Cohort_Analysis_and_Customer_Segmentation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cohort Analysis and Customer Segmentation Project\n",
    "\n",
    "\n",
    "### \n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A Cohort is simply a group of people with shared characteristics.\n",
    "Cohort analysis is a type of behavioral analytics in which you group your users based on their shared traits to better track and understand their actions over a specified period. Cohort analysis allows you to ask more specific, targeted questions and make informed product decisions that will reduce churn and drastically increase revenue.\n",
    "Customer segmentation is the act of dividing customers into groups such that each group has similar characteristics. Customer segmentation could be based on customer location, demographics, or behavior.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem Statements"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An e-commerce giant wants to improve its product offerings, customer relations and also maximize profit.\n",
    "To achieve this, the company wants to calculate the percentage of customers it's able to retain every month.\n",
    "The company also needs to segment its customers for target marketing and promotions.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objectives"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The project objectives are:\n",
    "1.\tTo calculate customer retention rate.\n",
    "2.\tTo segment the customers based on their behavior. The behavior under consideration includes customers’ recent transactions with the company, the frequency of purchases, and how much each customer spends on the company products.\n",
    "3.\tTo analyze customer segments based on Recency, Frequency, and Monetary value.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Definitions of metrics used"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recency (R) refers to the days elapsed since the last purchase.\n",
    "It is calculated by deducting the date that the customer made the last purchase from the offset date (the assumed date when the analysis was carried out).\n",
    "\n",
    "Frequency (F) refers to the total number of transactions a customer has made with the company.\n",
    "It represents how often a customer purchased a product/product from the company. It is calculated by counting the number of complete purchases a customer has made with the company.\n",
    "\n",
    "Monetary (M) refers to the sum of a customer’s monies on the company's products.\n",
    "It is calculated by multiplying the number of products by the unit price of products.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the necessary packages\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime as dt\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import style\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis and Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the data\n",
    "\n",
    "df = pd.read_csv(\"sample_superstore.csv\", parse_dates = [\"Order Date\",\"Ship Date\"], encoding = \"latin1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "\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>Row ID</th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Ship Date</th>\n",
       "      <th>Ship Mode</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Customer Name</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>...</th>\n",
       "      <th>Postal Code</th>\n",
       "      <th>Region</th>\n",
       "      <th>Product ID</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Product Name</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>CA-2016-152156</td>\n",
       "      <td>2016-11-08</td>\n",
       "      <td>2016-11-11</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>CG-12520</td>\n",
       "      <td>Claire Gute</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>...</td>\n",
       "      <td>42420</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-BO-10001798</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Bookcases</td>\n",
       "      <td>Bush Somerset Collection Bookcase</td>\n",
       "      <td>261.9600</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>41.9136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>CA-2016-152156</td>\n",
       "      <td>2016-11-08</td>\n",
       "      <td>2016-11-11</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>CG-12520</td>\n",
       "      <td>Claire Gute</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>...</td>\n",
       "      <td>42420</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-CH-10000454</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>Hon Deluxe Fabric Upholstered Stacking Chairs,...</td>\n",
       "      <td>731.9400</td>\n",
       "      <td>3</td>\n",
       "      <td>0.00</td>\n",
       "      <td>219.5820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>CA-2016-138688</td>\n",
       "      <td>2016-06-12</td>\n",
       "      <td>2016-06-16</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>DV-13045</td>\n",
       "      <td>Darrin Van Huff</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>...</td>\n",
       "      <td>90036</td>\n",
       "      <td>West</td>\n",
       "      <td>OFF-LA-10000240</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Labels</td>\n",
       "      <td>Self-Adhesive Address Labels for Typewriters b...</td>\n",
       "      <td>14.6200</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.8714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>US-2015-108966</td>\n",
       "      <td>2015-10-11</td>\n",
       "      <td>2015-10-18</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SO-20335</td>\n",
       "      <td>Sean O'Donnell</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>...</td>\n",
       "      <td>33311</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-TA-10000577</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Tables</td>\n",
       "      <td>Bretford CR4500 Series Slim Rectangular Table</td>\n",
       "      <td>957.5775</td>\n",
       "      <td>5</td>\n",
       "      <td>0.45</td>\n",
       "      <td>-383.0310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>US-2015-108966</td>\n",
       "      <td>2015-10-11</td>\n",
       "      <td>2015-10-18</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SO-20335</td>\n",
       "      <td>Sean O'Donnell</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>...</td>\n",
       "      <td>33311</td>\n",
       "      <td>South</td>\n",
       "      <td>OFF-ST-10000760</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Storage</td>\n",
       "      <td>Eldon Fold 'N Roll Cart System</td>\n",
       "      <td>22.3680</td>\n",
       "      <td>2</td>\n",
       "      <td>0.20</td>\n",
       "      <td>2.5164</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Row ID        Order ID Order Date  Ship Date       Ship Mode Customer ID  \\\n",
       "0       1  CA-2016-152156 2016-11-08 2016-11-11    Second Class    CG-12520   \n",
       "1       2  CA-2016-152156 2016-11-08 2016-11-11    Second Class    CG-12520   \n",
       "2       3  CA-2016-138688 2016-06-12 2016-06-16    Second Class    DV-13045   \n",
       "3       4  US-2015-108966 2015-10-11 2015-10-18  Standard Class    SO-20335   \n",
       "4       5  US-2015-108966 2015-10-11 2015-10-18  Standard Class    SO-20335   \n",
       "\n",
       "     Customer Name    Segment        Country             City  ...  \\\n",
       "0      Claire Gute   Consumer  United States        Henderson  ...   \n",
       "1      Claire Gute   Consumer  United States        Henderson  ...   \n",
       "2  Darrin Van Huff  Corporate  United States      Los Angeles  ...   \n",
       "3   Sean O'Donnell   Consumer  United States  Fort Lauderdale  ...   \n",
       "4   Sean O'Donnell   Consumer  United States  Fort Lauderdale  ...   \n",
       "\n",
       "  Postal Code  Region       Product ID         Category Sub-Category  \\\n",
       "0       42420   South  FUR-BO-10001798        Furniture    Bookcases   \n",
       "1       42420   South  FUR-CH-10000454        Furniture       Chairs   \n",
       "2       90036    West  OFF-LA-10000240  Office Supplies       Labels   \n",
       "3       33311   South  FUR-TA-10000577        Furniture       Tables   \n",
       "4       33311   South  OFF-ST-10000760  Office Supplies      Storage   \n",
       "\n",
       "                                        Product Name     Sales  Quantity  \\\n",
       "0                  Bush Somerset Collection Bookcase  261.9600         2   \n",
       "1  Hon Deluxe Fabric Upholstered Stacking Chairs,...  731.9400         3   \n",
       "2  Self-Adhesive Address Labels for Typewriters b...   14.6200         2   \n",
       "3      Bretford CR4500 Series Slim Rectangular Table  957.5775         5   \n",
       "4                     Eldon Fold 'N Roll Cart System   22.3680         2   \n",
       "\n",
       "   Discount    Profit  \n",
       "0      0.00   41.9136  \n",
       "1      0.00  219.5820  \n",
       "2      0.00    6.8714  \n",
       "3      0.45 -383.0310  \n",
       "4      0.20    2.5164  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display first few rows\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>9989</th>\n",
       "      <td>9990</td>\n",
       "      <td>CA-2014-110422</td>\n",
       "      <td>2014-01-21</td>\n",
       "      <td>2014-01-23</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>TB-21400</td>\n",
       "      <td>Tom Boeckenhauer</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Miami</td>\n",
       "      <td>...</td>\n",
       "      <td>33180</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-FU-10001889</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Furnishings</td>\n",
       "      <td>Ultra Door Pull Handle</td>\n",
       "      <td>25.248</td>\n",
       "      <td>3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>4.1028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9990</th>\n",
       "      <td>9991</td>\n",
       "      <td>CA-2017-121258</td>\n",
       "      <td>2017-02-26</td>\n",
       "      <td>2017-03-03</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>DB-13060</td>\n",
       "      <td>Dave Brooks</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Costa Mesa</td>\n",
       "      <td>...</td>\n",
       "      <td>92627</td>\n",
       "      <td>West</td>\n",
       "      <td>FUR-FU-10000747</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Furnishings</td>\n",
       "      <td>Tenex B1-RE Series Chair Mats for Low Pile Car...</td>\n",
       "      <td>91.960</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.6332</td>\n",
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       "    <tr>\n",
       "      <th>9991</th>\n",
       "      <td>9992</td>\n",
       "      <td>CA-2017-121258</td>\n",
       "      <td>2017-02-26</td>\n",
       "      <td>2017-03-03</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>DB-13060</td>\n",
       "      <td>Dave Brooks</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Costa Mesa</td>\n",
       "      <td>...</td>\n",
       "      <td>92627</td>\n",
       "      <td>West</td>\n",
       "      <td>TEC-PH-10003645</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Phones</td>\n",
       "      <td>Aastra 57i VoIP phone</td>\n",
       "      <td>258.576</td>\n",
       "      <td>2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>19.3932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9992</th>\n",
       "      <td>9993</td>\n",
       "      <td>CA-2017-121258</td>\n",
       "      <td>2017-02-26</td>\n",
       "      <td>2017-03-03</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>DB-13060</td>\n",
       "      <td>Dave Brooks</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Costa Mesa</td>\n",
       "      <td>...</td>\n",
       "      <td>92627</td>\n",
       "      <td>West</td>\n",
       "      <td>OFF-PA-10004041</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Paper</td>\n",
       "      <td>It's Hot Message Books with Stickers, 2 3/4\" x 5\"</td>\n",
       "      <td>29.600</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.3200</td>\n",
       "    </tr>\n",
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       "      <th>9993</th>\n",
       "      <td>9994</td>\n",
       "      <td>CA-2017-119914</td>\n",
       "      <td>2017-05-04</td>\n",
       "      <td>2017-05-09</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>CC-12220</td>\n",
       "      <td>Chris Cortes</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Westminster</td>\n",
       "      <td>...</td>\n",
       "      <td>92683</td>\n",
       "      <td>West</td>\n",
       "      <td>OFF-AP-10002684</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Appliances</td>\n",
       "      <td>Acco 7-Outlet Masterpiece Power Center, Wihtou...</td>\n",
       "      <td>243.160</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72.9480</td>\n",
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      "text/plain": [
       "      Row ID        Order ID Order Date  Ship Date       Ship Mode  \\\n",
       "9989    9990  CA-2014-110422 2014-01-21 2014-01-23    Second Class   \n",
       "9990    9991  CA-2017-121258 2017-02-26 2017-03-03  Standard Class   \n",
       "9991    9992  CA-2017-121258 2017-02-26 2017-03-03  Standard Class   \n",
       "9992    9993  CA-2017-121258 2017-02-26 2017-03-03  Standard Class   \n",
       "9993    9994  CA-2017-119914 2017-05-04 2017-05-09    Second Class   \n",
       "\n",
       "     Customer ID     Customer Name   Segment        Country         City  ...  \\\n",
       "9989    TB-21400  Tom Boeckenhauer  Consumer  United States        Miami  ...   \n",
       "9990    DB-13060       Dave Brooks  Consumer  United States   Costa Mesa  ...   \n",
       "9991    DB-13060       Dave Brooks  Consumer  United States   Costa Mesa  ...   \n",
       "9992    DB-13060       Dave Brooks  Consumer  United States   Costa Mesa  ...   \n",
       "9993    CC-12220      Chris Cortes  Consumer  United States  Westminster  ...   \n",
       "\n",
       "     Postal Code  Region       Product ID         Category Sub-Category  \\\n",
       "9989       33180   South  FUR-FU-10001889        Furniture  Furnishings   \n",
       "9990       92627    West  FUR-FU-10000747        Furniture  Furnishings   \n",
       "9991       92627    West  TEC-PH-10003645       Technology       Phones   \n",
       "9992       92627    West  OFF-PA-10004041  Office Supplies        Paper   \n",
       "9993       92683    West  OFF-AP-10002684  Office Supplies   Appliances   \n",
       "\n",
       "                                           Product Name    Sales  Quantity  \\\n",
       "9989                             Ultra Door Pull Handle   25.248         3   \n",
       "9990  Tenex B1-RE Series Chair Mats for Low Pile Car...   91.960         2   \n",
       "9991                              Aastra 57i VoIP phone  258.576         2   \n",
       "9992  It's Hot Message Books with Stickers, 2 3/4\" x 5\"   29.600         4   \n",
       "9993  Acco 7-Outlet Masterpiece Power Center, Wihtou...  243.160         2   \n",
       "\n",
       "      Discount   Profit  \n",
       "9989       0.2   4.1028  \n",
       "9990       0.0  15.6332  \n",
       "9991       0.2  19.3932  \n",
       "9992       0.0  13.3200  \n",
       "9993       0.0  72.9480  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display last few rows\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. About the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Row ID\n",
      "Order ID\n",
      "Order Date\n",
      "Ship Date\n",
      "Ship Mode\n",
      "Customer ID\n",
      "Customer Name\n",
      "Segment\n",
      "Country\n",
      "City\n",
      "State\n",
      "Postal Code\n",
      "Region\n",
      "Product ID\n",
      "Category\n",
      "Sub-Category\n",
      "Product Name\n",
      "Sales\n",
      "Quantity\n",
      "Discount\n",
      "Profit\n"
     ]
    }
   ],
   "source": [
    "# Show all columns\n",
    "for col in df.columns:\n",
    "    print(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 9994 rows and 21 columns in the dataset.\n"
     ]
    }
   ],
   "source": [
    "# Checking the the number of columns and rows of the data \n",
    "\n",
    "print(f\"There are {df.shape[0]} rows and {df.shape[1]} columns in the dataset.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.Exploratory data analysis, data cleaning, and Feature engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data has 21 columns. Row ID and default pandas index are the same. Hence Row ID is dropped."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>Order ID</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Ship Date</th>\n",
       "      <th>Ship Mode</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Customer Name</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Postal Code</th>\n",
       "      <th>Region</th>\n",
       "      <th>Product ID</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Product Name</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CA-2016-152156</td>\n",
       "      <td>2016-11-08</td>\n",
       "      <td>2016-11-11</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>CG-12520</td>\n",
       "      <td>Claire Gute</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>42420</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-BO-10001798</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Bookcases</td>\n",
       "      <td>Bush Somerset Collection Bookcase</td>\n",
       "      <td>261.9600</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>41.9136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CA-2016-152156</td>\n",
       "      <td>2016-11-08</td>\n",
       "      <td>2016-11-11</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>CG-12520</td>\n",
       "      <td>Claire Gute</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>42420</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-CH-10000454</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>Hon Deluxe Fabric Upholstered Stacking Chairs,...</td>\n",
       "      <td>731.9400</td>\n",
       "      <td>3</td>\n",
       "      <td>0.00</td>\n",
       "      <td>219.5820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CA-2016-138688</td>\n",
       "      <td>2016-06-12</td>\n",
       "      <td>2016-06-16</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>DV-13045</td>\n",
       "      <td>Darrin Van Huff</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>California</td>\n",
       "      <td>90036</td>\n",
       "      <td>West</td>\n",
       "      <td>OFF-LA-10000240</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Labels</td>\n",
       "      <td>Self-Adhesive Address Labels for Typewriters b...</td>\n",
       "      <td>14.6200</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.8714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>US-2015-108966</td>\n",
       "      <td>2015-10-11</td>\n",
       "      <td>2015-10-18</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SO-20335</td>\n",
       "      <td>Sean O'Donnell</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>Florida</td>\n",
       "      <td>33311</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-TA-10000577</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Tables</td>\n",
       "      <td>Bretford CR4500 Series Slim Rectangular Table</td>\n",
       "      <td>957.5775</td>\n",
       "      <td>5</td>\n",
       "      <td>0.45</td>\n",
       "      <td>-383.0310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>US-2015-108966</td>\n",
       "      <td>2015-10-11</td>\n",
       "      <td>2015-10-18</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SO-20335</td>\n",
       "      <td>Sean O'Donnell</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>Florida</td>\n",
       "      <td>33311</td>\n",
       "      <td>South</td>\n",
       "      <td>OFF-ST-10000760</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Storage</td>\n",
       "      <td>Eldon Fold 'N Roll Cart System</td>\n",
       "      <td>22.3680</td>\n",
       "      <td>2</td>\n",
       "      <td>0.20</td>\n",
       "      <td>2.5164</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Order ID Order Date  Ship Date       Ship Mode Customer ID  \\\n",
       "0  CA-2016-152156 2016-11-08 2016-11-11    Second Class    CG-12520   \n",
       "1  CA-2016-152156 2016-11-08 2016-11-11    Second Class    CG-12520   \n",
       "2  CA-2016-138688 2016-06-12 2016-06-16    Second Class    DV-13045   \n",
       "3  US-2015-108966 2015-10-11 2015-10-18  Standard Class    SO-20335   \n",
       "4  US-2015-108966 2015-10-11 2015-10-18  Standard Class    SO-20335   \n",
       "\n",
       "     Customer Name    Segment        Country             City       State  \\\n",
       "0      Claire Gute   Consumer  United States        Henderson    Kentucky   \n",
       "1      Claire Gute   Consumer  United States        Henderson    Kentucky   \n",
       "2  Darrin Van Huff  Corporate  United States      Los Angeles  California   \n",
       "3   Sean O'Donnell   Consumer  United States  Fort Lauderdale     Florida   \n",
       "4   Sean O'Donnell   Consumer  United States  Fort Lauderdale     Florida   \n",
       "\n",
       "   Postal Code Region       Product ID         Category Sub-Category  \\\n",
       "0        42420  South  FUR-BO-10001798        Furniture    Bookcases   \n",
       "1        42420  South  FUR-CH-10000454        Furniture       Chairs   \n",
       "2        90036   West  OFF-LA-10000240  Office Supplies       Labels   \n",
       "3        33311  South  FUR-TA-10000577        Furniture       Tables   \n",
       "4        33311  South  OFF-ST-10000760  Office Supplies      Storage   \n",
       "\n",
       "                                        Product Name     Sales  Quantity  \\\n",
       "0                  Bush Somerset Collection Bookcase  261.9600         2   \n",
       "1  Hon Deluxe Fabric Upholstered Stacking Chairs,...  731.9400         3   \n",
       "2  Self-Adhesive Address Labels for Typewriters b...   14.6200         2   \n",
       "3      Bretford CR4500 Series Slim Rectangular Table  957.5775         5   \n",
       "4                     Eldon Fold 'N Roll Cart System   22.3680         2   \n",
       "\n",
       "   Discount    Profit  \n",
       "0      0.00   41.9136  \n",
       "1      0.00  219.5820  \n",
       "2      0.00    6.8714  \n",
       "3      0.45 -383.0310  \n",
       "4      0.20    2.5164  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop Row ID\n",
    "\n",
    "df.drop('Row ID' ,inplace = True, axis = 1)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 9994 rows and 20 columns in the dataset.\n"
     ]
    }
   ],
   "source": [
    "# Re-checking the shape\n",
    "\n",
    "print(f\"There are {df.shape[0]} rows and {df.shape[1]} columns in the dataset.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inspecting the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Check the info for various colunms and datatype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9994 entries, 0 to 9993\n",
      "Data columns (total 20 columns):\n",
      " #   Column         Non-Null Count  Dtype         \n",
      "---  ------         --------------  -----         \n",
      " 0   Order ID       9994 non-null   object        \n",
      " 1   Order Date     9994 non-null   datetime64[ns]\n",
      " 2   Ship Date      9994 non-null   datetime64[ns]\n",
      " 3   Ship Mode      9994 non-null   object        \n",
      " 4   Customer ID    9994 non-null   object        \n",
      " 5   Customer Name  9994 non-null   object        \n",
      " 6   Segment        9994 non-null   object        \n",
      " 7   Country        9994 non-null   object        \n",
      " 8   City           9994 non-null   object        \n",
      " 9   State          9994 non-null   object        \n",
      " 10  Postal Code    9994 non-null   int64         \n",
      " 11  Region         9994 non-null   object        \n",
      " 12  Product ID     9994 non-null   object        \n",
      " 13  Category       9994 non-null   object        \n",
      " 14  Sub-Category   9994 non-null   object        \n",
      " 15  Product Name   9994 non-null   object        \n",
      " 16  Sales          9994 non-null   float64       \n",
      " 17  Quantity       9994 non-null   int64         \n",
      " 18  Discount       9994 non-null   float64       \n",
      " 19  Profit         9994 non-null   float64       \n",
      "dtypes: datetime64[ns](2), float64(3), int64(2), object(13)\n",
      "memory usage: 1.5+ MB\n"
     ]
    }
   ],
   "source": [
    "# Generate the info of the dataset\n",
    "\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are no missing values. Data types for all columns are correct except postal code which is in int."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Converting Postal Code column to strings\n",
    "\n",
    "df['Postal Code'] = df['Postal Code'].astype('str')\n",
    "df['Postal Code'].dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Check the summary statistics of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Sales</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9994.000000</td>\n",
       "      <td>9994.000000</td>\n",
       "      <td>9994.000000</td>\n",
       "      <td>9994.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>229.858001</td>\n",
       "      <td>3.789574</td>\n",
       "      <td>0.156203</td>\n",
       "      <td>28.656896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>623.245101</td>\n",
       "      <td>2.225110</td>\n",
       "      <td>0.206452</td>\n",
       "      <td>234.260108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.444000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-6599.978000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>17.280000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.728750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>54.490000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>8.666500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>209.940000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>29.364000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>22638.480000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>8399.976000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Sales     Quantity     Discount       Profit\n",
       "count   9994.000000  9994.000000  9994.000000  9994.000000\n",
       "mean     229.858001     3.789574     0.156203    28.656896\n",
       "std      623.245101     2.225110     0.206452   234.260108\n",
       "min        0.444000     1.000000     0.000000 -6599.978000\n",
       "25%       17.280000     2.000000     0.000000     1.728750\n",
       "50%       54.490000     3.000000     0.200000     8.666500\n",
       "75%      209.940000     5.000000     0.200000    29.364000\n",
       "max    22638.480000    14.000000     0.800000  8399.976000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Describe the dataset\n",
    "\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Check for duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The total number of duplicates\n",
    "\n",
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is one duplicate in the dataset which needs to be addressed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract the duplicate entry\n",
    "\n",
    "duplicates = df.duplicated(keep = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\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>Order ID</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Ship Date</th>\n",
       "      <th>Ship Mode</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Customer Name</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Postal Code</th>\n",
       "      <th>Region</th>\n",
       "      <th>Product ID</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Product Name</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3405</th>\n",
       "      <td>US-2014-150119</td>\n",
       "      <td>2014-04-23</td>\n",
       "      <td>2014-04-27</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>LB-16795</td>\n",
       "      <td>Laurel Beltran</td>\n",
       "      <td>Home Office</td>\n",
       "      <td>United States</td>\n",
       "      <td>Columbus</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>43229</td>\n",
       "      <td>East</td>\n",
       "      <td>FUR-CH-10002965</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>Global Leather Highback Executive Chair with P...</td>\n",
       "      <td>281.372</td>\n",
       "      <td>2</td>\n",
       "      <td>0.3</td>\n",
       "      <td>-12.0588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3406</th>\n",
       "      <td>US-2014-150119</td>\n",
       "      <td>2014-04-23</td>\n",
       "      <td>2014-04-27</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>LB-16795</td>\n",
       "      <td>Laurel Beltran</td>\n",
       "      <td>Home Office</td>\n",
       "      <td>United States</td>\n",
       "      <td>Columbus</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>43229</td>\n",
       "      <td>East</td>\n",
       "      <td>FUR-CH-10002965</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>Global Leather Highback Executive Chair with P...</td>\n",
       "      <td>281.372</td>\n",
       "      <td>2</td>\n",
       "      <td>0.3</td>\n",
       "      <td>-12.0588</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Order ID Order Date  Ship Date       Ship Mode Customer ID  \\\n",
       "3405  US-2014-150119 2014-04-23 2014-04-27  Standard Class    LB-16795   \n",
       "3406  US-2014-150119 2014-04-23 2014-04-27  Standard Class    LB-16795   \n",
       "\n",
       "       Customer Name      Segment        Country      City State Postal Code  \\\n",
       "3405  Laurel Beltran  Home Office  United States  Columbus  Ohio       43229   \n",
       "3406  Laurel Beltran  Home Office  United States  Columbus  Ohio       43229   \n",
       "\n",
       "     Region       Product ID   Category Sub-Category  \\\n",
       "3405   East  FUR-CH-10002965  Furniture       Chairs   \n",
       "3406   East  FUR-CH-10002965  Furniture       Chairs   \n",
       "\n",
       "                                           Product Name    Sales  Quantity  \\\n",
       "3405  Global Leather Highback Executive Chair with P...  281.372         2   \n",
       "3406  Global Leather Highback Executive Chair with P...  281.372         2   \n",
       "\n",
       "      Discount   Profit  \n",
       "3405       0.3 -12.0588  \n",
       "3406       0.3 -12.0588  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[duplicates]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a complete duplicate of Row ID 3405 in 3406. Therefore, it must be dropped.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop the duplicate\n",
    "\n",
    "df.drop_duplicates(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9993, 20)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Recheck for exiting duplicates\n",
    "\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Check for missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Order ID         0\n",
       "Order Date       0\n",
       "Ship Date        0\n",
       "Ship Mode        0\n",
       "Customer ID      0\n",
       "Customer Name    0\n",
       "Segment          0\n",
       "Country          0\n",
       "City             0\n",
       "State            0\n",
       "Postal Code      0\n",
       "Region           0\n",
       "Product ID       0\n",
       "Category         0\n",
       "Sub-Category     0\n",
       "Product Name     0\n",
       "Sales            0\n",
       "Quantity         0\n",
       "Discount         0\n",
       "Profit           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the sum of all missing values in the data\n",
    "\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are no missing values in the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inspecting and exploring various columns of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_var = df.select_dtypes(include = \"object\")\n",
    "numerical_var = df.select_dtypes(include = np.number)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Order ID\n",
      "Ship Mode\n",
      "Customer ID\n",
      "Customer Name\n",
      "Segment\n",
      "Country\n",
      "City\n",
      "State\n",
      "Postal Code\n",
      "Region\n",
      "Product ID\n",
      "Category\n",
      "Sub-Category\n",
      "Product Name\n"
     ]
    }
   ],
   "source": [
    "# Iterate all Categorical Variables in the data\n",
    "\n",
    "for col in categorical_var.columns:\n",
    "    print(col)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a function to explore the categorical variable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is to easily explore the numerical and graphical nature of the categorical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a function named \"explore_categorical_var\" to explore the categorical variables of the data\n",
    "\n",
    "def explore_categorical_var(df, x):\n",
    "    \n",
    "    \"\"\" To return the numerical and graphical summary of \n",
    "    categorical variables by taking df and x as argument \"\"\"\n",
    "    \n",
    "    nunique_categories = df[x].nunique()\n",
    "    if nunique_categories <= 6 :\n",
    "        print(\"Summary\")\n",
    "        print(\"===============================\")\n",
    "        print(df[x].describe(), \"\\n\")\n",
    "        sns.countplot(data = df, x = x)\n",
    "        plt.title(x)\n",
    "        plt.show()\n",
    "        print(\"===============================\")\n",
    "        print(\"Value count \\n\")\n",
    "        print(df[x].value_counts(), \"\\n\")\n",
    "        print(\"===============================\")\n",
    "        print(f\"Missing values: {df[x].isnull().sum()}\")\n",
    "        \n",
    "    elif nunique_categories > 6 and nunique_categories <= 17 :\n",
    "        print(\"Summary\")\n",
    "        print(\"===============================\")\n",
    "        print(df[x].describe(), \"\\n\")\n",
    "        sns.countplot(data = df, y = x)\n",
    "        plt.title(x)\n",
    "        plt.show()\n",
    "        print(\"===============================\")\n",
    "        print(\"Value count \\n\")\n",
    "        print(df[x].value_counts(), \"\\n\")\n",
    "        print(\"===============================\")\n",
    "        print(f\"Missing values: {df[x].isnull().sum()}\")\n",
    "        \n",
    "    else :\n",
    "        print(\"Summary\")\n",
    "        print(\"===============================\")\n",
    "        print(df[x].describe(), \"\\n\")\n",
    "        print(\"===============================\")\n",
    "        print(\"Value count \\n\")\n",
    "        print(df[x].value_counts(), \"\\n\")\n",
    "        print(\"===============================\")\n",
    "        print(f\"Missing values: {df[x].isnull().sum()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Explore Order ID column of the date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    CA-2016-152156\n",
       "1    CA-2016-152156\n",
       "2    CA-2016-138688\n",
       "3    US-2015-108966\n",
       "4    US-2015-108966\n",
       "Name: Order ID, dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Output the head of \"Order ID\"\n",
    "\n",
    "df[\"Order ID\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary\n",
      "===============================\n",
      "count               9993\n",
      "unique              5009\n",
      "top       CA-2017-100111\n",
      "freq                  14\n",
      "Name: Order ID, dtype: object \n",
      "\n",
      "===============================\n",
      "Value count \n",
      "\n",
      "CA-2017-100111    14\n",
      "CA-2017-157987    12\n",
      "US-2016-108504    11\n",
      "CA-2016-165330    11\n",
      "CA-2016-105732    10\n",
      "                  ..\n",
      "CA-2016-157259     1\n",
      "CA-2017-107986     1\n",
      "US-2015-112508     1\n",
      "US-2016-126844     1\n",
      "CA-2017-119914     1\n",
      "Name: Order ID, Length: 5009, dtype: int64 \n",
      "\n",
      "===============================\n",
      "Missing values: 0\n"
     ]
    }
   ],
   "source": [
    "# Call the \"explore_categorical_var\" function\n",
    "\n",
    "explore_categorical_var(df, \"Order ID\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Explore Ship Mode column of the date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      Second Class\n",
       "1      Second Class\n",
       "2      Second Class\n",
       "3    Standard Class\n",
       "4    Standard Class\n",
       "Name: Ship Mode, dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Output  the head of \"Ship Mode\"\n",
    "\n",
    "df[\"Ship Mode\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary\n",
      "===============================\n",
      "count               9993\n",
      "unique                 4\n",
      "top       Standard Class\n",
      "freq                5967\n",
      "Name: Ship Mode, dtype: object \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===============================\n",
      "Value count \n",
      "\n",
      "Standard Class    5967\n",
      "Second Class      1945\n",
      "First Class       1538\n",
      "Same Day           543\n",
      "Name: Ship Mode, dtype: int64 \n",
      "\n",
      "===============================\n",
      "Missing values: 0\n"
     ]
    }
   ],
   "source": [
    "# Call the \"explore_categorical_var\" function\n",
    "\n",
    "explore_categorical_var(df, \"Ship Mode\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Explore Customer ID column of the date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    CG-12520\n",
       "1    CG-12520\n",
       "2    DV-13045\n",
       "3    SO-20335\n",
       "4    SO-20335\n",
       "Name: Customer ID, dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Output the head of \"Customer ID\"\n",
    "\n",
    "df[\"Customer ID\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary\n",
      "===============================\n",
      "count         9993\n",
      "unique         793\n",
      "top       WB-21850\n",
      "freq            37\n",
      "Name: Customer ID, dtype: object \n",
      "\n",
      "===============================\n",
      "Value count \n",
      "\n",
      "WB-21850    37\n",
      "JL-15835    34\n",
      "MA-17560    34\n",
      "PP-18955    34\n",
      "CK-12205    32\n",
      "            ..\n",
      "LD-16855     1\n",
      "AO-10810     1\n",
      "CJ-11875     1\n",
      "RE-19405     1\n",
      "JR-15700     1\n",
      "Name: Customer ID, Length: 793, dtype: int64 \n",
      "\n",
      "===============================\n",
      "Missing values: 0\n"
     ]
    }
   ],
   "source": [
    "# Call the \"explore_categorical_var\" function\n",
    "\n",
    "explore_categorical_var(df, \"Customer ID\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Explore Customer Name column of the date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        Claire Gute\n",
       "1        Claire Gute\n",
       "2    Darrin Van Huff\n",
       "3     Sean O'Donnell\n",
       "4     Sean O'Donnell\n",
       "Name: Customer Name, dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Output the head of \"Customer Name\"\n",
    "df[\"Customer Name\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary\n",
      "===============================\n",
      "count              9993\n",
      "unique              793\n",
      "top       William Brown\n",
      "freq                 37\n",
      "Name: Customer Name, dtype: object \n",
      "\n",
      "===============================\n",
      "Value count \n",
      "\n",
      "William Brown          37\n",
      "John Lee               34\n",
      "Matt Abelman           34\n",
      "Paul Prost             34\n",
      "Chloris Kastensmidt    32\n",
      "                       ..\n",
      "Lela Donovan            1\n",
      "Anthony O'Donnell       1\n",
      "Carl Jackson            1\n",
      "Ricardo Emerson         1\n",
      "Jocasta Rupert          1\n",
      "Name: Customer Name, Length: 793, dtype: int64 \n",
      "\n",
      "===============================\n",
      "Missing values: 0\n"
     ]
    }
   ],
   "source": [
    "# Call the \"explore_categorical_var\" function\n",
    "\n",
    "explore_categorical_var(df, \"Customer Name\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Explore Segment column of the date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     Consumer\n",
       "1     Consumer\n",
       "2    Corporate\n",
       "3     Consumer\n",
       "4     Consumer\n",
       "Name: Segment, dtype: object"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Output the head of \"Segment\"\n",
    "\n",
    "df[\"Segment\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary\n",
      "===============================\n",
      "count         9993\n",
      "unique           3\n",
      "top       Consumer\n",
      "freq          5191\n",
      "Name: Segment, dtype: object \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===============================\n",
      "Value count \n",
      "\n",
      "Consumer       5191\n",
      "Corporate      3020\n",
      "Home Office    1782\n",
      "Name: Segment, dtype: int64 \n",
      "\n",
      "===============================\n",
      "Missing values: 0\n"
     ]
    }
   ],
   "source": [
    "# Call the \"explore_categorical_var\" function\n",
    "\n",
    "explore_categorical_var(df, \"Segment\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Explore Country column of the date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    United States\n",
       "1    United States\n",
       "2    United States\n",
       "3    United States\n",
       "4    United States\n",
       "Name: Country, dtype: object"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Output the head of \"Country\"\n",
    "\n",
    "df[\"Country\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary\n",
      "===============================\n",
      "count              9993\n",
      "unique                1\n",
      "top       United States\n",
      "freq               9993\n",
      "Name: Country, dtype: object \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===============================\n",
      "Value count \n",
      "\n",
      "United States    9993\n",
      "Name: Country, dtype: int64 \n",
      "\n",
      "===============================\n",
      "Missing values: 0\n"
     ]
    }
   ],
   "source": [
    "# Call the \"explore_categorical_var\" function\n",
    "\n",
    "explore_categorical_var(df, \"Country\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Explore City column of the data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          Henderson\n",
       "1          Henderson\n",
       "2        Los Angeles\n",
       "3    Fort Lauderdale\n",
       "4    Fort Lauderdale\n",
       "Name: City, dtype: object"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Output the head of \"City\"\n",
    "\n",
    "df[\"City\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary\n",
      "===============================\n",
      "count              9993\n",
      "unique              531\n",
      "top       New York City\n",
      "freq                915\n",
      "Name: City, dtype: object \n",
      "\n",
      "===============================\n",
      "Value count \n",
      "\n",
      "New York City      915\n",
      "Los Angeles        747\n",
      "Philadelphia       537\n",
      "San Francisco      510\n",
      "Seattle            428\n",
      "                  ... \n",
      "Glenview             1\n",
      "Missouri City        1\n",
      "Rochester Hills      1\n",
      "Palatine             1\n",
      "Manhattan            1\n",
      "Name: City, Length: 531, dtype: int64 \n",
      "\n",
      "===============================\n",
      "Missing values: 0\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Call the \"explore_categorical_var\" function\n",
    "\n",
    "explore_categorical_var(df, \"City\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Explore the  State column of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      Kentucky\n",
       "1      Kentucky\n",
       "2    California\n",
       "3       Florida\n",
       "4       Florida\n",
       "Name: State, dtype: object"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Output the head of \"State\"\n",
    "\n",
    "df[\"State\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary\n",
      "===============================\n",
      "count           9993\n",
      "unique            49\n",
      "top       California\n",
      "freq            2001\n",
      "Name: State, dtype: object \n",
      "\n",
      "===============================\n",
      "Value count \n",
      "\n",
      "California              2001\n",
      "New York                1128\n",
      "Texas                    985\n",
      "Pennsylvania             587\n",
      "Washington               506\n",
      "Illinois                 492\n",
      "Ohio                     468\n",
      "Florida                  383\n",
      "Michigan                 255\n",
      "North Carolina           249\n",
      "Arizona                  224\n",
      "Virginia                 224\n",
      "Georgia                  184\n",
      "Tennessee                183\n",
      "Colorado                 182\n",
      "Indiana                  149\n",
      "Kentucky                 139\n",
      "Massachusetts            135\n",
      "New Jersey               130\n",
      "Oregon                   124\n",
      "Wisconsin                110\n",
      "Maryland                 105\n",
      "Delaware                  96\n",
      "Minnesota                 89\n",
      "Connecticut               82\n",
      "Oklahoma                  66\n",
      "Missouri                  66\n",
      "Alabama                   61\n",
      "Arkansas                  60\n",
      "Rhode Island              56\n",
      "Utah                      53\n",
      "Mississippi               53\n",
      "Louisiana                 42\n",
      "South Carolina            42\n",
      "Nevada                    39\n",
      "Nebraska                  38\n",
      "New Mexico                37\n",
      "Iowa                      30\n",
      "New Hampshire             27\n",
      "Kansas                    24\n",
      "Idaho                     21\n",
      "Montana                   15\n",
      "South Dakota              12\n",
      "Vermont                   11\n",
      "District of Columbia      10\n",
      "Maine                      8\n",
      "North Dakota               7\n",
      "West Virginia              4\n",
      "Wyoming                    1\n",
      "Name: State, dtype: int64 \n",
      "\n",
      "===============================\n",
      "Missing values: 0\n"
     ]
    }
   ],
   "source": [
    "# Call the \"explore_categorical_var\" function\n",
    "\n",
    "explore_categorical_var(df, \"State\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Explore Region column of the data\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    South\n",
       "1    South\n",
       "2     West\n",
       "3    South\n",
       "4    South\n",
       "Name: Region, dtype: object"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Output the head\n",
    "\n",
    "df[\"Region\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary\n",
      "===============================\n",
      "count     9993\n",
      "unique       4\n",
      "top       West\n",
      "freq      3203\n",
      "Name: Region, dtype: object \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===============================\n",
      "Value count \n",
      "\n",
      "West       3203\n",
      "East       2847\n",
      "Central    2323\n",
      "South      1620\n",
      "Name: Region, dtype: int64 \n",
      "\n",
      "===============================\n",
      "Missing values: 0\n"
     ]
    }
   ],
   "source": [
    "# Call the \"explore_categorical_var\" function\n",
    "\n",
    "explore_categorical_var(df, \"Region\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Explore the Product ID column of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    FUR-BO-10001798\n",
       "1    FUR-CH-10000454\n",
       "2    OFF-LA-10000240\n",
       "3    FUR-TA-10000577\n",
       "4    OFF-ST-10000760\n",
       "Name: Product ID, dtype: object"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Output the head\n",
    "\n",
    "df[\"Product ID\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary\n",
      "===============================\n",
      "count                9993\n",
      "unique               1862\n",
      "top       OFF-PA-10001970\n",
      "freq                   19\n",
      "Name: Product ID, dtype: object \n",
      "\n",
      "===============================\n",
      "Value count \n",
      "\n",
      "OFF-PA-10001970    19\n",
      "TEC-AC-10003832    18\n",
      "FUR-FU-10004270    16\n",
      "TEC-AC-10002049    15\n",
      "FUR-CH-10001146    15\n",
      "                   ..\n",
      "TEC-PH-10003535     1\n",
      "TEC-MA-10002937     1\n",
      "OFF-AR-10004974     1\n",
      "TEC-AC-10004814     1\n",
      "OFF-ST-10001627     1\n",
      "Name: Product ID, Length: 1862, dtype: int64 \n",
      "\n",
      "===============================\n",
      "Missing values: 0\n"
     ]
    }
   ],
   "source": [
    "# Call the \"explore_categorical_var\" function\n",
    "\n",
    "explore_categorical_var(df, \"Product ID\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Explore the Category column of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          Furniture\n",
       "1          Furniture\n",
       "2    Office Supplies\n",
       "3          Furniture\n",
       "4    Office Supplies\n",
       "Name: Category, dtype: object"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Output the head\n",
    "\n",
    "df[\"Category\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary\n",
      "===============================\n",
      "count                9993\n",
      "unique                  3\n",
      "top       Office Supplies\n",
      "freq                 6026\n",
      "Name: Category, dtype: object \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===============================\n",
      "Value count \n",
      "\n",
      "Office Supplies    6026\n",
      "Furniture          2120\n",
      "Technology         1847\n",
      "Name: Category, dtype: int64 \n",
      "\n",
      "===============================\n",
      "Missing values: 0\n"
     ]
    }
   ],
   "source": [
    "# Call the \"explore_categorical_var\" function\n",
    "\n",
    "explore_categorical_var(df, \"Category\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Explore the Sub-Category column of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Bookcases\n",
       "1       Chairs\n",
       "2       Labels\n",
       "3       Tables\n",
       "4      Storage\n",
       "Name: Sub-Category, dtype: object"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Output the head\n",
    "\n",
    "df[\"Sub-Category\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary\n",
      "===============================\n",
      "count        9993\n",
      "unique         17\n",
      "top       Binders\n",
      "freq         1523\n",
      "Name: Sub-Category, dtype: object \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===============================\n",
      "Value count \n",
      "\n",
      "Binders        1523\n",
      "Paper          1370\n",
      "Furnishings     957\n",
      "Phones          889\n",
      "Storage         846\n",
      "Art             796\n",
      "Accessories     775\n",
      "Chairs          616\n",
      "Appliances      466\n",
      "Labels          364\n",
      "Tables          319\n",
      "Envelopes       254\n",
      "Bookcases       228\n",
      "Fasteners       217\n",
      "Supplies        190\n",
      "Machines        115\n",
      "Copiers          68\n",
      "Name: Sub-Category, dtype: int64 \n",
      "\n",
      "===============================\n",
      "Missing values: 0\n"
     ]
    }
   ],
   "source": [
    "# Call the \"explore_categorical_var\" function\n",
    "\n",
    "explore_categorical_var(df, \"Sub-Category\" )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Explore the Product Name column of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                    Bush Somerset Collection Bookcase\n",
       "1    Hon Deluxe Fabric Upholstered Stacking Chairs,...\n",
       "2    Self-Adhesive Address Labels for Typewriters b...\n",
       "3        Bretford CR4500 Series Slim Rectangular Table\n",
       "4                       Eldon Fold 'N Roll Cart System\n",
       "Name: Product Name, dtype: object"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Output head\n",
    "\n",
    "df[\"Product Name\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary\n",
      "===============================\n",
      "count                9993\n",
      "unique               1850\n",
      "top       Staple envelope\n",
      "freq                   48\n",
      "Name: Product Name, dtype: object \n",
      "\n",
      "===============================\n",
      "Value count \n",
      "\n",
      "Staple envelope                                                                        48\n",
      "Staples                                                                                46\n",
      "Easy-staple paper                                                                      46\n",
      "Avery Non-Stick Binders                                                                20\n",
      "Staples in misc. colors                                                                19\n",
      "                                                                                       ..\n",
      "Boston 1900 Electric Pencil Sharpener                                                   1\n",
      "RCA ViSYS 25423RE1 Corded phone                                                         1\n",
      "Canon Color ImageCLASS MF8580Cdw Wireless Laser All-In-One Printer, Copier, Scanner     1\n",
      "Newell 342                                                                              1\n",
      "Eldon Jumbo ProFile Portable File Boxes Graphite/Black                                  1\n",
      "Name: Product Name, Length: 1850, dtype: int64 \n",
      "\n",
      "===============================\n",
      "Missing values: 0\n"
     ]
    }
   ],
   "source": [
    "# Call the \"explore_categorical_var\" function\n",
    "\n",
    "explore_categorical_var(df, \"Product Name\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploring the Numerical Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sales\n",
      "Quantity\n",
      "Discount\n",
      "Profit\n"
     ]
    }
   ],
   "source": [
    "# Output the columns of the numerical variables\n",
    "\n",
    "for col in numerical_var :\n",
    "    print(col)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a function to explore the numerical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "def explor_numerical_var(df, x) :\n",
    "    \n",
    "    \"\"\" To return the numerical and graphical summary of \n",
    "    numerical variables by taking df and x as argument \"\"\"\n",
    "        \n",
    "    print(\"Summary statistics \\n\")\n",
    "    print(df[x].apply([len, min, max, np.mean, np.median, np.std]))\n",
    "    print(\"=================================================\")\n",
    "    print(f\"Missing values: {df[x].isnull().sum()}\")\n",
    "    print(\"==================================================\")\n",
    "    plt.figure(figsize = (8, 4))\n",
    "    #Plot a box plot\n",
    "    meanpointdrop = dict(marker = \"D\", markeredgecolor =\"black\", markerfacecolor =\"firebrick\")\n",
    "    medianprops = dict(linestyle =\"-.\", linewidth = 3, color=\"gold\")\n",
    "    plt.subplot(1,2,1)\n",
    "    plt.boxplot(df[x], vert = True, patch_artist = True, notch = True,\n",
    "            showmeans = True, medianprops = medianprops, meanprops = meanpointdrop)\n",
    "    plt.xlabel(df[x].name)\n",
    "    plt.title(\"Boxplot\")\n",
    "\n",
    "    # plotting a histogram\n",
    "    plt.subplot(1,2,2)\n",
    "    plt.hist(df[x], bins = 10)\n",
    "    plt.title(\"Histogram\")\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Explore Sales column of the date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    261.9600\n",
       "1    731.9400\n",
       "2     14.6200\n",
       "3    957.5775\n",
       "4     22.3680\n",
       "Name: Sales, dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Output the head of Sales\n",
    "\n",
    "df[\"Sales\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary statistics \n",
      "\n",
      "len        9993.000000\n",
      "min           0.444000\n",
      "max       22638.480000\n",
      "mean        229.852846\n",
      "median       54.480000\n",
      "std         623.276074\n",
      "Name: Sales, dtype: float64\n",
      "=================================================\n",
      "Missing values: 0\n",
      "==================================================\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Call explor_numerical_var function\n",
    "\n",
    "explor_numerical_var(df, \"Sales\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Further insight of the Sales column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>Order ID</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Ship Date</th>\n",
       "      <th>Ship Mode</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Customer Name</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Postal Code</th>\n",
       "      <th>Region</th>\n",
       "      <th>Product ID</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Product Name</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2697</th>\n",
       "      <td>CA-2014-145317</td>\n",
       "      <td>2014-03-18</td>\n",
       "      <td>2014-03-23</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SM-20320</td>\n",
       "      <td>Sean Miller</td>\n",
       "      <td>Home Office</td>\n",
       "      <td>United States</td>\n",
       "      <td>Jacksonville</td>\n",
       "      <td>Florida</td>\n",
       "      <td>32216</td>\n",
       "      <td>South</td>\n",
       "      <td>TEC-MA-10002412</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Machines</td>\n",
       "      <td>Cisco TelePresence System EX90 Videoconferenci...</td>\n",
       "      <td>22638.48</td>\n",
       "      <td>6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-1811.0784</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Order ID Order Date  Ship Date       Ship Mode Customer ID  \\\n",
       "2697  CA-2014-145317 2014-03-18 2014-03-23  Standard Class    SM-20320   \n",
       "\n",
       "     Customer Name      Segment        Country          City    State  \\\n",
       "2697   Sean Miller  Home Office  United States  Jacksonville  Florida   \n",
       "\n",
       "     Postal Code Region       Product ID    Category Sub-Category  \\\n",
       "2697       32216  South  TEC-MA-10002412  Technology     Machines   \n",
       "\n",
       "                                           Product Name     Sales  Quantity  \\\n",
       "2697  Cisco TelePresence System EX90 Videoconferenci...  22638.48         6   \n",
       "\n",
       "      Discount     Profit  \n",
       "2697       0.5 -1811.0784  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Output the maximum sales row\n",
    "\n",
    "max_sale =np.where(df[\"Sales\"]== df[\"Sales\"].max())\n",
    "df.iloc[max_sale]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>Order ID</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Ship Date</th>\n",
       "      <th>Ship Mode</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Customer Name</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Postal Code</th>\n",
       "      <th>Region</th>\n",
       "      <th>Product ID</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Product Name</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4101</th>\n",
       "      <td>US-2017-102288</td>\n",
       "      <td>2017-06-19</td>\n",
       "      <td>2017-06-23</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>ZC-21910</td>\n",
       "      <td>Zuschuss Carroll</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Houston</td>\n",
       "      <td>Texas</td>\n",
       "      <td>77095</td>\n",
       "      <td>Central</td>\n",
       "      <td>OFF-AP-10002906</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Appliances</td>\n",
       "      <td>Hoover Replacement Belt for Commercial Guardsm...</td>\n",
       "      <td>0.444</td>\n",
       "      <td>1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>-1.11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Order ID Order Date  Ship Date       Ship Mode Customer ID  \\\n",
       "4101  US-2017-102288 2017-06-19 2017-06-23  Standard Class    ZC-21910   \n",
       "\n",
       "         Customer Name   Segment        Country     City  State Postal Code  \\\n",
       "4101  Zuschuss Carroll  Consumer  United States  Houston  Texas       77095   \n",
       "\n",
       "       Region       Product ID         Category Sub-Category  \\\n",
       "4101  Central  OFF-AP-10002906  Office Supplies   Appliances   \n",
       "\n",
       "                                           Product Name  Sales  Quantity  \\\n",
       "4101  Hoover Replacement Belt for Commercial Guardsm...  0.444         1   \n",
       "\n",
       "      Discount  Profit  \n",
       "4101       0.8   -1.11  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Output the minimum sales row\n",
    "\n",
    "min_sale =np.where(df['Sales']== df['Sales'].min())\n",
    "df.iloc[min_sale]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The maximum sales is justified by the product(Cisco TelePresence System EX90 Videoconferenci) that was bought. A discount of 50% was given.\n",
    "The minimum sales is also justified. A high discount of 80% was given but only one item was bought."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Explore Quantity column of the date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2\n",
       "1    3\n",
       "2    2\n",
       "3    5\n",
       "4    2\n",
       "Name: Quantity, dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Output head\n",
    "\n",
    "df[\"Quantity\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary statistics \n",
      "\n",
      "len       9993.000000\n",
      "min          1.000000\n",
      "max         14.000000\n",
      "mean         3.789753\n",
      "median       3.000000\n",
      "std          2.225149\n",
      "Name: Quantity, dtype: float64\n",
      "=================================================\n",
      "Missing values: 0\n",
      "==================================================\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Call explor_numerical_var function\n",
    "\n",
    "explor_numerical_var(df, \"Quantity\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Explore Profit column of the date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     41.9136\n",
       "1    219.5820\n",
       "2      6.8714\n",
       "3   -383.0310\n",
       "4      2.5164\n",
       "Name: Profit, dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Output the head\n",
    "\n",
    "df[\"Profit\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary statistics \n",
      "\n",
      "len       9993.000000\n",
      "min      -6599.978000\n",
      "max       8399.976000\n",
      "mean        28.660971\n",
      "median       8.671000\n",
      "std        234.271476\n",
      "Name: Profit, dtype: float64\n",
      "=================================================\n",
      "Missing values: 0\n",
      "==================================================\n"
     ]
    },
    {
     "data": {
      "image/png": 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0VaiJ2bx5s3BzcxMApM3NzU1s3rxZ6apRE1PX90EAYsuWLdLr8vJyodVqxaeffiqV3bp1S2g0GrF06VIhhBDXr18XlpaWYuPGjVLMhQsXhJmZmUhKShJCCHHixAkBQOzbt0+KSU9PFwDEf//7XyGEENu3bxdmZmbiwoULUsyGDRuEWq1+oPbxs4GImrqa3gfZU0GkoPj4eAwdOhQeHh5IT09HUVER0tPT4eHhgaFDhyI+Pl7pKhLVmezsbOTl5SEwMFAqU6vV8Pf3x969ewEAGRkZKCsrk8XodDro9XopJj09HRqNBj169JBivL29odFoZDF6vR46nU6K6devH0pKSpCRkXHPOpaUlKCwsFC2ERHR/TGpIFKI0WhEVFQUgoKCkJCQAG9vbzRv3hze3t5ISEhAUFAQpkyZwqFQ9MjIy8sDADg7O8vKnZ2dpX15eXmwsrKCg4NDtTFOTk6Vzu/k5CSLqXgdBwcHWFlZSTFViYuLk57T0Gg0cHV1fcBWEhE1TUwqiBSSlpaGnJwcTJ06FWZm8j9FMzMzREdHIzs7G2lpaQrVkKh+qFQq2WshRKWyiirGVBX/Z2Iqio6OhsFgkLZz585VWy8iIrqDSQWRQnJzcwEAer2+yv2mclMc0cNOq9UCQKWegvz8fKlXQavVorS0FAUFBdXGXLp0qdL5L1++LIupeJ2CggKUlZVV6sG4m1qthr29vWwjIqL7s1C6AkRNlYuLCwAgKysL3t7elfZnZWXJ4ogedu7u7tBqtUhJSUG3bt0AAKWlpdi9ezdmzZoFAPD09ISlpSVSUlIwbNgwAHcS66ysLMyePRsA4OPjA4PBgAMHDuC5554DAOzfvx8GgwG+vr5SzMyZM5Gbmyv9DSUnJ0OtVsPT07NB202PFrf3tjX4NXM+HdDg1yR6UEwqiBTi5+cHNzc3xMbGIiEhQTYEqry8HHFxcXB3d4efn5+CtSR6MDdu3MCvv/4qvc7OzkZmZiZatGiBNm3aIDIyErGxsejQoQM6dOiA2NhYNGvWDKGhoQAAjUaDUaNGISoqCi1btkSLFi0wZcoUeHh4oE+fPgCATp06oX///hg9ejS+/PJLAMCYMWMQFBSEjh07AgACAwPRuXNnhIWFYc6cObh27RqmTJmC0aNHs/eBiKge1Ovwp9u3b+ODDz6Au7s7bGxs0K5dO8yYMQPl5eVSjGjAhZCIGhNzc3PMmzcPiYmJCA4Ols3+FBwcjMTERMydOxfm5uZKV5Woxg4dOoRu3bpJPRGTJ09Gt27d8NFHHwEA3n33XURGRiIiIgJeXl64cOECkpOTYWdnJ51jwYIFCA4OxrBhw9CzZ080a9YM33//vexvYd26dfDw8EBgYCACAwPRpUsXrFmzRtpvbm6Obdu2wdraGj179sSwYcMQHByMuXPnNtA7QUTUtKiEEKK+Tj5z5kwsWLAAq1evxtNPP41Dhw7h//2//4dPPvkEb7/9NgBg1qxZmDlzJlatWoUnn3wSn3zyCX766SecOnVK+pB566238P3332PVqlVo2bIloqKicO3aNWRkZEgfMi+99BLOnz+PZcuWAbjzrZWbmxu+//77GtW1sLAQGo0GBoOB32JRg4qPj0dUVBRycnKkMnd3d8ydOxchISHKVYyaHN4HK+N7QhVx+BM1NTW9D9ZrUhEUFARnZ2esWLFCKhsyZAiaNWuGNWvWQAgBnU6HyMhI/P3vfwdwp1fC2dkZs2bNwtixY2EwGNCqVSusWbMGw4cPBwBcvHgRrq6u2L59O/r164eTJ0+ic+fO2LdvnzRv+b59++Dj44P//ve/Und4dfjBQUoyGo1IS0uTxn/7+fmxh4IaHO+DlfE9oYqYVFBTU9P7YL0Of3r++efxww8/4PTp0wCAn3/+GXv27MHLL78MoGEXQiJqzMzNzREQEIDXX38dAQEBTCiIiIjooVKvD2r//e9/h8FgwFNPPQVzc3MYjUbMnDkTr7/+OoDqF0I6c+aMFFMXCyFVVFJSgpKSEuk1V00lIiIiIvpz6rWn4ptvvsHatWuxfv16HD58GKtXr8bcuXOxevVqWVxDLYR0N66aSkRERERUN+o1qfjb3/6G9957D6+99ho8PDwQFhaGd955B3FxcQAadiGkirhqKhERERFR3ajXpOLmzZuyufeBO2PHTVPK3r0QkolpISTTAkZ3L4RkYloI6e5FjkwLIZlUXAipIq6aSkRERERUN+r1mYqBAwdi5syZaNOmDZ5++mkcOXIE8+fPx1//+lcAd4YsNdRCSEREREREVD/qNalYtGgRPvzwQ0RERCA/Px86nQ5jx46VFkEC7iyEVFxcjIiICBQUFKBHjx5VLoRkYWGBYcOGobi4GL1798aqVasqLYQ0adIkaZaoQYMGYfHixfXZPCIiIiIiQj2vU/Ew4VzkRNTU8T5YGd8TqojrVFBT0yjWqSAiIiIiokcfkwoiIiIiIqoVJhVERERERFQrTCqIiIiIiKhWmFQQEREREVGtMKkgIiIiIqJaYVJBRERERES1wqSCiIiIiIhqhUkFERERERHVCpMKIiIiIiKqFSYVRERERERUK0wqiIiIiIioViyUrgARAUajEWlpacjNzYWLiwv8/Pxgbm6udLWIiIiIaoQ9FUQKi4+PR/v27dGrVy+EhoaiV69eaN++PeLj45WuGhEREVGNMKkgUlB8fDyGDh0KDw8PpKeno6ioCOnp6fDw8MDQoUOZWBAREdFDgUkFkUKMRiOioqIQFBSEhIQEeHt7o3nz5vD29kZCQgKCgoIwZcoUGI1GpatKREREVC0mFUQKSUtLQ05ODqZOnQozM/mfopmZGaKjo5GdnY20tDSFakhERERUM0wqiBSSm5sLANDr9VXuN5Wb4oiIiIgaKyYVRApxcXEBAGRlZVW531RuiiMiIiJqrJhUECnEz88Pbm5uiI2NRXl5uWxfeXk54uLi4O7uDj8/P4VqSERERFQzTCqIFGJubo558+YhMTERwcHBstmfgoODkZiYiLlz53K9CiIiImr0uPgdkYJCQkKwadMmREVFwdfXVyp3d3fHpk2bEBISomDtiIiIiGqGSQWRwkJCQjB48GCuqE1EREQPLSYVRI2Aubk5AgIClK4GERER0Z/CZyqIiIiIiKhWmFQQEREREVGt1HtSceHCBbzxxhto2bIlmjVrhq5duyIjI0PaL4RATEwMdDodbGxsEBAQgOPHj8vOUVJSgokTJ8LR0RG2trYYNGgQzp8/L4spKChAWFgYNBoNNBoNwsLCcP369fpuHhERERFRk1evSUVBQQF69uwJS0tL7NixAydOnMC8efPw2GOPSTGzZ8/G/PnzsXjxYhw8eBBarRZ9+/ZFUVGRFBMZGYktW7Zg48aN2LNnD27cuIGgoCAYjUYpJjQ0FJmZmUhKSkJSUhIyMzMRFhZWn80jIiIiIiLU84Pas2bNgqurK1auXCmVubm5ST8LIbBw4UK8//770tSZq1evhrOzM9avX4+xY8fCYDBgxYoVWLNmDfr06QMAWLt2LVxdXbFz507069cPJ0+eRFJSEvbt24cePXoAAJYvXw4fHx+cOnUKHTt2rM9mEhERERE1afXaU7F161Z4eXnh1VdfhZOTE7p164bly5dL+7Ozs5GXl4fAwECpTK1Ww9/fH3v37gUAZGRkoKysTBaj0+mg1+ulmPT0dGg0GimhAABvb29oNBophoiIiIiI6ke9JhW///47lixZgg4dOuDf//43xo0bh0mTJuHrr78GAOTl5QEAnJ2dZcc5OztL+/Ly8mBlZQUHB4dqY5ycnCpd38nJSYqpqKSkBIWFhbKNiIiIiIgeXL0OfyovL4eXlxdiY2MBAN26dcPx48exZMkS/OUvf5HiVCqV7DghRKWyiirGVBVf3Xni4uIwffr0GreFiIiIiIiqVq89FS4uLujcubOsrFOnTjh79iwAQKvVAkCl3oT8/Hyp90Kr1aK0tBQFBQXVxly6dKnS9S9fvlypF8QkOjoaBoNB2s6dO/cnWkhERERERPWaVPTs2ROnTp2SlZ0+fRpt27YFALi7u0Or1SIlJUXaX1pait27d8PX1xcA4OnpCUtLS1lMbm4usrKypBgfHx8YDAYcOHBAitm/fz8MBoMUU5FarYa9vb1sIyIiIiKiB1evw5/eeecd+Pr6IjY2FsOGDcOBAwewbNkyLFu2DMCdIUuRkZGIjY1Fhw4d0KFDB8TGxqJZs2YIDQ0FAGg0GowaNQpRUVFo2bIlWrRogSlTpsDDw0OaDapTp07o378/Ro8ejS+//BIAMGbMGAQFBXHmJyIiIiKielavSUX37t2xZcsWREdHY8aMGXB3d8fChQsxYsQIKebdd99FcXExIiIiUFBQgB49eiA5ORl2dnZSzIIFC2BhYYFhw4ahuLgYvXv3xqpVq2Bubi7FrFu3DpMmTZJmiRo0aBAWL15cn80jIiIiIiIAKiGEULoSjUFhYSE0Gg0MBgOHQhFRk8T7YGV8T6git/e2Nfg1cz4d0ODXJDKp6X2wXp+pICIiIiKiRx+TCiIiIiIiqhUmFURE1KBu376NDz74AO7u7rCxsUG7du0wY8YMlJeXSzFCCMTExECn08HGxgYBAQE4fvy47DwlJSWYOHEiHB0dYWtri0GDBuH8+fOymIKCAoSFhUGj0UCj0SAsLAzXr19viGYSETUpTCqIiKhBzZo1C0uXLsXixYtx8uRJzJ49G3PmzMGiRYukmNmzZ2P+/PlYvHgxDh48CK1Wi759+6KoqEiKiYyMxJYtW7Bx40bs2bMHN27cQFBQEIxGoxQTGhqKzMxMJCUlISkpCZmZmQgLC2vQ9hIRNQX1OvsTERFRRenp6Rg8eDAGDLjz8Kmbmxs2bNiAQ4cOAbjTS7Fw4UK8//77CAkJAQCsXr0azs7OWL9+PcaOHQuDwYAVK1ZgzZo10vTia9euhaurK3bu3Il+/frh5MmTSEpKwr59+9CjRw8AwPLly+Hj44NTp05xynEiojrEngoiImpQzz//PH744QecPn0aAPDzzz9jz549ePnllwEA2dnZyMvLk6YIB+4sWOrv74+9e/cCADIyMlBWViaL0el00Ov1Ukx6ejo0Go2UUACAt7c3NBqNFFNRSUkJCgsLZRsREd0feyqIiKhB/f3vf4fBYMBTTz0Fc3NzGI1GzJw5E6+//joAIC8vDwDg7OwsO87Z2RlnzpyRYqysrODg4FApxnR8Xl4enJycKl3fyclJiqkoLi4O06dPr10DiYiaIPZUEDUCRqMRqamp2LBhA1JTU2VjwokeNd988w3Wrl2L9evX4/Dhw1i9ejXmzp2L1atXy+JUKpXstRCiUllFFWOqiq/uPNHR0TAYDNJ27ty5mjaLiKhJY08FkcLi4+MxefJk6RtYAGjbti3mz58vjScnepT87W9/w3vvvYfXXnsNAODh4YEzZ84gLi4OI0eOhFarBXCnp8HFxUU6Lj8/X+q90Gq1KC0tRUFBgay3Ij8/H76+vlLMpUuXKl3/8uXLlXpBTNRqNdRqdd00lIioCWFPBZGC4uPjMWTIEOTn58vK8/PzMWTIEMTHxytUM6L6c/PmTZiZyT9+zM3NpSll3d3dodVqkZKSIu0vLS3F7t27pYTB09MTlpaWspjc3FxkZWVJMT4+PjAYDDhw4IAUs3//fhgMBimGiIjqBpMKIoUYjUaMGzcOANC7d2+kp6ejqKgI6enp6N27NwDgrbfe4lAoeuQMHDgQM2fOxLZt25CTk4MtW7Zg/vz5eOWVVwDcGbIUGRmJ2NhYbNmyBVlZWQgPD0ezZs0QGhoKANBoNBg1ahSioqLwww8/4MiRI3jjjTfg4eEhzQbVqVMn9O/fH6NHj8a+ffuwb98+jB49GkFBQZz5iYiojnH4E5FCUlNTcfnyZTz//PP47rvvpG9uvb298d1338Hf3x979uxBamqqlGQQPQoWLVqEDz/8EBEREcjPz4dOp8PYsWPx0UcfSTHvvvsuiouLERERgYKCAvTo0QPJycmws7OTYhYsWAALCwsMGzYMxcXF6N27N1atWgVzc3MpZt26dZg0aZI0S9SgQYOwePHihmssEVEToRJCCKUr0RgUFhZCo9HAYDDA3t5e6epQE/Dhhx/ik08+wQ8//IAXX3yx0v6dO3eib9+++OCDD/Dxxx8rUENqangfrIzvCVXk9t62Br9mzqcDGvyaRCY1vQ9y+BMREREREdUKkwoihQQEBAAApk2bhrKyMtmUsmVlZYiJiZHFERERETVWfKaCSCEBAQFwcnLCnj17YG9vj1u3bkn7rK2tcevWLTg5OTGpICIiokaPPRVECjE3N8fIkSMB3Jku825lZWUAgJEjR8oeOiUiIiJqjJhUECnEaDTi22+/hZeXF9q0aSPb16ZNG3h5eWHTpk2cUpaIiIgaPQ5/IlJIWloacnJysGHDBnTv3h1paWnIzc2Fi4sL/Pz8cODAAfj6+iItLY1DoIiIiKhRY1JBpJDc3FwAgF6vh7m5eaXEQa/Xy+KIiIiIGisOfyJSiIuLCwAgKyuryv2mclMcERERUWPFpIJIIX5+fnBzc0NsbCzKy8tl+8rLyxEXFwd3d3f4+fkpVEMiIiKimmFSQaQQc3NzzJs3D4mJiQgODkZ6ejqKioqQnp6O4OBgJCYmYu7cuZz9iYiIiBo9PlNBpKCQkBBs2rQJUVFR8PX1lcrd3d2xadMmhISEKFg7IiIiopphUkGksJCQEAwePLjS7E/soSAiIqKHBZMKokagqtmfiIiIiB4WDfZMRVxcHFQqFSIjI6UyIQRiYmKg0+lgY2ODgIAAHD9+XHZcSUkJJk6cCEdHR9ja2mLQoEE4f/68LKagoABhYWHQaDTQaDQICwvD9evXG6BVRERERETUIEnFwYMHsWzZMnTp0kVWPnv2bMyfPx+LFy/GwYMHodVq0bdvXxQVFUkxkZGR2LJlCzZu3Ig9e/bgxo0bCAoKkq0yHBoaiszMTCQlJSEpKQmZmZkICwtriKYR1Qmj0YjU1FRs2LABqampXEWbiIiIHir1nlTcuHEDI0aMwPLly+Hg4CCVCyGwcOFCvP/++wgJCYFer8fq1atx8+ZNrF+/HgBgMBiwYsUKzJs3D3369EG3bt2wdu1aHDt2DDt37gQAnDx5EklJSfjqq6/g4+MDHx8fLF++HImJiTh16lR9N4+o1uLj49G+fXv06tULoaGh6NWrF9q3b4/4+Hilq0ZERERUI/WeVIwfPx4DBgxAnz59ZOXZ2dnIy8tDYGCgVKZWq+Hv74+9e/cCADIyMlBWViaL0el00Ov1Ukx6ejo0Gg169OghxXh7e0Oj0UgxRI1VfHw8hg4dCg8PD9mUsh4eHhg6dCgTCyIiInoo1OuD2hs3bsThw4dx8ODBSvvy8vIAAM7OzrJyZ2dnnDlzRoqxsrKS9XCYYkzH5+XlwcnJqdL5nZycpJiqlJSUoKSkRHpdWFhYw1YR1Q2j0YioqCgEBQUhISEBZmZ3cnxvb28kJCQgODgYU6ZMweDBgzkTFBERETVq9dZTce7cObz99ttYu3YtrK2t7xmnUqlkr4UQlcoqqhhTVfz9zhMXFyc92K3RaODq6lrtNYnqWlpaGnJycjB16lQpoTAxMzNDdHQ0srOzkZaWplANiYiIiGqm3pKKjIwM5Ofnw9PTExYWFrCwsMDu3bvx2WefwcLCQuqhqNibkJ+fL+3TarUoLS1FQUFBtTGXLl2qdP3Lly9X6gW5W3R0NAwGg7SdO3euVu0lelC5ubkAAL1eX+V+U7kpjoiIiKixqrekonfv3jh27BgyMzOlzcvLCyNGjEBmZibatWsHrVaLlJQU6ZjS0lLs3r1bWlnY09MTlpaWspjc3FxkZWVJMT4+PjAYDDhw4IAUs3//fhgMBtkKxRWp1WrY29vLNqKG5OLiAgDIysqqcr+p3BRHRERE1FjV2zMVdnZ2lb6BtbW1RcuWLaXyyMhIxMbGokOHDujQoQNiY2PRrFkzhIaGAgA0Gg1GjRqFqKgotGzZEi1atMCUKVPg4eEhPfjdqVMn9O/fH6NHj8aXX34JABgzZgyCgoLQsWPH+moeUa35+fnBzc0NsbGxsmcqAKC8vBxxcXFwd3eHn5+fgrUkIiIiuj9FV9R+9913UVxcjIiICBQUFKBHjx5ITk6GnZ2dFLNgwQJYWFhg2LBhKC4uRu/evbFq1SrZg6vr1q3DpEmTpFmiBg0ahMWLFzd4e4gehLm5OebNm4ehQ4ciODgY0dHR0Ov1yMrKQlxcHBITE7Fp0yY+pE1ERESNnkoIIZSuRGNQWFgIjUYDg8HAoVDUoOLj4xEVFYWcnBypzN3dHXPnzkVISIhyFaMmh/fByvieUEVu721r8GvmfDqgwa9JZFLT+6CiPRVEBISEhGDw4MFIS0tDbm4uXFxc4Ofnxx4KIiIiemgwqSBqBMzNzREQEKB0NYiIiIj+lHpfUZuIiIiIiB5tTCqIiIiIiKhWOPyJqBEwGo18poKIiIgeWuypIFJYfHw82rdvj169eiE0NBS9evVC+/btER8fr3TViIiIiGqESQWRguLj4zF06FB4eHggPT0dRUVFSE9Ph4eHB4YOHcrEgoiIiB4KTCqIFGI0GhEVFYWgoCAkJCTA29sbzZs3h7e3NxISEhAUFIQpU6bAaDQqXVUiIiKiajGpIFJIWloacnJyMHXqVJiZyf8UzczMEB0djezsbKSlpSlUQyIiIqKaYVJBpJDc3FwAgF6vr3K/qdwUR0RERNRYMakgUoiLiwsAICsrq8r9pnJTHBEREVFjxaSCSCF+fn5wc3NDbGwsysrKkJqaig0bNiA1NRVlZWWIi4uDu7s7/Pz8lK4qERERUbW4TgWRQszNzTFv3jwMGTIEGo0GxcXF0j4bGxsUFxdj8+bNXK+CiIiIGj32VBApTKVSVVlWVTkRERFRY8Skgkghd08pazAYsGvXLqxfvx67du3C9evXOaUsERERPTSYVBAphFPKEhER0aOCz1QQKcQ0Vexvv/2G119/HTk5OdI+Nzc3fPLJJ7I4IiIiosaKSQWRQkxTxb7xxhsICgrC3/72N+kB7R07duCNN96QxRERERE1VkwqiBTi6+sLCwsL2Nra4ujRo0hMTJT2tWnTBhqNBn/88Qd8fX0VrCURERHR/TGpIFLI3r17cfv2bRgMBqjVagwbNgzNmjXDzZs3kZqaCoPBIMUFBAQoW1kiIiKiajCpIFLIhQsXAABOTk7Iz8/Hv/71L9l+U7kpjoiIiKix4uxPRAq5fPkyACA/P7/K2Z/y8/NlcURERESNFXsqiBTi4OAg/dyvXz88+eSTKC4uho2NDU6fPo0dO3ZUiiMiIiJqjNhTQaSQ/fv3Sz/v2LED//jHP7Bs2TL84x//kBKKinFEj4oLFy7gjTfeQMuWLdGsWTN07doVGRkZ0n4hBGJiYqDT6WBjY4OAgAAcP35cdo6SkhJMnDgRjo6OsLW1xaBBg3D+/HlZTEFBAcLCwqDRaKDRaBAWFobr1683RBOJiJoUJhVECrl48aL0c8XhT+bm5lXGET0KCgoK0LNnT1haWmLHjh04ceIE5s2bh8cee0yKmT17NubPn4/Fixfj4MGD0Gq16Nu3L4qKiqSYyMhIbNmyBRs3bsSePXtw48YNBAUFyVahDw0NRWZmJpKSkpCUlITMzEyEhYU1ZHOJiJoEDn8iUoitra30c//+/fHyyy9L61Rs374d27dvrxRH9CiYNWsWXF1dsXLlSqnMzc1N+lkIgYULF+L9999HSEgIAGD16tVwdnbG+vXrMXbsWBgMBqxYsQJr1qxBnz59AABr166Fq6srdu7ciX79+uHkyZNISkrCvn370KNHDwDA8uXL4ePjg1OnTqFjx44N12giokcceyqIFGJvbw8AsLKywvHjxzFhwgSMGjUKEyZMwIkTJ2BlZSWLI3pUbN26FV5eXnj11Vfh5OSEbt26Yfny5dL+7Oxs5OXlITAwUCpTq9Xw9/fH3r17AQAZGRkoKyuTxeh0Ouj1eikmPT0dGo1GSigAwNvbGxqNRoohIqK6Ua9JRVxcHLp37w47Ozs4OTkhODgYp06dksVw3Cw1VaYhT6WlpThz5oxsX05ODkpLS2VxRI+K33//HUuWLEGHDh3w73//G+PGjcOkSZPw9ddfAwDy8vIAAM7OzrLjnJ2dpX15eXmwsrKqNJFBxRgnJ6dK13dycpJiKiopKUFhYaFsIyKi+6vXf63s3r0b48ePx759+5CSkoLbt28jMDAQf/zxhxTDcbPUVHXo0KFO44geFuXl5Xj22WcRGxuLbt26YezYsRg9ejSWLFkii1OpVLLXQohKZRVVjKkqvrrzxMXFSV9OaTQauLq61rRZRERNWr0mFUlJSQgPD8fTTz+NZ555BitXrsTZs2elGT4qjpvV6/VYvXo1bt68ifXr1wOANG523rx56NOnD7p164a1a9fi2LFj2LlzJwBI42a/+uor+Pj4wMfHB8uXL0diYmKlnhGixmLs2LEAqv5Hz93lpjiiR4WLiws6d+4sK+vUqRPOnj0LANBqtQBQqTchPz9f6r3QarUoLS1FQUFBtTGXLl2qdP3Lly9X6gUxiY6OhsFgkLZz5879iRYSETU9DTquwmAwAABatGgBQNlxs+ziJqWZpooVQgAA+vTpg9jYWOmhU1M5p5SlR03Pnj0rfeFz+vRptG3bFgDg7u4OrVaLlJQUaX9paSl2794NX19fAICnpycsLS1lMbm5ucjKypJifHx8YDAYcODAASlm//79MBgMUkxFarUa9vb2so2IiO6vwWZ/EkJg8uTJeP7556HX6wFUP27WNMa8vsbNxsXFYfr06bVrFFEtmL4BbdasGW7duoWdO3dKvW/m5uZQq9W4efMmvymlR84777wDX19fxMbGYtiwYThw4ACWLVuGZcuWAbjTSxcZGYnY2Fh06NABHTp0QGxsLJo1a4bQ0FAAgEajwahRoxAVFYWWLVuiRYsWmDJlCjw8PKTEvFOnTujfvz9Gjx6NL7/8EgAwZswYBAUFceYnIqI61mA9FRMmTMDRo0exYcOGSvuUGDfLLm5SmqkHYtKkSbhx4wbGjx+PwMBAjB8/HkVFRZgwYYIsjuhR0b17d2zZsgUbNmyAXq/Hxx9/jIULF2LEiBFSzLvvvovIyEhERETAy8sLFy5cQHJyMuzs7KSYBQsWIDg4GMOGDUPPnj3RrFkzfP/997J1XtatWwcPDw8EBgYiMDAQXbp0wZo1axq0vURETUGD9FRMnDgRW7duxU8//YTHH39cKr973KyLi4tUfq9xs3f3VuTn50vd139m3KxarYZara5944j+JNPwpu3bt2Pjxo3IyckBACQnJ2Pbtm3SsAtTHNGjJCgoCEFBQffcr1KpEBMTg5iYmHvGWFtbY9GiRVi0aNE9Y1q0aIG1a9fWpqpERFQD9dpTIYTAhAkTEB8fjx9//BHu7u6y/UqOmyVSmmlWp6NHj0oPqJqcPXsWR48elcURERERNVb1mlSMHz8ea9euxfr162FnZ4e8vDzk5eWhuLgYgHzc7JYtW5CVlYXw8PB7jpv94YcfcOTIEbzxxhv3HDe7b98+7Nu3D6NHj+a4WWrU7p7VydLSUrbv7tec/YmIiIgau3od/mSaczwgIEBWvnLlSoSHhwO4M262uLgYERERKCgoQI8ePaocN2thYYFhw4ahuLgYvXv3xqpVqyqNm500aZI0S9SgQYOwePHi+mweUa3cPTOZvb09dDodSkpKoFarcfHiRVy+fFmK6927t1LVJCIiIrqvek0qajIWnONmqalKTU0FcGeK5LuTCBMXFxfk5uYiNTWVSQURERE1ag26TgURVXbx4sUqy3Nzcxu4JkRERER/DpMKIoV4e3vXaRwRERGRUphUECkkISFB+lmlUqF169Zo3749WrduLVtf5e44IiIiosaowVbUJiK5HTt2SD8LIXDhwoX7xhERERE1RuypIFJIQUFBncYRERERKYVJBZFCTCtm11UcERERkVKYVBApxMrKqk7jiIiIiJTCpIJIIba2tnUaR0RERKQUJhVECikuLq7TOCIiIiKlMKkgUgifqSAiIqJHBZMKIoXcayXtPxtHREREpBQmFUQKuXuBu7qIIyIiIlIKkwoihZSUlNRpHBEREZFSmFQQKeT27dt1GkdERESkFCYVRAq5efNmncYRERERKYVJBRERERER1QqTCiIiIiIiqhUmFUREREREVCtMKoiIiIiIqFaYVBARERERUa0wqSAiIiIiolphUkFERERERLXCpIKIiIiIiGqFSQUREREREdUKkwoiIiIiIqoVJhVEjcgzNjZKV4GIiIjogT1SScUXX3wBd3d3WFtbw9PTE2lpaUpXiajGRjg44GMXF4xwcFC6KkREREQPxELpCtSVb775BpGRkfjiiy/Qs2dPfPnll3jppZdw4sQJtGnTRunqUVN1+/I9dzk6ADeLgZu37iQUw/+XTJj+u66gAM2sgWY21ZzHolVd15iIiIjogT0yPRXz58/HqFGj8Oabb6JTp05YuHAhXF1dsWTJEqWrRk3UzZs3gV+d7rlNeqNyQmEy3MEBIxwccPMWMOkN3PMchw8fvnMdIiIiIgU9EklFaWkpMjIyEBgYKCsPDAzE3r17qzympKQEhYWFso2oOiqV6oE2W1vbe57r4y+AjxZVnVCYmBKLjxbdia+Kp6cnbG1ta1yn4cOH18VbQURERCTzSCQVV65cgdFohLOzs6zc2dkZeXl5VR4TFxcHjUYjba6urg1RVSIAwLTFdx7KvldCYTLcwQHP2Nhg2uK6uW5xcXHdnIiIiIjoLo9EUmGiUqlkr4UQlcpMoqOjYTAYpO3cuXMNUUV6iAkhHmjLyMi457mmTwB+Li7GNwUF1V7zm4IC/FxcjOkT7h2TkZFR4zpt3br1zzafiIiI6J4eiQe1HR0dYW5uXqlXIj8/v1LvhYlarYZarW6I6lET9dRTT+Hof3dWuW9wOPDp8j5Y97+koqoei28KCqSHtQeH78TRKh6dyMhwwFNPPVWX1SYiIiJ6YI9EUmFlZQVPT0+kpKTglVdekcpTUlIwePBgBWtGTVmzZs3Q5dne99x/89ad/1aVWJgSClNcdechIiIiUtojkVQAwOTJkxEWFgYvLy/4+Phg2bJlOHv2LMaNG6d01Yju6+7E4u6EgoiIiOhh8MgkFcOHD8fVq1cxY8YM5ObmQq/XY/v27Wjbtq3SVSOqkXUFBci6dQs/82FqIiIiesg8MkkFAERERCAiIkLpahD9aUwoiIiI6GH0SM3+REREREREDY9JBRERERER1QqTCiIiIiIiqhUmFUREREREVCtMKoiIiIiIqFaYVBApxMrKqk7jiIiIiJTCpIJIIWq1uk7jiIiIiJTCpIJIIeXl5XUaR/QwiouLg0qlQmRkpFQmhEBMTAx0Oh1sbGwQEBCA48ePy44rKSnBxIkT4ejoCFtbWwwaNAjnz5+XxRQUFCAsLAwajQYajQZhYWG4fv16A7SKiKjpYVJBpBAOf6Km7uDBg1i2bBm6dOkiK589ezbmz5+PxYsX4+DBg9Bqtejbty+KioqkmMjISGzZsgUbN27Enj17cOPGDQQFBcFoNEoxoaGhyMzMRFJSEpKSkpCZmYmwsLAGax8RUVPCpIJIIW5ubnUaR/QwuXHjBkaMGIHly5fDwcFBKhdCYOHChXj//fcREhICvV6P1atX4+bNm1i/fj0AwGAwYMWKFZg3bx769OmDbt26Ye3atTh27Bh27twJADh58iSSkpLw1VdfwcfHBz4+Pli+fDkSExNx6tQpRdpMRPQoY1JBpJCKQzVqG0f0MBk/fjwGDBiAPn36yMqzs7ORl5eHwMBAqUytVsPf3x979+4FAGRkZKCsrEwWo9PpoNfrpZj09HRoNBr06NFDivH29oZGo5FiiIio7lgoXQGipqqwsLBO44geFhs3bsThw4dx8ODBSvvy8vIAAM7OzrJyZ2dnnDlzRoqxsrKS9XCYYkzH5+XlwcnJqdL5nZycpJiqlJSUoKSkRHrNvz8iopphTwWRQszNzes0juhhcO7cObz99ttYu3YtrK2t7xmnUqlkr4UQlcoqqhhTVfz9zhMXFyc92K3RaODq6lrtNYmI6A4mFUQKadGihfSzpaUlWrduDWdnZ7Ru3RqWlpZVxhE97DIyMpCfnw9PT09YWFjAwsICu3fvxmeffQYLCwuph6Jib0J+fr60T6vVorS0FAUFBdXGXLp0qdL1L1++XKkX5G7R0dEwGAzSdu7cuVq1l4ioqWBSQaQQe3t76eeysjJcuHABly5dwoULF1BWVlZlHNHDrnfv3jh27BgyMzOlzcvLCyNGjEBmZibatWsHrVaLlJQU6ZjS0lLs3r0bvr6+AABPT09YWlrKYnJzc5GVlSXF+Pj4wGAw4MCBA1LM/v37YTAYpJiqqNVq2NvbyzYiIro/PlNBpBCtVosTJ07UKI7oUWFnZwe9Xi8rs7W1RcuWLaXyyMhIxMbGokOHDujQoQNiY2PRrFkzhIaGAgA0Gg1GjRqFqKgotGzZEi1atMCUKVPg4eEhPfjdqVMn9O/fH6NHj8aXX34JABgzZgyCgoLQsWPHBmwxEVHTwKSCSCGenp748ccfaxRH1JS8++67KC4uRkREBAoKCtCjRw8kJyfDzs5OilmwYAEsLCwwbNgwFBcXo3fv3li1apXsGaR169Zh0qRJ0ixRgwYNwuLFixu8PURETQGTCiKF3Lhxo07jiB5WqampstcqlQoxMTGIiYm55zHW1tZYtGgRFi1adM+YFi1aYO3atXVUSyIiqg6fqSBSyP1msnnQOCIiIiKlMKkgUsgTTzwh/Vxxas27X98dR0RERNQYMakgUoiHhweAO7PN3D3bE3BnNii1Wi2LIyIiImqs+EwFkUKuXLkC4M4KvpaWlhg2bBi6d++OgwcPYtOmTdKqvqY4IiIiosaKSQWRQpycnAAArVu3Rl5eHjZs2IANGzYAACwsLNC6dWtcuHBBiiMiIiJqrDj8iUhh9vb2cHFxkZVptVrZ9JlEREREjRl7KogUkp+fDwA4efJkpX3nz5+vFEdERETUWLGngkghdw9rMjOT/ynevYAXhz8RERFRY1dvSUVOTg5GjRoFd3d32NjY4IknnsC0adNQWloqizt79iwGDhwIW1tbODo6YtKkSZVijh07Bn9/f9jY2KB169aYMWMGhBCymN27d8PT0xPW1tZo164dli5dWl9NI6oTphmfLC0tcePGDezatQvr16/Hrl27UFRUBEtLS1kcERERUWNVb0nFf//7X5SXl+PLL7/E8ePHsWDBAixduhRTp06VYoxGIwYMGIA//vgDe/bswcaNG7F582ZERUVJMYWFhejbty90Oh0OHjyIRYsWYe7cuZg/f74Uk52djZdffhl+fn44cuQIpk6dikmTJmHz5s311TyiWlu3bh2AO0nD8OHDoVarERQUBLVajeHDh0vJhCmOiIiIqLGqt2cq+vfvj/79+0uv27Vrh1OnTmHJkiWYO3cuACA5ORknTpzAuXPnoNPpAADz5s1DeHg4Zs6cCXt7e6xbtw63bt3CqlWroFarodfrcfr0acyfPx+TJ0+GSqXC0qVL0aZNGyxcuBAA0KlTJxw6dAhz587FkCFD6quJRLVy48YNAEB0dDQ2bNgAX19faZ+7uzvee+89fPrpp1IcERERUWPVoM9UGAwGtGjRQnqdnp4OvV4vJRQA0K9fP5SUlCAjI0OK8ff3lxYCM8VcvHgROTk5UkxgYKDsWv369cOhQ4fuOXSkpKQEhYWFso2oIT3//PMAgG3btuH06dOy4U+nTp3C9u3bZXFEREREjVWDJRW//fYbFi1ahHHjxklleXl5cHZ2lsU5ODjAysoKeXl594wxvb5fzO3bt++5cFhcXBw0Go20ubq61q6BRA9o4sSJMDMzw9GjR/HKK6/Ihj+98sorOHr0KMzMzDBx4kSlq0pERERUrQdOKmJiYqBSqardDh06JDvm4sWL6N+/P1599VW8+eabsn0qlarSNYQQsvKKMaaHtB805m7R0dEwGAzSdu7cufs1nahOWVlZSc8P7dixA76+vrC3t4evry927NgBAIiKioKVlZWS1SQiIiK6rwd+pmLChAl47bXXqo1xc3OTfr548SJ69eoFHx8fLFu2TBan1Wqxf/9+WVlBQQHKysqkngetViv1SJiY5u2/X4yFhQVatmxZZR3VarVsSBWREmbPno3Tp0/ju+++k5WXl5dj8ODBmD17tkI1IyIiIqq5B04qHB0d4ejoWKPYCxcuoFevXvD09MTKlSsrzcXv4+ODmTNnIjc3V1pRODk5GWq1Gp6enlLM1KlTUVpaKn1jm5ycDJ1OJyUvPj4++P7772XnTk5OhpeXlzQtJ1FjFB8fj61bt2LAgAFo3749iouLYWNjg19//RVbt25FfHw8QkJClK4mERERUbVUouKCD3Xk4sWL8Pf3R5s2bfD111/LFvPSarUA7kwp27VrVzg7O2POnDm4du0awsPDERwcjEWLFgG483B3x44d8eKLL2Lq1Kn45ZdfEB4ejo8++kgaOpKdnQ29Xo+xY8di9OjRSE9Px7hx47Bhw4Yaz/5UWFgIjUYDg8EAe3v7On43iCozGo1o3749PDw8kJCQIEu6y8vLERwcjKysLPzyyy+yvx+i+sL7YGV8T6git/e2Nfg1cz4d0ODXJDKp6X2w3qaUTU5Oxq+//opff/0Vjz/+uGyfKY8xNzfHtm3bEBERgZ49e8LGxgahoaHSlLMAoNFokJKSgvHjx8PLywsODg6YPHkyJk+eLMW4u7tj+/bteOedd/D5559Dp9Phs88+43Sy1KilpaUhJycHGzZsqNSLZ2ZmhujoaPj6+iItLQ0BAQHKVJKIiIioBuotqQgPD0d4ePh949q0aYPExMRqYzw8PPDTTz9VG+Pv74/Dhw8/SBWJFJWbmwsA0Ov1Ve43lZviiIiIiBqrBl2ngoj+j+k5oqysLBiNRqSmpmLDhg1ITU2F0WhEVlaWLI6IiIiosaq3ngoiqp6fnx/c3NwwceJEXL58GWfOnJH2tW3bFq1atYK7uzv8/PwUrCURERHR/bGngkgh5ubmePXVV3Ho0CHcunULy5Ytw8WLF7Fs2TLcunULhw4dwtChQ/mQNhERETV6TCqIFGI0GvHtt9/Cy8sLNjY2GDNmDHQ6HcaMGYNmzZrBy8sLmzZtgtFoVLqqRERERNXi8Ccihdw9+1P37t2RlpYmrdni5+eHAwcOcPYnIiIieigwqSBSyN2zP5mbm1dKHDj7ExERET0sOPyJSCGc/YmIiIgeFeypIFLI3bM/XblyBTk5OdI+Nzc3ODo6cvYnIiIieiiwp4JIIXfP/lRcXCyb/am4uJizPxEREdFDg0kFkULunv3J2tpaNvuTjY0NZ38iIiKihwaHPxEphLM/ERER0aOCSQWRQjj7ExERET0qOPyJSCF3z/5UFc7+RERERA8LJhVECjHN/hQbG4vy8nLZvvLycsTFxXH2JyIiInoocPgTkULMzc0xb948DB06FIMHD0b//v1hY2OD4uJiJCUlYdu2bdi0aRNnfyIiIqJGj0kFkYJCQkIwZcoULFiwAImJiVK5hYUFpkyZgpCQEAVrR0RERFQzTCqIFBQfH4+5c+diwIABeOmll6Seih07dmDu3Lnw9vZmYkFERESNHpMKIoUYjUZERUUhKCgICQkJMDP7v0ecxo0bh+DgYEyZMgWDBw/mECgiIiJq1PigNpFCTOtUTJ06VZZQAICZmRmio6ORnZ2NtLQ0hWpIREREVDNMKogUcvc6FVXhOhVERET0sGBSQaSQu9epMBqNSE1NxYYNG5Camgqj0ch1KoiIiOihwWcqiBRiWqdi4sSJuHLlCnJycqR9bm5ucHR05DoVRERE9FBgTwWRQszNzfHqq6/i0KFDKC4uxrJly3Dx4kUsW7YMxcXFOHToEIYOHcqHtImIiKjRY1JBpBCj0Yhvv/0WXl5esLa2xpgxY6DT6TBmzBjY2NjAy8sLmzZtgtFoVLqqRERERNViUkGkENPsT0OGDKm0TwiBkJAQzv5EREREDwUmFUQKMc3qFB0djS5duiA9PR1FRUVIT09Hly5dMHXqVFkcERERUWPVIElFSUkJunbtCpVKhczMTNm+s2fPYuDAgbC1tYWjoyMmTZqE0tJSWcyxY8fg7+8PGxsbtG7dGjNmzIAQQhaze/dueHp6wtraGu3atcPSpUvru1lEteLk5AQAeP7555GQkABvb280b94c3t7eSEhIQM+ePWVxRERERI1Vg8z+9O6770Kn0+Hnn3+WlRuNRgwYMACtWrXCnj17cPXqVYwcORJCCCxatAgAUFhYiL59+6JXr144ePAgTp8+jfDwcNja2iIqKgoAkJ2djZdffhmjR4/G2rVr8Z///AcRERFo1apVlUNLiB4GKpVK6SoQERER1Ui991Ts2LEDycnJmDt3bqV9ycnJOHHiBNauXYtu3bqhT58+mDdvHpYvX47CwkIAwLp163Dr1i2sWrUKer0eISEhmDp1KubPny/1VixduhRt2rTBwoUL0alTJ7z55pv461//WuU1iRqL/Px8AMB//vMfBAcHy4Y/BQcH4z//+Y8sjoiIiKixqtek4tKlSxg9ejTWrFmDZs2aVdqfnp4OvV4PnU4nlfXr1w8lJSXIyMiQYvz9/aFWq2UxFy9elOb1T09PR2BgoOzc/fr1w6FDh1BWVlYPLSOqPdOidrGxsTh27Bh8fX1hb28PX19fZGVlYebMmbI4okdFXFwcunfvDjs7Ozg5OSE4OBinTp2SxQghEBMTA51OBxsbGwQEBOD48eOymJKSEkycOBGOjo6wtbXFoEGDcP78eVlMQUEBwsLCoNFooNFoEBYWhuvXr9d3E4mImpx6SyqEEAgPD8e4cePg5eVVZUxeXh6cnZ1lZQ4ODrCyskJeXt49Y0yv7xdz+/ZtXLlypcprl5SUoLCwULYRNSTT4nd79+7F6dOnsWvXLqxfvx67du3CqVOnkJ6ezsXv6JG0e/dujB8/Hvv27UNKSgpu376NwMBA/PHHH1LM7NmzMX/+fCxevBgHDx6EVqtF3759UVRUJMVERkZiy5Yt2LhxI/bs2YMbN24gKChINg1zaGgoMjMzkZSUhKSkJGRmZiIsLKxB20tE1BQ8cFIRExMDlUpV7Xbo0CEsWrQIhYWFiI6OrvZ8VY0bF0LIyivGmIY9PWjM3eLi4qRvrjQaDVxdXautJ1FdMzc3x7x585CYmIghQ4ZArVYjKCgIarUaQ4YMQWJiIubOncvF7+iRk5SUhPDwcDz99NN45plnsHLlSpw9e1bqoRZCYOHChXj//fcREhICvV6P1atX4+bNm1i/fj0AwGAwYMWKFZg3bx769OmDbt26Ye3atTh27Bh27twJADh58iSSkpLw1VdfwcfHBz4+Pli+fDkSExMr9YwQEVHtPHBSMWHCBJw8ebLaTa/X48cff8S+ffugVqthYWGB9u3bAwC8vLwwcuRIAIBWq5V6G0wKCgpQVlYm9TxUFWMaY36/GAsLC7Rs2bLKdkRHR8NgMEjbuXPnHvStIKq1kJAQbNq0qcrhT5s2bUJISIjSVSSqdwaDAQDQokULAHcm38jLy5MNa1Wr1fD398fevXsBABkZGSgrK5PF6HQ66PV6KSY9PR0ajQY9evSQYry9vaHRaKQYIiKqGw88+5OjoyMcHR3vG/fZZ5/hk08+kV5fvHgR/fr1wzfffCPd4H18fDBz5kzk5uZK48aTk5OhVqvh6ekpxUydOhWlpaWwsrKSYnQ6Hdzc3KSY77//Xnb95ORkeHl5wdLSssr6qdVq2XMaREoJCQnB4MGDkZaWJv0t+Pn5sYeCmgQhBCZPnoznn38eer0ewP8Nba1qWOuZM2ekGCsrKzg4OFSKuXtobFVTMjs5OVX6IsqkpKQEJSUl0msOjSUiqpl6m1K2TZs2stfNmzcHADzxxBN4/PHHAQCBgYHo3LkzwsLCMGfOHFy7dg1TpkzB6NGjYW9vD+DOeNjp06cjPDwcU6dOxS+//ILY2Fh89NFH0tCmcePGYfHixZg8eTJGjx6N9PR0rFixAhs2bKiv5hHVKXNzcwQEBChdDaIGN2HCBBw9ehR79uyptK+qYa33m2r5fsNn73eeuLg4TJ8+vSZVJyKiuyi6ora5uTm2bdsGa2tr9OzZE8OGDUNwcLBsKliNRoOUlBScP38eXl5eiIiIwOTJkzF58mQpxt3dHdu3b0dqaiq6du2Kjz/+GJ999hnXqCAiasQmTpyIrVu3YteuXdKXTcCdIa0AqhzWevew19LSUhQUFFQbc+nSpUrXvXz5cqVeEBMOjSUi+nMaZPE7AHBzc6u0CjZwp0cjMTGx2mM9PDzw008/VRvj7++Pw4cP16qORERU/4QQmDhxIrZs2YLU1FS4u7vL9ru7u0Or1SIlJQXdunUDAJSWlmL37t2YNWsWAMDT0xOWlpZISUnBsGHDAAC5ubnIysrC7NmzAdwZGmswGHDgwAE899xzAID9+/fDYDDA19e3yrpxaCwR0Z/TYEkFERERAIwfPx7r16/Hd999Bzs7O6lHQqPRwMbGBiqVCpGRkYiNjUWHDh3QoUMHxMbGolmzZggNDZViR40ahaioKLRs2RItWrTAlClT4OHhgT59+gAAOnXqhP79+2P06NH48ssvAQBjxoxBUFAQOnbsqEzjiYgeUUwqiIioQS1ZsgQAKj1HtHLlSoSHhwMA3n33XRQXFyMiIgIFBQXo0aMHkpOTYWdnJ8UvWLAAFhYWGDZsGIqLi9G7d2+sWrVKNsnBunXrMGnSJGmWqEGDBmHx4sX120AioiZIJaoak9QEFRYWQqPRwGAwSA+JExE1JbwPVsb3hCpye29bg18z59MBDX5NIpOa3gcVfVCbiIiIiIgefkwqiIiIiIioVphUEBERERFRrTCpICIiIiKiWmFSQUREREREtcKkgoiIiIiIaoVJBRERERER1QqTCiIiIiIiqhUmFUREREREVCtMKoiIiIiIqFaYVBARERERUa0wqSAiIiIiolphUkFERERERLXCpIKIiIiIiGqFSQUREREREdUKkwoiIiIiIqoVJhVERERERFQrTCqIiIiIiKhWmFQQEREREVGtMKkgIiIiIqJaYVJBRERERES1wqSCiIiIiIhqhUkFERERERHVSr0nFdu2bUOPHj1gY2MDR0dHhISEyPafPXsWAwcOhK2tLRwdHTFp0iSUlpbKYo4dOwZ/f3/Y2NigdevWmDFjBoQQspjdu3fD09MT1tbWaNeuHZYuXVrfTSMiIiIiIgAW9XnyzZs3Y/To0YiNjcWLL74IIQSOHTsm7TcajRgwYABatWqFPXv24OrVqxg5ciSEEFi0aBEAoLCwEH379kWvXr1w8OBBnD59GuHh4bC1tUVUVBQAIDs7Gy+//DJGjx6NtWvX4j//+Q8iIiLQqlUrDBkypD6bSERERETU5NVbUnH79m28/fbbmDNnDkaNGiWVd+zYUfo5OTkZJ06cwLlz56DT6QAA8+bNQ3h4OGbOnAl7e3usW7cOt27dwqpVq6BWq6HX63H69GnMnz8fkydPhkqlwtKlS9GmTRssXLgQANCpUyccOnQIc+fOZVJBRERERFTP6m340+HDh3HhwgWYmZmhW7ducHFxwUsvvYTjx49LMenp6dDr9VJCAQD9+vVDSUkJMjIypBh/f3+o1WpZzMWLF5GTkyPFBAYGyq7fr18/HDp0CGVlZfXVRCIiIiIiQj0mFb///jsAICYmBh988AESExPh4OAAf39/XLt2DQCQl5cHZ2dn2XEODg6wsrJCXl7ePWNMr+8Xc/v2bVy5cqXK+pWUlKCwsFC2ERERERHRg3vgpCImJgYqlara7dChQygvLwcAvP/++xgyZAg8PT2xcuVKqFQqfPvtt9L5VCpVpWsIIWTlFWNMD2k/aMzd4uLioNFopM3V1fVB3gYiIiIiIvqfB36mYsKECXjttdeqjXFzc0NRUREAoHPnzlK5Wq1Gu3btcPbsWQCAVqvF/v37ZccWFBSgrKxM6nnQarVSj4RJfn4+ANw3xsLCAi1btqyyjtHR0Zg8ebL0urCwkIkFEREREdGf8MBJhaOjIxwdHe8b5+npCbVajVOnTuH5558HAJSVlSEnJwdt27YFAPj4+GDmzJnIzc2Fi4sLgDsPb6vVanh6ekoxU6dORWlpKaysrKQYnU4HNzc3Keb777+XXT85ORleXl6wtLSssn5qtVr2nAYREREREf059fZMhb29PcaNG4dp06YhOTkZp06dwltvvQUAePXVVwEAgYGB6Ny5M8LCwnDkyBH88MMPmDJlCkaPHg17e3sAQGhoKNRqNcLDw5GVlYUtW7YgNjZWmvkJAMaNG4czZ85g8uTJOHnyJP75z39ixYoVmDJlSn01j4iIiIiI/qde16mYM2cOLCwsEBYWhuLiYvTo0QM//vgjHBwcAADm5ubYtm0bIiIi0LNnT9jY2CA0NBRz586VzqHRaJCSkoLx48fDy8sLDg4OmDx5smzokru7O7Zv34533nkHn3/+OXQ6HT777DNOJ0tERERE1ABUouLS1E1UYWEhNBoNDAaD1EtCRNSU8D5YGd8TqsjtvW0Nfs2cTwc0+DWJTGp6H6zXngoiqhmj0Yi0tDTp+SI/Pz+Ym5srXS0iIiKiGqm3ZyqIqGbi4+PRvn179OrVC6GhoejVqxfat2+P+Ph4patGREREVCNMKogUFB8fj6FDh8LDwwPp6ekoKipCeno6PDw8MHToUCYWRERE9FBgUkGkEKPRiKioKAQFBSEhIQHe3t5o3rw5vL29kZCQgKCgIEyZMgVGo1HpqhIRERFVi0kFkULS0tKQk5ODqVOnwsxM/qdoZmaG6OhoZGdnIy0tTaEaEhEREdUMkwoiheTm5gIA9Hp9lftN5aY4IiIiosaKSQWRQkyryGdlZVW531RuiiMiIiJqrJhUECnEz88Pbm5uiI2NRXl5uWxfeXk54uLi4O7uDj8/P4VqSERERFQzTCqIFGJubo558+YhMTERwcHBstmfgoODkZiYiLlz53K9CiIiImr0uPgdkYJCQkKwadMmREVFwdfXVyp3d3fHpk2bEBISomDtiIiIiGqGSQWRwkJCQjB48GCuqE1EREQPLSYVRI2Aubk5AgIClK4GERER0Z/CZyqIiIiIiKhWmFQQEREREVGtMKkgIqJH2hdffAF3d3dYW1vD09OTq9QTEdUDJhVERPTI+uabbxAZGYn3338fR44cgZ+fH1566SWcPXtW6aoRET1SmFQQEdEja/78+Rg1ahTefPNNdOrUCQsXLoSrqyuWLFmidNWIiB4pTCqIiOiRVFpaioyMDAQGBsrKAwMDsXfvXoVqRUT0aOKUsv8jhAAAFBYWKlwTIiJlmO5/pvvhw+7KlSswGo1wdnaWlTs7OyMvL6/KY0pKSlBSUiK9NhgMAPjZ0Bjpp/1b6So0mDbvfNvg18ya3q/Br0mNU00/G5hU/E9RUREAwNXVVeGaEBEpq6ioCBqNRulq1BmVSiV7LYSoVGYSFxeH6dOnVyrnZwM1NZqFSteAGpv7fTYwqfgfnU6Hc+fOwc7O7p4fNkT1qbCwEK6urjh37hzs7e2Vrg41QUIIFBUVQafTKV2VOuHo6Ahzc/NKvRL5+fmVei9MoqOjMXnyZOl1eXk5rl27hpYtWz5Unw2Pwv3kYW/Dw15/gG1oLJRuQ00/G5hU/I+ZmRkef/xxpatBBHt7+4f2xkcPv0eph8LKygqenp5ISUnBK6+8IpWnpKRg8ODBVR6jVquhVqtlZY899lh9VrNePQr3k4e9DQ97/QG2obFQsg01+WxgUkFERI+syZMnIywsDF5eXvDx8cGyZctw9uxZjBs3TumqERE9UphUEBHRI2v48OG4evUqZsyYgdzcXOj1emzfvh1t27ZVumpERI8UJhVEjYRarca0adMqDb0gotqJiIhARESE0tVoUI/C/eRhb8PDXn+AbWgsHpY2qMSjMncgEREREREpgovfERERERFRrTCpICIiIiKiWmFSQUREREREtcKkgkhhP/30EwYOHAidTgeVSoWEhASlq0REjZSbmxtUKpVse++992QxZ8+excCBA2FrawtHR0dMmjQJpaWlsphjx47B398fNjY2aN26NWbMmIGKj1ju3r0bnp6esLa2Rrt27bB06dJa1z8nJwejRo2Cu7s7bGxs8MQTT2DatGmV6lexjSqVqtL1lWpDTX3xxRdwd3eHtbU1PD09kZaW1mDXNomLi0P37t1hZ2cHJycnBAcH49SpU7KY8PDwSu+1t7e3LKakpAQTJ06Eo6MjbG1tMWjQIJw/f14WU1BQgLCwMGg0Gmg0GoSFheH69eu1bkNMTEyl+mm1Wmm/EAIxMTHQ6XSwsbFBQEAAjh8/3mjqD1T9d6tSqTB+/HgAjf93UGOCiBS1fft28f7774vNmzcLAGLLli1KV4mIGqm2bduKGTNmiNzcXGkrKiqS9t++fVvo9XrRq1cvcfjwYZGSkiJ0Op2YMGGCFGMwGISzs7N47bXXxLFjx8TmzZuFnZ2dmDt3rhTz+++/i2bNmom3335bnDhxQixfvlxYWlqKTZs21ar+O3bsEOHh4eLf//63+O2338R3330nnJycRFRUlCwOgFi5cqWsnTdv3mwUbaiJjRs3CktLS7F8+XJx4sQJ8fbbbwtbW1tx5syZer/23fr16ydWrlwpsrKyRGZmphgwYIBo06aNuHHjhhQzcuRI0b9/f9l7ffXqVdl5xo0bJ1q3bi1SUlLE4cOHRa9evcQzzzwjbt++LcX0799f6PV6sXfvXrF3716h1+tFUFBQrdswbdo08fTTT8vql5+fL+3/9NNPhZ2dndi8ebM4duyYGD58uHBxcRGFhYWNov5CCJGfny+rf0pKigAgdu3aJYRo/L+DmmJSQdSIMKkgouq0bdtWLFiw4J77t2/fLszMzMSFCxeksg0bNgi1Wi0MBoMQQogvvvhCaDQacevWLSkmLi5O6HQ6UV5eLoQQ4t133xVPPfWU7Nxjx44V3t7eddiaO2bPni3c3d1lZfe7Fza2NlT03HPPiXHjxsnKnnrqKfHee+/V+7Wrk5+fLwCI3bt3S2UjR44UgwcPvucx169fF5aWlmLjxo1S2YULF4SZmZlISkoSQghx4sQJAUDs27dPiklPTxcAxH//+99a1XnatGnimWeeqXJfeXm50Gq14tNPP5XKbt26JTQajVi6dGmjqH9V3n77bfHEE09I/6829t9BTXH4ExER0UNk1qxZaNmyJbp27YqZM2fKhg6lp6dDr9dDp9NJZf369UNJSQkyMjKkGH9/f9mc9/369cPFixeRk5MjxQQGBsqu269fPxw6dAhlZWV12h6DwYAWLVpUKp8wYQIcHR3RvXt3LF26FOXl5dK+xtaGu5WWliIjI6PStQMDA7F37956u25NGAwGAKj0fqempsLJyQlPPvkkRo8ejfz8fGlfRkYGysrKZO3R6XTQ6/VSe9LT06HRaNCjRw8pxtvbGxqNpk7a/Msvv0Cn08Hd3R2vvfYafv/9dwBAdnY28vLyZHVTq9Xw9/eXrtsY6n+30tJSrF27Fn/961+hUqmk8sb+O6gJLn5HRET0kHj77bfx7LPPwsHBAQcOHEB0dDSys7Px1VdfAQDy8vLg7OwsO8bBwQFWVlbIy8uTYtzc3GQxpmPy8vLg7u5e5XmcnZ1x+/ZtXLlyBS4uLnXSnt9++w2LFi3CvHnzZOUff/wxevfuDRsbG/zwww+IiorClStX8MEHHzS6NlR05coVGI3GKq9t+h0oQQiByZMn4/nnn4der5fKX3rpJbz66qto27YtsrOz8eGHH+LFF19ERkYG1Go18vLyYGVlBQcHB9n57m5PXl4enJycKl3Tycmp1m3u0aMHvv76azz55JO4dOkSPvnkE/j6+uL48ePSuat6r8+cOSPVTcn6V5SQkIDr168jPDxcKmvsv4OaYlJBRESkoJiYGEyfPr3amIMHD8LLywvvvPOOVNalSxc4ODhg6NChUu8FANm3nyZCCFl5xRjxvwecHzTmz7TB5OLFi+jfvz9effVVvPnmm7JYU/IAAF27dgUAzJgxQ1Ze122oa1VduyGuey8TJkzA0aNHsWfPHln58OHDpZ/1ej28vLzQtm1bbNu2DSEhIfc83/3+n6oq5s946aWXpJ89PDzg4+ODJ554AqtXr5YeZv4z73VD1b+iFStW4KWXXpL1Jjb230FNMakgIiJS0IQJE/Daa69VG1PxW3kT0z+qfv31V7Rs2RJarRb79++XxRQUFKCsrEz6Nler1Vb65tI01OJ+MRYWFlLyUps2XLx4Eb169YKPjw+WLVtW7XGmdhYWFuLSpUtwdnaulzbUFUdHR5ibm1d57YrfqDeUiRMnYuvWrfjpp5/w+OOPVxvr4uKCtm3b4pdffgFw530sLS1FQUGB7Jvy/Px8+Pr6SjGXLl2qdK7Lly/XeZttbW3h4eGBX375BcHBwQDufEt/d8/T3e91Y6r/mTNnsHPnTsTHx1cb19h/B/fCZyqIiIgU5OjoiKeeeqrazdrauspjjxw5AgDSP6h8fHyQlZWF3NxcKSY5ORlqtRqenp5SzE8//SR7FiM5ORk6nU76h7+Pjw9SUlJk10pOToaXlxcsLS1r1YYLFy4gICAAzz77LFauXAkzs/v/U+TIkSOwtrbGY489Vm9tqCtWVlbw9PSsdO2UlBTpH4ANRQiBCRMmID4+Hj/++CPc3d3ve8zVq1dx7tw56f8pT09PWFpaytqTm5uLrKwsqT0+Pj4wGAw4cOCAFLN//34YDIY6b3NJSQlOnjwJFxcXuLu7Q6vVyupWWlqK3bt3S9dtTPVfuXIlnJycMGDAgGrjGvvv4J4a5HFwIrqnoqIiceTIEXHkyBEBQMyfP18cOXKkwaceJKLGbe/evdL94ffffxfffPON0Ol0YtCgQVKMaUrZ3r17i8OHD4udO3eKxx9/XDal7PXr14Wzs7N4/fXXxbFjx0R8fLywt7evcjrWd955R5w4cUKsWLGiTqZjvXDhgmjfvr148cUXxfnz52VTaJps3bpVLFu2TBw7dkz8+uuvYvny5cLe3l5MmjSpUbShJkxTyq5YsUKcOHFCREZGCltbW5GTk1Pv177bW2+9JTQajUhNTa1yet6ioiIRFRUl9u7dK7Kzs8WuXbuEj4+PaN26daUpWR9//HGxc+dOcfjwYfHiiy9WOZ1ply5dRHp6ukhPTxceHh51Mp1pVFSUSE1NFb///rvYt2+fCAoKEnZ2dtJ7+emnnwqNRiPi4+PFsWPHxOuvv17llLJK1d/EaDSKNm3aiL///e+y8ofhd1BTTCqIFLZr1y4BoNI2cuRIpatGRI1IRkaG6NGjh9BoNMLa2lp07NhRTJs2Tfzxxx+yuDNnzogBAwYIGxsb0aJFCzFhwgTZ1KtCCHH06FHh5+cn1Gq10Gq1IiYmRpre0iQ1NVV069ZNWFlZCTc3N7FkyZJat2HlypVV3u/u/o5zx44domvXrqJ58+aiWbNmQq/Xi4ULF4qysrJG0Yaa+vzzz0Xbtm2FlZWVePbZZ2XTuDaUe73XK1euFEIIcfPmTREYGChatWolLC0tRZs2bcTIkSPF2bNnZecpLi4WEyZMEC1atBA2NjYiKCioUszVq1fFiBEjhJ2dnbCzsxMjRowQBQUFtW6Dad0JS0tLodPpREhIiDh+/Li0v7y8XEybNk1otVqhVqvFCy+8II4dO9Zo6m/y73//WwAQp06dkpU/DL+DmlIJUWH5SSIiIiIiogfAZyqIiIiIiKhWmFQQEREREVGtMKkgIiIiIqJaYVJBRERERES1wqSCiIiIiIhqhUkFERERERHVCpMKIiIiIiKqFSYVRERERERUK0wqiBq5ZcuWwdXVFWZmZli4cCFiYmLQtWtXpatFREREJOGK2kR1JDw8HKtXrwYAWFhYwNXVFSEhIZg+fTpsbW3/1DkLCwvh6OiI+fPnY8iQIdBoNCgvL0dJSQlatmwpXff69etISEioq6YQERERPRALpStA9Cjp378/Vq5cibKyMqSlpeHNN9/EH3/8gSVLlsjiysrKYGlped/znT17FmVlZRgwYABcXFyk8ubNm9d53YmIiIj+LA5/IqpDarUaWq0Wrq6uCA0NxYgRI5CQkCANWfrnP/+Jdu3aQa1WQwiBs2fPYvDgwWjevDns7e0xbNgwXLp0CQCwatUqeHh4AADatWsHlUqFnJwc2fCnmJgYrF69Gt999x1UKhVUKhVSU1MVaj0RERE1VeypIKpHNjY2KCsrAwD8+uuv+Ne//oXNmzfD3NwcABAcHAxbW1vs3r0bt2/fRkREBIYPH47U1FQMHz4crq6u6NOnDw4cOABXV1e0atVKdv4pU6bg5MmTKCwsxMqVKwEALVq0aNhGEhERUZPHpIKonhw4cADr169H7969AQClpaVYs2aNlBikpKTg6NGjyM7OhqurKwBgzZo1ePrpp3Hw4EF0795dem6iVatW0Gq1la7RvHlz2NjYoKSkpMr9RERERA2Bw5+I6lBiYiKaN28Oa2tr+Pj44IUXXsCiRYsAAG3btpX1NJw8eRKurq5SQgEAnTt3xmOPPYaTJ082eN2JiIiI/iz2VBDVoV69emHJkiWwtLSETqeTPYxdcQYoIQRUKlWlc9yrnIiIiKixYlJBVIdsbW3Rvn37GsV27twZZ8+exblz56TeihMnTsBgMKBTp041vqaVlRWMRuOfqi8RERFRXeDwJyKF9OnTB126dMGIESNw+PBhHDhwAH/5y1/g7+8PLy+vGp/Hzc0NR48exalTp3DlyhXpwXAiIiKihsKkgkghKpUKCQkJcHBwwAsvvIA+ffqgXbt2+Oabbx7oPKNHj0bHjh3h5eWFVq1a4T//+U891ZiIiIioalxRm4iIiIiIaoU9FUREREREVCtMKoiIiIiIqFaYVBARERERUa0wqSAiIiIiolphUkFERERERLXCpIKIiIiIiGqFSQUREREREdUKkwoiIiIiIqoVJhVERERERFQrTCqIiIiIiKhWmFQQEREREVGtMKkgIiIiIqJa+f9vcta7rI4UOgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Call explor_numerical_var function\n",
    "\n",
    "explor_numerical_var(df, \"Profit\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Further insight of the Profit column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>Order ID</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Ship Date</th>\n",
       "      <th>Ship Mode</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Customer Name</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Postal Code</th>\n",
       "      <th>Region</th>\n",
       "      <th>Product ID</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Product Name</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6826</th>\n",
       "      <td>CA-2016-118689</td>\n",
       "      <td>2016-10-02</td>\n",
       "      <td>2016-10-09</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>TC-20980</td>\n",
       "      <td>Tamara Chand</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Lafayette</td>\n",
       "      <td>Indiana</td>\n",
       "      <td>47905</td>\n",
       "      <td>Central</td>\n",
       "      <td>TEC-CO-10004722</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Copiers</td>\n",
       "      <td>Canon imageCLASS 2200 Advanced Copier</td>\n",
       "      <td>17499.95</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8399.976</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Order ID Order Date  Ship Date       Ship Mode Customer ID  \\\n",
       "6826  CA-2016-118689 2016-10-02 2016-10-09  Standard Class    TC-20980   \n",
       "\n",
       "     Customer Name    Segment        Country       City    State Postal Code  \\\n",
       "6826  Tamara Chand  Corporate  United States  Lafayette  Indiana       47905   \n",
       "\n",
       "       Region       Product ID    Category Sub-Category  \\\n",
       "6826  Central  TEC-CO-10004722  Technology      Copiers   \n",
       "\n",
       "                               Product Name     Sales  Quantity  Discount  \\\n",
       "6826  Canon imageCLASS 2200 Advanced Copier  17499.95         5       0.0   \n",
       "\n",
       "        Profit  \n",
       "6826  8399.976  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Output the maximum profit row\n",
    "\n",
    "max_profit = np.where(df['Profit'] == df['Profit'].max())\n",
    "df.iloc[max_profit]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>Order ID</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Ship Date</th>\n",
       "      <th>Ship Mode</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Customer Name</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Postal Code</th>\n",
       "      <th>Region</th>\n",
       "      <th>Product ID</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Product Name</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7772</th>\n",
       "      <td>CA-2016-108196</td>\n",
       "      <td>2016-11-25</td>\n",
       "      <td>2016-12-02</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>CS-12505</td>\n",
       "      <td>Cindy Stewart</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Lancaster</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>43130</td>\n",
       "      <td>East</td>\n",
       "      <td>TEC-MA-10000418</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Machines</td>\n",
       "      <td>Cubify CubeX 3D Printer Double Head Print</td>\n",
       "      <td>4499.985</td>\n",
       "      <td>5</td>\n",
       "      <td>0.7</td>\n",
       "      <td>-6599.978</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Order ID Order Date  Ship Date       Ship Mode Customer ID  \\\n",
       "7772  CA-2016-108196 2016-11-25 2016-12-02  Standard Class    CS-12505   \n",
       "\n",
       "      Customer Name   Segment        Country       City State Postal Code  \\\n",
       "7772  Cindy Stewart  Consumer  United States  Lancaster  Ohio       43130   \n",
       "\n",
       "     Region       Product ID    Category Sub-Category  \\\n",
       "7772   East  TEC-MA-10000418  Technology     Machines   \n",
       "\n",
       "                                   Product Name     Sales  Quantity  Discount  \\\n",
       "7772  Cubify CubeX 3D Printer Double Head Print  4499.985         5       0.7   \n",
       "\n",
       "        Profit  \n",
       "7772 -6599.978  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Output the minimum profit row\n",
    "\n",
    "min_profit =np.where(df['Profit']== df['Profit'].min())\n",
    "df.iloc[min_profit]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Explore Discount column of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.00\n",
       "1    0.00\n",
       "2    0.00\n",
       "3    0.45\n",
       "4    0.20\n",
       "Name: Discount, dtype: float64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Output the head\n",
    "\n",
    "df[\"Discount\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary statistics \n",
      "\n",
      "len       9993.000000\n",
      "min          0.000000\n",
      "max          0.800000\n",
      "mean         0.156188\n",
      "median       0.200000\n",
      "std          0.206457\n",
      "Name: Discount, dtype: float64\n",
      "=================================================\n",
      "Missing values: 0\n",
      "==================================================\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Call explore_numerical_var function\n",
    "\n",
    "explor_numerical_var(df, \"Discount\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time-based cohort analysis "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This phase involves calculating and visualizing the retention rate for each cohort."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis of Cohort for the year 2017"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>index</th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Ship Date</th>\n",
       "      <th>Ship Mode</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Customer Name</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>...</th>\n",
       "      <th>Region</th>\n",
       "      <th>Product ID</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Product Name</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12</td>\n",
       "      <td>CA-2017-114412</td>\n",
       "      <td>2017-04-15</td>\n",
       "      <td>2017-04-20</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>AA-10480</td>\n",
       "      <td>Andrew Allen</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Concord</td>\n",
       "      <td>...</td>\n",
       "      <td>South</td>\n",
       "      <td>OFF-PA-10002365</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Paper</td>\n",
       "      <td>Xerox 1967</td>\n",
       "      <td>15.552</td>\n",
       "      <td>3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>5.4432</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23</td>\n",
       "      <td>US-2017-156909</td>\n",
       "      <td>2017-07-16</td>\n",
       "      <td>2017-07-18</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>SF-20065</td>\n",
       "      <td>Sandra Flanagan</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>...</td>\n",
       "      <td>East</td>\n",
       "      <td>FUR-CH-10002774</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>Global Deluxe Stacking Chair, Gray</td>\n",
       "      <td>71.372</td>\n",
       "      <td>2</td>\n",
       "      <td>0.3</td>\n",
       "      <td>-1.0196</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   index        Order ID Order Date  Ship Date       Ship Mode Customer ID  \\\n",
       "0     12  CA-2017-114412 2017-04-15 2017-04-20  Standard Class    AA-10480   \n",
       "1     23  US-2017-156909 2017-07-16 2017-07-18    Second Class    SF-20065   \n",
       "\n",
       "     Customer Name   Segment        Country          City  ... Region  \\\n",
       "0     Andrew Allen  Consumer  United States       Concord  ...  South   \n",
       "1  Sandra Flanagan  Consumer  United States  Philadelphia  ...   East   \n",
       "\n",
       "        Product ID         Category Sub-Category  \\\n",
       "0  OFF-PA-10002365  Office Supplies        Paper   \n",
       "1  FUR-CH-10002774        Furniture       Chairs   \n",
       "\n",
       "                         Product Name   Sales Quantity  Discount  Profit  Year  \n",
       "0                          Xerox 1967  15.552        3       0.2  5.4432  2017  \n",
       "1  Global Deluxe Stacking Chair, Gray  71.372        2       0.3 -1.0196  2017  \n",
       "\n",
       "[2 rows x 22 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract 2017 from the Sales Dataset\n",
    "\n",
    "df[\"Year\"] = pd.DatetimeIndex(df[\"Order Date\"]).year\n",
    "\n",
    "data_2017 = np.where(df[\"Year\"] == 2017)\n",
    "\n",
    "data = df.iloc[data_2017].reset_index()\n",
    "\n",
    "data.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create InvoiceDay and CohortDay columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The year 2017 has 3312 rows of data.\n"
     ]
    }
   ],
   "source": [
    "print(f\"The year 2017 has {data.shape[0]} rows of data.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a function name get_day to trancate the day\n",
    "\n",
    "def get_day(x):\n",
    "    \"\"\"To convert a datetype column into day\"\"\"\n",
    "    \n",
    "    return dt.datetime(x.year, x.month, x.day)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Ship Date</th>\n",
       "      <th>Ship Mode</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Customer Name</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>...</th>\n",
       "      <th>Product ID</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Product Name</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Year</th>\n",
       "      <th>InvoiceDay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12</td>\n",
       "      <td>CA-2017-114412</td>\n",
       "      <td>2017-04-15</td>\n",
       "      <td>2017-04-20</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>AA-10480</td>\n",
       "      <td>Andrew Allen</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Concord</td>\n",
       "      <td>...</td>\n",
       "      <td>OFF-PA-10002365</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Paper</td>\n",
       "      <td>Xerox 1967</td>\n",
       "      <td>15.552</td>\n",
       "      <td>3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>5.4432</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-04-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23</td>\n",
       "      <td>US-2017-156909</td>\n",
       "      <td>2017-07-16</td>\n",
       "      <td>2017-07-18</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>SF-20065</td>\n",
       "      <td>Sandra Flanagan</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>...</td>\n",
       "      <td>FUR-CH-10002774</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>Global Deluxe Stacking Chair, Gray</td>\n",
       "      <td>71.372</td>\n",
       "      <td>2</td>\n",
       "      <td>0.3</td>\n",
       "      <td>-1.0196</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-07-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>34</td>\n",
       "      <td>CA-2017-107727</td>\n",
       "      <td>2017-10-19</td>\n",
       "      <td>2017-10-23</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>MA-17560</td>\n",
       "      <td>Matt Abelman</td>\n",
       "      <td>Home Office</td>\n",
       "      <td>United States</td>\n",
       "      <td>Houston</td>\n",
       "      <td>...</td>\n",
       "      <td>OFF-PA-10000249</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Paper</td>\n",
       "      <td>Easy-staple paper</td>\n",
       "      <td>29.472</td>\n",
       "      <td>3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>9.9468</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-10-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>41</td>\n",
       "      <td>CA-2017-120999</td>\n",
       "      <td>2017-09-10</td>\n",
       "      <td>2017-09-15</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>LC-16930</td>\n",
       "      <td>Linda Cazamias</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Naperville</td>\n",
       "      <td>...</td>\n",
       "      <td>TEC-PH-10004093</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Phones</td>\n",
       "      <td>Panasonic Kx-TS550</td>\n",
       "      <td>147.168</td>\n",
       "      <td>4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>16.5564</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-09-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>43</td>\n",
       "      <td>CA-2017-139619</td>\n",
       "      <td>2017-09-19</td>\n",
       "      <td>2017-09-23</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>ES-14080</td>\n",
       "      <td>Erin Smith</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Melbourne</td>\n",
       "      <td>...</td>\n",
       "      <td>OFF-ST-10003282</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Storage</td>\n",
       "      <td>Advantus 10-Drawer Portable Organizer, Chrome ...</td>\n",
       "      <td>95.616</td>\n",
       "      <td>2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>9.5616</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-09-19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   index        Order ID Order Date  Ship Date       Ship Mode Customer ID  \\\n",
       "0     12  CA-2017-114412 2017-04-15 2017-04-20  Standard Class    AA-10480   \n",
       "1     23  US-2017-156909 2017-07-16 2017-07-18    Second Class    SF-20065   \n",
       "2     34  CA-2017-107727 2017-10-19 2017-10-23    Second Class    MA-17560   \n",
       "3     41  CA-2017-120999 2017-09-10 2017-09-15  Standard Class    LC-16930   \n",
       "4     43  CA-2017-139619 2017-09-19 2017-09-23  Standard Class    ES-14080   \n",
       "\n",
       "     Customer Name      Segment        Country          City  ...  \\\n",
       "0     Andrew Allen     Consumer  United States       Concord  ...   \n",
       "1  Sandra Flanagan     Consumer  United States  Philadelphia  ...   \n",
       "2     Matt Abelman  Home Office  United States       Houston  ...   \n",
       "3   Linda Cazamias    Corporate  United States    Naperville  ...   \n",
       "4       Erin Smith    Corporate  United States     Melbourne  ...   \n",
       "\n",
       "        Product ID         Category Sub-Category  \\\n",
       "0  OFF-PA-10002365  Office Supplies        Paper   \n",
       "1  FUR-CH-10002774        Furniture       Chairs   \n",
       "2  OFF-PA-10000249  Office Supplies        Paper   \n",
       "3  TEC-PH-10004093       Technology       Phones   \n",
       "4  OFF-ST-10003282  Office Supplies      Storage   \n",
       "\n",
       "                                        Product Name    Sales Quantity  \\\n",
       "0                                         Xerox 1967   15.552        3   \n",
       "1                 Global Deluxe Stacking Chair, Gray   71.372        2   \n",
       "2                                  Easy-staple paper   29.472        3   \n",
       "3                                 Panasonic Kx-TS550  147.168        4   \n",
       "4  Advantus 10-Drawer Portable Organizer, Chrome ...   95.616        2   \n",
       "\n",
       "  Discount   Profit  Year  InvoiceDay  \n",
       "0      0.2   5.4432  2017  2017-04-15  \n",
       "1      0.3  -1.0196  2017  2017-07-16  \n",
       "2      0.2   9.9468  2017  2017-10-19  \n",
       "3      0.2  16.5564  2017  2017-09-10  \n",
       "4      0.2   9.5616  2017  2017-09-19  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create InvoiceDay column\n",
    "\n",
    "data[\"InvoiceDay\"] = data[\"Order Date\"].apply(get_day) \n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Group by CustomerID and select the InvoiceDay value\n",
    "\n",
    "grouping = data.groupby(\"Customer ID\")[\"InvoiceDay\"] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Ship Date</th>\n",
       "      <th>Ship Mode</th>\n",
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       "      <th>Customer Name</th>\n",
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       "      <th>CohortDay</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>12</td>\n",
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       "    </tr>\n",
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       "      <th>1</th>\n",
       "      <td>23</td>\n",
       "      <td>US-2017-156909</td>\n",
       "      <td>2017-07-16</td>\n",
       "      <td>2017-07-18</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>SF-20065</td>\n",
       "      <td>Sandra Flanagan</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>...</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>Global Deluxe Stacking Chair, Gray</td>\n",
       "      <td>71.372</td>\n",
       "      <td>2</td>\n",
       "      <td>0.3</td>\n",
       "      <td>-1.0196</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-07-16</td>\n",
       "      <td>2017-07-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>34</td>\n",
       "      <td>CA-2017-107727</td>\n",
       "      <td>2017-10-19</td>\n",
       "      <td>2017-10-23</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>MA-17560</td>\n",
       "      <td>Matt Abelman</td>\n",
       "      <td>Home Office</td>\n",
       "      <td>United States</td>\n",
       "      <td>Houston</td>\n",
       "      <td>...</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Paper</td>\n",
       "      <td>Easy-staple paper</td>\n",
       "      <td>29.472</td>\n",
       "      <td>3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>9.9468</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-10-19</td>\n",
       "      <td>2017-10-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>41</td>\n",
       "      <td>CA-2017-120999</td>\n",
       "      <td>2017-09-10</td>\n",
       "      <td>2017-09-15</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>LC-16930</td>\n",
       "      <td>Linda Cazamias</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Naperville</td>\n",
       "      <td>...</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Phones</td>\n",
       "      <td>Panasonic Kx-TS550</td>\n",
       "      <td>147.168</td>\n",
       "      <td>4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>16.5564</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-09-10</td>\n",
       "      <td>2017-06-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>43</td>\n",
       "      <td>CA-2017-139619</td>\n",
       "      <td>2017-09-19</td>\n",
       "      <td>2017-09-23</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>ES-14080</td>\n",
       "      <td>Erin Smith</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Melbourne</td>\n",
       "      <td>...</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Storage</td>\n",
       "      <td>Advantus 10-Drawer Portable Organizer, Chrome ...</td>\n",
       "      <td>95.616</td>\n",
       "      <td>2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>9.5616</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-09-19</td>\n",
       "      <td>2017-02-24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   index        Order ID Order Date  Ship Date       Ship Mode Customer ID  \\\n",
       "0     12  CA-2017-114412 2017-04-15 2017-04-20  Standard Class    AA-10480   \n",
       "1     23  US-2017-156909 2017-07-16 2017-07-18    Second Class    SF-20065   \n",
       "2     34  CA-2017-107727 2017-10-19 2017-10-23    Second Class    MA-17560   \n",
       "3     41  CA-2017-120999 2017-09-10 2017-09-15  Standard Class    LC-16930   \n",
       "4     43  CA-2017-139619 2017-09-19 2017-09-23  Standard Class    ES-14080   \n",
       "\n",
       "     Customer Name      Segment        Country          City  ...  \\\n",
       "0     Andrew Allen     Consumer  United States       Concord  ...   \n",
       "1  Sandra Flanagan     Consumer  United States  Philadelphia  ...   \n",
       "2     Matt Abelman  Home Office  United States       Houston  ...   \n",
       "3   Linda Cazamias    Corporate  United States    Naperville  ...   \n",
       "4       Erin Smith    Corporate  United States     Melbourne  ...   \n",
       "\n",
       "          Category Sub-Category  \\\n",
       "0  Office Supplies        Paper   \n",
       "1        Furniture       Chairs   \n",
       "2  Office Supplies        Paper   \n",
       "3       Technology       Phones   \n",
       "4  Office Supplies      Storage   \n",
       "\n",
       "                                        Product Name    Sales Quantity  \\\n",
       "0                                         Xerox 1967   15.552        3   \n",
       "1                 Global Deluxe Stacking Chair, Gray   71.372        2   \n",
       "2                                  Easy-staple paper   29.472        3   \n",
       "3                                 Panasonic Kx-TS550  147.168        4   \n",
       "4  Advantus 10-Drawer Portable Organizer, Chrome ...   95.616        2   \n",
       "\n",
       "  Discount   Profit  Year  InvoiceDay  CohortDay  \n",
       "0      0.2   5.4432  2017  2017-04-15 2017-04-15  \n",
       "1      0.3  -1.0196  2017  2017-07-16 2017-07-16  \n",
       "2      0.2   9.9468  2017  2017-10-19 2017-10-19  \n",
       "3      0.2  16.5564  2017  2017-09-10 2017-06-10  \n",
       "4      0.2   9.5616  2017  2017-09-19 2017-02-24  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Assign a minimum InvoiceDay value to the dataset\n",
    "\n",
    "data[\"CohortDay\"] = grouping.transform(\"min\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create InvoiceMonth and CohortMonth columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a function name get_month to trancate the month\n",
    "\n",
    "def get_month(x):\n",
    "    \"\"\"To extract the month from the datetime\"\"\"\n",
    "    return dt.datetime(x.year, x.month, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Ship Date</th>\n",
       "      <th>Ship Mode</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Customer Name</th>\n",
       "      <th>Segment</th>\n",
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       "      <th>City</th>\n",
       "      <th>...</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Product Name</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Year</th>\n",
       "      <th>InvoiceDay</th>\n",
       "      <th>CohortDay</th>\n",
       "      <th>InvoiceMonth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12</td>\n",
       "      <td>CA-2017-114412</td>\n",
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       "      <td>...</td>\n",
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       "      <td>3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>5.4432</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-04-15</td>\n",
       "      <td>2017-04-15</td>\n",
       "      <td>2017-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23</td>\n",
       "      <td>US-2017-156909</td>\n",
       "      <td>2017-07-16</td>\n",
       "      <td>2017-07-18</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>SF-20065</td>\n",
       "      <td>Sandra Flanagan</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>...</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>Global Deluxe Stacking Chair, Gray</td>\n",
       "      <td>71.372</td>\n",
       "      <td>2</td>\n",
       "      <td>0.3</td>\n",
       "      <td>-1.0196</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-07-16</td>\n",
       "      <td>2017-07-16</td>\n",
       "      <td>2017-07-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   index        Order ID Order Date  Ship Date       Ship Mode Customer ID  \\\n",
       "0     12  CA-2017-114412 2017-04-15 2017-04-20  Standard Class    AA-10480   \n",
       "1     23  US-2017-156909 2017-07-16 2017-07-18    Second Class    SF-20065   \n",
       "\n",
       "     Customer Name   Segment        Country          City  ... Sub-Category  \\\n",
       "0     Andrew Allen  Consumer  United States       Concord  ...        Paper   \n",
       "1  Sandra Flanagan  Consumer  United States  Philadelphia  ...       Chairs   \n",
       "\n",
       "                         Product Name   Sales Quantity Discount  Profit  Year  \\\n",
       "0                          Xerox 1967  15.552        3      0.2  5.4432  2017   \n",
       "1  Global Deluxe Stacking Chair, Gray  71.372        2      0.3 -1.0196  2017   \n",
       "\n",
       "   InvoiceDay  CohortDay  InvoiceMonth  \n",
       "0  2017-04-15 2017-04-15    2017-04-01  \n",
       "1  2017-07-16 2017-07-16    2017-07-01  \n",
       "\n",
       "[2 rows x 25 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create InvoiceMonth column\n",
    "data[\"InvoiceMonth\"] = data[\"Order Date\"].apply(get_month)\n",
    "\n",
    "data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Order ID</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Ship Date</th>\n",
       "      <th>Ship Mode</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Customer Name</th>\n",
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       "      <th>City</th>\n",
       "      <th>...</th>\n",
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       "      <th>Profit</th>\n",
       "      <th>Year</th>\n",
       "      <th>InvoiceDay</th>\n",
       "      <th>CohortDay</th>\n",
       "      <th>InvoiceMonth</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12</td>\n",
       "      <td>CA-2017-114412</td>\n",
       "      <td>2017-04-15</td>\n",
       "      <td>2017-04-20</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>AA-10480</td>\n",
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       "      <td>Concord</td>\n",
       "      <td>...</td>\n",
       "      <td>Paper</td>\n",
       "      <td>Xerox 1967</td>\n",
       "      <td>15.552</td>\n",
       "      <td>3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>5.4432</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-04-15</td>\n",
       "      <td>2017-04-15</td>\n",
       "      <td>2017-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23</td>\n",
       "      <td>US-2017-156909</td>\n",
       "      <td>2017-07-16</td>\n",
       "      <td>2017-07-18</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>SF-20065</td>\n",
       "      <td>Sandra Flanagan</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>...</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>Global Deluxe Stacking Chair, Gray</td>\n",
       "      <td>71.372</td>\n",
       "      <td>2</td>\n",
       "      <td>0.3</td>\n",
       "      <td>-1.0196</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-07-16</td>\n",
       "      <td>2017-07-16</td>\n",
       "      <td>2017-07-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   index        Order ID Order Date  Ship Date       Ship Mode Customer ID  \\\n",
       "0     12  CA-2017-114412 2017-04-15 2017-04-20  Standard Class    AA-10480   \n",
       "1     23  US-2017-156909 2017-07-16 2017-07-18    Second Class    SF-20065   \n",
       "\n",
       "     Customer Name   Segment        Country          City  ... Sub-Category  \\\n",
       "0     Andrew Allen  Consumer  United States       Concord  ...        Paper   \n",
       "1  Sandra Flanagan  Consumer  United States  Philadelphia  ...       Chairs   \n",
       "\n",
       "                         Product Name   Sales Quantity Discount  Profit  Year  \\\n",
       "0                          Xerox 1967  15.552        3      0.2  5.4432  2017   \n",
       "1  Global Deluxe Stacking Chair, Gray  71.372        2      0.3 -1.0196  2017   \n",
       "\n",
       "   InvoiceDay  CohortDay  InvoiceMonth  \n",
       "0  2017-04-15 2017-04-15    2017-04-01  \n",
       "1  2017-07-16 2017-07-16    2017-07-01  \n",
       "\n",
       "[2 rows x 25 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group by CustomerID and select the InvoiceMonth value\n",
    "\n",
    "grouping = data.groupby(\"Customer ID\")[\"InvoiceMonth\"] \n",
    "\n",
    "data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assign a minimum InvoiceMonth value to the dataset\n",
    "\n",
    "data[\"CohortMonth\"] = grouping.transform(\"min\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>...</td>\n",
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       "      <td>2017</td>\n",
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       "      <td>2017-04-15</td>\n",
       "      <td>2017-04-01</td>\n",
       "      <td>2017-04-01</td>\n",
       "    </tr>\n",
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       "      <th>1</th>\n",
       "      <td>23</td>\n",
       "      <td>US-2017-156909</td>\n",
       "      <td>2017-07-16</td>\n",
       "      <td>2017-07-18</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>SF-20065</td>\n",
       "      <td>Sandra Flanagan</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>...</td>\n",
       "      <td>Global Deluxe Stacking Chair, Gray</td>\n",
       "      <td>71.372</td>\n",
       "      <td>2</td>\n",
       "      <td>0.3</td>\n",
       "      <td>-1.0196</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-07-16</td>\n",
       "      <td>2017-07-16</td>\n",
       "      <td>2017-07-01</td>\n",
       "      <td>2017-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>34</td>\n",
       "      <td>CA-2017-107727</td>\n",
       "      <td>2017-10-19</td>\n",
       "      <td>2017-10-23</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>MA-17560</td>\n",
       "      <td>Matt Abelman</td>\n",
       "      <td>Home Office</td>\n",
       "      <td>United States</td>\n",
       "      <td>Houston</td>\n",
       "      <td>...</td>\n",
       "      <td>Easy-staple paper</td>\n",
       "      <td>29.472</td>\n",
       "      <td>3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>9.9468</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-10-19</td>\n",
       "      <td>2017-10-19</td>\n",
       "      <td>2017-10-01</td>\n",
       "      <td>2017-10-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>41</td>\n",
       "      <td>CA-2017-120999</td>\n",
       "      <td>2017-09-10</td>\n",
       "      <td>2017-09-15</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>LC-16930</td>\n",
       "      <td>Linda Cazamias</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Naperville</td>\n",
       "      <td>...</td>\n",
       "      <td>Panasonic Kx-TS550</td>\n",
       "      <td>147.168</td>\n",
       "      <td>4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>16.5564</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-09-10</td>\n",
       "      <td>2017-06-10</td>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>2017-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>43</td>\n",
       "      <td>CA-2017-139619</td>\n",
       "      <td>2017-09-19</td>\n",
       "      <td>2017-09-23</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>ES-14080</td>\n",
       "      <td>Erin Smith</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Melbourne</td>\n",
       "      <td>...</td>\n",
       "      <td>Advantus 10-Drawer Portable Organizer, Chrome ...</td>\n",
       "      <td>95.616</td>\n",
       "      <td>2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>9.5616</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-09-19</td>\n",
       "      <td>2017-02-24</td>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>2017-02-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   index        Order ID Order Date  Ship Date       Ship Mode Customer ID  \\\n",
       "0     12  CA-2017-114412 2017-04-15 2017-04-20  Standard Class    AA-10480   \n",
       "1     23  US-2017-156909 2017-07-16 2017-07-18    Second Class    SF-20065   \n",
       "2     34  CA-2017-107727 2017-10-19 2017-10-23    Second Class    MA-17560   \n",
       "3     41  CA-2017-120999 2017-09-10 2017-09-15  Standard Class    LC-16930   \n",
       "4     43  CA-2017-139619 2017-09-19 2017-09-23  Standard Class    ES-14080   \n",
       "\n",
       "     Customer Name      Segment        Country          City  ...  \\\n",
       "0     Andrew Allen     Consumer  United States       Concord  ...   \n",
       "1  Sandra Flanagan     Consumer  United States  Philadelphia  ...   \n",
       "2     Matt Abelman  Home Office  United States       Houston  ...   \n",
       "3   Linda Cazamias    Corporate  United States    Naperville  ...   \n",
       "4       Erin Smith    Corporate  United States     Melbourne  ...   \n",
       "\n",
       "                                        Product Name    Sales Quantity  \\\n",
       "0                                         Xerox 1967   15.552        3   \n",
       "1                 Global Deluxe Stacking Chair, Gray   71.372        2   \n",
       "2                                  Easy-staple paper   29.472        3   \n",
       "3                                 Panasonic Kx-TS550  147.168        4   \n",
       "4  Advantus 10-Drawer Portable Organizer, Chrome ...   95.616        2   \n",
       "\n",
       "  Discount   Profit  Year InvoiceDay  CohortDay  InvoiceMonth  CohortMonth  \n",
       "0      0.2   5.4432  2017 2017-04-15 2017-04-15    2017-04-01   2017-04-01  \n",
       "1      0.3  -1.0196  2017 2017-07-16 2017-07-16    2017-07-01   2017-07-01  \n",
       "2      0.2   9.9468  2017 2017-10-19 2017-10-19    2017-10-01   2017-10-01  \n",
       "3      0.2  16.5564  2017 2017-09-10 2017-06-10    2017-09-01   2017-06-01  \n",
       "4      0.2   9.5616  2017 2017-09-19 2017-02-24    2017-09-01   2017-02-01  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View the top 5 rows\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculating time offset in days \n",
    "\n",
    "Calculating time offset for each transaction allows you to report the metrics for each cohort in a comparable fashion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a function name get_date_int get extract the date into integer\n",
    "\n",
    "def get_date_int(data, column):\n",
    "    \"\"\"To take a dataframe and datetype column\n",
    "    and return the integer value of the column\"\"\"\n",
    "    \n",
    "    year = data[column].dt.year\n",
    "    month = data[column].dt.month\n",
    "    day = data[column].dt.day\n",
    "    return year, month, day"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we will create 6 variables that capture the integer value of years, months and days for Invoice and Cohort Date using the get_date_int() function above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assign integer values to invoice year, month and day using the \"InvoiceDay\" column\n",
    "\n",
    "invoice_year, invoice_month, invoice_day = get_date_int(data, \"InvoiceDay\")\n",
    "\n",
    "\n",
    "# Assign integer values to invoice year, month and day using the \"CohortDay\" column\n",
    "\n",
    "cohort_year, cohort_month, cohort_day = get_date_int(data,\"CohortDay\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate time offset in days\n",
    "calculate the difference between the Invoice and Cohort dates in years, months and days separately and then calculate the total days difference between the two.\n",
    "\n",
    "    Find the difference in years between the moment of the invoice and the moment the cohort was created.\n",
    "    Find the difference in months between the moment of the invoice and the moment the cohort was created.\n",
    "    Find the difference in days between the moment of the invoice and the moment the cohort was created.\n",
    "    Calculate the number of days for the CohortIndex (assume 365 days in a year, and 30 days in a month)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate difference in years\n",
    "\n",
    "years_diff = invoice_year - cohort_year\n",
    "\n",
    "\n",
    "# Calculate difference in months\n",
    "\n",
    "months_diff = invoice_month - cohort_month\n",
    "\n",
    "\n",
    "# Calculate difference in days\n",
    "\n",
    "days_diff = invoice_day - cohort_day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract the difference in months from all previous values\n",
    "\n",
    "data[\"CohortIndex\"] = years_diff * 12 + months_diff + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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>index</th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Ship Date</th>\n",
       "      <th>Ship Mode</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Customer Name</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>...</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Year</th>\n",
       "      <th>InvoiceDay</th>\n",
       "      <th>CohortDay</th>\n",
       "      <th>InvoiceMonth</th>\n",
       "      <th>CohortMonth</th>\n",
       "      <th>CohortIndex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12</td>\n",
       "      <td>CA-2017-114412</td>\n",
       "      <td>2017-04-15</td>\n",
       "      <td>2017-04-20</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>AA-10480</td>\n",
       "      <td>Andrew Allen</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Concord</td>\n",
       "      <td>...</td>\n",
       "      <td>15.552</td>\n",
       "      <td>3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>5.4432</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-04-15</td>\n",
       "      <td>2017-04-15</td>\n",
       "      <td>2017-04-01</td>\n",
       "      <td>2017-04-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23</td>\n",
       "      <td>US-2017-156909</td>\n",
       "      <td>2017-07-16</td>\n",
       "      <td>2017-07-18</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>SF-20065</td>\n",
       "      <td>Sandra Flanagan</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>...</td>\n",
       "      <td>71.372</td>\n",
       "      <td>2</td>\n",
       "      <td>0.3</td>\n",
       "      <td>-1.0196</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-07-16</td>\n",
       "      <td>2017-07-16</td>\n",
       "      <td>2017-07-01</td>\n",
       "      <td>2017-07-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   index        Order ID Order Date  Ship Date       Ship Mode Customer ID  \\\n",
       "0     12  CA-2017-114412 2017-04-15 2017-04-20  Standard Class    AA-10480   \n",
       "1     23  US-2017-156909 2017-07-16 2017-07-18    Second Class    SF-20065   \n",
       "\n",
       "     Customer Name   Segment        Country          City  ...   Sales  \\\n",
       "0     Andrew Allen  Consumer  United States       Concord  ...  15.552   \n",
       "1  Sandra Flanagan  Consumer  United States  Philadelphia  ...  71.372   \n",
       "\n",
       "  Quantity Discount  Profit  Year InvoiceDay  CohortDay  InvoiceMonth  \\\n",
       "0        3      0.2  5.4432  2017 2017-04-15 2017-04-15    2017-04-01   \n",
       "1        2      0.3 -1.0196  2017 2017-07-16 2017-07-16    2017-07-01   \n",
       "\n",
       "   CohortMonth  CohortIndex  \n",
       "0   2017-04-01            1  \n",
       "1   2017-07-01            1  \n",
       "\n",
       "[2 rows x 27 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Output of the dataset\n",
    "\n",
    "data.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Monthly active customers from each cohort\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Groupby \"CohortMonth\" and \"CohortIndex\" columns\n",
    "\n",
    "grouping = data.groupby([\"CohortMonth\", \"CohortIndex\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Count number of each group using the \"CustomerID\" column\n",
    "\n",
    "cohort_data = grouping[\"Customer ID\"].apply(pd.Series.nunique)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reset the column index\n",
    "\n",
    "cohort_data = cohort_data.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a pivot table and assign to a variable called cohort_count\n",
    "\n",
    "cohort_counts = cohort_data.pivot(index=\"CohortMonth\",\n",
    "                                  columns=\"CohortIndex\",\n",
    "                                  values=\"Customer ID\")"
   ]
  },
  {
   "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>CohortIndex</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CohortMonth</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>67.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-01</th>\n",
       "      <td>50.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-01</th>\n",
       "      <td>101.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-01</th>\n",
       "      <td>77.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-01</th>\n",
       "      <td>71.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-01</th>\n",
       "      <td>70.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-01</th>\n",
       "      <td>45.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-01</th>\n",
       "      <td>32.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-01</th>\n",
       "      <td>63.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-01</th>\n",
       "      <td>39.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01</th>\n",
       "      <td>45.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-01</th>\n",
       "      <td>33.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "CohortIndex     1     2     3     4     5     6     7     8     9     10  \\\n",
       "CohortMonth                                                                \n",
       "2017-01-01    67.0   3.0   8.0  13.0   9.0   4.0   9.0   9.0  16.0   9.0   \n",
       "2017-02-01    50.0   6.0   6.0   6.0   7.0   4.0   6.0  17.0  10.0  15.0   \n",
       "2017-03-01   101.0  13.0  15.0  13.0  15.0  14.0  27.0  20.0  27.0  23.0   \n",
       "2017-04-01    77.0  12.0  15.0  13.0  12.0  20.0  13.0  22.0  26.0   NaN   \n",
       "2017-05-01    71.0   9.0  10.0  12.0  17.0  11.0  26.0  17.0   NaN   NaN   \n",
       "2017-06-01    70.0   6.0  12.0  18.0  12.0  17.0  15.0   NaN   NaN   NaN   \n",
       "2017-07-01    45.0   7.0   9.0   9.0  10.0  11.0   NaN   NaN   NaN   NaN   \n",
       "2017-08-01    32.0   9.0   5.0  17.0   9.0   NaN   NaN   NaN   NaN   NaN   \n",
       "2017-09-01    63.0   8.0  15.0   8.0   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "2017-10-01    39.0   6.0   9.0   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "2017-11-01    45.0  11.0   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "2017-12-01    33.0   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "\n",
       "CohortIndex    11    12  \n",
       "CohortMonth              \n",
       "2017-01-01   16.0  18.0  \n",
       "2017-02-01   15.0   NaN  \n",
       "2017-03-01    NaN   NaN  \n",
       "2017-04-01    NaN   NaN  \n",
       "2017-05-01    NaN   NaN  \n",
       "2017-06-01    NaN   NaN  \n",
       "2017-07-01    NaN   NaN  \n",
       "2017-08-01    NaN   NaN  \n",
       "2017-09-01    NaN   NaN  \n",
       "2017-10-01    NaN   NaN  \n",
       "2017-11-01    NaN   NaN  \n",
       "2017-12-01    NaN   NaN  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Output of the cohort_counts\n",
    "\n",
    "cohort_counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculating Retention Rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Group the dataframe by \"CohortMonth\" and \"CohortIndex\" columns\n",
    "\n",
    "grouping = data.groupby([\"CohortMonth\", \"CohortIndex\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select the first column and store it to cohort_sizes\n",
    "\n",
    "cohort_sizes = cohort_counts.iloc[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Divide the cohort count by cohort sizes along the rows\n",
    "\n",
    "retention = cohort_counts.divide(cohort_sizes, axis=0)\n",
    "\n",
    "# Convert retention rate to percentages\n",
    "\n",
    "retention = (retention*100).round(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>CohortIndex</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CohortMonth</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>100.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>11.9</td>\n",
       "      <td>19.4</td>\n",
       "      <td>13.4</td>\n",
       "      <td>6.0</td>\n",
       "      <td>13.4</td>\n",
       "      <td>13.4</td>\n",
       "      <td>23.9</td>\n",
       "      <td>13.4</td>\n",
       "      <td>23.9</td>\n",
       "      <td>26.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-01</th>\n",
       "      <td>100.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-01</th>\n",
       "      <td>100.0</td>\n",
       "      <td>12.9</td>\n",
       "      <td>14.9</td>\n",
       "      <td>12.9</td>\n",
       "      <td>14.9</td>\n",
       "      <td>13.9</td>\n",
       "      <td>26.7</td>\n",
       "      <td>19.8</td>\n",
       "      <td>26.7</td>\n",
       "      <td>22.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-01</th>\n",
       "      <td>100.0</td>\n",
       "      <td>15.6</td>\n",
       "      <td>19.5</td>\n",
       "      <td>16.9</td>\n",
       "      <td>15.6</td>\n",
       "      <td>26.0</td>\n",
       "      <td>16.9</td>\n",
       "      <td>28.6</td>\n",
       "      <td>33.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-01</th>\n",
       "      <td>100.0</td>\n",
       "      <td>12.7</td>\n",
       "      <td>14.1</td>\n",
       "      <td>16.9</td>\n",
       "      <td>23.9</td>\n",
       "      <td>15.5</td>\n",
       "      <td>36.6</td>\n",
       "      <td>23.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-01</th>\n",
       "      <td>100.0</td>\n",
       "      <td>8.6</td>\n",
       "      <td>17.1</td>\n",
       "      <td>25.7</td>\n",
       "      <td>17.1</td>\n",
       "      <td>24.3</td>\n",
       "      <td>21.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-01</th>\n",
       "      <td>100.0</td>\n",
       "      <td>15.6</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>22.2</td>\n",
       "      <td>24.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-01</th>\n",
       "      <td>100.0</td>\n",
       "      <td>28.1</td>\n",
       "      <td>15.6</td>\n",
       "      <td>53.1</td>\n",
       "      <td>28.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-01</th>\n",
       "      <td>100.0</td>\n",
       "      <td>12.7</td>\n",
       "      <td>23.8</td>\n",
       "      <td>12.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-01</th>\n",
       "      <td>100.0</td>\n",
       "      <td>15.4</td>\n",
       "      <td>23.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01</th>\n",
       "      <td>100.0</td>\n",
       "      <td>24.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-01</th>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "CohortIndex     1     2     3     4     5     6     7     8     9     10  \\\n",
       "CohortMonth                                                                \n",
       "2017-01-01   100.0   4.5  11.9  19.4  13.4   6.0  13.4  13.4  23.9  13.4   \n",
       "2017-02-01   100.0  12.0  12.0  12.0  14.0   8.0  12.0  34.0  20.0  30.0   \n",
       "2017-03-01   100.0  12.9  14.9  12.9  14.9  13.9  26.7  19.8  26.7  22.8   \n",
       "2017-04-01   100.0  15.6  19.5  16.9  15.6  26.0  16.9  28.6  33.8   NaN   \n",
       "2017-05-01   100.0  12.7  14.1  16.9  23.9  15.5  36.6  23.9   NaN   NaN   \n",
       "2017-06-01   100.0   8.6  17.1  25.7  17.1  24.3  21.4   NaN   NaN   NaN   \n",
       "2017-07-01   100.0  15.6  20.0  20.0  22.2  24.4   NaN   NaN   NaN   NaN   \n",
       "2017-08-01   100.0  28.1  15.6  53.1  28.1   NaN   NaN   NaN   NaN   NaN   \n",
       "2017-09-01   100.0  12.7  23.8  12.7   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "2017-10-01   100.0  15.4  23.1   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "2017-11-01   100.0  24.4   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "2017-12-01   100.0   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "\n",
       "CohortIndex    11    12  \n",
       "CohortMonth              \n",
       "2017-01-01   23.9  26.9  \n",
       "2017-02-01   30.0   NaN  \n",
       "2017-03-01    NaN   NaN  \n",
       "2017-04-01    NaN   NaN  \n",
       "2017-05-01    NaN   NaN  \n",
       "2017-06-01    NaN   NaN  \n",
       "2017-07-01    NaN   NaN  \n",
       "2017-08-01    NaN   NaN  \n",
       "2017-09-01    NaN   NaN  \n",
       "2017-10-01    NaN   NaN  \n",
       "2017-11-01    NaN   NaN  \n",
       "2017-12-01    NaN   NaN  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "retention"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate a heatmap with the retention rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a haetmap using seaborn\n",
    "\n",
    "#Initialize your figure size\n",
    "plt.figure(figsize=(8,6))\n",
    "\n",
    "# Add a title\n",
    "plt.title(\"Customer Retention\")\n",
    "\n",
    "# Create the heatmap\n",
    "sns.heatmap(\n",
    "            data=retention, \n",
    "            annot=True, \n",
    "            fmt = \"g\",\n",
    "            cmap=\"BuPu\",\n",
    "            yticklabels = [\"2017-01-01\", \"2017-02-01\", \"2017-03-01\", \"2017-04-01\",\n",
    "                           \"2017-05-01\", \"2017-06-01\", \"2017-07-01\", \"2017-08-01\",\n",
    "                           \"2017-09-01\", \"2017-10-01\",\"2017-11-01\", \"2017-12-01\"]\n",
    "            )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The retention rate for each cohort is shown on the graph. There, the boxes with shading denote strong retention whereas the boxes with fading denote low retention.\n",
    "\n",
    "To understand the cohort with the highest retention rate and other groups, additional graphical analysis must be performed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Find the average retention rate for each cohort"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find the mean of various cohort retention rate \n",
    "\n",
    "retention_curve_value = []\n",
    "\n",
    "for col in retention.columns:\n",
    "    a = retention[col].mean()\n",
    "    retention_curve_value.append(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a dataframe\n",
    "\n",
    "retention_curve_values = pd.DataFrame(retention_curve_value)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer Retention</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>16.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>21.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>23.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>26.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>22.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>26.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Customer Retention\n",
       "0                100.0\n",
       "1                 14.8\n",
       "2                 17.2\n",
       "3                 21.1\n",
       "4                 18.7\n",
       "5                 16.9\n",
       "6                 21.2\n",
       "7                 23.9\n",
       "8                 26.1\n",
       "9                 22.1\n",
       "10                27.0\n",
       "11                26.9"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Rename column\n",
    "\n",
    "retention_curve_values.rename(columns = {0 : \"Customer Retention\"}, inplace = True)\n",
    "\n",
    "retention_curve_values.round(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot retention curve "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot retention curve\n",
    "\n",
    "plt.figure(figsize = (8, 6))\n",
    "\n",
    "ax = sns.pointplot(data = retention_curve_values,\n",
    "             x = retention_curve_values.index + 1,\n",
    "             y = retention_curve_values[\"Customer Retention\"])\n",
    "plt.xlabel(\"Month\")\n",
    "\n",
    "    \n",
    "for i in range(len(retention_curve_values.index)):\n",
    "    plt.text(i, retention_curve_values[\"Customer Retention\"][i]+2,\n",
    "             round(retention_curve_values[\"Customer Retention\"][i],1), \n",
    "             ha = \"center\")\n",
    "        \n",
    "plt.title(\"Retention Rate\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the cohort curve, the cohorts 11 and 12 had the highest retention rates at 27.0 and 26.9, respectively. Cohort 2 had the lowest retention rate, with a rate of 14.8 percent.\n",
    "\n",
    "Given the aforementioned chart and data, this also indicates that the business has a low retention rate.\n",
    "\n",
    "To gain insight into the cohort, feather analysis must be conducted."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Customer Segmentation Using K-means Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Already existing variables are transformed to build/extract metrics that reflect client behavior. These metrics include monetary value, frequency, and recency. These metrics will be used for segmentation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Recency, Frequency and Monetary(RFM) Values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Recency: days since last customer transaction.\n",
    "\n",
    "2. Frequency: number of transactions in the last 12 months.\n",
    "\n",
    "3. Monetary value: total money spend in the last 12 months."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min:2017-01-01 00:00:00; Max:2017-12-30 00:00:00\n"
     ]
    }
   ],
   "source": [
    "# Proint the minimum and the maximum \"Order Date\" from the dataframe\n",
    "\n",
    "print(\"Min:{}; Max:{}\".format(min(data[\"Order Date\"]),max(data[\"Order Date\"])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The analysis is being made on 2017-12-31 as this is the last day of the year. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.date(2017, 12, 31)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create a snapshot_day data as if we're doing analysis recently.\n",
    "\n",
    "snapshot_date = max(data[\"Order Date\"]) + dt.timedelta(days=1)\n",
    "\n",
    "snapshot_date.date()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate Recency, Frequency, and Monetary values for each customer \n",
    "\n",
    "datamart = data.groupby([\"Customer ID\"]).agg(\n",
    "            {\n",
    "            \"Order Date\": lambda x: (snapshot_date - x.max()).days,\n",
    "            \"Order ID\": \"count\",\n",
    "            \"Sales\": \"sum\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Rename the columns\n",
    "\n",
    "datamart.rename(columns={\n",
    "                         \"Order Date\": \"Recency\",\n",
    "                         \"Order ID\": \"Frequency\",\n",
    "                         \"Sales\": \"Monetary\"}, \n",
    "                         inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Customer ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AA-10315</th>\n",
       "      <td>185</td>\n",
       "      <td>2</td>\n",
       "      <td>374.480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AA-10375</th>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>206.732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AA-10480</th>\n",
       "      <td>260</td>\n",
       "      <td>1</td>\n",
       "      <td>15.552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AA-10645</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>12.960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AB-10060</th>\n",
       "      <td>55</td>\n",
       "      <td>8</td>\n",
       "      <td>2936.264</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Recency  Frequency  Monetary\n",
       "Customer ID                              \n",
       "AA-10315         185          2   374.480\n",
       "AA-10375          20          5   206.732\n",
       "AA-10480         260          1    15.552\n",
       "AA-10645          56          1    12.960\n",
       "AB-10060          55          8  2936.264"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datamart.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Preprocessing for k-means"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At this point, the metrics are scaled, fitted, and transformed for KMeans clustering."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Key k-means assumptions\n",
    "\n",
    "1. Symmetric distribution of variables(not skewed).\n",
    "\n",
    "2. Variables with same average values.\n",
    "\n",
    "3. Variables with same variance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exploring distribution of RFM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>693.00</td>\n",
       "      <td>693.00</td>\n",
       "      <td>693.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>88.34</td>\n",
       "      <td>4.78</td>\n",
       "      <td>1058.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>81.51</td>\n",
       "      <td>3.25</td>\n",
       "      <td>1463.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>29.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>204.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>58.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>604.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>119.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>1334.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>363.00</td>\n",
       "      <td>20.00</td>\n",
       "      <td>14203.28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Recency  Frequency  Monetary\n",
       "count   693.00     693.00    693.00\n",
       "mean     88.34       4.78   1058.03\n",
       "std      81.51       3.25   1463.91\n",
       "min       1.00       1.00      1.19\n",
       "25%      29.00       2.00    204.40\n",
       "50%      58.00       4.00    604.91\n",
       "75%     119.00       6.00   1334.64\n",
       "max     363.00      20.00  14203.28"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Explore the summary statistics of datamart\n",
    "\n",
    "datamart.describe().round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use a distplot to visualize the distribution of the data\n",
    "\n",
    "plt.figure(figsize=(20,4))\n",
    "\n",
    "# Plot distribution of Recency\n",
    "plt.subplot(1, 4, 1) \n",
    "sns.distplot(datamart[\"Recency\"])\n",
    "\n",
    "\n",
    "# Plot distribution of Frequency\n",
    "plt.subplot(1, 4, 2) \n",
    "sns.distplot(datamart[\"Frequency\"])\n",
    "\n",
    "\n",
    "# Plot distribution of Monetary\n",
    "plt.subplot(1, 4, 3) \n",
    "sns.distplot(datamart[\"Monetary\"])\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distribution of Recency, Frequency and Monetary values are right skewed. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>693.00</td>\n",
       "      <td>693.00</td>\n",
       "      <td>693.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.98</td>\n",
       "      <td>1.32</td>\n",
       "      <td>6.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.13</td>\n",
       "      <td>0.74</td>\n",
       "      <td>1.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.37</td>\n",
       "      <td>0.69</td>\n",
       "      <td>5.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.06</td>\n",
       "      <td>1.39</td>\n",
       "      <td>6.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.78</td>\n",
       "      <td>1.79</td>\n",
       "      <td>7.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.89</td>\n",
       "      <td>3.00</td>\n",
       "      <td>9.56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Recency  Frequency  Monetary\n",
       "count   693.00     693.00    693.00\n",
       "mean      3.98       1.32      6.12\n",
       "std       1.13       0.74      1.54\n",
       "min       0.00       0.00      0.17\n",
       "25%       3.37       0.69      5.32\n",
       "50%       4.06       1.39      6.41\n",
       "75%       4.78       1.79      7.20\n",
       "max       5.89       3.00      9.56"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Applying log to unskew the data\n",
    "\n",
    "# Apply log transformation to to RFM\n",
    "\n",
    "datamart_log = np.log(datamart)\n",
    "\n",
    "datamart_log.describe().round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use a distplot to visualize the distribution of the data\n",
    "\n",
    "plt.figure(figsize=(20,4))\n",
    "\n",
    "# Plot distribution of Recency\n",
    "plt.subplot(1, 4, 1) \n",
    "sns.distplot(datamart_log[\"Recency\"])\n",
    "\n",
    "\n",
    "# Plot distribution of Frequency\n",
    "plt.subplot(1, 4, 2) \n",
    "sns.distplot(datamart_log[\"Frequency\"])\n",
    "\n",
    "\n",
    "# Plot distribution of Monetary\n",
    "plt.subplot(1, 4, 3) \n",
    "sns.distplot(datamart_log[\"Monetary\"])\n",
    "\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distribution of Recency, Frequency and Monetary values after taking log are assuming normality."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Scaling and Centering the data using StandardScaler "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean:  [-0.  0. -0.]\n",
      "std:  [1. 1. 1.]\n"
     ]
    }
   ],
   "source": [
    "# Normalize the variables\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Instantiate scaler object\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(datamart_log)\n",
    "\n",
    "# Store for clustering\n",
    "datamart_normalized = scaler.transform(datamart_log)\n",
    "\n",
    "print(\"mean: \", datamart_normalized.mean(axis=0).round(2))\n",
    "print(\"std: \", datamart_normalized.std(axis=0).round(2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert into a dataframe\n",
    "\n",
    "datamart_normalized = pd.DataFrame(data=datamart_normalized,\n",
    "                                   index=datamart_log.index, \n",
    "                                   columns=datamart_log.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.930000e+02</td>\n",
       "      <td>6.930000e+02</td>\n",
       "      <td>6.930000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-3.748805e-17</td>\n",
       "      <td>1.438644e-16</td>\n",
       "      <td>-4.677996e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.000722e+00</td>\n",
       "      <td>1.000722e+00</td>\n",
       "      <td>1.000722e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-3.517407e+00</td>\n",
       "      <td>-1.791354e+00</td>\n",
       "      <td>-3.862901e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-5.449444e-01</td>\n",
       "      <td>-8.509592e-01</td>\n",
       "      <td>-5.193476e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.692751e-02</td>\n",
       "      <td>8.943583e-02</td>\n",
       "      <td>1.853523e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.013388e-01</td>\n",
       "      <td>6.395317e-01</td>\n",
       "      <td>6.993322e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.685846e+00</td>\n",
       "      <td>2.272965e+00</td>\n",
       "      <td>2.235293e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Recency     Frequency      Monetary\n",
       "count  6.930000e+02  6.930000e+02  6.930000e+02\n",
       "mean  -3.748805e-17  1.438644e-16 -4.677996e-17\n",
       "std    1.000722e+00  1.000722e+00  1.000722e+00\n",
       "min   -3.517407e+00 -1.791354e+00 -3.862901e+00\n",
       "25%   -5.449444e-01 -8.509592e-01 -5.193476e-01\n",
       "50%    6.692751e-02  8.943583e-02  1.853523e-01\n",
       "75%    7.013388e-01  6.395317e-01  6.993322e-01\n",
       "max    1.685846e+00  2.272965e+00  2.235293e+00"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Explore the summary statistics of datamart\n",
    "\n",
    "datamart_normalized.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize the normalized variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use a distplot to visualize the distribution of the data\n",
    "\n",
    "plt.figure(figsize=(20,4))\n",
    "\n",
    "# Plot recency distribution\n",
    "plt.subplot(1, 4, 1)\n",
    "sns.distplot(datamart_normalized[\"Recency\"])\n",
    "\n",
    "# Plot frequency distribution\n",
    "plt.subplot(1, 4, 2)\n",
    "sns.distplot(datamart_normalized[\"Frequency\"])\n",
    "\n",
    "# Plot monetary value distribution\n",
    "plt.subplot(1, 4, 3)\n",
    "sns.distplot(datamart_normalized[\"Monetary\"])\n",
    "\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K - Means Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Choosing the number of clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit KMeans and calculate SSE for each k\n",
    "\n",
    "sse = {}\n",
    "for k in range(1, 15):\n",
    "  \n",
    "    # Initialize KMeans with k clusters\n",
    "    kmeans = KMeans(n_clusters=k, random_state=42)\n",
    "    \n",
    "    # Fit KMeans on the normalized dataset\n",
    "    kmeans.fit(datamart_normalized)\n",
    "    \n",
    "    # Assign sum of squared distances to k element of dictionary\n",
    "    # sse is sum of squared distances to closest cluster center\n",
    "    sse[k] = kmeans.inertia_ "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ##### Creating clusters using KMeans Algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clusters represent groups with similar behaviours.\n",
    "\n",
    "A graphical approach ('the elbow method') is employed in selecting the best number of clusters.\n",
    "\n",
    "The number of clusters at which the sum of square distances reduces insignificantly is chosen as the optimum number of clusters.\n",
    "\n",
    "At this point, the curve seizes to bend significantly.\n",
    "\n",
    "It is important to further investigate for clusters in the neighbourhood of the elbow coupled with their corresponding summary RFM values in order to select the approprate cluster number that best explains group behaviour.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Add the plot title \"The Elbow Method\"\n",
    "plt.title(\"The Elbow Method\")\n",
    "\n",
    "# Add X-axis label \"k\"\n",
    "plt.xlabel(\"Number of clusters(k)\")\n",
    "\n",
    "# Add Y-axis label \"SSE\"\n",
    "plt.ylabel(\"Sum of square distances(SSE)\")\n",
    "\n",
    "# Plot SSE values for each key in the dictionary\n",
    "sns.pointplot(x=list(sse.keys()), y=list(sse.values()))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the elbow diagram, recommended number of cluster is 4. However there is a need to further investigate for clusters of 3,4,5 coupled with their corresponding RFM values in order to choose the approprate cluster number that best explain customer behavior."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Investigating a  Cluster of 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize KMeans\n",
    "kmeans = KMeans(n_clusters=3, random_state=1) \n",
    "\n",
    "# Fit k-means clustering on the normalized data set\n",
    "kmeans.fit(datamart_normalized)\n",
    "\n",
    "# Extract cluster labels\n",
    "cluster_labels = kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Recency</th>\n",
       "      <th>Frequency</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Monetary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100.8</td>\n",
       "      <td>5.9</td>\n",
       "      <td>1489.2</td>\n",
       "      <td>293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>131.3</td>\n",
       "      <td>1.8</td>\n",
       "      <td>176.4</td>\n",
       "      <td>217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17.3</td>\n",
       "      <td>6.5</td>\n",
       "      <td>1413.1</td>\n",
       "      <td>183</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Recency Frequency Monetary      \n",
       "           mean      mean     mean count\n",
       "Cluster                                 \n",
       "0         100.8       5.9   1489.2   293\n",
       "1         131.3       1.8    176.4   217\n",
       "2          17.3       6.5   1413.1   183"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a Dataframe by adding a new cluster label column\n",
    "datamart_rfm_k3 = datamart.assign(Cluster = cluster_labels)\n",
    "\n",
    "# Gruop the data by cluster\n",
    "grouped = datamart_rfm_k3.groupby([\"Cluster\"])\n",
    "\n",
    "# Calculate average RFM values and segment sizes per cluster value\n",
    "grouped.agg({\n",
    "    \"Recency\" : \"mean\", \n",
    "    \"Frequency\": \"mean\", \"Monetary\":[\"mean\", \"count\"]\n",
    "    }).round(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Investigating a  Cluster of 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize KMeans\n",
    "kmeans = KMeans(n_clusters=4, random_state=1) \n",
    "\n",
    "# Fit k-means clustering on the normalized data set\n",
    "kmeans.fit(datamart_normalized)\n",
    "\n",
    "# Extract cluster labels\n",
    "cluster_labels = kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Recency</th>\n",
       "      <th>Frequency</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Monetary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>70.4</td>\n",
       "      <td>7.3</td>\n",
       "      <td>1940.0</td>\n",
       "      <td>235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>135.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>65.0</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>124.3</td>\n",
       "      <td>3.2</td>\n",
       "      <td>537.2</td>\n",
       "      <td>215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.6</td>\n",
       "      <td>6.1</td>\n",
       "      <td>1271.8</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Recency Frequency Monetary      \n",
       "           mean      mean     mean count\n",
       "Cluster                                 \n",
       "0          70.4       7.3   1940.0   235\n",
       "1         135.5       1.4     65.0   122\n",
       "2         124.3       3.2    537.2   215\n",
       "3          11.6       6.1   1271.8   121"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a Dataframe by adding a new cluster label column\n",
    "datamart_rfm_k4 = datamart.assign(Cluster = cluster_labels)\n",
    "\n",
    "# Gruop the data by cluster\n",
    "grouped = datamart_rfm_k4.groupby([\"Cluster\"])\n",
    "\n",
    "# Calculate average RFM values and segment sizes per cluster value\n",
    "datamart_rfm_k4_grouped=grouped.agg({\n",
    "    \"Recency\" : \"mean\", \n",
    "    \"Frequency\": \"mean\", \"Monetary\":[\"mean\", \"count\"]\n",
    "    }).round(1)\n",
    "datamart_rfm_k4_grouped"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Investigating a  Cluster of 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize KMeans\n",
    "kmeans = KMeans(n_clusters=5, random_state=1) \n",
    "\n",
    "# Fit k-means clustering on the normalized data set\n",
    "kmeans.fit(datamart_normalized)\n",
    "\n",
    "# Extract cluster labels\n",
    "cluster_labels = kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "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 tr th {\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Recency</th>\n",
       "      <th>Frequency</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Monetary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>56.6</td>\n",
       "      <td>7.4</td>\n",
       "      <td>1918.6</td>\n",
       "      <td>219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>170.8</td>\n",
       "      <td>1.3</td>\n",
       "      <td>89.6</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1570.7</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32.6</td>\n",
       "      <td>2.6</td>\n",
       "      <td>205.2</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>142.9</td>\n",
       "      <td>3.7</td>\n",
       "      <td>790.0</td>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Recency Frequency Monetary      \n",
       "           mean      mean     mean count\n",
       "Cluster                                 \n",
       "0          56.6       7.4   1918.6   219\n",
       "1         170.8       1.3     89.6   107\n",
       "2           9.1       7.0   1570.7    87\n",
       "3          32.6       2.6    205.2    93\n",
       "4         142.9       3.7    790.0   187"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a Dataframe by adding a new cluster label column\n",
    "datamart_rfm_k5 = datamart.assign(Cluster = cluster_labels)\n",
    "\n",
    "# Gruop the data by cluster\n",
    "grouped = datamart_rfm_k5.groupby([\"Cluster\"])\n",
    "\n",
    "# Calculate average RFM values and segment sizes per cluster value\n",
    "grouped.agg({\n",
    "    \"Recency\" : \"mean\", \n",
    "    \"Frequency\": \"mean\", \"Monetary\":[\"mean\", \"count\"]\n",
    "    }).round(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the results above. A cluster of 4 present a more distinct grouping of customers. Hence the chosen Cluster is 4."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Customer Persona"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "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>Cluster</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Customer ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AA-10315</th>\n",
       "      <td>185</td>\n",
       "      <td>2</td>\n",
       "      <td>374.480</td>\n",
       "      <td>Silver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AA-10375</th>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>206.732</td>\n",
       "      <td>Gold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AA-10480</th>\n",
       "      <td>260</td>\n",
       "      <td>1</td>\n",
       "      <td>15.552</td>\n",
       "      <td>Bronze</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AA-10645</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>12.960</td>\n",
       "      <td>Bronze</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AB-10060</th>\n",
       "      <td>55</td>\n",
       "      <td>8</td>\n",
       "      <td>2936.264</td>\n",
       "      <td>Platinum</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Recency  Frequency  Monetary   Cluster\n",
       "Customer ID                                        \n",
       "AA-10315         185          2   374.480    Silver\n",
       "AA-10375          20          5   206.732      Gold\n",
       "AA-10480         260          1    15.552    Bronze\n",
       "AA-10645          56          1    12.960    Bronze\n",
       "AB-10060          55          8  2936.264  Platinum"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Rename Clusters to Platinum, Gold, Silver, and Bronze\n",
    "\n",
    "datamart_rfm_k4[\"Cluster\"] = datamart_rfm_k4[[\"Cluster\"]].replace(\n",
    "                    {0:\"Platinum\", 3:\"Gold\", 2:\"Silver\", 1: \"Bronze\"})\n",
    "datamart_rfm_k4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Recency</th>\n",
       "      <th>Frequency</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Monetary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Platinum</th>\n",
       "      <td>70.4</td>\n",
       "      <td>7.3</td>\n",
       "      <td>1940.0</td>\n",
       "      <td>235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gold</th>\n",
       "      <td>11.6</td>\n",
       "      <td>6.1</td>\n",
       "      <td>1271.8</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Silver</th>\n",
       "      <td>124.3</td>\n",
       "      <td>3.2</td>\n",
       "      <td>537.2</td>\n",
       "      <td>215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bronze</th>\n",
       "      <td>135.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>65.0</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Recency Frequency Monetary      \n",
       "            mean      mean     mean count\n",
       "Cluster                                  \n",
       "Platinum    70.4       7.3   1940.0   235\n",
       "Gold        11.6       6.1   1271.8   121\n",
       "Silver     124.3       3.2    537.2   215\n",
       "Bronze     135.5       1.4     65.0   122"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Gruop the data by cluster\n",
    "grouped = datamart_rfm_k4.groupby([\"Cluster\"])\n",
    "\n",
    "# Calculate average RFM values and segment sizes per cluster value\n",
    "datamart_rfm_k4_grouped=grouped.agg({\n",
    "    \"Recency\" : \"mean\", \n",
    "    \"Frequency\": \"mean\", \"Monetary\":[\"mean\", \"count\"]\n",
    "    }).round(1)\n",
    "datamart_rfm_k4_grouped.sort_values(by = ('Monetary', 'mean'), ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Number customers per a cluster"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "    Platinum contains 235 customers\n",
    "    Gold contains 121 customers\n",
    "    Silver  contains 215 customers\n",
    "    Bronze contains 122 customers\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Platinum Group"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "1. Customers in Platinum are the most frequent buyers and purchase the company's products more than customers in the other groups.\n",
    "2. On the average, they have 7 transactions with the company within the year 2017.\n",
    "3. They give the highest monetary value of about 1940 on an average to the company.\n",
    "4.A larger portion of the company's customers(235) fall within this group.\n",
    "5. They are the second most recent buyers. On an average, the last time a customer in this group made a transaction with the company is about 2 months away from the snap-shot date.\n",
    "\n",
    "The company must make a conscious effort to maintain customers in this group.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gold Group"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "They are the second important customer group to the company.\n",
    "1. They contributeed an average of 1272 towards revenue.\n",
    "2. They are great with recency and frequency\n",
    "3. Within a span of one year, a customer in this group makes 6 transactions on the average.\n",
    "4. On an average, the last time a customer in this group made a transaction with the company is about 10 days away from the snap-shot date.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Silver Group"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the second largest group with 215 customers.\n",
    "1. They contributed an average of 537 towards revenue.\n",
    "2. They are not performing well in terms of frenquency and recency. An avaerage of 3 transactions the whole year and last transaction is 4 months away from the snapshot date.\n",
    "\n",
    "The company should research more about their preferences and devise more strategies to attract them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Bronze Group"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The least active group in terms of all metrics.\n",
    "1. Within a span of one year, customers in this group made an avarage of a single transaction contributing an average monetary value of 65 to the company. \n",
    "2. They are the least frequent and recent buyers. On an average, the last time a customer in this group made a transaction with the company is a little over 4 months away from the snap-shot date.\n",
    "\n",
    "The company should research more about their preferences and devise more strategies to attract them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing clusters with regards to the metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Relative importance of the RFM values within each cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bronze</th>\n",
       "      <td>0.53</td>\n",
       "      <td>-0.71</td>\n",
       "      <td>-0.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gold</th>\n",
       "      <td>-0.87</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Platinum</th>\n",
       "      <td>-0.20</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Silver</th>\n",
       "      <td>0.41</td>\n",
       "      <td>-0.33</td>\n",
       "      <td>-0.49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Recency  Frequency  Monetary\n",
       "Cluster                               \n",
       "Bronze       0.53      -0.71     -0.94\n",
       "Gold        -0.87       0.28      0.20\n",
       "Platinum    -0.20       0.53      0.83\n",
       "Silver       0.41      -0.33     -0.49"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate average RFM values for each cluster\n",
    "cluster_avg = datamart_rfm_k4.groupby([\"Cluster\"]).mean() \n",
    "\n",
    "# Calculate average RFM values for the total customer population\n",
    "population_avg = datamart.mean()\n",
    "\n",
    "# Calculate relative importance of cluster's attribute value compared to population\n",
    "relative_imp =  cluster_avg/ population_avg - 1\n",
    "\n",
    "# Print relative importance scores rounded to 2 decimals\n",
    "relative_imp.round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ONG/eHICXX34Zf39/Jk6cyP3792nWrJluxX29evUYNGhQvq7l4+PDnj172LBhAy4uLlhaWuLl5UWXLl345JNPmDJlCq1ateLSpUtMnz4dDw8PMjIynqlf8+bNo2vXrjRu3Jj33nsPV1dXbty4wdatW3UB2TfffEPz5s1p0aIFI0eOxN3dnQcPHnD16lU2bNigm+eak8J6Th5XvXp1qlSpwgcffICiKNja2rJhwwbd1JTH+fj46PowZMgQjIyM8PLywtLSEh8fH3777TdWr15N5cqVMTEx0eUvqPzcHyqVioEDB7JkyRKqVKlCnTp1OHr0qC7QzU+73d3dmT59OpMmTeLatWu6OfB3797l6NGjmJubM23aNM6cOcPo0aPp1asXVatWxdjYmF27dnHmzBm9T1aEEHko3jVkQpQeD3chOHbsWLZzycnJiqurq1K1alXdyvPTp08rvXv3VhwdHRUjIyPF2dlZadu2rbJw4UJduZxWVd+6dUvp2bOnYmNjo1haWiodOnRQzp07p7i5uWVbHT937lzFw8NDMTQ01Fth/eQuBI9r2rSpAigDBgzI8Xx6eroya9YspU6dOoqJiYliYWGhVK9eXRkxYoRy5cqVPJ+j3HYh6Ny5c7a8gPL222/rpT1cKf7VV1/p0h6ujj9z5ozSunVrxdTUVLG1tVVGjhypJCQk6JVPTk5WJk6cqLi5uSlGRkaKi4uLMnLkSCUuLi5fbVKUrB0SmjVrppiZmSmAbqV8amqqMn78eKVChQqKiYmJ4uvrq6xbty7bc51THx7v8+Or3RVFUQ4dOqR07NhRsba2VjQajVKlShXlvffey/a8DB06VKlQoYJiZGSkODg4KE2bNlU+/fTTHPvwLM9JQXYhuHDhgvLyyy8rlpaWio2NjdKrVy/lxo0bOfYvMDBQKV++vGJgYKB3r4eFhSnt27dXLC0tFUD3HD78nfjjjz+yXTe3XQjye3/Ex8crw4YNU5ycnBRzc3Ola9euSlhYWIHarShZu2W0adNGsbKyUjQajeLm5qa89tpryo4dOxRFUZS7d+8qAQEBSvXq1RVzc3PFwsJCqV27tvL111/r7UwhhMidSlEUpcijZiGEKCQBAQH8+eefJCQkFHdThBBCFBGZAyuEEEIIIUoUCWCFEEIIIUSJIlMIhBBCCCFEiSIjsEIIIYQQokSRAFYIIYQQQpQoEsAKIYQQQogSRQJYIYQQQghRosg3cb2Auq0fXNxNEELPt9+ceXomIYrQgV2F9zWzQhSW/sqlYru2amTjXM8pCw4XYUuKhgSwQgghhBAlnIG6bH2oLgGsEEIIIUQJpzJQFXcTipQEsEIIIYQQJZyBoYzACiGEEEKIEkSmEAghhBBCiBJFphAIIYQQQogSRUZghRBCCCFEiSIBrBBCCCGEKFFUKplCIIQQQgghShAZgRVCCCGEECWKLOISQgghhBAliozACiGEEEKIEkUCWCGEEEIIUaLIFIIXXEBAAMuXL9c9trW1pUGDBnz55ZfUrl27GFsmnreO7u14tUonbEysufHgNj+dW8mF2Ms55q1lV53Pmn2YLX3UroncTggHoLGLH72qdsXZ3BG1Ss2dxAj+DvmHPbcOPtd+iNLFevBbWHTugYGlFWnB54j9dibp10Nyze80ezEmdf2ypScd3kfUpHcA0Pj4YtVnCMZVvVHbOxL58XskH9j93PogSh+fKaOp8mYfjG2siDlymuNvTyf+wtVc86vUamoGjsBjSHfMKjhx/1IopybOInzrvhzz1/jgTerO/B8X5y7nxHsznlc3RAHIV8mWAB06dGDp0qUAREREMHnyZLp06cKNGzdyzJ+eno6RkVFRNlEUsublG/FGrQEsOrOc4Ngr+Lu14ePG4xm9O5Do5Jhcy43cOYGkjGTd4/up93X/T0hL4I/L67mVEE5GZgZ+TnV5t+5w4lMfcDLq7HPtjygdrPoGYPXaQKK//JiMW9exHjgcxy8XcCegO0pyUo5loqaOA/Wj1yNDq3K4/LiapH+369JUpqakh1wmYcvfOE6b89z7IUoX7wnDqT7udQ4FfMCDy2HUmjySNtuXstGrAxkJiTmWqfPpWNwHvsLR4ZOJv3gNF/8WtPjre7Y37UvcqWC9vLZ+Pni+2Ye40xeLojsin8raFIIS2VuNRoOzszPOzs7UrVuXiRMncvPmTaKioggLC0OlUvH777/TunVrTExM+OWXX8jMzGT69OlUrFgRjUZD3bp12bJli67Oh+XWrl1LmzZtMDMzo06dOhw6dEiXp3Xr1qhUqmxHWFgYAPHx8bz55ps4OjpiZWVF27ZtOX36dFE/PaVStyod2HFjL9tv7OVWwh1+Or+S6ORYOrq3zbNcfOp97qXG645MFN25czEXORwRxK2EO0QkRbIxdBth92/ibVvteXdHlBKWPQYQv2oxyft3kR4WQvQXH2FgYop5u465lsl8cJ/MuBjdYVK/MUpKCkl7t+nypBw9wL2l80jev6souiFKmepjB3Pus4Xc+ms78eevcGjIRNRmJrj375JrGfdB3Tg/YyF3/vmXxNBbXF34K+Fb91P9f0P18qnNzWi68iuODJ9MWlz88+6KKAADg9yP0qjEdyshIYGVK1fi6emJnZ2dLn3ixIm8++67BAcH4+/vzzfffMPs2bOZNWsWZ86cwd/fn1deeYUrV67o1Tdp0iTGjx/PqVOnqFatGv369SMjIwOAtWvXEh4erjt69OiBl5cXTk5OKIpC586diYiIYPPmzQQFBeHr60u7du2IjY0t0uektFGrDKli7c6pyHN66aeizlLdpmqeZb9u9QlL23/L9CYT8bHzzjNvbfsaVLBw4XysjCqIp1O7VEBt50Dy8UdvcklPJ+X0cTQ16+a7HouO3UncvRUlJaXwGynKHHOPipi6OBKxbb8uLTMtnci9x7BvWi/XcoYaI7QpaXpp2uQUHJr76qX5zfuYO5v2cnfnIcSLxdjQINejNCqRUwg2btyIhYUFAImJibi4uLBx40YMHnubMXbsWHr06KF7PGvWLCZOnEjfvn0B+OKLL9i9ezdz585l3rx5unzjx4+nc+fOAEybNo2aNWty9epVqlevjq2trS7f119/za5duzhy5Aimpqbs2rWLs2fPEhkZiUaj0V1z3bp1/Pnnn7z55pvP7wkp5ayMLTE0MOReqv67/Xup97Exsc6xTFzKPb4/tYSQ+FCMDIxoXbEZ05tOZNKBmVyIvaTLZ6Y2ZUn7bzAyUJOpZLLwzM+cjjr/XPsjSgdDG3sAMuP036Bq42JRO7nkqw5jr1oYV65KzKxphd4+UTaZOjsAkHJXf2pVyt1ozN3K51oufOt+qo8LIOrfYzwIuYFzuyZU7NYOlaGhLo9bn07Y+tZgS4PXnk/jxX9ibCiLuF54bdq0YcGCBQDExsYyf/58OnbsyNGjR3V5/PweLZK4f/8+d+7coVmzZnr1NGvWLNtH/I8vBHNxyfojFBkZSfXq1XXp//zzDx988AEbNmygWrWsj5uDgoJISEjQGwUGSE5OJiQk9wUdqamppKam6qVp07UYGhnmUqLsUp54rAIU5cnULLcTI7idGKF7fCnuKvamtrzq2ZELRx8FsMkZKYzdOxlTQxNqO9RgaK1+3E2K5FyMjMIKfebtOmH73mTd48gPsxZc8eQ9qFJlT8uFRafupF27Qtqlc0/PLEQO3Pt3pcGiR2+A9nYeAeTw2qhS5XlbBo35jIY/fkrni/+AopAQcpNrS9dS+fWsgSCzis74fjOJ3e2HkpmalntFotgYylfJvvjMzc3x9PTUPa5fvz7W1tb8+OOPDBs2TJfnSU9+T7CiKNnSHl/s9fBcZmamLu3ChQv07duXzz//nPbt2+vSMzMzcXFxYc+ePdmuW65cuVz7MnPmTKZN0x99qda3NtX71cm1TFlzP+0B2kwtNhr90VZrjRX3HluU9TSX467SqqL+mxgFhYjESABC79+gkkV5XqvaVQJYkU3SwT2kBj9a3KcyMgbAwNYObWy0Lt2wnA3ae0+fNqTSmGDe2p97yxcUfmNFmXFr/S6ijzwaiDHUZN2Xps72pERE6dJNHO1IuRudrfxDqdFx7Hv1bQw0xmjsypF8J5K6n48nIfQWALb1a2LqZE+HoLW6MgZqNY4tG1Bt9ABWa3xQHvtbKYpeaZ0qkJsSGcA+SaVSYWBgQHJyco7nraysKF++PPv376dly5a69IMHD9KwYcN8XycmJoauXbvSo0cP3nvvPb1zvr6+REREoFarcXd3z3edgYGBjBs3Ti+t//aR+S5fFmQoWkLiw6jjUIvDEUG69LoOtTgScSLf9VS2diMu5d5TcqlQG5SKXwtRyJTkJDKe2FkgIyYK0/pNSL/6/6P6ajUmdfyI+3HuU+sza90elbExiTs2PYfWirIiIyGRhCd2FkgOj8T55Wa63QMMjIxwbNWAUxNnPbW+zNQ0ku9EolKrqdSzPTd+/weAiJ2H2VRLfxFY46UzuX/xGhe++FGC1xeATCEoAVJTU4mIyPp4OC4uju+//56EhAS6du2aa5n333+fKVOmUKVKFerWrcvSpUs5deoUK1euzPd1e/TogampKVOnTtVdH8DBwYGXXnqJJk2a0L17d7744gu8vLy4c+cOmzdvpnv37npTGh6n0Wh0c2YfkukD2f0dsoWxviO4ei+US3FX8Xdrjb2pHVvCslZpD/LuhZ2JDXNP/gBA18r+RCZFcePBbdQGalpXbErT8g2ZeexbXZ09PbtwNT6UiMRI1AZq6jvWoU2lZiw8szzHNgjxpAdrV2Ld/w3Sb10n4/YNrPsPIzMlmcSd/+jy2E38BG10JPd++k6vrEXH7iQd2E3m/ewruVUmpqgruOoeq50rYFTFi8wH8WgjI7LlF+JxF+f+TM0PR/DgShgPrlyn5ocjyEhKIWzVRl2eJsu/IOn2XU5/mLVNm13D2phWcCLuVDBmFZzwmfoOKgMDLny5GMgKlOPP6y96zkhMIjXmXrZ0UTxkCkEJsGXLFt38VEtLS6pXr84ff/xB69atdVtaPendd9/l/v37/O9//yMyMpIaNWqwfv16qlbNexX74/7991+AbCOsoaGhuLu7s3nzZiZNmsTQoUOJiorC2dmZli1b4uTk9Ez9FI/sv3MES2ML+nh1w1ZTjusPbjH98Gyi/n8PWBtNOexNH80/VhsY8nrNftia2JCmTePGg9tMPzyLoMgzujwmag1v+QzBztSWNG0atxPC+frEIvbfOVLk/RMl0/3flqEyNsF2zIcYWlqRGnyWyIkj9faAVTu6ZJsTq67oiomPL3cnvJVjvcZeNXGes1j32HbUeAAStq4n5suPn0NPRGkS/OWPqE01NJg/BWMba6KPnGZ3+6F6e8CaubrojZoammio8+lYLCpXIj0hifDNezk0aALp8Q+KowviGZS1EViVktsqGFFsuq0fXNxNEELPt9+ceXomIYrQgV05TxkTojj1Vy49PdNz0n7twFzPbevxSxG2pGiUrRm/QgghhBClkKFKlevxLObPn4+HhwcmJibUr1+fffty/lphgD179uT4RU8XLz6/BdElcgqBEEIIIYR4pDCnEKxevZqxY8cyf/58mjVrxqJFi+jYsSMXLlzA1dU113KXLl3CyspK99jBwaHQ2vQkGYEVQgghhCjhCvObuObMmcMbb7zBsGHD8Pb2Zu7cuVSqVEm3B39uHB0dcXZ21h2Ghs9vUboEsEIIIYQQJZyhQe5Hamoq9+/f1zue/BKlh9LS0ggKCtLb6x6gffv2HDx4MM821KtXDxcXF9q1a8fu3bsLrW85kQBWCCGEEKKEy2sEdubMmVhbW+sdM2fOzLGe6OhotFptth2UnJyc9LYQfZyLiws//PADa9asYe3atXh5edGuXTvd7k3Pg8yBFUIIIYQo4fKaA5vTlyY9uQf9k/Lz7aUPeXl54eXlpXvcpEkTbt68yaxZs/S+QKowSQArhBBCCFHC5bXbQE5fmpQbe3t7DA0Ns422RkZGFmhf+8aNG/PLL89v+y6ZQiCEEEIIUcIZGahyPQrC2NiY+vXrs337dr307du307Rp03zXc/LkSd2XTj0PMgIrhBBCCFHCGRXikOS4ceMYNGgQfn5+NGnShB9++IEbN27w1ltZ3x4YGBjI7du3+fnnnwGYO3cu7u7u1KxZk7S0NH755RfWrFnDmjVrCq9RT5AAVgghhBCihCvMb5Lt06cPMTExTJ8+nfDwcGrVqsXmzZtxc3MDIDw8nBs3bujyp6WlMX78eG7fvo2pqSk1a9Zk06ZNdOrUqfAa9QT5KtkXkHyVrHjRyFfJiheNfJWseBEV51fJBh58M9dzM5v+UIQtKRoyAiuEEEIIUcIV5hSCkkACWCGEEEKIEs4gj10ISiMJYIUQQgghSjgZgRXF7u2hR4q7CULoCTrSsbibIIQeVzOr4m6CEC+Ugm6XVdJJACuEEEIIUcIV5i4EJYEEsEIIIYQQJZxRGYtgJYAVQgghhCjhZA6sEEIIIYQoUcrYFFgJYIUQQgghSjpZxCWEEEIIIUoUCWCFEEIIIUSJUsbWcEkAK4QQQghR0qkNytYqLglghRBCCCFKOLWBYXE3oUhJACuEEEIIUcIZqMrWHAIJYIUQQgghSriyNoWgbPW2kE2dOpW6devmmScgIIDu3bsXSXuEEEIIUTapVYa5HqVRmR2BjYiIYObMmWzatIlbt25hbW1N1apVGThwIIMHD8bMzKy4myhyUGXCaCoM7oORtRXxJ04TPGE6iZeu5lnGdcQQKr3eD5MKLqTHxnF3w1aufDKbzNQ0AFqc2Impa8Vs5W78tJKLE6c/l36I0uHQxuvsWxPKg9hUHN0s6PKmNx61bHPMe+5ABEc23SD82n0y0jNxdLPkpQGeVKvvoJdv/7pQjmy6yb2oZMytjKnV3Bn/gGoYGZfOP0KicO1ad4Wtv13iXkwyFTys6Tu6HtVqO+SYN+jfW+z5+yo3rt4jI11LeXdrugXUpFZDF718x/feZN2Sc0TdScChvAU9hvng2yL7a6YoXjKFoAy4du0azZo1o1y5csyYMQMfHx8yMjK4fPkyS5YsoXz58rzyyivF3UzxBPd3huM28nXOjf6ApJAwPP43kvprlnKgcQe0CYk5lnF+rStVP/of58d8yL2jJzGv4k7N7z8H4NLkmQAcfvk1VIaPggOL6lXxW7uMu+u3PP9OiRLrzN5wNv0QTLdRNXGrYcORf26w7OPjvLewBeUcTbPlDz0Xi2c9e9oHVMPU3Iig7bf4eVoQo75uQvkq1gCc3H2brUsv03OsD241yhF1O5E/55wFoMub3kXaP1HyHN11g9++P8XAsb54+jiwd/1V5k74l0+Wd8DOyTxb/sunI6nh50SP4T6YWRiz/59Qvv1wP5MWvIRbVRsArp6PZtG0Q3R/oxa+zStyYv8tFk49yAfftaNyDbui7qLIQ1lbxFXgKQRarZa9e/cSFxf3PNpTJEaNGoVareb48eP07t0bb29vfHx86NmzJ5s2baJr164A3Lhxg27dumFhYYGVlRW9e/fm7t27udar1WoZN24c5cqVw87OjgkTJqAoSlF1q9Rze2sw1+YsJHLTdhIuXuHc2xMxNDXBpWeXXMuU86vLvaMniFizkZSbt4nZc4CItRuxqlNLlyc9Jo60yGjd4dC+DUnXrhN34GhRdEuUUPv+CsWvfUUadKiEo6sFXUfUwNrBhMObbuSYv+uIGrTqVZlK1cphX8Ec/wAv7MqbE3wkUpfnRvA93GrYULdNeWyczKjm60CdVi7cvhJfVN0SJdi2Py7RopMHLbtUobybFf3e8cXW0ZQ9f4fkmL/fO7507OeNR3U7nCpa0nN4bZwqWnD64B1dnh1/XqaGnxOdB9TAxc2KzgNq4O3rxPY/LxdVt0Q+qQ0Mcj1KowL3ytDQEH9/f+7du/ccmvP8xcTEsG3bNt5++23MzbO/IwVQqVQoikL37t2JjY1l7969bN++nZCQEPr06ZNr3bNnz2bJkiX89NNP7N+/n9jYWP7666/n1ZUyxdStIhonR2L27NelKWnpxB08RrkG9XItd+9IEFZ1amJVz0dXj/1LrYjavifH/CojI1x6vcLtVWsKtf2idMlIz+TO1ftU9bXXS69az54bwfl7c5+ZqZCanIGppbEuzb2mDbevxnPz0j0AYsOTuHQ8Cq8GOX8ELMRDGelarl+Ko2YDZ730Gg2cuXo+Ol91ZGYqpCRlYP7YPRlyPiZbnTUb5r9OUXQMVAa5HqXRM00h8PHx4dq1a3h4eBR2e567q1evoigKXl5eeun29vakpKQA8Pbbb/PSSy9x5swZQkNDqVSpEgArVqygZs2aHDt2jAYNGmSre+7cuQQGBtKzZ08AFi5cyNatW59zj8oGY8esP+BpUTF66WlR0ZhULJ9ruYi/NmNkZ0vDTatApcLAyIibS1YR9u2POeZ37PQSamtL7vwmbzxE7pLup5GZqWBRTqOXbmGj4UFcWr7q2L82lLQULbVbPAoO6rQqT2J8GoveP4yiQKZWoVFnV1r3rlKo7Relz4P4rHvSysZEL93axoRzsSn5qmPb75dITcmgQZtKurT42JRsdVrZmHA/n3WKoqMupYFqbp4pgP3ss88YP348n3zyCfXr1882kmllZVUojXueVE9Mdj569CiZmZkMGDCA1NRUgoODqVSpki54BahRowblypUjODg4WwAbHx9PeHg4TZo00aWp1Wr8/PzynEaQmppKamqqXlqakolxGbsRn+T8WldqzJqme3yy/wiA7M+lSgV5zNKwadaQyu+9RfCEacQHncHMwxWvGZOofDeKa7PnZ8tfYUBPYnb+S2pEZA61CfGEJ9dMKAr5WUdxas8ddqy8yuCPffWC4GtnYti9OoRuo2pSyascMeGJbFgUzE4bDe36exZu20Xp9MT9pyjZb9OcHNl5nb+XneOdT5tnC1iz3dMyNe6FVNbmwD5TANuhQwcAXnnlFb1AUFEUVCoVWq22cFr3HHh6eqJSqbh48aJeeuXKlQEwNc1afPGwL0/KLf1ZzZw5k2nTpumlDTS1ZZCZfS4lyoaoLbs4FHRa99jAOOsjLY2jPWl3o3TpxvZ2pEXl/lGW5wdjCP9jPbd/+ROAhODLGJqbUWP2dK7NWaD3QmxSsTx2rZpyKuCdwu6OKGXMrIwxMFCREKf/5jPhXhoW5YxzKZXlzN5w1n5zlv6B9fCsp/97vn3FFeq1rUCDDllvnJ09LElL0fLXd+do07cKBgZla5WxyD9L66x78smR0fv3UrCyNcmlVJaju26w7MtjvDW1KTX89KcLWNuaEJ+tztSn1imKXlnbheCZhvl2796tO3bt2qU7Hj5+kdnZ2fHyyy/z/fffk5iY88p1yBptvXHjBjdv3tSlXbhwgfj4eLy9s68Gtra2xsXFhcOHD+vSMjIyCAoKyrM9gYGBxMfH6x19THPehqcs0SYkkhx6Q3ckXrpK6t1I7Fo30+VRGRlh07QB946dzLUeQzMTlMxMvTRFq80aUnjil71C/x6kRccQvW1PofZFlD5qIwPKe1px5aT+lJarJ6Nx9bbJtdypPXf44+sz9Hm/LtUbOmY7n5aqzTbapTJQZb3PkkEvkQe1kSFuXjacPx6hl37h+F08a+Y+IHJk53WWfH6U4ZMbU6dJ9ulYVWraceG4/uLl88ci8qxTFA+1gWGuR2n0TCOwrVq1Kux2FKn58+fTrFkz/Pz8mDp1KrVr18bAwIBjx45x8eJF6tevz0svvUTt2rUZMGAAc+fOJSMjg1GjRtGqVSv8/PxyrHfMmDF8/vnnVK1aFW9vb+bMmfPUxW4ajQaNRn8eXVmfPpCb6wt/xmPsCJJCwki6dh2P90agTU4hfM1GXZ5a874gJfwuVz+dA0DU1t24jXydB2cvEB90BlMPVzw/GEPU1l3weGCrUlG+Xw/u/LYuK8AV4ilavOrB77NPU7GqFa7VbTi65Sb3olJo1MkVgC1LL3E/JoXe4+sA/x+8zj5DlxHeuFYvx4PYrNFbI40BJuZGAHg3dGT/X6GUr2KVNYXgThLbV1zBu5EjBoZla3RFFFz7Xl4snnEEdy9bqtS0598NIcTeTaLVK1lzqNf8cIa46CSGfdgYyApef5pxhL7v1KNKDTviY5IBMNIYYmaR9UnCSz2r8cW7u9i8Kph6zSpw8sBtgoPu8sF37YqnkyJXMgc2n/bt28eiRYu4du0af/zxBxUqVGDFihV4eHjQvHnzwmxjoatSpQonT55kxowZBAYGcuvWLTQaDTVq1GD8+PGMGjUKlUrFunXreOedd2jZsiUGBgZ06NCB7777Ltd6//e//xEeHk5AQAAGBgYMHTqUV199lfh42QKnMIR99yOGphq8v5qC2tqa+BOnOfHaUL09YE0quuiNuF6bvQBFUfAMHIvGxYm0mFiitu7m6mdf69Vt16opppUqyO4DIt9qt3Ih8UEaO1eF8CA2BSd3SwKm+WHjlDUN6UFcKveiHn30evSfm2RqFdbPv8D6+Rd06b4vVaDXuNoAtOlXBVSw7ecr3I9JwdzaGO+GjrQfUq1oOydKpIZtXUm4n8qG5eeJj02hgoc1Y75ogb1z1jqVezHJxN5N0uXfuz4ErVZh5dwTrJx7Qpfe1N+dNwIbAeBZy54RHzfhr5/Osm7JORzLmzNiSlPZA/YFVFp3G8iNSnmGjUrXrFnDoEGDGDBgACtWrODChQtUrlyZ+fPns3HjRjZv3vw82lpmbLP3enomIYpQwpGOxd0EIfQ4mr34i4VF2dPcpfi+vfFK/Jxcz1W1HleELSkazxSuf/rppyxcuJAff/wRIyMjXXrTpk05ceJEHiWFEEIIIURhM1Spcz1Ko2fq1aVLl2jZsmW2dCsrqxL7BQdCCCGEECVVWZtC8Ey9dXFx4erVq9nS9+/fr9uOSgghhBBCFI2yNgL7TAHsiBEjGDNmDEeOHEGlUnHnzh1WrlypWwAlhBBCCCGKTlkLYJ+pVxMmTCA+Pp42bdqQkpJCy5Yt0Wg0jB8/ntGjRxd2G4UQQgghRB5UMoUgfz777DOio6M5evQohw8fJioqik8++aQw2yaEEEIIIfKhsEdg58+fj4eHByYmJtSvX599+/blmX/v3r3Ur18fExMTKleuzMKFC5/puvn1TAHs0KFDefDgAWZmZvj5+dGwYUMsLCxITExk6NChhd1GIYQQQgiRB0OVUa5HQa1evZqxY8cyadIkTp48SYsWLejYsSM3btzIMX9oaCidOnWiRYsWnDx5kg8//JB3332XNWue397qz7QPrKGhIeHh4Tg66n8VYnR0NM7OzmRkZBRaA8si2QdWvGhkH1jxopF9YMWLqDj3gY1LXZ3rORtNnwLV1ahRI3x9fVmwYIEuzdvbm+7duzNz5sxs+SdOnMj69esJDg7Wpb311lucPn2aQ4cOFeja+VWgEdj79+8THx+Poig8ePCA+/fv6464uDg2b96cLagVQgghhBDPV2FNIUhLSyMoKIj27dvrpbdv356DBw/mWObQoUPZ8vv7+3P8+HHS09ML1pF8KlCvypUrh0qlQqVSUa1a9q82VKlUTJs2rdAaJ4QQQgghni6vQDU1NZXU1FS9NI1Gg0ajyZY3OjoarVaLk5OTXrqTkxMRERE51h8REZFj/oyMDKKjo3FxcclvN/KtQAHs7t27URSFtm3bsmbNGmxtbXXnjI2NcXNzo3z58oXeSCGEEEIIkbu8diGYOXNmtgHGKVOmMHXq1DzqU+k9VhQlW9rT8ueUXlgKFMC2atUKyJqs6+rq+twaJYQQQggh8i+vEdjAwEDGjRunl5bT6CuAvb09hoaG2UZbIyMjs42yPuTs7JxjfrVajZ2dXX6aX2DPtAtBcHAwBw4c0D2eN28edevWpX///sTFxRVa44QQQgghxNPlNQdWo9FgZWWld+QWwBobG1O/fn22b9+ul759+3aaNm2aY5kmTZpky79t2zb8/PwwMir4Lgj58UwB7Pvvv8/9+/cBOHv2LOPGjaNTp05cu3YtW4QvhBBCCCGeLwOVQa5HQY0bN47FixezZMkSgoODee+997hx4wZvvfUWkDWiO3jwYF3+t956i+vXrzNu3DiCg4NZsmQJP/30E+PHjy+0/j3pmXa3DQ0NpUaNGgCsWbOGrl27MmPGDE6cOEGnTp0KtYFl0ctn3inuJgihp/vxo8XdBCH0rP/ncHE3QYhslAXFt42WwbN/N1U2ffr0ISYmhunTpxMeHk6tWrXYvHkzbm5uAISHh+vtCevh4cHmzZt57733mDdvHuXLl+fbb7+lZ8+ehdamJz1TAGtsbExSUhIAO3bs0EXhtra2upFZIYQQQghRRDLz2IPfsODVjRo1ilGjRuV4btmyZdnSWrVqxYkTJwp+oWf0TAFs8+bNGTduHM2aNePo0aOsXp21ee7ly5epWLFioTZQCCGEEEI8hZJZ3C0oUs803vz999+jVqv5888/WbBgARUqVADgn3/+oUOHDoXaQCGEEEII8RTajNyPUuiZRmBdXV3ZuHFjtvSvv/76PzdICCGEEEIUUF5TCEqhZwpgH5+4mxNXV9dnaowQQgghhHgGZWwKwTMFsO7u7nl+iYFWq33mBgkhhBBCiAIqpVMFcvNMAezJkyf1Hqenp3Py5EnmzJnDZ599VigNE0IIIYQQ+SRTCJ6uTp062dL8/PwoX748X331FT169PjPDRNCCCGEEPmjKLl/+p37Z+Yl1zMFsLmpVq0ax44dK8wqhRBCCCHE08gUgqd78ssKFEUhPDycqVOnUrVq1UJpmBBCCCGEyCdZxPV05cqVy7aIS1EUKlWqxG+//VYoDRNCCCGEEPkkc2Cfbvfu3XqPDQwMcHBwwNPTE7W6UGclCCGEEEKIp5EpBE/XqlWrwm5HNnv27KFNmzbExcVRrly5Yq9HCCGEEOKFJVMIcrZ+/fp8V/rKK6/kK19AQADLly/PaohaTaVKlejRowfTpk3L97Ue17p1a+rWrcvcuXN1aU2bNiU8PBxra+tnqlO8OBRF4fvlR/l943nuP0ihtrczH49pRVUPu1zL/L7xHH9vu8iV0FgAalZz4L1hTajt7ayXb9W6M/y0+iRRMYl4utvy4egW+NWu8Fz7I0q+ju7teLVKJ2xMrLnx4DY/nVvJhdjLOeatZVedz5p9mC191K6J3E4IB6Cxix+9qnbF2dwRtUrNncQI/g75hz23Dj7XfojSY2TLnrz/8gBcrO04Hx7K2D++Zv/V07nm79/AnwntB1LVsRLxyQlsOX+Y8Wu/JTYxa63Lq3Vb82GHIXg6VMTIUM2VyJvM3rGKX45uKaouifySEdicde/ePV/5VCpVgb7IoEOHDixdupT09HT27dvHsGHDSExMpE+fPvmuIy/GxsY4Ozs/PaN44S3+7QTL/jjJzIkv4V7JhoUrjjH0/b/55+eBWJgZ51jm6KnbdG5bjXq1XNAYG7L41xO88f7fbFw6ACcHCwA277rMzHn7+Hhsa3xrubB6wznenLiBjcsGUN7Jsii7KEqQ5uUb8UatASw6s5zg2Cv4u7Xh48bjGb07kOjkmFzLjdw5gaSMZN3j+6mPFsUmpCXwx+X13EoIJyMzAz+nurxbdzjxqQ84GXX2ufZHlHy967/E3F5jGfXbVxwIOcOIFt355+2vqTG9Hzfj7mbL36xKHX4O+Jj3/vyGDWf2UaGcIwv7T2DxwA/psegDAGIT7/PZP8u4ePc6aRnpdPFpxtLBk4l8EMe24CNF3UWRlzI2B9YgvxkzMzPzdRT0W7g0Gg3Ozs5UqlSJ/v37M2DAANatW5ctX0xMDP369aNixYqYmZnh4+PDr7/+qjsfEBDA3r17+eabb1CpVKhUKsLCwtizZw8qlYp79+4BsGzZMsqVK8fWrVvx9vbGwsKCDh06EB4erqurdevWjB07Vu/63bt3JyAgQPfY3d2dTz/9lMGDB2NhYYGbmxt///03UVFRdOvWDQsLC3x8fDh+/HiBng+RM0VR+PnPU7w1sAHtW3pSzcOOzz94mZSUdDbuyHnEC2DWZH/6d6+Nt6cDlV1t+WR8WzIVhUMnburyLPvjFD071aBX55pUcbPlw9EtcXa04Nf1EjCI3HWr0oEdN/ay/cZebiXc4afzK4lOjqWje9s8y8Wn3udearzuyETRnTsXc5HDEUHcSrhDRFIkG0O3EXb/Jt621Z53d0QpMK5dP346uIGfDqznYkQY7/0xl5txkYxsmfPe7I09ahIWE853u38nLCacAyGnWbRvHX6u3ro8e6+cYN3pvVyMCONa9G2+3f07Z26H0Nwz+37wophlZuZ+lEL5DmABdu3aRY0aNbJtowUQHx9PzZo12bdv339qkKmpKenp6dnSU1JSqF+/Phs3buTcuXO8+eabDBo0iCNHst4BfvPNNzRp0oThw4cTHh5OeHg4lSpVyvEaSUlJzJo1ixUrVvDvv/9y48YNxo8fX+C2fv311zRr1oyTJ0/SuXNnBg0axODBgxk4cCAnTpzA09OTwYMHoyjK0ysTeboVfp+o2CSa+bnq0oyNDWlQpwInz4fnUVJfcmoGGRmZWFuZAJCWruX85Ui9egGa+bly8lz+6xVli1plSBVrd05FntNLPxV1luo2eW8l+HWrT1ja/lumN5mIj513nnlr29eggoUL52Mv/uc2i9LNyFBNfVcvtl3QHxXdFnyEppV9cixz8NpZKpZzpGPNJgA4WtryWr02bDp3INfrtPXyw8vJlX+vnCq0totCkqHN/SiFCrSIa+7cuQwfPhwrK6ts56ytrRkxYgRz5syhRYsWz9SYo0ePsmrVKtq1a5ftXIUKFfSCzHfeeYctW7bwxx9/0KhRI6ytrTE2NsbMzOypUwbS09NZuHAhVapUAWD06NFMnz69wO3t1KkTI0aMAODjjz9mwYIFNGjQgF69egEwceJEmjRpwt27d2Uaw38UFZsEgJ2NqV66nY0Zd+4+yHc9c344iJO9BU3rZ725iYtPRpupYGdj9kS9pkTHJf3HVovSysrYEkMDQ+6lxuul30u9j41JzvPt41Lu8f2pJYTEh2JkYETris2Y3nQikw7M5ELsJV0+M7UpS9p/g5GBmkwlk4VnfuZ01Pnn2h9R8tlblENtqObug1i99LsPYnG2znmdwKFrZxmwdAqrh32KiZEGI0M1f5/+l3dWz9bLZ2Vizu2ZG9AYGaPN1DLq16/YcfHoc+uLeEalNFDNTYEC2NOnT/PFF1/ker59+/bMmjWrQA3YuHEjFhYWZGRkkJ6eTrdu3fjuu++4cOGCXj6tVsvnn3/O6tWruX37NqmpqaSmpmJubl6g6wGYmZnpglcAFxcXIiMjC1xP7dq1df93cnICwMfHJ1taZGRkrgHsw348zjg1HY3GqMDtKU02bL/ElDmPtmtbOLMrQLb9h0EhW1IuFv8axKZdl/n56x5ojPVv/ZzqKI1fvScK15Ofragg109cbidGcDsxQvf4UtxV7E1tedWzIxeOPgpgkzNSGLt3MqaGJtR2qMHQWv24mxTJuRgZhRVP9+T9p0KV6z3p7ezOt73HMX3zErZeOIKLlR1f9XiHhf0nMuyXGbp8D1KTqDtjMBYaU9p5NWDOa2O4Fn2HvVdOPNe+iAIqpVMFclOgAPbu3bsYGeUeWKnVaqKiogrUgDZt2rBgwQKMjIwoX768rv4nA9jZs2fz9ddfM3fuXHx8fDA3N2fs2LGkpaUV6HpAtj6oVPq/4AYGBtl+4XOa1vB4PQ8Dq5zSMvO4qWbOnJlt14WPx3Vk6v86Pa0bpVqbZh7UruGke5yWlvXOMjo2CUe7R29aYuKSs42e5uSn1SdYtPI4S2Z3x6uKvS7dxtoUQwMV0bH6o635rVeUTffTHqDN1GKj0R9ttdZYcS81+xSr3FyOu0qris300hQUIhKz3lCH3r9BJYvyvFa1qwSwIk/RCffI0GbgbKU/2upoacPd+7E5lgnsMIQDIWeYtX0lAGdvXyXxtxT2j1/E5PWLiLiftRhRURRCom4BcPrWFbxd3AnsMFgC2BdNGRuBLdAc2AoVKnD2bO4LW86cOYOLi0uBGmBubo6npydubm55Bsf79u2jW7duDBw4kDp16lC5cmWuXLmil8fY2LjAi8hy4uDgoLeoS6vVcu7cuTxKPLvAwEDi4+P1jsDRLz+Xa5UkFmbGuFUopzs83W1xsDXj4PEbujxp6VqOnb5NvZp533M//XaCBSuO8eOX3fDxctI7Z2xkSM1qjhw8flMv/WDQDerVKti9LMqODEVLSHwYdRxq6aXXdajFxbgruZTKrrK1G3Ep956SS4XaQL4gRuQtXZtB0I1LvOzdUC/9Ze+GHLyW899tM2MTMp8YrNFmZv0Nzf5p1yMqQKPOeecXUYzK2BzYAgWwnTp14uOPPyYlJSXbueTkZKZMmUKXLl0KrXGP8/T0ZPv27Rw8eJDg4GBGjBhBRESEXh53d3eOHDlCWFgY0dHReY585qVt27Zs2rSJTZs2cfHiRUaNGqXbxaCwaTQarKys9I6yPn0gJyqVisGv1WXRyuNs3xfC5dAYAj/fgYmJEV1eerRCe+KMbcz+8dGemYt/DWLukkN89n47KjhbEhWbSFRsIonJj0buA3rV5c/N51mz+QIh12OZOW8f4XcT6NtVPzgR4nF/h2zhZbdWtKvUkooW5XmjZn/sTe3YErYLgEHevRhb701d/q6V/Wnk7IuLuROVLCswyLsXTcs3ZFPYDl2enp5dqONQEyczBypYuPBK5Q60qdSMvbIPrMiHOTt/ZVizV3i9SReqO7sz57UxuNo4sXDfXwDM6DaS5UM+1uXfcGY/Peq15q2WPfCwL0/TyrX5tvc4joSeJzw+GoAP/AfzUvWGeNiXx8vJjffa9WNw406yD+yLqIztQlCgt/WTJ09m7dq1VKtWjdGjR+Pl5YVKpSI4OJh58+ah1WqZNGnSc2noRx99RGhoKP7+/piZmfHmm2/SvXt34uMfLaIYP348Q4YMoUaNGiQnJxMaGvpM1xo6dCinT59m8ODBqNVq3nvvPdq0aVNYXRHPaFhfX1JSM5g+dw/xD1Kp7e3ET19109sD9k5kAiqDRyMHq/4+S3p6JmOm/qNX19tDGvJOQCMAOrWtxr37Kcz7+ShRsYlUdbdj0eddqeCcfbGiEA/tv3MES2ML+nh1w1ZTjusPbjH98Gyi/n8PWBtNOexNH32cqzYw5PWa/bA1sSFNm8aNB7eZfngWQZFndHlM1Bre8hmCnaktado0bieE8/WJRey/I/ttiqf7PWgHdubWfNz5DVys7DgXfo1O88ZxIzZrsMfF2h5X20frMZYf3oSliRmjW73G7J7vci/pAbsuBTHxr3m6POYaU+b3e5+K5RxITk/lYsR1Bi6dyu9BO7JdXxQvJY9PoEvjmg6VUsA9nq5fv87IkSPZunWrbp6oSqXC39+f+fPn4+7u/jzaWaYod74v7iYIoaf7cVlxLF4s6//Jff9nIYqLsuBwsV07c///cj1n0Hx2rudKqgJPrHJzc2Pz5s3ExcVx9epVFEWhatWq2NjYPI/2CSGEEEKIp8ksW3vOP/PKABsbGxo0aFCYbRFCCCGEEM8io2x9lawsbRVCCCGEKOlK6W4DuZEAVgghhBCipCuluw3kRgJYIYQQQoiSTkZghRBCCCFEiSIBrBBCCCGEKFFkCoEQQgghhChJlHQJYIUQQgghRAkiAawQQgghhChZtGXriwwMirsBQgghhBDiv1HStbkez1NcXByDBg3C2toaa2trBg0axL179/IsExAQgEql0jsaN25coOvKCKwQQgghRElXTFMI+vfvz61bt9iyZQsAb775JoMGDWLDhg15luvQoQNLly7VPTY2Ni7QdSWAFUIIIYQo4ZRimEIQHBzMli1bOHz4MI0aNQLgxx9/pEmTJly6dAkvL69cy2o0GpydnZ/52jKFQAghhBCipEvX5nqkpqZy//59vSM1NfU/X/LQoUNYW1vrgleAxo0bY21tzcGDB/Msu2fPHhwdHalWrRrDhw8nMjKyQNeWEdgX0LSbZ4u7CULo+cvWobibIISejGqJxd0EIV4oee1CMHPmTKZNm6aXNmXKFKZOnfqfrhkREYGjo2O2dEdHRyIiInIt17FjR3r16oWbmxuhoaF89NFHtG3blqCgIDQaTb6uLQGsEEIIIURJl8cUgsDAQMaNG6eXllegOHXq1GwB75OOHTsGgEqlynZOUZQc0x/q06eP7v+1atXCz88PNzc3Nm3aRI8ePfK87kMSwAohhBBClHB57TZgotHke2QTYPTo0fTt2zfPPO7u7pw5c4a7d+9mOxcVFYWTk1O+r+fi4oKbmxtXrlzJdxkJYIUQQgghSrjC/CIDe3t77O3tn5qvSZMmxMfHc/ToURo2bAjAkSNHiI+Pp2nTpvm+XkxMDDdv3sTFxSXfZWQRlxBCCCFESZep5H48J97e3nTo0IHhw4dz+PBhDh8+zPDhw+nSpYveDgTVq1fnr7/+AiAhIYHx48dz6NAhwsLC2LNnD127dsXe3p5XX30139eWAFYIIYQQooRT0jNzPZ6nlStX4uPjQ/v27Wnfvj21a9dmxYoVenkuXbpEfHw8AIaGhpw9e5Zu3bpRrVo1hgwZQrVq1Th06BCWlpb5vq5MIRBCCCGEKOGe9zdu5cbW1pZffvklzzyK8mgU2NTUlK1bt/7n60oAK4QQQghR0hXDFxkUJwlghRBCCCFKuMxi+irZ4iIBrBBCCCFECaeVAFYIIYQQQpQkynPcbeBFJAGsEEIIIUQJl1lMi7iKS5ndRkulUrFu3ToAwsLCUKlUnDp1qljbJIQQQgjxLDLTM3M9SqNSOwIbGRnJRx99xD///MPdu3exsbGhTp06TJ06lSZNmhAeHo6NjU1xN1MUwJUdtwnefJPk+FSsK5jjO8ATR69yTy0XdTmenTNOYl3RnI6fNtClx99K5MzaUOLCHpAYnUq9/lWo3qHSc+yBKG0URWHe+sv8vvcG95PSqV25HB8N8KFqhdz3MtwWFM4Pm65yIzKRDK2Cm5M5Ae0r061pRV2eX3eH8due69yOTgbAs7wFo16pRksfx+feJ1E6GDbujYHPy2BijhJ+Be3uxSgxN/NV1qBaM9Sdx5F59SgZG754dMLIBMOm/TDwbARmViiRoWj3LEG5G/KceiEKQqYQlBI9e/YkPT2d5cuXU7lyZe7evcvOnTuJjY0FwNnZuUjbk56ejpGRUZFeszS5fjiSEyuv4jekKvZVrbm6+w57Z52h08yGmNub5FouLSmDwz8E41TDhpT7aXrnMtK0WDiY4trQgRMr5QVYFNzif0JYti2UGUPr4O5kzsKNV3lj9mH++awN5qY5v7yWMzdiRBdPKjtbYKQ2YM/pu0xaeho7K2Oa18oKUJ1tTBnXszqujuYA/H3wFqO/O8aaKS3zDI6FADDw646Bb1cytn0PcXcwaPQa6h4fk77sHUhPybuwpQOGLYeQeetCtlOGL49CZe9KxpZvURJiMfRuibrnFNKXj4XE2OfTGZFvZW0RV6mcQnDv3j3279/PF198QZs2bXBzc6Nhw4YEBgbSuXNnQH8KweMyMzOpWLEiCxcu1Es/ceIEKpWKa9euARAfH8+bb76Jo6MjVlZWtG3bltOnT+vyT506lbp167JkyRIqV66MRqPR28hXFMylLTep3MqFKq3LY13BnPoDq2Jma8KVXXfyLHds6SXcGjth72mV7ZxdZSvq9auCW2MnDI1Uz6vpopRSFIWfd4QyorMn7eu7UK2iFZ+/UYeUNC0bj9zOtVzD6va87OtClfKWuDqaM/jlylSraEnQlThdnjZ1nWhV2wkPZws8nC0Y26M6Zho1p6/F5VqvEA8Z+nZBe3QNytUjKDE30W79DtQaDKq3yLugygB1xzFoD61Gib/7RKXGGFRtjHbfzyi3L0B8BNrDv6PER2JYx//5dUbkW2ZGZq5HaVQqA1gLCwssLCxYt24dqampBSprYGBA3759WblypV76qlWraNKkCZUrV0ZRFDp37kxERASbN28mKCgIX19f2rVrpxvhBbh69Sq///47a9askfm1/4E2I5PYsAc419Kf8uHsY0P0lfhcy137N5yEyBRqver2vJsoyqBb0UlEx6fSrKaDLs3YyJAGXnacDMlfoKkoCocuRBMWkYhfNdsc82gzFTYduU1Smpa6VWTak3gKaydU5jYo1x8NqKDNQLl9HlV5r9zLAYaNe0HyfTLP78x+0sAAlYEhZKTrp2ekoSpfvRAaLv4rRavkepRGpXIKgVqtZtmyZQwfPpyFCxfi6+tLq1at6Nu3L7Vr135q+QEDBjBnzhyuX7+Om5sbmZmZ/Pbbb3z44YcA7N69m7NnzxIZGYlGowFg1qxZrFu3jj///JM333wTgLS0NFasWIGDg0Ou1xJPl/ogHSUTTKyN9dJNrIxJiU/LscyDiCRO/X6NlybVw8CwVL5PE8UsOj7rzbG9lUYv3c5Kw52Y5DzLPkhKp/X4HaRlZGKgUvHxwFp6gTDA5Vv36TfjAKnpmZhpDPnu7fp4lpfpAyJvKrNyAChJ9/TSlaR4VJa5/y1SlffCoGY70n/5X84Z0lPIvHMRw0avkRF7C5LiMfBqjsqlKsSFF1LrxX9R1nYhKJUBLGTNge3cuTP79u3j0KFDbNmyhS+//JLFixcTEBCQZ9l69epRvXp1fv31Vz744AP27t1LZGQkvXv3BiAoKIiEhATs7Oz0yiUnJxMS8mgupZub21OD19TU1GyjxBlpWtTGhgXobdmQ44f8OSRmZiocXBCMTw93rFzMnnezRBmx4fAtpv58Vvd4wZiGWf954h5UFAXVU2akmJuoWTulJUmpGRwOjuaL1Reo5GBGw+r2ujzuzhasndKSB8npbAsKJ/Cn0/w8sYkEsUKPQfUWGLYboXucsW7G//8vp1G3XEbijExQdxhDxo4FkPIg12tlbPkWdfu3MX5zMUqmFiXyGpkX92HgWPnZOyAKjbaUThXITakNYAFMTEx4+eWXefnll/n4448ZNmwYU6ZMeWoAC1mjsKtWreKDDz5g1apV+Pv7Y2+f9cclMzMTFxcX9uzZk61cuXLldP83Nzd/6nVmzpzJtGnT9NJaveFL6+F+Ty1bVmgsjVAZQPITo60p99MwsTLOlj8jOYPY0AfEXX9A0M9XAFAUQIHfAvbQekIdnGvIR7GiYNrWcab2lEf3Tdr//7GIjk/FsdyjhYSxD9Kwe2JU9kkGBircnLJeH7xdrQkJT+CHzSF6Aayx2kCXp5Z7Oc6GxrNiRyjTBj/9UyRRdmSGHCMz/IrusUqdtVhYZWaDknjvUbqZdbZRWd25cs6orJ1Qdwt8LDHrXZjRmN+zFn/F34X4u2T88TGoNaAxhcR7GHYahxIfWej9EgVXWqcK5KZUB7BPqlGjRo4Lt3LSv39/Jk+eTFBQEH/++ScLFizQnfP19SUiIgK1Wo27u/t/alNgYCDjxo3TS/vy9LhccpdNhmoDbN0tiTgXRyW/RyPaEefiqOBrny2/kamajjP03wBc2XGHu8FxNH+nJhYOps+9zaL0MTdV6+0soCgK9tYaDl6IooabNZAV1B67FMP/XvMuUN2KAmkZT/n4T1FIK2OrjEU+pKdAfITuoQIoiXGo3GqjRIVmJRqoUVWoSeb+FTlWocTeJv3nsXpphk37g7EJ2j1L4EGMfoGM1KxDY46BW120udQrilZp3e81N6UygI2JiaFXr14MHTqU2rVrY2lpyfHjx/nyyy/p1q1bvurw8PCgadOmvPHGG2RkZOiVe+mll2jSpAndu3fniy++wMvLizt37rB582a6d++On1/+R081Go1uHu1DMn0gO68OlTi8KBhbD0vsPa0I2RNOUkwKVduWB+DU79dIjkulyQhvVAYqylW00CtvYmWEoZGBXro2I5P7txMByMxQSI5LI+76A9Qmhlg6ydQDkTeVSsXglzz4YdNV3JzMcXM054fNVzExNqRLowq6fBMXn8TJxoRxPbOC2h82XaWmuzWujmakZyj8eyaS9Ydu8fFAH12Zr9dcpIWPAy62piSmZLD56B2OXorhh/caFXk/RcmjPbERwwY9UeLC4V44Bg17QkYqmRf36fIY+r8DCbFoD6wEbXq2PWKV1ERUoJeucqubdS7uDqpyzhi2GIwSd5vM87uKolviKUrrbgO5KZUBrIWFBY0aNeLrr78mJCSE9PR0KlWqxPDhw3ULsfJjwIABvP322wwePBhT00ejdiqVis2bNzNp0iSGDh1KVFQUzs7OtGzZEicnp+fRpTLPrbEjaQnpnP87jOR7aVhXNKfV/2rr9oBNuZdKUsxT9jd8QnJcKls+CtI9vvjPTS7+cxPH6ta0+7BeobZflE7DOlYhNV3L9F/OcT8x64sMFo9rpDdSGx6bjMFjk2KTUjOY/stZ7salYGJkiIeLBV8Mq0enhuV1eaLvpzJx8Smi4lOxNFVTraIVP7zXKNtCLyFyknl8HSq1Mep2b4LGHCXiChlrp+vtAauytC/w1o4qjRmGzQaAhR2kJpB55TDaA6sgs2wtHnpRlbUpBCpFNid94Uw9MuLpmYQoQh+nWzw9kxBFKONYWHE3QYhsjN9bU2zXPl879+3Map65WIQtKRqlcgRWCCGEEKIsycgo7hYULQlghRBCCCFKuMwy9nm6BLBCCCGEECWcjMAKIYQQQogSRQJYIYQQQghRomSWrV20JIAVQgghhCjpZARWCCGEEEKUKNoytg+sBLBCCCGEECWcTCEQQgghhBAlikwhEEIIIYQQJYoEsEIIIYQQokSRKQRCCCGEEKJEydAWdwuKlgSwQgghhBAlnEwhEEIIIYQQJUpZm0KgUhSlbG0cJsqE1NRUZs6cSWBgIBqNpribIwQg96V48cg9KUoqCWBFqXT//n2sra2Jj4/HysqquJsjBCD3pXjxyD0pSiqD4m6AEEIIIYQQBSEBrBBCCCGEKFEkgBVCCCGEECWKBLCiVNJoNEyZMkUWJYgXityX4kUj96QoqWQRlxBCCCGEKFFkBFYIIYQQQpQoEsAKIYQQQogSRQJYIYQQQghRokgAK4QQQgghShQJYEWRCggIQKVSoVKpUKvVuLq6MnLkSOLi4oq7aaIUe/y+e/y4evVqcTdNCN39+dZbb2U7N2rUKFQqFQEBAUXaJpVKxbp164r0mkIUhASwosh16NCB8PBwwsLCWLx4MRs2bGDUqFHF3SxRyj287x4/PDw89PKkpaUVU+tEWVepUiV+++03kpOTdWkpKSn8+uuvuLq6FmPL/hv5nRLPiwSwoshpNBqcnZ2pWLEi7du3p0+fPmzbtk13funSpXh7e2NiYkL16tWZP3++Xvlbt27Rt29fbG1tMTc3x8/PjyNHjujOb9iwgfr162NiYkLlypWZNm0aGRkZuvMqlYrFixfz6quvYmZmRtWqVVm/fr3eNc6fP0/nzp2xsrLC0tKSFi1aEBISwr///ouRkRERERF6+f/3v//RsmXLwnyaRCF7eN89frRr147Ro0czbtw47O3tefnllwG4cOECnTp1wsLCAicnJwYNGkR0dLSursTERAYPHoyFhQUuLi7Mnj2b1q1bM3bsWF2enEawypUrx7Jly3SPb9++TZ8+fbCxscHOzo5u3boRFhamOx8QEED37t2ZNWsWLi4u2NnZ8fbbb5Oenq7Lk5qayoQJE6hUqRIajYaqVavy008/oSgKnp6ezJo1S68N586dw8DAgJCQkP/+pIpC4+vri6urK2vXrtWlrV27lkqVKlGvXj1dWmpqKu+++y6Ojo6YmJjQvHlzjh07pju/Z88eVCoVO3fuxM/PDzMzM5o2bcqlS5f0rpfX66S7uzsAr776KiqVSvc4JCSEbt264eTkhIWFBQ0aNGDHjh169bq7u/Ppp58SEBCAtbU1w4cPp23btowePVovX0xMDBqNhl27dv3n506UTRLAimJ17do1tmzZgpGREQA//vgjkyZN4rPPPiM4OJgZM2bw0UcfsXz5cgASEhJo1aoVd+7cYf369Zw+fZoJEyaQmZkJwNatWxk4cCDvvvsuFy5cYNGiRSxbtozPPvtM77rTpk2jd+/enDlzhk6dOjFgwABiY2OBrKCiZcuWmJiYsGvXLoKCghg6dCgZGRm0bNmSypUrs2LFCl1dGRkZ/PLLL7z++utF8ZSJQrZ8+XLUajUHDhxg0aJFhIeH06pVK+rWrcvx48fZsmULd+/epXfv3roy77//Prt37+avv/5i27Zt7Nmzh6CgoAJdNykpiTZt2mBhYcG///7L/v37sbCwoEOHDnqjVrt37yYkJITdu3ezfPlyli1bphcEDx48mN9++41vv/2W4OBgFi5ciIWFBSqViqFDh7J06VK96y5ZsoQWLVpQpUqVZ3vCxHPz+uuv6/28lixZwtChQ/XyTJgwgTVr1rB8+XJOnDiBp6cn/v7+utevhyZNmsTs2bM5fvw4arVar56nvU4+DIiXLl1KeHi47nFCQgKdOnVix44dnDx5En9/f7p27cqNGzf0rv3VV19Rq1YtgoKC+Oijjxg2bBirVq0iNTVVl2flypWUL1+eNm3aFMIzJ8okRYgiNGTIEMXQ0FAxNzdXTExMFEABlDlz5iiKoiiVKlVSVq1apVfmk08+UZo0aaIoiqIsWrRIsbS0VGJiYnKsv0WLFsqMGTP00lasWKG4uLjoHgPK5MmTdY8TEhIUlUql/PPPP4qiKEpgYKDi4eGhpKWl5XiNL774QvH29tY9XrdunWJhYaEkJCTk92kQRezx++7h8dprrymtWrVS6tatq5f3o48+Utq3b6+XdvPmTQVQLl26pDx48EAxNjZWfvvtN935mJgYxdTUVBkzZowuDVD++usvvXqsra2VpUuXKoqiKD/99JPi5eWlZGZm6s6npqYqpqamytatW3XtdnNzUzIyMnR5evXqpfTp00dRFEW5dOmSAijbt2/Psd937txRDA0NlSNHjiiKoihpaWmKg4ODsmzZsnw8a6KoDBkyROnWrZsSFRWlaDQaJTQ0VAkLC1NMTEyUqKgopVu3bsqQIUOUhIQExcjISFm5cqWubFpamlK+fHnlyy+/VBRFUXbv3q0Ayo4dO3R5Nm3apABKcnKyoij5f5188v7NSY0aNZTvvvtO99jNzU3p3r27Xp6UlBTF1tZWWb16tS6tbt26ytSpU/Px7AiRM3WxRc6izGrTpg0LFiwgKSmJxYsXc/nyZd555x2ioqK4efMmb7zxBsOHD9flz8jIwNraGoBTp05Rr149bG1tc6w7KCiIY8eO6Y24arVaUlJSSEpKwszMDIDatWvrzpubm2NpaUlkZKTuGi1atNCNCj8pICCAyZMnc/jwYRo3bsySJUvo3bs35ubm/+2JEc/Vw/vuIXNzc/r164efn59evqCgIHbv3o2FhUW2OkJCQkhOTiYtLY0mTZro0m1tbfHy8ipQe4KCgrh69SqWlpZ66SkpKXof79esWRNDQ0PdYxcXF86ePQtk3auGhoa0atUqx2u4uLjQuXNnlixZQsOGDdm4cSMpKSn06tWrQG0VRcPe3p7OnTuzfPlyFEWhc+fO2Nvb686HhISQnp5Os2bNdGlGRkY0bNiQ4OBgvboef41zcXEBIDIyEldX13y/Tj4pMTGRadOmsXHjRu7cuUNGRgbJycnZRmCf/J3SaDQMHDhQ91p56tQpTp8+LYvExH8iAawocubm5nh6egLw7bff0qZNG6ZNm6abI/Xjjz/SqFEjvTIP/4CbmprmWXdmZibTpk2jR48e2c6ZmJjo/v9kcKpSqXTTEJ52DUdHR7p27crSpUupXLkymzdvZs+ePXmWEcXv8fvuyfTHZWZm0rVrV7744otseV1cXLhy5Uq+rqdSqVCe+Kbux+euZmZmUr9+fVauXJmtrIODg+7//+VeBRg2bBiDBg3i66+/ZunSpfTp0yfXAEUUv6FDh+peC+fNm6d37uH9pFKpsqU/mfb4ffPw3MP7Jr+vk096//332bp1K7NmzcLT0xNTU1Nee+21bAu1cnozP2zYMOrWrcutW7dYsmQJ7dq1w83NLddrCfE0EsCKYjdlyhQ6duzIyJEjqVChAteuXWPAgAE55q1duzaLFy8mNjY2x1FYX19fLl26lGOgkl+1a9dm+fLlpKen5zoKO2zYMPr27UvFihWpUqWK3oiIKNl8fX1Zs2YN7u7uqNXZXyI9PT0xMjLi8OHDutXhcXFxXL58WW8k1MHBgfDwcN3jK1eukJSUpHed1atX4+joiJWV1TO11cfHh8zMTPbu3ctLL72UY55OnTphbm7OggUL+Oeff/j333+f6VqiaDw+B9rf31/vnKenJ8bGxuzfv5/+/fsDWW+Kjh8/rreA8Gny8zppZGSEVqvVS9u3bx8BAQG8+uqrQNac2McXHebFx8cHPz8/fvzxR1atWsV3332X7/YKkRNZxCWKXevWralZsyYzZsxg6tSpzJw5k2+++YbLly9z9uxZli5dypw5cwDo168fzs7OdO/enQMHDnDt2jXWrFnDoUOHAPj444/5+eefmTp1KufPnyc4OJjVq1czefLkfLdn9OjR3L9/n759+3L8+HGuXLnCihUr9Fbx+vv7Y21tzaeffiqLt0qZt99+m9jYWPr168fRo0e5du0a27ZtY+jQoWi1WiwsLHjjjTd4//332blzJ+fOnSMgIAADA/2X07Zt2/L9999z4sQJjh8/zltvvaX3hmjAgAHY29vTrVs39u3bR2hoKHv37mXMmDHcunUrX211d3dnyJAhDB06lHXr1hEaGsqePXv4/fffdXkMDQ0JCAggMDAQT09PvakP4sVjaGhIcHAwwcHBelNHIGtkc+TIkbz//vts2bKFCxcuMHz4cJKSknjjjTfyfY38vE66u7uzc+dOIiIidPt0e3p6snbtWt0UgP79++tGdfNj2LBhfP7552i1Wl0QLMSzkgBWvBDGjRvHjz/+iL+/P4sXL2bZsmX4+PjQqlUrli1bptuv09jYmG3btuHo6EinTp3w8fHh888/173Q+/v7s3HjRrZv306DBg1o3Lgxc+bMKdBHVXZ2duzatUu340H9+vX58ccf9YIPAwMDAgIC0Gq1DB48uHCfDFGsypcvz4EDB9Bqtfj7+1OrVi3GjBmDtbW1Lkj96quvaNmyJa+88govvfQSzZs3p379+nr1zJ49m0qVKtGyZUv69+/P+PHj9T66NzMz499//8XV1ZUePXrg7e3N0KFDSU5OLtCI7IIFC3jttdcYNWoU1atXZ/jw4SQmJurleeONN0hLS8u2ol28mKysrHK9Bz7//HN69uzJoEGD8PX15erVq2zduhUbG5t815+f18nZs2ezfft2vW28vv76a2xsbGjatCldu3bF398fX1/ffF+3X79+qNVq+vfvn+dUBSHyQ6U8OUlLCJEvw4cP5+7du9n2kBVlU+vWralbty5z584t7qZkc+DAAVq3bs2tW7dwcnIq7uaIMurmzZu4u7tz7NixAgW+QuRE5sAKUUDx8fEcO3aMlStX8vfffxd3c4TIVWpqKjdv3uSjjz6id+/eEryKYpGenk54eDgffPABjRs3luBVFAqZQiBEAXXr1o1XXnmFESNG6L65SYgX0a+//oqXlxfx8fF8+eWXxd0cUUYdOHAANzc3goKCWLhwYXE3R5QSMoVACCGEEEKUKDICK4QQQgghShQJYIUQQgghRIkiAawQQgghhChRJIAVQgghhBAligSwQgghhBCiRJEAVgghhBBClCgSwAohhBBCiBJFAlghhBBCCFGiSAArhBBCCCFKlP8DXVHs0/fq0LkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 800x200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize a plot with a figure size of 8 by 2 inches \n",
    "plt.figure(figsize=(8,2))\n",
    "\n",
    "# Add the plot title\n",
    "plt.title(\"Relative importance of attributes\")\n",
    "\n",
    "# Plot the heatmap\n",
    "sns.heatmap(data=relative_imp, annot=True, fmt=\".2f\", cmap=\"RdYlGn\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "The heatmap of the relative importance with regards to the metrics buttresses the findings from the snake plot below.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare data for the snake plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "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>Cluster</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Customer ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AA-10315</th>\n",
       "      <td>1.090834</td>\n",
       "      <td>-0.850959</td>\n",
       "      <td>-0.126113</td>\n",
       "      <td>Silver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AA-10375</th>\n",
       "      <td>-0.872940</td>\n",
       "      <td>0.392175</td>\n",
       "      <td>-0.511995</td>\n",
       "      <td>Gold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AA-10480</th>\n",
       "      <td>1.391255</td>\n",
       "      <td>-1.791354</td>\n",
       "      <td>-2.192422</td>\n",
       "      <td>Bronze</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AA-10645</th>\n",
       "      <td>0.035951</td>\n",
       "      <td>-1.791354</td>\n",
       "      <td>-2.310841</td>\n",
       "      <td>Bronze</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AB-10060</th>\n",
       "      <td>0.020045</td>\n",
       "      <td>1.029831</td>\n",
       "      <td>1.211453</td>\n",
       "      <td>Platinum</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Recency  Frequency  Monetary   Cluster\n",
       "Customer ID                                         \n",
       "AA-10315     1.090834  -0.850959 -0.126113    Silver\n",
       "AA-10375    -0.872940   0.392175 -0.511995      Gold\n",
       "AA-10480     1.391255  -1.791354 -2.192422    Bronze\n",
       "AA-10645     0.035951  -1.791354 -2.310841    Bronze\n",
       "AB-10060     0.020045   1.029831  1.211453  Platinum"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datamart_normalized = pd.DataFrame(datamart_normalized,\n",
    "                                   index=datamart_rfm_k4.index,\n",
    "                                   columns=datamart_rfm_k4.columns)\n",
    "datamart_normalized['Cluster'] = datamart_rfm_k4['Cluster']\n",
    "datamart_normalized.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "datamart_normalized['Cluster'] = datamart_normalized[\n",
    "                                    [\"Cluster\"]].replace(\n",
    "                                        {0 : \"Platinum\", 3 : \"Gold\", 2 : \"Silver\", 1 : \"Bronze\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Melt the normalized dataset and reset the index\n",
    "datamart_melt = pd.melt(datamart_normalized.reset_index(),\n",
    "                        \n",
    "                        \n",
    "# Assign Customer ID and Cluster as ID variables\n",
    "                    id_vars = [\"Customer ID\", \"Cluster\"],\n",
    "\n",
    "# Assign RFM values as value variables\n",
    "                    value_vars = [\"Recency\", \"Frequency\", \"Monetary\"], \n",
    "                        \n",
    "# Name the variable and value\n",
    "                    var_name = \"Metric\", value_name= \"Value\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Cluster</th>\n",
       "      <th>Metric</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AA-10315</td>\n",
       "      <td>Silver</td>\n",
       "      <td>Recency</td>\n",
       "      <td>1.090834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AA-10375</td>\n",
       "      <td>Gold</td>\n",
       "      <td>Recency</td>\n",
       "      <td>-0.872940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AA-10480</td>\n",
       "      <td>Bronze</td>\n",
       "      <td>Recency</td>\n",
       "      <td>1.391255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AA-10645</td>\n",
       "      <td>Bronze</td>\n",
       "      <td>Recency</td>\n",
       "      <td>0.035951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AB-10060</td>\n",
       "      <td>Platinum</td>\n",
       "      <td>Recency</td>\n",
       "      <td>0.020045</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Customer ID   Cluster   Metric     Value\n",
       "0    AA-10315    Silver  Recency  1.090834\n",
       "1    AA-10375      Gold  Recency -0.872940\n",
       "2    AA-10480    Bronze  Recency  1.391255\n",
       "3    AA-10645    Bronze  Recency  0.035951\n",
       "4    AB-10060  Platinum  Recency  0.020045"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datamart_melt.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a Snake plot\n",
    "\n",
    "plt.figure(figsize = (10,6))\n",
    "\n",
    "# Add the plot title\n",
    "plt.title(\"Snake plot of normalized variables\")\n",
    "\n",
    "# Add the x axis label\n",
    "plt.xlabel(\"Metric\")\n",
    "\n",
    "# Add the y axis label\n",
    "plt.ylabel(\"Value\")\n",
    "\n",
    "# Plot a line for each value of the cluster variable\n",
    "sns.lineplot(x =\"Metric\", y =\"Value\", hue = \"Cluster\", data=datamart_melt, \n",
    "             palette = [\"dimgrey\",\"yellow\",\"darkorange\",\"blue\"], ci = None)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion and recommendation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On a general note, the company has a low retention rate. This implies that customers easily churn from the company.\n",
    "\n",
    "I therefore recommend that research should be conducted to find out why customers easily turn away from the company.\n",
    "\n",
    "Kmeans clustering was used in grouping customers into four clusters (Groups).\n",
    "\n",
    "Based on the results, I advise the business to conduct additional study on the Silver and Bronze Groups, which is made up of inactive customers.An in-depth investigation of their preferred products should be conducted. \n",
    "\n",
    "Also, for the Platinum and Gold customer groups, discount packages, loyalty packages and other customer incencentives should be made available for them to retain them.  \n",
    "\n",
    "In order to improve target marketing, I also advise conducting further research into the products that the various clusters are associated with.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data['Customer ID'].sort_values().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "#new_data = datamart_melt.merge(data, on = 'Customer ID' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "#new_data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "#new_data['Year'] = pd.to_datetime(new_data['Year'], format = '%Y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#new_data['Year'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "#new_data.to_csv('superstore.csv', index = False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}