DQA and Data Cleaning CustomerDemographic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "from datetime import datetime, date\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading the Customer Demographics Data from the excel file\n",
    "\n",
    "cust_demo = pd.read_excel('Raw_data.xlsx' , sheet_name='CustomerDemographic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>customer_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>gender</th>\n",
       "      <th>past_3_years_bike_related_purchases</th>\n",
       "      <th>DOB</th>\n",
       "      <th>job_title</th>\n",
       "      <th>job_industry_category</th>\n",
       "      <th>wealth_segment</th>\n",
       "      <th>deceased_indicator</th>\n",
       "      <th>default</th>\n",
       "      <th>owns_car</th>\n",
       "      <th>tenure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Laraine</td>\n",
       "      <td>Medendorp</td>\n",
       "      <td>F</td>\n",
       "      <td>93</td>\n",
       "      <td>1953-10-12</td>\n",
       "      <td>Executive Secretary</td>\n",
       "      <td>Health</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>\"'</td>\n",
       "      <td>Yes</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Eli</td>\n",
       "      <td>Bockman</td>\n",
       "      <td>Male</td>\n",
       "      <td>81</td>\n",
       "      <td>1980-12-16</td>\n",
       "      <td>Administrative Officer</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>&lt;script&gt;alert('hi')&lt;/script&gt;</td>\n",
       "      <td>Yes</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Arlin</td>\n",
       "      <td>Dearle</td>\n",
       "      <td>Male</td>\n",
       "      <td>61</td>\n",
       "      <td>1954-01-20</td>\n",
       "      <td>Recruiting Manager</td>\n",
       "      <td>Property</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>2018-02-01 00:00:00</td>\n",
       "      <td>Yes</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Talbot</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>33</td>\n",
       "      <td>1961-10-03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IT</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>() { _; } &gt;_[$($())] { touch /tmp/blns.shellsh...</td>\n",
       "      <td>No</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Sheila-kathryn</td>\n",
       "      <td>Calton</td>\n",
       "      <td>Female</td>\n",
       "      <td>56</td>\n",
       "      <td>1977-05-13</td>\n",
       "      <td>Senior Editor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>NIL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   customer_id      first_name  last_name  gender  \\\n",
       "0            1         Laraine  Medendorp       F   \n",
       "1            2             Eli    Bockman    Male   \n",
       "2            3           Arlin     Dearle    Male   \n",
       "3            4          Talbot        NaN    Male   \n",
       "4            5  Sheila-kathryn     Calton  Female   \n",
       "\n",
       "   past_3_years_bike_related_purchases        DOB               job_title  \\\n",
       "0                                   93 1953-10-12     Executive Secretary   \n",
       "1                                   81 1980-12-16  Administrative Officer   \n",
       "2                                   61 1954-01-20      Recruiting Manager   \n",
       "3                                   33 1961-10-03                     NaN   \n",
       "4                                   56 1977-05-13           Senior Editor   \n",
       "\n",
       "  job_industry_category     wealth_segment deceased_indicator  \\\n",
       "0                Health      Mass Customer                  N   \n",
       "1    Financial Services      Mass Customer                  N   \n",
       "2              Property      Mass Customer                  N   \n",
       "3                    IT      Mass Customer                  N   \n",
       "4                   NaN  Affluent Customer                  N   \n",
       "\n",
       "                                             default owns_car  tenure  \n",
       "0                                                 \"'      Yes    11.0  \n",
       "1                       <script>alert('hi')</script>      Yes    16.0  \n",
       "2                                2018-02-01 00:00:00      Yes    15.0  \n",
       "3  () { _; } >_[$($())] { touch /tmp/blns.shellsh...       No     7.0  \n",
       "4                                                NIL      Yes     8.0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking first 5 records from Customer Demographics Data\n",
    "\n",
    "cust_demo.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4000 entries, 0 to 3999\n",
      "Data columns (total 13 columns):\n",
      "customer_id                            4000 non-null int64\n",
      "first_name                             4000 non-null object\n",
      "last_name                              3875 non-null object\n",
      "gender                                 4000 non-null object\n",
      "past_3_years_bike_related_purchases    4000 non-null int64\n",
      "DOB                                    3913 non-null datetime64[ns]\n",
      "job_title                              3494 non-null object\n",
      "job_industry_category                  3344 non-null object\n",
      "wealth_segment                         4000 non-null object\n",
      "deceased_indicator                     4000 non-null object\n",
      "default                                3698 non-null object\n",
      "owns_car                               4000 non-null object\n",
      "tenure                                 3913 non-null float64\n",
      "dtypes: datetime64[ns](1), float64(1), int64(2), object(9)\n",
      "memory usage: 406.3+ KB\n"
     ]
    }
   ],
   "source": [
    "# Information of columns and data-types of Customer Demographics Data.\n",
    "\n",
    "cust_demo.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data-type of columns looks fine. However here <b>default</b> is an irrelevent column which should be dropped / deleted from the dataset. Let's check for the data quality and apply data cleaning process where ever applicable to clean our dataset before performing any analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Total Records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total records (rows) in the dataset : 4000\n",
      "Total columns (features) in the dataset : 13\n"
     ]
    }
   ],
   "source": [
    "print(\"Total records (rows) in the dataset : {}\".format(cust_demo.shape[0]))\n",
    "print(\"Total columns (features) in the dataset : {}\".format(cust_demo.shape[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Numeric Columns and Non-Numeric Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The numeric columns are : ['customer_id' 'past_3_years_bike_related_purchases' 'tenure']\n",
      "The non-numeric columns are : ['first_name' 'last_name' 'gender' 'DOB' 'job_title'\n",
      " 'job_industry_category' 'wealth_segment' 'deceased_indicator' 'default'\n",
      " 'owns_car']\n"
     ]
    }
   ],
   "source": [
    "# select numeric columns\n",
    "df_numeric = cust_demo.select_dtypes(include=[np.number])\n",
    "numeric_cols = df_numeric.columns.values\n",
    "print(\"The numeric columns are : {}\".format(numeric_cols))\n",
    "\n",
    "\n",
    "# select non-numeric columns\n",
    "df_non_numeric = cust_demo.select_dtypes(exclude=[np.number])\n",
    "non_numeric_cols = df_non_numeric.columns.values\n",
    "print(\"The non-numeric columns are : {}\".format(non_numeric_cols))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Dropping Irrelevent Columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>default is an irrelevent column. Hence it should be dropped.</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping the default column\n",
    "\n",
    "cust_demo.drop(labels={'default'}, axis=1 , inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Missing Values Check"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking for the presence of any missing values in the dataset. If missing values are present for a particular feature then depending upon the situation the feature may be either dropped (cases when a major amount of data is missing) or an appropiate value will be imputed in the feature column with missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customer_id                              0\n",
       "first_name                               0\n",
       "last_name                              125\n",
       "gender                                   0\n",
       "past_3_years_bike_related_purchases      0\n",
       "DOB                                     87\n",
       "job_title                              506\n",
       "job_industry_category                  656\n",
       "wealth_segment                           0\n",
       "deceased_indicator                       0\n",
       "owns_car                                 0\n",
       "tenure                                  87\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total number of missing values\n",
    "\n",
    "cust_demo.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customer_id                             0.000\n",
       "first_name                              0.000\n",
       "last_name                               3.125\n",
       "gender                                  0.000\n",
       "past_3_years_bike_related_purchases     0.000\n",
       "DOB                                     2.175\n",
       "job_title                              12.650\n",
       "job_industry_category                  16.400\n",
       "wealth_segment                          0.000\n",
       "deceased_indicator                      0.000\n",
       "owns_car                                0.000\n",
       "tenure                                  2.175\n",
       "dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Percentage of missing values\n",
    "\n",
    "cust_demo.isnull().mean()*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here it is observed that columns like gender, DOB, job_title, job_industry_category and tenure have missing values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 Last Name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "first_name     0\n",
       "customer_id    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for the presence of first name and customer id in records where last name is missing.\n",
    "\n",
    "cust_demo[cust_demo['last_name'].isnull()][['first_name', 'customer_id']].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since All customers have a customer_id and First name, all the customers are identifiable. <b>Hence it is okay for to not have a last name. Filling null last names with \"None\"</b>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>gender</th>\n",
       "      <th>past_3_years_bike_related_purchases</th>\n",
       "      <th>DOB</th>\n",
       "      <th>job_title</th>\n",
       "      <th>job_industry_category</th>\n",
       "      <th>wealth_segment</th>\n",
       "      <th>deceased_indicator</th>\n",
       "      <th>owns_car</th>\n",
       "      <th>tenure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Talbot</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>33</td>\n",
       "      <td>1961-10-03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IT</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
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       "      <th>66</th>\n",
       "      <td>67</td>\n",
       "      <td>Vernon</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>67</td>\n",
       "      <td>1960-06-14</td>\n",
       "      <td>Web Developer II</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>18.0</td>\n",
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       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>106</td>\n",
       "      <td>Glyn</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>54</td>\n",
       "      <td>1966-07-03</td>\n",
       "      <td>Software Test Engineer III</td>\n",
       "      <td>Health</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>139</td>\n",
       "      <td>Gar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>1</td>\n",
       "      <td>1964-07-28</td>\n",
       "      <td>Operator</td>\n",
       "      <td>Telecommunications</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>197</td>\n",
       "      <td>Avis</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>32</td>\n",
       "      <td>1977-01-27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>211</td>\n",
       "      <td>Beitris</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>6</td>\n",
       "      <td>1974-03-04</td>\n",
       "      <td>VP Marketing</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>250</td>\n",
       "      <td>Kristofer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>53</td>\n",
       "      <td>1988-04-15</td>\n",
       "      <td>Legal Assistant</td>\n",
       "      <td>Health</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>251</td>\n",
       "      <td>Mala</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>88</td>\n",
       "      <td>1977-12-24</td>\n",
       "      <td>VP Sales</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>257</td>\n",
       "      <td>Marissa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>70</td>\n",
       "      <td>1966-02-08</td>\n",
       "      <td>Sales Associate</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>275</td>\n",
       "      <td>Dud</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>7</td>\n",
       "      <td>1955-07-27</td>\n",
       "      <td>VP Sales</td>\n",
       "      <td>Health</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>356</td>\n",
       "      <td>Nichole</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>10</td>\n",
       "      <td>1975-03-30</td>\n",
       "      <td>Librarian</td>\n",
       "      <td>Entertainment</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>459</th>\n",
       "      <td>460</td>\n",
       "      <td>Illa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>1986-01-23</td>\n",
       "      <td>Electrical Engineer</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>475</td>\n",
       "      <td>Vernor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>1996-11-14</td>\n",
       "      <td>Nuclear Power Engineer</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>494</td>\n",
       "      <td>Gaby</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>33</td>\n",
       "      <td>1975-06-02</td>\n",
       "      <td>Design Engineer</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>513</th>\n",
       "      <td>514</td>\n",
       "      <td>Trent</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>9</td>\n",
       "      <td>1996-06-20</td>\n",
       "      <td>Associate Professor</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>525</th>\n",
       "      <td>526</td>\n",
       "      <td>Ardelle</td>\n",
       "      <td>NaN</td>\n",
       "      <td>U</td>\n",
       "      <td>9</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Social Worker</td>\n",
       "      <td>Health</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>656</th>\n",
       "      <td>657</td>\n",
       "      <td>Hoyt</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>66</td>\n",
       "      <td>1993-02-18</td>\n",
       "      <td>Safety Technician II</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>659</th>\n",
       "      <td>660</td>\n",
       "      <td>Stormi</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>82</td>\n",
       "      <td>1995-07-29</td>\n",
       "      <td>Geological Engineer</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>675</th>\n",
       "      <td>676</td>\n",
       "      <td>Curtis</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>51</td>\n",
       "      <td>1968-05-19</td>\n",
       "      <td>Senior Editor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>683</th>\n",
       "      <td>684</td>\n",
       "      <td>Malvin</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>88</td>\n",
       "      <td>1987-07-03</td>\n",
       "      <td>Desktop Support Technician</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>689</th>\n",
       "      <td>690</td>\n",
       "      <td>Lindsey</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>95</td>\n",
       "      <td>1987-03-27</td>\n",
       "      <td>Assistant Professor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>702</th>\n",
       "      <td>703</td>\n",
       "      <td>Ethelda</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>66</td>\n",
       "      <td>1966-10-31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Property</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>743</th>\n",
       "      <td>744</td>\n",
       "      <td>Heinrik</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>54</td>\n",
       "      <td>1977-08-30</td>\n",
       "      <td>Graphic Designer</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>779</th>\n",
       "      <td>780</td>\n",
       "      <td>Kim</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>24</td>\n",
       "      <td>1973-10-12</td>\n",
       "      <td>Professor</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>789</th>\n",
       "      <td>790</td>\n",
       "      <td>Yvonne</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>22</td>\n",
       "      <td>1968-03-24</td>\n",
       "      <td>Senior Editor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>856</th>\n",
       "      <td>857</td>\n",
       "      <td>Theo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>15</td>\n",
       "      <td>1964-08-14</td>\n",
       "      <td>General Manager</td>\n",
       "      <td>NaN</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>859</th>\n",
       "      <td>860</td>\n",
       "      <td>Ida</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>80</td>\n",
       "      <td>1980-08-12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>915</th>\n",
       "      <td>916</td>\n",
       "      <td>Joycelin</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>18</td>\n",
       "      <td>1991-06-18</td>\n",
       "      <td>Recruiter</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>926</th>\n",
       "      <td>927</td>\n",
       "      <td>Jarret</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>25</td>\n",
       "      <td>1966-02-19</td>\n",
       "      <td>Cost Accountant</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>937</th>\n",
       "      <td>938</td>\n",
       "      <td>Corabelle</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>18</td>\n",
       "      <td>1996-04-06</td>\n",
       "      <td>Technical Writer</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>7.0</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>3179</th>\n",
       "      <td>3180</td>\n",
       "      <td>Gage</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>96</td>\n",
       "      <td>1974-06-14</td>\n",
       "      <td>Business Systems Development Analyst</td>\n",
       "      <td>IT</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3187</th>\n",
       "      <td>3188</td>\n",
       "      <td>Boyd</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>94</td>\n",
       "      <td>1999-07-07</td>\n",
       "      <td>Actuary</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3199</th>\n",
       "      <td>3200</td>\n",
       "      <td>Marna</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>51</td>\n",
       "      <td>1995-11-03</td>\n",
       "      <td>Environmental Tech</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3258</th>\n",
       "      <td>3259</td>\n",
       "      <td>Rabi</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>74</td>\n",
       "      <td>1953-11-04</td>\n",
       "      <td>Quality Control Specialist</td>\n",
       "      <td>NaN</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3318</th>\n",
       "      <td>3319</td>\n",
       "      <td>Erda</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>67</td>\n",
       "      <td>1966-04-04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3320</th>\n",
       "      <td>3321</td>\n",
       "      <td>Ives</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>38</td>\n",
       "      <td>1980-05-10</td>\n",
       "      <td>Software Test Engineer I</td>\n",
       "      <td>NaN</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3323</th>\n",
       "      <td>3324</td>\n",
       "      <td>Sholom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>32</td>\n",
       "      <td>1973-07-11</td>\n",
       "      <td>Research Nurse</td>\n",
       "      <td>Health</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3324</th>\n",
       "      <td>3325</td>\n",
       "      <td>Sylas</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>80</td>\n",
       "      <td>1996-10-08</td>\n",
       "      <td>Database Administrator IV</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3346</th>\n",
       "      <td>3347</td>\n",
       "      <td>Nichols</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>99</td>\n",
       "      <td>1985-11-08</td>\n",
       "      <td>Computer Systems Analyst II</td>\n",
       "      <td>Entertainment</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3363</th>\n",
       "      <td>3364</td>\n",
       "      <td>Trueman</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>77</td>\n",
       "      <td>1993-08-19</td>\n",
       "      <td>Engineer IV</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3384</th>\n",
       "      <td>3385</td>\n",
       "      <td>Ronda</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>23</td>\n",
       "      <td>1975-02-10</td>\n",
       "      <td>Systems Administrator III</td>\n",
       "      <td>Argiculture</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3396</th>\n",
       "      <td>3397</td>\n",
       "      <td>Melisande</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>70</td>\n",
       "      <td>1985-08-19</td>\n",
       "      <td>Product Engineer</td>\n",
       "      <td>IT</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3400</th>\n",
       "      <td>3401</td>\n",
       "      <td>Cristie</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>92</td>\n",
       "      <td>1993-07-28</td>\n",
       "      <td>Tax Accountant</td>\n",
       "      <td>Telecommunications</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3442</th>\n",
       "      <td>3443</td>\n",
       "      <td>Fran</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>11</td>\n",
       "      <td>1995-04-12</td>\n",
       "      <td>Technical Writer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3444</th>\n",
       "      <td>3445</td>\n",
       "      <td>Craggy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>62</td>\n",
       "      <td>1966-06-23</td>\n",
       "      <td>Database Administrator I</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3446</th>\n",
       "      <td>3447</td>\n",
       "      <td>Linell</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>43</td>\n",
       "      <td>1977-11-23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3479</th>\n",
       "      <td>3480</td>\n",
       "      <td>Jarib</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>30</td>\n",
       "      <td>1959-06-24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3554</th>\n",
       "      <td>3555</td>\n",
       "      <td>Latashia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>96</td>\n",
       "      <td>1976-02-26</td>\n",
       "      <td>Programmer Analyst II</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3596</th>\n",
       "      <td>3597</td>\n",
       "      <td>Giorgi</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>71</td>\n",
       "      <td>1954-06-16</td>\n",
       "      <td>Analog Circuit Design manager</td>\n",
       "      <td>Property</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3623</th>\n",
       "      <td>3624</td>\n",
       "      <td>Lenka</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>54</td>\n",
       "      <td>1984-10-16</td>\n",
       "      <td>Cost Accountant</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3634</th>\n",
       "      <td>3635</td>\n",
       "      <td>Elset</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>51</td>\n",
       "      <td>1977-07-06</td>\n",
       "      <td>VP Marketing</td>\n",
       "      <td>Retail</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3650</th>\n",
       "      <td>3651</td>\n",
       "      <td>Baxie</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>91</td>\n",
       "      <td>1999-11-15</td>\n",
       "      <td>Human Resources Assistant I</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3717</th>\n",
       "      <td>3718</td>\n",
       "      <td>Damiano</td>\n",
       "      <td>NaN</td>\n",
       "      <td>U</td>\n",
       "      <td>22</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Geologist IV</td>\n",
       "      <td>IT</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3755</th>\n",
       "      <td>3756</td>\n",
       "      <td>Barry</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>22</td>\n",
       "      <td>1977-07-08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3816</th>\n",
       "      <td>3817</td>\n",
       "      <td>Tuckie</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>65</td>\n",
       "      <td>1957-05-02</td>\n",
       "      <td>VP Product Management</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3884</th>\n",
       "      <td>3885</td>\n",
       "      <td>Asher</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>55</td>\n",
       "      <td>1978-06-17</td>\n",
       "      <td>Actuary</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3915</th>\n",
       "      <td>3916</td>\n",
       "      <td>Myrtia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>31</td>\n",
       "      <td>1958-10-17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3926</th>\n",
       "      <td>3927</td>\n",
       "      <td>Conway</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>29</td>\n",
       "      <td>1978-01-07</td>\n",
       "      <td>Electrical Engineer</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3961</th>\n",
       "      <td>3962</td>\n",
       "      <td>Benoit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>17</td>\n",
       "      <td>1977-10-06</td>\n",
       "      <td>Project Manager</td>\n",
       "      <td>Argiculture</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3998</th>\n",
       "      <td>3999</td>\n",
       "      <td>Patrizius</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>11</td>\n",
       "      <td>1973-10-24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>125 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      customer_id first_name last_name  gender  \\\n",
       "3               4     Talbot       NaN    Male   \n",
       "66             67     Vernon       NaN    Male   \n",
       "105           106       Glyn       NaN    Male   \n",
       "138           139        Gar       NaN    Male   \n",
       "196           197       Avis       NaN  Female   \n",
       "210           211    Beitris       NaN  Female   \n",
       "249           250  Kristofer       NaN    Male   \n",
       "250           251       Mala       NaN  Female   \n",
       "256           257    Marissa       NaN  Female   \n",
       "274           275        Dud       NaN    Male   \n",
       "355           356    Nichole       NaN  Female   \n",
       "459           460       Illa       NaN  Female   \n",
       "474           475     Vernor       NaN    Male   \n",
       "493           494       Gaby       NaN    Male   \n",
       "513           514      Trent       NaN    Male   \n",
       "525           526    Ardelle       NaN       U   \n",
       "656           657       Hoyt       NaN    Male   \n",
       "659           660     Stormi       NaN  Female   \n",
       "675           676     Curtis       NaN    Male   \n",
       "683           684     Malvin       NaN    Male   \n",
       "689           690    Lindsey       NaN    Male   \n",
       "702           703    Ethelda       NaN  Female   \n",
       "743           744    Heinrik       NaN    Male   \n",
       "779           780        Kim       NaN  Female   \n",
       "789           790     Yvonne       NaN  Female   \n",
       "856           857       Theo       NaN  Female   \n",
       "859           860        Ida       NaN  Female   \n",
       "915           916   Joycelin       NaN  Female   \n",
       "926           927     Jarret       NaN    Male   \n",
       "937           938  Corabelle       NaN  Female   \n",
       "...           ...        ...       ...     ...   \n",
       "3179         3180       Gage       NaN    Male   \n",
       "3187         3188       Boyd       NaN    Male   \n",
       "3199         3200      Marna       NaN  Female   \n",
       "3258         3259       Rabi       NaN    Male   \n",
       "3318         3319       Erda       NaN  Female   \n",
       "3320         3321       Ives       NaN    Male   \n",
       "3323         3324     Sholom       NaN    Male   \n",
       "3324         3325      Sylas       NaN    Male   \n",
       "3346         3347    Nichols       NaN    Male   \n",
       "3363         3364    Trueman       NaN    Male   \n",
       "3384         3385      Ronda       NaN  Female   \n",
       "3396         3397  Melisande       NaN  Female   \n",
       "3400         3401    Cristie       NaN  Female   \n",
       "3442         3443       Fran       NaN    Male   \n",
       "3444         3445     Craggy       NaN    Male   \n",
       "3446         3447     Linell       NaN  Female   \n",
       "3479         3480      Jarib       NaN    Male   \n",
       "3554         3555   Latashia       NaN  Female   \n",
       "3596         3597     Giorgi       NaN    Male   \n",
       "3623         3624      Lenka       NaN  Female   \n",
       "3634         3635      Elset       NaN  Female   \n",
       "3650         3651      Baxie       NaN    Male   \n",
       "3717         3718    Damiano       NaN       U   \n",
       "3755         3756      Barry       NaN    Male   \n",
       "3816         3817     Tuckie       NaN    Male   \n",
       "3884         3885      Asher       NaN    Male   \n",
       "3915         3916     Myrtia       NaN  Female   \n",
       "3926         3927     Conway       NaN    Male   \n",
       "3961         3962     Benoit       NaN    Male   \n",
       "3998         3999  Patrizius       NaN    Male   \n",
       "\n",
       "      past_3_years_bike_related_purchases        DOB  \\\n",
       "3                                      33 1961-10-03   \n",
       "66                                     67 1960-06-14   \n",
       "105                                    54 1966-07-03   \n",
       "138                                     1 1964-07-28   \n",
       "196                                    32 1977-01-27   \n",
       "210                                     6 1974-03-04   \n",
       "249                                    53 1988-04-15   \n",
       "250                                    88 1977-12-24   \n",
       "256                                    70 1966-02-08   \n",
       "274                                     7 1955-07-27   \n",
       "355                                    10 1975-03-30   \n",
       "459                                     0 1986-01-23   \n",
       "474                                     0 1996-11-14   \n",
       "493                                    33 1975-06-02   \n",
       "513                                     9 1996-06-20   \n",
       "525                                     9        NaT   \n",
       "656                                    66 1993-02-18   \n",
       "659                                    82 1995-07-29   \n",
       "675                                    51 1968-05-19   \n",
       "683                                    88 1987-07-03   \n",
       "689                                    95 1987-03-27   \n",
       "702                                    66 1966-10-31   \n",
       "743                                    54 1977-08-30   \n",
       "779                                    24 1973-10-12   \n",
       "789                                    22 1968-03-24   \n",
       "856                                    15 1964-08-14   \n",
       "859                                    80 1980-08-12   \n",
       "915                                    18 1991-06-18   \n",
       "926                                    25 1966-02-19   \n",
       "937                                    18 1996-04-06   \n",
       "...                                   ...        ...   \n",
       "3179                                   96 1974-06-14   \n",
       "3187                                   94 1999-07-07   \n",
       "3199                                   51 1995-11-03   \n",
       "3258                                   74 1953-11-04   \n",
       "3318                                   67 1966-04-04   \n",
       "3320                                   38 1980-05-10   \n",
       "3323                                   32 1973-07-11   \n",
       "3324                                   80 1996-10-08   \n",
       "3346                                   99 1985-11-08   \n",
       "3363                                   77 1993-08-19   \n",
       "3384                                   23 1975-02-10   \n",
       "3396                                   70 1985-08-19   \n",
       "3400                                   92 1993-07-28   \n",
       "3442                                   11 1995-04-12   \n",
       "3444                                   62 1966-06-23   \n",
       "3446                                   43 1977-11-23   \n",
       "3479                                   30 1959-06-24   \n",
       "3554                                   96 1976-02-26   \n",
       "3596                                   71 1954-06-16   \n",
       "3623                                   54 1984-10-16   \n",
       "3634                                   51 1977-07-06   \n",
       "3650                                   91 1999-11-15   \n",
       "3717                                   22        NaT   \n",
       "3755                                   22 1977-07-08   \n",
       "3816                                   65 1957-05-02   \n",
       "3884                                   55 1978-06-17   \n",
       "3915                                   31 1958-10-17   \n",
       "3926                                   29 1978-01-07   \n",
       "3961                                   17 1977-10-06   \n",
       "3998                                   11 1973-10-24   \n",
       "\n",
       "                                 job_title job_industry_category  \\\n",
       "3                                      NaN                    IT   \n",
       "66                        Web Developer II                Retail   \n",
       "105             Software Test Engineer III                Health   \n",
       "138                               Operator    Telecommunications   \n",
       "196                                    NaN                   NaN   \n",
       "210                           VP Marketing         Manufacturing   \n",
       "249                        Legal Assistant                Health   \n",
       "250                               VP Sales    Financial Services   \n",
       "256                        Sales Associate         Manufacturing   \n",
       "274                               VP Sales                Health   \n",
       "355                              Librarian         Entertainment   \n",
       "459                    Electrical Engineer         Manufacturing   \n",
       "474                 Nuclear Power Engineer         Manufacturing   \n",
       "493                        Design Engineer         Manufacturing   \n",
       "513                    Associate Professor    Financial Services   \n",
       "525                          Social Worker                Health   \n",
       "656                   Safety Technician II         Manufacturing   \n",
       "659                    Geological Engineer         Manufacturing   \n",
       "675                          Senior Editor                   NaN   \n",
       "683             Desktop Support Technician    Financial Services   \n",
       "689                    Assistant Professor                   NaN   \n",
       "702                                    NaN              Property   \n",
       "743                       Graphic Designer         Manufacturing   \n",
       "779                              Professor    Financial Services   \n",
       "789                          Senior Editor                   NaN   \n",
       "856                        General Manager                   NaN   \n",
       "859                                    NaN                   NaN   \n",
       "915                              Recruiter                   NaN   \n",
       "926                        Cost Accountant    Financial Services   \n",
       "937                       Technical Writer                Retail   \n",
       "...                                    ...                   ...   \n",
       "3179  Business Systems Development Analyst                    IT   \n",
       "3187                               Actuary    Financial Services   \n",
       "3199                    Environmental Tech         Manufacturing   \n",
       "3258            Quality Control Specialist                   NaN   \n",
       "3318                                   NaN    Financial Services   \n",
       "3320              Software Test Engineer I                   NaN   \n",
       "3323                        Research Nurse                Health   \n",
       "3324             Database Administrator IV         Manufacturing   \n",
       "3346           Computer Systems Analyst II         Entertainment   \n",
       "3363                           Engineer IV         Manufacturing   \n",
       "3384             Systems Administrator III           Argiculture   \n",
       "3396                      Product Engineer                    IT   \n",
       "3400                        Tax Accountant    Telecommunications   \n",
       "3442                      Technical Writer                   NaN   \n",
       "3444              Database Administrator I    Financial Services   \n",
       "3446                                   NaN    Financial Services   \n",
       "3479                                   NaN                   NaN   \n",
       "3554                 Programmer Analyst II         Manufacturing   \n",
       "3596         Analog Circuit Design manager              Property   \n",
       "3623                       Cost Accountant    Financial Services   \n",
       "3634                          VP Marketing                Retail   \n",
       "3650           Human Resources Assistant I         Manufacturing   \n",
       "3717                          Geologist IV                    IT   \n",
       "3755                                   NaN                   NaN   \n",
       "3816                 VP Product Management         Manufacturing   \n",
       "3884                               Actuary    Financial Services   \n",
       "3915                                   NaN                Retail   \n",
       "3926                   Electrical Engineer         Manufacturing   \n",
       "3961                       Project Manager           Argiculture   \n",
       "3998                                   NaN         Manufacturing   \n",
       "\n",
       "         wealth_segment deceased_indicator owns_car  tenure  \n",
       "3         Mass Customer                  N       No     7.0  \n",
       "66        Mass Customer                  N       No    18.0  \n",
       "105      High Net Worth                  N      Yes    18.0  \n",
       "138   Affluent Customer                  N       No     4.0  \n",
       "196      High Net Worth                  N       No     5.0  \n",
       "210       Mass Customer                  N      Yes     5.0  \n",
       "249       Mass Customer                  N      Yes    13.0  \n",
       "250   Affluent Customer                  N      Yes    10.0  \n",
       "256   Affluent Customer                  N      Yes    19.0  \n",
       "274      High Net Worth                  N       No    13.0  \n",
       "355      High Net Worth                  N       No     5.0  \n",
       "459   Affluent Customer                  N      Yes    16.0  \n",
       "474   Affluent Customer                  N       No     1.0  \n",
       "493       Mass Customer                  N       No     9.0  \n",
       "513       Mass Customer                  N      Yes     4.0  \n",
       "525       Mass Customer                  N      Yes     NaN  \n",
       "656   Affluent Customer                  N       No    10.0  \n",
       "659      High Net Worth                  N       No     6.0  \n",
       "675      High Net Worth                  N      Yes    14.0  \n",
       "683       Mass Customer                  N       No    14.0  \n",
       "689   Affluent Customer                  N      Yes    17.0  \n",
       "702       Mass Customer                  N       No    15.0  \n",
       "743   Affluent Customer                  N      Yes    14.0  \n",
       "779       Mass Customer                  N       No    20.0  \n",
       "789   Affluent Customer                  N       No    15.0  \n",
       "856      High Net Worth                  N       No     4.0  \n",
       "859      High Net Worth                  N      Yes     7.0  \n",
       "915   Affluent Customer                  N       No     8.0  \n",
       "926       Mass Customer                  N      Yes    18.0  \n",
       "937       Mass Customer                  N       No     7.0  \n",
       "...                 ...                ...      ...     ...  \n",
       "3179      Mass Customer                  N      Yes    19.0  \n",
       "3187      Mass Customer                  N       No     1.0  \n",
       "3199      Mass Customer                  N       No     1.0  \n",
       "3258     High Net Worth                  N       No    10.0  \n",
       "3318  Affluent Customer                  N      Yes    19.0  \n",
       "3320     High Net Worth                  N      Yes    14.0  \n",
       "3323      Mass Customer                  N      Yes    10.0  \n",
       "3324     High Net Worth                  N       No     1.0  \n",
       "3346     High Net Worth                  N      Yes    18.0  \n",
       "3363      Mass Customer                  N      Yes     3.0  \n",
       "3384      Mass Customer                  N       No     9.0  \n",
       "3396      Mass Customer                  N       No    11.0  \n",
       "3400      Mass Customer                  N      Yes     4.0  \n",
       "3442      Mass Customer                  N      Yes     5.0  \n",
       "3444  Affluent Customer                  N      Yes    11.0  \n",
       "3446     High Net Worth                  N       No    17.0  \n",
       "3479      Mass Customer                  N       No    20.0  \n",
       "3554      Mass Customer                  N       No    21.0  \n",
       "3596  Affluent Customer                  N      Yes    16.0  \n",
       "3623      Mass Customer                  N      Yes     7.0  \n",
       "3634     High Net Worth                  N       No     9.0  \n",
       "3650      Mass Customer                  N       No     2.0  \n",
       "3717      Mass Customer                  N      Yes     NaN  \n",
       "3755  Affluent Customer                  N       No    10.0  \n",
       "3816     High Net Worth                  N       No    13.0  \n",
       "3884      Mass Customer                  N      Yes     8.0  \n",
       "3915  Affluent Customer                  N      Yes    17.0  \n",
       "3926      Mass Customer                  N      Yes     7.0  \n",
       "3961     High Net Worth                  N      Yes    14.0  \n",
       "3998  Affluent Customer                  N      Yes    10.0  \n",
       "\n",
       "[125 rows x 12 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fetching records where last name is missing.\n",
    "\n",
    "cust_demo[cust_demo['last_name'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "cust_demo['last_name'].fillna('None',axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cust_demo['last_name'].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Currently there are no missing values for last name column."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Date of Birth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>customer_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>gender</th>\n",
       "      <th>past_3_years_bike_related_purchases</th>\n",
       "      <th>DOB</th>\n",
       "      <th>job_title</th>\n",
       "      <th>job_industry_category</th>\n",
       "      <th>wealth_segment</th>\n",
       "      <th>deceased_indicator</th>\n",
       "      <th>owns_car</th>\n",
       "      <th>tenure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>144</td>\n",
       "      <td>Jory</td>\n",
       "      <td>Barrabeale</td>\n",
       "      <td>U</td>\n",
       "      <td>71</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Environmental Tech</td>\n",
       "      <td>IT</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>168</td>\n",
       "      <td>Reggie</td>\n",
       "      <td>Broggetti</td>\n",
       "      <td>U</td>\n",
       "      <td>8</td>\n",
       "      <td>NaT</td>\n",
       "      <td>General Manager</td>\n",
       "      <td>IT</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>267</td>\n",
       "      <td>Edgar</td>\n",
       "      <td>Buckler</td>\n",
       "      <td>U</td>\n",
       "      <td>53</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IT</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>290</td>\n",
       "      <td>Giorgio</td>\n",
       "      <td>Kevane</td>\n",
       "      <td>U</td>\n",
       "      <td>42</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Senior Sales Associate</td>\n",
       "      <td>IT</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>450</th>\n",
       "      <td>451</td>\n",
       "      <td>Marlow</td>\n",
       "      <td>Flowerdew</td>\n",
       "      <td>U</td>\n",
       "      <td>37</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Quality Control Specialist</td>\n",
       "      <td>IT</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>452</th>\n",
       "      <td>453</td>\n",
       "      <td>Cornelius</td>\n",
       "      <td>Yarmouth</td>\n",
       "      <td>U</td>\n",
       "      <td>81</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Assistant Professor</td>\n",
       "      <td>IT</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453</th>\n",
       "      <td>454</td>\n",
       "      <td>Eugenie</td>\n",
       "      <td>Domenc</td>\n",
       "      <td>U</td>\n",
       "      <td>58</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Research Nurse</td>\n",
       "      <td>Health</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>480</td>\n",
       "      <td>Darelle</td>\n",
       "      <td>Ive</td>\n",
       "      <td>U</td>\n",
       "      <td>67</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Registered Nurse</td>\n",
       "      <td>Health</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512</th>\n",
       "      <td>513</td>\n",
       "      <td>Kienan</td>\n",
       "      <td>Soar</td>\n",
       "      <td>U</td>\n",
       "      <td>30</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Tax Accountant</td>\n",
       "      <td>IT</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>525</th>\n",
       "      <td>526</td>\n",
       "      <td>Ardelle</td>\n",
       "      <td>None</td>\n",
       "      <td>U</td>\n",
       "      <td>9</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Social Worker</td>\n",
       "      <td>Health</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>547</th>\n",
       "      <td>548</td>\n",
       "      <td>Georgie</td>\n",
       "      <td>Cudbertson</td>\n",
       "      <td>U</td>\n",
       "      <td>84</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IT</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>581</th>\n",
       "      <td>582</td>\n",
       "      <td>Rhoda</td>\n",
       "      <td>McKeown</td>\n",
       "      <td>U</td>\n",
       "      <td>21</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Staff Scientist</td>\n",
       "      <td>IT</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>598</th>\n",
       "      <td>599</td>\n",
       "      <td>Ernestus</td>\n",
       "      <td>Cruden</td>\n",
       "      <td>U</td>\n",
       "      <td>48</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Senior Financial Analyst</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679</th>\n",
       "      <td>680</td>\n",
       "      <td>Gay</td>\n",
       "      <td>Pickersgill</td>\n",
       "      <td>U</td>\n",
       "      <td>22</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IT</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>684</th>\n",
       "      <td>685</td>\n",
       "      <td>Booth</td>\n",
       "      <td>Birkin</td>\n",
       "      <td>U</td>\n",
       "      <td>28</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Senior Developer</td>\n",
       "      <td>IT</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>798</th>\n",
       "      <td>799</td>\n",
       "      <td>Harland</td>\n",
       "      <td>Spilisy</td>\n",
       "      <td>U</td>\n",
       "      <td>39</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Programmer I</td>\n",
       "      <td>IT</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>838</th>\n",
       "      <td>839</td>\n",
       "      <td>Charis</td>\n",
       "      <td>Greaves</td>\n",
       "      <td>U</td>\n",
       "      <td>14</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Structural Analysis Engineer</td>\n",
       "      <td>IT</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>883</td>\n",
       "      <td>Lolita</td>\n",
       "      <td>Bennie</td>\n",
       "      <td>U</td>\n",
       "      <td>73</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Recruiter</td>\n",
       "      <td>IT</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891</th>\n",
       "      <td>892</td>\n",
       "      <td>Conroy</td>\n",
       "      <td>Healy</td>\n",
       "      <td>U</td>\n",
       "      <td>22</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Office Assistant II</td>\n",
       "      <td>IT</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>949</th>\n",
       "      <td>950</td>\n",
       "      <td>Bret</td>\n",
       "      <td>Ivakhnov</td>\n",
       "      <td>U</td>\n",
       "      <td>24</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Recruiter</td>\n",
       "      <td>IT</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>974</th>\n",
       "      <td>975</td>\n",
       "      <td>Goldarina</td>\n",
       "      <td>Rzehorz</td>\n",
       "      <td>U</td>\n",
       "      <td>26</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Automation Specialist IV</td>\n",
       "      <td>IT</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>982</th>\n",
       "      <td>983</td>\n",
       "      <td>Shaylyn</td>\n",
       "      <td>Riggs</td>\n",
       "      <td>U</td>\n",
       "      <td>49</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IT</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>996</td>\n",
       "      <td>Aura</td>\n",
       "      <td>Bemlott</td>\n",
       "      <td>U</td>\n",
       "      <td>67</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Assistant Manager</td>\n",
       "      <td>IT</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1037</th>\n",
       "      <td>1038</td>\n",
       "      <td>Fraser</td>\n",
       "      <td>Acome</td>\n",
       "      <td>U</td>\n",
       "      <td>57</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Engineer I</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1043</th>\n",
       "      <td>1044</td>\n",
       "      <td>Frederico</td>\n",
       "      <td>Whilder</td>\n",
       "      <td>U</td>\n",
       "      <td>4</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Food Chemist</td>\n",
       "      <td>Health</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1081</th>\n",
       "      <td>1082</td>\n",
       "      <td>Guinevere</td>\n",
       "      <td>Kelby</td>\n",
       "      <td>U</td>\n",
       "      <td>90</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Financial Analyst</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1173</th>\n",
       "      <td>1174</td>\n",
       "      <td>Shellysheldon</td>\n",
       "      <td>Gooderridge</td>\n",
       "      <td>U</td>\n",
       "      <td>9</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Executive Secretary</td>\n",
       "      <td>IT</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1209</th>\n",
       "      <td>1210</td>\n",
       "      <td>Shandie</td>\n",
       "      <td>Sprigg</td>\n",
       "      <td>U</td>\n",
       "      <td>81</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Programmer II</td>\n",
       "      <td>IT</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1243</th>\n",
       "      <td>1244</td>\n",
       "      <td>Glenn</td>\n",
       "      <td>Tinham</td>\n",
       "      <td>U</td>\n",
       "      <td>80</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Financial Analyst</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1350</th>\n",
       "      <td>1351</td>\n",
       "      <td>Lorettalorna</td>\n",
       "      <td>None</td>\n",
       "      <td>U</td>\n",
       "      <td>32</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Office Assistant IV</td>\n",
       "      <td>IT</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</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>2695</th>\n",
       "      <td>2696</td>\n",
       "      <td>Isabelle</td>\n",
       "      <td>Bursnoll</td>\n",
       "      <td>U</td>\n",
       "      <td>42</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Social Worker</td>\n",
       "      <td>Health</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2696</th>\n",
       "      <td>2697</td>\n",
       "      <td>Klarika</td>\n",
       "      <td>Yerby</td>\n",
       "      <td>U</td>\n",
       "      <td>70</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Legal Assistant</td>\n",
       "      <td>IT</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2853</th>\n",
       "      <td>2854</td>\n",
       "      <td>Vikky</td>\n",
       "      <td>Dyde</td>\n",
       "      <td>U</td>\n",
       "      <td>49</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Project Manager</td>\n",
       "      <td>IT</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2919</th>\n",
       "      <td>2920</td>\n",
       "      <td>Casar</td>\n",
       "      <td>Ritchley</td>\n",
       "      <td>U</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Business Systems Development Analyst</td>\n",
       "      <td>IT</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2962</th>\n",
       "      <td>2963</td>\n",
       "      <td>Christin</td>\n",
       "      <td>Fricke</td>\n",
       "      <td>U</td>\n",
       "      <td>17</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Safety Technician II</td>\n",
       "      <td>IT</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2998</th>\n",
       "      <td>2999</td>\n",
       "      <td>Rinaldo</td>\n",
       "      <td>Diggin</td>\n",
       "      <td>U</td>\n",
       "      <td>28</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Business Systems Development Analyst</td>\n",
       "      <td>IT</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3011</th>\n",
       "      <td>3012</td>\n",
       "      <td>Devland</td>\n",
       "      <td>Probart</td>\n",
       "      <td>U</td>\n",
       "      <td>81</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Technical Writer</td>\n",
       "      <td>IT</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3085</th>\n",
       "      <td>3086</td>\n",
       "      <td>Pieter</td>\n",
       "      <td>Gadesby</td>\n",
       "      <td>U</td>\n",
       "      <td>18</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Biostatistician I</td>\n",
       "      <td>IT</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3150</th>\n",
       "      <td>3151</td>\n",
       "      <td>Thorn</td>\n",
       "      <td>Choffin</td>\n",
       "      <td>U</td>\n",
       "      <td>20</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Senior Developer</td>\n",
       "      <td>IT</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3221</th>\n",
       "      <td>3222</td>\n",
       "      <td>Caralie</td>\n",
       "      <td>Sellors</td>\n",
       "      <td>U</td>\n",
       "      <td>40</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Senior Editor</td>\n",
       "      <td>IT</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3222</th>\n",
       "      <td>3223</td>\n",
       "      <td>Tiffi</td>\n",
       "      <td>Wortt</td>\n",
       "      <td>U</td>\n",
       "      <td>44</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Database Administrator III</td>\n",
       "      <td>IT</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3254</th>\n",
       "      <td>3255</td>\n",
       "      <td>Sutherlan</td>\n",
       "      <td>Truin</td>\n",
       "      <td>U</td>\n",
       "      <td>47</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Engineer IV</td>\n",
       "      <td>IT</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3287</th>\n",
       "      <td>3288</td>\n",
       "      <td>Fair</td>\n",
       "      <td>Dewen</td>\n",
       "      <td>U</td>\n",
       "      <td>47</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Engineer III</td>\n",
       "      <td>IT</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3297</th>\n",
       "      <td>3298</td>\n",
       "      <td>Christine</td>\n",
       "      <td>Baignard</td>\n",
       "      <td>U</td>\n",
       "      <td>1</td>\n",
       "      <td>NaT</td>\n",
       "      <td>VP Quality Control</td>\n",
       "      <td>IT</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3311</th>\n",
       "      <td>3312</td>\n",
       "      <td>Franky</td>\n",
       "      <td>Nanninini</td>\n",
       "      <td>U</td>\n",
       "      <td>49</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Administrative Officer</td>\n",
       "      <td>IT</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3321</th>\n",
       "      <td>3322</td>\n",
       "      <td>Hew</td>\n",
       "      <td>Sworder</td>\n",
       "      <td>U</td>\n",
       "      <td>24</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Financial Analyst</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3342</th>\n",
       "      <td>3343</td>\n",
       "      <td>Cristabel</td>\n",
       "      <td>Bim</td>\n",
       "      <td>U</td>\n",
       "      <td>3</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Recruiter</td>\n",
       "      <td>IT</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3364</th>\n",
       "      <td>3365</td>\n",
       "      <td>Karlens</td>\n",
       "      <td>Chaffyn</td>\n",
       "      <td>U</td>\n",
       "      <td>29</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Engineer III</td>\n",
       "      <td>IT</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3472</th>\n",
       "      <td>3473</td>\n",
       "      <td>Sanderson</td>\n",
       "      <td>Alloway</td>\n",
       "      <td>U</td>\n",
       "      <td>34</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Analog Circuit Design manager</td>\n",
       "      <td>IT</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3509</th>\n",
       "      <td>3510</td>\n",
       "      <td>Jemima</td>\n",
       "      <td>Izaac</td>\n",
       "      <td>U</td>\n",
       "      <td>48</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Safety Technician II</td>\n",
       "      <td>IT</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3512</th>\n",
       "      <td>3513</td>\n",
       "      <td>Enriqueta</td>\n",
       "      <td>Waterhowse</td>\n",
       "      <td>U</td>\n",
       "      <td>80</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Internal Auditor</td>\n",
       "      <td>IT</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3564</th>\n",
       "      <td>3565</td>\n",
       "      <td>Charyl</td>\n",
       "      <td>Pottiphar</td>\n",
       "      <td>U</td>\n",
       "      <td>14</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Structural Engineer</td>\n",
       "      <td>IT</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3653</th>\n",
       "      <td>3654</td>\n",
       "      <td>Kenyon</td>\n",
       "      <td>Paddefield</td>\n",
       "      <td>U</td>\n",
       "      <td>78</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Electrical Engineer</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3717</th>\n",
       "      <td>3718</td>\n",
       "      <td>Damiano</td>\n",
       "      <td>None</td>\n",
       "      <td>U</td>\n",
       "      <td>22</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Geologist IV</td>\n",
       "      <td>IT</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3726</th>\n",
       "      <td>3727</td>\n",
       "      <td>Eba</td>\n",
       "      <td>Youle</td>\n",
       "      <td>U</td>\n",
       "      <td>65</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Assistant Professor</td>\n",
       "      <td>IT</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3778</th>\n",
       "      <td>3779</td>\n",
       "      <td>Ulick</td>\n",
       "      <td>Daspar</td>\n",
       "      <td>U</td>\n",
       "      <td>68</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IT</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3882</th>\n",
       "      <td>3883</td>\n",
       "      <td>Nissa</td>\n",
       "      <td>Conrad</td>\n",
       "      <td>U</td>\n",
       "      <td>35</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Legal Assistant</td>\n",
       "      <td>IT</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3930</th>\n",
       "      <td>3931</td>\n",
       "      <td>Kylie</td>\n",
       "      <td>Epine</td>\n",
       "      <td>U</td>\n",
       "      <td>19</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IT</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3934</th>\n",
       "      <td>3935</td>\n",
       "      <td>Teodor</td>\n",
       "      <td>Alfonsini</td>\n",
       "      <td>U</td>\n",
       "      <td>72</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IT</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3997</th>\n",
       "      <td>3998</td>\n",
       "      <td>Sarene</td>\n",
       "      <td>Woolley</td>\n",
       "      <td>U</td>\n",
       "      <td>60</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Assistant Manager</td>\n",
       "      <td>IT</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>87 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      customer_id     first_name    last_name gender  \\\n",
       "143           144           Jory   Barrabeale      U   \n",
       "167           168         Reggie    Broggetti      U   \n",
       "266           267          Edgar      Buckler      U   \n",
       "289           290        Giorgio       Kevane      U   \n",
       "450           451         Marlow    Flowerdew      U   \n",
       "452           453      Cornelius     Yarmouth      U   \n",
       "453           454        Eugenie       Domenc      U   \n",
       "479           480        Darelle          Ive      U   \n",
       "512           513         Kienan         Soar      U   \n",
       "525           526        Ardelle         None      U   \n",
       "547           548        Georgie   Cudbertson      U   \n",
       "581           582          Rhoda      McKeown      U   \n",
       "598           599       Ernestus       Cruden      U   \n",
       "679           680            Gay  Pickersgill      U   \n",
       "684           685          Booth       Birkin      U   \n",
       "798           799        Harland      Spilisy      U   \n",
       "838           839         Charis      Greaves      U   \n",
       "882           883         Lolita       Bennie      U   \n",
       "891           892         Conroy        Healy      U   \n",
       "949           950           Bret     Ivakhnov      U   \n",
       "974           975      Goldarina      Rzehorz      U   \n",
       "982           983        Shaylyn        Riggs      U   \n",
       "995           996           Aura      Bemlott      U   \n",
       "1037         1038         Fraser        Acome      U   \n",
       "1043         1044      Frederico      Whilder      U   \n",
       "1081         1082      Guinevere        Kelby      U   \n",
       "1173         1174  Shellysheldon  Gooderridge      U   \n",
       "1209         1210        Shandie       Sprigg      U   \n",
       "1243         1244          Glenn       Tinham      U   \n",
       "1350         1351   Lorettalorna         None      U   \n",
       "...           ...            ...          ...    ...   \n",
       "2695         2696       Isabelle     Bursnoll      U   \n",
       "2696         2697        Klarika        Yerby      U   \n",
       "2853         2854          Vikky         Dyde      U   \n",
       "2919         2920          Casar     Ritchley      U   \n",
       "2962         2963       Christin       Fricke      U   \n",
       "2998         2999        Rinaldo       Diggin      U   \n",
       "3011         3012        Devland      Probart      U   \n",
       "3085         3086         Pieter      Gadesby      U   \n",
       "3150         3151          Thorn      Choffin      U   \n",
       "3221         3222        Caralie      Sellors      U   \n",
       "3222         3223          Tiffi        Wortt      U   \n",
       "3254         3255      Sutherlan        Truin      U   \n",
       "3287         3288           Fair        Dewen      U   \n",
       "3297         3298      Christine     Baignard      U   \n",
       "3311         3312         Franky    Nanninini      U   \n",
       "3321         3322            Hew      Sworder      U   \n",
       "3342         3343      Cristabel          Bim      U   \n",
       "3364         3365        Karlens      Chaffyn      U   \n",
       "3472         3473      Sanderson      Alloway      U   \n",
       "3509         3510         Jemima        Izaac      U   \n",
       "3512         3513      Enriqueta   Waterhowse      U   \n",
       "3564         3565         Charyl    Pottiphar      U   \n",
       "3653         3654         Kenyon   Paddefield      U   \n",
       "3717         3718        Damiano         None      U   \n",
       "3726         3727            Eba        Youle      U   \n",
       "3778         3779          Ulick       Daspar      U   \n",
       "3882         3883          Nissa       Conrad      U   \n",
       "3930         3931          Kylie        Epine      U   \n",
       "3934         3935         Teodor    Alfonsini      U   \n",
       "3997         3998         Sarene      Woolley      U   \n",
       "\n",
       "      past_3_years_bike_related_purchases DOB  \\\n",
       "143                                    71 NaT   \n",
       "167                                     8 NaT   \n",
       "266                                    53 NaT   \n",
       "289                                    42 NaT   \n",
       "450                                    37 NaT   \n",
       "452                                    81 NaT   \n",
       "453                                    58 NaT   \n",
       "479                                    67 NaT   \n",
       "512                                    30 NaT   \n",
       "525                                     9 NaT   \n",
       "547                                    84 NaT   \n",
       "581                                    21 NaT   \n",
       "598                                    48 NaT   \n",
       "679                                    22 NaT   \n",
       "684                                    28 NaT   \n",
       "798                                    39 NaT   \n",
       "838                                    14 NaT   \n",
       "882                                    73 NaT   \n",
       "891                                    22 NaT   \n",
       "949                                    24 NaT   \n",
       "974                                    26 NaT   \n",
       "982                                    49 NaT   \n",
       "995                                    67 NaT   \n",
       "1037                                   57 NaT   \n",
       "1043                                    4 NaT   \n",
       "1081                                   90 NaT   \n",
       "1173                                    9 NaT   \n",
       "1209                                   81 NaT   \n",
       "1243                                   80 NaT   \n",
       "1350                                   32 NaT   \n",
       "...                                   ...  ..   \n",
       "2695                                   42 NaT   \n",
       "2696                                   70 NaT   \n",
       "2853                                   49 NaT   \n",
       "2919                                    0 NaT   \n",
       "2962                                   17 NaT   \n",
       "2998                                   28 NaT   \n",
       "3011                                   81 NaT   \n",
       "3085                                   18 NaT   \n",
       "3150                                   20 NaT   \n",
       "3221                                   40 NaT   \n",
       "3222                                   44 NaT   \n",
       "3254                                   47 NaT   \n",
       "3287                                   47 NaT   \n",
       "3297                                    1 NaT   \n",
       "3311                                   49 NaT   \n",
       "3321                                   24 NaT   \n",
       "3342                                    3 NaT   \n",
       "3364                                   29 NaT   \n",
       "3472                                   34 NaT   \n",
       "3509                                   48 NaT   \n",
       "3512                                   80 NaT   \n",
       "3564                                   14 NaT   \n",
       "3653                                   78 NaT   \n",
       "3717                                   22 NaT   \n",
       "3726                                   65 NaT   \n",
       "3778                                   68 NaT   \n",
       "3882                                   35 NaT   \n",
       "3930                                   19 NaT   \n",
       "3934                                   72 NaT   \n",
       "3997                                   60 NaT   \n",
       "\n",
       "                                 job_title job_industry_category  \\\n",
       "143                     Environmental Tech                    IT   \n",
       "167                        General Manager                    IT   \n",
       "266                                    NaN                    IT   \n",
       "289                 Senior Sales Associate                    IT   \n",
       "450             Quality Control Specialist                    IT   \n",
       "452                    Assistant Professor                    IT   \n",
       "453                         Research Nurse                Health   \n",
       "479                       Registered Nurse                Health   \n",
       "512                         Tax Accountant                    IT   \n",
       "525                          Social Worker                Health   \n",
       "547                                    NaN                    IT   \n",
       "581                        Staff Scientist                    IT   \n",
       "598               Senior Financial Analyst    Financial Services   \n",
       "679                                    NaN                    IT   \n",
       "684                       Senior Developer                    IT   \n",
       "798                           Programmer I                    IT   \n",
       "838           Structural Analysis Engineer                    IT   \n",
       "882                              Recruiter                    IT   \n",
       "891                    Office Assistant II                    IT   \n",
       "949                              Recruiter                    IT   \n",
       "974               Automation Specialist IV                    IT   \n",
       "982                                    NaN                    IT   \n",
       "995                      Assistant Manager                    IT   \n",
       "1037                            Engineer I         Manufacturing   \n",
       "1043                          Food Chemist                Health   \n",
       "1081                     Financial Analyst    Financial Services   \n",
       "1173                   Executive Secretary                    IT   \n",
       "1209                         Programmer II                    IT   \n",
       "1243                     Financial Analyst    Financial Services   \n",
       "1350                   Office Assistant IV                    IT   \n",
       "...                                    ...                   ...   \n",
       "2695                         Social Worker                Health   \n",
       "2696                       Legal Assistant                    IT   \n",
       "2853                       Project Manager                    IT   \n",
       "2919  Business Systems Development Analyst                    IT   \n",
       "2962                  Safety Technician II                    IT   \n",
       "2998  Business Systems Development Analyst                    IT   \n",
       "3011                      Technical Writer                    IT   \n",
       "3085                     Biostatistician I                    IT   \n",
       "3150                      Senior Developer                    IT   \n",
       "3221                         Senior Editor                    IT   \n",
       "3222            Database Administrator III                    IT   \n",
       "3254                           Engineer IV                    IT   \n",
       "3287                          Engineer III                    IT   \n",
       "3297                    VP Quality Control                    IT   \n",
       "3311                Administrative Officer                    IT   \n",
       "3321                     Financial Analyst    Financial Services   \n",
       "3342                             Recruiter                    IT   \n",
       "3364                          Engineer III                    IT   \n",
       "3472         Analog Circuit Design manager                    IT   \n",
       "3509                  Safety Technician II                    IT   \n",
       "3512                      Internal Auditor                    IT   \n",
       "3564                   Structural Engineer                    IT   \n",
       "3653                   Electrical Engineer         Manufacturing   \n",
       "3717                          Geologist IV                    IT   \n",
       "3726                   Assistant Professor                    IT   \n",
       "3778                                   NaN                    IT   \n",
       "3882                       Legal Assistant                    IT   \n",
       "3930                                   NaN                    IT   \n",
       "3934                                   NaN                    IT   \n",
       "3997                     Assistant Manager                    IT   \n",
       "\n",
       "         wealth_segment deceased_indicator owns_car  tenure  \n",
       "143       Mass Customer                  N       No     NaN  \n",
       "167   Affluent Customer                  N      Yes     NaN  \n",
       "266      High Net Worth                  N       No     NaN  \n",
       "289       Mass Customer                  N       No     NaN  \n",
       "450      High Net Worth                  N       No     NaN  \n",
       "452      High Net Worth                  N       No     NaN  \n",
       "453   Affluent Customer                  N      Yes     NaN  \n",
       "479       Mass Customer                  N      Yes     NaN  \n",
       "512       Mass Customer                  N       No     NaN  \n",
       "525       Mass Customer                  N      Yes     NaN  \n",
       "547      High Net Worth                  N      Yes     NaN  \n",
       "581   Affluent Customer                  N       No     NaN  \n",
       "598       Mass Customer                  N      Yes     NaN  \n",
       "679      High Net Worth                  N      Yes     NaN  \n",
       "684       Mass Customer                  N       No     NaN  \n",
       "798       Mass Customer                  N      Yes     NaN  \n",
       "838       Mass Customer                  N      Yes     NaN  \n",
       "882       Mass Customer                  N      Yes     NaN  \n",
       "891       Mass Customer                  N      Yes     NaN  \n",
       "949      High Net Worth                  N      Yes     NaN  \n",
       "974       Mass Customer                  N       No     NaN  \n",
       "982   Affluent Customer                  N       No     NaN  \n",
       "995       Mass Customer                  N      Yes     NaN  \n",
       "1037      Mass Customer                  N      Yes     NaN  \n",
       "1043     High Net Worth                  N       No     NaN  \n",
       "1081      Mass Customer                  N      Yes     NaN  \n",
       "1173      Mass Customer                  N       No     NaN  \n",
       "1209      Mass Customer                  N       No     NaN  \n",
       "1243      Mass Customer                  N      Yes     NaN  \n",
       "1350     High Net Worth                  N       No     NaN  \n",
       "...                 ...                ...      ...     ...  \n",
       "2695      Mass Customer                  N      Yes     NaN  \n",
       "2696     High Net Worth                  N       No     NaN  \n",
       "2853     High Net Worth                  N      Yes     NaN  \n",
       "2919      Mass Customer                  N      Yes     NaN  \n",
       "2962  Affluent Customer                  N      Yes     NaN  \n",
       "2998  Affluent Customer                  N      Yes     NaN  \n",
       "3011      Mass Customer                  N      Yes     NaN  \n",
       "3085     High Net Worth                  N       No     NaN  \n",
       "3150  Affluent Customer                  N      Yes     NaN  \n",
       "3221  Affluent Customer                  N       No     NaN  \n",
       "3222      Mass Customer                  N      Yes     NaN  \n",
       "3254     High Net Worth                  N       No     NaN  \n",
       "3287     High Net Worth                  N       No     NaN  \n",
       "3297  Affluent Customer                  N      Yes     NaN  \n",
       "3311     High Net Worth                  N       No     NaN  \n",
       "3321  Affluent Customer                  N      Yes     NaN  \n",
       "3342      Mass Customer                  N      Yes     NaN  \n",
       "3364      Mass Customer                  N       No     NaN  \n",
       "3472      Mass Customer                  N       No     NaN  \n",
       "3509  Affluent Customer                  N      Yes     NaN  \n",
       "3512  Affluent Customer                  N      Yes     NaN  \n",
       "3564     High Net Worth                  N      Yes     NaN  \n",
       "3653      Mass Customer                  N       No     NaN  \n",
       "3717      Mass Customer                  N      Yes     NaN  \n",
       "3726      Mass Customer                  N       No     NaN  \n",
       "3778  Affluent Customer                  N       No     NaN  \n",
       "3882      Mass Customer                  N       No     NaN  \n",
       "3930     High Net Worth                  N      Yes     NaN  \n",
       "3934     High Net Worth                  N      Yes     NaN  \n",
       "3997     High Net Worth                  N       No     NaN  \n",
       "\n",
       "[87 rows x 12 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cust_demo[cust_demo['DOB'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(cust_demo['DOB'].isnull().mean()*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Since less than 5 % of data has null date of birth. we can remove the records where date of birth is null.</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([ 143,  167,  266,  289,  450,  452,  453,  479,  512,  525,  547,\n",
       "             581,  598,  679,  684,  798,  838,  882,  891,  949,  974,  982,\n",
       "             995, 1037, 1043, 1081, 1173, 1209, 1243, 1350, 1476, 1508, 1582,\n",
       "            1627, 1682, 1739, 1772, 1779, 1805, 1917, 1937, 1989, 1999, 2020,\n",
       "            2068, 2164, 2204, 2251, 2294, 2334, 2340, 2413, 2425, 2468, 2539,\n",
       "            2641, 2646, 2695, 2696, 2853, 2919, 2962, 2998, 3011, 3085, 3150,\n",
       "            3221, 3222, 3254, 3287, 3297, 3311, 3321, 3342, 3364, 3472, 3509,\n",
       "            3512, 3564, 3653, 3717, 3726, 3778, 3882, 3930, 3934, 3997],\n",
       "           dtype='int64')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dob_index_drop = cust_demo[cust_demo['DOB'].isnull()].index\n",
    "dob_index_drop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "cust_demo.drop(index=dob_index_drop, inplace=True, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cust_demo['DOB'].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Currently there are no missing values for DOB column."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating Age Column for checking further descripency in data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to calculate the age as of today based on the DOB of the customer.\n",
    "\n",
    "def age(born):\n",
    "    today = date.today()\n",
    "    \n",
    "    return today.year - born.year - ((today.month, today.day) < (born.month, born.day))\n",
    "\n",
    "cust_demo['Age'] = cust_demo['DOB'].apply(age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ef4c9deb00>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Viz to find out the Age Distribution\n",
    "plt.figure(figsize=(20,8))\n",
    "sns.distplot(cust_demo['Age'], kde=False, bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Statistics of the Age column</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    3913.000000\n",
       "mean       43.346026\n",
       "std        12.803129\n",
       "min        19.000000\n",
       "25%        34.000000\n",
       "50%        43.000000\n",
       "75%        53.000000\n",
       "max       177.000000\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cust_demo['Age'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we find there is only 1 customer with an age of 177. Clearly this is an outlier since the 75th percentile of Age is 53."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>customer_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>gender</th>\n",
       "      <th>past_3_years_bike_related_purchases</th>\n",
       "      <th>DOB</th>\n",
       "      <th>job_title</th>\n",
       "      <th>job_industry_category</th>\n",
       "      <th>wealth_segment</th>\n",
       "      <th>deceased_indicator</th>\n",
       "      <th>owns_car</th>\n",
       "      <th>tenure</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>34</td>\n",
       "      <td>Jephthah</td>\n",
       "      <td>Bachmann</td>\n",
       "      <td>U</td>\n",
       "      <td>59</td>\n",
       "      <td>1843-12-21</td>\n",
       "      <td>Legal Assistant</td>\n",
       "      <td>IT</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>20.0</td>\n",
       "      <td>177</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    customer_id first_name last_name gender  \\\n",
       "33           34   Jephthah  Bachmann      U   \n",
       "\n",
       "    past_3_years_bike_related_purchases        DOB        job_title  \\\n",
       "33                                   59 1843-12-21  Legal Assistant   \n",
       "\n",
       "   job_industry_category     wealth_segment deceased_indicator owns_car  \\\n",
       "33                    IT  Affluent Customer                  N       No   \n",
       "\n",
       "    tenure  Age  \n",
       "33    20.0  177  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cust_demo[cust_demo['Age'] > 100]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Here we see a customer with age 177 which is an outlier. hence we need to remove this record.</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "age_index_drop = cust_demo[cust_demo['Age']>100].index\n",
    "\n",
    "cust_demo.drop(index=age_index_drop, inplace=True , axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Tenure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>When Date of Birth was Null the Tenure was also Null. Hence after removing null DOBs from dataframe , null tenures were also removed.</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cust_demo['tenure'].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are no missing values for Tenure column."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4 Job Title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>customer_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>gender</th>\n",
       "      <th>past_3_years_bike_related_purchases</th>\n",
       "      <th>DOB</th>\n",
       "      <th>job_title</th>\n",
       "      <th>job_industry_category</th>\n",
       "      <th>wealth_segment</th>\n",
       "      <th>deceased_indicator</th>\n",
       "      <th>owns_car</th>\n",
       "      <th>tenure</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Talbot</td>\n",
       "      <td>None</td>\n",
       "      <td>Male</td>\n",
       "      <td>33</td>\n",
       "      <td>1961-10-03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IT</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>7.0</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Curr</td>\n",
       "      <td>Duckhouse</td>\n",
       "      <td>Male</td>\n",
       "      <td>35</td>\n",
       "      <td>1966-09-16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Retail</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>13.0</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Fina</td>\n",
       "      <td>Merali</td>\n",
       "      <td>Female</td>\n",
       "      <td>6</td>\n",
       "      <td>1976-02-23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>11.0</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>Uriah</td>\n",
       "      <td>Bisatt</td>\n",
       "      <td>Male</td>\n",
       "      <td>99</td>\n",
       "      <td>1954-04-30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Property</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>9.0</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>Deeanne</td>\n",
       "      <td>Durtnell</td>\n",
       "      <td>Female</td>\n",
       "      <td>79</td>\n",
       "      <td>1962-12-10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IT</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>11.0</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>Olav</td>\n",
       "      <td>Polak</td>\n",
       "      <td>Male</td>\n",
       "      <td>43</td>\n",
       "      <td>1995-02-10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>Darrick</td>\n",
       "      <td>Helleckas</td>\n",
       "      <td>Male</td>\n",
       "      <td>18</td>\n",
       "      <td>1961-10-18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IT</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>6.0</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>46</td>\n",
       "      <td>Kaila</td>\n",
       "      <td>Allin</td>\n",
       "      <td>Female</td>\n",
       "      <td>98</td>\n",
       "      <td>1972-02-26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>15.0</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>52</td>\n",
       "      <td>Curran</td>\n",
       "      <td>Bentson</td>\n",
       "      <td>Male</td>\n",
       "      <td>57</td>\n",
       "      <td>1988-06-22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>13.0</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>60</td>\n",
       "      <td>Nadiya</td>\n",
       "      <td>Champerlen</td>\n",
       "      <td>Female</td>\n",
       "      <td>18</td>\n",
       "      <td>1970-02-04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>10.0</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>62</td>\n",
       "      <td>Sorcha</td>\n",
       "      <td>Roggers</td>\n",
       "      <td>Female</td>\n",
       "      <td>38</td>\n",
       "      <td>1979-07-06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IT</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>22.0</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>74</td>\n",
       "      <td>Pansy</td>\n",
       "      <td>Kiddie</td>\n",
       "      <td>Female</td>\n",
       "      <td>94</td>\n",
       "      <td>1969-06-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>6.0</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>81</td>\n",
       "      <td>Bee</td>\n",
       "      <td>Blazewicz</td>\n",
       "      <td>Female</td>\n",
       "      <td>58</td>\n",
       "      <td>1986-09-04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Health</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>13.0</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>108</td>\n",
       "      <td>Kayle</td>\n",
       "      <td>Mingaud</td>\n",
       "      <td>Female</td>\n",
       "      <td>4</td>\n",
       "      <td>1994-03-14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>3.0</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>110</td>\n",
       "      <td>Sascha</td>\n",
       "      <td>St. Quintin</td>\n",
       "      <td>Male</td>\n",
       "      <td>23</td>\n",
       "      <td>2000-07-31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>161</td>\n",
       "      <td>Tadd</td>\n",
       "      <td>Bloss</td>\n",
       "      <td>Male</td>\n",
       "      <td>49</td>\n",
       "      <td>1976-01-21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>16.0</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>167</td>\n",
       "      <td>Nathalie</td>\n",
       "      <td>Tideswell</td>\n",
       "      <td>Female</td>\n",
       "      <td>95</td>\n",
       "      <td>1969-10-27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Health</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>17.0</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>178</td>\n",
       "      <td>Matthieu</td>\n",
       "      <td>Bertelmot</td>\n",
       "      <td>Male</td>\n",
       "      <td>2</td>\n",
       "      <td>1967-04-03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>8.0</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>185</td>\n",
       "      <td>Crosby</td>\n",
       "      <td>Walcot</td>\n",
       "      <td>Male</td>\n",
       "      <td>80</td>\n",
       "      <td>1979-12-13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Property</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>13.0</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>197</td>\n",
       "      <td>Avis</td>\n",
       "      <td>None</td>\n",
       "      <td>Female</td>\n",
       "      <td>32</td>\n",
       "      <td>1977-01-27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>5.0</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>207</td>\n",
       "      <td>Adena</td>\n",
       "      <td>Whyman</td>\n",
       "      <td>Female</td>\n",
       "      <td>9</td>\n",
       "      <td>1994-08-10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>7.0</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>217</td>\n",
       "      <td>Jeralee</td>\n",
       "      <td>Quartly</td>\n",
       "      <td>Female</td>\n",
       "      <td>63</td>\n",
       "      <td>1979-12-09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>16.0</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>229</td>\n",
       "      <td>Vaughn</td>\n",
       "      <td>Lambis</td>\n",
       "      <td>Male</td>\n",
       "      <td>30</td>\n",
       "      <td>1966-03-06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Property</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>19.0</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>244</td>\n",
       "      <td>Germayne</td>\n",
       "      <td>Sperry</td>\n",
       "      <td>Male</td>\n",
       "      <td>57</td>\n",
       "      <td>1974-11-25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>8.0</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>262</td>\n",
       "      <td>Cordie</td>\n",
       "      <td>Petrelli</td>\n",
       "      <td>Male</td>\n",
       "      <td>97</td>\n",
       "      <td>1977-12-23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Health</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>10.0</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>276</td>\n",
       "      <td>Goldi</td>\n",
       "      <td>Dwine</td>\n",
       "      <td>Female</td>\n",
       "      <td>47</td>\n",
       "      <td>1990-03-25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>22.0</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>288</td>\n",
       "      <td>Ebenezer</td>\n",
       "      <td>Seedman</td>\n",
       "      <td>Male</td>\n",
       "      <td>71</td>\n",
       "      <td>1985-09-08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>9.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>296</td>\n",
       "      <td>Marshal</td>\n",
       "      <td>Rathbone</td>\n",
       "      <td>Male</td>\n",
       "      <td>34</td>\n",
       "      <td>1972-06-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Health</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>17.0</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>302</td>\n",
       "      <td>Laurice</td>\n",
       "      <td>Colgrave</td>\n",
       "      <td>Female</td>\n",
       "      <td>32</td>\n",
       "      <td>1977-03-27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Health</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>13.0</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>319</td>\n",
       "      <td>Madelle</td>\n",
       "      <td>Matteris</td>\n",
       "      <td>Female</td>\n",
       "      <td>32</td>\n",
       "      <td>1971-10-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>14.0</td>\n",
       "      <td>49</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>3797</th>\n",
       "      <td>3798</td>\n",
       "      <td>Yorker</td>\n",
       "      <td>Dennison</td>\n",
       "      <td>Male</td>\n",
       "      <td>13</td>\n",
       "      <td>1968-02-22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>17.0</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3803</th>\n",
       "      <td>3804</td>\n",
       "      <td>Andria</td>\n",
       "      <td>Keays</td>\n",
       "      <td>Female</td>\n",
       "      <td>23</td>\n",
       "      <td>1986-08-21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4.0</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3805</th>\n",
       "      <td>3806</td>\n",
       "      <td>Ado</td>\n",
       "      <td>Gailor</td>\n",
       "      <td>Male</td>\n",
       "      <td>1</td>\n",
       "      <td>1954-02-08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Property</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>7.0</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3810</th>\n",
       "      <td>3811</td>\n",
       "      <td>Etta</td>\n",
       "      <td>Leele</td>\n",
       "      <td>Female</td>\n",
       "      <td>60</td>\n",
       "      <td>1997-03-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>4.0</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3821</th>\n",
       "      <td>3822</td>\n",
       "      <td>Conny</td>\n",
       "      <td>Speechley</td>\n",
       "      <td>Male</td>\n",
       "      <td>37</td>\n",
       "      <td>1959-03-09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>18.0</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3823</th>\n",
       "      <td>3824</td>\n",
       "      <td>Giffard</td>\n",
       "      <td>Stollman</td>\n",
       "      <td>Male</td>\n",
       "      <td>33</td>\n",
       "      <td>1994-11-21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Property</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>3.0</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3825</th>\n",
       "      <td>3826</td>\n",
       "      <td>Marlow</td>\n",
       "      <td>Balffye</td>\n",
       "      <td>Male</td>\n",
       "      <td>33</td>\n",
       "      <td>1978-09-25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Health</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>7.0</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3826</th>\n",
       "      <td>3827</td>\n",
       "      <td>Cherida</td>\n",
       "      <td>Whyffen</td>\n",
       "      <td>Female</td>\n",
       "      <td>10</td>\n",
       "      <td>1976-09-05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>8.0</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3839</th>\n",
       "      <td>3840</td>\n",
       "      <td>Marc</td>\n",
       "      <td>Torrans</td>\n",
       "      <td>Male</td>\n",
       "      <td>27</td>\n",
       "      <td>1962-09-30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>5.0</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3843</th>\n",
       "      <td>3844</td>\n",
       "      <td>Clotilda</td>\n",
       "      <td>Oret</td>\n",
       "      <td>Female</td>\n",
       "      <td>87</td>\n",
       "      <td>1987-12-06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>15.0</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3864</th>\n",
       "      <td>3865</td>\n",
       "      <td>Urbanus</td>\n",
       "      <td>Fuxman</td>\n",
       "      <td>Male</td>\n",
       "      <td>49</td>\n",
       "      <td>1978-03-15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>11.0</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3880</th>\n",
       "      <td>3881</td>\n",
       "      <td>Olivie</td>\n",
       "      <td>Nazair</td>\n",
       "      <td>Female</td>\n",
       "      <td>50</td>\n",
       "      <td>1971-01-12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>18.0</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3892</th>\n",
       "      <td>3893</td>\n",
       "      <td>Hadria</td>\n",
       "      <td>Moles</td>\n",
       "      <td>Female</td>\n",
       "      <td>7</td>\n",
       "      <td>1996-11-18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4.0</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3908</th>\n",
       "      <td>3909</td>\n",
       "      <td>Micheil</td>\n",
       "      <td>McGeorge</td>\n",
       "      <td>Male</td>\n",
       "      <td>1</td>\n",
       "      <td>1987-10-04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>18.0</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3915</th>\n",
       "      <td>3916</td>\n",
       "      <td>Myrtia</td>\n",
       "      <td>None</td>\n",
       "      <td>Female</td>\n",
       "      <td>31</td>\n",
       "      <td>1958-10-17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>17.0</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3927</th>\n",
       "      <td>3928</td>\n",
       "      <td>Kristin</td>\n",
       "      <td>Way</td>\n",
       "      <td>Female</td>\n",
       "      <td>71</td>\n",
       "      <td>1982-04-16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Property</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>6.0</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3928</th>\n",
       "      <td>3929</td>\n",
       "      <td>Jacqui</td>\n",
       "      <td>Fortnam</td>\n",
       "      <td>Female</td>\n",
       "      <td>50</td>\n",
       "      <td>1989-10-18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>10.0</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3929</th>\n",
       "      <td>3930</td>\n",
       "      <td>Blancha</td>\n",
       "      <td>Baldi</td>\n",
       "      <td>Female</td>\n",
       "      <td>43</td>\n",
       "      <td>1988-01-06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>22.0</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3932</th>\n",
       "      <td>3933</td>\n",
       "      <td>Chiarra</td>\n",
       "      <td>Cops</td>\n",
       "      <td>Female</td>\n",
       "      <td>65</td>\n",
       "      <td>1983-07-05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>10.0</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3938</th>\n",
       "      <td>3939</td>\n",
       "      <td>Georges</td>\n",
       "      <td>Dumbelton</td>\n",
       "      <td>Male</td>\n",
       "      <td>67</td>\n",
       "      <td>1981-06-25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>15.0</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3944</th>\n",
       "      <td>3945</td>\n",
       "      <td>Lazarus</td>\n",
       "      <td>Donaghy</td>\n",
       "      <td>Male</td>\n",
       "      <td>77</td>\n",
       "      <td>1994-10-21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Retail</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>7.0</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3945</th>\n",
       "      <td>3946</td>\n",
       "      <td>Wylie</td>\n",
       "      <td>FitzGilbert</td>\n",
       "      <td>Male</td>\n",
       "      <td>85</td>\n",
       "      <td>1960-06-23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Retail</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>10.0</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3951</th>\n",
       "      <td>3952</td>\n",
       "      <td>Di</td>\n",
       "      <td>Borsnall</td>\n",
       "      <td>Female</td>\n",
       "      <td>96</td>\n",
       "      <td>1968-05-09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>10.0</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3958</th>\n",
       "      <td>3959</td>\n",
       "      <td>Dannie</td>\n",
       "      <td>Sowray</td>\n",
       "      <td>Male</td>\n",
       "      <td>76</td>\n",
       "      <td>1992-12-07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>3.0</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3959</th>\n",
       "      <td>3960</td>\n",
       "      <td>Hobart</td>\n",
       "      <td>Burgan</td>\n",
       "      <td>Male</td>\n",
       "      <td>6</td>\n",
       "      <td>2000-03-16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Property</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3967</th>\n",
       "      <td>3968</td>\n",
       "      <td>Alexandra</td>\n",
       "      <td>Kroch</td>\n",
       "      <td>Female</td>\n",
       "      <td>99</td>\n",
       "      <td>1977-12-22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Property</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>22.0</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3971</th>\n",
       "      <td>3972</td>\n",
       "      <td>Maribelle</td>\n",
       "      <td>Schaffel</td>\n",
       "      <td>Female</td>\n",
       "      <td>6</td>\n",
       "      <td>1979-03-28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>8.0</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3978</th>\n",
       "      <td>3979</td>\n",
       "      <td>Kleon</td>\n",
       "      <td>Adam</td>\n",
       "      <td>Male</td>\n",
       "      <td>67</td>\n",
       "      <td>1974-07-13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>18.0</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3986</th>\n",
       "      <td>3987</td>\n",
       "      <td>Beckie</td>\n",
       "      <td>Wakeham</td>\n",
       "      <td>Female</td>\n",
       "      <td>18</td>\n",
       "      <td>1964-05-29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Argiculture</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>7.0</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3998</th>\n",
       "      <td>3999</td>\n",
       "      <td>Patrizius</td>\n",
       "      <td>None</td>\n",
       "      <td>Male</td>\n",
       "      <td>11</td>\n",
       "      <td>1973-10-24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>10.0</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>497 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      customer_id first_name    last_name  gender  \\\n",
       "3               4     Talbot         None    Male   \n",
       "5               6       Curr    Duckhouse    Male   \n",
       "6               7       Fina       Merali  Female   \n",
       "10             11      Uriah       Bisatt    Male   \n",
       "21             22    Deeanne     Durtnell  Female   \n",
       "22             23       Olav        Polak    Male   \n",
       "29             30    Darrick    Helleckas    Male   \n",
       "45             46      Kaila        Allin  Female   \n",
       "51             52     Curran      Bentson    Male   \n",
       "59             60     Nadiya   Champerlen  Female   \n",
       "61             62     Sorcha      Roggers  Female   \n",
       "73             74      Pansy       Kiddie  Female   \n",
       "80             81        Bee    Blazewicz  Female   \n",
       "107           108      Kayle      Mingaud  Female   \n",
       "109           110     Sascha  St. Quintin    Male   \n",
       "160           161       Tadd        Bloss    Male   \n",
       "166           167   Nathalie    Tideswell  Female   \n",
       "177           178   Matthieu    Bertelmot    Male   \n",
       "184           185     Crosby       Walcot    Male   \n",
       "196           197       Avis         None  Female   \n",
       "206           207      Adena       Whyman  Female   \n",
       "216           217    Jeralee      Quartly  Female   \n",
       "228           229     Vaughn       Lambis    Male   \n",
       "243           244   Germayne       Sperry    Male   \n",
       "261           262     Cordie     Petrelli    Male   \n",
       "275           276      Goldi        Dwine  Female   \n",
       "287           288   Ebenezer      Seedman    Male   \n",
       "295           296    Marshal     Rathbone    Male   \n",
       "301           302    Laurice     Colgrave  Female   \n",
       "318           319    Madelle     Matteris  Female   \n",
       "...           ...        ...          ...     ...   \n",
       "3797         3798     Yorker     Dennison    Male   \n",
       "3803         3804     Andria        Keays  Female   \n",
       "3805         3806        Ado       Gailor    Male   \n",
       "3810         3811       Etta        Leele  Female   \n",
       "3821         3822      Conny    Speechley    Male   \n",
       "3823         3824    Giffard     Stollman    Male   \n",
       "3825         3826     Marlow      Balffye    Male   \n",
       "3826         3827    Cherida      Whyffen  Female   \n",
       "3839         3840       Marc      Torrans    Male   \n",
       "3843         3844   Clotilda         Oret  Female   \n",
       "3864         3865    Urbanus       Fuxman    Male   \n",
       "3880         3881     Olivie       Nazair  Female   \n",
       "3892         3893     Hadria        Moles  Female   \n",
       "3908         3909    Micheil     McGeorge    Male   \n",
       "3915         3916     Myrtia         None  Female   \n",
       "3927         3928    Kristin          Way  Female   \n",
       "3928         3929     Jacqui      Fortnam  Female   \n",
       "3929         3930    Blancha        Baldi  Female   \n",
       "3932         3933    Chiarra         Cops  Female   \n",
       "3938         3939    Georges    Dumbelton    Male   \n",
       "3944         3945    Lazarus      Donaghy    Male   \n",
       "3945         3946      Wylie  FitzGilbert    Male   \n",
       "3951         3952         Di     Borsnall  Female   \n",
       "3958         3959     Dannie       Sowray    Male   \n",
       "3959         3960     Hobart       Burgan    Male   \n",
       "3967         3968  Alexandra        Kroch  Female   \n",
       "3971         3972  Maribelle     Schaffel  Female   \n",
       "3978         3979      Kleon         Adam    Male   \n",
       "3986         3987     Beckie      Wakeham  Female   \n",
       "3998         3999  Patrizius         None    Male   \n",
       "\n",
       "      past_3_years_bike_related_purchases        DOB job_title  \\\n",
       "3                                      33 1961-10-03       NaN   \n",
       "5                                      35 1966-09-16       NaN   \n",
       "6                                       6 1976-02-23       NaN   \n",
       "10                                     99 1954-04-30       NaN   \n",
       "21                                     79 1962-12-10       NaN   \n",
       "22                                     43 1995-02-10       NaN   \n",
       "29                                     18 1961-10-18       NaN   \n",
       "45                                     98 1972-02-26       NaN   \n",
       "51                                     57 1988-06-22       NaN   \n",
       "59                                     18 1970-02-04       NaN   \n",
       "61                                     38 1979-07-06       NaN   \n",
       "73                                     94 1969-06-19       NaN   \n",
       "80                                     58 1986-09-04       NaN   \n",
       "107                                     4 1994-03-14       NaN   \n",
       "109                                    23 2000-07-31       NaN   \n",
       "160                                    49 1976-01-21       NaN   \n",
       "166                                    95 1969-10-27       NaN   \n",
       "177                                     2 1967-04-03       NaN   \n",
       "184                                    80 1979-12-13       NaN   \n",
       "196                                    32 1977-01-27       NaN   \n",
       "206                                     9 1994-08-10       NaN   \n",
       "216                                    63 1979-12-09       NaN   \n",
       "228                                    30 1966-03-06       NaN   \n",
       "243                                    57 1974-11-25       NaN   \n",
       "261                                    97 1977-12-23       NaN   \n",
       "275                                    47 1990-03-25       NaN   \n",
       "287                                    71 1985-09-08       NaN   \n",
       "295                                    34 1972-06-19       NaN   \n",
       "301                                    32 1977-03-27       NaN   \n",
       "318                                    32 1971-10-11       NaN   \n",
       "...                                   ...        ...       ...   \n",
       "3797                                   13 1968-02-22       NaN   \n",
       "3803                                   23 1986-08-21       NaN   \n",
       "3805                                    1 1954-02-08       NaN   \n",
       "3810                                   60 1997-03-19       NaN   \n",
       "3821                                   37 1959-03-09       NaN   \n",
       "3823                                   33 1994-11-21       NaN   \n",
       "3825                                   33 1978-09-25       NaN   \n",
       "3826                                   10 1976-09-05       NaN   \n",
       "3839                                   27 1962-09-30       NaN   \n",
       "3843                                   87 1987-12-06       NaN   \n",
       "3864                                   49 1978-03-15       NaN   \n",
       "3880                                   50 1971-01-12       NaN   \n",
       "3892                                    7 1996-11-18       NaN   \n",
       "3908                                    1 1987-10-04       NaN   \n",
       "3915                                   31 1958-10-17       NaN   \n",
       "3927                                   71 1982-04-16       NaN   \n",
       "3928                                   50 1989-10-18       NaN   \n",
       "3929                                   43 1988-01-06       NaN   \n",
       "3932                                   65 1983-07-05       NaN   \n",
       "3938                                   67 1981-06-25       NaN   \n",
       "3944                                   77 1994-10-21       NaN   \n",
       "3945                                   85 1960-06-23       NaN   \n",
       "3951                                   96 1968-05-09       NaN   \n",
       "3958                                   76 1992-12-07       NaN   \n",
       "3959                                    6 2000-03-16       NaN   \n",
       "3967                                   99 1977-12-22       NaN   \n",
       "3971                                    6 1979-03-28       NaN   \n",
       "3978                                   67 1974-07-13       NaN   \n",
       "3986                                   18 1964-05-29       NaN   \n",
       "3998                                   11 1973-10-24       NaN   \n",
       "\n",
       "     job_industry_category     wealth_segment deceased_indicator owns_car  \\\n",
       "3                       IT      Mass Customer                  N       No   \n",
       "5                   Retail     High Net Worth                  N      Yes   \n",
       "6       Financial Services  Affluent Customer                  N      Yes   \n",
       "10                Property      Mass Customer                  N       No   \n",
       "21                      IT      Mass Customer                  N       No   \n",
       "22                     NaN     High Net Worth                  N      Yes   \n",
       "29                      IT  Affluent Customer                  N      Yes   \n",
       "45                     NaN  Affluent Customer                  N      Yes   \n",
       "51      Financial Services      Mass Customer                  N      Yes   \n",
       "59           Manufacturing      Mass Customer                  N       No   \n",
       "61                      IT      Mass Customer                  N      Yes   \n",
       "73                     NaN      Mass Customer                  N      Yes   \n",
       "80                  Health     High Net Worth                  N       No   \n",
       "107                    NaN     High Net Worth                  N       No   \n",
       "109     Financial Services  Affluent Customer                  N       No   \n",
       "160                    NaN      Mass Customer                  N       No   \n",
       "166                 Health     High Net Worth                  N      Yes   \n",
       "177                    NaN  Affluent Customer                  N       No   \n",
       "184               Property      Mass Customer                  N      Yes   \n",
       "196                    NaN     High Net Worth                  N       No   \n",
       "206                    NaN      Mass Customer                  N       No   \n",
       "216          Manufacturing     High Net Worth                  N       No   \n",
       "228               Property     High Net Worth                  N       No   \n",
       "243                 Retail  Affluent Customer                  N       No   \n",
       "261                 Health     High Net Worth                  N      Yes   \n",
       "275     Financial Services      Mass Customer                  N       No   \n",
       "287          Manufacturing     High Net Worth                  N       No   \n",
       "295                 Health     High Net Worth                  N      Yes   \n",
       "301                 Health      Mass Customer                  N       No   \n",
       "318                 Retail      Mass Customer                  N      Yes   \n",
       "...                    ...                ...                ...      ...   \n",
       "3797         Manufacturing      Mass Customer                  N      Yes   \n",
       "3803         Manufacturing      Mass Customer                  N      Yes   \n",
       "3805              Property      Mass Customer                  N       No   \n",
       "3810    Financial Services     High Net Worth                  N       No   \n",
       "3821         Manufacturing     High Net Worth                  N      Yes   \n",
       "3823              Property      Mass Customer                  N       No   \n",
       "3825                Health      Mass Customer                  N       No   \n",
       "3826                Retail  Affluent Customer                  N       No   \n",
       "3839                   NaN     High Net Worth                  N       No   \n",
       "3843         Manufacturing  Affluent Customer                  N       No   \n",
       "3864         Manufacturing      Mass Customer                  N      Yes   \n",
       "3880    Financial Services  Affluent Customer                  N       No   \n",
       "3892                   NaN     High Net Worth                  N      Yes   \n",
       "3908         Manufacturing     High Net Worth                  N      Yes   \n",
       "3915                Retail  Affluent Customer                  N      Yes   \n",
       "3927              Property  Affluent Customer                  N      Yes   \n",
       "3928                   NaN  Affluent Customer                  N      Yes   \n",
       "3929    Financial Services     High Net Worth                  N       No   \n",
       "3932                   NaN     High Net Worth                  N      Yes   \n",
       "3938         Manufacturing  Affluent Customer                  N       No   \n",
       "3944                Retail     High Net Worth                  N       No   \n",
       "3945                Retail     High Net Worth                  N      Yes   \n",
       "3951         Manufacturing  Affluent Customer                  N       No   \n",
       "3958                   NaN      Mass Customer                  N       No   \n",
       "3959              Property      Mass Customer                  N       No   \n",
       "3967              Property     High Net Worth                  N       No   \n",
       "3971                Retail      Mass Customer                  N       No   \n",
       "3978    Financial Services      Mass Customer                  N      Yes   \n",
       "3986           Argiculture      Mass Customer                  N       No   \n",
       "3998         Manufacturing  Affluent Customer                  N      Yes   \n",
       "\n",
       "      tenure  Age  \n",
       "3        7.0   59  \n",
       "5       13.0   54  \n",
       "6       11.0   45  \n",
       "10       9.0   67  \n",
       "21      11.0   58  \n",
       "22       1.0   26  \n",
       "29       6.0   59  \n",
       "45      15.0   49  \n",
       "51      13.0   32  \n",
       "59      10.0   51  \n",
       "61      22.0   41  \n",
       "73       6.0   51  \n",
       "80      13.0   34  \n",
       "107      3.0   27  \n",
       "109      1.0   20  \n",
       "160     16.0   45  \n",
       "166     17.0   51  \n",
       "177      8.0   54  \n",
       "184     13.0   41  \n",
       "196      5.0   44  \n",
       "206      7.0   26  \n",
       "216     16.0   41  \n",
       "228     19.0   55  \n",
       "243      8.0   46  \n",
       "261     10.0   43  \n",
       "275     22.0   31  \n",
       "287      9.0   35  \n",
       "295     17.0   48  \n",
       "301     13.0   44  \n",
       "318     14.0   49  \n",
       "...      ...  ...  \n",
       "3797    17.0   53  \n",
       "3803     4.0   34  \n",
       "3805     7.0   67  \n",
       "3810     4.0   24  \n",
       "3821    18.0   62  \n",
       "3823     3.0   26  \n",
       "3825     7.0   42  \n",
       "3826     8.0   44  \n",
       "3839     5.0   58  \n",
       "3843    15.0   33  \n",
       "3864    11.0   43  \n",
       "3880    18.0   50  \n",
       "3892     4.0   24  \n",
       "3908    18.0   33  \n",
       "3915    17.0   62  \n",
       "3927     6.0   39  \n",
       "3928    10.0   31  \n",
       "3929    22.0   33  \n",
       "3932    10.0   37  \n",
       "3938    15.0   39  \n",
       "3944     7.0   26  \n",
       "3945    10.0   60  \n",
       "3951    10.0   53  \n",
       "3958     3.0   28  \n",
       "3959     1.0   21  \n",
       "3967    22.0   43  \n",
       "3971     8.0   42  \n",
       "3978    18.0   46  \n",
       "3986     7.0   56  \n",
       "3998    10.0   47  \n",
       "\n",
       "[497 rows x 13 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fetching records where Job Title is missing.\n",
    "\n",
    "cust_demo[cust_demo['job_title'].isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Since Percentage of missing Job is 13. We will replace null values with Missing.</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "cust_demo['job_title'].fillna('Missing', inplace=True, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cust_demo['job_title'].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Currently there are no mssing values for job_title column."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.5 Job Industry Category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>customer_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>gender</th>\n",
       "      <th>past_3_years_bike_related_purchases</th>\n",
       "      <th>DOB</th>\n",
       "      <th>job_title</th>\n",
       "      <th>job_industry_category</th>\n",
       "      <th>wealth_segment</th>\n",
       "      <th>deceased_indicator</th>\n",
       "      <th>owns_car</th>\n",
       "      <th>tenure</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Sheila-kathryn</td>\n",
       "      <td>Calton</td>\n",
       "      <td>Female</td>\n",
       "      <td>56</td>\n",
       "      <td>1977-05-13</td>\n",
       "      <td>Senior Editor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>8.0</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Rod</td>\n",
       "      <td>Inder</td>\n",
       "      <td>Male</td>\n",
       "      <td>31</td>\n",
       "      <td>1962-03-30</td>\n",
       "      <td>Media Manager I</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>7.0</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>Harlin</td>\n",
       "      <td>Parr</td>\n",
       "      <td>Male</td>\n",
       "      <td>38</td>\n",
       "      <td>1977-02-27</td>\n",
       "      <td>Media Manager IV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>18.0</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>Heath</td>\n",
       "      <td>Faraday</td>\n",
       "      <td>Male</td>\n",
       "      <td>57</td>\n",
       "      <td>1962-03-19</td>\n",
       "      <td>Sales Associate</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>15.0</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>Marjie</td>\n",
       "      <td>Neasham</td>\n",
       "      <td>Female</td>\n",
       "      <td>79</td>\n",
       "      <td>1967-07-06</td>\n",
       "      <td>Professor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>11.0</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>Olav</td>\n",
       "      <td>Polak</td>\n",
       "      <td>Male</td>\n",
       "      <td>43</td>\n",
       "      <td>1995-02-10</td>\n",
       "      <td>Missing</td>\n",
       "      <td>NaN</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>33</td>\n",
       "      <td>Ernst</td>\n",
       "      <td>Hacon</td>\n",
       "      <td>Male</td>\n",
       "      <td>44</td>\n",
       "      <td>1957-06-25</td>\n",
       "      <td>Product Engineer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>11.0</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>36</td>\n",
       "      <td>Lurette</td>\n",
       "      <td>Stonnell</td>\n",
       "      <td>Female</td>\n",
       "      <td>33</td>\n",
       "      <td>1977-11-09</td>\n",
       "      <td>VP Quality Control</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>22.0</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>46</td>\n",
       "      <td>Kaila</td>\n",
       "      <td>Allin</td>\n",
       "      <td>Female</td>\n",
       "      <td>98</td>\n",
       "      <td>1972-02-26</td>\n",
       "      <td>Missing</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>15.0</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>48</td>\n",
       "      <td>Rebbecca</td>\n",
       "      <td>Casone</td>\n",
       "      <td>Female</td>\n",
       "      <td>46</td>\n",
       "      <td>1975-08-15</td>\n",
       "      <td>Biostatistician II</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>8.0</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>49</td>\n",
       "      <td>Nolly</td>\n",
       "      <td>Ownsworth</td>\n",
       "      <td>Male</td>\n",
       "      <td>63</td>\n",
       "      <td>1994-01-26</td>\n",
       "      <td>VP Quality Control</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>1.0</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>57</td>\n",
       "      <td>Abba</td>\n",
       "      <td>Masedon</td>\n",
       "      <td>M</td>\n",
       "      <td>87</td>\n",
       "      <td>1988-06-13</td>\n",
       "      <td>Chief Design Engineer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>13.0</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>59</td>\n",
       "      <td>Niki</td>\n",
       "      <td>Heathcote</td>\n",
       "      <td>Male</td>\n",
       "      <td>60</td>\n",
       "      <td>2000-02-08</td>\n",
       "      <td>Physical Therapy Assistant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>3.0</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>68</td>\n",
       "      <td>Dahlia</td>\n",
       "      <td>Eddoes</td>\n",
       "      <td>Female</td>\n",
       "      <td>37</td>\n",
       "      <td>1974-04-21</td>\n",
       "      <td>Information Systems Manager</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>9.0</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>69</td>\n",
       "      <td>Heidi</td>\n",
       "      <td>Milner</td>\n",
       "      <td>Female</td>\n",
       "      <td>16</td>\n",
       "      <td>1969-06-22</td>\n",
       "      <td>Web Developer II</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>6.0</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>73</td>\n",
       "      <td>Minette</td>\n",
       "      <td>Worters</td>\n",
       "      <td>Female</td>\n",
       "      <td>16</td>\n",
       "      <td>1960-05-27</td>\n",
       "      <td>Teacher</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>5.0</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>74</td>\n",
       "      <td>Pansy</td>\n",
       "      <td>Kiddie</td>\n",
       "      <td>Female</td>\n",
       "      <td>94</td>\n",
       "      <td>1969-06-19</td>\n",
       "      <td>Missing</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>6.0</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>84</td>\n",
       "      <td>Rich</td>\n",
       "      <td>Mathiasen</td>\n",
       "      <td>Male</td>\n",
       "      <td>78</td>\n",
       "      <td>1958-02-07</td>\n",
       "      <td>Accountant III</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>14.0</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>85</td>\n",
       "      <td>Kane</td>\n",
       "      <td>Tixall</td>\n",
       "      <td>Male</td>\n",
       "      <td>1</td>\n",
       "      <td>1958-05-21</td>\n",
       "      <td>Analyst Programmer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>8.0</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>108</td>\n",
       "      <td>Kayle</td>\n",
       "      <td>Mingaud</td>\n",
       "      <td>Female</td>\n",
       "      <td>4</td>\n",
       "      <td>1994-03-14</td>\n",
       "      <td>Missing</td>\n",
       "      <td>NaN</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>3.0</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>109</td>\n",
       "      <td>Cody</td>\n",
       "      <td>Blabey</td>\n",
       "      <td>Male</td>\n",
       "      <td>16</td>\n",
       "      <td>1978-12-11</td>\n",
       "      <td>Marketing Assistant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4.0</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>111</td>\n",
       "      <td>Cele</td>\n",
       "      <td>Evason</td>\n",
       "      <td>Female</td>\n",
       "      <td>65</td>\n",
       "      <td>1993-08-29</td>\n",
       "      <td>Analyst Programmer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>2.0</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>113</td>\n",
       "      <td>Gage</td>\n",
       "      <td>Nickless</td>\n",
       "      <td>Male</td>\n",
       "      <td>67</td>\n",
       "      <td>1956-05-06</td>\n",
       "      <td>Staff Scientist</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>20.0</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>118</td>\n",
       "      <td>Prentice</td>\n",
       "      <td>Pearmain</td>\n",
       "      <td>Male</td>\n",
       "      <td>43</td>\n",
       "      <td>1959-11-12</td>\n",
       "      <td>Budget/Accounting Analyst IV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>19.0</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>119</td>\n",
       "      <td>Willey</td>\n",
       "      <td>Chastanet</td>\n",
       "      <td>Male</td>\n",
       "      <td>9</td>\n",
       "      <td>1981-12-04</td>\n",
       "      <td>Associate Professor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>9.0</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>148</td>\n",
       "      <td>Jaquith</td>\n",
       "      <td>Maffey</td>\n",
       "      <td>Female</td>\n",
       "      <td>69</td>\n",
       "      <td>1981-05-08</td>\n",
       "      <td>Programmer Analyst III</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>5.0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>154</td>\n",
       "      <td>Faydra</td>\n",
       "      <td>Dulieu</td>\n",
       "      <td>Female</td>\n",
       "      <td>90</td>\n",
       "      <td>1958-02-13</td>\n",
       "      <td>Junior Executive</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>11.0</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>158</td>\n",
       "      <td>Hamlin</td>\n",
       "      <td>Odams</td>\n",
       "      <td>Male</td>\n",
       "      <td>99</td>\n",
       "      <td>1984-09-03</td>\n",
       "      <td>Internal Auditor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>5.0</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>161</td>\n",
       "      <td>Tadd</td>\n",
       "      <td>Bloss</td>\n",
       "      <td>Male</td>\n",
       "      <td>49</td>\n",
       "      <td>1976-01-21</td>\n",
       "      <td>Missing</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>16.0</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>178</td>\n",
       "      <td>Matthieu</td>\n",
       "      <td>Bertelmot</td>\n",
       "      <td>Male</td>\n",
       "      <td>2</td>\n",
       "      <td>1967-04-03</td>\n",
       "      <td>Missing</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>8.0</td>\n",
       "      <td>54</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>3851</th>\n",
       "      <td>3852</td>\n",
       "      <td>Zerk</td>\n",
       "      <td>Merrien</td>\n",
       "      <td>Male</td>\n",
       "      <td>44</td>\n",
       "      <td>1982-02-04</td>\n",
       "      <td>Help Desk Operator</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>4.0</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3852</th>\n",
       "      <td>3853</td>\n",
       "      <td>Kerri</td>\n",
       "      <td>Marrington</td>\n",
       "      <td>Female</td>\n",
       "      <td>91</td>\n",
       "      <td>1975-06-26</td>\n",
       "      <td>Accounting Assistant IV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>19.0</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3854</th>\n",
       "      <td>3855</td>\n",
       "      <td>Brnaby</td>\n",
       "      <td>Doughtery</td>\n",
       "      <td>Male</td>\n",
       "      <td>89</td>\n",
       "      <td>1965-02-26</td>\n",
       "      <td>General Manager</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>16.0</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3859</th>\n",
       "      <td>3860</td>\n",
       "      <td>Sheila-kathryn</td>\n",
       "      <td>Conklin</td>\n",
       "      <td>Female</td>\n",
       "      <td>14</td>\n",
       "      <td>1986-04-05</td>\n",
       "      <td>Mechanical Systems Engineer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>13.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3863</th>\n",
       "      <td>3864</td>\n",
       "      <td>Ilyssa</td>\n",
       "      <td>Piaggia</td>\n",
       "      <td>Female</td>\n",
       "      <td>23</td>\n",
       "      <td>1963-08-27</td>\n",
       "      <td>Help Desk Technician</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>10.0</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3870</th>\n",
       "      <td>3871</td>\n",
       "      <td>Magda</td>\n",
       "      <td>Shugg</td>\n",
       "      <td>Female</td>\n",
       "      <td>80</td>\n",
       "      <td>1983-11-13</td>\n",
       "      <td>Recruiting Manager</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>4.0</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3876</th>\n",
       "      <td>3877</td>\n",
       "      <td>Georgine</td>\n",
       "      <td>Poutress</td>\n",
       "      <td>Female</td>\n",
       "      <td>55</td>\n",
       "      <td>1971-01-28</td>\n",
       "      <td>Account Coordinator</td>\n",
       "      <td>NaN</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>11.0</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3877</th>\n",
       "      <td>3878</td>\n",
       "      <td>Waldon</td>\n",
       "      <td>Digges</td>\n",
       "      <td>Male</td>\n",
       "      <td>99</td>\n",
       "      <td>1978-02-24</td>\n",
       "      <td>Programmer III</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>9.0</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3878</th>\n",
       "      <td>3879</td>\n",
       "      <td>Vin</td>\n",
       "      <td>Attack</td>\n",
       "      <td>Male</td>\n",
       "      <td>74</td>\n",
       "      <td>1979-08-28</td>\n",
       "      <td>Payment Adjustment Coordinator</td>\n",
       "      <td>NaN</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>19.0</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3886</th>\n",
       "      <td>3887</td>\n",
       "      <td>Dulcie</td>\n",
       "      <td>Nealon</td>\n",
       "      <td>Female</td>\n",
       "      <td>66</td>\n",
       "      <td>1964-07-16</td>\n",
       "      <td>Computer Systems Analyst IV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>7.0</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3891</th>\n",
       "      <td>3892</td>\n",
       "      <td>Roma</td>\n",
       "      <td>Finlater</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>1978-01-29</td>\n",
       "      <td>Staff Scientist</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>15.0</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3892</th>\n",
       "      <td>3893</td>\n",
       "      <td>Hadria</td>\n",
       "      <td>Moles</td>\n",
       "      <td>Female</td>\n",
       "      <td>7</td>\n",
       "      <td>1996-11-18</td>\n",
       "      <td>Missing</td>\n",
       "      <td>NaN</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4.0</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3895</th>\n",
       "      <td>3896</td>\n",
       "      <td>Perla</td>\n",
       "      <td>Blakiston</td>\n",
       "      <td>Female</td>\n",
       "      <td>3</td>\n",
       "      <td>1979-10-15</td>\n",
       "      <td>Tax Accountant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>13.0</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3902</th>\n",
       "      <td>3903</td>\n",
       "      <td>Dayna</td>\n",
       "      <td>Cawthera</td>\n",
       "      <td>Female</td>\n",
       "      <td>69</td>\n",
       "      <td>1981-02-13</td>\n",
       "      <td>Research Assistant III</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>17.0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3906</th>\n",
       "      <td>3907</td>\n",
       "      <td>Adriana</td>\n",
       "      <td>Heam</td>\n",
       "      <td>Female</td>\n",
       "      <td>8</td>\n",
       "      <td>1996-01-11</td>\n",
       "      <td>Technical Writer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>5.0</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3910</th>\n",
       "      <td>3911</td>\n",
       "      <td>Valeda</td>\n",
       "      <td>Ezele</td>\n",
       "      <td>Female</td>\n",
       "      <td>81</td>\n",
       "      <td>1954-05-25</td>\n",
       "      <td>Recruiting Manager</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>5.0</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3917</th>\n",
       "      <td>3918</td>\n",
       "      <td>Rosalia</td>\n",
       "      <td>Skedge</td>\n",
       "      <td>Female</td>\n",
       "      <td>52</td>\n",
       "      <td>1977-07-05</td>\n",
       "      <td>Junior Executive</td>\n",
       "      <td>NaN</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>18.0</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3924</th>\n",
       "      <td>3925</td>\n",
       "      <td>Cally</td>\n",
       "      <td>Chaim</td>\n",
       "      <td>Female</td>\n",
       "      <td>81</td>\n",
       "      <td>1978-11-25</td>\n",
       "      <td>Statistician I</td>\n",
       "      <td>NaN</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>7.0</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3928</th>\n",
       "      <td>3929</td>\n",
       "      <td>Jacqui</td>\n",
       "      <td>Fortnam</td>\n",
       "      <td>Female</td>\n",
       "      <td>50</td>\n",
       "      <td>1989-10-18</td>\n",
       "      <td>Missing</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>10.0</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3932</th>\n",
       "      <td>3933</td>\n",
       "      <td>Chiarra</td>\n",
       "      <td>Cops</td>\n",
       "      <td>Female</td>\n",
       "      <td>65</td>\n",
       "      <td>1983-07-05</td>\n",
       "      <td>Missing</td>\n",
       "      <td>NaN</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>10.0</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3946</th>\n",
       "      <td>3947</td>\n",
       "      <td>Tanitansy</td>\n",
       "      <td>McTrustam</td>\n",
       "      <td>Female</td>\n",
       "      <td>26</td>\n",
       "      <td>1970-05-12</td>\n",
       "      <td>GIS Technical Architect</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>12.0</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3950</th>\n",
       "      <td>3951</td>\n",
       "      <td>Ephrem</td>\n",
       "      <td>Hollerin</td>\n",
       "      <td>Male</td>\n",
       "      <td>39</td>\n",
       "      <td>1975-02-10</td>\n",
       "      <td>Quality Control Specialist</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>9.0</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3956</th>\n",
       "      <td>3957</td>\n",
       "      <td>Bernice</td>\n",
       "      <td>Scotchforth</td>\n",
       "      <td>Female</td>\n",
       "      <td>4</td>\n",
       "      <td>1978-07-20</td>\n",
       "      <td>Business Systems Development Analyst</td>\n",
       "      <td>NaN</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>14.0</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3958</th>\n",
       "      <td>3959</td>\n",
       "      <td>Dannie</td>\n",
       "      <td>Sowray</td>\n",
       "      <td>Male</td>\n",
       "      <td>76</td>\n",
       "      <td>1992-12-07</td>\n",
       "      <td>Missing</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>3.0</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3962</th>\n",
       "      <td>3963</td>\n",
       "      <td>Ardelle</td>\n",
       "      <td>Dasent</td>\n",
       "      <td>Female</td>\n",
       "      <td>10</td>\n",
       "      <td>1954-08-22</td>\n",
       "      <td>Software Test Engineer II</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>13.0</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3965</th>\n",
       "      <td>3966</td>\n",
       "      <td>Astrix</td>\n",
       "      <td>Sigward</td>\n",
       "      <td>Female</td>\n",
       "      <td>53</td>\n",
       "      <td>1968-09-15</td>\n",
       "      <td>Geologist I</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>11.0</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3973</th>\n",
       "      <td>3974</td>\n",
       "      <td>Misha</td>\n",
       "      <td>Ranklin</td>\n",
       "      <td>Female</td>\n",
       "      <td>82</td>\n",
       "      <td>1961-02-11</td>\n",
       "      <td>Technical Writer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>9.0</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3975</th>\n",
       "      <td>3976</td>\n",
       "      <td>Gretel</td>\n",
       "      <td>Chrystal</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>1957-11-20</td>\n",
       "      <td>Internal Auditor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>13.0</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3982</th>\n",
       "      <td>3983</td>\n",
       "      <td>Jarred</td>\n",
       "      <td>Lyste</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>1965-04-21</td>\n",
       "      <td>Graphic Designer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>9.0</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3999</th>\n",
       "      <td>4000</td>\n",
       "      <td>Kippy</td>\n",
       "      <td>Oldland</td>\n",
       "      <td>Male</td>\n",
       "      <td>76</td>\n",
       "      <td>1991-11-05</td>\n",
       "      <td>Software Engineer IV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>11.0</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>656 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      customer_id      first_name    last_name  gender  \\\n",
       "4               5  Sheila-kathryn       Calton  Female   \n",
       "7               8             Rod        Inder    Male   \n",
       "15             16          Harlin         Parr    Male   \n",
       "16             17           Heath      Faraday    Male   \n",
       "17             18          Marjie      Neasham  Female   \n",
       "22             23            Olav        Polak    Male   \n",
       "32             33           Ernst        Hacon    Male   \n",
       "35             36         Lurette     Stonnell  Female   \n",
       "45             46           Kaila        Allin  Female   \n",
       "47             48        Rebbecca       Casone  Female   \n",
       "48             49           Nolly    Ownsworth    Male   \n",
       "56             57            Abba      Masedon       M   \n",
       "58             59            Niki    Heathcote    Male   \n",
       "67             68          Dahlia       Eddoes  Female   \n",
       "68             69           Heidi       Milner  Female   \n",
       "72             73         Minette      Worters  Female   \n",
       "73             74           Pansy       Kiddie  Female   \n",
       "83             84            Rich    Mathiasen    Male   \n",
       "84             85            Kane       Tixall    Male   \n",
       "107           108           Kayle      Mingaud  Female   \n",
       "108           109            Cody       Blabey    Male   \n",
       "110           111            Cele       Evason  Female   \n",
       "112           113            Gage     Nickless    Male   \n",
       "117           118        Prentice     Pearmain    Male   \n",
       "118           119          Willey    Chastanet    Male   \n",
       "147           148         Jaquith       Maffey  Female   \n",
       "153           154          Faydra       Dulieu  Female   \n",
       "157           158          Hamlin        Odams    Male   \n",
       "160           161            Tadd        Bloss    Male   \n",
       "177           178        Matthieu    Bertelmot    Male   \n",
       "...           ...             ...          ...     ...   \n",
       "3851         3852            Zerk      Merrien    Male   \n",
       "3852         3853           Kerri   Marrington  Female   \n",
       "3854         3855          Brnaby    Doughtery    Male   \n",
       "3859         3860  Sheila-kathryn      Conklin  Female   \n",
       "3863         3864          Ilyssa      Piaggia  Female   \n",
       "3870         3871           Magda        Shugg  Female   \n",
       "3876         3877        Georgine     Poutress  Female   \n",
       "3877         3878          Waldon       Digges    Male   \n",
       "3878         3879             Vin       Attack    Male   \n",
       "3886         3887          Dulcie       Nealon  Female   \n",
       "3891         3892            Roma     Finlater    Male   \n",
       "3892         3893          Hadria        Moles  Female   \n",
       "3895         3896           Perla    Blakiston  Female   \n",
       "3902         3903           Dayna     Cawthera  Female   \n",
       "3906         3907         Adriana         Heam  Female   \n",
       "3910         3911          Valeda        Ezele  Female   \n",
       "3917         3918         Rosalia       Skedge  Female   \n",
       "3924         3925           Cally        Chaim  Female   \n",
       "3928         3929          Jacqui      Fortnam  Female   \n",
       "3932         3933         Chiarra         Cops  Female   \n",
       "3946         3947       Tanitansy    McTrustam  Female   \n",
       "3950         3951          Ephrem     Hollerin    Male   \n",
       "3956         3957         Bernice  Scotchforth  Female   \n",
       "3958         3959          Dannie       Sowray    Male   \n",
       "3962         3963         Ardelle       Dasent  Female   \n",
       "3965         3966          Astrix      Sigward  Female   \n",
       "3973         3974           Misha      Ranklin  Female   \n",
       "3975         3976          Gretel     Chrystal  Female   \n",
       "3982         3983          Jarred        Lyste    Male   \n",
       "3999         4000           Kippy      Oldland    Male   \n",
       "\n",
       "      past_3_years_bike_related_purchases        DOB  \\\n",
       "4                                      56 1977-05-13   \n",
       "7                                      31 1962-03-30   \n",
       "15                                     38 1977-02-27   \n",
       "16                                     57 1962-03-19   \n",
       "17                                     79 1967-07-06   \n",
       "22                                     43 1995-02-10   \n",
       "32                                     44 1957-06-25   \n",
       "35                                     33 1977-11-09   \n",
       "45                                     98 1972-02-26   \n",
       "47                                     46 1975-08-15   \n",
       "48                                     63 1994-01-26   \n",
       "56                                     87 1988-06-13   \n",
       "58                                     60 2000-02-08   \n",
       "67                                     37 1974-04-21   \n",
       "68                                     16 1969-06-22   \n",
       "72                                     16 1960-05-27   \n",
       "73                                     94 1969-06-19   \n",
       "83                                     78 1958-02-07   \n",
       "84                                      1 1958-05-21   \n",
       "107                                     4 1994-03-14   \n",
       "108                                    16 1978-12-11   \n",
       "110                                    65 1993-08-29   \n",
       "112                                    67 1956-05-06   \n",
       "117                                    43 1959-11-12   \n",
       "118                                     9 1981-12-04   \n",
       "147                                    69 1981-05-08   \n",
       "153                                    90 1958-02-13   \n",
       "157                                    99 1984-09-03   \n",
       "160                                    49 1976-01-21   \n",
       "177                                     2 1967-04-03   \n",
       "...                                   ...        ...   \n",
       "3851                                   44 1982-02-04   \n",
       "3852                                   91 1975-06-26   \n",
       "3854                                   89 1965-02-26   \n",
       "3859                                   14 1986-04-05   \n",
       "3863                                   23 1963-08-27   \n",
       "3870                                   80 1983-11-13   \n",
       "3876                                   55 1971-01-28   \n",
       "3877                                   99 1978-02-24   \n",
       "3878                                   74 1979-08-28   \n",
       "3886                                   66 1964-07-16   \n",
       "3891                                   19 1978-01-29   \n",
       "3892                                    7 1996-11-18   \n",
       "3895                                    3 1979-10-15   \n",
       "3902                                   69 1981-02-13   \n",
       "3906                                    8 1996-01-11   \n",
       "3910                                   81 1954-05-25   \n",
       "3917                                   52 1977-07-05   \n",
       "3924                                   81 1978-11-25   \n",
       "3928                                   50 1989-10-18   \n",
       "3932                                   65 1983-07-05   \n",
       "3946                                   26 1970-05-12   \n",
       "3950                                   39 1975-02-10   \n",
       "3956                                    4 1978-07-20   \n",
       "3958                                   76 1992-12-07   \n",
       "3962                                   10 1954-08-22   \n",
       "3965                                   53 1968-09-15   \n",
       "3973                                   82 1961-02-11   \n",
       "3975                                    0 1957-11-20   \n",
       "3982                                   19 1965-04-21   \n",
       "3999                                   76 1991-11-05   \n",
       "\n",
       "                                 job_title job_industry_category  \\\n",
       "4                            Senior Editor                   NaN   \n",
       "7                          Media Manager I                   NaN   \n",
       "15                        Media Manager IV                   NaN   \n",
       "16                         Sales Associate                   NaN   \n",
       "17                               Professor                   NaN   \n",
       "22                                 Missing                   NaN   \n",
       "32                        Product Engineer                   NaN   \n",
       "35                      VP Quality Control                   NaN   \n",
       "45                                 Missing                   NaN   \n",
       "47                      Biostatistician II                   NaN   \n",
       "48                      VP Quality Control                   NaN   \n",
       "56                   Chief Design Engineer                   NaN   \n",
       "58              Physical Therapy Assistant                   NaN   \n",
       "67             Information Systems Manager                   NaN   \n",
       "68                        Web Developer II                   NaN   \n",
       "72                                 Teacher                   NaN   \n",
       "73                                 Missing                   NaN   \n",
       "83                          Accountant III                   NaN   \n",
       "84                      Analyst Programmer                   NaN   \n",
       "107                                Missing                   NaN   \n",
       "108                    Marketing Assistant                   NaN   \n",
       "110                     Analyst Programmer                   NaN   \n",
       "112                        Staff Scientist                   NaN   \n",
       "117           Budget/Accounting Analyst IV                   NaN   \n",
       "118                    Associate Professor                   NaN   \n",
       "147                 Programmer Analyst III                   NaN   \n",
       "153                       Junior Executive                   NaN   \n",
       "157                       Internal Auditor                   NaN   \n",
       "160                                Missing                   NaN   \n",
       "177                                Missing                   NaN   \n",
       "...                                    ...                   ...   \n",
       "3851                    Help Desk Operator                   NaN   \n",
       "3852               Accounting Assistant IV                   NaN   \n",
       "3854                       General Manager                   NaN   \n",
       "3859           Mechanical Systems Engineer                   NaN   \n",
       "3863                  Help Desk Technician                   NaN   \n",
       "3870                    Recruiting Manager                   NaN   \n",
       "3876                   Account Coordinator                   NaN   \n",
       "3877                        Programmer III                   NaN   \n",
       "3878        Payment Adjustment Coordinator                   NaN   \n",
       "3886           Computer Systems Analyst IV                   NaN   \n",
       "3891                       Staff Scientist                   NaN   \n",
       "3892                               Missing                   NaN   \n",
       "3895                        Tax Accountant                   NaN   \n",
       "3902                Research Assistant III                   NaN   \n",
       "3906                      Technical Writer                   NaN   \n",
       "3910                    Recruiting Manager                   NaN   \n",
       "3917                      Junior Executive                   NaN   \n",
       "3924                        Statistician I                   NaN   \n",
       "3928                               Missing                   NaN   \n",
       "3932                               Missing                   NaN   \n",
       "3946               GIS Technical Architect                   NaN   \n",
       "3950            Quality Control Specialist                   NaN   \n",
       "3956  Business Systems Development Analyst                   NaN   \n",
       "3958                               Missing                   NaN   \n",
       "3962             Software Test Engineer II                   NaN   \n",
       "3965                           Geologist I                   NaN   \n",
       "3973                      Technical Writer                   NaN   \n",
       "3975                      Internal Auditor                   NaN   \n",
       "3982                      Graphic Designer                   NaN   \n",
       "3999                  Software Engineer IV                   NaN   \n",
       "\n",
       "         wealth_segment deceased_indicator owns_car  tenure  Age  \n",
       "4     Affluent Customer                  N      Yes     8.0   44  \n",
       "7         Mass Customer                  N       No     7.0   59  \n",
       "15        Mass Customer                  N      Yes    18.0   44  \n",
       "16    Affluent Customer                  N      Yes    15.0   59  \n",
       "17    Affluent Customer                  N       No    11.0   53  \n",
       "22       High Net Worth                  N      Yes     1.0   26  \n",
       "32    Affluent Customer                  N      Yes    11.0   63  \n",
       "35    Affluent Customer                  N       No    22.0   43  \n",
       "45    Affluent Customer                  N      Yes    15.0   49  \n",
       "47        Mass Customer                  N      Yes     8.0   45  \n",
       "48    Affluent Customer                  N       No     1.0   27  \n",
       "56        Mass Customer                  N      Yes    13.0   32  \n",
       "58       High Net Worth                  N       No     3.0   21  \n",
       "67    Affluent Customer                  N       No     9.0   47  \n",
       "68        Mass Customer                  N       No     6.0   51  \n",
       "72    Affluent Customer                  N      Yes     5.0   60  \n",
       "73        Mass Customer                  N      Yes     6.0   51  \n",
       "83        Mass Customer                  N      Yes    14.0   63  \n",
       "84        Mass Customer                  N       No     8.0   62  \n",
       "107      High Net Worth                  N       No     3.0   27  \n",
       "108   Affluent Customer                  N      Yes     4.0   42  \n",
       "110       Mass Customer                  N       No     2.0   27  \n",
       "112       Mass Customer                  N       No    20.0   65  \n",
       "117      High Net Worth                  N       No    19.0   61  \n",
       "118      High Net Worth                  N      Yes     9.0   39  \n",
       "147       Mass Customer                  N      Yes     5.0   40  \n",
       "153       Mass Customer                  N       No    11.0   63  \n",
       "157   Affluent Customer                  N       No     5.0   36  \n",
       "160       Mass Customer                  N       No    16.0   45  \n",
       "177   Affluent Customer                  N       No     8.0   54  \n",
       "...                 ...                ...      ...     ...  ...  \n",
       "3851      Mass Customer                  N       No     4.0   39  \n",
       "3852      Mass Customer                  N      Yes    19.0   45  \n",
       "3854      Mass Customer                  N       No    16.0   56  \n",
       "3859  Affluent Customer                  N      Yes    13.0   35  \n",
       "3863      Mass Customer                  N      Yes    10.0   57  \n",
       "3870      Mass Customer                  N       No     4.0   37  \n",
       "3876     High Net Worth                  N       No    11.0   50  \n",
       "3877      Mass Customer                  N       No     9.0   43  \n",
       "3878     High Net Worth                  N       No    19.0   41  \n",
       "3886  Affluent Customer                  N       No     7.0   56  \n",
       "3891      Mass Customer                  N      Yes    15.0   43  \n",
       "3892     High Net Worth                  N      Yes     4.0   24  \n",
       "3895      Mass Customer                  N      Yes    13.0   41  \n",
       "3902      Mass Customer                  N      Yes    17.0   40  \n",
       "3906     High Net Worth                  N      Yes     5.0   25  \n",
       "3910      Mass Customer                  N       No     5.0   66  \n",
       "3917     High Net Worth                  N       No    18.0   43  \n",
       "3924     High Net Worth                  N       No     7.0   42  \n",
       "3928  Affluent Customer                  N      Yes    10.0   31  \n",
       "3932     High Net Worth                  N      Yes    10.0   37  \n",
       "3946      Mass Customer                  N       No    12.0   51  \n",
       "3950  Affluent Customer                  N      Yes     9.0   46  \n",
       "3956     High Net Worth                  N      Yes    14.0   42  \n",
       "3958      Mass Customer                  N       No     3.0   28  \n",
       "3962      Mass Customer                  N       No    13.0   66  \n",
       "3965      Mass Customer                  N      Yes    11.0   52  \n",
       "3973  Affluent Customer                  N      Yes     9.0   60  \n",
       "3975  Affluent Customer                  N      Yes    13.0   63  \n",
       "3982      Mass Customer                  N      Yes     9.0   56  \n",
       "3999  Affluent Customer                  N       No    11.0   29  \n",
       "\n",
       "[656 rows x 13 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cust_demo[cust_demo['job_industry_category'].isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Since Percentage of missing Job Industry Category is 16. We will replace null values with Missing</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "cust_demo['job_industry_category'].fillna('Missing', inplace=True, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cust_demo['job_industry_category'].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Finally there are no Missing Values in the dataset.</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customer_id                            0\n",
       "first_name                             0\n",
       "last_name                              0\n",
       "gender                                 0\n",
       "past_3_years_bike_related_purchases    0\n",
       "DOB                                    0\n",
       "job_title                              0\n",
       "job_industry_category                  0\n",
       "wealth_segment                         0\n",
       "deceased_indicator                     0\n",
       "owns_car                               0\n",
       "tenure                                 0\n",
       "Age                                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cust_demo.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total records after removing Missing Values: 3912\n"
     ]
    }
   ],
   "source": [
    "print(\"Total records after removing Missing Values: {}\".format(cust_demo.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Inconsistency Check in Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will check whether there is inconsistent data / typo error data is present in the categorical columns.<br>\n",
    "The columns to be checked are <b>'gender', 'wealth_segment' ,'deceased_indicator', 'owns_car'</b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 Gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Female    2037\n",
       "Male      1872\n",
       "F            1\n",
       "M            1\n",
       "Femal        1\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cust_demo['gender'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here there are inconsistent data in gender column.There are spelling mistakes and typos. For gender with value <b>M will be replaced with Male</b>, <b>F will be replaced by Female</b> and <b>Femal will be replaced by Female</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "def replace_gender_names(gender):\n",
    "    \n",
    "    # Making Gender as Male and Female as standards\n",
    "    if gender=='M':\n",
    "        return 'Male'\n",
    "    elif gender=='F':\n",
    "        return 'Female'\n",
    "    elif gender=='Femal':\n",
    "        return 'Female'\n",
    "    else :\n",
    "        return gender\n",
    "\n",
    "cust_demo['gender'] = cust_demo['gender'].apply(replace_gender_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Female    2039\n",
       "Male      1873\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cust_demo['gender'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The inconsistent data ,spelling mistakes and typos in gender column are removed. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Wealth Segment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is <b>no inconsistent data</b> in <b>wealth_segment</b> column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mass Customer        1954\n",
       "High Net Worth        996\n",
       "Affluent Customer     962\n",
       "Name: wealth_segment, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cust_demo['wealth_segment'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 Deceased Indicator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is <b>no inconsistent data</b> in <b>deceased_indicator</b> column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "N    3910\n",
       "Y       2\n",
       "Name: deceased_indicator, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cust_demo['deceased_indicator'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4 Owns a Car"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is <b>no inconsistent data</b> in <b>owns_car</b> column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Yes    1974\n",
       "No     1938\n",
       "Name: owns_car, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cust_demo['owns_car'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Duplication Checks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to ensure that there is no duplication of records in the dataset. This may lead to error in data analysis due to poor data quality. If there are duplicate rows of data then we need to drop such records.<br>For checking for duplicate records we need to firstly remove the primary key column of the dataset then apply drop_duplicates() function provided by Python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of records after removing customer_id (pk), duplicates : 3912\n",
      "Number of records in original dataset : 3912\n"
     ]
    }
   ],
   "source": [
    "cust_demo_dedupped = cust_demo.drop('customer_id', axis=1).drop_duplicates()\n",
    "\n",
    "print(\"Number of records after removing customer_id (pk), duplicates : {}\".format(cust_demo_dedupped.shape[0]))\n",
    "print(\"Number of records in original dataset : {}\".format(cust_demo.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Since both the numbers are same. There are no duplicate records in the dataset.</b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Exporting the Cleaned Customer Demographic Data Set to csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Currently the Customer Demographics dataset is clean. Hence we can export the data to a csv to continue our data analysis of Customer Segments by joining it to other tables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "cust_demo.to_csv('CustomerDemographic_Cleaned.csv', index=False)"
   ]
  }
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