KPMG TASK 1.0.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing Important Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing Customer Demographic Dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "CustomerDemographic = pd.read_excel(\"C:/Users/My/Desktop/KPMG 2/KPMG_VI_New_raw_data_update_final.xlsx\",sheet_name = 'CustomerDemographic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>default</th>\n",
       "      <th>owns_car</th>\n",
       "      <th>tenure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\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",
       "      <td>1</td>\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",
       "      <td>2</td>\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",
       "      <td>3</td>\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",
       "      <td>4</td>\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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CustomerDemographic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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: 406.4+ KB\n"
     ]
    }
   ],
   "source": [
    "CustomerDemographic.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customer_id                            4000\n",
       "first_name                             3139\n",
       "last_name                              3725\n",
       "gender                                    6\n",
       "past_3_years_bike_related_purchases     100\n",
       "DOB                                    3448\n",
       "job_title                               195\n",
       "job_industry_category                     9\n",
       "wealth_segment                            3\n",
       "deceased_indicator                        2\n",
       "default                                  90\n",
       "owns_car                                  2\n",
       "tenure                                   22\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CustomerDemographic.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Female    2037\n",
       "Male      1872\n",
       "U           88\n",
       "F            1\n",
       "M            1\n",
       "Femal        1\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CustomerDemographic['gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "CustomerDemographic['gender'].replace('F','Female',True)\n",
    "CustomerDemographic['gender'].replace('Femal','Female',True)\n",
    "CustomerDemographic['gender'].replace('U','Undefined',True)\n",
    "CustomerDemographic['gender'].replace('M','Male',True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x16c9bb93a48>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "CustomerDemographic['gender'].value_counts().plot(kind = 'pie')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x16c9bbb7648>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "CustomerDemographic['job_industry_category'].value_counts().plot(kind = 'pie')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x16c9bf3fb08>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "CustomerDemographic['wealth_segment'].value_counts().plot(kind = 'bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x16c9bf96e88>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "CustomerDemographic['deceased_indicator'].value_counts().plot(kind = 'pie')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x16c9bfc0788>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "CustomerDemographic['owns_car'].value_counts().plot(kind = 'pie')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x16c9bfed988>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "CustomerDemographic['tenure'].value_counts().plot(kind = 'pie')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Missing value treatment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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",
      "default                                302\n",
      "owns_car                                 0\n",
      "tenure                                  87\n",
      "dtype: int64\n",
      "AxesSubplot(0.260833,0.125;0.503333x0.755)\n",
      "--------------------------------------------------\n",
      "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",
      "default                                 7.550\n",
      "owns_car                                0.000\n",
      "tenure                                  2.175\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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Nr6Ws4CrasG9o8+olk6vPauwd9+sN7hNAp4quiNSrapeHyETkG8DtqtrY1X39YFcv5IY5fgfojiaJTPtL0fJBKwvfWKzkz0cuVa1ZfOCRbbsaXkvJvBWK1uwc2bjwofeGW56/8OBljb3jo1LRTiddm4Y2vgH0SUM7SWFFNzdc7XeAbhP6VRbuuvS+4tCGY9Kw0+84qaaqh1/ad/++/U3bJyf92OihN0rqFz1w5e7Ay9MOzW4pSgxJdhvdMKb0ntIujR+LSD8ReUlE1opIpYhc4z3eV0SeEpENIrJJROaKyNdwlwx6WURe7uCY9SLyE2/fFSIy0nv8ahFZKSLrROTFVo87InKPiDwvIlUi8hER+bmX51kRCXrbTRORRSKyRkSeE5GTnquwMd0s5zjOqcAuv3MkhdJ4Trxk9czYmZcIknMdgoQm9j2/5+6mmuihM5J6XNE9r42te3PtmcdmxAvUt1uGO/DFynmVfzzZRseHF0SkEOijqrUiMgxYgXvTz0eAD6jqF73tB6pqjYhUAdNV9XAHx1bgQ6r6pIj8HKhV1f8RkcHAMVVVEfkCcI6q3iQiDvBe4HJgIrAc+KiqPiMijwH3AE8Bi4BrVPWQiMwF3q+qHV4vb2O62W+m3wGSRujzWmH40jcL9m+cE5nWf4j2y5h5Y3sqofGdz4T/WFAfO5a0ghsP6I6N42v2bhxfM0MDnJKs46bAZcBJi24rAvxURC4FEsApwEigEviFiPwMWKCqoS4cMwIs8L5fA7zP+74EeNjroRYBO1rt84yqRkWkEnc+k2e9xyuBscBZwCTgBRHB22bfyYJY0c1+OTfHQURik/9WtLL5rPiYhRfHzp4VQNI6gU+yxRPRbU+Fb+/fFK8fmYzjRQsSW9acdezoltPrLkTIhjemzk6Uc9yngOHANK/oVQG9VPV1EZkGXAX8r4g8r6qdnbs3qm99rI/zVu37DfBLVX1CRGbz9luXWwBUNSEirfdPePsLsFlVL+rKi7Oim/269D88awi9thbunb294MDmssj5xcN0wLv8jtQd0UTLqwt23zYqkmjq8fhqSzC+ceXEo5HtpzRk28Txp5beU3pK5bzKPZ3cfiBw0Cu4l4M7B4SIjAGOqOp9IlIPfNbbvg7oD7Q7vHCSto7nmtfFfbcCw0XkIlVd7o3znqmqmzvayYpuFnMcpxhI5R1FvotK/NzHi16JjE+MXHhZdOIlAQJZ8zvbEm9cv2D3beNjGunfk+M0FsdWLy09UrRnRFPST76l0XTeKm4ncz/wpIisBtYDW7zHS4GbRSQBRIGveI/fDjwjIvtU9fIu5nKAv4rIHtyx405/clDViIh8DPi1iAzEraf/B3RYdO1EWhZzHOciIG8mlCnUwJarIufLCB2YUYsytqUxVvfK0+HbJ8U11rs7+yuaqOsTW7VkcvXAg0Nazkl2Ph/8pHJe5X/7HSITZE2vwbTpAr8DpFNMEmc/UbQ6OjYxfOF7opMuDhAI+p2pLXXRI8ufCf9pupLocj5FY0f7R1eEphwec3RANJfG6/Pqd7UjVnSzW8b3+JJOCFYVHJp9T2DRGx+ITI2O1sET/Y7U2pGWfaEX9t7b5blwFW0+OLhl1ZLJ1WfU9Y3l4qRAaRmHFpGVQPEJD39GVSvT0X5nWNHNbmf6HcAvcUlMeKpobfzUxNCF741OvqiAwIl/aGl3oKlq0cL9D3fpTL2idXuGN69ZWlp9blOv+KWpypYBhpTeUzqgcl5lbSobUdULU3n8ZLCim938XSXCb0LB7oLq2fcGFr15ZXRK0ymJIZP8irKr/rWFyw89Mbuz2yt6ZMfoxsoV5x6ZEilKdHq/LFcCvOp3CL9Z0c1SjuP0Ak71O0cmiEti/DPBdYkxicGLroxOmVFIQbdOXnWHquq2unWL11a/MLsz2yfQ/a+fVr919dlHp8c6v9BjrjgVK7pWdLPYeGzujLcIgb0FRy+7N7Co6r3RyTWnJYZNSXWTqprYfGzp0s3Hlp60eCZEd20aV7tz/YRjMxIFXb5ZIFdYJwErut0iIpcAE1T1LhEZDvRT1R0n2y/JsvJmgVRLiI59PrhBR+qgxR+ITJkWpLBvKtpR1ei6Iy+tfqN2TYdTJsYCiW3rJ9Qc2HxG7btVOC0VWbJI3k3h2RYrul0kIj/APRN7FnAXEATuw53TNp2ScktpThLkgBy79N7ixbuviE56fWxiRFJvIFHVppWHn9q0s35zu3cDRgoSr64+52j966fWX4DYG6THerpY0e2OD+PeBbYWQFX3ikiP7jjqpqE+tJlVVPTUF4OVJcN1QOiDkfOmFFE4oMfHVK0LHXh0+76mN9u87rQ5GF+3fNIRdo5uzOk7BbvJii5WdLsj4k0Dp+DO8elTjkyYKzXzCXJIamf9uXjR3tnRc18fnxjV7etFVfXIy/sf2H+oOfyO8eKGXrFXlkyu7rUvtQs9ZjsbXsCKbnf8RURuAwaJyBeBzwF3+JDDerpdoMKYl4s2j9mQ2LmkLHJ+aTHBgV3aXxMHXth7b93RyIF/3oyhaKK2b2xFaMrhYYcHReyOq5Ozni4290K3iMj7gCtxp3Z7TlVfSHcGx3EeB65Jd7u5QJT9l8TO2XVWfMyMzmyf0PjuZ8N/0rrY0dPAXeixekBkVWhKdUlN/+jYlIbNPX0q51U2+R3CT9bT7QavyKa90J7Ahhe6SYVRoeBroyoLdi4ri0w7uzdF7f4s44nYm0+Hb+/TGK8brWjjgSEtq0OTD09o6BPPxVt10yEI5HXRtes8u8hbK+kNEakRkVoRqRORlN7a2A6/xpJzxrFA48z7i0PxVwvCK9p6PpqIvPbk7j8MaojX9tk1onHRw1eEG59994FLG/rET7oOlmlXRk5SlE7W0+26nwNXq+prPufI6tUUMoYwfFlw6/BNBbtWzIlMG9+H4uEALfGmDU/uuXXg1tFHNq2ceGRqNJh3d4+lihVdvwNkoQMZUHDBPqUkVW2g6d0PFC+pnhF717IzYkMK5xfMb3jlvYfOjBfoWL+z5Zi8rzl5/wPohtUi8jDwON4aSgCq+rc055A0t5f7hKGrgttmfic44sXI4FMJcmyLEDkN+1mbJLKi23UDgEbcqxeOUyDdRTee5vbyRq+WoUV1+z5+acu+jxMo3r+jaMjiXYX9Nw2XgkhGzd2bpRJ+B/CbFd0uUtXr/c7gifkdIFeNCtTJoXg/ABIto8Y177tuHPuuQ4JH9hYNCW0LDtgwkILGUhEb4umGvO8sWNHtIhEpwV22+WLcHu4S4OuqGk5zlEia28sbYwI1QyrbuEBBo0PGtBy4ZkzLgWuQgrpDwSHLtgQHru4thXWTRSjyIWo2yvvfWyu6XXcX8ADwce/fn/Yee1+acxxNc3t5Y3ig/gzQOEi7V4hovP/wyKH3D48cej8EmmqCg1atLhq8MiDBI5NF6JPOvFkkDtT4HcJvVnS7briq3tXq33eLyDd8yHHYhzbzQqFo7wC6PYGc0akdEr0HRo9cNjN65DKQaFNw4NqVwcHLYoHiA5NE6NLtxjnuaOW8ShvT9TtAFjosIp8GHvT+/Qmg2occfrSZN/pJy4Fa7d25otuaBntHj114YfTYhUA8Wjigck3RkCWNgV57zhbR4clPmlWso4AV3e74HPBb4BbcMd1l3mPpZr/AKTQi0BCpjfd01Z+CYKx26rRY7VQgkSjo+/rGoqGhowV9dowXSeTjjFv2O4sV3S5T1V3Ah/zOgfV0U2pMoKbftviwJB4xEIg3nD25qeFsAAp679hSNHTxgYK+r58qgXjXe9TZyYouVnQ7TUS+38HTqqo/TlsYl/0Cp9CoQF1KpyGMN407uyk87myAQPHe7UVDF+8u7P/qSAlEzk5luz6zjgJWdLuioY3H+gKfx53bNt1FtyrN7eWVPhIdAXoIJOXjsImWMWc07/2XMwAkWB0uGhp6Mzhgw2ACTZNy7FrgvX4HyARWdDtJVecf/95bnufrwPXAQ8D89vZLodd9aDOv9Ca6q4mitJ780ujQkpb915a07L8WKaw9FBy8dEtw0No+UlA3WSTrJ4vZdrINRGQQ8ElV/X0a8vgil95FU05EhojI/wAbcd+wzlfV76jqwXRncRynDtif7nbzyZBAY72f7WtswPDIoQ/Oanjje9PqX/9+Y8vB9y9NRAavVM3a+Wjf6MQ2g4B/T2UIEfG1s2k93U4SkZuBjwC3A6Wq6usfpGcrMMrvELlqdKA2uCcxyO8YrkSfgZHqyy+OVF8OEmkMDlqzIjh4eSJQdHCSCD1ecDNNOlN0K4DxIrIed6GAg8B1QDHwmKr+QETGAs/g3g06E9gDXKOqTSKyEPimqq4WkWHAalUdKyKfBcqAXrjDgu8RkW+deOykvdIOWE+3824CxgD/Dez1JjD3cxJzsCGGlBodqMvMZe61qE/06EXvbtz+nzPrt/xPr6Y9162ON52yRFUy+eTqwcp5lZ3JVw68qapTcYvuBGAGMBWYJiKXettNAH6nqucCx4CPduLYFwHzVPU9InJlB8dOKevpdpKqZuIblBXdFBosjWNBm0B6esFuChUWxWrPnx6rPR9IxAv7bdkQHBKqKehTNUFEM2mFi83d2OdK72ud9+9+uIVyF7BDVdd7j68BxnbieC+o6pGTHHtxN3J2iRXd7LbB7wC5LCAUBEnsiFKQJVM6Bgpi9ROnxOonAqoFfba/WjR08aGCPttOl0B8rM/hNnVjHwH+V1Vve9uD7vBCS6uH4sDxN8YYb32C73XC8VpfgdTmsdPBim52W4k7P2km9sJzwgBprq7WbFyOTiTeOH5iU+N4AAK9wtuKhizeU9j/tdESiJ7pQ6CVndyuDujvff8c8GMRuV9V60XkFCB6kv2rgGnAKuBjHWzX5rHTcVLc/lizmOM4tXTvY5vppJGBOvU7QzIkmkve1bz3k5fVb/3xmfVv3rQrcnTGIo332qRKul7fks5spKrVwFIR2YQ7c98DwHIRqQQe4a2C3J5fAF8RkWVAu7cUqurz3Th2UohqTvxO5S3HcW4Fvux3jly1Jz6g8oXoWaV+50gVKaw5UDRkydbCgWv7S0FDqUhKPv3urpxXeVoKjpuVbHgh+y3Him7KjAjUjwNVkJxcJ01jA0e2HCwb2XKwDAoajhYNXvFqcNArQSk8NlnkHWOi3RVK0nFyghXd7LfM7wC5LCiJfgF0ZwI53e8sKRfvOzhy+IqLI4evgEBLfXDg6uVFg5cjRYcnifToo3enhhbyhQ0v5ADHcfbgXkNsUuDRltIVddrr3X7n8I3EWgoHrN9YNGRZS6B470QRhnTxCKWV8yq7c/VCTrKebm5YAHzJ7xC5arg0tNRpsj5pZyEtLI7VTL8gVjMdSMQL+7+6PjhkSW1B751niujJ7og8jJ3sfRsrurnhCazopszoQE2f7YmhfsfIEIGCWN2kqbG6SYBqQd9tm4qGLK4u6PvmWJFEW0MwCyrnVdrH6Vas6OaGl4BGsAURU2F0QV2JLXjfFpF4w4RJTQ0TAAj02vV60dDF+wr7bRkjgdgEb6Mn/MuXmWxMN0c4jvM4cI3fOXLV3c3Tj4B0dSwzbwWKDlQVDV20LTho7bWV8yrbmos6b9nNEbnjSb8D5LJexHb6nSGbJCIjxzbvu67OCu47WdHNHU9w8lskTTcNDjT6NZNcNvur3wEykRXdHOE4ziHgKb9z5KrRgTo7/9E1DdinrzZZ0c0tf/I7QK4aHagd4XeGLPNQVUVZJkz0n3Gs6OaWZ4B9fofIRUPduXVbTrqhOe4OvwNkKiu6OcRxnDhwj985clFANFhIYoffObLExqqKss5O5Zh3rOjmnjv9DpCrBkhzJi+Hk0msl9sBK7o5xnGcN4AX/c6Ri0YE6hN+Z8gCTcB9fofIZFZ0c9PP/A6Qi8YEagf6nSEL3FlVUXbM7xCZzIpuDnIc50Vgtd85cs2oQN1YvzNkuCjwc79DZDorurmrwu8AuaZI4gMFDfudI4M9UFVRtsvvEJnOim7uegzY6neIXNOHyF6/M2SoBPZG3ylWdHOU4zgJ7KNe0g0PNDT5nSFDPVZVUbbF7xDZwIpubrsX6+0m1ehAbW+/M2SgOPBDv0NkCyu6OcxxnBjwLb9z5JLRgVpbFnLaFvgAAAxWSURBVOmd7qyqKKv0O0S2sKKb4xzHeRJ3knOTBAMCLSWgNX7nyCB1wP/zO0Q2saKbH27CPdFhkqCYeJXfGTJIRVVF2QG/Q2QTK7p5wHGcDcDdfufIFYOk0Xq6rl3AL/0OkW2s6OaP7wE2EXcSjC6os78b141VFWXNfofINvbLkyccx9kPfNvvHLlgdKB2mN8ZMsAjVRVlf/M7RDayoptfbgcW+R0i2w2ThnGg+bw00hHgP/wOka2s6OYRx3EU+ALucu2mmwpEiwtIVPmdw0ffsJNn3depoisiyzp4braILOhsgyLytIgM6sL2Y0VkU2e3P2Hfz4pI2q+r9DJ/Mt3tdobjONuwYYYe6y8tB/3O4JOnqyrK/ux3iGzWqaKrqjOT1aCqXqWq6Zr67bNAm0VXRApS2O5YIOVFtwev4ffYnLs9MiJQH/M7gw8OAV/0O0S262xPt15cN4vIJhGpFJG5rTYZICKPicirInKriLR7XBGpEpFhXm/wNRG5Q0Q2i8jzItLb22aaiGwQkeXAV1vt+1kR+W2rfy/wetoFInJ3q2w3isjHgOnA/SKyXkR6e21/X0SWAOUisrbVsSaIyJoOcl8gIsu8XKtEpL/3GkIistb7Ov7mVAHM8tq90ct3s4i8IiIbReTL3jEDIvJ77/Uv8D4FfMx77goRWee9njtFpLjVz69br+E4b5jhM9h6at02JlA7wO8MaZYAPlVVUWYT/vRQV8Z0PwJMBaYA7wVuFpHR3nMzcC/ALwXGe9t2xgTgd6p6LnAM+Kj3+F3A11T1ok4eZypwiqpOUtVS4C5VfQR3TtlPqepUVT0+UUmzql6iqj8BakRkqvf49bRzLauIFAEPA19X1eOvvwk4CLxPVc8H5gK/9nYpB0Jeu7cAnwdqVPUC4ALgiyIyDvfnNBb35/YF4CKvvV5elrne6ykEvtIqUpdfw4m8qxmuA/Kxx9ZjowJ1p/udIc1+UlVR9oLfIXJBV4ruJcCDqhpX1QO4Z8Ev8J5bparbVTUOPOht2xk7VHW99/0aYKyIDAQGqerxs+ydGT/aDpwhIr8RkQ/Q8fWoD7f6/o/A9d7H9LnAA+3scxawT1VfAVDVWlWNAUHgDhGpBP4KTGxn/yuBfxWR9cBKYCjuG84lwF9VNaGq+4GXW7W3Q1Vf9/59D3BpD1/DOziOswT4Tme3N2/pJbEhgubLJ4V/AI7fIXJFV4qudPCcnuTf7Wm9pHUct0cnHewf4+2ZewGo6lHcHvhC3OGIP3bQZkOr7x8FPgjMAdaoanU7+7SX6UbggNf2dKCog/1v8Hq+U1V1nKo+T/s/045+1t19DW1yHOeXuG8Ypot6E82HCc334w4r2G3kSdKVorsYmOuNTw7H7Xmt8p6bISLjvLHcucCS7gbyTrLViMjx3vKnWj1dBUz1xkJPxR3WQESGAQFVfRR38o3zve3rgP4dtNUMPAf8AXdIoz1bgDEicoHXXn8RKQQG4vaAE7hjpMdPbJ3Y7nPAV0Qk6O1/poj0xf05fdR7PSOB2a3aGysi7/L+/Rnaub62C6+hI5/z2jRdMCzQkOuX3jUCV1dVlO33O0gu6WzRVdyVCDYCG3A/bnzb+0gMsBz35NEmYIe3bU9cD/zOO5HWetLopd7xK4FfAMdPIp0CLPQ+vt8NfNd7/G7g1uMn0tpp637v9T3fXhhVjeC+mfxGRDYAL+D2sn8PzBORFcCZvNUD3QjEvJNuN+L2vF8F1nqXv92G26t/FAjj/txuwx16qPEK6fXAX72hiwRwa7s/rU68ho44jlMPlOH22k0njQ7UFvudIYUSwCeqKspsrb0kE9WORwJEZCiwVlVz8sSBiHwTGKiqvkxPJyL9VLXe+zmvAi5u9WbW2WMk5TU4jnMebo+63U8H5i3HEr12Ph4pzcm/C9wbIH7ld4hc1GFPV9wbC5bj9ipzjog8Bvwr4Ocv1wKvhx4CftyNgpu01+A4zjrgw0Ckp8fKBwOl+TTQer9zpMCvreCmzkl7ut0+sMhK4MSPX59R1YyeYd4rYuNOePg7qvqcH3n84DjOXNyrUE52Qi/vPdB8XmWEwlK/cyTRg8Cn7cRZ6qSs6Jrs5jjODbx13bFpx9MtZy8+qP0vPfmWWeFh3CsV4n4HyWU24Y1pk+M4vwH+nc5f/peXRgXqcuXTwF9xe7hWcFPMiq5pl+M4f8C9nMw+arZjdKB2qN8ZkuBR4JNVFWV2d2IaWNE1HXIc527ca6XtD7INwwP140CzuXf4MO6lYfb/N02s6JqTchznIeBjvP0OQgMUivYOoFV+5+imX+EW3HyekD3trOiaTnEc5+/A+3Cn9zOt9JeWbLupRIHvVFWUfaOqoszG7NPMiq7pNMdxQri3Xmf0ZX/pNjxQn009xWbguqqKsp/7HSRfWdE1XeI4ThUwE3jC5ygZY0ygtp/fGTppF3BZVUXZI34HyWdWdE2XeXM1fBh3vo28NzpQe5rfGTrhOeD8qoqyVSfd8gQi4ni3mrf3/HARWelNuj+rG8f/5+IEInKtiLQ3RWpOsKJrusVxnITjON/FLb5dmk4y1/SW2HDQTF0zLYE7F+5VVRVlqfr/dAWwRVXPU9VQD491Le3PS50TrOiaHnEc53FgMnm+5lpvorv9ztCGQ8AHqyrKftjV23pF5HsislVEXsSdVB8RGS8iz4rIGm+ZqrO9VUt+DlzValmsP4jIam8Zqh+2OmaVNw0rIjJdRBae0OZM4EO4q9KsF5HxPXr1GcqKrukxx3H24q6O8S3ydLKcoYHGTJv45hHg3KqKsi5P9yki04B/Ac7DXVLq+Aoxt+NOxj8N+Cbwe2/ll+8DD7daFut7qjod9834MhGZ3Jl2VXUZ7rmCb3nHerOr2bNBod8BTG7wFrv8heM4L+EusXSuz5HSanSgtiicGOR3DIDDwFerKsr+0oNjzAIeU9VGABF5Anf+6Jm4czwf3669+YSvE5Ev4daX0bjDBRt7kCenWE/XJJU3PeR5uItzNpxk85wxOlA70u8MuL3biT0suMedeP1uADjWasmpqap6zok7eQuufhO4QlUnA0/hLavF25fb6nXivvnCiq5JOsdxoo7j/Aw4h56vIpIVBkvTWFC/lu95HXdZnY9XVZQl4+aVxcCHvfHZ/sDVuEv37BCRjwOIa0ob+w7AfbOt8Zag+mCr56qAad73H6VtHS6xlQus6JqUcRxnt+M4H8FdCmi733lSSYRAkMSONDd7DPhPYFJVRdmCZB1UVdfizsmwHncynONXJHwK+Ly3ZNVm4Jo29t0ArPOevxN3ia3jfgj8SkRCuAvRtuUh4Fve5Wc5eSLN5tM1aeE4TjHwReC/cMf5cs6ClnNCh7Vfl69T7YY47kmt71dVlB1OQ3smiazomrRyHKc37jy93wGG+xwnqV6JlizeHB+dygnNo8B9wE+rKsq2pbAdk0JWdI0vHMfpB9yAe9JliM9xkmJPfMCmF6JnTUrBoVuAu4CKqoqynSk4vkkjK7rGV47j9AHmAV8DzvY5To9ENVB/f8v5fUCSda7kKG6x/WVVRdmeJB3T+MyKrskIjuMI7g0WXwHmAAX+Juqee5unVSUIjO3hYVYAtwIPV1WUNfc8lckkVnRNxnEcpwT4LDAXSMXH9ZR5tKV0eZ32uqgbux4B/gLcWlVRtiHJsUwGsaJrMprjOBNxi+91ZMHww+LIuEXbE8Mu6+Tmh4HHcReF/IctmZMfrOiarOE4zmTcWc2uAN4NBP1N9E7b4kNfWRI944IONtkKvIR708hCK7T5x4quyUqO4/QFLsEtwO/BvfXY95t96rVo/yMtU0a1eqgK+AfwMm5vdq8vwUzGsKJrcoLjOP1xZ7Wa0uq/pUDfNEVI4N51V3lv87S1CQIbgDVWZM2JrOianOU4TgAYB4wFSoBTTvjvYKB3q6+iEw4RB5q8r0bcOQX2AbuB8An/fdNxHL/mXjBZxIquMR6vSPfGHStuchzHlpw3SWdF1xhj0sj3Ew/GGJNPrOgaY0waWdE1xpg0sqJrjDFpZEXXGGPSyIquMcakkRVdY4xJIyu6xhiTRlZ0jTEmjazoGmNMGlnRNcaYNLKia4wxaWRF1xhj0siKrjHGpJEVXWOMSSMrusYYk0ZWdI0xJo2s6BpjTBpZ0TXGmDSyomuMMWlkRdcYY9LIiq4xxqSRFV1jjEkjK7rGGJNGVnSNMSaNrOgaY0waWdE1xpg0+v/xrlo/FKJa6QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(CustomerDemographic.isnull().sum())\n",
    "print(CustomerDemographic.isnull().sum().plot(kind = 'pie'))\n",
    "print('-'*50)\n",
    "print(CustomerDemographic.isnull().sum()/CustomerDemographic.shape[0]*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#CustomerDemographic.dropna(inplace = True)\n",
    "CustomerDemographic = CustomerDemographic[-CustomerDemographic['last_name'].isnull()]\n",
    "CustomerDemographic = CustomerDemographic[-CustomerDemographic['DOB'].isnull()]\n",
    "CustomerDemographic = CustomerDemographic[-CustomerDemographic['job_title'].isnull()]\n",
    "CustomerDemographic = CustomerDemographic[-CustomerDemographic['job_industry_category'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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",
      "default                                150\n",
      "owns_car                                 0\n",
      "tenure                                   0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(CustomerDemographic.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "CustomerDemographic['year'] = pd.DatetimeIndex(CustomerDemographic['DOB']).year\n",
    "CustomerDemographic['month'] = pd.DatetimeIndex(CustomerDemographic['DOB']).month\n",
    "CustomerDemographic['age'] = 2021- CustomerDemographic['year']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['customer_id', 'first_name', 'last_name', 'gender',\n",
       "       'past_3_years_bike_related_purchases', 'DOB', 'job_title',\n",
       "       'job_industry_category', 'wealth_segment', 'deceased_indicator',\n",
       "       'default', 'owns_car', 'tenure', 'year', 'month', 'age'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CustomerDemographic.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Droping all the misleading columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['DOB','default','year','month']\n",
    "CustomerDemographic.drop(cols,axis = 1,inplace =True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['customer_id', 'first_name', 'last_name', 'gender',\n",
       "       'past_3_years_bike_related_purchases', 'job_title',\n",
       "       'job_industry_category', 'wealth_segment', 'deceased_indicator',\n",
       "       'owns_car', 'tenure', 'age'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CustomerDemographic.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "customer_id                            0\n",
      "first_name                             0\n",
      "last_name                              0\n",
      "gender                                 0\n",
      "past_3_years_bike_related_purchases    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\n"
     ]
    }
   ],
   "source": [
    "print(CustomerDemographic.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------------------------------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing Transaction Data Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "Transactions = pd.read_excel(\"C:/Users/My/Desktop/KPMG 2/KPMG_VI_New_raw_data_update_final.xlsx\",sheet_name = 'Transactions')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>transaction_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>transaction_date</th>\n",
       "      <th>online_order</th>\n",
       "      <th>order_status</th>\n",
       "      <th>brand</th>\n",
       "      <th>product_line</th>\n",
       "      <th>product_class</th>\n",
       "      <th>product_size</th>\n",
       "      <th>list_price</th>\n",
       "      <th>standard_cost</th>\n",
       "      <th>product_first_sold_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2950</td>\n",
       "      <td>2017-02-25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Solex</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>71.49</td>\n",
       "      <td>53.62</td>\n",
       "      <td>41245.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3120</td>\n",
       "      <td>2017-05-21</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Trek Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>large</td>\n",
       "      <td>2091.47</td>\n",
       "      <td>388.92</td>\n",
       "      <td>41701.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>37</td>\n",
       "      <td>402</td>\n",
       "      <td>2017-10-16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Approved</td>\n",
       "      <td>OHM Cycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>low</td>\n",
       "      <td>medium</td>\n",
       "      <td>1793.43</td>\n",
       "      <td>248.82</td>\n",
       "      <td>36361.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>88</td>\n",
       "      <td>3135</td>\n",
       "      <td>2017-08-31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Norco Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>1198.46</td>\n",
       "      <td>381.10</td>\n",
       "      <td>36145.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>78</td>\n",
       "      <td>787</td>\n",
       "      <td>2017-10-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Giant Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>large</td>\n",
       "      <td>1765.30</td>\n",
       "      <td>709.48</td>\n",
       "      <td>42226.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   transaction_id  product_id  customer_id transaction_date  online_order  \\\n",
       "0               1           2         2950       2017-02-25           0.0   \n",
       "1               2           3         3120       2017-05-21           1.0   \n",
       "2               3          37          402       2017-10-16           0.0   \n",
       "3               4          88         3135       2017-08-31           0.0   \n",
       "4               5          78          787       2017-10-01           1.0   \n",
       "\n",
       "  order_status           brand product_line product_class product_size  \\\n",
       "0     Approved           Solex     Standard        medium       medium   \n",
       "1     Approved   Trek Bicycles     Standard        medium        large   \n",
       "2     Approved      OHM Cycles     Standard           low       medium   \n",
       "3     Approved  Norco Bicycles     Standard        medium       medium   \n",
       "4     Approved  Giant Bicycles     Standard        medium        large   \n",
       "\n",
       "   list_price  standard_cost  product_first_sold_date  \n",
       "0       71.49          53.62                  41245.0  \n",
       "1     2091.47         388.92                  41701.0  \n",
       "2     1793.43         248.82                  36361.0  \n",
       "3     1198.46         381.10                  36145.0  \n",
       "4     1765.30         709.48                  42226.0  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Transactions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20000 entries, 0 to 19999\n",
      "Data columns (total 13 columns):\n",
      "transaction_id             20000 non-null int64\n",
      "product_id                 20000 non-null int64\n",
      "customer_id                20000 non-null int64\n",
      "transaction_date           20000 non-null datetime64[ns]\n",
      "online_order               19640 non-null float64\n",
      "order_status               20000 non-null object\n",
      "brand                      19803 non-null object\n",
      "product_line               19803 non-null object\n",
      "product_class              19803 non-null object\n",
      "product_size               19803 non-null object\n",
      "list_price                 20000 non-null float64\n",
      "standard_cost              19803 non-null float64\n",
      "product_first_sold_date    19803 non-null float64\n",
      "dtypes: datetime64[ns](1), float64(4), int64(3), object(5)\n",
      "memory usage: 2.0+ MB\n"
     ]
    }
   ],
   "source": [
    "Transactions.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "transaction_id             20000\n",
       "product_id                   101\n",
       "customer_id                 3494\n",
       "transaction_date             364\n",
       "online_order                   2\n",
       "order_status                   2\n",
       "brand                          6\n",
       "product_line                   4\n",
       "product_class                  3\n",
       "product_size                   3\n",
       "list_price                   296\n",
       "standard_cost                103\n",
       "product_first_sold_date      100\n",
       "dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Transactions.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x16c9b6f52c8>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Transactions['online_order'].value_counts().plot(kind = 'pie')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x16c9b702088>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "Transactions['order_status'].value_counts().plot(kind = 'bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x16c9c8adac8>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Transactions['brand'].value_counts().plot(kind = 'bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x16c9c6749c8>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Transactions['product_line'].value_counts().plot(kind = 'pie')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x16c9bf3ac48>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Transactions['product_size'].value_counts().plot(kind = 'pie')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Missing Value Treatment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(Transactions.isnull().sum())\n",
    "print(Transactions.isnull().sum().plot(kind = 'pie'))\n",
    "print('-'*50)\n",
    "print(Transactions.isnull().sum()/Transactions.shape[0]*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "Transactions = Transactions[-Transactions['brand'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "Transactions = Transactions[-Transactions['online_order'].isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Making a Profit Name Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "Transactions['Profit'] = Transactions['list_price']-Transactions['standard_cost']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['transaction_id', 'product_id', 'customer_id', 'transaction_date',\n",
       "       'online_order', 'order_status', 'brand', 'product_line',\n",
       "       'product_class', 'product_size', 'list_price', 'standard_cost',\n",
       "       'product_first_sold_date', 'Profit'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Transactions.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------------------------------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing Customer Address Data Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "CustomerAddress = pd.read_excel(\"C:/Users/My/Desktop/KPMG 2/KPMG_VI_New_raw_data_update_final.xlsx\",sheet_name = 'CustomerAddress')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>address</th>\n",
       "      <th>postcode</th>\n",
       "      <th>state</th>\n",
       "      <th>country</th>\n",
       "      <th>property_valuation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>060 Morning Avenue</td>\n",
       "      <td>2016</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6 Meadow Vale Court</td>\n",
       "      <td>2153</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0 Holy Cross Court</td>\n",
       "      <td>4211</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>17979 Del Mar Point</td>\n",
       "      <td>2448</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>9 Oakridge Court</td>\n",
       "      <td>3216</td>\n",
       "      <td>VIC</td>\n",
       "      <td>Australia</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   customer_id              address  postcode            state    country  \\\n",
       "0            1   060 Morning Avenue      2016  New South Wales  Australia   \n",
       "1            2  6 Meadow Vale Court      2153  New South Wales  Australia   \n",
       "2            4   0 Holy Cross Court      4211              QLD  Australia   \n",
       "3            5  17979 Del Mar Point      2448  New South Wales  Australia   \n",
       "4            6     9 Oakridge Court      3216              VIC  Australia   \n",
       "\n",
       "   property_valuation  \n",
       "0                  10  \n",
       "1                  10  \n",
       "2                   9  \n",
       "3                   4  \n",
       "4                   9  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CustomerAddress.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3999 entries, 0 to 3998\n",
      "Data columns (total 6 columns):\n",
      "customer_id           3999 non-null int64\n",
      "address               3999 non-null object\n",
      "postcode              3999 non-null int64\n",
      "state                 3999 non-null object\n",
      "country               3999 non-null object\n",
      "property_valuation    3999 non-null int64\n",
      "dtypes: int64(3), object(3)\n",
      "memory usage: 187.6+ KB\n"
     ]
    }
   ],
   "source": [
    "CustomerAddress.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NSW                2054\n",
       "VIC                 939\n",
       "QLD                 838\n",
       "New South Wales      86\n",
       "Victoria             82\n",
       "Name: state, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CustomerAddress['state'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "CustomerAddress['state'].replace('NSW','New South Wales',True)\n",
    "CustomerAddress['state'].replace('VIC','Victoria',True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "New South Wales    2140\n",
       "Victoria           1021\n",
       "QLD                 838\n",
       "Name: state, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CustomerAddress['state'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customer_id           0\n",
       "address               0\n",
       "postcode              0\n",
       "state                 0\n",
       "country               0\n",
       "property_valuation    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CustomerAddress.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "source": []
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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