       0.0          High School   \n",
       "72           Single   Male        30000.0       0.0          High School   \n",
       "76           Single   Male       100000.0       3.0      Partial College   \n",
       "79           Single   Male        30000.0       0.0            Bachelors   \n",
       "82          Married   Male        40000.0       1.0            Bachelors   \n",
       "87          Married   Male        70000.0       0.0      Graduate Degree   \n",
       "88           Single   Male        30000.0       0.0          High School   \n",
       "89          Married   Male        40000.0       0.0          High School   \n",
       "90           Single   Male        40000.0       0.0            Bachelors   \n",
       "92          Married   Male        60000.0       0.0      Partial College   \n",
       "96          Married   Male        60000.0       1.0            Bachelors   \n",
       "99          Married   Male        60000.0       0.0            Bachelors   \n",
       "107         Married   Male       100000.0       1.0            Bachelors   \n",
       "108         Married   Male        20000.0       0.0      Partial College   \n",
       "110          Single   Male        70000.0       0.0      Graduate Degree   \n",
       "112         Married   Male        70000.0       2.0            Bachelors   \n",
       "113          Single   Male        40000.0       0.0      Graduate Degree   \n",
       "116         Married   Male        30000.0       0.0      Graduate Degree   \n",
       "...             ...    ...            ...       ...                  ...   \n",
       "1834         Single   Male        70000.0       0.0          High School   \n",
       "1837        Married   Male        40000.0       0.0      Partial College   \n",
       "1839         Single   Male        70000.0       4.0      Partial College   \n",
       "1846        Married   Male        30000.0       1.0            Bachelors   \n",
       "1850        Married   Male        90000.0       0.0            Bachelors   \n",
       "1853        Married   Male        60000.0       2.0            Bachelors   \n",
       "1857         Single   Male        60000.0       2.0            Bachelors   \n",
       "1861        Married   Male        30000.0       0.0            Bachelors   \n",
       "1863        Married   Male        20000.0       0.0            Bachelors   \n",
       "1870         Single   Male        40000.0       0.0      Partial College   \n",
       "1872        Married   Male        60000.0       0.0      Partial College   \n",
       "1875         Single   Male        90000.0       0.0            Bachelors   \n",
       "1881        Married   Male        60000.0       0.0          High School   \n",
       "1887        Married   Male        70000.0       0.0      Graduate Degree   \n",
       "1899         Single   Male        60000.0       0.0      Partial College   \n",
       "1905        Married   Male        60000.0       0.0      Graduate Degree   \n",
       "1909        Married   Male       130000.0       4.0      Partial College   \n",
       "1921        Married   Male        10000.0       1.0          High School   \n",
       "1922         Single   Male       110000.0       5.0          High School   \n",
       "1924        Married   Male        80000.0       0.0      Graduate Degree   \n",
       "1925        Married   Male        60000.0       4.0          High School   \n",
       "1926         Single   Male        50000.0       0.0      Partial College   \n",
       "1932        Married   Male        60000.0       0.0      Partial College   \n",
       "1934        Married   Male        40000.0       0.0      Partial College   \n",
       "1935        Married   Male        60000.0       0.0      Partial College   \n",
       "1939        Married   Male        30000.0       0.0      Graduate Degree   \n",
       "1941        Married   Male        50000.0       1.0            Bachelors   \n",
       "1944        Married   Male        60000.0       0.0          High School   \n",
       "1946        Married   Male        40000.0       0.0          High School   \n",
       "1958        Married   Male        40000.0       0.0            Bachelors   \n",
       "\n",
       "          Occupation Home Owner  Cars  Commute Distance         Region   Age  \\\n",
       "0           Clerical         No   1.0          1.000000         Europe  33.0   \n",
       "12    Skilled Manual         No   2.0          1.000000  North America  31.0   \n",
       "18          Clerical        Yes   1.0          2.000000  North America  48.0   \n",
       "29          Clerical        Yes   0.0          1.000000         Europe  37.0   \n",
       "31      Professional         No   1.0          1.000000        Pacific  42.0   \n",
       "33          Clerical        Yes   0.0          2.000000         Europe  63.0   \n",
       "44        Management        Yes   1.0          1.000000        Pacific  63.0   \n",
       "45      Professional        Yes   0.0          1.000000  North America  47.0   \n",
       "48          Clerical        Yes   2.0          1.000000        Pacific  58.0   \n",
       "50      Professional        Yes   0.0          1.000000  North America  39.0   \n",
       "54      Professional        Yes   1.0          4.210103        Pacific  35.0   \n",
       "58          Clerical        Yes   1.0          1.000000  North America  52.0   \n",
       "66    Skilled Manual         No   2.0          6.000000  North America  27.0   \n",
       "72    Skilled Manual        Yes   2.0          9.000000  North America  48.0   \n",
       "76      Professional        Yes   1.0          6.000000         Europe  49.0   \n",
       "79          Clerical         No   0.0          4.210103         Europe  37.0   \n",
       "82    Skilled Manual        Yes   1.0          5.000000         Europe  34.0   \n",
       "87      Professional        Yes   1.0          2.000000  North America  47.0   \n",
       "88            Manual         No   1.0          3.000000         Europe  35.0   \n",
       "89      Professional        Yes   3.0          6.000000  North America  60.0   \n",
       "90        Management        Yes   2.0          4.000000        Pacific  63.0   \n",
       "92    Skilled Manual        Yes   1.0          3.000000        Pacific  46.0   \n",
       "96        Management        Yes   0.0          2.000000  North America  58.0   \n",
       "99      Professional        Yes   1.0          6.000000  North America  48.0   \n",
       "107       Management        Yes   3.0          1.000000        Pacific  47.0   \n",
       "108           Manual        Yes   1.0          1.000000         Europe  47.0   \n",
       "110       Management        Yes   2.0          1.000000  North America  65.0   \n",
       "112     Professional        Yes   2.0          1.000000  North America  43.0   \n",
       "113         Clerical        Yes   0.0          2.000000         Europe  38.0   \n",
       "116         Clerical        Yes   0.0          6.000000         Europe  45.0   \n",
       "...              ...        ...   ...               ...            ...   ...   \n",
       "1834  Skilled Manual         No   2.0          5.000000        Pacific  50.0   \n",
       "1837        Clerical        Yes   1.0          4.000000  North America  51.0   \n",
       "1839    Professional        Yes   3.0          5.000000  North America  45.0   \n",
       "1846        Clerical        Yes   1.0          6.000000         Europe  40.0   \n",
       "1850    Professional        Yes   0.0          7.000000        Pacific  35.0   \n",
       "1853  Skilled Manual         No   2.0          2.000000  North America  42.0   \n",
       "1857  Skilled Manual         No   0.0          7.000000  North America  37.0   \n",
       "1861        Clerical        Yes   0.0          1.000000         Europe  47.0   \n",
       "1863        Clerical        Yes   0.0          6.000000        Pacific  27.0   \n",
       "1870        Clerical        Yes   1.0          4.000000  North America  48.0   \n",
       "1872  Skilled Manual        Yes   1.0          3.000000  North America  45.0   \n",
       "1875    Professional        Yes   0.0          9.000000        Pacific  40.0   \n",
       "1881    Professional         No   2.0          1.000000  North America  51.0   \n",
       "1887    Professional        Yes   0.0          1.000000  North America  36.0   \n",
       "1899    Professional         No   2.0          2.000000  North America  32.0   \n",
       "1905    Professional        Yes   0.0          8.000000  North America  34.0   \n",
       "1909    Professional        Yes   2.0          6.000000         Europe  49.0   \n",
       "1921          Manual        Yes   2.0          2.000000  North America  53.0   \n",
       "1922      Management        Yes   1.0          3.000000        Pacific  34.0   \n",
       "1924    Professional        Yes   0.0          8.000000  North America  37.0   \n",
       "1925    Professional        Yes   3.0          9.000000         Europe  51.0   \n",
       "1926  Skilled Manual         No   1.0          2.000000  North America  39.0   \n",
       "1932  Skilled Manual        Yes   1.0          2.000000  North America  45.0   \n",
       "1934        Clerical        Yes   1.0          1.000000  North America  48.0   \n",
       "1935  Skilled Manual         No   1.0          2.000000  North America  39.0   \n",
       "1939        Clerical        Yes   0.0          1.000000         Europe  44.0   \n",
       "1941  Skilled Manual        Yes   0.0          5.000000  North America  36.0   \n",
       "1944    Professional        Yes   2.0          1.000000  North America  49.0   \n",
       "1946  Skilled Manual        Yes   2.0          1.000000  North America  30.0   \n",
       "1958  Skilled Manual        Yes   1.0          2.000000         Europe  44.0   \n",
       "\n",
       "     Bike Buyer  \n",
       "0           Yes  \n",
       "12          Yes  \n",
       "18          Yes  \n",
       "29          Yes  \n",
       "31          Yes  \n",
       "33          Yes  \n",
       "44          Yes  \n",
       "45          Yes  \n",
       "48          Yes  \n",
       "50          Yes  \n",
       "54          Yes  \n",
       "58          Yes  \n",
       "66          Yes  \n",
       "72          Yes  \n",
       "76          Yes  \n",
       "79          Yes  \n",
       "82          Yes  \n",
       "87          Yes  \n",
       "88          Yes  \n",
       "89          Yes  \n",
       "90          Yes  \n",
       "92          Yes  \n",
       "96          Yes  \n",
       "99          Yes  \n",
       "107         Yes  \n",
       "108         Yes  \n",
       "110         Yes  \n",
       "112         Yes  \n",
       "113         Yes  \n",
       "116         Yes  \n",
       "...         ...  \n",
       "1834        Yes  \n",
       "1837        Yes  \n",
       "1839        Yes  \n",
       "1846        Yes  \n",
       "1850        Yes  \n",
       "1853        Yes  \n",
       "1857        Yes  \n",
       "1861        Yes  \n",
       "1863        Yes  \n",
       "1870        Yes  \n",
       "1872        Yes  \n",
       "1875        Yes  \n",
       "1881        Yes  \n",
       "1887        Yes  \n",
       "1899        Yes  \n",
       "1905        Yes  \n",
       "1909        Yes  \n",
       "1921        Yes  \n",
       "1922        Yes  \n",
       "1924        Yes  \n",
       "1925        Yes  \n",
       "1926        Yes  \n",
       "1932        Yes  \n",
       "1934        Yes  \n",
       "1935        Yes  \n",
       "1939        Yes  \n",
       "1941        Yes  \n",
       "1944        Yes  \n",
       "1946        Yes  \n",
       "1958        Yes  \n",
       "\n",
       "[478 rows x 12 columns]"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Person whose is male and yes\n",
    "data1[data1['Gender'].str.contains('Male') & data1['Bike Buyer'].str.contains('Yes')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [],
   "source": [
    "def match_it2(p,q,r,s) : # p is column1 and q its value , r is second column and s its value \n",
    "    print(data1[data1[p].str.contains(q) & data1[r].str.contains(s)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Marital Status Gender  Yearly Income  Children            Education  \\\n",
      "0            Single   Male        30000.0       0.0      Partial College   \n",
      "12           Single   Male        60000.0       0.0      Partial College   \n",
      "18           Single   Male            0.0       0.0      Partial College   \n",
      "29           Single   Male        40000.0       0.0      Graduate Degree   \n",
      "31          Married   Male        70000.0       0.0            Bachelors   \n",
      "33           Single   Male        30000.0       0.0            Bachelors   \n",
      "44          Married   Male        50000.0       0.0      Graduate Degree   \n",
      "45          Married   Male        80000.0       0.0      Graduate Degree   \n",
      "48           Single   Male        20000.0       1.0  Partial High School   \n",
      "50           Single   Male        60000.0       0.0      Graduate Degree   \n",
      "54          Married   Male       110000.0       5.0  Partial High School   \n",
      "58          Married   Male        40000.0       0.0      Partial College   \n",
      "66           Single   Male        40000.0       0.0          High School   \n",
      "72           Single   Male        30000.0       0.0          High School   \n",
      "76           Single   Male       100000.0       3.0      Partial College   \n",
      "79           Single   Male        30000.0       0.0            Bachelors   \n",
      "82          Married   Male        40000.0       1.0            Bachelors   \n",
      "87          Married   Male        70000.0       0.0      Graduate Degree   \n",
      "88           Single   Male        30000.0       0.0          High School   \n",
      "89          Married   Male        40000.0       0.0          High School   \n",
      "90           Single   Male        40000.0       0.0            Bachelors   \n",
      "92          Married   Male        60000.0       0.0      Partial College   \n",
      "96          Married   Male        60000.0       1.0            Bachelors   \n",
      "99          Married   Male        60000.0       0.0            Bachelors   \n",
      "107         Married   Male       100000.0       1.0            Bachelors   \n",
      "108         Married   Male        20000.0       0.0      Partial College   \n",
      "110          Single   Male        70000.0       0.0      Graduate Degree   \n",
      "112         Married   Male        70000.0       2.0            Bachelors   \n",
      "113          Single   Male        40000.0       0.0      Graduate Degree   \n",
      "116         Married   Male        30000.0       0.0      Graduate Degree   \n",
      "...             ...    ...            ...       ...                  ...   \n",
      "1834         Single   Male        70000.0       0.0          High School   \n",
      "1837        Married   Male        40000.0       0.0      Partial College   \n",
      "1839         Single   Male        70000.0       4.0      Partial College   \n",
      "1846        Married   Male        30000.0       1.0            Bachelors   \n",
      "1850        Married   Male        90000.0       0.0            Bachelors   \n",
      "1853        Married   Male        60000.0       2.0            Bachelors   \n",
      "1857         Single   Male        60000.0       2.0            Bachelors   \n",
      "1861        Married   Male        30000.0       0.0            Bachelors   \n",
      "1863        Married   Male        20000.0       0.0            Bachelors   \n",
      "1870         Single   Male        40000.0       0.0      Partial College   \n",
      "1872        Married   Male        60000.0       0.0      Partial College   \n",
      "1875         Single   Male        90000.0       0.0            Bachelors   \n",
      "1881        Married   Male        60000.0       0.0          High School   \n",
      "1887        Married   Male        70000.0       0.0      Graduate Degree   \n",
      "1899         Single   Male        60000.0       0.0      Partial College   \n",
      "1905        Married   Male        60000.0       0.0      Graduate Degree   \n",
      "1909        Married   Male       130000.0       4.0      Partial College   \n",
      "1921        Married   Male        10000.0       1.0          High School   \n",
      "1922         Single   Male       110000.0       5.0          High School   \n",
      "1924        Married   Male        80000.0       0.0      Graduate Degree   \n",
      "1925        Married   Male        60000.0       4.0          High School   \n",
      "1926         Single   Male        50000.0       0.0      Partial College   \n",
      "1932        Married   Male        60000.0       0.0      Partial College   \n",
      "1934        Married   Male        40000.0       0.0      Partial College   \n",
      "1935        Married   Male        60000.0       0.0      Partial College   \n",
      "1939        Married   Male        30000.0       0.0      Graduate Degree   \n",
      "1941        Married   Male        50000.0       1.0            Bachelors   \n",
      "1944        Married   Male        60000.0       0.0          High School   \n",
      "1946        Married   Male        40000.0       0.0          High School   \n",
      "1958        Married   Male        40000.0       0.0            Bachelors   \n",
      "\n",
      "          Occupation Home Owner  Cars  Commute Distance         Region   Age  \\\n",
      "0           Clerical         No   1.0          1.000000         Europe  33.0   \n",
      "12    Skilled Manual         No   2.0          1.000000  North America  31.0   \n",
      "18          Clerical        Yes   1.0          2.000000  North America  48.0   \n",
      "29          Clerical        Yes   0.0          1.000000         Europe  37.0   \n",
      "31      Professional         No   1.0          1.000000        Pacific  42.0   \n",
      "33          Clerical        Yes   0.0          2.000000         Europe  63.0   \n",
      "44        Management        Yes   1.0          1.000000        Pacific  63.0   \n",
      "45      Professional        Yes   0.0          1.000000  North America  47.0   \n",
      "48          Clerical        Yes   2.0          1.000000        Pacific  58.0   \n",
      "50      Professional        Yes   0.0          1.000000  North America  39.0   \n",
      "54      Professional        Yes   1.0          4.210103        Pacific  35.0   \n",
      "58          Clerical        Yes   1.0          1.000000  North America  52.0   \n",
      "66    Skilled Manual         No   2.0          6.000000  North America  27.0   \n",
      "72    Skilled Manual        Yes   2.0          9.000000  North America  48.0   \n",
      "76      Professional        Yes   1.0          6.000000         Europe  49.0   \n",
      "79          Clerical         No   0.0          4.210103         Europe  37.0   \n",
      "82    Skilled Manual        Yes   1.0          5.000000         Europe  34.0   \n",
      "87      Professional        Yes   1.0          2.000000  North America  47.0   \n",
      "88            Manual         No   1.0          3.000000         Europe  35.0   \n",
      "89      Professional        Yes   3.0          6.000000  North America  60.0   \n",
      "90        Management        Yes   2.0          4.000000        Pacific  63.0   \n",
      "92    Skilled Manual        Yes   1.0          3.000000        Pacific  46.0   \n",
      "96        Management        Yes   0.0          2.000000  North America  58.0   \n",
      "99      Professional        Yes   1.0          6.000000  North America  48.0   \n",
      "107       Management        Yes   3.0          1.000000        Pacific  47.0   \n",
      "108           Manual        Yes   1.0          1.000000         Europe  47.0   \n",
      "110       Management        Yes   2.0          1.000000  North America  65.0   \n",
      "112     Professional        Yes   2.0          1.000000  North America  43.0   \n",
      "113         Clerical        Yes   0.0          2.000000         Europe  38.0   \n",
      "116         Clerical        Yes   0.0          6.000000         Europe  45.0   \n",
      "...              ...        ...   ...               ...            ...   ...   \n",
      "1834  Skilled Manual         No   2.0          5.000000        Pacific  50.0   \n",
      "1837        Clerical        Yes   1.0          4.000000  North America  51.0   \n",
      "1839    Professional        Yes   3.0          5.000000  North America  45.0   \n",
      "1846        Clerical        Yes   1.0          6.000000         Europe  40.0   \n",
      "1850    Professional        Yes   0.0          7.000000        Pacific  35.0   \n",
      "1853  Skilled Manual         No   2.0          2.000000  North America  42.0   \n",
      "1857  Skilled Manual         No   0.0          7.000000  North America  37.0   \n",
      "1861        Clerical        Yes   0.0          1.000000         Europe  47.0   \n",
      "1863        Clerical        Yes   0.0          6.000000        Pacific  27.0   \n",
      "1870        Clerical        Yes   1.0          4.000000  North America  48.0   \n",
      "1872  Skilled Manual        Yes   1.0          3.000000  North America  45.0   \n",
      "1875    Professional        Yes   0.0          9.000000        Pacific  40.0   \n",
      "1881    Professional         No   2.0          1.000000  North America  51.0   \n",
      "1887    Professional        Yes   0.0          1.000000  North America  36.0   \n",
      "1899    Professional         No   2.0          2.000000  North America  32.0   \n",
      "1905    Professional        Yes   0.0          8.000000  North America  34.0   \n",
      "1909    Professional        Yes   2.0          6.000000         Europe  49.0   \n",
      "1921          Manual        Yes   2.0          2.000000  North America  53.0   \n",
      "1922      Management        Yes   1.0          3.000000        Pacific  34.0   \n",
      "1924    Professional        Yes   0.0          8.000000  North America  37.0   \n",
      "1925    Professional        Yes   3.0          9.000000         Europe  51.0   \n",
      "1926  Skilled Manual         No   1.0          2.000000  North America  39.0   \n",
      "1932  Skilled Manual        Yes   1.0          2.000000  North America  45.0   \n",
      "1934        Clerical        Yes   1.0          1.000000  North America  48.0   \n",
      "1935  Skilled Manual         No   1.0          2.000000  North America  39.0   \n",
      "1939        Clerical        Yes   0.0          1.000000         Europe  44.0   \n",
      "1941  Skilled Manual        Yes   0.0          5.000000  North America  36.0   \n",
      "1944    Professional        Yes   2.0          1.000000  North America  49.0   \n",
      "1946  Skilled Manual        Yes   2.0          1.000000  North America  30.0   \n",
      "1958  Skilled Manual        Yes   1.0          2.000000         Europe  44.0   \n",
      "\n",
      "     Bike Buyer  \n",
      "0           Yes  \n",
      "12          Yes  \n",
      "18          Yes  \n",
      "29          Yes  \n",
      "31          Yes  \n",
      "33          Yes  \n",
      "44          Yes  \n",
      "45          Yes  \n",
      "48          Yes  \n",
      "50          Yes  \n",
      "54          Yes  \n",
      "58          Yes  \n",
      "66          Yes  \n",
      "72          Yes  \n",
      "76          Yes  \n",
      "79          Yes  \n",
      "82          Yes  \n",
      "87          Yes  \n",
      "88          Yes  \n",
      "89          Yes  \n",
      "90          Yes  \n",
      "92          Yes  \n",
      "96          Yes  \n",
      "99          Yes  \n",
      "107         Yes  \n",
      "108         Yes  \n",
      "110         Yes  \n",
      "112         Yes  \n",
      "113         Yes  \n",
      "116         Yes  \n",
      "...         ...  \n",
      "1834        Yes  \n",
      "1837        Yes  \n",
      "1839        Yes  \n",
      "1846        Yes  \n",
      "1850        Yes  \n",
      "1853        Yes  \n",
      "1857        Yes  \n",
      "1861        Yes  \n",
      "1863        Yes  \n",
      "1870        Yes  \n",
      "1872        Yes  \n",
      "1875        Yes  \n",
      "1881        Yes  \n",
      "1887        Yes  \n",
      "1899        Yes  \n",
      "1905        Yes  \n",
      "1909        Yes  \n",
      "1921        Yes  \n",
      "1922        Yes  \n",
      "1924        Yes  \n",
      "1925        Yes  \n",
      "1926        Yes  \n",
      "1932        Yes  \n",
      "1934        Yes  \n",
      "1935        Yes  \n",
      "1939        Yes  \n",
      "1941        Yes  \n",
      "1944        Yes  \n",
      "1946        Yes  \n",
      "1958        Yes  \n",
      "\n",
      "[478 rows x 12 columns]\n"
     ]
    }
   ],
   "source": [
    "match_it2('Gender','Male','Bike Buyer','Yes')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [],
   "source": [
    "def match_it1(p,q) :\n",
    "    print(data1[data1[p].str.contains(q)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Marital Status Gender  Yearly Income  Children            Education  \\\n",
      "0            Single   Male        30000.0  0.000000      Partial College   \n",
      "2           Married   Male        60000.0  5.000000      Partial College   \n",
      "3            Single   Male        60000.0  0.000000      Graduate Degree   \n",
      "5           Married   Male       120000.0  2.000000            Bachelors   \n",
      "10           Single   Male        50000.0  0.000000            Bachelors   \n",
      "12           Single   Male        60000.0  0.000000      Partial College   \n",
      "14          Married   Male        90000.0  0.000000      Partial College   \n",
      "15           Single   Male        70000.0  0.000000      Partial College   \n",
      "18           Single   Male            0.0  0.000000      Partial College   \n",
      "21           Single   Male        80000.0  5.000000            Bachelors   \n",
      "22          Married   Male        70000.0  4.000000            Bachelors   \n",
      "26          Married   Male        70000.0  4.000000      Partial College   \n",
      "27          Married   Male        70000.0  4.000000      Partial College   \n",
      "28           Single   Male        80000.0  1.111621      Partial College   \n",
      "29           Single   Male        40000.0  0.000000      Graduate Degree   \n",
      "31          Married   Male        70000.0  0.000000            Bachelors   \n",
      "33           Single   Male        30000.0  0.000000            Bachelors   \n",
      "34           Single   Male        30000.0  0.000000      Partial College   \n",
      "35          Married   Male        70000.0  4.000000      Partial College   \n",
      "36          Married   Male        70000.0  4.000000      Partial College   \n",
      "37          Married   Male        70000.0  4.000000      Partial College   \n",
      "44          Married   Male        50000.0  0.000000      Graduate Degree   \n",
      "45          Married   Male        80000.0  0.000000      Graduate Degree   \n",
      "48           Single   Male        20000.0  1.000000  Partial High School   \n",
      "50           Single   Male        60000.0  0.000000      Graduate Degree   \n",
      "54          Married   Male       110000.0  5.000000  Partial High School   \n",
      "58          Married   Male        40000.0  0.000000      Partial College   \n",
      "59           Single   Male        30000.0  0.000000  Partial High School   \n",
      "63           Single   Male        40000.0  0.000000          High School   \n",
      "65          Married   Male        40000.0  0.000000          High School   \n",
      "...             ...    ...            ...       ...                  ...   \n",
      "6931        Married   Male        70000.0  0.000000            Bachelors   \n",
      "6939        Married   Male        40000.0  0.000000          High School   \n",
      "6940         Single   Male        40000.0  0.000000          High School   \n",
      "6942         Single   Male        40000.0  0.000000          High School   \n",
      "6945        Married   Male        70000.0  0.000000            Bachelors   \n",
      "6946        Married   Male        60000.0  0.000000            Bachelors   \n",
      "6947        Married   Male        60000.0  0.000000            Bachelors   \n",
      "6948        Married   Male        70000.0  1.000000      Partial College   \n",
      "6949        Married   Male        70000.0  1.000000      Partial College   \n",
      "6950        Married   Male        70000.0  1.000000      Partial College   \n",
      "6953         Single   Male        60000.0  1.000000      Partial College   \n",
      "6956        Married   Male        60000.0  1.000000      Partial College   \n",
      "6958        Married   Male        60000.0  1.000000      Partial College   \n",
      "6959        Married   Male        60000.0  1.000000      Partial College   \n",
      "6960        Married   Male        40000.0  1.000000  Partial High School   \n",
      "6963        Married   Male        60000.0  1.000000      Partial College   \n",
      "6966        Married   Male        60000.0  1.000000          High School   \n",
      "6967        Married   Male        60000.0  1.000000      Partial College   \n",
      "6968        Married   Male        70000.0  2.000000      Partial College   \n",
      "6969        Married   Male        70000.0  1.000000  Partial High School   \n",
      "6970        Married   Male        70000.0  1.000000  Partial High School   \n",
      "6971        Married   Male        70000.0  3.000000      Partial College   \n",
      "6972         Single   Male        70000.0  3.000000      Partial College   \n",
      "6975        Married   Male       110000.0  4.000000      Partial College   \n",
      "6984        Married   Male        80000.0  0.000000      Partial College   \n",
      "6985        Married   Male       110000.0  4.000000            Bachelors   \n",
      "6987        Married   Male       130000.0  4.000000            Bachelors   \n",
      "6988        Married   Male       100000.0  3.000000            Bachelors   \n",
      "6992        Married   Male       100000.0  4.000000          High School   \n",
      "6996        Married   Male       130000.0  5.000000          High School   \n",
      "\n",
      "          Occupation Home Owner  Cars  Commute Distance         Region   Age  \\\n",
      "0           Clerical         No   1.0          1.000000         Europe  33.0   \n",
      "2       Professional         No   3.0          2.000000         Europe  55.0   \n",
      "3         Management        Yes   2.0          5.000000        Pacific  47.0   \n",
      "5         Management        Yes   3.0          1.000000  North America  40.0   \n",
      "10        Management         No   2.0          2.000000  North America  64.0   \n",
      "12    Skilled Manual         No   2.0          1.000000  North America  31.0   \n",
      "14      Professional        Yes   1.0          1.000000  North America  49.0   \n",
      "15    Skilled Manual         No   1.0          1.000000        Pacific  44.0   \n",
      "18          Clerical        Yes   1.0          2.000000  North America  48.0   \n",
      "21      Professional        Yes   1.0         12.000000        Pacific  41.0   \n",
      "22      Professional        Yes   2.0          1.000000        Pacific  44.0   \n",
      "26    Skilled Manual        Yes   2.0          1.000000        Pacific  44.0   \n",
      "27    Skilled Manual        Yes   2.0          9.000000        Pacific  44.0   \n",
      "28    Skilled Manual         No   1.0          5.000000        Pacific  49.0   \n",
      "29          Clerical        Yes   0.0          1.000000         Europe  37.0   \n",
      "31      Professional         No   1.0          1.000000        Pacific  42.0   \n",
      "33          Clerical        Yes   0.0          2.000000         Europe  63.0   \n",
      "34          Clerical         No   2.0          2.000000        Pacific  71.0   \n",
      "35    Skilled Manual        Yes   3.0          1.000000        Pacific  45.0   \n",
      "36    Skilled Manual        Yes   3.0          1.000000        Pacific  46.0   \n",
      "37    Skilled Manual        Yes   3.0          2.000000        Pacific  46.0   \n",
      "44        Management        Yes   1.0          1.000000        Pacific  63.0   \n",
      "45      Professional        Yes   0.0          1.000000  North America  47.0   \n",
      "48          Clerical        Yes   2.0          1.000000        Pacific  58.0   \n",
      "50      Professional        Yes   0.0          1.000000  North America  39.0   \n",
      "54      Professional        Yes   1.0          4.210103        Pacific  35.0   \n",
      "58          Clerical        Yes   1.0          1.000000  North America  52.0   \n",
      "59          Clerical         No   2.0          5.000000  North America  29.0   \n",
      "63    Skilled Manual         No   2.0          1.000000  North America  29.0   \n",
      "65    Skilled Manual        Yes   2.0          3.000000  North America  29.0   \n",
      "...              ...        ...   ...               ...            ...   ...   \n",
      "6931      Management        Yes   2.0          2.000000  North America  63.0   \n",
      "6939    Professional        Yes   2.0          7.000000  North America  60.0   \n",
      "6940    Professional         No   2.0          1.000000  North America  61.0   \n",
      "6942    Professional         No   3.0          1.000000  North America  61.0   \n",
      "6945      Management        Yes   1.0          7.000000  North America  60.0   \n",
      "6946      Management         No   2.0          8.000000  North America  60.0   \n",
      "6947      Management         No   2.0          9.000000  North America  60.0   \n",
      "6948    Professional        Yes   1.0          2.000000  North America  59.0   \n",
      "6949    Professional        Yes   1.0          3.000000  North America  59.0   \n",
      "6950    Professional        Yes   1.0          5.000000  North America  59.0   \n",
      "6953    Professional        Yes   1.0          1.000000  North America  58.0   \n",
      "6956    Professional        Yes   2.0          1.000000  North America  58.0   \n",
      "6958    Professional        Yes   2.0          7.000000  North America  57.0   \n",
      "6959    Professional        Yes   1.0          8.000000  North America  57.0   \n",
      "6960  Skilled Manual         No   2.0          9.000000  North America  55.0   \n",
      "6963    Professional        Yes   2.0          5.000000  North America  56.0   \n",
      "6966    Professional        Yes   2.0          1.000000  North America  55.0   \n",
      "6967    Professional        Yes   2.0          2.000000  North America  54.0   \n",
      "6968    Professional        Yes   1.0          1.000000  North America  55.0   \n",
      "6969  Skilled Manual        Yes   2.0          1.000000  North America  54.0   \n",
      "6970  Skilled Manual        Yes   3.0          2.000000  North America  54.0   \n",
      "6971    Professional        Yes   1.0          7.000000  North America  53.0   \n",
      "6972    Professional        Yes   1.0          8.000000  North America  53.0   \n",
      "6975    Professional         No   3.0          3.000000         Europe  49.0   \n",
      "6984    Professional        Yes   2.0          7.000000         Europe  61.0   \n",
      "6985      Management        Yes   1.0          8.000000         Europe  61.0   \n",
      "6987      Management        Yes   1.0          2.000000         Europe  61.0   \n",
      "6988      Management        Yes   1.0          3.000000         Europe  60.0   \n",
      "6992    Professional        Yes   3.0          1.000000         Europe  52.0   \n",
      "6996    Professional        Yes   3.0          2.000000         Europe  54.0   \n",
      "\n",
      "     Bike Buyer  \n",
      "0           Yes  \n",
      "2            No  \n",
      "3            No  \n",
      "5            No  \n",
      "10           No  \n",
      "12          Yes  \n",
      "14           No  \n",
      "15           No  \n",
      "18          Yes  \n",
      "21           No  \n",
      "22           No  \n",
      "26           No  \n",
      "27           No  \n",
      "28           No  \n",
      "29          Yes  \n",
      "31          Yes  \n",
      "33          Yes  \n",
      "34           No  \n",
      "35           No  \n",
      "36           No  \n",
      "37           No  \n",
      "44          Yes  \n",
      "45          Yes  \n",
      "48          Yes  \n",
      "50          Yes  \n",
      "54          Yes  \n",
      "58          Yes  \n",
      "59           No  \n",
      "63           No  \n",
      "65           No  \n",
      "...         ...  \n",
      "6931         No  \n",
      "6939         No  \n",
      "6940         No  \n",
      "6942         No  \n",
      "6945         No  \n",
      "6946         No  \n",
      "6947         No  \n",
      "6948         No  \n",
      "6949         No  \n",
      "6950         No  \n",
      "6953         No  \n",
      "6956         No  \n",
      "6958         No  \n",
      "6959         No  \n",
      "6960         No  \n",
      "6963         No  \n",
      "6966         No  \n",
      "6967         No  \n",
      "6968         No  \n",
      "6969         No  \n",
      "6970         No  \n",
      "6971         No  \n",
      "6972         No  \n",
      "6975         No  \n",
      "6984         No  \n",
      "6985         No  \n",
      "6987         No  \n",
      "6988         No  \n",
      "6992         No  \n",
      "6996         No  \n",
      "\n",
      "[3556 rows x 12 columns]\n"
     ]
    }
   ],
   "source": [
    "match_it1('Gender','Male')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "def matchit_counts2(p,q,r,s) :\n",
    "    print(data1[data1[p].str.contains(q) & data1[r].str.contains(s)].count())\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Marital Status      478\n",
       "Gender              478\n",
       "Yearly Income       478\n",
       "Children            478\n",
       "Education           478\n",
       "Occupation          478\n",
       "Home Owner          478\n",
       "Cars                478\n",
       "Commute Distance    478\n",
       "Region              478\n",
       "Age                 478\n",
       "Bike Buyer          478\n",
       "dtype: int64"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1[data1['Gender'].str.contains('Male') & data1['Bike Buyer'].str.contains('Yes')].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "def matchit_counts1(p,q) :\n",
    "    print(data1[data1[p].str.contains(q)].count())\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Marital Status      478\n",
      "Gender              478\n",
      "Yearly Income       478\n",
      "Children            478\n",
      "Education           478\n",
      "Occupation          478\n",
      "Home Owner          478\n",
      "Cars                478\n",
      "Commute Distance    478\n",
      "Region              478\n",
      "Age                 478\n",
      "Bike Buyer          478\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "matchit_counts2('Gender','Male','Bike Buyer','Yes')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Marital Status      3556\n",
      "Gender              3556\n",
      "Yearly Income       3556\n",
      "Children            3556\n",
      "Education           3556\n",
      "Occupation          3556\n",
      "Home Owner          3556\n",
      "Cars                3556\n",
      "Commute Distance    3556\n",
      "Region              3556\n",
      "Age                 3556\n",
      "Bike Buyer          3556\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "matchit_counts1('Gender','Male')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# write csv function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1.to_csv('C:\\\\Users\\\\Satwik\\\\Desktop\\\\Data Sets\\\\bikebuyerne.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=data1.iloc[:,:-1].values\n",
    "y=data1.iloc[:,-1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['Single', 'Male', 30000.0, ..., 1.0, 'Europe', 33.0],\n",
       "       ['Married', 'Female', 40000.0, ..., 1.0, 'Europe', 37.0],\n",
       "       ['Married', 'Male', 60000.0, ..., 2.0, 'Europe', 55.0],\n",
       "       ...,\n",
       "       ['Married', 'Female', 60000.0, ..., 1.0, 'Europe', 53.0],\n",
       "       ['Single', 'Female', 120000.0, ..., 1.0, 'Europe', 54.0],\n",
       "       ['Married', 'Male', 130000.0, ..., 2.0, 'Europe', 54.0]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Yes', 'Yes', 'No', ..., 'No', 'No', 'No'], dtype=object)"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
    "labelencoder_x=LabelEncoder()\n",
    "x[:,0]=labelencoder_x.fit_transform(x[:,0])\n",
    "x[:,1]=labelencoder_x.fit_transform(x[:,1])\n",
    "x[:,4]=labelencoder_x.fit_transform(x[:,4])\n",
    "x[:,5]=labelencoder_x.fit_transform(x[:,5])\n",
    "x[:,6]=labelencoder_x.fit_transform(x[:,6])\n",
    "x[:,9]=labelencoder_x.fit_transform(x[:,9])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1, 30000.0, ..., 1.0, 0, 33.0],\n",
       "       [0, 0, 40000.0, ..., 1.0, 0, 37.0],\n",
       "       [0, 1, 60000.0, ..., 2.0, 0, 55.0],\n",
       "       ...,\n",
       "       [0, 0, 60000.0, ..., 1.0, 0, 53.0],\n",
       "       [1, 0, 120000.0, ..., 1.0, 0, 54.0],\n",
       "       [0, 1, 130000.0, ..., 2.0, 0, 54.0]], dtype=object)"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [],
   "source": [
    "onehotencoder=OneHotEncoder(categorical_features=[4,5,9])\n",
    "x=onehotencoder.fit_transform(x).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0.,  0., ...,  1.,  1., 33.],\n",
       "       [ 0.,  1.,  0., ...,  0.,  1., 37.],\n",
       "       [ 0.,  0.,  0., ...,  3.,  2., 55.],\n",
       "       ...,\n",
       "       [ 1.,  0.,  0., ...,  2.,  1., 53.],\n",
       "       [ 0.,  0.,  0., ...,  3.,  1., 54.],\n",
       "       [ 0.,  0.,  1., ...,  3.,  2., 54.]])"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        , 0.        , 0.        , ..., 0.33333333, 0.        ,\n",
       "        0.1509434 ],\n",
       "       [0.        , 1.        , 0.        , ..., 0.        , 0.        ,\n",
       "        0.22641509],\n",
       "       [0.        , 0.        , 0.        , ..., 1.        , 0.08333333,\n",
       "        0.56603774],\n",
       "       ...,\n",
       "       [1.        , 0.        , 0.        , ..., 0.66666667, 0.        ,\n",
       "        0.52830189],\n",
       "       [0.        , 0.        , 0.        , ..., 1.        , 0.        ,\n",
       "        0.54716981],\n",
       "       [0.        , 0.        , 1.        , ..., 1.        , 0.08333333,\n",
       "        0.54716981]])"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Normalization\n",
    "from sklearn import preprocessing\n",
    "minmax=preprocessing.MinMaxScaler(feature_range=(0,1))\n",
    "minmax.fit(x).transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [],
   "source": [
    "#splitting the data\n",
    "from sklearn import cross_validation, neighbors\n",
    "from sklearn.cross_validation import train_test_split\n",
    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multiple Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data1[['Marital Status','Gender','Yearly Income','Children','Education','Occupation','Home Owner','Cars','Commute Distance','Region','Age']]\n",
    "X=X.values\n",
    "X[:,0]=labelencoder_x.fit_transform(X[:,0])\n",
    "X[:,1]=labelencoder_x.fit_transform(X[:,1])\n",
    "X[:,4]=labelencoder_x.fit_transform(X[:,4])\n",
    "X[:,5]=labelencoder_x.fit_transform(X[:,5])\n",
    "X[:,6]=labelencoder_x.fit_transform(X[:,6])\n",
    "X[:,9]=labelencoder_x.fit_transform(X[:,9])\n",
    "Y = data1['Bike Buyer']\n",
    "Y=labelencoder_x.fit_transform(Y)\n",
    "X=pd.DataFrame(X)\n",
    "Y=pd.DataFrame(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>0</td>        <th>  R-squared:         </th> <td>   0.159</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.157</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   119.8</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 30 May 2018</td> <th>  Prob (F-statistic):</th> <td>3.32e-252</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>21:36:44</td>     <th>  Log-Likelihood:    </th> <td> -2517.6</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  6997</td>      <th>  AIC:               </th> <td>   5057.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  6986</td>      <th>  BIC:               </th> <td>   5133.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    11</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "   <td></td>     <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>0</th>  <td>    0.0839</td> <td>    0.008</td> <td>   10.064</td> <td> 0.000</td> <td>    0.068</td> <td>    0.100</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>1</th>  <td>   -0.0021</td> <td>    0.008</td> <td>   -0.255</td> <td> 0.799</td> <td>   -0.018</td> <td>    0.014</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>2</th>  <td> 8.378e-07</td> <td> 1.73e-07</td> <td>    4.848</td> <td> 0.000</td> <td> 4.99e-07</td> <td> 1.18e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>3</th>  <td>   -0.0104</td> <td>    0.003</td> <td>   -3.384</td> <td> 0.001</td> <td>   -0.016</td> <td>   -0.004</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>4</th>  <td>    0.0043</td> <td>    0.003</td> <td>    1.320</td> <td> 0.187</td> <td>   -0.002</td> <td>    0.011</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>5</th>  <td>   -0.0004</td> <td>    0.003</td> <td>   -0.149</td> <td> 0.881</td> <td>   -0.006</td> <td>    0.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>6</th>  <td>    0.0276</td> <td>    0.009</td> <td>    2.936</td> <td> 0.003</td> <td>    0.009</td> <td>    0.046</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>7</th>  <td>   -0.0372</td> <td>    0.005</td> <td>   -7.203</td> <td> 0.000</td> <td>   -0.047</td> <td>   -0.027</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>8</th>  <td>    0.0026</td> <td>    0.001</td> <td>    1.913</td> <td> 0.056</td> <td> -6.5e-05</td> <td>    0.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>9</th>  <td>    0.0476</td> <td>    0.007</td> <td>    6.935</td> <td> 0.000</td> <td>    0.034</td> <td>    0.061</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>10</th> <td>    0.0009</td> <td>    0.000</td> <td>    3.246</td> <td> 0.001</td> <td>    0.000</td> <td>    0.001</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>2350.118</td> <th>  Durbin-Watson:     </th> <td>   1.121</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>5678.504</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 1.954</td>  <th>  Prob(JB):          </th> <td>    0.00</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td> 5.051</td>  <th>  Cond. No.          </th> <td>1.46e+05</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      0   R-squared:                       0.159\n",
       "Model:                            OLS   Adj. R-squared:                  0.157\n",
       "Method:                 Least Squares   F-statistic:                     119.8\n",
       "Date:                Wed, 30 May 2018   Prob (F-statistic):          3.32e-252\n",
       "Time:                        21:36:44   Log-Likelihood:                -2517.6\n",
       "No. Observations:                6997   AIC:                             5057.\n",
       "Df Residuals:                    6986   BIC:                             5133.\n",
       "Df Model:                          11                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "0              0.0839      0.008     10.064      0.000       0.068       0.100\n",
       "1             -0.0021      0.008     -0.255      0.799      -0.018       0.014\n",
       "2           8.378e-07   1.73e-07      4.848      0.000    4.99e-07    1.18e-06\n",
       "3             -0.0104      0.003     -3.384      0.001      -0.016      -0.004\n",
       "4              0.0043      0.003      1.320      0.187      -0.002       0.011\n",
       "5             -0.0004      0.003     -0.149      0.881      -0.006       0.005\n",
       "6              0.0276      0.009      2.936      0.003       0.009       0.046\n",
       "7             -0.0372      0.005     -7.203      0.000      -0.047      -0.027\n",
       "8              0.0026      0.001      1.913      0.056    -6.5e-05       0.005\n",
       "9              0.0476      0.007      6.935      0.000       0.034       0.061\n",
       "10             0.0009      0.000      3.246      0.001       0.000       0.001\n",
       "==============================================================================\n",
       "Omnibus:                     2350.118   Durbin-Watson:                   1.121\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             5678.504\n",
       "Skew:                           1.954   Prob(JB):                         0.00\n",
       "Kurtosis:                       5.051   Cond. No.                     1.46e+05\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.46e+05. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.formula.api as sm\n",
    "model = sm.OLS(Y,X.astype(float)).fit()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data1[['Marital Status','Yearly Income','Children','Home Owner','Cars','Region','Age']]\n",
    "X=X.values\n",
    "X[:,0]=labelencoder_x.fit_transform(X[:,0])\n",
    "#X[:,1]=labelencoder_x.fit_transform(X[:,1])\n",
    "X[:,3]=labelencoder_x.fit_transform(X[:,3])\n",
    "X[:,5]=labelencoder_x.fit_transform(X[:,5])\n",
    "#X[:,6]=labelencoder_x.fit_transform(X[:,6])\n",
    "#X[:,9]=labelencoder_x.fit_transform(X[:,9])\n",
    "Y = data1['Bike Buyer']\n",
    "Y=labelencoder_x.fit_transform(Y)\n",
    "X=pd.DataFrame(X)\n",
    "Y=pd.DataFrame(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>0</td>        <th>  R-squared:         </th> <td>   0.158</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.157</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   187.3</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 30 May 2018</td> <th>  Prob (F-statistic):</th> <td>2.55e-255</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>21:38:49</td>     <th>  Log-Likelihood:    </th> <td> -2520.6</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  6997</td>      <th>  AIC:               </th> <td>   5055.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  6990</td>      <th>  BIC:               </th> <td>   5103.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     7</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <td></td>     <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>0</th> <td>    0.0888</td> <td>    0.008</td> <td>   11.096</td> <td> 0.000</td> <td>    0.073</td> <td>    0.104</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>1</th> <td> 8.123e-07</td> <td> 1.68e-07</td> <td>    4.840</td> <td> 0.000</td> <td> 4.83e-07</td> <td> 1.14e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>2</th> <td>   -0.0100</td> <td>    0.003</td> <td>   -3.283</td> <td> 0.001</td> <td>   -0.016</td> <td>   -0.004</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>3</th> <td>    0.0294</td> <td>    0.009</td> <td>    3.172</td> <td> 0.002</td> <td>    0.011</td> <td>    0.048</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>4</th> <td>   -0.0349</td> <td>    0.005</td> <td>   -7.057</td> <td> 0.000</td> <td>   -0.045</td> <td>   -0.025</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>5</th> <td>    0.0471</td> <td>    0.007</td> <td>    7.166</td> <td> 0.000</td> <td>    0.034</td> <td>    0.060</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>6</th> <td>    0.0012</td> <td>    0.000</td> <td>    4.489</td> <td> 0.000</td> <td>    0.001</td> <td>    0.002</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>2343.986</td> <th>  Durbin-Watson:     </th> <td>   1.122</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>5651.190</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 1.950</td>  <th>  Prob(JB):          </th> <td>    0.00</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td> 5.043</td>  <th>  Cond. No.          </th> <td>1.43e+05</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      0   R-squared:                       0.158\n",
       "Model:                            OLS   Adj. R-squared:                  0.157\n",
       "Method:                 Least Squares   F-statistic:                     187.3\n",
       "Date:                Wed, 30 May 2018   Prob (F-statistic):          2.55e-255\n",
       "Time:                        21:38:49   Log-Likelihood:                -2520.6\n",
       "No. Observations:                6997   AIC:                             5055.\n",
       "Df Residuals:                    6990   BIC:                             5103.\n",
       "Df Model:                           7                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "0              0.0888      0.008     11.096      0.000       0.073       0.104\n",
       "1           8.123e-07   1.68e-07      4.840      0.000    4.83e-07    1.14e-06\n",
       "2             -0.0100      0.003     -3.283      0.001      -0.016      -0.004\n",
       "3              0.0294      0.009      3.172      0.002       0.011       0.048\n",
       "4             -0.0349      0.005     -7.057      0.000      -0.045      -0.025\n",
       "5              0.0471      0.007      7.166      0.000       0.034       0.060\n",
       "6              0.0012      0.000      4.489      0.000       0.001       0.002\n",
       "==============================================================================\n",
       "Omnibus:                     2343.986   Durbin-Watson:                   1.122\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             5651.190\n",
       "Skew:                           1.950   Prob(JB):                         0.00\n",
       "Kurtosis:                       5.043   Cond. No.                     1.43e+05\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.43e+05. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = sm.OLS(Y,X.astype(float)).fit()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20ac35ced68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(X[6],Y,'.')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Bike Buyer')\n",
    "a=Y.mean()\n",
    "plt.axhline(float(a),color='r',linestyle='-')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20ac3631780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(X[1],Y,'.')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Bike Buyer')\n",
    "a=Y.mean()\n",
    "plt.axhline(float(a),color='r',linestyle='-')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model.logistic import LogisticRegression\n",
    "cls = LogisticRegression(random_state = 0)\n",
    "lr_cls = cls.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['No', 'No', 'No', ..., 'No', 'No', 'No'], dtype=object)"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_y_test = lr_cls.predict(x_test)\n",
    "lr_y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['No', 'No', 'No', ..., 'No', 'No', 'No'], dtype=object)"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_y_train = lr_cls.predict(x_train)\n",
    "lr_y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1212,    0],\n",
       "       [ 188,    0]], dtype=int64)"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "clf_cm_test = confusion_matrix(y_test,lr_y_test)\n",
    "clf_cm_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy = cls.score(x_test,lr_y_test) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    }
   ],
   "source": [
    "print(accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# KNN classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Fitting Classifier to the training set\n",
    "clf = neighbors.KNeighborsClassifier()\n",
    "clf.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predicting the Test Set results\n",
    "y_pred=clf.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['No', 'No', 'No', ..., 'No', 'No', 'No'], dtype=object)"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['No', 'No', 'No', ..., 'No', 'No', 'No'], dtype=object)"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1171,   41],\n",
       "       [ 156,   32]], dtype=int64)"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#build the confusion matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "clf_cm_test = confusion_matrix(y_test,y_pred)\n",
    "clf_cm_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8592857142857143"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#obtain accuracy\n",
    "accuracy = clf.score(x_test,y_test)\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Support Vector Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "# support vector classification\n",
    "from sklearn.svm import SVC\n",
    "sc = SVC(kernel='rbf')\n",
    "sc_classifier = sc.fit(x_train,y_train) #model building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['No' 'No' 'No' ... 'No' 'No' 'No']\n"
     ]
    }
   ],
   "source": [
    "#Predictong on test and train data\n",
    "svc_y_test = sc_classifier.predict(x_test)\n",
    "print(svc_y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['No' 'No' 'No' ... 'No' 'Yes' 'No']\n"
     ]
    }
   ],
   "source": [
    "svc_y_train = sc_classifier.predict(x_train)\n",
    "print(svc_y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1200,   12],\n",
       "       [ 176,   12]], dtype=int64)"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# building confusion matrux on test and train\n",
    "from sklearn.metrics import confusion_matrix\n",
    "svc_cm_test = confusion_matrix(y_test,svc_y_test)\n",
    "svc_cm_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4769,   16],\n",
       "       [ 705,  107]], dtype=int64)"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc_cm_train = confusion_matrix(y_train,svc_y_train)\n",
    "svc_cm_train "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8657142857142858"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#accuracy classification score \n",
    "from sklearn.metrics import accuracy_score\n",
    "svc_acc_test = accuracy_score(y_test,svc_y_test)  # for test\n",
    "svc_acc_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8711809898159728"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc_acc_train = accuracy_score(y_train , svc_y_train) #for train\n",
    "svc_acc_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classification Using Decision Trees "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Decision Tree Classifier \n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "dtc_clf = DecisionTreeClassifier()\n",
    "dtc_clf.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['No', 'No', 'No', ..., 'No', 'No', 'No'], dtype=object)"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# predicting on test and train data\n",
    "dtc_y_test = dtc_clf.predict(x_test)\n",
    "dtc_y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['No', 'No', 'Yes', ..., 'No', 'Yes', 'No'], dtype=object)"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtc_y_train = dtc_clf.predict(x_train) \n",
    "dtc_y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1110,  102],\n",
       "       [ 121,   67]], dtype=int64)"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# buildind the confusion matrix\n",
    "from sklearn.metrics import confusion_matrix \n",
    "dtc_cm_test = confusion_matrix(y_test,dtc_y_test)\n",
    "dtc_cm_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8407142857142857"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "dtc_accu_test = accuracy_score(y_test,dtc_y_test)\n",
    "dtc_accu_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Builds Random  Forest Classifier \n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "rmf = RandomForestClassifier(max_depth = 3 , random_state = 0)\n",
    "rf_classi = rmf.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['No', 'No', 'No', ..., 'No', 'No', 'No'], dtype=object)"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pedicting on the test and trian data\n",
    "rf_y_test  =rf_classi.predict(x_test)\n",
    "rf_y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['No', 'No', 'No', ..., 'No', 'No', 'No'], dtype=object)"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_y_train = rf_classi.predict(x_train)\n",
    "rf_y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1212,    0],\n",
       "       [ 188,    0]], dtype=int64)"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# buliding the confudion matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "rf_cm_test = confusion_matrix(y_test,rf_y_test)\n",
    "rf_cm_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4785,    0],\n",
       "       [ 812,    0]], dtype=int64)"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_cm_train = confusion_matrix(y_train,rf_y_train)\n",
    "rf_cm_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8657142857142858"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "rf_accu_test  = accuracy_score(y_test,rf_y_test)   # test\n",
    "rf_accu_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8549222797927462"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_accu_train  = accuracy_score(y_train,rf_y_train)   # test\n",
    "rf_accu_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7494612244897959\n",
      "0.7308921044860265\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ml\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "#Pecision Score on tets and training dataa\n",
    "from sklearn.metrics import precision_score\n",
    "rf_precision_test = precision_score(y_test ,rf_y_test , average = 'weighted')\n",
    "print(rf_precision_test)\n",
    "rf_precision_train = precision_score(y_train ,rf_y_train , average = 'weighted')\n",
    "print(rf_precision_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8657142857142858\n",
      "0.8549222797927462\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import recall_score\n",
    "rf_recall_test = recall_score(y_test,rf_y_test ,average ='weighted')\n",
    "print(rf_recall_test)\n",
    "rf_recall_train = recall_score(y_train,rf_y_train ,average ='weighted')\n",
    "print(rf_recall_train)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ADA Boosting : Adaptive Bosoting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import model_selection\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "seed = 7\n",
    "num_trees = 30\n",
    "kfold = model_selection.KFold(n_splits = 10,random_state = seed)\n",
    "model = AdaBoostClassifier(n_estimators=num_trees,random_state = seed)\n",
    "results = model_selection.cross_val_score(model,x_train,y_train,cv=kfold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.8375    , 0.83214286, 0.875     , 0.87321429, 0.84642857,\n",
       "       0.86964286, 0.87142857, 0.84794275, 0.84615385, 0.85509839])"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.85545521339126"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FPR Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "# FPR :(False Positive Rate) = # False Positives / # negatives = FP / (FP+TN)\n",
    "# TPR :(True Positive Rate) = # True positives / # positives = Recall = TP / (TP+FN)\n",
    "# Precision =# True positives / # predicted positive = TP/(TP+FP)\n",
    "# Recall = # True positives / # positives = TP / (TP+FN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Recall and True Positive Rate (TPR) are exactly the same. \\nSo the difference is in the precision and the false positive rate.\\n\\nThe main difference between these two types of metrics is that precision denominator contains the False positives\\nwhile false positive rate denominator contains the true negatives.'"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"Recall and True Positive Rate (TPR) are exactly the same. \n",
    "So the difference is in the precision and the false positive rate.\n",
    "\n",
    "The main difference between these two types of metrics is that precision denominator contains the False positives\n",
    "while false positive rate denominator contains the true negatives.\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "#    While precision measures the probability of a sample classified as positive to actually be positive,\n",
    "#   the false positive rate measures the ratio of false positives within the negative samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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