RFM Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import math\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": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading the Transactions and Customer Demographics Datasets\n",
    "\n",
    "trans = pd.read_csv('Transactions_Cleaned.csv')\n",
    "cust = pd.read_csv('CustomerDemographic_Cleaned.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "        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",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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",
       "      <td>17.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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",
       "      <td>1702.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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",
       "      <td>1544.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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",
       "      <td>817.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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",
       "      <td>1055.82</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   Profit  \n",
       "0       71.49          53.62                  41245.0    17.87  \n",
       "1     2091.47         388.92                  41701.0  1702.55  \n",
       "2     1793.43         248.82                  36361.0  1544.61  \n",
       "3     1198.46         381.10                  36145.0   817.36  \n",
       "4     1765.30         709.48                  42226.0  1055.82  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fetching first 5 transaction records\n",
    "\n",
    "trans.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total records (rows) in the Transaction Dataset : 19803\n",
      "Total features (columns) in the Transaction Dataset : 14\n"
     ]
    }
   ],
   "source": [
    "print(\"Total records (rows) in the Transaction Dataset : {}\".format(trans.shape[0]))\n",
    "print(\"Total features (columns) in the Transaction Dataset : {}\".format(trans.shape[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        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>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Laraine</td>\n",
       "      <td>Medendorp</td>\n",
       "      <td>Female</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>Yes</td>\n",
       "      <td>11.0</td>\n",
       "      <td>67</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>Yes</td>\n",
       "      <td>16.0</td>\n",
       "      <td>40</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>Yes</td>\n",
       "      <td>15.0</td>\n",
       "      <td>67</td>\n",
       "    </tr>\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>Missing</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>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>Missing</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   customer_id      first_name  last_name  gender  \\\n",
       "0            1         Laraine  Medendorp  Female   \n",
       "1            2             Eli    Bockman    Male   \n",
       "2            3           Arlin     Dearle    Male   \n",
       "3            4          Talbot       None    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                 Missing   \n",
       "4                                   56  1977-05-13           Senior Editor   \n",
       "\n",
       "  job_industry_category     wealth_segment deceased_indicator owns_car  \\\n",
       "0                Health      Mass Customer                  N      Yes   \n",
       "1    Financial Services      Mass Customer                  N      Yes   \n",
       "2              Property      Mass Customer                  N      Yes   \n",
       "3                    IT      Mass Customer                  N       No   \n",
       "4               Missing  Affluent Customer                  N      Yes   \n",
       "\n",
       "   tenure  Age  \n",
       "0    11.0   67  \n",
       "1    16.0   40  \n",
       "2    15.0   67  \n",
       "3     7.0   59  \n",
       "4     8.0   44  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fetching first 5 Customer Demographics records\n",
    "\n",
    "cust.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total records (rows) in the Customer Demographics Dataset : 3912\n",
      "Total features (columns) in the Customer Demographics Dataset : 13\n"
     ]
    }
   ],
   "source": [
    "print(\"Total records (rows) in the Customer Demographics Dataset : {}\".format(cust.shape[0]))\n",
    "print(\"Total features (columns) in the Customer Demographics Dataset : {}\".format(cust.shape[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Merging both the Transaction Dataset and Customer Demographics Dataset based on customer_id.\n",
    "\n",
    "merged_trans_cust = pd.merge(trans, cust, left_on='customer_id', right_on='customer_id', how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</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>...</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>0</th>\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>...</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>1955-01-11</td>\n",
       "      <td>Software Engineer I</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>10.0</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11065</td>\n",
       "      <td>1</td>\n",
       "      <td>2950</td>\n",
       "      <td>2017-10-16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Giant Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>...</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>1955-01-11</td>\n",
       "      <td>Software Engineer I</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>10.0</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18923</td>\n",
       "      <td>62</td>\n",
       "      <td>2950</td>\n",
       "      <td>2017-04-26</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>...</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>1955-01-11</td>\n",
       "      <td>Software Engineer I</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>10.0</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>...</td>\n",
       "      <td>Female</td>\n",
       "      <td>89</td>\n",
       "      <td>1979-02-04</td>\n",
       "      <td>Clinical Specialist</td>\n",
       "      <td>Health</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>10.0</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6862</td>\n",
       "      <td>4</td>\n",
       "      <td>3120</td>\n",
       "      <td>2017-10-05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Giant Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>high</td>\n",
       "      <td>medium</td>\n",
       "      <td>...</td>\n",
       "      <td>Female</td>\n",
       "      <td>89</td>\n",
       "      <td>1979-02-04</td>\n",
       "      <td>Clinical Specialist</td>\n",
       "      <td>Health</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>10.0</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\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           11065           1         2950       2017-10-16           0.0   \n",
       "2           18923          62         2950       2017-04-26           0.0   \n",
       "3               2           3         3120       2017-05-21           1.0   \n",
       "4            6862           4         3120       2017-10-05           0.0   \n",
       "\n",
       "  order_status           brand product_line product_class product_size  ...  \\\n",
       "0     Approved           Solex     Standard        medium       medium  ...   \n",
       "1     Approved  Giant Bicycles     Standard        medium       medium  ...   \n",
       "2     Approved           Solex     Standard        medium       medium  ...   \n",
       "3     Approved   Trek Bicycles     Standard        medium        large  ...   \n",
       "4     Approved  Giant Bicycles     Standard          high       medium  ...   \n",
       "\n",
       "   gender  past_3_years_bike_related_purchases         DOB  \\\n",
       "0    Male                                   19  1955-01-11   \n",
       "1    Male                                   19  1955-01-11   \n",
       "2    Male                                   19  1955-01-11   \n",
       "3  Female                                   89  1979-02-04   \n",
       "4  Female                                   89  1979-02-04   \n",
       "\n",
       "             job_title job_industry_category wealth_segment  \\\n",
       "0  Software Engineer I    Financial Services  Mass Customer   \n",
       "1  Software Engineer I    Financial Services  Mass Customer   \n",
       "2  Software Engineer I    Financial Services  Mass Customer   \n",
       "3  Clinical Specialist                Health  Mass Customer   \n",
       "4  Clinical Specialist                Health  Mass Customer   \n",
       "\n",
       "  deceased_indicator  owns_car tenure Age  \n",
       "0                  N       Yes   10.0  66  \n",
       "1                  N       Yes   10.0  66  \n",
       "2                  N       Yes   10.0  66  \n",
       "3                  N       Yes   10.0  42  \n",
       "4                  N       Yes   10.0  42  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fetching the first 5 records of the merged dataset.\n",
    "\n",
    "merged_trans_cust.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total records (rows) in the Merged Dataset : 19354\n",
      "Total features (columns) in the Merged Dataset : 26\n"
     ]
    }
   ],
   "source": [
    "print(\"Total records (rows) in the Merged Dataset : {}\".format(merged_trans_cust.shape[0]))\n",
    "print(\"Total features (columns) in the Merged Dataset : {}\".format(merged_trans_cust.shape[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 19354 entries, 0 to 19353\n",
      "Data columns (total 26 columns):\n",
      "transaction_id                         19354 non-null int64\n",
      "product_id                             19354 non-null int64\n",
      "customer_id                            19354 non-null int64\n",
      "transaction_date                       19354 non-null object\n",
      "online_order                           19354 non-null float64\n",
      "order_status                           19354 non-null object\n",
      "brand                                  19354 non-null object\n",
      "product_line                           19354 non-null object\n",
      "product_class                          19354 non-null object\n",
      "product_size                           19354 non-null object\n",
      "list_price                             19354 non-null float64\n",
      "standard_cost                          19354 non-null float64\n",
      "product_first_sold_date                19354 non-null float64\n",
      "Profit                                 19354 non-null float64\n",
      "first_name                             19354 non-null object\n",
      "last_name                              19354 non-null object\n",
      "gender                                 19354 non-null object\n",
      "past_3_years_bike_related_purchases    19354 non-null int64\n",
      "DOB                                    19354 non-null object\n",
      "job_title                              19354 non-null object\n",
      "job_industry_category                  19354 non-null object\n",
      "wealth_segment                         19354 non-null object\n",
      "deceased_indicator                     19354 non-null object\n",
      "owns_car                               19354 non-null object\n",
      "tenure                                 19354 non-null float64\n",
      "Age                                    19354 non-null int64\n",
      "dtypes: float64(6), int64(5), object(15)\n",
      "memory usage: 4.0+ MB\n"
     ]
    }
   ],
   "source": [
    "merged_trans_cust.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>The data-type of transaction_date column is not in date-time format. Hence the data-type of the column should be changed from object to datetime type.</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_trans_cust['transaction_date']= pd.to_datetime(merged_trans_cust['transaction_date'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. RFM Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "RFM (Recency, Frequency, Monetary) analysis is a behavior-based approach grouping customers into segments. It groups the customers on the basis of their previous purchase transactions. How recently, how often, and how much did a customer buy. RFM filters customers into various groups for the purpose of better service. There is a segment of customer who is the big spender but what if they purchased only once or how recently they purchased? Do they often purchase our product? Also, It helps managers to run an effective promotional campaign for personalized service.<br>\n",
    "\n",
    "- Recency (R): Who have purchased recently? Number of days since last purchase (least recency)\n",
    "- Frequency (F): Who has purchased frequently? It means the total number of purchases. ( high frequency)\n",
    "- Monetary Value(M): Who have high purchase amount? It means the total money customer spent (high monetary value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.date(2017, 12, 30)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Maximum Transaction Date or the latest transaction date.\n",
    "\n",
    "max_trans_date = max(merged_trans_cust['transaction_date']).date()\n",
    "max_trans_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Taking the last transaction date as a reference date for comparision and \n",
    "# finding the number of days between a transaction date and last transaction date to compute the recency.\n",
    "\n",
    "comparison_date = datetime.strptime(str(max_trans_date), \"%Y-%m-%d\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a RFM table that will contain all the values for recency , frequency and Monetray data. \n",
    "\n",
    "rfm_table = merged_trans_cust.groupby(['customer_id']).agg({'transaction_date': lambda date : (comparison_date - date.max()).days,\n",
    "                                                            'product_id' : lambda prod_id : len(prod_id), \n",
    "                                                            'Profit' : lambda p : sum(p)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['transaction_date', 'product_id', 'Profit'], dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The columns in rfm_table dataframe are not properly named. Renaming of the columns to appropiate name is needed\n",
    "\n",
    "rfm_table.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Renaming column names to appropiate names\n",
    "\n",
    "rfm_table.rename(columns={'transaction_date' : 'recency', \n",
    "                        'product_id' : 'frequency',\n",
    "                        'Profit' : 'monetary'} , inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dividing the recency, frequency and monetary into 4 quartiles (min, 25%, 50%, 75% and max).\n",
    "# These values will help us to calculate RFM score for a customer and classify based on their RFM score.\n",
    "\n",
    "rfm_table['r_quartile'] = pd.qcut(rfm_table['recency'], 4, ['4','3','2','1'])\n",
    "rfm_table['f_quartile'] = pd.qcut(rfm_table['frequency'], 4, ['1','2','3','4'])\n",
    "rfm_table['m_quartile'] = pd.qcut(rfm_table['monetary'], 4, ['1','2','3','4'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>recency</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary</th>\n",
       "      <th>r_quartile</th>\n",
       "      <th>f_quartile</th>\n",
       "      <th>m_quartile</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>3018.09</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>128</td>\n",
       "      <td>3</td>\n",
       "      <td>2226.26</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>102</td>\n",
       "      <td>8</td>\n",
       "      <td>3362.81</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>195</td>\n",
       "      <td>2</td>\n",
       "      <td>220.57</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>2394.94</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>64</td>\n",
       "      <td>5</td>\n",
       "      <td>3946.55</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>253</td>\n",
       "      <td>3</td>\n",
       "      <td>220.11</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>22</td>\n",
       "      <td>10</td>\n",
       "      <td>7066.94</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>78</td>\n",
       "      <td>6</td>\n",
       "      <td>2353.11</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>43</td>\n",
       "      <td>5</td>\n",
       "      <td>3358.28</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>46</td>\n",
       "      <td>6</td>\n",
       "      <td>3638.84</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>67</td>\n",
       "      <td>7</td>\n",
       "      <td>3540.03</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>27</td>\n",
       "      <td>7</td>\n",
       "      <td>4337.38</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>47</td>\n",
       "      <td>3</td>\n",
       "      <td>1713.90</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>35</td>\n",
       "      <td>6</td>\n",
       "      <td>1728.39</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>99</td>\n",
       "      <td>5</td>\n",
       "      <td>4521.84</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2015.61</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>134</td>\n",
       "      <td>7</td>\n",
       "      <td>3543.38</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>102</td>\n",
       "      <td>3</td>\n",
       "      <td>2951.79</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>3608.28</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>4229.41</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>97</td>\n",
       "      <td>8</td>\n",
       "      <td>5159.84</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>4376.15</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>17</td>\n",
       "      <td>7</td>\n",
       "      <td>3689.35</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>5333.66</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>259</td>\n",
       "      <td>2</td>\n",
       "      <td>268.24</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>143</td>\n",
       "      <td>7</td>\n",
       "      <td>3274.25</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>83</td>\n",
       "      <td>6</td>\n",
       "      <td>3366.56</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>22</td>\n",
       "      <td>9</td>\n",
       "      <td>6175.30</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>2633.95</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3470</th>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>7228.80</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3471</th>\n",
       "      <td>148</td>\n",
       "      <td>4</td>\n",
       "      <td>1914.10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3472</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>2779.58</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3474</th>\n",
       "      <td>71</td>\n",
       "      <td>5</td>\n",
       "      <td>2358.99</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3475</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2515.14</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3476</th>\n",
       "      <td>50</td>\n",
       "      <td>5</td>\n",
       "      <td>1152.36</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3477</th>\n",
       "      <td>64</td>\n",
       "      <td>8</td>\n",
       "      <td>4401.92</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3478</th>\n",
       "      <td>29</td>\n",
       "      <td>6</td>\n",
       "      <td>4297.85</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3479</th>\n",
       "      <td>31</td>\n",
       "      <td>6</td>\n",
       "      <td>2265.96</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3480</th>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>1770.26</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3481</th>\n",
       "      <td>54</td>\n",
       "      <td>7</td>\n",
       "      <td>2438.45</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3482</th>\n",
       "      <td>27</td>\n",
       "      <td>8</td>\n",
       "      <td>5549.59</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3483</th>\n",
       "      <td>117</td>\n",
       "      <td>6</td>\n",
       "      <td>2193.04</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3484</th>\n",
       "      <td>79</td>\n",
       "      <td>7</td>\n",
       "      <td>5924.55</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3485</th>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "      <td>2491.47</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3486</th>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>2972.34</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3487</th>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>1837.75</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3488</th>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>815.95</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3489</th>\n",
       "      <td>108</td>\n",
       "      <td>6</td>\n",
       "      <td>2644.44</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3490</th>\n",
       "      <td>166</td>\n",
       "      <td>5</td>\n",
       "      <td>2379.57</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3491</th>\n",
       "      <td>189</td>\n",
       "      <td>4</td>\n",
       "      <td>1430.28</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3492</th>\n",
       "      <td>80</td>\n",
       "      <td>3</td>\n",
       "      <td>2193.81</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3493</th>\n",
       "      <td>93</td>\n",
       "      <td>6</td>\n",
       "      <td>3728.88</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3494</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2755.11</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3495</th>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>3847.65</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3496</th>\n",
       "      <td>256</td>\n",
       "      <td>4</td>\n",
       "      <td>2045.84</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3497</th>\n",
       "      <td>52</td>\n",
       "      <td>3</td>\n",
       "      <td>1648.32</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3498</th>\n",
       "      <td>127</td>\n",
       "      <td>6</td>\n",
       "      <td>3147.33</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3499</th>\n",
       "      <td>51</td>\n",
       "      <td>7</td>\n",
       "      <td>4955.25</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3500</th>\n",
       "      <td>144</td>\n",
       "      <td>6</td>\n",
       "      <td>1785.86</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3416 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             recency  frequency  monetary r_quartile f_quartile m_quartile\n",
       "customer_id                                                               \n",
       "1                  7         11   3018.09          4          4          3\n",
       "2                128          3   2226.26          1          1          2\n",
       "3                102          8   3362.81          1          4          3\n",
       "4                195          2    220.57          1          1          1\n",
       "5                 16          6   2394.94          4          2          2\n",
       "6                 64          5   3946.55          2          2          3\n",
       "7                253          3    220.11          1          1          1\n",
       "8                 22         10   7066.94          3          4          4\n",
       "9                 78          6   2353.11          2          2          2\n",
       "10                43          5   3358.28          3          2          3\n",
       "11                46          6   3638.84          2          2          3\n",
       "12                67          7   3540.03          2          3          3\n",
       "13                27          7   4337.38          3          3          4\n",
       "14                47          3   1713.90          2          1          1\n",
       "15                35          6   1728.39          3          2          1\n",
       "16                99          5   4521.84          1          2          4\n",
       "17                 0          5   2015.61          4          2          2\n",
       "18               134          7   3543.38          1          3          3\n",
       "19               102          3   2951.79          1          1          3\n",
       "20                31          4   3608.28          3          1          3\n",
       "21                 6          5   4229.41          4          2          4\n",
       "22                97          8   5159.84          1          4          4\n",
       "23                10          6   4376.15          4          2          4\n",
       "24                17          7   3689.35          4          3          3\n",
       "25                 5         12   5333.66          4          4          4\n",
       "26               259          2    268.24          1          1          1\n",
       "27               143          7   3274.25          1          3          3\n",
       "28                83          6   3366.56          2          2          3\n",
       "29                22          9   6175.30          3          4          4\n",
       "30                11          3   2633.95          4          1          2\n",
       "...              ...        ...       ...        ...        ...        ...\n",
       "3470               2          8   7228.80          4          4          4\n",
       "3471             148          4   1914.10          1          1          2\n",
       "3472               5          6   2779.58          4          2          2\n",
       "3474              71          5   2358.99          2          2          2\n",
       "3475               3          4   2515.14          4          1          2\n",
       "3476              50          5   1152.36          2          2          1\n",
       "3477              64          8   4401.92          2          4          4\n",
       "3478              29          6   4297.85          3          2          4\n",
       "3479              31          6   2265.96          3          2          2\n",
       "3480              16          4   1770.26          4          1          1\n",
       "3481              54          7   2438.45          2          3          2\n",
       "3482              27          8   5549.59          3          4          4\n",
       "3483             117          6   2193.04          1          2          2\n",
       "3484              79          7   5924.55          2          3          4\n",
       "3485              25          3   2491.47          3          1          2\n",
       "3486               9          4   2972.34          4          1          3\n",
       "3487              10          3   1837.75          4          1          2\n",
       "3488              17          2    815.95          4          1          1\n",
       "3489             108          6   2644.44          1          2          2\n",
       "3490             166          5   2379.57          1          2          2\n",
       "3491             189          4   1430.28          1          1          1\n",
       "3492              80          3   2193.81          2          1          2\n",
       "3493              93          6   3728.88          1          2          3\n",
       "3494               4          4   2755.11          4          1          2\n",
       "3495              13          7   3847.65          4          3          3\n",
       "3496             256          4   2045.84          1          1          2\n",
       "3497              52          3   1648.32          2          1          1\n",
       "3498             127          6   3147.33          1          2          3\n",
       "3499              51          7   4955.25          2          3          4\n",
       "3500             144          6   1785.86          1          2          1\n",
       "\n",
       "[3416 rows x 6 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# RFM_table dataset\n",
    "\n",
    "rfm_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Caluclation of RFM Score.\n",
    "# Max weightage is given to recency then frequency and then  monetary.\n",
    "\n",
    "rfm_table['rfm_score'] = 100*rfm_table['r_quartile'].astype(int)+10*rfm_table['f_quartile'].astype(int)+rfm_table['m_quartile'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assigning a title to a cuustomer.\n",
    "# Platinum corresponds to highest range of RFM score down to Bronze to lowest range of RFM score.\n",
    "\n",
    "rfm_table['customer_title'] = pd.qcut(rfm_table['rfm_score'], 4, ['Bronze','Silver','Gold','Platinum'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>recency</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary</th>\n",
       "      <th>r_quartile</th>\n",
       "      <th>f_quartile</th>\n",
       "      <th>m_quartile</th>\n",
       "      <th>rfm_score</th>\n",
       "      <th>customer_title</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>3018.09</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>443</td>\n",
       "      <td>Platinum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>128</td>\n",
       "      <td>3</td>\n",
       "      <td>2226.26</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>112</td>\n",
       "      <td>Bronze</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>102</td>\n",
       "      <td>8</td>\n",
       "      <td>3362.81</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>143</td>\n",
       "      <td>Bronze</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>195</td>\n",
       "      <td>2</td>\n",
       "      <td>220.57</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "      <td>Bronze</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>2394.94</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>422</td>\n",
       "      <td>Platinum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>64</td>\n",
       "      <td>5</td>\n",
       "      <td>3946.55</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>223</td>\n",
       "      <td>Silver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>253</td>\n",
       "      <td>3</td>\n",
       "      <td>220.11</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "      <td>Bronze</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>22</td>\n",
       "      <td>10</td>\n",
       "      <td>7066.94</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>344</td>\n",
       "      <td>Gold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>78</td>\n",
       "      <td>6</td>\n",
       "      <td>2353.11</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>222</td>\n",
       "      <td>Silver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>43</td>\n",
       "      <td>5</td>\n",
       "      <td>3358.28</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>323</td>\n",
       "      <td>Gold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>46</td>\n",
       "      <td>6</td>\n",
       "      <td>3638.84</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>223</td>\n",
       "      <td>Silver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>67</td>\n",
       "      <td>7</td>\n",
       "      <td>3540.03</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>233</td>\n",
       "      <td>Silver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>27</td>\n",
       "      <td>7</td>\n",
       "      <td>4337.38</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>334</td>\n",
       "      <td>Gold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>47</td>\n",
       "      <td>3</td>\n",
       "      <td>1713.90</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>211</td>\n",
       "      <td>Bronze</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>35</td>\n",
       "      <td>6</td>\n",
       "      <td>1728.39</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>321</td>\n",
       "      <td>Gold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>99</td>\n",
       "      <td>5</td>\n",
       "      <td>4521.84</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>124</td>\n",
       "      <td>Bronze</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2015.61</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>422</td>\n",
       "      <td>Platinum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>134</td>\n",
       "      <td>7</td>\n",
       "      <td>3543.38</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>133</td>\n",
       "      <td>Bronze</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>102</td>\n",
       "      <td>3</td>\n",
       "      <td>2951.79</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>113</td>\n",
       "      <td>Bronze</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>3608.28</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>313</td>\n",
       "      <td>Gold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>4229.41</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>424</td>\n",
       "      <td>Platinum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>97</td>\n",
       "      <td>8</td>\n",
       "      <td>5159.84</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>144</td>\n",
       "      <td>Bronze</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>4376.15</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>424</td>\n",
       "      <td>Platinum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>17</td>\n",
       "      <td>7</td>\n",
       "      <td>3689.35</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>433</td>\n",
       "      <td>Platinum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>5333.66</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>444</td>\n",
       "      <td>Platinum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>259</td>\n",
       "      <td>2</td>\n",
       "      <td>268.24</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "      <td>Bronze</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>143</td>\n",
       "      <td>7</td>\n",
       "      <td>3274.25</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>133</td>\n",
       "      <td>Bronze</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>83</td>\n",
       "      <td>6</td>\n",
       "      <td>3366.56</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>223</td>\n",
       "      <td>Silver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>22</td>\n",
       "      <td>9</td>\n",
       "      <td>6175.30</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>344</td>\n",
       "      <td>Gold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>2633.95</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>412</td>\n",
       "      <td>Platinum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3470</th>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>7228.80</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>444</td>\n",
       "      <td>Platinum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3471</th>\n",
       "      <td>148</td>\n",
       "      <td>4</td>\n",
       "      <td>1914.10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>112</td>\n",
       "      <td>Bronze</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3472</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>2779.58</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>422</td>\n",
       "      <td>Platinum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3474</th>\n",
       "      <td>71</td>\n",
       "      <td>5</td>\n",
       "      <td>2358.99</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>222</td>\n",
       "      <td>Silver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3475</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2515.14</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>412</td>\n",
       "      <td>Platinum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3476</th>\n",
       "      <td>50</td>\n",
       "      <td>5</td>\n",
       "      <td>1152.36</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>221</td>\n",
       "      <td>Silver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3477</th>\n",
       "      <td>64</td>\n",
       "      <td>8</td>\n",
       "      <td>4401.92</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>244</td>\n",
       "      <td>Silver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3478</th>\n",
       "      <td>29</td>\n",
       "      <td>6</td>\n",
       "      <td>4297.85</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>324</td>\n",
       "      <td>Gold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3479</th>\n",
       "      <td>31</td>\n",
       "      <td>6</td>\n",
       "      <td>2265.96</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>322</td>\n",
       "      <td>Gold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3480</th>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>1770.26</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>411</td>\n",
       "      <td>Gold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3481</th>\n",
       "      <td>54</td>\n",
       "      <td>7</td>\n",
       "      <td>2438.45</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>232</td>\n",
       "      <td>Silver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3482</th>\n",
       "      <td>27</td>\n",
       "      <td>8</td>\n",
       "      <td>5549.59</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>344</td>\n",
       "      <td>Gold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3483</th>\n",
       "      <td>117</td>\n",
       "      <td>6</td>\n",
       "      <td>2193.04</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>122</td>\n",
       "      <td>Bronze</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3484</th>\n",
       "      <td>79</td>\n",
       "      <td>7</td>\n",
       "      <td>5924.55</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>234</td>\n",
       "      <td>Silver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3485</th>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "      <td>2491.47</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>312</td>\n",
       "      <td>Gold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3486</th>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>2972.34</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>413</td>\n",
       "      <td>Platinum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3487</th>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>1837.75</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>412</td>\n",
       "      <td>Platinum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3488</th>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>815.95</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>411</td>\n",
       "      <td>Gold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3489</th>\n",
       "      <td>108</td>\n",
       "      <td>6</td>\n",
       "      <td>2644.44</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>122</td>\n",
       "      <td>Bronze</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3490</th>\n",
       "      <td>166</td>\n",
       "      <td>5</td>\n",
       "      <td>2379.57</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>122</td>\n",
       "      <td>Bronze</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3491</th>\n",
       "      <td>189</td>\n",
       "      <td>4</td>\n",
       "      <td>1430.28</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "      <td>Bronze</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3492</th>\n",
       "      <td>80</td>\n",
       "      <td>3</td>\n",
       "      <td>2193.81</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>212</td>\n",
       "      <td>Silver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3493</th>\n",
       "      <td>93</td>\n",
       "      <td>6</td>\n",
       "      <td>3728.88</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>123</td>\n",
       "      <td>Bronze</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3494</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2755.11</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>412</td>\n",
       "      <td>Platinum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3495</th>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>3847.65</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>433</td>\n",
       "      <td>Platinum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3496</th>\n",
       "      <td>256</td>\n",
       "      <td>4</td>\n",
       "      <td>2045.84</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>112</td>\n",
       "      <td>Bronze</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3497</th>\n",
       "      <td>52</td>\n",
       "      <td>3</td>\n",
       "      <td>1648.32</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>211</td>\n",
       "      <td>Bronze</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3498</th>\n",
       "      <td>127</td>\n",
       "      <td>6</td>\n",
       "      <td>3147.33</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>123</td>\n",
       "      <td>Bronze</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3499</th>\n",
       "      <td>51</td>\n",
       "      <td>7</td>\n",
       "      <td>4955.25</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>234</td>\n",
       "      <td>Silver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3500</th>\n",
       "      <td>144</td>\n",
       "      <td>6</td>\n",
       "      <td>1785.86</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>121</td>\n",
       "      <td>Bronze</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3416 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             recency  frequency  monetary r_quartile f_quartile m_quartile  \\\n",
       "customer_id                                                                  \n",
       "1                  7         11   3018.09          4          4          3   \n",
       "2                128          3   2226.26          1          1          2   \n",
       "3                102          8   3362.81          1          4          3   \n",
       "4                195          2    220.57          1          1          1   \n",
       "5                 16          6   2394.94          4          2          2   \n",
       "6                 64          5   3946.55          2          2          3   \n",
       "7                253          3    220.11          1          1          1   \n",
       "8                 22         10   7066.94          3          4          4   \n",
       "9                 78          6   2353.11          2          2          2   \n",
       "10                43          5   3358.28          3          2          3   \n",
       "11                46          6   3638.84          2          2          3   \n",
       "12                67          7   3540.03          2          3          3   \n",
       "13                27          7   4337.38          3          3          4   \n",
       "14                47          3   1713.90          2          1          1   \n",
       "15                35          6   1728.39          3          2          1   \n",
       "16                99          5   4521.84          1          2          4   \n",
       "17                 0          5   2015.61          4          2          2   \n",
       "18               134          7   3543.38          1          3          3   \n",
       "19               102          3   2951.79          1          1          3   \n",
       "20                31          4   3608.28          3          1          3   \n",
       "21                 6          5   4229.41          4          2          4   \n",
       "22                97          8   5159.84          1          4          4   \n",
       "23                10          6   4376.15          4          2          4   \n",
       "24                17          7   3689.35          4          3          3   \n",
       "25                 5         12   5333.66          4          4          4   \n",
       "26               259          2    268.24          1          1          1   \n",
       "27               143          7   3274.25          1          3          3   \n",
       "28                83          6   3366.56          2          2          3   \n",
       "29                22          9   6175.30          3          4          4   \n",
       "30                11          3   2633.95          4          1          2   \n",
       "...              ...        ...       ...        ...        ...        ...   \n",
       "3470               2          8   7228.80          4          4          4   \n",
       "3471             148          4   1914.10          1          1          2   \n",
       "3472               5          6   2779.58          4          2          2   \n",
       "3474              71          5   2358.99          2          2          2   \n",
       "3475               3          4   2515.14          4          1          2   \n",
       "3476              50          5   1152.36          2          2          1   \n",
       "3477              64          8   4401.92          2          4          4   \n",
       "3478              29          6   4297.85          3          2          4   \n",
       "3479              31          6   2265.96          3          2          2   \n",
       "3480              16          4   1770.26          4          1          1   \n",
       "3481              54          7   2438.45          2          3          2   \n",
       "3482              27          8   5549.59          3          4          4   \n",
       "3483             117          6   2193.04          1          2          2   \n",
       "3484              79          7   5924.55          2          3          4   \n",
       "3485              25          3   2491.47          3          1          2   \n",
       "3486               9          4   2972.34          4          1          3   \n",
       "3487              10          3   1837.75          4          1          2   \n",
       "3488              17          2    815.95          4          1          1   \n",
       "3489             108          6   2644.44          1          2          2   \n",
       "3490             166          5   2379.57          1          2          2   \n",
       "3491             189          4   1430.28          1          1          1   \n",
       "3492              80          3   2193.81          2          1          2   \n",
       "3493              93          6   3728.88          1          2          3   \n",
       "3494               4          4   2755.11          4          1          2   \n",
       "3495              13          7   3847.65          4          3          3   \n",
       "3496             256          4   2045.84          1          1          2   \n",
       "3497              52          3   1648.32          2          1          1   \n",
       "3498             127          6   3147.33          1          2          3   \n",
       "3499              51          7   4955.25          2          3          4   \n",
       "3500             144          6   1785.86          1          2          1   \n",
       "\n",
       "             rfm_score customer_title  \n",
       "customer_id                            \n",
       "1                  443       Platinum  \n",
       "2                  112         Bronze  \n",
       "3                  143         Bronze  \n",
       "4                  111         Bronze  \n",
       "5                  422       Platinum  \n",
       "6                  223         Silver  \n",
       "7                  111         Bronze  \n",
       "8                  344           Gold  \n",
       "9                  222         Silver  \n",
       "10                 323           Gold  \n",
       "11                 223         Silver  \n",
       "12                 233         Silver  \n",
       "13                 334           Gold  \n",
       "14                 211         Bronze  \n",
       "15                 321           Gold  \n",
       "16                 124         Bronze  \n",
       "17                 422       Platinum  \n",
       "18                 133         Bronze  \n",
       "19                 113         Bronze  \n",
       "20                 313           Gold  \n",
       "21                 424       Platinum  \n",
       "22                 144         Bronze  \n",
       "23                 424       Platinum  \n",
       "24                 433       Platinum  \n",
       "25                 444       Platinum  \n",
       "26                 111         Bronze  \n",
       "27                 133         Bronze  \n",
       "28                 223         Silver  \n",
       "29                 344           Gold  \n",
       "30                 412       Platinum  \n",
       "...                ...            ...  \n",
       "3470               444       Platinum  \n",
       "3471               112         Bronze  \n",
       "3472               422       Platinum  \n",
       "3474               222         Silver  \n",
       "3475               412       Platinum  \n",
       "3476               221         Silver  \n",
       "3477               244         Silver  \n",
       "3478               324           Gold  \n",
       "3479               322           Gold  \n",
       "3480               411           Gold  \n",
       "3481               232         Silver  \n",
       "3482               344           Gold  \n",
       "3483               122         Bronze  \n",
       "3484               234         Silver  \n",
       "3485               312           Gold  \n",
       "3486               413       Platinum  \n",
       "3487               412       Platinum  \n",
       "3488               411           Gold  \n",
       "3489               122         Bronze  \n",
       "3490               122         Bronze  \n",
       "3491               111         Bronze  \n",
       "3492               212         Silver  \n",
       "3493               123         Bronze  \n",
       "3494               412       Platinum  \n",
       "3495               433       Platinum  \n",
       "3496               112         Bronze  \n",
       "3497               211         Bronze  \n",
       "3498               123         Bronze  \n",
       "3499               234         Silver  \n",
       "3500               121         Bronze  \n",
       "\n",
       "[3416 rows x 8 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# RFM table dataset\n",
    "\n",
    "rfm_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merging both RFM Table with Transaction and Customer Tables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The RFM_Table dataframe is merged with the Transactions and Customer Demographics datasets, to gain depper insights of Customer Segemnts along with transactions. The dataframes are joined based on customer_ids from both the datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "cust_trans_rfm = pd.merge(merged_trans_cust, rfm_table, left_on='customer_id', right_on='customer_id', how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 19354 entries, 0 to 19353\n",
      "Data columns (total 34 columns):\n",
      "transaction_id                         19354 non-null int64\n",
      "product_id                             19354 non-null int64\n",
      "customer_id                            19354 non-null int64\n",
      "transaction_date                       19354 non-null datetime64[ns]\n",
      "online_order                           19354 non-null float64\n",
      "order_status                           19354 non-null object\n",
      "brand                                  19354 non-null object\n",
      "product_line                           19354 non-null object\n",
      "product_class                          19354 non-null object\n",
      "product_size                           19354 non-null object\n",
      "list_price                             19354 non-null float64\n",
      "standard_cost                          19354 non-null float64\n",
      "product_first_sold_date                19354 non-null float64\n",
      "Profit                                 19354 non-null float64\n",
      "first_name                             19354 non-null object\n",
      "last_name                              19354 non-null object\n",
      "gender                                 19354 non-null object\n",
      "past_3_years_bike_related_purchases    19354 non-null int64\n",
      "DOB                                    19354 non-null object\n",
      "job_title                              19354 non-null object\n",
      "job_industry_category                  19354 non-null object\n",
      "wealth_segment                         19354 non-null object\n",
      "deceased_indicator                     19354 non-null object\n",
      "owns_car                               19354 non-null object\n",
      "tenure                                 19354 non-null float64\n",
      "Age                                    19354 non-null int64\n",
      "recency                                19354 non-null int64\n",
      "frequency                              19354 non-null int64\n",
      "monetary                               19354 non-null float64\n",
      "r_quartile                             19354 non-null category\n",
      "f_quartile                             19354 non-null category\n",
      "m_quartile                             19354 non-null category\n",
      "rfm_score                              19354 non-null int32\n",
      "customer_title                         19354 non-null category\n",
      "dtypes: category(4), datetime64[ns](1), float64(7), int32(1), int64(7), object(14)\n",
      "memory usage: 4.6+ MB\n"
     ]
    }
   ],
   "source": [
    "cust_trans_rfm.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> The datatypes of the columns looks fine.</b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating an Age Group Feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "cust_trans_rfm['Age_Group'] = cust_trans_rfm['Age'].apply(lambda x : (math.floor(x/10)+1)*10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a Detail Customer title / tag based on RFM Score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An extended version of customer title is made which divides the entire number of customers into 11 groups. The groups are mainly Platinum Customers, Very Loyal, Becoming Loyal, Recent Customers, Potential Customers, Late Bloomer, Loosing Customers, High Risk Customers, Almost Lost Customers, Evasive Customers and Lost Customers.<br>\n",
    "The demarkation of customers into the above mentioned groups is based on their RFM scores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function as a lookup to appropiate customer titles based on RFM score.\n",
    "\n",
    "def cust_score_title_lkup(cols):\n",
    "    \n",
    "    rfm_score = cols[0]\n",
    "    \n",
    "    if rfm_score >= 444:\n",
    "        return 'Platinum Customer'\n",
    "    elif rfm_score >=433 and rfm_score < 444:\n",
    "        return 'Very Loyal'\n",
    "    elif rfm_score >=421 and rfm_score < 433:\n",
    "        return 'Becoming Loyal'\n",
    "    elif rfm_score >=344 and rfm_score < 421:\n",
    "        return 'Recent Customer'\n",
    "    elif rfm_score >=323 and rfm_score < 344:\n",
    "        return 'Potential Customer'\n",
    "    elif rfm_score >=311 and rfm_score < 323:\n",
    "        return 'Late Bloomer'\n",
    "    elif rfm_score >=224 and rfm_score < 311:\n",
    "        return 'Loosing Customer'\n",
    "    elif rfm_score >=212 and rfm_score < 224:\n",
    "        return 'High Risk Customer'\n",
    "    elif rfm_score >=124 and rfm_score < 212:\n",
    "        return 'Almost Lost Customer'\n",
    "    elif rfm_score >=112 and rfm_score < 124:\n",
    "        return 'Evasive Customer'\n",
    "    else :\n",
    "        return 'Lost Customer'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Applying the above functions and creating a new feature detail_cust_title\n",
    "\n",
    "cust_trans_rfm['detail_cust_title']=cust_trans_rfm[['rfm_score']].apply(cust_score_title_lkup, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to provide ranks to the customers based on their title.\n",
    "\n",
    "def get_rank(cols):\n",
    "    \n",
    "    title = cols[0]\n",
    "    \n",
    "    if title=='Platinum Customer':\n",
    "        return 1\n",
    "    elif title=='Very Loyal':\n",
    "        return 2\n",
    "    elif title == 'Becoming Loyal':\n",
    "        return 3\n",
    "    elif title == 'Recent Customer':\n",
    "        return 4\n",
    "    elif title=='Potential Customer':\n",
    "        return 5\n",
    "    elif title == 'Late Bloomer':\n",
    "        return 6\n",
    "    elif title == 'Loosing Customer':\n",
    "        return 7\n",
    "    elif title=='High Risk Customer':\n",
    "        return 8\n",
    "    elif title == 'Almost Lost Customer':\n",
    "        return 9\n",
    "    elif title == 'Evasive Customer':\n",
    "        return 10\n",
    "    else :\n",
    "        return 11\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Applying the above functions and creating a new feature rank\n",
    "\n",
    "cust_trans_rfm['rank']=cust_trans_rfm[['detail_cust_title']].apply(get_rank, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Exporting to CSV File"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After performing data quality assessment(DQA), data cleaning and RFM Analysis on the dataset, it's time to export the dataset to a csv file for further <b>exploratory data analysis (EDA)</b> and this data will drive the <b>Sales Customer Segmenation Dashboard</b> developed in <b>Tableau</b>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "cust_trans_rfm.to_csv('Customer_Trans_RFM_Analysis.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total records in final dataset : 19354\n"
     ]
    }
   ],
   "source": [
    "print(\"Total records in final dataset : {}\".format(cust_trans_rfm.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Data Analysis and Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1. New Customer vs Old Customer Age Distributions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most New / Old Customers are aged between 40-49. The lowest age groups are under 20 and 80+ for both Old and New Customers dataset.<br>\n",
    "Among the New Customers the most populated age bracket is 20-29 and 60-69, while the maximum Old Customers are from the age bracket 50-69.<br>\n",
    "There is a steep drop in number of customers in 30-39 age groupsd among the New Customers.<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading the New Customers Dataset\n",
    "\n",
    "new_cust = pd.read_csv('NewCustomerList_Cleaned.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "sns.distplot(new_cust['Age Group'], kde=False, bins=15)\n",
    "plt.xlabel('Age Group')\n",
    "plt.ylabel('Number of Customers')\n",
    "plt.title('New Customers - Age Distribution')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Here 20 = under 20, 30 = 20-29 age bracket</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "sns.distplot(cust_trans_rfm['Age_Group'], kde=False, bins=15)\n",
    "plt.xlabel('Age Group')\n",
    "plt.ylabel('Number of Customers')\n",
    "plt.title('Old Customers - Age Distribution')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Here 20 = under 20, 30 = 20-29 age bracket</b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2. Bike related purchases over last 3 years by gender"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Over the last 3 years approximately 51% of the buyers are women and 49% were male buyers.<br>\n",
    "Female purchases are approximately 10,000 more than male (numerically). Gender wise majority of the bike sales comes from female customers.<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "cust_bike_purchase_by_gender = cust_trans_rfm.groupby('gender').agg({'past_3_years_bike_related_purchases' : sum}\n",
    "                                                                 ).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_records = cust_trans_rfm['past_3_years_bike_related_purchases'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "cust_bike_purchase_by_gender['Percent_of_total'] = (cust_bike_purchase_by_gender['past_3_years_bike_related_purchases']\n",
    "                                                        /total_records)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>gender</th>\n",
       "      <th>past_3_years_bike_related_purchases</th>\n",
       "      <th>Percent_of_total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Female</td>\n",
       "      <td>478488</td>\n",
       "      <td>50.503731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>468943</td>\n",
       "      <td>49.496269</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender  past_3_years_bike_related_purchases  Percent_of_total\n",
       "0  Female                               478488         50.503731\n",
       "1    Male                               468943         49.496269"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cust_bike_purchase_by_gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "sns.barplot(x='gender',y='Percent_of_total',data=cust_bike_purchase_by_gender)\n",
    "plt.xlabel('Gender')\n",
    "plt.ylabel('Percent of Total Purchases')\n",
    "plt.title('Female vs Male past 3 years Bike purchases')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 Job Industry Customer Distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Among the <b>New Customers</b> the highest amount of sales comes from customers having a job in Manufacturing and Financial services sector. The samllest chunk of sales comes from customers in Agriculture sector and from Telecom sector with 3% sales only. Similar trend is observed among <b>Old Customers</b>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "sns.countplot(x='job_industry_category',data=new_cust[~(new_cust['job_industry_category']=='Missing')])\n",
    "plt.xlabel('Job Industry')\n",
    "plt.ylabel('Number of Customers')\n",
    "plt.title('New Customers - Job Industry Customer Distribution')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "sns.countplot(x='job_industry_category',data=cust_trans_rfm[~(cust_trans_rfm['job_industry_category']=='Missing')])\n",
    "plt.xlabel('Job Industry')\n",
    "plt.ylabel('Number of Customers')\n",
    "plt.title('Old Customers - Job Industry Customer Distribution')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4. Wealth Segmentation by Age Group"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Across all Age Groups the maximum number of customers are classified as 'Mass Customers'. The next being 'High Net Worth'. However among 40-49 aged customers 'Affluent Customers' outperforms the 'High Net Worth' customers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>New Customers</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "wealth_age_seg_new = new_cust.groupby(['wealth_segment', 'Age Group']).size().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>wealth_segment</th>\n",
       "      <th>Age Group</th>\n",
       "      <th>Number of Customers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>30</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>40</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>50</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>60</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>70</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>80</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>90</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>30</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>40</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>50</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>60</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>70</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>80</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>90</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>30</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>40</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>50</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>60</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>70</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>80</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>90</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       wealth_segment  Age Group  Number of Customers\n",
       "0   Affluent Customer         20                    3\n",
       "1   Affluent Customer         30                   49\n",
       "2   Affluent Customer         40                   15\n",
       "3   Affluent Customer         50                   58\n",
       "4   Affluent Customer         60                   40\n",
       "5   Affluent Customer         70                   41\n",
       "6   Affluent Customer         80                   20\n",
       "7   Affluent Customer         90                    9\n",
       "8      High Net Worth         30                   42\n",
       "9      High Net Worth         40                   34\n",
       "10     High Net Worth         50                   53\n",
       "11     High Net Worth         60                   36\n",
       "12     High Net Worth         70                   50\n",
       "13     High Net Worth         80                   24\n",
       "14     High Net Worth         90                   10\n",
       "15      Mass Customer         20                    5\n",
       "16      Mass Customer         30                   74\n",
       "17      Mass Customer         40                   52\n",
       "18      Mass Customer         50                  121\n",
       "19      Mass Customer         60                   93\n",
       "20      Mass Customer         70                   81\n",
       "21      Mass Customer         80                   51\n",
       "22      Mass Customer         90                   22"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wealth_age_seg_new.rename(columns={0:'Number of Customers'}, inplace=True)\n",
    "wealth_age_seg_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "sns.barplot(x='Age Group', y='Number of Customers' , hue='wealth_segment', data=wealth_age_seg_new)\n",
    "plt.xlabel('Age Group')\n",
    "plt.ylabel('Number of Customers')\n",
    "plt.title('New Customers - Wealth Segmentation by Age Group')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In all the age groups the most number of customers are 'Mass Customers'. The 2nd largest customer base being the 'High Net Worth' group.<br>In the age group 40-49 the 'Affluent Customer' group outperforms 'High Net Worth' group"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Old Customers</b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar treand (like that of New Customers) is observed among Old Customers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "wealth_age_seg_old = cust_trans_rfm.groupby(['wealth_segment', 'Age_Group']).size().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "wealth_age_seg_old.rename(columns={0:'Number of Customers'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yj5ANa2xCwX2eaTa77BSgBSveP0iapg+SnfPdyWYGnUb2R4BP+WKI5PIhmZPJJnS5h+yY6/NzsiGOU8k+k3p7lVfhYbKE9kayXpAHyc7jt9M0/X1+HO/kZe+TPe7hVbJz+1WyHs3l1/Jx+etFsmtuRN5GvT27q5Km6Stks3E2J7u2XwZ+R/YohKJn1E3T9BmySYxGkfXc35AnTEeR3Yc3jay37BfLj6vAuWQzoM4gO+e17b9k34vc7WTXzmNkw4r/QTastzCGRWTJUCOyR7PUZxjZ0OmV7ltO0/QNsp7ZU/OiH5E9zuTPZNfqVmTnalHBNmv03c2vs2+Q/Vs0luz6ehQ4Oj++q+reurjznqbpq2Q9nsfkdd+hYHbjAsvy8t/mx98SOKxG0i3pSyx88fuLJElan0IIJ/DFcxdX51EoWgshhITs+YQDStzOA8CHaZquq4R3vQrZYzN+n6apc1JIGzC/wJIkrSchhP8m62GcRza5z1XAbSaD5RGy56J+g2z4cq3PPl2Lfe9Ods/dk2ST7RxPNjT90HXZjiStLRNCSZLWn6+TDa38Ctl9qLeQDTlWeTxPdp/hz9I0fWgd7zsFTid71mED4N9kz1+9u96tJKnMHDIqSZIkSRXKSWUkSZIkqUKZEEqSJElShaqEewgdEytJkiSp0oXaCishIWTOnDnrOwRJkiRJWi9atWpVZ51DRiVJkiSpQpkQSpIkSVKFMiGUJEmSpApVEfcQ1pSmKYsWLWLZsmWEUOu9ldoIpWlKgwYNaNKkiZ+7JEmSRIUmhIsWLWKTTTahUaOKPPyKVlVVxaJFi2jatOn6DkWSJEla7ypyyOiyZctMBitUo0aNWLZs2foOQ5IkSfpSqMiE0OGClc3PX5IkScpUZEL4ZTZr1ixat25NVVVVneu0bt2aGTNmlDEqSZIkSRujsoybjDE2AR4BGudt3p4kyUUxxjFAD+DjfNUTkyR5IcYYgOuBQ4GFeflz+b6GAiPy9S9LkuSmchzD+nLkkUfyzW9+k29961vrO5T16oknnuD73/8+U6ZMWd+hSJIkSRuNct1ItxjomSTJ/BjjJsBjMca787ofJklye431+wPt81dX4DdA1xjjV4CLgL2BFJgSY5yYJMmHZTkKSZIkSdqIlGXIaJIkaZIk8/PFTfJXWs8mg4Cx+XZPAVvGGFsChwD3JUkyL08C7wP6lTL2msaNG8fQoUOrl7t3786wYcOql/fee2/+9a9/MX36dI455hh23XVXvvGNbzBx4sTqdSZNmkTfvn3p0KEDe++9Nz//+c9rbevKK6/k6aefZsSIEbRv354LL7ywuu7RRx+le/fudO7cmQsuuIA0re90wowZMxgyZAgdO3Zkt91247TTTquuqy/WefPmMXToUDp06MChhx7KVVddxRFHHFFd37p1a8aMGUP37t3ZZZdd+NnPfsbMmTMZMGAAHTp0YNiwYSxZsqR6/fvuu48+ffrQqVMnBg4cyMsvv1xd17VrV0aNGkXv3r3p2LEjp512GosWLWLhwoUcf/zxvPPOO7Rv35727dvz9ttv13u8kiRJklatbFNtxhgbAlOAdsCvkiR5OsZ4OnB5jPF/gPuBHydJshhoDcwq2PytvKyu8pptnQqcCpAkCdtss80K9e+8884azzLavXt3Lr74Yho0aMB7771HVVUVzz77LI0aNWLmzJksXLiQ9u3bc8ABB3Deeedx66238vLLL3P00UfTuXNnOnbsSIsWLbjhhhvo2LEjr7zyCjFGdt99dw499FAaNmwIZLNhjhgxgilTpjBkyBC+/e1vrxDH/fffzz//+U8+/fRT+vbtS79+/ejZs2edcV9zzTUcdNBB3HHHHSxZsoSpU6fSqFEjFixYwLHHHltnrCNGjKBZs2a8+OKLzJo1i2OOOYY2bdqscP4efvhhJk2axOzZs+nTpw9TpkzhN7/5DV/5ylc47LDDuPPOOzn66KOZNm0a5557LjfffDN77rknt99+O9/5znd4/PHHady4MSEE7rzzTm699VYaN27MgAED+Otf/8rQoUP585//zBlnnMELL7ywRp9bocaNG690TUiSJEmVqGwJYZIkS4E9Y4xbAnfEGHcDzgfeBjYFRgM/Ai4BapsGMq2nvGZbo/P9AaTvv//+CvWLFy+uTrxWV5s2bWjWrBlTp07ljTfeoEePHrz00kv8+9//ZsqUKey7777cc889tGnThqOOOgqAzp07079/fyZOnEi7du3o2rUrkD3+okOHDgwaNIjHH3+cvn37snTpUoDqSWXSNGXZsmUrTTJzxhln0KxZM5o1a8Z+++3HtGnTOPDAA+uMu2HDhsyaNYu33nqLVq1a0aVLF6qqquqN9Wtf+xp33XUX999/P5tuuilt27blyCOP5Mknn1whnu9973s0bdqUdu3a0aFDBw488EDatGkDwEEHHcTUqVMZMmQIY8eO5dvf/jZ77LEHaZoyZMgQfvGLXzB58mT2228/0jTlO9/5TnWy1rt3b6ZNm0ZVVRVLly4lTdN6J9sp1uLFi6l5TUiSJEkbq1atWtVZV/aH8SVJ8lGM8SGgX5Ik1+TFi2OMNwL/nS+/BexQsFkbYE5eflCN8odKGW9t9ttvP5544glmzpxJt27d2HzzzXnyySeZMmUK3bp1Y/bs2Tz//PN06tSpepuqqiqGDBkCwHPPPcfIkSN59dVX+fzzz1myZAmHHXbYasWw7bbbVr9v2rQpCxYsqHf9ESNGcPXVV3P44YezxRZbMGzYMI455ph6Y/3ggw+oqqqidesvOmFru5gKe9uaNGmy0vJ7770HwOzZs7ntttu48cYbq+uXLFnCO++8U+dxFdZJkiRJWrfKNcvotsDneTLYFOgNXBVjbJkkydx8VtEjgH/lm0wEzowx3ko2qczH+Xr3AiNjjFvl6/Ul62Usq27dunHfffcxa9Ysvv/977P55ptzxx13MGXKFE466SRmzJhBt27duPXWW2vd/swzz+TEE0/klltuoUmTJvzP//wPH35Y2nlxtttuO66++moAJk+ezDHHHEPXrl1p1apVnbEuXbqURo0aMWfOHNq2bQvAnDlz1jiGli1bctZZZ/GDH/xgtbf12YGSJEnSuleu5xC2BB6MMU4DniGbGObvwJ9ijC8CLwLbAJfl6/8DeAOYDvwO+B5AkiTzgEvzfTwDXJKXlVW3bt144oknWLRoEa1ataJr16489NBDfPjhh+y222707t2bN954g9tvv53PP/+czz//nBdeeIHXXnsNgPnz57PlllvSpEkTnn/+ecaPH19nW9tuuy1vvvnmWsd85513VidzW2yxBSEEGjZsWG+sDRs2pH///lx77bV89tlnTJ8+ndtvrzkhbPGOO+44br75Zp577jnSNGXhwoVMmjSJ+fPnr3Lbbbfdlo8++ohPPvlkjduXJEmStKKy9BAmSTIN2KuW8lpnQUmSJAXOqKPuj8Af12mAq6lt27Y0a9aMfffdF4AWLVqw4447svXWW9OwYUOaN2/On//8Z37605/y05/+lGXLltG5c2cuuugiAEaOHMkll1zCiBEj6NatGwMGDKgz0Tn55JMZPnw4N998M0OGDOHSSy9do5inTp3KxRdfzCeffMK2227LT3/6U3bccUeAemO9/PLLGT58OHvuuSdt27bliCOOYOrUqWsUwx577MHVV1/NiBEjmDFjBk2aNGGfffahW7duq9y2Xbt2DBo0iP32249ly5bx4IMPsv32269RHJIkSZIyYVWPK9gIpDWHOS5cuJDNNttsPYWzYbv88st59913uf7669d3KGvMz1+SJEmVJJ8HpNZ7sMo1ZFQbqOnTp/Pyyy+TpinPP/88f/nLX+jfv//6DkuSJEnSOlD2WUZVOj/60Y/429/+tlL5N7/5Ta666qo12uf8+fM544wzePvtt9lmm20YNmwYhxxyyNqGKkklce/EuWVr65CBLcvWliRJpeKQUVUcP39p42VCKEnSyhwyKkmSJElaiQmhJEmSJFUoE0JJkiRJqlAmhJIkSZJUoUwI16O7776b1q1bM3369BXKL730Ug4++GAuvfRSPvjgAw4//HD69u3L008/TdeuXZk3b946jWPWrFnccccddda//vrrHH/88XTv3p0ePXowbNgw3nvvvdVuZ9y4cbz99ttrE6okSZKkdcjHTgBzf/jddbq/llf/vqj1xo8fz7777suECRM499xzq8tvueUWpk2bRuPGjZkwYQJt27Yt6YPglyeEgwcPXqlu0aJFnHDCCVx00UX07dsXgMcff5wPPviAbbfddrXaue222+jYsSPbb7/9Ool7VdI0JU1TGjTw7x6SJElSbfxNeT1ZsGABzz77LNdccw0TJkyoLj/xxBNZuHAhhx9+OL/61a+47LLLeOCBB+jTpw+fffZZ9XqzZs2iZ8+e1cujRo3i5z//OQAzZ87kuOOOo1+/fgwePLi6B3L48OH85Cc/YeDAgey33378/e9/B2DkyJFMnjyZPn36MHr06BXiHD9+PF26dKlOBgG6d+9Ox44dGTduHBdeeGF1+QknnMATTzzB0qVLGT58OD179qRXr16MHj2av//970ydOpUzzzyz+lgeffRR+vbtS69evTjnnHNYvHgxAF27duWKK65gwIAB9O/fnxdffJFvfetb7L///owdO7a6vd/85jcceuih9O7dm2uuuab6vPTo0YPzzz+fQw45hJqPHJEkSZL0BXsI15N77rmHgw46iLZt27Llllvy4osvsvvuuzNmzBjat2/PfffdB8A222zDtGnTuPzyy4ve93nnnceVV17JzjvvzHPPPcf555/PbbfdBsA777zD+PHjmT59OieddBKHH344F1xwAaNGjVoh2Vru3//+N1//+tdX69heeukl3n77bR544AEAPv74Y7bYYgvGjBnDT37yE/bYYw8WLVrE2Wefzbhx42jbti1nnXUWY8eO5ZRTTgGyZ6XceeedXHTRRZx99tmMHz+exYsXc/DBB3PCCSfw8MMPM2PGDO666y7SNOXEE0/kqaeeonXr1rz++utce+21XHHFFasVtyRJklRpTAjXk/Hjx1cnP4MGDWL8+PHsvvvua73fBQsWMGXKFIYNG1ZdtmTJkur3/fr1o0GDBuyyyy5rdB9gMXbccUf+85//MGLECHr16kWPHj1WWuf1119nxx13pG3btgAcddRR3HTTTdXnZHmPZKdOnVi4cCHNmzenefPmNG7cmI8//piHH36Yhx9+uHq9hQsXMmPGDFq3bk2bNm3o0qVLSY5NkiRJ2piYEK4H8+bN44knnuDVV18lhMDSpUsJITBixAhCCEXto2HDhixbtqx6edGiRQAsW7aMzTffvLqHsaZNN920+n2apqtsp0OHDjz55JO11jVq1GiFGJYP+dxyyy257777eOihhxgzZgx33nkn11577Qrbrqrtxo0bAxBCWCHmBg0asHTpUtI05cwzz+T4449fYbtZs2ax2WabrfK4JEmSJHkP4Xpx1113MWTIECZPnszTTz/Ns88+y4477sjkyZOL3se2227L+++/z7x581i8eDGTJk0CoEWLFuywww7ceeedQJZ4vfTSS/Xuq3nz5ixYsKDWuiOOOIIpU6ZU7x/gwQcf5JVXXmGHHXbgpZdeYtmyZcyePZsXXngByBLeZcuWcdhhh/HDH/6QF198EYBmzZoxf/58ANq1a8esWbOYMWMGAH/961/p1q1b0cd/0EEHMW7cuOq4586dy/vvv1/09pIkSZLsIVwvJkyYwBlnnLFC2aGHHsodd9xB165di9rHJptswtlnn82AAQPYYYcdaNeuXXXdDTfcwPnnn8/1119PVVUVgwYNYtddd61zX506daJhw4b07t2bGCOnnnpqdV3Tpk256aabuOiii7jooovYZJNN6NSpE5dccgkdO3Zkxx13pFevXnTo0KF6yOvcuXM555xzqnsPzz//fABijPz4xz+mSZMmTJw4kWuvvZZhw4axdOlS9thjj5V6++rTo0cPXnvtNQYOHAjAZpttxi9/+UsaNmxY9D4kSZKkSheKGTa4gUtrzjS5cOFChxVWMD9/aeN178S5ZWvrkIEty9aWJElro1WrVgC13pvmkFFJkiRJqnH4LC0AACAASURBVFAmhJIkSZJUoUwIJUmSJKlCmRBKkiRJUoUyIZQkSZKkCmVCKEmSJEkVyoRwPWnfvv0Ky+PGjePCCy8EYOzYsdx22231bl+4fn2OPPJI+vfvX708depUjjzyyHq3mTVrFnfccUetdSeffDL33HNP9fI3vvENfvGLX1Qvn3LKKfzjH/9YZVx1tVXscUmSJElaez6YHjjxpifX6f7GDN1vrbY/4YQT1lEkmffff58HHniAnj17FrX+8iRt8ODBK9XtvffePPvss/Tr14958+bRrFkzpkyZUl0/ZcoULr/88qLaqaqqqrctSZIkSaVlD+GX0M9//nNGjRoFwAsvvEDv3r0ZMGAAl1566QpJ3TvvvMNxxx1H9+7dueyyy+rc3+mnn87111+/UvnSpUu59NJLOfTQQ+nduzc333wzACNHjmTy5Mn06dOH0aNHr7DN8oQQ4Nlnn6V3797MmzePNE35z3/+Q5MmTdhuu+1YtGgRZ599Nr169aJv3748/vjjQNYDeOqppzJ06FCOPfbYWtsq9rgkSZIkrR17CNeTRYsW0adPn+rljz76iL59+6603jnnnMNVV13FPvvsw8iRI1eoe+mll7j33nvZdNNNOfDAAznppJNo3br1Svvo0qULd999N48//jjNmzevLv/LX/5CixYt+Mc//sHixYs54ogj6NGjBxdccAGjRo1i7NixK+3r61//Oq+++ipLlixhypQpdOvWjTfffJPXXnuNf/3rX+y9994AjBkzBoD777+f6dOnc+yxx/Loo48CWS/ipEmT2GqrrXjiiSdWaGvcuHFFH5ckSZKktWMP4XrSpEkT7rvvvurXf//3f6+0zscff8z8+fPZZ599ADjiiCNWqD/ggAPYfPPNadKkCbvssguzZ8+us70f/OAHK/USPvzww9x+++306dOHww8/nA8//JAZM2bUG3fjxo3ZZZddePHFF5kyZQp77bUXXbp04dlnn+XZZ5+tTgifeeYZhgwZAkC7du1o06YNb7zxBgAHHnggW221VZ1trM5xSZIkSVpzJoRfYmma1lu/6aabVr9v0KABVVVVda57wAEHsHjxYp577rkVyi+77LLqpPSpp56iR48eq4xr77335umnn2bBggVsueWWKySEy5PX+mLfbLPN1tlxSZIkSVpzJoRfYltuuSXNmzevnrRlwoQJa7W/s846i1//+tfVyz169GDs2LF8/vnnALz++ussXLiQ5s2bs2DBgjr3s88++3DLLbfQuXNnADp16sRzzz3H7Nmz6dChAwBdu3atnj309ddfZ/bs2bRt23alfa2qLUmSJEmlY0L4JXfNNdfwox/9iAEDBgDQokWLNd5Xr1692HrrrauXv/Wtb9G+fXv69etHz549+dGPfkRVVRWdOnWiYcOG9O7de6VJZSDrIXzzzTfp0qULAI0aNWKbbbZhjz32oEGD7JIaOnQoS5cupVevXpx++ulcd911NG7ceKV9raotSZIkSaUTVjUscSOQzpkzZ4WChQsXrnLY4pfFggULaNasGQA33HAD7777Lpdccsl6jmrDtiF9/pJWz70T55atrUMGtixbW5IkrY1WrVoBhNrqnGX0S27SpEnccMMNLF26lNatW6/wEHhJkiRJWhsmhF9ygwYNYtCgQes7DEmSJEkbIe8hlCRJkqQKZUIoSZIkSRXKhFCSJEmSKpQJoSRJkiRVKBPC9aR169Z8//vfr16uqqpi991354QTTihZm6NGjeLAAw+kZ8+e9O7dm9tuu2219zFr1qzqB85LkiRJ2rA5yyjr/rlVxTybarPNNuPVV1/ls88+o2nTpjzyyCNsv/326zSOQmPHjuWRRx7hrrvuokWLFnzyySfcc889q72f5Qnh4MGDSxBl7aqqqmjUyEtVkiRJWtfsIVyPDj74YO6//34Axo8fzxFHHFFd9/zzzzNw4ED69u3LwIEDmT59OgCvvvoqhx12GH369KF379688cYbLFy4kOOPP57evXvTs2dPJkyYsFJbv/zlLxk5ciQtWrQAYPPNNyfGCEDXrl2ZN28eAFOnTuXII48E4Mknn6RPnz706dOHvn37Mn/+fEaOHMnkyZPp06cPo0ePZtGiRZx99tn06tWLvn378vjjjwMwbtw4vvOd7zB06FC6devGjTfeyG9/+1v69u3L4YcfzocffgjAzJkzOe644+jXrx+DBw+uPs7hw4dz8cUXc+SRR3L55Zev83MvSZIkyR7C9WrQoEFcd9119O7dm1deeYVjjjmGp59+GoB27drxt7/9jUaNGvHII49w1VVX8bvf/Y6bb76Zk08+mW9+85ssWbKEpUuX8sADD7D99ttz8803A/DJJ5+s0M78+fNZsGABO+2002rFN2rUKEaOHMk+++zDggULaNy4MRdccAGjRo1i7Nix1esA3H///UyfPp1jjz2WRx99FMiS13vvvZfFixfTvXt3LrjgAv75z39y0UUXcfvtt3PKKadw3nnnceWVV7Lzzjvz3HPPcf7551cPZX3jjTcYN24cDRs2XONzLEmSJKluJoTrUefOnXnrrbeYMGECPXv2XKHuk08+Yfjw4cyYMYMQAp9//jkAXbp04X//93+ZO3cu/fv3Z+edd6Zjx45ceumlXH755fTu3ZuuXbuusK80TQkhrHZ8++yzDz/96U8ZPHgw/fv3p1WrViut88wzz3DSSScBWRLbpk0b3njjDQD2339/mjdvTvPmzWnRogV9+vQBoFOnTrz88sssWLCAKVOmMGzYsOr9LVmypPr94YcfbjIoSZIklZBDRtezvn37cskll6wwXBTg6quvZv/99+eBBx5gzJgxLF68GIDBgwdz44030qRJE4477jgee+wx2rZty913303Hjh254ooruO6661bYV4sWLWjatClvvvlmrTE0atSIZcuWAVS3A3DmmWdy9dVXs2jRIgYMGFA9nLNQmqZ1Htumm25a/b5BgwY0btwYgBACS5cuZdmyZWy++ebcd9991a+HH364epvNNtuszn1LkiRJWnsmhOvZ0Ucfzdlnn02nTp1WKP/000+rJ5lJkqS6/M033+SrX/0qJ598Mn369OGVV17h7bffpmnTpgwZMoTTTjuNF198caV2zjzzTC688EI+/fTT6v3fcsstALRp04Zp06YBcNddd1VvM3PmTDp16sQZZ5zBHnvswfTp02nevDkLFiyoXqdr167Vs46+/vrrzJ49m7Zt2xZ17C1atGCHHXbgzjvvBLLk8qWXXipqW0mSJElrzyGj61mrVq347ne/u1L56aefzvDhwxk9ejTdu3evLp84cWL1vYXbbbcdZ599NlOnTuWyyy4jhMAmm2zCFVdcsdL+hg4dysKFCzn00EPZZJNNaNSoUfVQzXPOOYdzzz2XX/7yl+y1117V2/z+97/niSeeoEGDBuyyyy4cfPDBNGjQgIYNG9K7d29ijAwdOpQf//jH9OrVi4YNG3LddddV9wQW44YbbuD888/n+uuvp6qqikGDBrHrrruuzimUJEmStIZCfUP+NhLpnDlzVihYuHChwxErmJ+/tPFa148Rqk8xjxiSJOnLIJ8LpNZJRRwyKkmSJEkVyoRQkiRJkiqUCaEkSZIkVaiKTAgr4L5J1cPPX5IkScqUZZbRGGMT4BGgcd7m7UmSXBRj/BpwK/AV4Dng+CRJlsQYGwNjgS7AB8DRSZLMzPd1PnAysBQ4K0mSe1c3ngYNGlBVVUWjRk6yWmmqqqpo0KAi/w4iSZIkraRcGdFioGeSJPNjjJsAj8UY7wbOAa5LkuTWGOMoskTvN/nPD5MkaRdjPAa4Cjg6xtgZOAbYFWgFTIox7pIkydLVCaZJkyYsWrSIxYsXE0Ktk+1oI5SmKQ0aNKBJkybrOxRJkiTpS6EsCWGSJCkwP1/cJH+lQE/gW3n5TcDFZAnhoPw9wO3ADTHGkJffmiTJYmBGjHE6sC/w5OrEE0KgadOma3o4kiRJkrRRKNvYuRhjwxjjC8C7wH3A68BHSZJU5au8BbTO37cGZgHk9R8DWxeW17KNJEmSJGk1lO0munxY554xxi2BO4BOtay2fLaP2sZxpvWUryDGeCpwat4u22yzzRrFLEna0JTvwfT+3yJJ2hiUfVaVJEk+ijE+BHQDtowxNsp7AdsAc/LV3gJ2AN6KMTYCtgDmFZQvV7hNYRujgdH5Yvr++++X4lAkSRXM/1skSRuKVq1a1VlXliGjMcZt855BYoxNgd7AK8CDwJH5akOBCfn7ifkyef0D+X2IE4FjYoyN8xlK2wOTy3EMkiRJkrSxKdc9hC2BB2OM04BngPuSJPk78CPgnHxymK2BP+Tr/wHYOi8/B/gxQJIkLwEJ8DJwD3DG6s4wKkmSJEnKhAp4SHc6Z85Ko0olSRuheyeW7x7CQwa2LFtbkiStjXzIaK3P2/MJ3ZIkSZJUoUwIJUmSJKlCmRBKkiRJUoUyIZQkSZKkCmVCKEmSJEkVyoRQkiRJkiqUCaEkSZIkVSgTQkmSJEmqUCaEkiRJklShTAglSZIkqUKZEEqSJElShTIhlCRJkqQKZUIoSZIkSRXKhFCSJEmSKpQJoSRJkiRVKBNCSZIkSapQJoSSJEmSVKFMCCVJkiSpQpkQSpIkSVKFMiGUJEmSpAplQihJkiRJFcqEUJIkSZIqlAmhJEmSJFUoE0JJkiRJqlAmhJIkSZJUoUwIJUmSJKlCmRBKkiRJUoUyIZQkSZKkCmVCKEmSJEkVyoRQkiRJkiqUCaEkSZIkVSgTQkmSJEmqUCaEkiRJklShTAglSZIkqUKZEEqSJElShTIhlCRJkqQKZUIoSZIkSRXKhFCSJEmSKpQJoSRJkiRVKBNCSZIkSapQJoSSJEmSVKFMCCVJkiSpQpkQSpIkSVKFMiGUJEmSpAplQihJkiRJFcqEUJIkSZIqlAmhJEmSJFUoE0JJkiRJqlAmhJIkSZJUoUwIJUmSJKlCmRBKkiRJUoVqVI5GYow7AGOB7YFlwOgkSa6PMV4MnAK8l696QZIk/8i3OR84GVgKnJUkyb15eT/geqAh8PskSa4sxzFIkiRJ0sYmpGla8kZijC2BlkmSPBdjbAFMAY4AIjA/SZJraqzfGfgLsC/QCpgE7JJX/x/QB3gLeAY4NkmSl+tpPp0zZ866PBxJ0pfUvRPnlq2tQwa2LFtbkiStjVatWgGE2urK0kOYJMlcYG7+/tMY4ytA63o2GQTcmiTJYmBGjHE6WXIIMD1JkjcAYoy35uvWlxBKkiRJkmpRloSwUIxxJ2Av4GmgO3BmjPEE4Fng3CRJPiRLFp8q2OwtvkggZ9Uo71rqmCVJkiRpY1TWhDDG2Bz4KzA8SZJPYoy/AS4F0vznz4HvUHt3Zkrtk+CsNOY1xngqcCpAkiRss8026+YAJElfcuUbMur/LZKkjUHZEsIY4yZkyeCfkiT5G0CSJO8U1P8O+Hu++BawQ8HmbYDlNwLWVV4tSZLRwOh8MX3//ffXxSFIklTN/1skSRuK/B7CWpVrltEA/AF4JUmSawvKW+b3FwIMBv6Vv58I/DnGeC3ZpDLtgclkPYftY4xfA2YDxwDfKscxSJIkSdLGplw9hN2B44EXY4wv5GUXAMfGGPckG/Y5ExgGkCTJSzHGhGyymCrgjCRJlgLEGM8E7iV77MQfkyR5qUzHIEmSJEkblbI8dmI987ETklQhfOyEJEkrq++xE7VN0iJJkiRJqgAmhJIkSZJUoUwIJUmSJKlCmRBKkiRJUoUyIZQkSZKkCmVCKEmSJEkVyoRQkiRJkiqUCaEkSZIkVSgTQkmSJEmqUCaEkiRJklShTAglSZIkqUKZEEqSJElShTIhlCRJkqQKZUIoSZIkSRWqUTErxRgPBmYmSTIjxtgSuBJYClyQJMnbpQxQkiRJklQaxfYQ/posAQT4ObAJkAKjSxGUJEmSJKn0iuohBFonSfKfGGMj4BDgq8ASYE7JIpMkSZIklVSxPYSfxBj/C+gBvJwkyfy8fJPShCVJkiRJKrViewh/CTwDbAoMz8u6A/8uRVCSJEmSpNIrNiG8GrgDWJokyet52WzguyWJSpIkSZJUcqtMCGOMDYH5wJZJkixeXp4kyf+VMjBJkiRJUmmt8h7CJEmWAv8HbF36cCRJkiRJ5VLskNE/AX+PMV4PvEX2yAkAkiR5oBSBSZIkSZJKq9iE8PT858U1ylNg53UWjSRJkiSpbIpKCJMk+VqpA5EkSZIklVexPYTEGDcBugGtkiQZF2NsBpAkyYJSBSdJkiRJKp2iHkwfY9ydbGKZ3wF/yIt7AH8sUVySJEmSpBIrKiEEfgP8T5IkHYHP87KHgQNKEpUkSZIkqeSKTQh3BW7J36dQPVS0aSmCkiRJkiSVXrEJ4UygS2FBjHFfYPq6DkiSJEmSVB7FTirzE+CuGOMoYNMY4/nAacApJYtMkiRJklRSRfUQJknyd6A/sC3ZvYNfBb6ZJMk/SxibJEmSJKmEin7sRJIkzwHfK2EskiRJkqQyKiohjDE2Ao4F9gKaF9YlSXJqCeKSJEmSJJVYsT2EtwC7A3cD75QuHEmSJElSuRSbEPYDdkiS5NNSBiNJkiRJKp9iHzvxMvCVUgYiSZIkSSqvYnsIvw38Psb4T2oMGU2SZOw6j0qSJEmSVHLFJoQnAt8AtgI+KyhPARNCSZIkSdoAFZsQ/gDYK0mSV0oZjCRJkiSpfIq9h/Ad4D+lDESSJEmSVF7F9hBeB/wpxngl8G5hRZIkb6zzqCRJkiRJJVdsQvir/OfAGuUp0HDdhSNJkiRJKpeiEsIkSYodWipJkiRJ2kAU20MIQIxxR6A18FaSJLNKE5IkSZIkqRyKSghjjC2BW4H9gA+ArWOMTwHHJEkyp4TxSZIkSZJKpNihoL8BpgJbJUnSkux5hM8Do0oVmCRJkiSptIpNCA8Azk2SZAFA/vM8YP9SBSZJkiRJKq1iE8IPgc41yjoAH63bcCRJkiRJ5VLspDI/AybFGP8AvAl8FTgJ+EmpApMkSZIklVZRPYRJkvwOOBrYBhiQ/zw2SZLRJYxNkiRJklRCxc4yelSSJLcBD9QoPzJJktuL2H4HYCywPbAMGJ0kyfUxxq8A44CdgJlATJLkwxhjAK4HDgUWAicmSfJcvq+hwIh815clSXJTMccgSZIkSVpRsfcQ/qGO8mJ7CKvIJqXpBHQDzogxdgZ+DNyfJEl74P58GaA/0D5/nUo2yyl5AnkR0BXYF7goxrhVkTFIkiRJkgrU20MYY9w5f9sgxvg1IBRU7wwsKqaRJEnmAnPz95/GGF8he8D9IOCgfLWbgIeAH+XlY5MkSYGnYoxb5s9CPAi4L0mSeXl89wH9gL8UE4ckSZIk6Qur6iGcDrwGbAa8ni8vf40FLl7dBmOMOwF7AU8D/5Uni8uTxu3y1VoDswo2eysvq6tckiRJkrSa6u0hTJKkAUCM8eEkSXqsbWMxxubAX4HhSZJ8EmOsa9VQS1laT3nNdk4lG2pKkiRss802axawJGkDM7dsLfl/iyRpY1DUpDI1k8F8KOnSJEneLLahGOMmZMngn5Ik+Vte/E6MsWWSJHPzIaHv5uVvATsUbN4GmJOXH1Sj/KFa4h3NF/c3pu+//36xYUqSVBT/b5EkbShatWpVZ11Rk8rEGP8SY9w/f38S8BLwcozx5CK3D2QT07ySJMm1BVUTgaH5+6HAhILyE2KMIcbYDfg4H1J6L9A3xrhVPplM37xMkiRJkrSain0wfS++SNzOAXoDHwHjqXsG0kLdgeOBF2OML+RlFwBXAkmeWP4HOCqv+wfZIyemkz124iSAJEnmxRgvBZ7J17tk+QQzkiRJkqTVE9J0pVvwVhJj/ChJki1jjK2ByUmStM7LP0mSZPNSB7mW0jlz5qzvGCRJZXDvxPLdQ3jIwJZla0uSpLWRDxmtbT6WonsIX4gxng98FbgLIE8OP1kXAUqSJEmSyq/YB9OfDOwONAVG5GX7AX8qRVCSJEmSpNIrasjoBs4ho5JUIRwyKknSytZ6yGiM8Tt11SVJ8sc1C0uSJEmStD4Vew/h8TWWtwfaAo8DJoSSJEmStAEq9sH0B9csy3sNO63ziCRJkiRJZVHspDK1GUM22YwkSZIkaQNU7D2ENRPHzYBvkz2cXpIkSZK0ASr2HsIqoOZ0pLOBU9ZtOJIkSZKkcik2IfxajeUFSZK8v66DkSRJkiSVT70JYYyxKdA2SZJ/1VK3GzA9SZJFpQpOkiRJklQ6q5pU5jzqnjjmJOCH6zYcSZIkSVK5rCohPBq4po66a4Fj1204kiRJkqRyWVVC2DpJktm1VeTlrdd9SJIkSZKkclhVQrggxrhDbRUxxh2Bhes+JEmSJElSOawqIfwHMLKOukuBu9ZtOJIkSZKkclnVYydGAE/GGKcCfwPmAi2BwcDmwP6lDU+SJEmSVCr19hAmSfI28P+AO4F+wH/nP+8EuuT1kiRJkqQNUEjTdH3HUGrpnDlz1ncMkqQyuHfi3LK1dcjAlmVrS5KktdGqVSuAUFvdqu4hlCRJkiRtpEwIJUmSJKlCmRBKkiRJUoWqMyGMMT5V8P6i8oQjSZIkSSqX+noId4kxNsnfn1uOYCRJkiRJ5VPfcwgnAP8XY5wJNI0xPlLbSkmSHFiKwCRJG74Tb3qyrO0du9VOZW1PkqQNXZ0JYZIkJ8UYDwB2AvYB/lCuoCRJkiRJpVdfDyFJkjwGPBZj3DRJkpvKFJMkSZIkqQzqTQiXS5LkjzHGg4HjgdbAbOCWJEkeKGVwkiRJkqTSKeqxEzHG7wLjgLeBvwFzgT/HGE8pYWySJEmSpBIqqocQOA/okyTJ1OUFMcZxwF+B35UiMEmSJElSaRX7YPqtgZdrlL0KfGXdhiNJkiRJKpdiE8LHgGtjjJsBxBibAVcDT5QqMEmSJElSaRWbEJ4GfB34OMb4DvARsAcwrFSBSZIkSZJKq9hZRucCPWKMbYBWwJwkSd4qaWSSJEmSpJIqdlIZAPIk0ERQkiRJkjYCxQ4ZlSRJkiRtZEwIJUmSJKlCrXLIaIyxAXAQ8FiSJEtKHpEkSdoo3TtxblnbO2Rgy7K2J0kbolX2ECZJsgyYYDIoSZIkSRuXYieVeSTG2C1JkqdKGo0kfYnZuyFJkjY2xSaEbwJ3xxgnALOAdHlFkiT/U4rAJEmSJEmlVWxC2BQYn79vU6JYJEmSJEllVOyD6U8qdSBSpXM4oiRJksqt6AfTxxg7AUcC/5UkyZkxxg5A4yRJppUsOkmSJElSyRT1HMIY41HAI0Br4IS8uAVwbYnikiRJkiSVWLEPpr8E6JMkyWnA0rxsKrBHSaKSJEmSJJVcsUNGtyNLAOGLGUbTgveSpA3A3B9+t7wN7nZyeduTJEmrpdgewinA8TXKjgEmr9twJEmSJEnlUmwP4VnAP2OMJwPNYoz3ArsAfUsWmSRJkiSppIrqIUyS5N9AR+BXwAjgRmD3JEleK2FskiRJkqQSKnbIKEmSLAQeBx4CHk2SZH6pgpIkSZIklV5RQ0ZjjDsCf4L/3969B1tW1XcC//ZANKJmRDpgN6BoQhKVKYkyyIyO42MQZCgwD38jEgEFSU1Ek4nFqPE5alKah0pNHGtQEDQq/OJjZJQSCdFYZnwzGhWjoqI0tGAHND4iDM6dP/Zu59rchiv2Pefc3p9P1alz9tr7nLUuq/Y5/WWtvXaOSHJjkr2r6qNJTuzur63i/ecmOTbJ9d19yFj24iRPS/LN8bA/6O6Lx33PTXJqhhVNn9ndl4zlRyc5K8keSV7f3S9f5d8JAADADlZ7DeH5GRaWObq7v1dVd0vy0rH8kat4/3lJ/jzJG3cof1V3/+nygqp6QIYFax6YZHOSv6qqXxp3vybJkUm2JPl4VV3U3Ves8m8AAABgmdVOGX1IkjO7+3tJMk4XffZYfru6+4NJblhlXccnuaC7b+rurya5Msnh4+PK7v5Kd9+c5ILxWAAAAO6A1Y4QfiRDIPvbZWWHJfnwT1n/GVV1UpJPJHlWd9+YZP+xvu22jGVJcvUO5Q/9KesHAACYrJ0Gwqp6ybLNLye5uKrekyGUHZjkmCRv+Snqfm2GaadL4/OfJXlqkg0rHLuUlUczl1b64Ko6PcnpSdLd2bhx40/RTJiVrTOtzXlxR6z/PprtX7B7cw7dEev/HALY3dzWCOGBO2y/Y3zeN8lNSd6Z5GfvaMXdfd3211X1uiTvHje37FD3AUmuXaFNy8t3/Oyzk5w9bi5t27btjjYTdlvOi8Wnjxab/ll8+ghgsHnz5p3u22kg7O6nrElrRlW1qbu3/6/CX0vy2fH1RUneUlWvzLCozMFJPpZh5PDgqrpvkmsyLDzzpLVsIwAAwO5stdcQpqr2SvKLSe62vLy7/9cq3vvWDKuRbqyqLUlelOSRVXVohmmfVyX57fHzPldVneSKJLckeXp3/3D8nDOSXJLhthPndvfnVtt+AAAAftxq70N4UobbRtyc5J+W7VpKcu/be393n7BC8Tm3cfwfJvnDFcovTnLx7dUHAADA7VvtCOEfJ/mN7r50LRsDAADA7Kz2PoQ3J/nAGrYDAACAGVttIHxBkldWlfWbAQAAdhOrnTL6xSQvSfI7VbW9bEOSpe7eYy0aBgAAwNpabSB8U5I3JrkwP76oDAAAAOvUagPhPkle2N1La9kYAAAAZme11xC+IcmT17IhAAAAzNZqRwgPT3JGVT0vyXXLd3T3I3Z5qwAAAFhzqw2ErxsfAAAA7CZWFQi7+/y1bggAAACztapAWFVP3dm+7j531zUHAACAWVntlNEdF5S5V5JfSPK3SQRCAACAdWi1U0YftWPZOGp4/13eIgAAAGZitbedWMl5SU7dRe0AAABgxlZ7DeGOwXGvJL+V5Fu7vEUAAADMxGqvIbwlydIOZdckedqubQ4AAACzstpAeN8dtr/X3dt2dWMAAACYmUwV+AAAEixJREFUndUuKvO1tW4IAAAAs3WbgbCq3p9bTxVdbqm7H7NrmwQAAMAs3N4I4V/spHz/JM/MsLgMAAAA69BtBsLuPmf5dlXtk+S5GRaTuTDJS9auaQAAAKyl1d524ueSnJnkjCTvTvLg7v7yWjYMAACAtXV71xDeJcnvJXlWkg8keXh3f24G7QIAYMYuuWjrTOs76rhNM60PuLXbGyH8apI9kvxxkk8k2a+q9lt+QHf/9Rq1DQAAgDV0e4HwBxlWGf2PO9m/lOR+u7RFAAAAzMTtLSpz0IzaAQAAwIz9s3k3AAAAgPkQCAEAACZKIAQAAJgogRAAAGCiBEIAAICJEggBAAAmSiAEAACYKIEQAABgogRCAACAiRIIAQAAJkogBAAAmCiBEAAAYKIEQgAAgIkSCAEAACZKIAQAAJgogRAAAGCiBEIAAICJEggBAAAmSiAEAACYKIEQAABgogRCAACAiRIIAQAAJkogBAAAmCiBEAAAYKIEQgAAgIkSCAEAACZqz1lUUlXnJjk2yfXdfchYds8kFyY5KMlVSaq7b6yqDUnOSnJMku8nOaW7Lx/fc3KS548f+7LuPn8W7QcAANgdzWqE8LwkR+9Q9pwkl3X3wUkuG7eT5HFJDh4fpyd5bfKjAPmiJA9NcniSF1XV3mvecgAAgN3UTAJhd38wyQ07FB+fZPsI3/lJHr+s/I3dvdTdH0lyj6ralOSoJJd29w3dfWOSS3PrkAkAAMAqzWTK6E7s191bk6S7t1bVvmP5/kmuXnbclrFsZ+W3UlWnZxhdTHdn48aNu7jpsBa2zrQ258Udsf77aLZ/we7NOXRHrP9zaPenj2Bq5hkId2bDCmVLt1F+K919dpKztx+zbdu2XdQ02H04LxafPlps+mfx6aPFp49gNjZv3rzTffNcZfS6cSpoxufrx/ItSQ5cdtwBSa69jXIAAADugHkGwouSnDy+PjnJu5aVn1RVG6rqiCTfHqeWXpLksVW197iYzGPHMgAAAO6AWd124q1JHplkY1VtybBa6MuTdFWdmuTrSZ4wHn5xhltOXJnhthNPSZLuvqGqXprk4+NxL+nuHReqAQAAYJVmEgi7+4Sd7HrMCscuJXn6Tj7n3CTn7sKmAQAATNY8p4wCAAAwRwIhAADARAmEAAAAEyUQAgAATJRACAAAMFECIQAAwETN5LYTsKtsPfO0mdW16U9eP7O6AABgHgRCAJioU87/8EzrO2Hvg2ZaHwC3z5RRAACAiRIIAQAAJkogBAAAmCiBEAAAYKIEQgAAgIkSCAEAACbKbSeAdcuS+QAAPx2BEAAWxNYzT5tthYecOtv6AFg4powCAABMlEAIAAAwUaaMwk64Pg0AgN2dQAjsMq5/AgBYX0wZBQAAmCiBEAAAYKIEQgAAgIkSCAEAACZKIAQAAJgogRAAAGCiBEIAAICJEggBAAAmSiAEAACYKIEQAABgogRCAACAiRIIAQAAJkogBAAAmCiBEAAAYKIEQgAAgIkSCAEAACZKIAQAAJgogRAAAGCiBEIAAICJEggBAAAmSiAEAACYqD3n3QAAAFZ2yvkfnml9J+x90EzrA+bPCCEAAMBECYQAAAATJRACAABMlEAIAAAwUQIhAADARFllFABglbaeedpsKzzk1NnWB0yOEUIAAICJEggBAAAmSiAEAACYqLlfQ1hVVyX5TpIfJrmluw+rqnsmuTDJQUmuSlLdfWNVbUhyVpJjknw/ySndffk82g0AALDeLcoI4aO6+9DuPmzcfk6Sy7r74CSXjdtJ8rgkB4+P05O8duYtBQAA2E0sSiDc0fFJzh9fn5/k8cvK39jdS939kST3qKpN82ggAADAercIgXApyfuq6pNVdfpYtl93b02S8XnfsXz/JFcve++WsQwAAICf0NyvIUzysO6+tqr2TXJpVf39bRy7YYWypR0LxmB5epJ0dzZu3LhrWsrcbZ13A3Yja3Fe6J9dSx8tNv2z+PTR4vNvNJi/uQfC7r52fL6+qt6Z5PAk11XVpu7eOk4JvX48fEuSA5e9/YAk167wmWcnOXvcXNq2bduatR/WK+fF4tNHi03/LD59tPj0EczG5s2bd7pvrlNGq+quVXX37a+TPDbJZ5NclOTk8bCTk7xrfH1RkpOqakNVHZHk29unlgIAAPCTmfc1hPsl+VBVfTrJx5K8p7vfm+TlSY6sqi8lOXLcTpKLk3wlyZVJXpfkd2bfZAAAgN3DXKeMdvdXkjxohfJ/SPKYFcqXkjx9Bk0DAADY7c17hBAAAIA5EQgBAAAmSiAEAACYKIEQAABgogRCAACAiRIIAQAAJkogBAAAmCiBEAAAYKIEQgAAgIkSCAEAACZKIAQAAJgogRAAAGCiBEIAAICJEggBAAAmSiAEAACYKIEQAABgogRCAACAiRIIAQAAJkogBAAAmCiBEAAAYKIEQgAAgIkSCAEAACZKIAQAAJgogRAAAGCiBEIAAICJEggBAAAmSiAEAACYKIEQAABgogRCAACAiRIIAQAAJkogBAAAmCiBEAAAYKIEQgAAgIkSCAEAACZKIAQAAJgogRAAAGCiBEIAAICJEggBAAAmSiAEAACYKIEQAABgogRCAACAiRIIAQAAJkogBAAAmCiBEAAAYKIEQgAAgInac94NYHYuuWjrTOs76rhNM60PAAD4yRghBAAAmCiBEAAAYKIEQgAAgIkSCAEAACZKIAQAAJiodbnKaFUdneSsJHskeX13v3xXfO7WM0/bFR+zapv+5PUzrQ8AAGC5dTdCWFV7JHlNkscleUCSE6rqAfNtFQAAwPqzHkcID09yZXd/JUmq6oIkxye5Yq6tugNOOf/DM63vhL0Pmml9AADAYluPgXD/JFcv296S5KFzagsAAAtk1pcAPfeQU2dW13kn/6uZ1bVWdvdLtC65aOtM6zvquE0/9WdsWFpa2gVNmZ2qekKSo7r7tHH7yUkO7+5nLDvm9CSnJ0l3P2QuDQUAAFgcG1YqXHfXEGYYETxw2fYBSa5dfkB3n93dh3X3YRn+8N3qUVWfnHcbPPTPen7oo8V/6KPFfuifxX/oo8V/6KPFfuym/bOi9Thl9ONJDq6q+ya5JskTkzxpvk0CAABYf9bdCGF335LkjCSXJPn8UNSfm2+rAAAA1p/1OEKY7r44ycXzbsccnT3vBnCb9M/i00eLTx8tNv2z+PTR4tNHi20y/bPuFpUBAABg11h3U0YBAADYNdbllNGpqKoDk7wxyb2S/N8kZ3f3WVV1zyQXJjkoyVVJqrtvnFc7p6yqfjbJB5PcOcP59LbuftG46NEFSe6Z5PIkT+7um+fX0mmrqj2SfCLJNd19rP5ZLFV1VZLvJPlhklu6+zDfc4ulqu6R5PVJDkmylOSpSb4QfTR3VfXLGfphu/sleWGGfz/onwVRVf8pyWkZzp/PJHlKkk3xW7QQqup3kzwtw0qcr+vuV0/pd8gI4WK7Jcmzuvv+SY5I8vSqekCS5yS5rLsPTnLZuM183JTk0d39oCSHJjm6qo5I8ookrxr76MYks7trLSv53QyLUG2nfxbPo7r70PF2QYnvuUVzVpL3dvevJHlQhvNJHy2A7v7CeO4cmuQhSb6f5J3RPwujqvZP8swkh3X3IUn2yLBKvt+iBVBVh2QIg4dn+H47tqoOzoTOIYFwgXX31u6+fHz9nQw/wPsnOT7J+eNh5yd5/HxaSHcvdfd3x82fGR9LSR6d5G1juT6ao6o6IMm/zzC6karaEP2zHvieWxBV9XNJHpHknCTp7pu7+1vRR4voMUm+3N1fi/5ZNHsmuUtV7ZlkryRb47doUdw/yUe6+/vj3Qz+JsmvZULnkEC4TlTVQUl+NclHk+zX3VuTITQm2XeOTZu8qtqjqj6V5Poklyb5cpJvjV8qSbIlQ5BnPl6d5D9nmHadJPtE/yyapSTvq6pPVtXpY5nvucVxvyTfTPKGqvrfVfX6qrpr9NEiemKSt46v9c+C6O5rkvxpkq9nCILfTvLJ+C1aFJ9N8oiq2qeq9kpyTJIDM6FzSCBcB6rqbknenuT3uvsf590eflx3/3CcqnNAhukG91/hMMv5zkFVHZvk+u7+5LLiDSscqn/m62Hd/eAkj8swNf4R824QP2bPJA9O8tru/tUk38tuPHVqvaqqOyU5Lslfzrst/Liq2jvDaNN9k2xOctcM33c78ls0B939+QzTdy9N8t4kn85w2dZkCIQLrqp+JkMYfHN3v2Msvq6qNo37N2UYmWLOxilUH8hwvec9xmkhyRAUr51XuybuYUmOGxctuSDD9JxXR/8slO6+dny+PsO1T4fH99wi2ZJkS3d/dNx+W4aAqI8Wy+OSXN7d143b+mdx/LskX+3ub3b3/0nyjiT/On6LFkZ3n9PdD+7uRyS5IcmXMqFzSCBcYOO1Tuck+Xx3v3LZrouSnDy+PjnJu2bdNgZV9fPj6nupqrtk+NL/fJL3J/nN8TB9NCfd/dzuPqC7D8owleqvu/vE6J+FUVV3raq7b3+d5LEZpu/4nlsQ3f2NJFePq1kmw3VqV0QfLZoT8v+niyb6Z5F8PckRVbXX+G+77eeQ36IFUVX7js/3TvLrGc6lyZxDAuFie1iSJyd5dFV9anwck+TlSY6sqi8lOXLcZj42JXl/Vf1dko8nubS7353k2Ul+v6quzHDN2jlzbCO3pn8Wx35JPlRVn07ysSTv6e73xvfconlGkjeP33WHJvmj6KOFMV73dGSGkaft9M+CGEfX35bh1hKfyfDv77Pjt2iRvL2qrkjyP5M8fby9xGTOoQ1LS6YrAwAATJERQgAAgIkSCAEAACZKIAQAAJgogRAAAGCiBEIAAICJEggBAAAmas95NwAAZqmqPpDkQUnu1d03rWE9Ryb5gySHJbk5ydUZbnZ8Vnf/YK3qBYCfhBFCACajqg5K8m+SLCU5bg3reUKGG1G/Jcl9unufJP8hyQFJDtzJe/xPWgBmzo3pAZiMqnphkqOSfDTJL3X3scv27ZPkvCT/NskXklyS5JHd/fBx/68k+a9JHpLkm0le0N29Qh0bknw9yau7+89uoy0vTnJIkh9kCKe/n+RNSV6RpMbDOsmzu/umqjolyWnb2zN+xlKSg7v7yqo6b/ysX0hyRJLLk5zU3V9b/X8hAKbGCCEAU3JSkjePj6Oqar9l+16T5HtJ7pXk5PGRJKmquya5NMOI375JTkjy36rqgSvU8csZRgLfvor2HJ9hJPEeY5uelyHMHZphWuvhSZ6/+j8vJyZ5aZKNST41fiYA7JTpKQBMQlU9PMl9knR3b6uqLyd5UpJXVdUeSX4jySHd/f0kV1TV+UkeOb792CRXdfcbxu3Lq+rtSX4zyed2qGrj+PyNZXVfkOToJHdK8tvd/aZx14e7+3+Mr/+pqk5M8ozuvn58339J8t+TvGCVf+Z7uvuD43ufl+TbVXVgd1+9yvcDMDECIQBTcXKS93X3tnH7LWPZq5L8fIbfxOXBafnr+yR5aFV9a1nZnhmmeO7oH8bnTUm+miTd/cQkqaoPJdljJ3UkyeYky6d4fm0sW60ffV53f7eqbhjfLxACsCKBEIDdXlXdJcN1eXtU1faRuzsnuUdVPSjJZ5PckmGq5xfH/csXf7k6yd9095GrqO7vk1yT5NeT7PQawtGOF/JfmyF8bh91vPdYlgzTWfda9jfda4XPO3DZ/rslueey9wPArQiEAEzB45P8MMm/yHALiO06w8Irz6qqdyR5cVWdliGInZRhcZgkeXeSl1fVk5NcMJYdmuS73f355RV191JVPSvJ66rqHzNcI/itJL+YZPk1iyt5a5LnV9XHM4TFFyb5i3Hfp5M8sKoOzRA6X7zC+48Zp8Z+LMO1hB81XRSA22JRGQCm4OQkb+jur3f3N7Y/kvx5khPHWz6ckeSfZ7j2700ZwtlNSdLd30ny2CRPzDDi9o0Mq4HeeaXKuvvCDCOSv5VhdHFbhvB5dpK/vI12vizJJ5L8XZLPZFgp9GXjZ34xyUuS/FWSLyX50Arvf0uSFyW5IcNqqCfezn8XACbObScAYAVV9YoMN68/+XYPXgDjbSe2dPdPsiopABNnyigA5Ef3GbxThpG5f5nk1CSnzbVRALDGBEIAGNw9wzTRzUmuz7AgzLvm2iIAWGOmjAIAAEyURWUAAAAmSiAEAACYKIEQAABgogRCAACAiRIIAQAAJkogBAAAmKj/Bx9HiUXgWKGyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "sns.barplot(x='Age_Group', y='Number of Customers' , hue='wealth_segment', data=wealth_age_seg_old)\n",
    "plt.xlabel('Age Group')\n",
    "plt.ylabel('Number of Customers')\n",
    "plt.title('Old Customers - Wealth Segmentation by Age Group')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.5. Car owner across each State"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The customer base of the automobile bike company lies in New South Wales, Queensland and Victoria, Australia. <br>\n",
    "In New South Wales (NSW) it seems there is a greater amount of people who donot own a car. In Victoria (VIC) the proportion is evenly split. However in Queensland (QLD) there are relatively more people who own the car."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading the Customer Address Dataset.\n",
    "\n",
    "cust_addr_info = pd.read_csv('CustomerAddress_Cleaned.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Merging the RFM data with Customer Address dataset.\n",
    "\n",
    "cust_trans_addr = pd.merge(cust_trans_rfm , cust_addr_info, left_on = 'customer_id' , \n",
    "                           right_on = 'customer_id', how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RFM table Records count : 19354\n",
      "Address Table Records count :3999\n"
     ]
    }
   ],
   "source": [
    "print(\"RFM table Records count : {}\\nAddress Table Records count :{}\".format(cust_trans_rfm.shape[0] ,cust_addr_info.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "state_car_owners = cust_trans_addr[['state' , 'owns_car' , 'customer_id']].drop_duplicates().groupby(['state', 'owns_car']).size().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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>state</th>\n",
       "      <th>owns_car</th>\n",
       "      <th>Number of Customers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NSW</td>\n",
       "      <td>No</td>\n",
       "      <td>889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NSW</td>\n",
       "      <td>Yes</td>\n",
       "      <td>935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>QLD</td>\n",
       "      <td>No</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>QLD</td>\n",
       "      <td>Yes</td>\n",
       "      <td>363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>VIC</td>\n",
       "      <td>No</td>\n",
       "      <td>435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>VIC</td>\n",
       "      <td>Yes</td>\n",
       "      <td>425</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  state owns_car  Number of Customers\n",
       "0   NSW       No                  889\n",
       "1   NSW      Yes                  935\n",
       "2   QLD       No                  365\n",
       "3   QLD      Yes                  363\n",
       "4   VIC       No                  435\n",
       "5   VIC      Yes                  425"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state_car_owners.rename(columns={0:'Number of Customers'}, inplace=True)\n",
    "state_car_owners"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,7))\n",
    "sns.barplot(x='state', y='Number of Customers' , hue='owns_car', data=state_car_owners)\n",
    "plt.xlabel('States')\n",
    "plt.ylabel('Number of Customers')\n",
    "plt.title('Number of Customers who own a car')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NSW has the largest number of people that donot own a car. It seems that a higher amount of data is collected from NSW compared to other states. In QLD the distribution between customers having a car or not having is even. In Victoria the number is split evenly. Both the numbers are significantly lower than that of NSW"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. RFM Analysis Scatter Plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1. Recency vs Monetary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The chart shows that customers who purchased recently generated more revenue than customers who visited long time ago. Customers from recent past (50-100) days generated a moderate revenue. Customers who visited 200 days ago generated a low revenue."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 576x504 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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F+xZv6NieMr4/Z01qzDimWAf8zgOtDDPXzgPDIqkVRVFKiyqUPJk8pm+GmWvymL6xx/uVCcC8xRs4a1K6MqkDXlGUakAVSp7MPHgnZlJbseeKoijlQBVKlVPJHBXNj1EUJR/UKV9ipozvH7udi2JzVIqhkvdWFKX20BVKiTlrUmPqPhNFUZRqRBVKF0Gz5RVFqTRq8uoilCJbXvNjFEXJB12hVDlhVYfLhTrgFUXJB1UoKfLtu5t5f1Pn9og+cONJxU3K2vZXUZRaQRVKiviVCcDKwHZaYbhh/eaD2fIAU5uaGdEH7jnrC3nfQ1EUJV9UoZSRtMJww/rNz5nmKKapTc0ZrwWVmqIoSqlQp7yiKIqSCqpQUmREn/htRVGUroyavFIklwO+sV+mmavQMNy4yK8RfTLNXJ5Su2Dem7yxtj3rWpNG9ubHh48rTBBFURQfqlBSIGl0V5QDPuhk9xOWoBgX+RWl1MKUCcDC1s2R11IURckHVSgpEBbd5TnHk6wAopQJaDtfRVFqB/WhlBhdASiK0l3QFUoRBE1d1czEIXU0R/hQapWwfBxNBFWUyqEKpQjSUiZBJ7uftNr5XjVlQtYE3K+Hs4LyzHOTx/Rl5sE7pXK/chCWj1MOrn78HR4LdO2spXFTlFKhCqUAnIip5twH0rkCiHuaLsVTddikF5yAP96Wec6CZRuZmbokXQ//uIKOm6J4qEIpgKiIqTA+aduWlb0OpXuajgoPXhCYBBVFUdJGnfJ5cva9yVYm4KxC4iK4SkE+yq7WCVZeLmclZkVRsqn4CsUYMxP4FmADrwDfAEYCdwJDgeeB00VkizGmN3Ar8BngA+BUEVnqXudHwAygHfieiMwvhbzBelxxJF2FFNIcK7gSmTikjqumTIg9J+ir6d8DNvjMXpPH9E0kb7VQKQf85DF9M1Z8tTZuilIqKqpQjDGjge8Bu4vIRmOMAF8BpgCzROROY8xvcRTF9e7vtSIy3hjzFeAq4FRjzO7ueXsAo4AHjTG7iEjVPq77n6YLaY4VXImERXD5Ucdxesw8eCdmAg0NDaxevbrS4ihK1VDxFQqODH2NMVuB7YBW4HDgq+7rtwA/w1EoU92/Af4M/NoYY7n77xSRzcDbxpjFwAHAP8v0HhJT6tDWYHjwxCF1zD7j86lOfBrlpChKGBVVKCKy3BjzS2AZsBG4H3gO+FBE2tzDWoDR7t+jgXfdc9uMMR8Bw9z9T/su7T8nA2PMmcCZ7jVoaGjIS+adBtbzzrq23Af6ePK8/PuR5CuXd87sM7LPq6+vj7ze9FufYfHaLR3bE4b24ubTD4i9z2PLMv1IC5Zt5IoC5A0jTtZqQ2UtDbUkK9SWvKWWNadCMcaMAI4C9gYGAx8CLwEPiMh7xdzcGDMEZ3Uxzr3un4BjQw71bEBhSRl2zP4sRORG4EbvmHyf3P/fl8cDzuR99u0LM3wSvYFgXnxjP3KuDsKaYx107RNA9IombCUSdZ8404xfmQC8uWZLQauZtFZAtWRGUllLQy3JCrUlb1qyjho1KnR/pEIxxuwGXAYchrNqeB14DxgAnA78yhjzCHCxiLxWoFxHAm+LyCr3nvcABwKDjTH17iqlEVjhHt8C7Ai0GGPqgUHAGt9+D/85JeHk3z9Fqy+Ro5h2v34HfDDEOMqxH+WAv/zht3nWV+5l0sjeXGNq4+lJUZTaJm6FcjPwv8A01zeRgTGmF87qYjbw+QLvvwz4nDFmOxyT1xHAv4BHgJNxIr2mA3Pc4+e62/90X39YRGxjzFzgdmPMNThO+QnAMwXKlIjWQFbgyk0w6/F3Qn0JYZN8qUrGPxuoHVaKWmIa5aQoShiRCkVEJsWdKCJbcExUfyr05iKy0BjzZ5zQ4DbgBRxz1N+BO40xl7v7ZrunzAZuc53ua3AiuxCRV90Isdfc65xTiQivqIzpckzycXjmM8hWZkFzW5JSL16UU5DrF7Zw3+INHdtTxvfnrEmNOa/XFZ385XyIUJRqoeJRXiJyCXBJYPcSnCit4LGbgFMirnMFcEXqAhZAVLa6n6jy9nHNs+KI66niJ6jMcuW75INfmQDMW7yBs2IfSxy6YimTSj9EKEolSOKU/xKwTERecbf/GzgXx58yXUSeKq2I1cmofj1YESyGRbYPJBfeRFPo071HuTPyk+KNh1YCVpSuT5IVyi+B4wCMMZ/FyWQ/EtgLJzdk75JJV8X86VsH8pO/PJdajaywp/t5i4ubjOdMm5iXgiul6alclYAVRakccVFenhlqJ+BrxhhwzFCtwNE4obrjjDEXA4jIpaUVtfoI+hLyXZ0kpZjJeNLI3hnmlrj+J8WanqaM78+8gGJMQld08ucz7orSVcgV5WUB3wHuxckTORL4NfCge8wM33HdhnzK1+di0sjeXP34OzmP85RVY7/s3vTn/XVR5Hk/Pnxc2eLkz5rU2OEzyUe5+hVzLcX0x6EOeKU7Ehfl9Q6AMeYBHCXyGjAeuEtENhhjdgTWiMiyskhaRSSt6Bt88vbYZUhdxzXydda2hBSnDNYCqwYKDS5QFKV2SeJDOQungOMI4CgR8Wwa+wBXlkqwWqee8B4kw3qVr8S8t0o4epchnL3/iJzH9+uR3XRralNzQb4UdcArSvcjp0IRkU8IURwi8teSSNRFCKv2NWeaY6pKYg7K59hczH9jbSKFEhK0BnSNMN5iCQtYuOI/tAKBoviJc8rvLSIv5bpA0uO6EsE6WqWksV+2mcuvZHYeaIXWAgty9r3Nsb1cJg6pK0bMoohrj1wthAUsKIqSSdwK5TfGmHXAbcCjItJRG8sYMxI4FDgD6A8cUlIpq4yrpkwoWUQXOEokKi8leN8l6+yO1YxHmGy5GoOVS0GGEcyheWnV1oIaiCmKUlninPJfMMYcB/wnMNsY0w6sxykOaeFEev1aROaVRdJuQpSpK2nWOWQHAxy9yxDmv7E253lBR7r/eklJSxHk20BMUZTKE+tDEZG/AX8zxvTEKbg4GFgLvOnrV9ItOXjsQB5fui5jX9SE7DG1qZm9h/eMNZnN/OuiosuhBPNjGhoamP/GE5HHe6RhZuqqiqAr5sooStokquUlIltxwoYVl483Z0+USRIQX1q1lTnTJkbW3srlCwkr5pikEGGYLybIxfe/VVLfRVQmfliIcTky6/Mp4BhVEDPf6yhKV6bixSFrjaRFGHOR6xrBrPMp4/sDncUcvUlsyTqbJetyFyIcObA3LR/H57yETeJpOsyjMvH91/MSGy+c92ZWA7G0SauAoxaCVBQHVSh5Uo4ijFObmhnRhyxnu5/gJBZ2DfB6yjfkPD6KMId5HGGdJAshTQd8VyyPryjViCqUCnDx/W/lPGblpnTuVW4fRjVGYnXF8viKUo0kUijGmOOBed3dEZ8WL63aWjY/QT4kLY9SaCRXtTm2owo45vv+tBCkojgkXaFchhM6fBdwm4gsLKFMVU1aisDzG8Tls0xtasYC/G56z4fRvwdsiMhsDyM46QWJ8o0kcZgnXQXFObYrQZTjPN9INXXAK4pD0iivvY0xewNfA+42xnyMk/D4RxFZWkL5qo5Lj/pUqkmNubLcg694k3lSZeL5MFZ+vCX2uCglGaZkSpnUWQqqbWWkKF2VxD4Ut7zKS8aYHwBHAFcDPzfGPAncANwhInk8M3dvZj3+DjMP3olZX941Kys+F0km9OCKI0lF4q7aXbHaVkaK0lXJyylvjPkUzirla8A24GJgGU5L4JOAE9MWsNq4YN6bqVzH7xjOR5kkpRiFUGgk160vtHL3ax917Dd7DGLaPiMLliMpaeeBpBWppijdjaRO+XNxlMh4QIDTReRp3+t3A++XRMIqI83S81ObmjvyS6oN/yoouGKJclD7lQmAvPpRWRRK2nkg1Rippii1QNIVyjE4Jq45IpJljBeRT4wxXX51UgoKaZmbi8lj+uasLuyRpHJyuaPRKrXSURSlOHrkOsAYU4dTUXhumDLxEJH70xRMKZwFyzYmUiZzpk3kqikTStpNcWpTM2fclZ8TP2yloyhK9ZNToYhIOzCObtY3PoquaE+/9KhPMWfaxI6fQjF7DArd/5FmLylKtyCpyevnwG+NMZcALfiiWbtbZJdnX6+10NkwwkqShH2YSVcw0/YZybR9RpZ9bDQsWFGqg6QK5ffu79N9+7ycu6Ie2Y0xg93rf9q93jeBRcBdwFhgKWBEZK0xxgKuBaYAnwBfF5Hn3etMBy5yL3u5iNxSjFxdnUkjeyfuQljKEOJ7X1vFH174oGP7e4eMwewxKMPMFbXy8dCwYEWpDpIqlFKmAl8L3CciJxtjegHbAT8GHhKRK40xPwR+CFwIHIvTl2UCMAm4HphkjBkKXAJ8FkcpPWeMmSsiubtKFcDVj79TisuWlR8fPq5kK4lB9ZlmrkEx3zK/MgH4f48tY860iZFO+DQLPWrZeUVJl6SZ8iWZQY0xA3HaB3/dvc8WYIsxZiow2T3sFmABjkKZCtwqIjbwtDFmsNuOeDLwgIisca/7AE5k2h2lkDv4ZF9t+P0gUUojrXyaMG49tXA/TC7SLPSoZecVJV0SJza6BSIPBRrwOehF5Iwi7r8zsAr4g1va5TngPGCEiLS61281xmzvHj8aeNd3fou7L2p/2Ps4EzjTvTYNDQ15CXzm7f/K6/hK4H9PJ+45nHteWZV1TNJ8mqN3GUJDQwMX/OVlnlq2LtHxFx+7R3JhQ8j3MwkqzRP3HM73Dy+s62U+966vr89b1kpRqKz/8+Ai5rza+f0pZmyTUkvjCrUlb6llTZrYeAlOb/k7gVNwSq18FcfPUez99wO+KyILjTHX4pi3ogiLNLNj9mchIjcCN3rHrF69Og9x4dVVKdWVLyH+9zR9r2F8//BdOeja3C2Awzh7/xGsXr06kTIBmP/GWs7eP/mYzthvGLOfz/Sh5PuZBLnnlVVM32tYQefmc2+vGVgtUKisfmUCxY1tUmppXKG25E1L1lGjRoXuzxk27PJN4IsiMhPY4v7+Mo7TvBhagBZf9eI/4yiYla4pC/f3+77jd/Sd3wisiNmvFMkNC1tKev3jdxueEbJ86r5jYo8vJILr6sffYWpTc8fPLNcHFiwzr2XnFaU4kpq8BovIv92/txhjeorIM8aYQ4u5uYi8Z4x51xizq4gswik6+Zr7Mx240v09xz1lLnCuMeZOHKf8R65JbD7wC2PMEPe4o4AfFSNbLeM3ASXpJQ9Ofs2gPvVZfoR5izdw1qTC75+2o9uL6MonoCDK76IOeEVJl6QrlLeMMZ5h/N/Ad4wxpwNpRFF9F2gyxrwM7AP8AkeRfNEY8ybwRXcbYB6wBFgM/A44G8B1xl8GPOv+XOo56NOmkhkOhxTwdB6lTCaP6cucaRPZ330qb17bHuuULvTpfWHr5o6VQZJOlbm4fmFLImXi3bPUK6yuTrDWXLXWnlOqg6QrlIsAz3D6Q+B2nHIs5xQrgIi8iBPuG+SIkGPtqHuKyE3ATcXKk4tKxnelGV22YNlGNgbCZqPwJvDJY/rywoqNBWe+p1ETLKwyc1xUWylqpXUnzprUmPcKVem+JA0bnuf7+xmcqsNKGQlmg6dBvmGyC5ZtTBSSnA/BXjAn7jk8daevZtIrSnlIGuW1RkSGhux/X0S2DztHSZeZB+/Egi5Q7iVIcMXhjyJK2njMv4IKQzPpMwmO65Tx/TlrUmMFJVK6Ckl9KFnFnIwxPSmy7IqSH9XwZD21qZlv/qm5oNVJvlWN8208tmDZRrX5JyA4rmoWVNIidoVijHkcJ5+jjzHmscDLjcBTpRKsuzFpZO9YE1Q1FaP8IKSJwZxpE2NlzCfaq+nF1oL7n6jNX1EqRy6T1+9xkgb3B2b79tvASuDhEsnV7ejqZT+i3t+U8f2znpDL1emx1tFGZEq1EatQvIq9xpinRaR6HpGVqqPQgpkbtkSXgAlTNnF4JsHu4iMotOVycFzVLKikRdKw4UXGmG8DpwENIrKXMeYQYAcRkdKJp9QKSUKa/SaxGfsN4/jdhkeeV4iJb8GyjfD4O1nRcPMWb2De4uYuq1jyRc2CSqlIqlAuxUkw/BXwW3dfCzALUIXSxch3ZVAIs5//gON3G576deNCqz3F4uGZiNIsiUVeXXcAACAASURBVK8o3ZmkUV5fB44TkTvpLLr4Nk61YKULsffwnqk9xU8a2bvotsKlxGvilbTRWLURbDyWqxGZopSapCuUOsB7ZPUUSn/fPqWLkEY2u4dXdiWNcGdPKSUxhZVjhVUNeC2XFaVaSLpCmQdcY4zpDeC24r0M+GupBFMqR6EhynsP7xm6GlmwbCMz9svMfg9u57puUiaP6ctZkxqZM22iOpsVpcwkXaGcD9wKfIST5LgBuB8oprmW0oXI5Xdoej6z1e/trg8l2C44SDB/pV8P+Hhb5+v9e0DTaeEmtaDzOUpRamkWRUmHpLW81gEnuJ0TdwLeFZH3SiqZUlMsWLYxtjRMsC3ZRpKthIL5K35lArAhsF0IXak0i+amKJUkqcnLzwfAdsaYnY0x6pRXclItT/zdoSxLWG6KopSLpMUhj8HJlA8+6thoPS8lB2s3Fljv3mVqU3NRT9r61F55avkz6C6JsmmQ1IfyGxwn/C0iUhsxlUrV8NKqrfSluF4yhZZjCU5kxVxLKZxa/gzCimlqYmg4SU1eQ4AbVJkoUeTq55jGF+cXD7+d9znBiayro7kpSiVJukKZDXyDMnREVGqTcpS2jCow6e+HUmiGexpmjWowjWhuilJJkiqUzwHfM8b8EMiI7hKRQ1KXSlEKwB9pFlcu33tqj2vgVYhZQ00jXRMtppmcpArl9+6PopSFXP1VcuGtZsweg7IinQb0dr72+TbwUronWkwzOUnzUG4ptSCKEiTYdCxXE7Iwpu0zMkuhlKowZRjFRqh1FWbsN4zZvuTWfColKLVD0hUKxphvAKcDo4HlwG0i8odSCVatjOgDK4NZekqqeCapMJNV04utqeRW3PpCa+zrhZg1omqI1VJEU6k4frfhZVPiSuVImofyE5wyK1cD7+Bky//AGDNKRK4ooXxVhyqT0uDVALvpxQ+QV1eFKg3vSb/dTha9FVeYMtf5hTjTPdNINbVrVpRyknSF8i1gsoh0tOUzxswHHgO6lUJRSscF897kjbXRHRy9J/18QoG9wpRBc8vsQG2xUlMNEWCKUmqSKpR+wKrAvg+A6qipodQ03ioiTpl4xD39hzngIdzckkuhhN0nLHIsLAM8KEeYXBoBpnRFkiqU+4AmN2x4GY7J6wpgfhpCGGPqgH8By0XkOGPMOOBOYCjwPHC6iGxxy+ffCnwGR6GdKiJL3Wv8CJgBtAPfE5FUZFNKT67CkknwTGZR/pWz721m+cdF3SI0ICAsA3zOtIlZPhOtqaV0B5Jmyp8LrAdewild/yLwMfDdlOQ4D3jdt30VMEtEJgBrcRQF7u+1IjIep/3wVQDGmN2BrwB7AMcA17lKKnU087i6CfpMvO1ilUmtcOsLrUxtau74aXoxPvhAUdIkkUIRkXUicgawHU6ByO1E5AwR+bBYAYwxjcCXcPNc3OZdhwN/dg+5BTjB/Xuqu437+hHu8VOBO0Vks4i8DSwGDihWtjC6e7RONeJX8ilUsy+asEm9XJWOtdqwUkliTV7GmDERLzUaYwAQkWVFyvAr4AfAAHd7GPChiHglaltwQpVxf7/r3rfNGPORe/xo4GnfNf3nZGCMORM4070GDQ0NRYqvVIKDxw7kyql7Ze1/bFmm6WzBso1ckdJnPKpfj4zvS319PdP3H8ktz3auAoLb4EzqT573BX6S4B6X/P1VHly8tmP76F2GcPGxexQld0NDA/X19TXzXa8lWaG25C21rLl8KEvp7CFvhbxeVPl6Y8xxwPsi8pwxZnKO+8S9FndOBiJyI3Cjd8zq1auTC6xUlGCtrqSf3UHXPkFviq83tuLjbRn3bGho4MRdBnHiLp0rpKj8Fu+8sOrHAI394DcnTMxQJgDz31jL2fsX9x1dvXo1DQ0Nicer0tSSrFBb8qYl66hRo0L351IoLwN9cMxMfwRWFC1JJgcBxxtjprj3GYizYhlsjKl3VymNvvu2ADsCLcaYemAQsMa338N/TqpojkHlCDrvvbyUqEnaTxJlkqTcS66+HlFy5LpuS0o+nrAIszS4+vF3eCzQJrnQQpxK1yXWhyIi+wAn40RbPQHMw3F+9xKRdhHJHecZf/0fiUijiIx1r/uwiEwDHnHvCzAdmOP+Pdfdxn39YRGx3f1fMcb0diPEJgDPFCObUv14E2daJeqTPCxE+Sg8v0kx5MreT8K0fUYyZ9pE5kybyEm7O8plalMzB137RFEOer8yAUe5K0qQnE55Efm3iFwAjAOuAY4DWo0x+5VQrguB840xi3F8JLPd/bOBYe7+84EfujK+CgjwGk6I8znFKjulNqiWFWMaSi3sGsW0T1YHvVJuEtfywnnqPxT4PPACTjhvaojIAmCB+/cSQqK0RGQTcErE+VegWftKBFPG92fkwN4lyZBPQ6k19gs3e6VtVvLLqtn6StrkivIaCpyGY2YaANwGHJJCZJeilAUv4dHj+N2GZ/kDws5JQ0nMmTYxq5hlXOXhcq+28snWnzymb4aZq5iVk9J1sWw7NBgKAGPMJuBtHEXydNgxIvJwaUQrC/aKFfn57qvFxNKVSbPWVlChQPxnuOuQOhYlKAGT773vfW0Vf3ghs55YsBxMWCVlC5h54AgOHTck7/snqcxs0RkmmeQ+Sd5HsdRS1BTUlrwpR3llRdfmMnm9hxN99W33J4gN7FyscIri8eR5X2D16tVlL97okZYy8YjqCjn7+Q8y3qO3cpm2z0hOaGruiHm3gVlPrSxIofjbATc0NHDQtU9kHZPvffzKxHsf5ShL//J7G7ji0eVsbbfpWWdx0eTR7DlCOydWG7EKxY2+UpSyETbpJaGQkN9ykLQrpL9nStBmEG1DyI+ofi1p36cUXPHocja1ORK2t9lcvmA5d526a4WlUoLk45RXqEzpcyWepLkWaSsTbzUF6ZpCPTOUfzsNgq1s/SuhNO9TCra227HbSnWgCiVPHlq8ptIiKAG8J/tgUl9jv/L5vNJIKJz1+DvMPHgnJgypyyjlv+uQzmIUC5as5Vf/XJnh91i7sS3LFGUBR4/vz/zFGyJ9JDMPHMGspzKvlYuw3jK5kj3ToGedRXubnbGtVB+xTvlugDrlq4Bio6rCHO9Q3GeVRCb/CiWMBUvWMuufK0OvnW8rY+89hq0qkv4HW8CJuw/KWqkVGz4cNk5Rn0kSwhzHr6zcwOULqtOHok75TnSFolScqU3NTBrZO7TfSNLzAUb3S69MfRJl5Pl7op7IfxWiTDym7TOyoETDYvwrNuFmv1po9rXniP7qM6kBVKEoVcHC1s18Y99hWaabfKhUzxO/Q91P2GTvmcIKLbMS5l9Jw8ZQDrOV0vVJ2mBLUUpOPsqkGJNKuQjaAyw6/T35Bgh4vVWCymTmgSOYsd+wnPfORTFlWoL+Im1C133RFYqSRZqmozDSyESvBV9WIU7vfLCBa55aidljUOIEzjoLggFSucKJc+HPd6l2gnlBWn4mXVShKFmUw3S0SyCSqRTsPNBiybrSB51EPZEfOm5IXgmJnlLIV1nKqx+FlncJRp5BpjLpU291+CXmLc59z7AIs0ISLitJMC+oFvxHtYQqFKXslHJ1Edeno1T39Sb0YBmSuB4icWHGYYogX3m8VYM/U/6Q//dEhkLx53IkCXv2lAl0ro6CK7BaUzBKuqhCUboUC5ZtpE+FzBrBMiRhPURmun97k73nDPeUktljEP16Zf5bThxSR3MBq7mgQgumbvhzOUYP7IPFRx3KoXFQn6zrha310igRkwZamqU6UIWidDlKadbw+yrCVjzevjBHeRhJnOHNa9sz7ps0hyWo0Nptx8zln3Q9gquPMOWQK6KskhltSUuzBP1FU8ar0kkTVShKt6DpxdZUyuYkNZulXZ7HS2jsdO5HK5S4KKuoXI4k+S3BIIPgMZXMXU9amiVYfkZJFw0bVqqSQSk/6sirH5WlKq6fnQdasdv5EFw9hK2Azj9wBHOmTSwo4iosxDnIoeOGcO+0icz8fHa0Wimi2PIhWIpFS7NUBlUoSlWyrq00101qikrKMTEmk2CEWVjEWVgORy4ZbcJXQLOeyszMn+irARa27WfmgSM6lEgu5eA3j3nH3zttYkUd8hdNHk2feos6yzHr+c15SvlQk5dSlZTKHn/8bsNTNUdFlaePMq/NfX1VxkopLIfj3tdWFSRLcMyCvV3ier3kCnGOK/1fDdUAtTRLdaAKRemSFFL918vfSCO8OEppBRtrQXaoc1jFAH9nxagJPGjkCfOL+IMG8jEB5srsn9rUXJHQ4XJ0kFSSowpF6XJMGd+/oOztYIJgufCHE0dxb47oMsjPh1GKTouVCB2uVAdJJRxVKEqXYOeBFrO+nMzkMXlMXxYEQmpzkVYRxiiaXmxl3eb2xB0ew7jmqZVc81R0heO0CSuhUw3mL6VyqEJRaoJcIb+ew/vyh9/mWV8Z/O37wPubMo+1cCKikk6+nimnlJN13MrIc9JH9acvB1EmxFJ1lwyipq3aQBWKUhMkcaSHmYKCygQ6TTNJ8PsFyvn07+FPaExTmeQb7RZlQix1AUyPKNNWWAfJKFQplR5VKHnS2A9aKtR3Q0kPm9yrHu+4jzaVKIa5zHjK6d7XVmUFBxTqUM+3AGbaHL/b8MRKQf0tpaeiCsUYsyNwK7ADsA24UUSuNcYMBe4CxgJLASMia40xFnAtMAX4BPi6iDzvXms6cJF76ctF5JZSyFypJk5Kulh0TkYX3/8WL63aGnls3MRz2RGNGWU/yo2nJOa+vipWOX7vkDEdf4dFkfkd6uV+kg+GJE/ffyQn7tIZlVdoGLVSfiqd2NgGfF9EdgM+B5xjjNkd+CHwkIhMAB5ytwGOBSa4P2cC1wO4CugSYBJwAHCJMaYkj03qdKx9gqaZS4/6VKLzgqHH0/cfWZQyydffsNfwnhnbe/u2j99tOHOmTYz8OXXfMcHLZeG9i7An+VISDEm+5dnMbpZhCjDtBNVS8vJ7Gzj1rkWceHszp961iFdWVsYPVg4qukIRkVag1f17vTHmdWA0MBWY7B52C7AAuNDdf6uI2MDTxpjBxpiR7rEPiMgaAGPMA8AxwB1lezNKzXBvSDOq/Uf2znDmB5n7+qosP0JDQwN//FdhrXwh/1a+rwRWUS/HrKqiyNV6+IQaaFwGFLRiysffEqSYXjBJC1d2BarGh2KMGQvsCywERrjKBhFpNcZs7x42GnjXd1qLuy9qf9h9zsRZ3SAiNDQ0pPgulFrg/xau5MHFazu2j95lSKwygfCERICePSza7eLXrTZw8NiBPL50Xce+g8cO5PlVbVx6/+LI0vENDQ389om3uO25TkUxff+RnHlg5qqrvr6ehoYG7n4tXmFEvRMvcfGSY8bzxV13SPSeisH7v7zzuWWxr+fDNw9u4JsHFybPr3ztlz3z4En7TwA6xzaKre3NgW27YvNOLlmLvn7JrpwHxpj+wN3Af4nIOmNM1KFhVgI7Zn8WInIjcKN3zOrVq/OUVqllGvuRoUwA5r+xNuLo3GzdVri5K3imp0z2Gt6Tl1dt5fGl6zIUTNg1Vq9enaFMwDEZ+X0Q4EzAxX7XbeDn9y1m32HpThvBkOTp+4/skPX/nshWKDP2G1b0e8mXsKoDngy5xrZnnUW7zyzas84qu/weaXwPAEaNGhW6v9I+FIwxPXGUSZOI3OPuXumasnB/v+/ubwF29J3eCKyI2a8oHVjAb07INnelcd18mDSyd2xRyXxMWafetSjPuycn8VNakUzbZ2SGvye4ugoy+/kPmNrUzAlNzTz6duEPA/mQpBpzFN2pcGVFFYobtTUbeF1ErvG9NBeY7v49HZjj23+GMcYyxnwO+Mg1jc0HjjLGDHGd8Ue5+5QqYNLI3olqaZWSUuZIBCv15uKZ1s2p5JTYkHdAQNLPwRuvYibSMBYsWcsJTc2pKIR88omKJZ9qzEG8wpX3fHUid526a5fuJFlpk9dBwOnAK8aYF919PwauBMQYMwNYBpzivjYPJ2R4MU7Y8DcARGSNMeYy4Fn3uEs9B71SeX58+DggPhs8jPNdx2cxxRqPHd+fR5Z+zNZ2m+ueeZ+h2/WkzoKI/kt5M2O/YVm5GLnkLXWkYJzS8AILTvD5BPyEhQinmbiYpDNkUJ64KLNyRV1WOt+mVrDsFByKNYy9YkV+lrE0KtEmIaxOUi2TK4oqimIm/z71zjOl/yneMzlcvqDwcF8v9yPKHn3R/W9lRWSlSa86iy3tme8pV9RQUNZH316bpSjKMWGGfafnBKLugrJGKT9wZP+vz49IHIFVTLRWFGn5JcpByj6UrAVrpVcoSgRdSZkABSkTKG4lEdYGdlObzdtrNnZMwIWMs/+ciUPquGrKhIzX01ImfeotNrfZWeHFFx/mKMSw3vBx5JOwGEw29Er7F0shtb/85V2C1wq+lmvVk+8KSckPVSh5suuQuthGRUppqLPgnq/mt2rz2sC2B1Yi/sz3KeP7M68If0Zzyt+FPvVWhqJY88nWrJVEoc2k8ik9Ekw2lFc/ykuhRCmvqNpfL7+3gSsedZRkr/oe/OTQUR2+hlzmpmCNtbhnkLBorWDTsyB+2bzPpSv7QYpBFUqeqDKpDO128tVEnUXHP/5LK9bzp4jmUC+/t4GHl36camn6l98rXDkFG215eJPpgiWOmeqap1ZiAQeP6ctjvjL81VDs0G9S8uMpr0PHDcG2O1cKnlP9umfe7zBBbty6LSP5L9fKKp9VT9hnnaumV3dKTCyWiocNK9XJlJiw1nLh+UDy5Z6vTuQ7+2/PTx9siVQmc19f1TFReBNMGqXXr3h0ecHnhikTP0FzzWOBni6lLpGShDBlEneMp1SC5kn/dq5SMGERWFHlTgoJKoiTTclEVyhKKLsO71eUKSgN7jp111gnbtyKJdfENvv5D3K2zA2jfw/YsK1ze+KQuo6/FyxZm0qRyNb1W7jwH0v4qABXzKl3LYo0zeRTeqSQFsqQbAzDzE5hyX+QbMUXZhI79a5FoauKQtoQRMkWRXc2kalCUbKYsd8wfvXP8vf+8JNrtZCrAm0hE1sS01fTaZ0RScGImbAx80dgxSnAyWP6dvw966kVBSkTINI089sn3uI2nzIxewyKNfMU0kIZosfQr7zCTFRe5J0XhLCpzeaEpmbqC7ShxK0q8q3p5cmWNAiiO5vIVKEoWbSu21zxqso2cMPCltA8hEffXhtagdZjwZLCkuW+maA/yql3LYp84gwbs01tdk7fT9AnsH5ztJ/Om4wt4NBAK+PgRO2fRIPlWfJ1ssfhfyL3HgTiwnLDHPNeoIG/OKUNbN1GFkmKOsatKvLpoQLkHQQRXKVWqrVBJVCFomRRaVOXx7zFG5gzrZGbnv8gY6LMlR1d6OrqiWW5m91sinnizNe570WuBRnQuw7Why9RghP1TN9rfjMP5DbNQLR5xtu/pc3Gm9P71FucfcD2/P2Nj1i/uZ0Bves4/6BRWSX8c+XFxEVt5Rq/PvVWImWQ76pCSQd1yitVT5jNPZ/jkxK2Mpix3zCC87K36jjo2idoerHzyT/fEixRE/75B41iUM/Ql4DokiPBmlGTx/ZjqlvmJApPGbTbncrSv9+/QNjUZjPrqZUsWr2JFeu3smj1JmY9uSJVp3XYiBRSB6uS5U6Ck2p3mmR1hZInk0b2ZmGBSXqFMKwXfLClbLerOk68Pf/Ew0LCgGfsN8xZofhWBsN6546c8puOgk/eURne/rDmMHbo34tbTebKJagUvOvGhdQm8dlEKYMopRDcu25ze95O6zjCzGHlSjxMkkUftqI7LFAOvle9lbFi61VgtGItogolTw4cO5hnWnOHRqbFAWP6c9/iDRX3aeTCAsYNtFiyLl1J833YnTymL/s1DgzNrI6j6eU1nH3A9lg4k+SA3nUsWr0p2bkvtob6I2YeOCIroigsVySYu9EDuPTIxoyn6jAleeLtzVnjk7RP+tMrnPcWVAbtNh3O8LCxD8oxoHcdZ08akZp56dBxQzhp/wlZ5UH8E3kPq9O3kqbSSZJFH+ZwP2yPsRnHdGdzm9byyrOWV1xdoe7M+QeOYEjfnvz8kRbaQhyppcL/tB80a4R9VnGrlx7AX3x1pfLJyg/Wo8r1tBuVABiU1TvPX3srF0n63Hv+m1dWboisa9an3gr1ocx746MOpXv+QaPYoX+vBFJlE7W6Cqs3FfQP+bGA3oEKA/mauF5+bwM/fagla3/wcw0q8ToLHvveF0pWy6t1/RZmPbUiw2dV6HiD1vKqOlSZhJNvbH9ahDm1PaLMJ1GKIk09mOtpN0kCoI0zrtc98z5b2216uz6ESx5qiV25Jelz75mlPF9D2JictufQjAl/2l5DQx3qhU56YQmL/3hzHUP7Lee7k4ZnXCPOL+OFGUPyMN2g6SqsT1qYoSpN814SZj21onOlvH4rs55cwVVHjy3pPYtBFUoXIs0SIrXC9QtbmO+aBIMrgWJLjucqne7HPyGbPQblDCTI53MKTpbBSc3PjP2GcXNESHXPHrDNDvffhOWGJK39leakt2L9Vlas38p/zllPD9/qM+49B0kSFBA0XYVxSkgyZznNWa3rt7BkTabZdV1MSHk10J0CELo8YQ2Rujp+/1JU9FOQpGVljt9tOJcf2Zh3CRh59aOcjakK/Zw2tdmxpp/jdxseuXpp20Zk1FMxDaSC0XHNqzdllTwBssqhxGFDRuSZP4Ktp2/WssiexJKsGpIonbmLstsvN2zXi50G92ZE/57sNLg3w/sVbn7KxaynVmTl4QzoXRd+cJWgK5Qa5PIjG7nowWx7r/dEXsrS99XepyXJM+xZkxpZ9fHboSX1w6J4fnLoaH7+UAttecgRVlZ9alMzPXvAJYc3Rprjgn1K6nuEJ/eFkcSE9srKDR3KJMrP45mwkhKWN9NuZ5ufgquC+h4k8rdtbbdjkws9P1A+q4YkK54wpRO2Gps9Nry/erEEFXXPHk5IeTWjCiVPqsGs5OUKRFENMlaKpE/+Uf1ZwqJ4gLyUCXQq92BgwNZtZNSVijrPwz9Z5nqoTvLe/RN8lJ8nY9L0ccoeg/jB/KVZvpLzDxrFrCdXsG5zOysCisU/KQcnaNvudHrHBbvkWnEUUs4/SZO1sPsGJ/k0TFBRyaVBRb3z0D5FOeTLgZq88qRUfcnzIZfDtRpkrBTFvPdJI3uH5mUUkqg3tamZk+8InyTzuZ4/QS+Xwkjy3v33jvLrBCfNUQN6MmfaRF5euTkrqRGcvJmrjh7L9cd/Kss86LUdOKGpmR6BN+CfsHuHmBXzTWbMB29cw3RV3H2DJqd8TVALlqzlBDfZ9ISmZh59e21kcun5B41iYkMfRg3oya4Nfap+dQK6Qskb7wmyoaGBg659otLiZBAMw+wKWMDX9x2W833lk4gWFVK6sHWzU4zQN9OGNenySovkMv1Fmar8E2lY+HCSGlhBLDr7pgQrBfvx8kw8n0nwvpBtwhrQu46X39uQtWoJezr3O639etOry9XHl/S3pc3uqI3mPy/YYCsphUSbBU1fucrG+Fdj3j3yIWxVGFS0ntL3FHUtoXkoeeahXL+whfuqpNaVn5552Nr97DW8Jy+HtKy13B//Jb1/tpPvaC7oXmEEE/1e+KCNn9+3OMOuHxeS7D8/LK+h6eU1oXWm5r6+KjKCK9g1Eciy0e85on9BviTPh+JNllHXsIB7p0WHROfqCR+W9Bi8fpQf570NW7ImzfP+/nbWynjXhj78T8yEF9V6IJhTEpzE88mV8CuRNRvbMq47saFPzgk5zP+SryLLR96wMekTyKzPpdSKQfNQqoxqVCaQnzLJlZPhTdJRzs5LDg8PCsiXYNIYwBd33YF9h2V+LeMUij+UNSzMNawO16Nvr+2oOBv2/sP+mYP7WtdvYXi/nqz6OFmdec+EknSy8pz4UV0Yc4VE53I62zhdEv2huZ5sYU/GYWa6XE/nUSugNGt/Rfl7IJl/o9B2yknKtIQRV7o/V1BB2kmOpUAVSjckqqyEhzeBRf2zRU2K+dY5C/unPClQF6lYwibWuPfuJ85cNuupFYmVCTiK7KIHW7g8UFIlF0lLqUDmhDO8X0/e/Si+CFw+yYBh47jq4y3s0L9X5OQa1T8+zeTAuFL/pQyxTVKmJYy40v25qIUkR1Uo3ZQkz4T5PoU907o5dNURtgowewwK/ac8af8JScTP4MTbmyMnpbAABv+euGZLcY2S4iayOApptpTUD3TFoy05lQh0rhQyos9yrBIumjw6a1XqvZfg5+hl9180eXSo2S7JE3nYd29I354Z4/DVvYbSGogq61NvMbRvfZZ/I+0uimEBDd+Z+1bOlUMxybbB79yajW2hUXeVRBVKnkwZ378q+oWkFRocNaG2rt/CLF9fEf9TWFRbVhtCv+Bh9zh+t+FZjmP/+/GettduzB2w6+U8FEJcs6U400xUz5Jcn8uWgJznxzjaPZJ2AMylTDzbfOv6LXxn7pKM16IU8svvbeCyBcvZEqJwvPEIkz2ub0ySJ/Kwh43ePl9De5vNTQEfmAVc+6VxoZNq2l0Uwz7nFeu3Fr1yiDNrBb9z6za38/7H7v9HlaxYVKHkyVmTGpm3uDyJfXHRRP/1+REZE75H1IQ2YrserPxkW8Y2RE+oYYltNnDKHc1sifHX+JfkVz3awqwv7Rx5jygbu3f/pNV+464ZRZInuzors+KuRWfP9joLxg3ty9pPNvPhpm0dr+cKIggOnf+J9Qfzl2a957DyL/l2AKyz6IicAmdsw+z4YVzxaLgygU4lFDXmW9vtgu3+YSuAXKuoHhaR107TbwPxEXfF5KbEmbWCEWZrPtnKprbOeyW57/IPN/LTEq5qupRCMcYcA1wL1AG/F5ErKyxSLL16wMWHN0YW+/O+9MEwULPHoFBl4pmbwhTQpV8cy6wnV7By/WY+3GyzauM2TrmjmREDerG5bZvzJe3Vg6Hb9eT8dh97XgAADOZJREFUg0ZFmnTilEmQd9dlPzH7TQ/+LPBguY9CTEpJw3mBRLboEQN6ZTz1t9nQ5j3l2vDe+s3caXbJOi9poczgZLvmk8wVz6gBPTl+t+Gh0WhhE3WvHtmfjzcm/uie4NgO7VvHzx7srATQq87i4sMck1DUxOvP04iaXHvWWQXb/cMeNnIFGsT5Yor124SNt2fO+8lDy/n3e+s7ji3GdxOXOBkMlvjB/KWs+qTz9ST3vXT+GyX1w3SZxEZjTB3wG+BYYHfgNGPM7qW4V5Ke1nFYOJP/n06byJ4j+kd+ub390/YZyZxpEzt+CukFvkP/Xkzbu4G1m21snCKBW7Y5ZpL3P3bCLVd90t6RsFYqh6Y/icvLS5gzbSL3TpuYYVtOev96shPReoWMZ686KzSJDbKf7FrXb+EH85eyIkQh+tkSUTck7vvhT/zzJlsvUXB9QBt4YxDWATB47qwnV3Dx4Y0ZT4i9IvwTwbFds7E9oxLAlvbO5Lqw76anpDwfxKHjhnDvtIkddc/8n0ehmeVhtcWCHSln7DcscTfH4Ln5JkuGjbf3PVn18Wb61Fts36++6ATEfBInC0l8/HBT5kNL2sUmu9IK5QBgsYgsATDG3AlMBV5L+0bH7zacbx68W8cTX65Y9rCcAT+ekzLYe6LQDOHgimb6/o4CuuLR+JItHus2t3PJ4Tty7l+XFJVvsuOg3ln7kpoevOX9mo1tnXbiAFERUxcflu309Y4Ly88I/tNGmduCT8296sOfx/wmvrg6U8HJdmDvOsYOrs9KmgvrABg2Ue85oj93x+SueARNJ2+EvFfvc7lo8mgufaTT7FVPtHkszDcyoPfqrCTJJEQ5r4PXTxoBV2h4sEfYeAe/J2MH1xf9tJ9P4mQhiY+D+/SkhU6Z035w7EoKZTTwrm+7BZgUPMgYcyZwJoCI0FBgmGp9fX3HuYc1NGR1bfNzUkNDbPRSrvOT4slz3pENnHdkpqxtbW1sbU/m+xnarw+fHjuKXbdflbGUB+hdBwP7ZOdfWMB3DxnDw2+s5cNNWxncpyeXHLMLDYP6ZhzXq74HG31aqld9j4zPwBvXhgY6iu4ded1TGecA/PqkPdi3MTxaJm48g/e3gMuP2yNDzo/blmacU2fBbiMGcPI+I7jqoSVsadtGr/oeXPMfe9LQMCD0PklkGdpveUbtq+0H9OWGU/fOOu6XU+u4YO7rHff93+N347dPLss4d2i/PrHfZf/31T+2ED6+3udS7Hfz8uP68fP73oj9TsTJWi0EP6uh/fpkPe1vaKNouYOfTdpcetwgLv7ba3l9HvnQZTLljTGnAEeLyLfc7dOBA0TkuzGn5Z0p75FWxmmh5MqU9uPJGtb1bsdB4T6UHfr3Cs2YLtaBl2s1FzauaWQz53OtoHM8KiO82O9AMeOb77lxsr6yckOkD6USVPp/K4yw8b7myRWJvifVRKkz5buSQvk88DMROdrd/hGAiPx3zGk1q1DywZM1zYm5VFTDuCadrKtB1qSorOnjfU82tEG/eqoiDyQXWnolOc8CE4wx44DlwFeAr1ZWpOqiWDtyd6EWi/Ip5cf7ntSKAiwHXSbKS0TagHOB+cDrzi55tbJSKYqidB+60goFEZkHzKu0HIqiKN2RLrNCURRFUSqLKhRFURQlFVShKIqiKKmgCkVRFEVJhS6Th1Ig3frNK4qiFEFWHkp3X6FYhf4YY54r5vxy/qisKqvKqvKWQNYsurtCURRFUVJCFYqiKIqSCqpQCufGSguQBypraVBZS0MtyQq1JW9JZe3uTnlFURQlJXSFoiiKoqSCKhRFURQlFbpUcchyYIw5BrgWqAN+LyJXVlikLIwxS4H1QDvQJiKfNcYMBe4CxgJLASMiaysg203AccD7IvJpd1+obMYYC2espwCfAF8XkecrLOvPgG8Dq9zDfuwWJfV68MzAGffvicj8Msq6I3ArsAOwDbhRRK6txrGNkfVnVNnYGmP6AI8BvXHmyz+LyCVum4w7gaHA88DpIrLFGNPbfW+fAT4AThWRpRWW9WbgUMDrC/51EXmxFN8BXaHkgTGmDvgNcCywO3CaMWb3ykoVyWEiso+IfNbd/iHwkIhMAB5ytyvBzcAxgX1Rsh0LTHB/zgSuL5OMHjeTLSvALHds9/FNeLvj9ODZwz3nOvf7Ui7agO+LyG7A54BzXJmqcWyjZIXqG9vNwOEisjewD3CMMeZzwFWurBOAtTjKDvf3WhEZD8xyjysXUbICXOAb1xfdfal/B1Sh5McBwGIRWSIiW3CeUKZWWKakTAVucf++BTihEkKIyGPAmsDuKNmmAreKiC0iTwODjTEjyyNppKxRTAXuFJHNIvI2sBjn+1IWRKTVe7oUkfU4PYFGU4VjGyNrFBUbW3d8NribPd0fGzgc+LO7Pziu3nj/GTjCXQlUUtYoUv8OqELJj9HAu77tFuL/ESqFDdxvjHnOGHOmu2+EiLSC8w8NbF8x6bKJkq1ax/tcY8zLxpibjDFD3H1VI6sxZiywL7CQKh/bgKxQhWNrjKkzxrwIvA88ALwFfOg29QvK0yGr+/pHwLBKySoi3rhe4Y7rLNcslyGrS9HjqgolP8KeNKox7vogEdkPZ0l7jjHmkEoLVCDVON7XA5/CMSm0Ale7+6tCVmNMf+Bu4L9EZF3MoRWXN0TWqhxbEWkXkX2ARpyV0W4x8lSVrMaYTwM/AiYC++P4fC50D09dVlUo+dEC7OjbbgRWVEiWSERkhfv7feAvOP8EK73lrPv7/cpJmEWUbFU33iKy0v2n3Qb8jk7TS8VlNcb0xJmgm0TkHnd3VY5tmKzVPLaufB8CC3D8PoONMV5Qk1+eDlnd1weR3GyaGj5Zj3FNjLaIbAb+QAnHVRVKfjwLTDDGjDPG9MJxFM6tsEwZGGP6GWMGeH8DRwH/xpFzunvYdGBOZSQMJUq2ucAZxhjLdS5+5JlvKkXAxvwfOGMLjqxfMcb0diOAJgDPlFEuC5gNvC4i1/heqrqxjZK1GsfWGDPcGDPY/bsvcCSOz+cR4GT3sOC4euN9MvCwiJRlhRIha7PvgcLC8fX4xzXV74CGDeeBiLQZY84F5uOEDd8kIq9WWKwgI4C/GGPA+XxvF5H7jDHPAmKMmQEsA06phHDGmDuAyUCDMaYFuAS4MkK2eTghjYtxwhq/UQWyTjbG7INjGlgKnAUgIq8aYwR4DSeK6RwRaS+juAcBpwOvuDZ0gB9TnWMbJetpVTi2I4Fb3KiyHo448jdjzGvAncaYy4EXcBQk7u/bjDGLcVYmXymTnHGyPmyMGY5j4noR+E/3+NS/A1p6RVEURUkFNXkpiqIoqaAKRVEURUkFVSiKoihKKqhCURRFUVJBFYqiKIqSCqpQFEVRlFTQPBRFKQC3RcAInHLqG4D7gHN9xfkUpduhKxRFKZwvi0h/nNpT++LUTFKUbouuUBSlSETkPWPMfBzFglvN9QrA4DQ7+gswU0Q2uq9PBX4O7IzTTOoct5rBIOAanOzlbTh1ly4RkXZjzNeBbwFP4/Tc+BA4W0T+4V5zKE4xxaOBvsCjInKCMebfwI9E5K/ucT1xCi8e6euLoSipoCsURSkSY0wjTmXnxe6uq4BdcBTMeJyS4Be7xx6A09HvAmAwcAhOmRFw+mi0uefsi1OH7Vu+W00CFgENwP8As329Nm4DtsNpQrU9TnMn3Ht9zXeNKUCrKhOlFOgKRVEK515jjA30Bx4GLnEn+G8De4nIGgBjzC+A23FMYjNwasA94F5juXvMCBylNNhdyXxsjJmF00nvBvfYd0Tkd+7xtwDXASPcex4LDJPOts6Pur//CPzUGDPQLRF/Oo7yUZTU0RWKohTOCSIyAKeA5ESclcNwnJXCc8aYD40xH+I47Ie75+yI06ApyE44HfZafefdQGYjtPe8P0TkE/fP/u411/iUCb7jVgBPAie5lWiPBZoKe7uKEo+uUBSlSETkUWPMzcAvgROBjcAeIrI85PB3cZpIhe3fDDT4OgEm5V1gqDFmsNsHI8gtOKazeuCfEXIpStGoQlGUdPgVji9kL5zmULOMMeeKyPvGmNHAp0VkPk558/uNMX/D6akxEhggIs3GmPuBq40xP8UJRR4HNIrIoyH360BEWo0x/wCuM8ac4577eRF5zD3kXlzzGI7vRVFKgpq8FCUFRGQVjgP8pzgtVhcDTxtj1gEPAru6xz2D03diFk6/8UdxzF0AZwC9cPp+rAX+jKNwknA6sBVoxunK+F8+2TbidEccB9wTeraipID2Q1GUboAx5mJgFxH5Ws6DFaVA1OSlKF0cN0dlBs4qRlFKhpq8FKULY4z5No7T/h8+n4qilAQ1eSmKoiipoCsURVEUJRVUoSiKoiipoApFURRFSQVVKIqiKEoqqEJRFEVRUuH/AwT/pJYQreBdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,7))\n",
    "cust_trans_rfm.plot.scatter(x='recency' , y='monetary')\n",
    "plt.xlabel('Recency')\n",
    "plt.ylabel('Monetary ($)')\n",
    "plt.title('Recency vs Monetary')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 Frequency vs Monetary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Customers classified as \"Platinum Custoers\" , \"Very Loyal\" and \"Becoming Loyal\" visit frequently, which correlated with increased revenue for the business. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 576x504 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,7))\n",
    "cust_trans_rfm.plot.scatter(x='frequency' , y='monetary')\n",
    "plt.xlabel('Frequency')\n",
    "plt.ylabel('Monetary ($)')\n",
    "plt.title('Frequency vs Monetary')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Customer Segment Distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Finally we can plot the Number of Customers present under a Customer Segment.</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculating the number of unique customers under a customer title.\n",
    "\n",
    "cust_per_title = cust_trans_rfm[['detail_cust_title', 'customer_id','rank']].drop_duplicates().groupby(\n",
    "    ['detail_cust_title','rank']).size().reset_index().sort_values('rank')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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>detail_cust_title</th>\n",
       "      <th>rank</th>\n",
       "      <th>Number of Customers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Platinum Customer</td>\n",
       "      <td>1</td>\n",
       "      <td>164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Very Loyal</td>\n",
       "      <td>2</td>\n",
       "      <td>181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Becoming Loyal</td>\n",
       "      <td>3</td>\n",
       "      <td>344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Recent Customer</td>\n",
       "      <td>4</td>\n",
       "      <td>357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Potential Customer</td>\n",
       "      <td>5</td>\n",
       "      <td>340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Late Bloomer</td>\n",
       "      <td>6</td>\n",
       "      <td>332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Loosing Customer</td>\n",
       "      <td>7</td>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>High Risk Customer</td>\n",
       "      <td>8</td>\n",
       "      <td>371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Almost Lost Customer</td>\n",
       "      <td>9</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Evasive Customer</td>\n",
       "      <td>10</td>\n",
       "      <td>388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Lost Customer</td>\n",
       "      <td>11</td>\n",
       "      <td>291</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       detail_cust_title  rank  Number of Customers\n",
       "7      Platinum Customer     1                  164\n",
       "10            Very Loyal     2                  181\n",
       "1         Becoming Loyal     3                  344\n",
       "9        Recent Customer     4                  357\n",
       "8     Potential Customer     5                  340\n",
       "4           Late Bloomer     6                  332\n",
       "5       Loosing Customer     7                  333\n",
       "3     High Risk Customer     8                  371\n",
       "0   Almost Lost Customer     9                  315\n",
       "2       Evasive Customer    10                  388\n",
       "6          Lost Customer    11                  291"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cust_per_title.rename(columns={0:'Number of Customers'}, inplace=True)\n",
    "cust_per_title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the Number of Customers\n",
    "\n",
    "plt.figure(figsize=(15,8))\n",
    "sns.barplot(y='detail_cust_title' , x='Number of Customers', data=cust_per_title)\n",
    "plt.xlabel('Number of Customers')\n",
    "plt.ylabel('Customer Segment')\n",
    "plt.title('Number of Customers by Customer Segment')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}