Part1_Starbucks_Capstone_Project_SooyeonWon.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
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   "source": [
    "# (Part 1) Customer Analytics in Retail Industry - Starbucks \n",
    "\n",
    "#### by Sooyeon Won \n",
    "\n",
    "\n",
    "### Contents \n",
    "\n",
    "<ul>\n",
    "    ------------------ Part 1 ------------------\n",
    "<li><a href=\"#Introduction\">1. Introduction</a></li>\n",
    "\n",
    "<li><a href=\"#Preparation\">2. Data Preparation/ Cleaning</a></li>\n",
    "&emsp;2.1. Portfolio Dataframe  <br>\n",
    "&emsp;2.2. Profile Dataframe <br> \n",
    "&emsp;2.3. Transcript Dataframe<br> \n",
    "\n",
    "<li><a href=\"#Exploration\">3. Data Exploration</a></li>\n",
    "&emsp;3.1. Current Business Situations <br>\n",
    "&emsp;3.2. Customer Demographics <br>  \n",
    "&emsp;3.3. Characteristics of Starbucks Offers <br>\n",
    "&emsp;3.4. Customer Purchasing Patterns <br>  \n",
    " \n",
    "<li><a href=\"#Analysis\">4. Data Analysis</a></li>\n",
    "&emsp;4.1. Feature Engineering  <br>  \n",
    "    \n",
    "------------------ Part 2 ------------------    <br>   \n",
    "&emsp;4.2. Data Spliting <br>\n",
    "&emsp;4.3. Data Balancing <br>\n",
    "&emsp;4.4. Model Comparison <br>\n",
    "&emsp;4.5. Model Evaluation & Selection<br>\n",
    "\n",
    "<li><a href=\"#Conclusion\">5. Conclusion</a></li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Introduction'></a>\n",
    "## 1. Introduction "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> This project is derived from the field of Customer Relationship Management (CRM) in Starbucks. One of the main concerns for CRM field is to interact with not only current customers, but also previous and potential customers, so that a company can boost its business relationship with the customers, and eventually expect its continuous sales growth. As mentioned in the provided business case, one of its marketing campaigns is to send out an offer to customers through various channels. An offer can be either just an advertisement for a certain beverage, or a coupon-type offer such as a ‘discount’ or ‘buy 1, get 1 (BOGO)’. Each offer is valid for certain number of days. The validity periods are different from offers.  <br> <br>\n",
    "To maximize a return-on-investment(ROI), we should properly figure out which customers are effectively using their received offers. If a customer feels reluctant to receive advertisement offers, this might be able to result in losing users or lead to decrease customer retention rate in the long run. These types of customers should be removed in advance from the “offering-list”. In this analysis, I classify the customers into 2 groups who use appropriately our offers and who do not based on customers' individual demographic features and their purchasing patterns. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Preparation'></a>\n",
    "## 2. Data Preparation/ Cleaning "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the relevant libraries \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt \n",
    "%matplotlib inline\n",
    "import math\n",
    "import json\n",
    "plt.rc(\"font\", size=14)\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "sns.set(style=\"whitegrid\", color_codes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in the json files\n",
    "portfolio = pd.read_json('portfolio.json', orient='records', lines=True)\n",
    "profile = pd.read_json('profile.json', orient='records', lines=True)\n",
    "transcript = pd.read_json('transcript.json', orient='records', lines=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. 1. Portfolio Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>reward</th>\n",
       "      <th>channels</th>\n",
       "      <th>difficulty</th>\n",
       "      <th>duration</th>\n",
       "      <th>offer_type</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>[email, mobile, social]</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>bogo</td>\n",
       "      <td>ae264e3637204a6fb9bb56bc8210ddfd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>[web, email, mobile, social]</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>bogo</td>\n",
       "      <td>4d5c57ea9a6940dd891ad53e9dbe8da0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>[web, email, mobile]</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>informational</td>\n",
       "      <td>3f207df678b143eea3cee63160fa8bed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>[web, email, mobile]</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>bogo</td>\n",
       "      <td>9b98b8c7a33c4b65b9aebfe6a799e6d9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>[web, email]</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>discount</td>\n",
       "      <td>0b1e1539f2cc45b7b9fa7c272da2e1d7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>[web, email, mobile, social]</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>discount</td>\n",
       "      <td>2298d6c36e964ae4a3e7e9706d1fb8c2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>[web, email, mobile, social]</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>discount</td>\n",
       "      <td>fafdcd668e3743c1bb461111dcafc2a4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>[email, mobile, social]</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>informational</td>\n",
       "      <td>5a8bc65990b245e5a138643cd4eb9837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5</td>\n",
       "      <td>[web, email, mobile, social]</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>bogo</td>\n",
       "      <td>f19421c1d4aa40978ebb69ca19b0e20d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>[web, email, mobile]</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>discount</td>\n",
       "      <td>2906b810c7d4411798c6938adc9daaa5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   reward                      channels  difficulty  duration     offer_type  \\\n",
       "0      10       [email, mobile, social]          10         7           bogo   \n",
       "1      10  [web, email, mobile, social]          10         5           bogo   \n",
       "2       0          [web, email, mobile]           0         4  informational   \n",
       "3       5          [web, email, mobile]           5         7           bogo   \n",
       "4       5                  [web, email]          20        10       discount   \n",
       "5       3  [web, email, mobile, social]           7         7       discount   \n",
       "6       2  [web, email, mobile, social]          10        10       discount   \n",
       "7       0       [email, mobile, social]           0         3  informational   \n",
       "8       5  [web, email, mobile, social]           5         5           bogo   \n",
       "9       2          [web, email, mobile]          10         7       discount   \n",
       "\n",
       "                                 id  \n",
       "0  ae264e3637204a6fb9bb56bc8210ddfd  \n",
       "1  4d5c57ea9a6940dd891ad53e9dbe8da0  \n",
       "2  3f207df678b143eea3cee63160fa8bed  \n",
       "3  9b98b8c7a33c4b65b9aebfe6a799e6d9  \n",
       "4  0b1e1539f2cc45b7b9fa7c272da2e1d7  \n",
       "5  2298d6c36e964ae4a3e7e9706d1fb8c2  \n",
       "6  fafdcd668e3743c1bb461111dcafc2a4  \n",
       "7  5a8bc65990b245e5a138643cd4eb9837  \n",
       "8  f19421c1d4aa40978ebb69ca19b0e20d  \n",
       "9  2906b810c7d4411798c6938adc9daaa5  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio.head(10) # Glance at the first dataset(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10 entries, 0 to 9\n",
      "Data columns (total 6 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   reward      10 non-null     int64 \n",
      " 1   channels    10 non-null     object\n",
      " 2   difficulty  10 non-null     int64 \n",
      " 3   duration    10 non-null     int64 \n",
      " 4   offer_type  10 non-null     object\n",
      " 5   id          10 non-null     object\n",
      "dtypes: int64(3), object(3)\n",
      "memory usage: 608.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "portfolio.info() # Glance at the first dataset(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Assessment of **Portfolio Dataframe**\n",
    "- Dataframe \"portfolio\" contains all offers information. \n",
    "- It has no missing values nor duplicates. \n",
    "- The datatype of each column is correct. \n",
    "- To avoid a confusion, I will change the 'id' column name into 'offer_id'.\n",
    "\n",
    "Cleaning **Portfolio Dataframe**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>reward</th>\n",
       "      <th>channels</th>\n",
       "      <th>difficulty</th>\n",
       "      <th>duration</th>\n",
       "      <th>offer_type</th>\n",
       "      <th>offer_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>[email, mobile, social]</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>bogo</td>\n",
       "      <td>ae264e3637204a6fb9bb56bc8210ddfd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>[web, email, mobile, social]</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>bogo</td>\n",
       "      <td>4d5c57ea9a6940dd891ad53e9dbe8da0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>[web, email, mobile]</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>informational</td>\n",
       "      <td>3f207df678b143eea3cee63160fa8bed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>[web, email, mobile]</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>bogo</td>\n",
       "      <td>9b98b8c7a33c4b65b9aebfe6a799e6d9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>[web, email]</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>discount</td>\n",
       "      <td>0b1e1539f2cc45b7b9fa7c272da2e1d7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   reward                      channels  difficulty  duration     offer_type  \\\n",
       "0      10       [email, mobile, social]          10         7           bogo   \n",
       "1      10  [web, email, mobile, social]          10         5           bogo   \n",
       "2       0          [web, email, mobile]           0         4  informational   \n",
       "3       5          [web, email, mobile]           5         7           bogo   \n",
       "4       5                  [web, email]          20        10       discount   \n",
       "\n",
       "                           offer_id  \n",
       "0  ae264e3637204a6fb9bb56bc8210ddfd  \n",
       "1  4d5c57ea9a6940dd891ad53e9dbe8da0  \n",
       "2  3f207df678b143eea3cee63160fa8bed  \n",
       "3  9b98b8c7a33c4b65b9aebfe6a799e6d9  \n",
       "4  0b1e1539f2cc45b7b9fa7c272da2e1d7  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio= portfolio.rename(columns={'id': 'offer_id'})\n",
    "portfolio.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. 2. Profile Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(17000, 5)\n"
     ]
    },
    {
     "data": {
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       "\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>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>id</th>\n",
       "      <th>became_member_on</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>None</td>\n",
       "      <td>118</td>\n",
       "      <td>68be06ca386d4c31939f3a4f0e3dd783</td>\n",
       "      <td>20170212</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>F</td>\n",
       "      <td>55</td>\n",
       "      <td>0610b486422d4921ae7d2bf64640c50b</td>\n",
       "      <td>20170715</td>\n",
       "      <td>112000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>None</td>\n",
       "      <td>118</td>\n",
       "      <td>38fe809add3b4fcf9315a9694bb96ff5</td>\n",
       "      <td>20180712</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F</td>\n",
       "      <td>75</td>\n",
       "      <td>78afa995795e4d85b5d9ceeca43f5fef</td>\n",
       "      <td>20170509</td>\n",
       "      <td>100000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>None</td>\n",
       "      <td>118</td>\n",
       "      <td>a03223e636434f42ac4c3df47e8bac43</td>\n",
       "      <td>20170804</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  gender  age                                id  became_member_on    income\n",
       "0   None  118  68be06ca386d4c31939f3a4f0e3dd783          20170212       NaN\n",
       "1      F   55  0610b486422d4921ae7d2bf64640c50b          20170715  112000.0\n",
       "2   None  118  38fe809add3b4fcf9315a9694bb96ff5          20180712       NaN\n",
       "3      F   75  78afa995795e4d85b5d9ceeca43f5fef          20170509  100000.0\n",
       "4   None  118  a03223e636434f42ac4c3df47e8bac43          20170804       NaN"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(profile.shape)\n",
    "profile.head() # Glance at the second dataset(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 17000 entries, 0 to 16999\n",
      "Data columns (total 5 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   gender            14825 non-null  object \n",
      " 1   age               17000 non-null  int64  \n",
      " 2   id                17000 non-null  object \n",
      " 3   became_member_on  17000 non-null  int64  \n",
      " 4   income            14825 non-null  float64\n",
      "dtypes: float64(1), int64(2), object(2)\n",
      "memory usage: 664.2+ KB\n"
     ]
    }
   ],
   "source": [
    "profile.info() # Glance at the second dataset(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(profile.duplicated()) # No Duplicates in the Profile Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "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>age</th>\n",
       "      <th>became_member_on</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>14825.00</td>\n",
       "      <td>14825.00</td>\n",
       "      <td>14825.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>54.39</td>\n",
       "      <td>20166887.15</td>\n",
       "      <td>65404.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>17.38</td>\n",
       "      <td>11885.65</td>\n",
       "      <td>21598.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.00</td>\n",
       "      <td>20130729.00</td>\n",
       "      <td>30000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>42.00</td>\n",
       "      <td>20160520.00</td>\n",
       "      <td>49000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>55.00</td>\n",
       "      <td>20170802.00</td>\n",
       "      <td>64000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>66.00</td>\n",
       "      <td>20171230.00</td>\n",
       "      <td>80000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>101.00</td>\n",
       "      <td>20180726.00</td>\n",
       "      <td>120000.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           age  became_member_on    income\n",
       "count 14825.00          14825.00  14825.00\n",
       "mean     54.39       20166887.15  65404.99\n",
       "std      17.38          11885.65  21598.30\n",
       "min      18.00       20130729.00  30000.00\n",
       "25%      42.00       20160520.00  49000.00\n",
       "50%      55.00       20170802.00  64000.00\n",
       "75%      66.00       20171230.00  80000.00\n",
       "max     101.00       20180726.00 120000.00"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Turn off scientific notation in pandas \n",
    "pd.set_option('display.float_format', lambda x: '%.2f' % x)\n",
    "profile.query('age != 118').describe() # Descriptive statistics of the dataset with non-missing values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Assessment of **Profile Dataframe**\n",
    "- Profile Dataframe contains customer information. \n",
    "- There are 2,175 missing datapoints. These missing values are represented as 'None', 'NaN in 'gender', 'income' column, respectively. \n",
    "- All the missing datapoints has 118 age value equally. \n",
    "- These missing values are replaced with each median value.\n",
    "- The missing values in 'gender' column will be replaced with 'O', meaning 'Others'. [Reference - Gender Classification](https://de.wikipedia.org/wiki/Datenstandards_zur_Beschreibung_des_Geschlechts)\n",
    "- The datatype of the column \"became_member_on\" should be changed to datetime."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cleaning **Profile Dataframe**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "profile_wo_mv = profile.query('age != 118') # Profile Dataframe without missing values \n",
    "median_age = profile_wo_mv.age.median() # The median value of ages \n",
    "median_income = profile_wo_mv.income.median() # The median value of incomes\n",
    "\n",
    "# Replace the missing values in gender column with U (Unknown), in income column with its median value. \n",
    "profile.fillna({'gender' : 'O', 'income':median_income }, inplace= True) \n",
    "# Replace the missing values (118) in age column with its median value. \n",
    "profile_cleaned = profile.replace({'age':118}, median_age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Gender')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualisation of each feature after cleaning the dataset. \n",
    "plt.figure(figsize=[15,3]) \n",
    "plt.subplots_adjust(wspace = 0.3 , hspace= 0.5)\n",
    "base_color =sns.color_palette()[0]\n",
    "\n",
    "plt.subplot(1, 3, 1)\n",
    "profile_cleaned.age.hist()\n",
    "plt.title('Distribution of Age (Cleaned)')\n",
    "plt.ylabel('No. of Customers')\n",
    "plt.xlabel('Age')\n",
    "\n",
    "plt.subplot(1, 3, 2)\n",
    "profile_cleaned.income.hist()\n",
    "plt.title('Distribution of Income (Cleaned)')\n",
    "plt.ylabel('No. of Customers')\n",
    "plt.xlabel('Income')\n",
    "\n",
    "plt.subplot(1, 3, 3)\n",
    "sns.countplot(data = profile_cleaned, x='gender', color = base_color)\n",
    "plt.title('Distribution of Gender (Cleaned)')\n",
    "plt.ylabel('No. of Customers')\n",
    "plt.xlabel('Gender')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Datatype of the column : 'became_member_on' is changed to 'datetime'\n",
    "profile_cleaned['became_member_on'] = pd.to_datetime(profile_cleaned['became_member_on'], format = '%Y%m%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2018-07-26 00:00:00')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The latest date of 'became_member_on'\n",
    "profile_cleaned['became_member_on'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calcuate the number of days as a member  \n",
    "def calculate_days(dates):\n",
    "    last_record = pd.Timestamp('2018-07-31')\n",
    "    return last_record - dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the number of days as member for each customer. \n",
    "profile_cleaned['days_as_member'] =  profile_cleaned['became_member_on'].apply(calculate_days).dt.days\n",
    "\n",
    "# To avoid confusion, I rename 'id' column with 'customer_id'. \n",
    "profile_cleaned= profile_cleaned.rename(columns={'id': 'customer_id'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>became_member_on</th>\n",
       "      <th>income</th>\n",
       "      <th>days_as_member</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>O</td>\n",
       "      <td>55</td>\n",
       "      <td>68be06ca386d4c31939f3a4f0e3dd783</td>\n",
       "      <td>2017-02-12</td>\n",
       "      <td>64000.00</td>\n",
       "      <td>534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>F</td>\n",
       "      <td>55</td>\n",
       "      <td>0610b486422d4921ae7d2bf64640c50b</td>\n",
       "      <td>2017-07-15</td>\n",
       "      <td>112000.00</td>\n",
       "      <td>381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>O</td>\n",
       "      <td>55</td>\n",
       "      <td>38fe809add3b4fcf9315a9694bb96ff5</td>\n",
       "      <td>2018-07-12</td>\n",
       "      <td>64000.00</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F</td>\n",
       "      <td>75</td>\n",
       "      <td>78afa995795e4d85b5d9ceeca43f5fef</td>\n",
       "      <td>2017-05-09</td>\n",
       "      <td>100000.00</td>\n",
       "      <td>448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>O</td>\n",
       "      <td>55</td>\n",
       "      <td>a03223e636434f42ac4c3df47e8bac43</td>\n",
       "      <td>2017-08-04</td>\n",
       "      <td>64000.00</td>\n",
       "      <td>361</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  gender  age                       customer_id became_member_on    income  \\\n",
       "0      O   55  68be06ca386d4c31939f3a4f0e3dd783       2017-02-12  64000.00   \n",
       "1      F   55  0610b486422d4921ae7d2bf64640c50b       2017-07-15 112000.00   \n",
       "2      O   55  38fe809add3b4fcf9315a9694bb96ff5       2018-07-12  64000.00   \n",
       "3      F   75  78afa995795e4d85b5d9ceeca43f5fef       2017-05-09 100000.00   \n",
       "4      O   55  a03223e636434f42ac4c3df47e8bac43       2017-08-04  64000.00   \n",
       "\n",
       "   days_as_member  \n",
       "0             534  \n",
       "1             381  \n",
       "2              19  \n",
       "3             448  \n",
       "4             361  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile_cleaned.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. 3. Transcript Dataframe "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>person</th>\n",
       "      <th>event</th>\n",
       "      <th>value</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>78afa995795e4d85b5d9ceeca43f5fef</td>\n",
       "      <td>offer received</td>\n",
       "      <td>{'offer id': '9b98b8c7a33c4b65b9aebfe6a799e6d9'}</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a03223e636434f42ac4c3df47e8bac43</td>\n",
       "      <td>offer received</td>\n",
       "      <td>{'offer id': '0b1e1539f2cc45b7b9fa7c272da2e1d7'}</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>e2127556f4f64592b11af22de27a7932</td>\n",
       "      <td>offer received</td>\n",
       "      <td>{'offer id': '2906b810c7d4411798c6938adc9daaa5'}</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8ec6ce2a7e7949b1bf142def7d0e0586</td>\n",
       "      <td>offer received</td>\n",
       "      <td>{'offer id': 'fafdcd668e3743c1bb461111dcafc2a4'}</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>68617ca6246f4fbc85e91a2a49552598</td>\n",
       "      <td>offer received</td>\n",
       "      <td>{'offer id': '4d5c57ea9a6940dd891ad53e9dbe8da0'}</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306529</th>\n",
       "      <td>b3a1272bc9904337b331bf348c3e8c17</td>\n",
       "      <td>transaction</td>\n",
       "      <td>{'amount': 1.5899999999999999}</td>\n",
       "      <td>714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306530</th>\n",
       "      <td>68213b08d99a4ae1b0dcb72aebd9aa35</td>\n",
       "      <td>transaction</td>\n",
       "      <td>{'amount': 9.53}</td>\n",
       "      <td>714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306531</th>\n",
       "      <td>a00058cf10334a308c68e7631c529907</td>\n",
       "      <td>transaction</td>\n",
       "      <td>{'amount': 3.61}</td>\n",
       "      <td>714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306532</th>\n",
       "      <td>76ddbd6576844afe811f1a3c0fbb5bec</td>\n",
       "      <td>transaction</td>\n",
       "      <td>{'amount': 3.5300000000000002}</td>\n",
       "      <td>714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306533</th>\n",
       "      <td>c02b10e8752c4d8e9b73f918558531f7</td>\n",
       "      <td>transaction</td>\n",
       "      <td>{'amount': 4.05}</td>\n",
       "      <td>714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>306534 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  person           event  \\\n",
       "0       78afa995795e4d85b5d9ceeca43f5fef  offer received   \n",
       "1       a03223e636434f42ac4c3df47e8bac43  offer received   \n",
       "2       e2127556f4f64592b11af22de27a7932  offer received   \n",
       "3       8ec6ce2a7e7949b1bf142def7d0e0586  offer received   \n",
       "4       68617ca6246f4fbc85e91a2a49552598  offer received   \n",
       "...                                  ...             ...   \n",
       "306529  b3a1272bc9904337b331bf348c3e8c17     transaction   \n",
       "306530  68213b08d99a4ae1b0dcb72aebd9aa35     transaction   \n",
       "306531  a00058cf10334a308c68e7631c529907     transaction   \n",
       "306532  76ddbd6576844afe811f1a3c0fbb5bec     transaction   \n",
       "306533  c02b10e8752c4d8e9b73f918558531f7     transaction   \n",
       "\n",
       "                                                   value  time  \n",
       "0       {'offer id': '9b98b8c7a33c4b65b9aebfe6a799e6d9'}     0  \n",
       "1       {'offer id': '0b1e1539f2cc45b7b9fa7c272da2e1d7'}     0  \n",
       "2       {'offer id': '2906b810c7d4411798c6938adc9daaa5'}     0  \n",
       "3       {'offer id': 'fafdcd668e3743c1bb461111dcafc2a4'}     0  \n",
       "4       {'offer id': '4d5c57ea9a6940dd891ad53e9dbe8da0'}     0  \n",
       "...                                                  ...   ...  \n",
       "306529                    {'amount': 1.5899999999999999}   714  \n",
       "306530                                  {'amount': 9.53}   714  \n",
       "306531                                  {'amount': 3.61}   714  \n",
       "306532                    {'amount': 3.5300000000000002}   714  \n",
       "306533                                  {'amount': 4.05}   714  \n",
       "\n",
       "[306534 rows x 4 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transcript # Glance at the third dataset(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "120"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total Days of testing\n",
    "transcript.time.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 306534 entries, 0 to 306533\n",
      "Data columns (total 4 columns):\n",
      " #   Column  Non-Null Count   Dtype \n",
      "---  ------  --------------   ----- \n",
      " 0   person  306534 non-null  object\n",
      " 1   event   306534 non-null  object\n",
      " 2   value   306534 non-null  object\n",
      " 3   time    306534 non-null  int64 \n",
      "dtypes: int64(1), object(3)\n",
      "memory usage: 9.4+ MB\n"
     ]
    }
   ],
   "source": [
    "transcript.info() # Glance at the third dataset(2) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Assessment of **Transcript Dataframe**\n",
    "- Semi-structured JSON data should be normalized into a flat table.\n",
    "- Note that the keys in 'value' column: 'offer_id' and 'offer id' are identical. They should be unified. \n",
    "- The column 'person' is equivalent to 'customer_id' column in Profile dataset. To avoid any confusion, I will match the name of the columns.\n",
    "- The numerical values are an arithmetical sequence, starting from 0 to 714 with the interval of 6. This implies that the testing lasted for 6 hours per days for 120 days (about 4 months). Therefore, I generate the new columns: days, months. \n",
    "<br><br>\n",
    "- It is more informative if \"offer_id\" is presented with the name of corresponding offers. The name of offers can be obtain from the Portfolio dataframe.\n",
    "- It is a untidy dataset. It contais more than one information. [Reference- Tidy Data](https://cran.r-project.org/web/packages/tidyr/vignettes/tidy-data.html)\n",
    "- The dataset should be split into 2 different datasets: <br>\n",
    "&emsp; 1. offer_based_transaction with columns (person, time, amount) <br>\n",
    "&emsp; 2. sales_based_transaction with columns (person, time, event, offer_id) <br>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cleaning **Transcript Dataframe**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To normalize a JSON column into a flat table.\n",
    "transcript_normalized = pd.json_normalize(transcript.value)\n",
    "# To unify the keys in 'value' column: 'offer_id' &  'offer id' \n",
    "transcript_normalized['offer_id'] = transcript_normalized['offer id'].fillna('') + transcript_normalized['offer_id'].fillna('')\n",
    "transcript_mod = pd.concat([transcript, transcript_normalized], axis = 1)\n",
    "\n",
    "# To drop unnecessary features \n",
    "transcript_mod.drop(['value', 'offer id'], axis =1, inplace = True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To rename 'person' column with 'customer_id' \n",
    "transcript_mod= transcript_mod.rename(columns={'person': 'customer_id'})\n",
    "\n",
    "# Calcuate the number of days/ months after starting the test\n",
    "## Nth day since testing\n",
    "transcript_mod['days'] = transcript_mod['time']/6 +1 \n",
    "transcript_mod['days'] = transcript_mod['days'].astype(int)\n",
    "## Nth month since testing\n",
    "transcript_mod['months'] = (transcript_mod['days']-1)//30 +1\n",
    "transcript_mod['months'] = transcript_mod['months'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# \"offer_id\" is presented with the name of corresponding offers\n",
    "transcript_offer_name = transcript_mod.merge(portfolio[['offer_type', 'offer_id']], \n",
    "                                                      left_on = 'offer_id', right_on = 'offer_id', how ='left' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>event</th>\n",
       "      <th>time</th>\n",
       "      <th>amount</th>\n",
       "      <th>offer_id</th>\n",
       "      <th>reward</th>\n",
       "      <th>days</th>\n",
       "      <th>months</th>\n",
       "      <th>offer_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>78afa995795e4d85b5d9ceeca43f5fef</td>\n",
       "      <td>offer received</td>\n",
       "      <td>0</td>\n",
       "      <td>nan</td>\n",
       "      <td>9b98b8c7a33c4b65b9aebfe6a799e6d9</td>\n",
       "      <td>nan</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>bogo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a03223e636434f42ac4c3df47e8bac43</td>\n",
       "      <td>offer received</td>\n",
       "      <td>0</td>\n",
       "      <td>nan</td>\n",
       "      <td>0b1e1539f2cc45b7b9fa7c272da2e1d7</td>\n",
       "      <td>nan</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>discount</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>e2127556f4f64592b11af22de27a7932</td>\n",
       "      <td>offer received</td>\n",
       "      <td>0</td>\n",
       "      <td>nan</td>\n",
       "      <td>2906b810c7d4411798c6938adc9daaa5</td>\n",
       "      <td>nan</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>discount</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8ec6ce2a7e7949b1bf142def7d0e0586</td>\n",
       "      <td>offer received</td>\n",
       "      <td>0</td>\n",
       "      <td>nan</td>\n",
       "      <td>fafdcd668e3743c1bb461111dcafc2a4</td>\n",
       "      <td>nan</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>discount</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>68617ca6246f4fbc85e91a2a49552598</td>\n",
       "      <td>offer received</td>\n",
       "      <td>0</td>\n",
       "      <td>nan</td>\n",
       "      <td>4d5c57ea9a6940dd891ad53e9dbe8da0</td>\n",
       "      <td>nan</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>bogo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306529</th>\n",
       "      <td>b3a1272bc9904337b331bf348c3e8c17</td>\n",
       "      <td>transaction</td>\n",
       "      <td>714</td>\n",
       "      <td>1.59</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>120</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306530</th>\n",
       "      <td>68213b08d99a4ae1b0dcb72aebd9aa35</td>\n",
       "      <td>transaction</td>\n",
       "      <td>714</td>\n",
       "      <td>9.53</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>120</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306531</th>\n",
       "      <td>a00058cf10334a308c68e7631c529907</td>\n",
       "      <td>transaction</td>\n",
       "      <td>714</td>\n",
       "      <td>3.61</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>120</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306532</th>\n",
       "      <td>76ddbd6576844afe811f1a3c0fbb5bec</td>\n",
       "      <td>transaction</td>\n",
       "      <td>714</td>\n",
       "      <td>3.53</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>120</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306533</th>\n",
       "      <td>c02b10e8752c4d8e9b73f918558531f7</td>\n",
       "      <td>transaction</td>\n",
       "      <td>714</td>\n",
       "      <td>4.05</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>120</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>306534 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             customer_id           event  time  amount  \\\n",
       "0       78afa995795e4d85b5d9ceeca43f5fef  offer received     0     nan   \n",
       "1       a03223e636434f42ac4c3df47e8bac43  offer received     0     nan   \n",
       "2       e2127556f4f64592b11af22de27a7932  offer received     0     nan   \n",
       "3       8ec6ce2a7e7949b1bf142def7d0e0586  offer received     0     nan   \n",
       "4       68617ca6246f4fbc85e91a2a49552598  offer received     0     nan   \n",
       "...                                  ...             ...   ...     ...   \n",
       "306529  b3a1272bc9904337b331bf348c3e8c17     transaction   714    1.59   \n",
       "306530  68213b08d99a4ae1b0dcb72aebd9aa35     transaction   714    9.53   \n",
       "306531  a00058cf10334a308c68e7631c529907     transaction   714    3.61   \n",
       "306532  76ddbd6576844afe811f1a3c0fbb5bec     transaction   714    3.53   \n",
       "306533  c02b10e8752c4d8e9b73f918558531f7     transaction   714    4.05   \n",
       "\n",
       "                                offer_id  reward  days  months offer_type  \n",
       "0       9b98b8c7a33c4b65b9aebfe6a799e6d9     nan     1       1       bogo  \n",
       "1       0b1e1539f2cc45b7b9fa7c272da2e1d7     nan     1       1   discount  \n",
       "2       2906b810c7d4411798c6938adc9daaa5     nan     1       1   discount  \n",
       "3       fafdcd668e3743c1bb461111dcafc2a4     nan     1       1   discount  \n",
       "4       4d5c57ea9a6940dd891ad53e9dbe8da0     nan     1       1       bogo  \n",
       "...                                  ...     ...   ...     ...        ...  \n",
       "306529                                       nan   120       4        NaN  \n",
       "306530                                       nan   120       4        NaN  \n",
       "306531                                       nan   120       4        NaN  \n",
       "306532                                       nan   120       4        NaN  \n",
       "306533                                       nan   120       4        NaN  \n",
       "\n",
       "[306534 rows x 9 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transcript_offer_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\wonso\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:4170: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  errors=errors,\n"
     ]
    }
   ],
   "source": [
    "# Tidiness of the Dataset - seperate into two datasets: offer_based, and sales_based \n",
    "\n",
    "# Offer_based_transaction: event != \"transaction\"\n",
    "# From offer_based_transaction, we can figure out which offers are provided which customers. \n",
    "offer_based_transaction = transcript_offer_name.query('event != \"transaction\"')\n",
    "offer_based_transaction.drop(['amount'], axis = 1, inplace=True)\n",
    "\n",
    "# sales_based_transaction: event == \"transaction\"\n",
    "# From sales_based_transaction, we can figure out how much customers spent at our stores. \n",
    "sales_based_transaction = transcript_offer_name.query('event == \"transaction\"')\n",
    "sales_based_transaction.drop(['offer_id'], axis = 1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make Checkpoints\n",
    "offer_info_df = portfolio.copy()\n",
    "customer_info_df = profile_cleaned.copy()\n",
    "transactions_df = transcript_offer_name.copy()\n",
    "sales_transaction_df = sales_based_transaction.copy()\n",
    "offers_transaction_df = offer_based_transaction.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Exploration'></a>\n",
    "## 3. Data Exploration  \n",
    "- 3.1. Current Business Situations\n",
    "- 3.2. Customer Demographics\n",
    "- 3.3. Characteristics of Starbucks Offers\n",
    "- 3.4. Customer Purchasing Patterns <br>\n",
    "\n",
    "\n",
    "### 3. 1. Current Business Situations\n",
    "\n",
    "#### The Change of traffics during the test period"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'Daily and Monthly Traffics')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[15,4]) \n",
    "plt.subplots_adjust(wspace = 0.3 , hspace= 0.5)\n",
    "base_color =sns.color_palette()[0]\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "sales_transaction_df.groupby('days')['customer_id'].count().plot(kind ='line')\n",
    "plt.ylabel('Daily Traffics')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "sns.countplot(data = sales_transaction_df, x = 'months', color = base_color)\n",
    "plt.ylabel('Monthly Traffics')\n",
    "\n",
    "plt.suptitle('Daily and Monthly Traffics')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The Sales Trend  Amount during the test period"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'Daily and Monthly Sales Amount')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[15,4]) \n",
    "plt.subplots_adjust(wspace = 0.3 , hspace= 0.5)\n",
    "\n",
    "plt.subplot(1, 3, 1)\n",
    "sales = sales_transaction_df.groupby('days',  as_index= False)['amount'].sum()\n",
    "sales['sales_MA(6)'] = sales.iloc[:,1].rolling(window =6).mean()\n",
    "plt.plot(sales['days'], sales['sales_MA(6)'])\n",
    "plt.xlabel('Testing days')\n",
    "plt.ylabel('sales_MA(6)')\n",
    "\n",
    "plt.subplot(1, 3, 2)\n",
    "sales_transaction_df.groupby('months')['amount'].sum().plot(kind ='bar')\n",
    "plt.ylabel('Total Monthly Average Sales')\n",
    "\n",
    "plt.subplot(1, 3, 3)\n",
    "sns.barplot(data = sales_transaction_df, x = 'months', y= 'amount',color = base_color)\n",
    "plt.ylabel('Monthly Average Sales per each traffic')\n",
    "\n",
    "\n",
    "plt.suptitle('Daily and Monthly Sales Amount')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=0.2517026240434585, pvalue=0.7774763978214865)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "month_1 = sales_transaction_df.query('months == 1').amount\n",
    "month_2 = sales_transaction_df.query('months == 2').amount\n",
    "month_3 = sales_transaction_df.query('months == 3').amount\n",
    "month_4 = sales_transaction_df.query('months == 4').amount\n",
    "\n",
    "import scipy \n",
    "scipy.stats.f_oneway(month_1, month_2, month_3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">**Comments:** \n",
    ">- The number of traffics varies in each month. It increased until the third month. \n",
    ">- In the fourth month, however, the total number of traffics slightly decreased. Note that the 4th month traffics are still as large as that in the third month. \n",
    ">- The change of sales amounts follows almost identical patterns with the change of traffics. We can understand that more traffics bring better sales performances. \n",
    ">- Interestingly, although the number of traffics and sales amount are different in each month, the average spendings per each transaction are very similar across the months. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  3. 2. Customer Demographics\n",
    "\n",
    "> In this section, I will explore whether there is a significant difference of issued offers on various demographic factors.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Making a new dataframe by Modifying the dataframe: offers_transaction_df\n",
    "# The new dataframe will indicates the number of offers that each customer received \n",
    "all_offers_infos = pd.concat([offers_transaction_df, pd.get_dummies(offers_transaction_df.offer_type)], axis = 1)\n",
    "all_issued_offers =all_offers_infos.query('event == \"offer received\"')\n",
    "\n",
    "# all_issued_offers per customers\n",
    "issued_offers_per_customer_df  =all_issued_offers.groupby('customer_id', as_index= False)[['bogo','discount', 'informational']].sum()\n",
    "\n",
    "# Dataframe (all_issued_offers per customers) is merged with customer profile\n",
    "customers_offers_merged_profile = issued_offers_per_customer_df.merge(customer_info_df, \n",
    "                                                                      left_on = 'customer_id', right_on= 'customer_id', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16994, 9)\n"
     ]
    },
    {
     "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>bogo</th>\n",
       "      <th>discount</th>\n",
       "      <th>informational</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>became_member_on</th>\n",
       "      <th>income</th>\n",
       "      <th>days_as_member</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0009655768c64bdeb2e877511632db8f</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>M</td>\n",
       "      <td>33</td>\n",
       "      <td>2017-04-21</td>\n",
       "      <td>72000.00</td>\n",
       "      <td>466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00116118485d4dfda04fdbaba9a87b5c</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>O</td>\n",
       "      <td>55</td>\n",
       "      <td>2018-04-25</td>\n",
       "      <td>64000.00</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0011e0d4e6b944f998e987f904e8c1e5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>O</td>\n",
       "      <td>40</td>\n",
       "      <td>2018-01-09</td>\n",
       "      <td>57000.00</td>\n",
       "      <td>203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0020c2b971eb4e9188eac86d93036a77</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>59</td>\n",
       "      <td>2016-03-04</td>\n",
       "      <td>90000.00</td>\n",
       "      <td>879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0020ccbbb6d84e358d3414a3ff76cffd</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>24</td>\n",
       "      <td>2016-11-11</td>\n",
       "      <td>60000.00</td>\n",
       "      <td>627</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        customer_id  bogo  discount  informational gender  \\\n",
       "0  0009655768c64bdeb2e877511632db8f     1         2              2      M   \n",
       "1  00116118485d4dfda04fdbaba9a87b5c     2         0              0      O   \n",
       "2  0011e0d4e6b944f998e987f904e8c1e5     1         2              2      O   \n",
       "3  0020c2b971eb4e9188eac86d93036a77     2         2              1      F   \n",
       "4  0020ccbbb6d84e358d3414a3ff76cffd     2         1              1      F   \n",
       "\n",
       "   age became_member_on   income  days_as_member  \n",
       "0   33       2017-04-21 72000.00             466  \n",
       "1   55       2018-04-25 64000.00              97  \n",
       "2   40       2018-01-09 57000.00             203  \n",
       "3   59       2016-03-04 90000.00             879  \n",
       "4   24       2016-11-11 60000.00             627  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(customers_offers_merged_profile.shape)\n",
    "customers_offers_merged_profile.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": false
   },
   "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>bogo</th>\n",
       "      <th>discount</th>\n",
       "      <th>informational</th>\n",
       "      <th>age</th>\n",
       "      <th>income</th>\n",
       "      <th>days_as_member</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>16994.00</td>\n",
       "      <td>16994.00</td>\n",
       "      <td>16994.00</td>\n",
       "      <td>16994.00</td>\n",
       "      <td>16994.00</td>\n",
       "      <td>16994.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.79</td>\n",
       "      <td>1.80</td>\n",
       "      <td>0.90</td>\n",
       "      <td>54.47</td>\n",
       "      <td>65226.90</td>\n",
       "      <td>522.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.12</td>\n",
       "      <td>1.13</td>\n",
       "      <td>0.87</td>\n",
       "      <td>16.23</td>\n",
       "      <td>20174.70</td>\n",
       "      <td>411.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>18.00</td>\n",
       "      <td>30000.00</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>51000.00</td>\n",
       "      <td>213.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>55.00</td>\n",
       "      <td>64000.00</td>\n",
       "      <td>363.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>65.00</td>\n",
       "      <td>76000.00</td>\n",
       "      <td>796.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>101.00</td>\n",
       "      <td>120000.00</td>\n",
       "      <td>1828.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          bogo  discount  informational      age    income  days_as_member\n",
       "count 16994.00  16994.00       16994.00 16994.00  16994.00        16994.00\n",
       "mean      1.79      1.80           0.90    54.47  65226.90          522.44\n",
       "std       1.12      1.13           0.87    16.23  20174.70          411.27\n",
       "min       0.00      0.00           0.00    18.00  30000.00            5.00\n",
       "25%       1.00      1.00           0.00    45.00  51000.00          213.00\n",
       "50%       2.00      2.00           1.00    55.00  64000.00          363.00\n",
       "75%       3.00      3.00           1.00    65.00  76000.00          796.00\n",
       "max       6.00      6.00           5.00   101.00 120000.00         1828.00"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers_offers_merged_profile.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  Gender (categorical variable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Informational')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jkZCQIHQMIqJKefbsGTZv3oyjR4+WeZBq/PjxAqQiIkC7zx6z0SMinWNjY4OrV6+iXbt20NPTEzoOEVG5wsLCcOzYMRQUFODOnTtCxyGit2jz2WM2ekSkc37//XeMHDkSBgYGMDIygkwmg0gkwtWrV4WORkT0jp07d+Lbb7/F9u3bMXHiRKHjEJGOYKNHRDonOlr9z7khIlLV77//jvj4eOzduxcfffQRZDJZqeX9+/cXKBkRaTM2ekSkc2xsbJCQkIBbt25h6tSpOHnyJAYOHCh0LCKiMvn5+eHgwYN4/vw5du/eXWqZSCRio0dEKqmw0Vu5ciXmzJmjjixERNVi69atOHfuHJ48eYJx48Zhw4YNePDgAWbMmCF0NCKid3h6esLT0xORkZFYsGCB0HGISEdUOErBqVOn1BCDiKj6HDt2DNu2bYOpqSnq1auH2NhYHD16VOhYREQK5eXlQSwWY8aMGZg5cyZ27NiB169fV2rd+Ph4uLm5oX///mVeun7jxg0MGTIEn3/+OaZMmYJXr15Vd3wi0kAVntFr1KgRJkyYAGdnZ5ibm8vnc6hfItJUbwZheaN27dowMOCV6kSkmV6+fAlvb280a9YMXbt2RVFRES5evIj9+/dj3759qFevnsJ1MzMzsXr1asTFxcHIyAjDhw9H586dYWdnJ3/N119/DT8/P/To0QNLly7F9u3b4e/vr46PRkQCqvCbT926dQEAGRkZNZ2FiKhaNGzYEKdOnYJIJEJRURG2b98OGxsboWMREZVp3bp18PLywqRJk+TzJkyYgM2bN2Pt2rVYvHixwnXPnz+PLl26yL+vubq6IiEhATNnzpS/RiqVIjc3FwCQn5+POnXq1MjnICLNUmGjFxkZCQB49eoVateuXeOBiIiqKjg4GAEBAfjtt9/Qrl07ODk5YeXKlULHIiIq06VLl3D48OF35k+ePBlubm7lrvv06VNYWlrKp8ViMVJTU0u9Zv78+ZgwYQIiIiJgamqK2NhYpTOmpaWVOb9Dhw5Kb0tXpKSkqLwu95vyuM+UV2Gj98cff2DGjBnIycnBwYMH5QMbNGvWTKU3JCKqKWvWrMGsWbOQnp6OXbt2IT8/HyUlJbCwsBA6GhFRufT19d+Zp6enV+oy9LJIpVKIRCL59Jvnhr5RUFCAwMBAfPfdd3B0dMTOnTvx1VdfYevWrUrls7e3h7GxsVLr6Lr3ufGoCu435SnaZ4WFhQoPwgCVGIwlLCwMgYGBaNCgAaysrDB69GgsWrRI9aRERDXk6NGjyMzMxJIlS/Dy5UsUFhaiuLgY2dnZyM7OFjoeEVGZpFIpcnJy3plfmUFTrK2tkZWVJZ/OysqCWCyWT9+5cwfGxsZwdHQEAHh7e+PSpUvVkJqINF2FjV52dja6d+8unx41alSlR4EiIlKnTz75BD179sTdu3fRuXNndOnSRf5f165dhY5HRFSmgQMHYsmSJaUelF5YWIiQkBAMGjSo3HW7deuG5ORkvHjxAvn5+UhMTISLi4t8ua2tLZ48eYL79+8DAE6ePAkHB4ea+SBEpFEqNQxdYWGh/DKArKwsSKXSGg1FRKSK5s2b49atW+jZs2eVHg3z+vVrDB8+HJs3b0ajRo1KLduwYQMOHTokv2d52LBhGDVqVFViE9F7bvLkyfD390evXr3Qrl07FBcXIzU1FZ06dcKECRPKXdfKygr+/v7w8fGBRCKBl5cXHB0d4evrCz8/Pzg4OCAyMhKzZs2CTCZDgwYNEBERoaZPRkRCqrDRGzFiBCZOnIjnz59j5cqVOHbsWKlRoYiINMWePXswcuTIcocir8i1a9cQFBSEP//8s8zlaWlpWLVqFdq3b6/yexARvU1fXx/r1q3Dr7/+il9++QUA4OvrCycnp0qt7+7uDnd391Lztm3bJv+5R48e6NGjR/UFJiKtoLDRe/ToET788EMMHToU//rXv3Dq1CkUFxcjLCys1KWc5eFRcSJSpyZNmsDJyQklJSVwdnaWz38zOMHVq1cr3EZsbCxCQkIQEBBQ5vK0tDRs2bIFGRkZ6NSpE7766isOUEBE1aJdu3Zo166d0DGISEcobPS++OILHDp0CPPmzUNUVBQ6deqk1IZ5VJyI1G3jxo148uQJfH19lR5R7o2vv/5a4bLc3Fy0bt0a8+bNg62tLebPn49vvvlGqQcPlzc61vs8EhmHKVcNhylXXlV+14iItInCRu/Vq1cIDg7GuXPnEB4e/s7yoKCgcjfMo+JEpG56enr48MMPsWPHDly/fh0ikQiOjo6lnjFVFebm5qUuh5owYQIWLlyoVKPHIcrL9j43HlXB/aY8VYcpJyLSNgobvaioKJw9exZ6enqoW7eu0huu6aPiAI+MK8IjvMrj2QTVaOKR8VWrVmHPnj1o0qQJJBIJ0tPTMWrUKMydO7fK23706BHOnz8PLy8vAP9cEmpgUKkxrYiIiIjUSuE3lIiICMTGxiI+Ph4zZ86s1jetjqPiAI+MK/I+Nx6q4j5TjaYdGT906BB++eUX/Pzzz2jQoAEAIDMzE3PmzMGBAwcwdOjQKm3fxMQEUVFR6Ny5Mxo1aoTo6Gj069evOqIT0Xts6tSp5S7fvHmzmpIQkS5R2Og9e/YMmzdvRkFBAXbu3PnO8vHjx6v8pjwqTkQ1Yf/+/diwYYO8yQP+GXp8xYoVmDFjhsqN3tvDlIeGhmLatGmQSCRwdnauUi0kIgIAV1dXoSMQkQ5S2F2FhYXh2LFjKCgowJ07d6r1TXlUnIhqQlFREcRi8Tvzra2tUVRUpNS2kpKS5D+/fQWCq6srv5QRUbXy9PQsc75MJsODBw/UnIaIdIXCRq979+7o3r07tm/fjokTJ1bLm/GoOBHVpLy8PIXLJBKJGpMQESlv3759WL58OfLz8+Xz6tevj3PnzgmYioi0VbnXS2ZmZiIjIwMeHh4QiURo3749Jk+eDGtr60q/AY+KE5G62Nvb48iRIxg0aFCp+YcPH4ajo6NAqYiIKmfr1q3YuXMnNm3ahFmzZuE///kPnjx5InQsItJSChu9x48fw9vbG5999hm+/PJLFBUV4eLFi/Dy8sL+/fthY2OjzpxERBXy9/fHmDFjcO/ePXTs2BHFxcW4ePEiEhISsH//fqHjERGVq27dunByckLr1q3x/PlzTJs2DW5ubkLHIiItpbDRW7NmDWbPng0PDw/5PFdXV7Rt2xZr1qxBVFSUOvIREVVa48aNER0djS1btmDFihUAAGdnZ8TGxip1JQIRkRAMDAzw8uVL2NraIjU1Fd27d0dJSYnQsYhISyls9G7evIlly5a9M3/IkCHYunVrjYYiIlKVjY0NQkNDhY5BRKS0YcOGYcqUKdi8eTM8PDzw008/oWnTpkLHIiItpbDRk8lkClcyMjKqkTBERERE7ysvLy+4ubnBzMwM+/fvx/Xr1/Hpp58KHYuItJTCRk9fXx+ZmZmwsrIqNT8zM5ONHhEREVE1y8/Px4kTJ/Dy5Uv5AfeYmBiOTE5EKlHY6A0fPhwLFy7E2rVrYWFhAQB4/vw5AgICMHLkSLUFJCIiInofBAQEICMjAy1atIBIJBI6DhFpOYWN3ogRI/Dw4UN8+umnsLOzQ3FxMf7880/4+PhgyJAh6sxIRKSUSZMm4dtvvy01b9iwYYiNjRUoERFRxX777TccP34cBgblPv2KiKhSyq0kX331FcaNG4dr164BAJycnN65lJOISFP4+fnhjz/+wF9//QV3d3f5/OLiYl5yTkQaj6MDE1F1qvCQkZWVFfr376+OLEREVfLmsqfg4GAEBwfL5+vr68POzk7AZEREFWvRogV8fHzw6aefwsTERD6f9+gRkSp4bQAR6YxGjRqhUaNGSEhIgJ6entBxiIiUkpubC1tbWzx8+FDoKESkA9joEZHOSUpKQkREhHzkOplMBpFIhKtXrwodjYhIocjISABARkYGiouLYWtrK3AiItJmlWr0cnJyIBKJ5KNvEhFpsqioKMyfPx9t2rThyHVEpDUePHiA6dOn4+nTp5BKpahXrx62bNmCZs2aCR2NiLRQuY3emTNnsHz5cty7dw8ikQjNmjXDvHnz0KNHD3XlIyJSWu3atXlvMRFpndDQUEyaNAmenp4AgEOHDmHJkiXYvXu3wMmISBspvInlypUrWLx4MWbNmoWUlBQkJydjxowZWLx4MS5fvqzOjERESnFycsLp06eFjkFEpJTnz5/LmzwAGDJkCP7++28BExGRNlN4Rm/Tpk1YvXo1nJyc5PMGDBgAa2trrF+/Hp06dVJLQCIiZZ0+fRrff/89DA0NYWhoyHv0iEgrlJSUIDs7G3Xr1gUAvHjxQthARKTVFDZ6z58/L9XkvdG+fXs8efKkRkMREVXFd999J3QEIiKljR49Gt7e3hgwYABEIhGOHz+OsWPHCh2LiLSUwkYvPz9f4UoctpyINFl2dnaZ821sbNQbhIhICd7e3rC1tcV///tfSKVShISEoFu3bhWuFx8fj02bNqG4uBhjx47FqFGjSi2/f/8+QkJC8PLlS1haWmLVqlWoU6dOTX0MItIQChs9KysrpKamwtHRsdT81NRUiMXiGg9GRKSqL774Qv6zRCJBVlYW7O3tcfDgQQFTERGV7ffff0ezZs1w48YN1KpVC25ubvJlN27cQNu2bRWum5mZidWrVyMuLg5GRkYYPnw4OnfuDDs7OwCATCbDtGnTEBgYCBcXF6xYsQJbt27FvHnzavxzEZGwFDZ606ZNw7x58xAcHIyOHTuiuLgYFy9eREREBJYtW6bOjERESklKSio1ffHiRcTHxwuUhoiofMuXL8eWLVtKHaR6QyQS4eTJkwrXPX/+PLp06SK/r8/V1RUJCQmYOXMmgH8aRTMzM7i4uAAApk6dilevXlX/hyAijaOw0evatSsCAgIQHh6OBw8eAACaNGmCRYsWoWPHjmoLSERUVZ07d8bSpUuFjkFEVKYtW7YAAGJiYmBtbV1q2d27d8td9+nTp7C0tJRPi8VipKamyqcfPnyIDz74AAsXLsStW7fQtGlTBAcHK50xLS2tzPkdOnRQelu6IiUlReV1ud+Ux32mvHKfo9enTx/06dNHfr/Lm6NFRESa7MaNG/KfZTIZ0tLSUFBQIGAiIiLF3nzPmjx5Mvbs2SMfKVgikeCLL75AQkKCwnWlUilEIpF8+s26bxQXF+PSpUv4/vvv4eDggDVr1mDp0qVKH/yyt7eHsbGxch9Mx73PjUdVcL8pT9E+KywsVHgQBqig0cvPz8fr169haWmJffv2IT8/H3p6ehg9ejT09fWrlpiIqIa8ffmTSCRC/fr1sXjxYuECERGVY86cOTh37hyAf65AeMPAwACurq7lrmttbY0rV67Ip7OyskqNpWBpaQlbW1s4ODgAAAYOHAg/P7/qjE9EGkpho/fHH39g/Pjx8PX1xahRo7Bt2zZ8/PHHuHXrFszMzDB06FB15iQiqrT/vUePiEiTbd++HQCwYMECREZGKrVut27dsH79erx48QKmpqZITExEWFiYfHn79u3x4sUL3L59G61atUJSUlK5g7sQke5Q2OitXLkSc+fOxcCBAwEAtWvXRmRkJO7du4eQkBA2ekSksfLy8rB8+XKcOXMGxcXF6N69OwIDA2FhYSF0NCIihSIjI5GdnY38/HzIZDKUlJTg4cOH6N69u8J1rKys4O/vDx8fH0gkEnh5ecHR0RG+vr7w8/ODg4MDNm7ciKCgIOTn58Pa2hrLly9X46ciIqEobPRu376NDRs2yKdlMhkAwM7ODo8fP675ZEREKoqMjERJSQk2btyIkpISxMTEICwsjCMGE5FGW7dunXxgFn19fUgkEtjZ2VU4arC7uzvc3d1Lzdu2bZv8ZycnJz5ehug9pLDRMzU1LTW9evVq+c8clIWINNm1a9fw448/yqfDw8Pxf//3fwImIiKq2OHDh/Gf//wHS5cuRUBAAC5cuIDTp08LHYuItJSeogX6+vqlnrPSpEkTAMDff//NgViISKOVlJRAKpXKp6VSKesWEWm8+vXrQywWo2nTprh9+zY8PDxw584doWMRkZZS2Oi5u7sjMDAQRUVF8nklJSUIDw+Hh4eHOrIREamka9eumDVrFpKTk5GcnIzZs2fj448/FjoWEVG5DAwM8PDhQzRt2hRXrlxBcXExCgsLhY5FRFpKYaM3btw4GBkZoXfv3vDz88OXX36JXr16QU9PD6NGjVJnRiIipcyfPx/NmzfHqlWrEBUVhaZNm+Krr74SOhYRUbmmTJmC4OBg9OzZE4mJiejZs2epxy0QESlD4T16+vr6WLlyJdLS0pCSkgKZTIaJEyfC0dFRnfmIiFRia2uLAwcOICsrC8eOHYOhoaHQkYiIytWrVy/06tULAHDkyBE8ePAArVq1EjgVEWmrch+YDvzzIE4bGxuIRCI0bNhQHZmIiKpk8eLFyMvLw+effw49PT2kpKQgPT0dQUFBQkcjIlIoKysLP/zwA7Kzs+XzfvzxRwQEBAgXioi0VrmN3qpVq7B79240a9YMRUVFSE9Px6hRozB37lx15SMiUtqvv/6Ko0ePAgAaNGiAtWvXYtCgQQKnIiIq37Rp02BtbY3GjRsLHYWIdIDCRu/QoUP45ZdfcPLkSTRo0AAAkJmZiTlz5uDAgQN8YDoRaSyJRIKioiIYGRkBAIqLi5Va//Xr1xg+fDg2b96MRo0alVp269YtBAYGIjc3Fx07dsSSJUtgYFDhxRFERBWSSCSlnmFMRFQVCgdj2b9/P1auXClv8gDAysoKK1aswL59+yq18devX2PgwIFIT09/Z9mtW7cwePBguLq6IjAwUOkvYkREivTs2RMTJ07EwYMHcejQIUyZMgU9evSo1LrXrl3DiBEj8Oeff5a5fN68eVi0aBFOnDgBmUyG2NjYakxORO+ztm3b8nEKRFRtFDZ6RUVFEIvF78y3trYu9cgFRfhliYiEEhAQgH79+uHkyZM4deoU+vXrh9mzZ1dq3djYWISEhJRZ/zIyMlBQUIB27doBAAYPHoyEhITqjE5E7zFnZ2d4eHigR48e6NOnj/w/IiJVKLzeKC8vT+FKEomkwg2/+bJU1g3EZX1ZWrduHUaOHFmJyERE5dPX14ePjw98fHyQmZmJv/76C3p6Co9rlfL1118rXPb06VNYWlrKpy0tLZGZmalUtrS0NIXLOnTooNS2dElKSorK63K/KY/7TDNt374dK1aswEcffSR0FCLSAQobPXt7exw5cuSdAQwOHz5cqUcs1PSXJYBfmBThP/zK45dM1WjqF6aYmBikpKQgMDAQgwcPhoWFBfr37485c+ZUabtSqRQikUg+LZPJSk1Xhr29PYyNjauUQxe9z39HVcH9pjxF+6ywsLDc7xXqULt2bbi5uQmagYh0h8JGz9/fH2PGjMG9e/fQsWNHFBcX4+LFi0hISMD+/fur9KbV8WUJ4BcmRfgPv/K4z1SjqV+YDh48iK1btyIhIQG9e/dGSEgIhg0bVuVGz9raGllZWfLpZ8+elXmJJxGRKrp06YJly5ahf//+8sGkgH/u3SMiUpbCRq9x48b4/vvvsXXrVqxYsQLAP9eOx8bGwtraukpvyi9LRFSTRCIRPvjgAyQnJ2PAgAEwMDCAVCqt8nZtbGxgbGyMlJQUdOjQAUeOHIGLi0s1JCYiAuLj4wEAJ06ckM8TiUQ4efKkUJGISIuVOyZ4o0aNEBoaWu1vyi9LRFSTjIyMsG3bNly6dAnh4eGIiYmBqampytvz9fWFn58fHBwcsGLFCgQFBeH169do27YtfHx8qjE5Eb3PFi5ciL59+wodg4h0hMJGTyqV4vjx47C0tISzszPCw8Nx5coVODo6YuHChahVq5bSb8YvS0SkDuHh4dixYweWLVuGOnXqICUlBeHh4UptIykpSf7ztm3b5D+3atUKBw8erLasRERvrF69mo0eEVUbhY1eREQErl69Kn/MgpGREfz9/XHmzBksXrwYK1eurNQb8MsSEalbs2bNSg0IVdl6RUQkpBYtWmDTpk3o2LEjzMzM5PN5jx4RqUJho3f+/HkcPnwYubm56NWrFy5cuAATExP07t0b//d//6fOjERElTJixAjs3bsX7du3L3OAp6tXrwqQioiocq5du4Zr167hwIED8nm8R4+IVKWw0TM0NISRkRGMjIzQuHFjmJiYAAD09PTkPxMRaZK1a9cCAI4ePSpwEiIi5b19FRQRUVUpbPTefriwgUG5Y7YQEWkEsVgMmUyGP/74A3fu3IGJiQlatGiBjh07Ch2NiKhCeXl5WL58Oc6cOYPi4mJ0794dgYGBsLCwEDoaEWkhhR3c8+fPsXPnznd+BoAXL17UfDIiIiU9e/YMkyZNQn5+Plq1agWRSISdO3eifv362Lp1K+rUqSN0RCIihSIjI1FSUoKNGzeipKQEMTExCAsLw7Jly4SORkRaSGGj1717d9y5c+ednwGgW7duNZ+MiEhJGzZswIABAzBlypR35kdFRSk98iYRkTpdu3YNP/74o3w6PDyc4yIQkcoUNnqRkZHqzEFEVGVXrlwp8/686dOnw9PTU4BERESVV1JSAqlUKr99RiqVQl9fX+BURKStePMdEekMRfcT6+nplbrvmIhIE3Xp0gWzZs3CiBEjAAB79+5F586dBU5FRNqK33yISGeU9UgFIiJNV1RUBABYsGAB7OzssGrVKkRFRaFJkyYICAiocP34+Hi4ubmhf//+iI6OVvi6U6dOoXfv3tWWm4g0m8Izej///DP69u2LoqIiGBkZqTMTEZFK/nfgqLdxECki0lSjR49GbGwsVq9ejXnz5sHPz6/S62ZmZmL16tWIi4uDkZERhg8fjs6dO8POzq7U6549e8ZBXYjeMwobvbVr16Jv377w9vbGDz/8oM5MREQq+d+Bo97GQaSISFM9e/YMmzdvxtGjR/HBBx+8s3z8+PEK1z1//jy6dOmCunXrAgBcXV2RkJCAmTNnlnpdUFAQZs6ciZUrV1ZrdiLSXAobPXNzc7i6uiIzMxPu7u7vLI+Pj6/RYEREyuIgUkSkjcLCwnDs2DEUFBQoPFilyNOnT2FpaSmfFovFSE1NLfWa3bt3o02bNnByclI5Y1paWpnzO3TooPI2tV1KSorK63K/KY/7THkKG71vv/0Wt27dQmBgIIKDg1UORkRERESKde/eHd27d8f27dsxceJEpdaVSqWl7k+WyWSlpu/cuYPExER89913ePLkicoZ7e3tYWxsrPL6uuh9bjyqgvtNeYr2WWFhocKDMEA5jZ6FhQU6deqELVu2QCwW48aNGyguLoajoyMsLCyqnpiIiIiI5D7//HNs2LAB2dnZpeYHBQUpXMfa2hpXrlyRT2dlZUEsFsunExISkJWVhSFDhkAikeDp06cYOXIkYmJiqj0/EWmWCh+vkJOTgzFjxuCDDz5ASUkJMjMzsXnzZjg7O6sjHxFRpXEQKSLSZgEBATAxMUGbNm0qPYpwt27dsH79erx48QKmpqZITExEWFiYfLmfn598cJf09HT4+PiwySN6T1TY6C1btgwrVqxAly5dAADJyclYunQpYmNjazwcEZEyOIgUEWmzJ0+e4N///rdS61hZWcHf3x8+Pj6QSCTw8vKCo6MjfH194efnBwcHhxpKS0SarsJGLzc3V97kAUDXrl0RERFRo6GIiFTBQaSISJt9+OGHyMvLg5mZmVLrubu7v1Pztm3b9s7rGjVqhKSkpCplJCLtUWGjJxKJkJGRARsbGwD/nPbX19ev8WBERMriIFJEpM3EYjE8PDzw8ccfw8TERD6/vHv0iIgUqbDRmzFjBry9vdG1a1eIRCKcPXsWISEh6shGRKQUDiJFRNrMxsZGfmCdiKiqKmz0+vbti6ZNm+LChQuQSqWYMmUKmjVrpo5sREQq4SBSRKSN/vch50REVVFhowcATZs2RdOmTWs6CxFRteAgUkSkTaZOnVru8s2bN6spCRHpkko1ekRE2oSDSBGRNnF1dRU6AhHpIDZ6RKRzOIgUEWkTT09PoSMQkQ7SU7SgqKgIALBmzRr5vLd/JiLSVG8GkZo3bx4CAgIwbNgwTJs2TehYRERERGqj8Iyeh4cH6tevj/T0dDg4OKB169Y4ffo0Zs2apcZ4RETK4yBSRERE9L5T2OgdP34cGRkZGDFiBC5fvoxdu3bh/v37mD59Oj7++GOMGzdOjTGJiJTDQaSISFv8/PPP6Nu3L4qKimBkZCR0HCLSEQov3Tx37hzq16+PBg0aYP78+di9ezf+9a9/wc/PDwYGvLWPiIiIqDqsXbsWAODt7S1wEiLSJQo7thMnTmDp0qV4/PgxQkJC0KZNGxQWFqJFixZo1aqVOjMSERER6Sxzc3O4uroiMzMT7u7u7yyPj48XIBURaTuFjV5oaCgAYMCAAejTpw9u376N58+f47PPPkPjxo2xfft2tYUkIqqMN5c9rVmzRn4/8ds/ExFpom+//Ra3bt1CYGAggoODhY5DRDqiwmswO3ToABcXF7i4uODhw4cIDw/HgwcP1JGNiEgpHESKiLSRhYUFOnXqhC1btkAsFuPGjRsoLi6Go6MjLCwshI5HRFqqwkYvPDz8nZ9tbW1rLhERkYo4iBQRabOcnByMGTMGH3zwAUpKSpCZmYnNmzfD2dlZ6GhEpIU4qgoR6Yxz587B2dlZPogUAAwaNAh+fn64cuWKwOmIiMq3bNkyrFixAl26dAEAJCcnY+nSpYiNjRU4GRFpI4WjbhIRaZsTJ05g2LBh+OuvvxASEoL9+/fLB5EaPXq00PGIiMqVm5srb/IAoGvXrsjPzxcwERFpsxpt9OLj4+Hm5ob+/fsjOjr6neUbNmxAr169MGjQIAwaNKjM1xARVVZoaCji4+NhaWmJPn364OXLl/JBpCZOnFipbbBuEZFQRCIRMjIy5NPp6enQ19cXMBERaTOVLt0MDg5GWFhYua/JzMzE6tWrERcXByMjIwwfPhydO3eGnZ2d/DVpaWlYtWoV2rdvr0oMIqIyqTqIFOsWEQlpxowZ8Pb2RteuXSESiXD27FmEhIQIHYuItJRKZ/Tatm1b4WvOnz+PLl26oG7dujAzM4OrqysSEhJKvSYtLQ1btmyBu7s7QkNDUVhYqEocIqJSVB1EinWLiITUt29f7N69G+3bt4ejoyP27NkDV1dXoWMRkZZS6Yze8OHDK3zN06dPYWlpKZ8Wi8VITU2VT+fm5qJ169aYN28ebG1tMX/+fHzzzTfw9/evdI60tDSFyzp06FDp7eialJQUldbjPlMN95vuYN0SDv8GVcN6rzxNr1tNmzZF06ZNhY5BRDqgwkZvwYIFpaZFIhFMTU3RvHlzDB06VOG141KpFCKRSD4tk8lKTZubm2Pbtm3y6QkTJmDhwoVKfWGyt7eHsbFxpV//vnif/wFXFfeZahTtt8LCwnIbGk3FuiUc/g2qhvtNebpWt4iIFKnUpZs3b95Ey5Yt0bp1a9y7dw+PHj3C2bNnERERoXAda2trZGVlyaezsrIgFovl048ePcLBgwfl0zKZDAYGfNoDEQmHdYuIiIh0RYWN3u+//47o6GiMGzcOPj4+2LVrF7Kzs7F+/XpcuHBB4XrdunVDcnIyXrx4gfz8fCQmJsLFxUW+3MTEBFFRUfjrr78gk8kQHR2Nfv36Vc+nIiL6H8HBwRW+hnWLiIRQVFQEAFizZo183ts/ExGposJD0S9fvoSFhYV82sTEBK9fv4ZIJIKhoaHC9aysrODv7w8fHx9IJBJ4eXnB0dERvr6+8PPzg4ODA0JDQzFt2jRIJBI4Oztj/Pjx1fOpiIj+R2UGkWLdIiIheHh4oH79+khPT4eDgwNat26N06dPY9asWUJHIyItVmGj5+TkhLlz58LLywsymQxxcXFwdHTE6dOnYWpqWu667u7ucHd3LzXv7ftbXF1dOZoUEalFZQaRAli3iEj9jh8/joyMDIwYMQKXL1/Grl27cP/+fUyfPh0ff/wxxo0bJ3REItJCFTZ6oaGh2LhxIyIjI6Gvr49evXph8uTJOHnyJEJDQ9WRkYhIKaoOIkVEJIRz587B2dkZDRo0wPz58wEAgwYNgp+fH65cuVLh+vHx8di0aROKi4sxduxYjBo1qtTyn3/+GevXr4dMJkOjRo0QGRmJOnXq1MhnISLNUWGjd/jwYUyZMgVz5swpNd/Nza3GQhERVdXNmzfh6ekJPT09HD16FA0aNEBmZibu3r1bqfv1iIjU5cSJE1i6dCkeP36MkJAQtGnTBoWFhWjRogVatWpV7rqZmZlYvXo14uLiYGRkhOHDh6Nz586ws7MDALx+/RqLFy/GoUOHYGVlhbVr12L9+vUICgpSx0cjIgFVOBjLxYsX0bdvXyxcuBC//vqrGiIREVWNqoNIEREJITQ0FPHx8bC0tESfPn3w8uVLPH/+HJ999hkmTpxY7rrnz59Hly5dULduXZiZmcHV1RUJCQny5RKJBCEhIbCysgIAtGzZEo8fP67Rz0NEmqHCM3qrV6/Gy5cvcfToUYSHh6OgoABDhw7F2LFj1ZGPiEhpqg4iRUQkpA4dOsDFxQUuLi54+PAhwsPD8eDBg3LXefr0KSwtLeXTYrEYqamp8ul69erJRwcuKCjA1q1bMWbMGKWzKXrG4Pv8LMeUlBSV1+V+Ux73mfIq9QCoOnXqwNvbG2KxGNu2bcO2bdvY6BGRxqrKIFJEREIJDw9/52dbW9ty15FKpRCJRPJpmUxWavqNnJwczJgxA61atYKnp6fS2ezt7WFsbKz0errsfW48qoL7TXmK9llhYaHCgzBAJS7dvHnzJsLCwtCjRw/ExsbC19cXp0+fVj0pEVENCw0NRcOGDREZGYmoqCh89NFHWLRoEXJzczmIFBHpFGtra2RlZcmns7KyIBaLS73m6dOnGDlyJFq2bImvv/5a3RGJSCAVNnrTp09HvXr1cODAAWzatAmFhYWVHqaciEgIbwaROnLkCOLi4vDFF1/A2NgYbm5uaN68udDxiIiqTbdu3ZCcnIwXL14gPz8fiYmJcHFxkS8vKSnB1KlTMWDAAAQGBpZ5to+IdFOFl24mJSUhJycH+/fvR3R0NPLy8jB69Gh1ZCMiUsnFixexZs0a9O7dG8OGDUO7du2EjkREVCOsrKzg7+8PHx8fSCQSeHl5wdHREb6+vvDz88OTJ09w8+ZNlJSU4MSJEwD+uQyTZ/aIdF+5jd79+/exe/duHDlyBDY2NigoKEBSUhJq1aqlrnxERErjIFJEpCuCg4MRFhZW7mvc3d3h7u5eat62bdsAAA4ODrh9+3aN5SMizaXw0s3Jkydj9OjRMDAwwO7du3H06FGYm5uzySMirfBmEKkpU6bAzMxM/qWHiEibtG3bVugIRKSlFJ7Ru3nzJtq2bYvmzZvLR3zidd1EpA1u3ryJQ4cOISEhAW3atIGvry969+4tdCwiIqVxXAQiUpXCRu/UqVNITEzE3r178fXXX6Nnz54oLCxUZzYiIpVMnz4dXl5eOHDgAMRiMRISEjB8+HAcOHBA6GhERAotWLCg1LRIJIKpqSmaN2+OoUOHQl9fX6BkRKSNFF66aWBgADc3N+zZswdxcXEQi8UoLCxE//79sXfvXnVmJCJSSlJSEsaMGYOjR4+iT58+WLJkCT755BOhYxERVejmzZto2bIlWrdujXv37uHRo0c4e/YsIiIihI5GRFqmUg9Mt7OzQ1BQEObMmYMff/wR+/btw4gRI2o6GxGR0jiIFBFpq99//x3R0dGwsLAAAAwdOhQTJkxATEwMBg4cKHA6ItI2FT5H722mpqbw9vbGDz/8UFN5iIhUxkGkiEibvXz5Ut7kAYCJiQlev34NkUgEQ0NDAZMRkTaq1Bk9IiJtwEGkiEibOTk5Ye7cufDy8oJMJkNcXBwcHR1x+vRpmJqaCh2PiLSMUmf0iIg02alTp+Dp6YmjR4/ik08+gZ+fHweRIiKtERoaioYNGyIyMhJRUVH46KOPsGjRIuTm5iI0NFToeESkZXhGj4h0xptBpNzc3HDv3j3s27dPPojU+PHjeW8xEWm0w4cPY8qUKZgzZ06p+W5ubgIlIiJtxjN6RKST3gwidebMGUycOBGxsbFCRyIiKtfFixfRt29fLFy4EL/++qvQcYhIy7HRIyKdxkGkiEhbrF69GidOnEDbtm0RHh6OgQMHYteuXULHIiItxUaPiIiISEPUqVMH3t7emDJlCszMzLBt2zahIxGRluI9ekREREQa4ObNmzh06BASEhLQpk0b+Pr6onfv3kLHIiItxTN6RERERBpg+vTpqFevHg4cOIBNmzahsLAQw4cPFzoWEWkpntEjIiIi0gBJSUnIycnB/v37ER0djby8PIwePVroWESkpdjoEREREQns/v372L17N44cOQIbGxsUFBQgKSkJtWrVEjoaEWkpXrpJREREJKDJkydj9OjRMDAwwO7du3H06FGYm5uzySOiKmGjR0RERCSgmzdvom3btmjevDlsbW0BACKRSOBURKTt2OgRERERCejUqVPw9PTE0aNH8cknn8DPzw+FhYVCxyIiLcdGj4iIiEhABgYGcHNzw549exAXFwexWIzCwkL0798fe/fuFToeEWkpNnpEREREGsLOzg5BQUE4c+YMJk6ciNjYWKEjEZGWYqNHREREpGFMTU3h7e2NH374QegoRKSl2OgRERERERHpGDZ6REREREREOqZGG734+Hi4ubmhf//+iI6Ofmf5rVu3MHjwYLi6uiIwMBDFxcU1GYeIqEKsW0SkbVi3iKgsNdboZWZmYvXq1YiJicHhw4exf/9+3Lt3r9Rr5s2bh0WLFuHEiROQyWS84ZiIBMW6RUTahnWLiBQxqKkNnz9/Hl26dEHdunUBAK6urkhISMDMmTMBABkZGSgoKEC7du0AAIMHD8a6deswcuTICrctk8kAAEVFReW+rraZoeofQEtV9bk73Geq4X4r7c3f5pu/VW3BuiUM/g2qhvVeeaxbytUtoHK1i79LquF+Ux73WWkV1a0aa/SePn0KS0tL+bRYLEZqaqrC5ZaWlsjMzKzUtiUSCQDgzp075b7O172ZMpF1QlpaWpXW5z5TDfdb2SQSCUxMTNSQpnqwbgmDf4OqYb1XHuuWcnULqFzt4u+SarjflMd9VjZFdavGGj2pVAqRSCSflslkpaYrWl4ec3NztGjRAoaGhpVeh4jURyaTQSKRwNzcXOgoSmHdInp/sW6VjbWLSHNVVLdqrNGztrbGlStX5NNZWVkQi8WllmdlZcmnnz17Vmp5efT09FCrVq3qC0tE1U6bjoi/wbpF9H5j3XoXaxeRZiuvbtXYYCzdunVDcnIyXrx4gfz8fCQmJsLFxUW+3MbGBsbGxkhJSQEAHDlypNRyIiJ1Y90iIm3DukVEiohkNXjXcXx8PLZs2QKJRAIvLy/4+vrC19cXfn5+cHBwwO3btxEUFITXr1+jbdu2iIyMhJGRUU3FISKqEOsWEWkb1i0iKkuNNnpERERERESkfjX6wHQiIiIiIiJSPzZ6REREREREOoaNHhERERERkY5ho0dERERERKRj2OgRERERERHpGDZ61SA9PR0tW7bEokWLSs2/desWWrZsibi4OIGSab709HTY29tj0KBBpf57/Pix0NE0Vm5uLpYsWYJ+/frh888/x8iRI5GcnCx0LNIyrFuqY91SHusWVRfWLtWwbqlG22uXgdABdEXdunXx3//+FyUlJdDX1wcAHD9+HPXr1xc4meYTi8U4cuSI0DG0gkwmw9SpU9G6dWscO3YMRkZGuHnzJiZPnoyVK1eic+fOQkckLcK6pTrWrcpj3aLqxtqlGtYt5ehC7eIZvWpibm6O1q1b4/Lly/J5586dQ7du3QRMRbrm0qVLePToERYsWCB/2G2bNm0wbdo0fPPNNwKnI23DukXqwLpF1Y21i9RBF2oXG71qNGDAAJw4cQIAkJqaipYtW8LQ0FDgVJrv6dOnpS4j+Pbbb4WOpLGuX78Oe3t7iESiUvM7deqE69evC5SKtBnrlmpYtyqPdYtqAmuX8li3lKMLtYuXblaj3r17Y82aNZBKpfj3v/+NAQMG4Pjx40LH0ni8lKDyRCIRSkpK3pkvkUjeKURElcG6pRrWrcpj3aKawNqlPNYt5ehC7eIZvWpkbm6OVq1aISUlBRcuXOAlBFTtnJyckJaWBolEUmr+r7/+Cnt7e4FSkTZj3aKaxrpFNYG1i2qaLtQuNnrVbMCAAVi5ciXs7e1hYMATplS9OnbsCDs7O0RERMgLT1paGjZt2oTp06cLnI60FesW1STWLaoprF1Uk3ShdrHRq2a9evXCrVu34ObmJnQU0lEbNmyAkZERBg4cCDc3N3z99deIiorSitGfSDOxblFNY92imsDaRTVN22uXSCaTyYQOQURERERERNWHZ/SIiIiIiIh0DBs9IiIiIiIiHcNGj4iIiIiISMew0SMiIiIiItIxbPSIiIiIiIh0DBs90lgJCQkYM2aM0DGIiCqNdYuItBFrl25io0dERERERKRjDIQOQNpv69atOHjwIMzNzdGxY0ecPHkSCQkJWLFiBS5fvoySkhK0adMGQUFBsLCwQO/eveHp6Ynk5GQ8fvwYgwYNwqxZswAAa9euRXx8POrWrQtbW1v5exQVFZW7PUdHR/z222+YPXs2+vXrJ9CeICJtwbpFRNqItYuUwTN6VCX//e9/ERcXh4MHDyIuLg65ubkA/ilE+vr6iIuLw48//gixWIwVK1bI18vLy0NMTAz27duHHTt24K+//sLPP/+MxMREHD58GPv27cPr16/lr69oe82bN8e///1vFhwiqhDrFhFpI9YuUhbP6FGVnD59Gp999hlq164NABg1ahQuXLiAU6dOIScnB+fPnwcASCQSNGjQQL5enz59AABWVlZo0KABXr58ieTkZPTr1w8WFhYAgCFDhmDPnj0AUOH2OnbsWPMfloh0AusWEWkj1i5SFhs9qhIDAwPIZDL5tL6+PgBAKpVi4cKF6NGjBwAgNzcXhYWF8tcZGxvLfxaJRPJtlLWtymzPzMysOj8WEekw1i0i0kasXaQsXrpJVdKjRw8kJiYiJycHAHDw4EEAwCeffILo6GgUFRVBKpUiODgYq1atKndbLi4uSEhIwKtXryCVSnHkyBH5MlW2R0RUFtYtItJGrF2kLDZ6VCVdu3bFsGHD4O3tjcGDByMnJwempqaYPn06bGxs4OnpCTc3N8hkMsyfP7/cbfXo0QNDhgzBkCFDMHToUNSqVUu+TJXtERGVhXWLiLQRaxcpSyR7+7wtkZKuX7+OX375BT4+PgCAnTt34tq1a1izZo2wwYiIFGDdIiJtxNpFymKjR1Xy+vVrLFy4EPfv34dIJELDhg0RFhYGKysroaMREZWJdYuItBFrFymLjR4REREREZGO4T16REREREREOoaNHhERERERkY5ho0dERERERKRj2OgRERERERHpGDZ6REREREREOub/AeCPSO/3mNqwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x216 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[15,3]) \n",
    "plt.subplots_adjust(wspace = 0.3 , hspace= 0.5)\n",
    "gender_order = customers_offers_merged_profile.gender.value_counts()\n",
    "\n",
    "plt.subplot(1, 3, 1)\n",
    "sns.barplot(data = customers_offers_merged_profile, x = 'gender', y= 'bogo', color = base_color, order = gender_order.index)\n",
    "plt.ylabel('Avg. # of BOGO Offer') \n",
    "plt.title('BOGO')\n",
    "\n",
    "plt.subplot(1, 3, 2)\n",
    "sns.barplot(data = customers_offers_merged_profile, x = 'gender', y= 'discount', color = base_color, order = gender_order.index)\n",
    "plt.ylabel('Avg. # of Discount Offer') \n",
    "plt.title('Discount')\n",
    "\n",
    "plt.subplot(1, 3, 3)\n",
    "sns.barplot(data = customers_offers_merged_profile, x = 'gender', y= 'informational', color = base_color, order = gender_order.index)\n",
    "plt.ylabel('Avg. # of Informational Offer') \n",
    "plt.title('Informational')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Income, Age, Days_as_member (numeric variables)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Correlation in Heatmap form \n",
    "plt.figure(figsize=(8,4))\n",
    "s = sns.heatmap(customers_offers_merged_profile.corr(), annot = True, cmap = 'RdBu', vmin = -1, vmax=1)\n",
    "\n",
    "s.set_yticklabels(s.get_yticklabels(), rotation = 0, fontsize =12) \n",
    "s.set_xticklabels(s.get_xticklabels(), rotation = 90, fontsize =12) \n",
    "plt.title('Correlation Heatmap', fontsize =18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Comments:**\n",
    "> - All customers in the profle dataset purchased products at Starbucks, although not all of them received offers. \n",
    "> - The average age of customers are 54.5 years old, and their average yearly income is around 65227. \n",
    "> - There is no significant difference between genders regarding the received type of offers. \n",
    "> - Finally the total number of offers and the number of each type of offers are not correlated with customer's ages, incomes, the number of days as a Starbucks member."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. 3. Starbucks Offers Exploration\n",
    "\n",
    "> In this subsection, I investigate the transaction data based on the perspective of offer types. For instance, how the offers are distributed during the test period."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To avoid double counting, I filtered the 'offer received' only. \n",
    "offers_received = offers_transaction_df.query('event==\"offer received\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The number of different offers which are issued in each month\n",
    "plt.figure(figsize =[7,5])\n",
    "ax = sns.countplot(data = offers_received, x = 'months', hue = 'offer_type')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "discount         30543\n",
       "bogo             30499\n",
       "informational    15235\n",
       "Name: offer_type, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total number of issued offers \n",
    "offers_received.offer_type.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='offer_type', ylabel='count'>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The number of different events on each offer type. \n",
    "plt.figure(figsize =[7,5])\n",
    "sns.countplot(data = offers_transaction_df, x = 'offer_type', hue = 'event', color = base_color)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 1. The number of issued offers are unbalanced. 'BOGO' and 'Discount' types of offers are almost evenly distributed; around 30000. On the other hand, \"Informational\" type of offer is issued only the half of them (ca. 15000). \n",
    "> 2. Not all of the issued offers are viewed. Only 75,68% (= 57725/76277)of offers are checked by customers. \n",
    "> 3. Only half of issued 'BOGO' and 'discount' offers are completed. Note that the event whether the informational type of data is completed, is not collected.\n",
    "> 4. From the last bar graph, we can recognize that customers viewed more 'BOGO' offers than 'Discount' offers. On the other hand, more 'Discount' type of offers are completed. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. 4. Customer Purchasing Patterns\n",
    "\n",
    "> In this section, I explore customer purchasing patterns based on RFM analysis. [Reference - RFM analysis](https://en.wikipedia.org/wiki/RFM_(market_research))\n",
    "RFM is an evaluation method to analyse customer value. It is often used in database marketing especially in retail and professional services industries. RFM indicates the following 3 dimensions:\n",
    "> - Recency – How recently did the customer purchase?\n",
    "> - Frequency – How often do they purchase?\n",
    "> - Monetary Value – How much do they spend? \n",
    "<br><br>\n",
    "> These features can be obtained from the provided transaction dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Recency \n",
    "recency = sales_transaction_df.groupby('customer_id', as_index= False)['days'].max()\n",
    "recency['days'] = 120 - recency['days']\n",
    "recency.rename(columns={'days':'recency'}, inplace = True)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Frequency \n",
    "transaction_cnt = sales_transaction_df.groupby(['customer_id','days'], as_index= False)['amount'].count()\n",
    "frequency = transaction_cnt.groupby('customer_id')['days'].count().reset_index()\n",
    "frequency.rename(columns={'days':'frequency'}, inplace = True)      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Monetary Values \n",
    "monetary = sales_transaction_df.groupby('customer_id', as_index= False)['amount'].sum()\n",
    "monetary.rename(columns={'amount':'monetary'}, inplace = True)      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The size of the RFM dataframe:  (16578, 4)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>recency</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0009655768c64bdeb2e877511632db8f</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>127.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00116118485d4dfda04fdbaba9a87b5c</td>\n",
       "      <td>40</td>\n",
       "      <td>3</td>\n",
       "      <td>4.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0011e0d4e6b944f998e987f904e8c1e5</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>79.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0020c2b971eb4e9188eac86d93036a77</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>196.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0020ccbbb6d84e358d3414a3ff76cffd</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>154.05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        customer_id  recency  frequency  monetary\n",
       "0  0009655768c64bdeb2e877511632db8f        3          8    127.60\n",
       "1  00116118485d4dfda04fdbaba9a87b5c       40          3      4.09\n",
       "2  0011e0d4e6b944f998e987f904e8c1e5       10          5     79.46\n",
       "3  0020c2b971eb4e9188eac86d93036a77        1          8    196.86\n",
       "4  0020ccbbb6d84e358d3414a3ff76cffd        7         12    154.05"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge the 3 elements \n",
    "rfm = recency.merge(frequency, left_on = 'customer_id', right_on = 'customer_id', how = 'inner')\n",
    "rfm_df = rfm.merge(monetary, left_on = 'customer_id', right_on = 'customer_id', how = 'inner')\n",
    "\n",
    "print('The size of the RFM dataframe: ' , rfm_df.shape)\n",
    "rfm_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>recency</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>16578.00</td>\n",
       "      <td>16578.00</td>\n",
       "      <td>16578.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>14.24</td>\n",
       "      <td>8.38</td>\n",
       "      <td>107.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13.79</td>\n",
       "      <td>5.01</td>\n",
       "      <td>126.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>23.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>10.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>72.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>20.00</td>\n",
       "      <td>11.00</td>\n",
       "      <td>150.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>117.00</td>\n",
       "      <td>36.00</td>\n",
       "      <td>1608.69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       recency  frequency  monetary\n",
       "count 16578.00   16578.00  16578.00\n",
       "mean     14.24       8.38    107.10\n",
       "std      13.79       5.01    126.39\n",
       "min       0.00       1.00      0.05\n",
       "25%       4.00       5.00     23.68\n",
       "50%      10.00       7.00     72.41\n",
       "75%      20.00      11.00    150.94\n",
       "max     117.00      36.00   1608.69"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Descriptive Statistics of RFM dimensions\n",
    "rfm_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:xlabel='recency', ylabel='recency'>,\n",
       "        <AxesSubplot:xlabel='frequency', ylabel='recency'>,\n",
       "        <AxesSubplot:xlabel='monetary', ylabel='recency'>],\n",
       "       [<AxesSubplot:xlabel='recency', ylabel='frequency'>,\n",
       "        <AxesSubplot:xlabel='frequency', ylabel='frequency'>,\n",
       "        <AxesSubplot:xlabel='monetary', ylabel='frequency'>],\n",
       "       [<AxesSubplot:xlabel='recency', ylabel='monetary'>,\n",
       "        <AxesSubplot:xlabel='frequency', ylabel='monetary'>,\n",
       "        <AxesSubplot:xlabel='monetary', ylabel='monetary'>]], dtype=object)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualisation of customer purchasing patterns. \n",
    "pd.plotting.scatter_matrix(rfm_df, figsize = (8,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Correlation in Heatmap form \n",
    "plt.figure(figsize=(6,4))\n",
    "s = sns.heatmap(rfm_df.corr(), annot = True, cmap = 'RdBu', vmin = -1, vmax=1)\n",
    "s.set_yticklabels(s.get_yticklabels(), rotation = 0, fontsize =12) \n",
    "s.set_xticklabels(s.get_xticklabels(), rotation = 90, fontsize =12) \n",
    "plt.title('Correlation Heatmap', fontsize =15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 1. I explore purchasing patterns of customers according to RFM Analysis. RFM analysis evaluates customer purchasing patterns with 3 elements: How recently does a customer purchase a product at Starbucks (Recency)? How often does the customer purchase a product during the period (Frequency)? How much does the customer spend at Starbucks (Monetary Value)? \n",
    "> 2. The latest purchase of customers is 14 days ago on average. Customers bought products 8 times at Starbucks on average. Also, the mean value of 'monetary' indicates that their average spendings during the test period is a little bit higher than 100 dollars. \n",
    "> 3. The scatter matrix shows interesting purchase patterns of customers. The histograms of recency, frequency and monetary values are all skewed to the right. Recency is negatively correlated with frequency and monetary values. On the other hand, frequency is positively related with monetary values. \n",
    "> 4. This indicates that the more recently customers purchase Starbucks products (lower recency value), the more frequently the customers buy our products, and the more they spend at Starbucks. Intuitively, the more frequently customers purchase, the more they spend at our store. \n",
    "> 5. The second heatmap indicates that the number of different offers that Starbucks sent to its customers are not highly related to their recency, frequency and monetary elements.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------------------------\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Analysis'></a>\n",
    "## 4. Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. 1. Feature Engineering \n",
    "\n",
    "> During the test period, customers are received different offer(s). Some customers followed desirable processes to be rewarded. However, not all of them. Some other customers get rewarded without noticing that they received the offers. In this subsection, I define the \"desirably-used\" offers. Then I identify each offer and subsequent events were either 'desirable' or 'non-desirable'.  "
   ]
  },
  {
   "attachments": {
    "image-2.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image-2.png](attachment:image-2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The Definition of \"Desirably-used\" Offers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I defined the meaning of \"desirable\" more broadly. \n",
    "- **Case 1**: If a person received an offer, viewed it, purchased a product, then met the level of difficulty (the minimum amount of spending to complete and get reward),and if all these processes occured during the offer duration, this person followed the \"typical\" desirable path of using offers. \n",
    "- **Case 2**: A person received an offer, viewed it, purchased a product. All these 3 processes occured during the offer duration. However, this person could not reach the difficulty of corresponding offers.  \n",
    "\n",
    "In the provided dataset, some customers show different purchasing patterns. \n",
    "\n",
    "- **Case 3**: A person received an offer, viewed it during the offer duration. However after the person checked the offer, he/she did not purchase during the offer duration. \n",
    "- **Case 4**: A person received an offer. However the customer has not viewed it during the offer duration. Then he/she satisfy the difficulty of corresponding offers, so the customer got rewards. \n",
    "- **Case 5**: Similarly, a person received an offer. However the customer has not viewed it during the offer duration. Since this type of customer has not satisfied the minimum level of spendings, so they could not enjoy any rewards.\n",
    "- **Case 6**: A customer who received an offer. However, the customer has not checked it and showed any transactions during the offer duration. \n",
    "- **Case 7**: These customers groups are actually ideal for our company. Although they didnt receive any offers, they purchased products at our stores.\n",
    "<br><br>\n",
    "\n",
    "Therefore, it is more efficient to analyse differrent demographic characters of the customer groups who used our offers properly (Case 1, Case 2), comparing to other customers (Case 3, Case 4, Case 5, Case 6, Case 7), to maximize the effect of offers in the future. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 1: Restructuring the Transaction Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\wonso\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:4304: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  errors=errors,\n"
     ]
    }
   ],
   "source": [
    "# To figure out which customers belong to which cases, I restructured the transaction dataframe.\n",
    "transactions_event_df = pd.concat([transactions_df, pd.get_dummies(transactions_df.event)], axis = 1)\n",
    "transactions_event_df.drop(['event','months'], axis = 1, inplace=True)\n",
    "\n",
    "# To avoid any confusions, I selected necessary columns for the purpose of analysis in this section and rename the columns.\n",
    "trans_event_mod_df = transactions_event_df[['customer_id', 'time','offer_id','days','offer received','offer viewed', 'transaction','offer completed','reward']]\n",
    "trans_event_mod_df.rename(columns={'offer viewed': 'offer_viewed', 'offer received':'offer_received', 'offer completed':'offer_completed' },inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 2: Computing the valid offer Day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Then I combined the dataframe with offer type and its duration from offer information dataset. \n",
    "trans_offer_info = trans_event_mod_df.merge(offer_info_df[['offer_id', 'offer_type','duration']], \n",
    "                                    left_on = 'offer_id', right_on = 'offer_id', how='left')\n",
    "\n",
    "# For the calculation convenience, I fill up zeros into null values. \n",
    "trans_offer_info = trans_offer_info.fillna(0)\n",
    "\n",
    "# I created a new column represening the last day of each offer. \n",
    "trans_offer_info['max_offer_day'] = trans_offer_info.offer_received*(trans_offer_info.duration + trans_offer_info.days)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": false
   },
   "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>time</th>\n",
       "      <th>offer_id</th>\n",
       "      <th>days</th>\n",
       "      <th>offer_received</th>\n",
       "      <th>offer_viewed</th>\n",
       "      <th>transaction</th>\n",
       "      <th>offer_completed</th>\n",
       "      <th>reward</th>\n",
       "      <th>offer_type</th>\n",
       "      <th>duration</th>\n",
       "      <th>max_offer_day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>78afa995795e4d85b5d9ceeca43f5fef</td>\n",
       "      <td>0</td>\n",
       "      <td>9b98b8c7a33c4b65b9aebfe6a799e6d9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>bogo</td>\n",
       "      <td>7.00</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a03223e636434f42ac4c3df47e8bac43</td>\n",
       "      <td>0</td>\n",
       "      <td>0b1e1539f2cc45b7b9fa7c272da2e1d7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>discount</td>\n",
       "      <td>10.00</td>\n",
       "      <td>11.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>e2127556f4f64592b11af22de27a7932</td>\n",
       "      <td>0</td>\n",
       "      <td>2906b810c7d4411798c6938adc9daaa5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>discount</td>\n",
       "      <td>7.00</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8ec6ce2a7e7949b1bf142def7d0e0586</td>\n",
       "      <td>0</td>\n",
       "      <td>fafdcd668e3743c1bb461111dcafc2a4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>discount</td>\n",
       "      <td>10.00</td>\n",
       "      <td>11.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>68617ca6246f4fbc85e91a2a49552598</td>\n",
       "      <td>0</td>\n",
       "      <td>4d5c57ea9a6940dd891ad53e9dbe8da0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>bogo</td>\n",
       "      <td>5.00</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306529</th>\n",
       "      <td>b3a1272bc9904337b331bf348c3e8c17</td>\n",
       "      <td>714</td>\n",
       "      <td></td>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306530</th>\n",
       "      <td>68213b08d99a4ae1b0dcb72aebd9aa35</td>\n",
       "      <td>714</td>\n",
       "      <td></td>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306531</th>\n",
       "      <td>a00058cf10334a308c68e7631c529907</td>\n",
       "      <td>714</td>\n",
       "      <td></td>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306532</th>\n",
       "      <td>76ddbd6576844afe811f1a3c0fbb5bec</td>\n",
       "      <td>714</td>\n",
       "      <td></td>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306533</th>\n",
       "      <td>c02b10e8752c4d8e9b73f918558531f7</td>\n",
       "      <td>714</td>\n",
       "      <td></td>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>306534 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             customer_id  time  \\\n",
       "0       78afa995795e4d85b5d9ceeca43f5fef     0   \n",
       "1       a03223e636434f42ac4c3df47e8bac43     0   \n",
       "2       e2127556f4f64592b11af22de27a7932     0   \n",
       "3       8ec6ce2a7e7949b1bf142def7d0e0586     0   \n",
       "4       68617ca6246f4fbc85e91a2a49552598     0   \n",
       "...                                  ...   ...   \n",
       "306529  b3a1272bc9904337b331bf348c3e8c17   714   \n",
       "306530  68213b08d99a4ae1b0dcb72aebd9aa35   714   \n",
       "306531  a00058cf10334a308c68e7631c529907   714   \n",
       "306532  76ddbd6576844afe811f1a3c0fbb5bec   714   \n",
       "306533  c02b10e8752c4d8e9b73f918558531f7   714   \n",
       "\n",
       "                                offer_id  days  offer_received  offer_viewed  \\\n",
       "0       9b98b8c7a33c4b65b9aebfe6a799e6d9     1               1             0   \n",
       "1       0b1e1539f2cc45b7b9fa7c272da2e1d7     1               1             0   \n",
       "2       2906b810c7d4411798c6938adc9daaa5     1               1             0   \n",
       "3       fafdcd668e3743c1bb461111dcafc2a4     1               1             0   \n",
       "4       4d5c57ea9a6940dd891ad53e9dbe8da0     1               1             0   \n",
       "...                                  ...   ...             ...           ...   \n",
       "306529                                     120               0             0   \n",
       "306530                                     120               0             0   \n",
       "306531                                     120               0             0   \n",
       "306532                                     120               0             0   \n",
       "306533                                     120               0             0   \n",
       "\n",
       "        transaction  offer_completed  reward offer_type  duration  \\\n",
       "0                 0                0    0.00       bogo      7.00   \n",
       "1                 0                0    0.00   discount     10.00   \n",
       "2                 0                0    0.00   discount      7.00   \n",
       "3                 0                0    0.00   discount     10.00   \n",
       "4                 0                0    0.00       bogo      5.00   \n",
       "...             ...              ...     ...        ...       ...   \n",
       "306529          120                0    0.00          0      0.00   \n",
       "306530          120                0    0.00          0      0.00   \n",
       "306531          120                0    0.00          0      0.00   \n",
       "306532          120                0    0.00          0      0.00   \n",
       "306533          120                0    0.00          0      0.00   \n",
       "\n",
       "        max_offer_day  \n",
       "0                8.00  \n",
       "1               11.00  \n",
       "2                8.00  \n",
       "3               11.00  \n",
       "4                6.00  \n",
       "...               ...  \n",
       "306529           0.00  \n",
       "306530           0.00  \n",
       "306531           0.00  \n",
       "306532           0.00  \n",
       "306533           0.00  \n",
       "\n",
       "[306534 rows x 12 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Then, I multiply the days column with event columns: 'offer_received', 'offer_viewed', 'transaction', 'offer_completed'\n",
    "# These event columns now also explain which day does the event occur. \n",
    "event_cols = ['offer_received', 'offer_viewed', 'transaction', 'offer_completed']\n",
    "trans_offer_info[event_cols]= trans_offer_info[event_cols].multiply(trans_offer_info.days, axis=\"index\" )\n",
    "\n",
    "trans_offer_info # Check the result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 3. Groupping the modified transaction dataframe by 'customer_id',  'days', and 'offer_id'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Third, I groupped the transaction dataframe by Customer_id, days, and offer_id.\n",
    "# The following dataframe (trans_per_pp_days_offer) represents transaction histories per customers, per days, per offers. \n",
    "trans_per_pp_days_offer = trans_offer_info.groupby(['customer_id',  'days', 'offer_id'], as_index= False)[['max_offer_day', 'offer_received', 'offer_viewed', 'transaction', 'offer_completed']].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(289408, 8)\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>days</th>\n",
       "      <th>offer_id</th>\n",
       "      <th>max_offer_day</th>\n",
       "      <th>offer_received</th>\n",
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       "      <th>0</th>\n",
       "      <td>0009655768c64bdeb2e877511632db8f</td>\n",
       "      <td>29</td>\n",
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       "      <td>32.00</td>\n",
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       "      <td>0009655768c64bdeb2e877511632db8f</td>\n",
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       "      <th>2</th>\n",
       "      <td>0009655768c64bdeb2e877511632db8f</td>\n",
       "      <td>39</td>\n",
       "      <td>5a8bc65990b245e5a138643cd4eb9837</td>\n",
       "      <td>32.00</td>\n",
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       "      <td>0</td>\n",
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       "      <th>3</th>\n",
       "      <td>0009655768c64bdeb2e877511632db8f</td>\n",
       "      <td>57</td>\n",
       "      <td>3f207df678b143eea3cee63160fa8bed</td>\n",
       "      <td>61.00</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>4</th>\n",
       "      <td>0009655768c64bdeb2e877511632db8f</td>\n",
       "      <td>63</td>\n",
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       "      <td>61.00</td>\n",
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       "      <th>6</th>\n",
       "      <td>0009655768c64bdeb2e877511632db8f</td>\n",
       "      <td>70</td>\n",
       "      <td>f19421c1d4aa40978ebb69ca19b0e20d</td>\n",
       "      <td>74.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
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       "      <th>7</th>\n",
       "      <td>0009655768c64bdeb2e877511632db8f</td>\n",
       "      <td>70</td>\n",
       "      <td>f19421c1d4aa40978ebb69ca19b0e20d</td>\n",
       "      <td>74.00</td>\n",
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       "      <td>70</td>\n",
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       "      <th>8</th>\n",
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       "      <td>95.00</td>\n",
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      ],
      "text/plain": [
       "                        customer_id  days                          offer_id  \\\n",
       "0  0009655768c64bdeb2e877511632db8f    29  5a8bc65990b245e5a138643cd4eb9837   \n",
       "1  0009655768c64bdeb2e877511632db8f    33  5a8bc65990b245e5a138643cd4eb9837   \n",
       "2  0009655768c64bdeb2e877511632db8f    39  5a8bc65990b245e5a138643cd4eb9837   \n",
       "3  0009655768c64bdeb2e877511632db8f    57  3f207df678b143eea3cee63160fa8bed   \n",
       "4  0009655768c64bdeb2e877511632db8f    63  3f207df678b143eea3cee63160fa8bed   \n",
       "5  0009655768c64bdeb2e877511632db8f    69  f19421c1d4aa40978ebb69ca19b0e20d   \n",
       "6  0009655768c64bdeb2e877511632db8f    70  f19421c1d4aa40978ebb69ca19b0e20d   \n",
       "7  0009655768c64bdeb2e877511632db8f    70  f19421c1d4aa40978ebb69ca19b0e20d   \n",
       "8  0009655768c64bdeb2e877511632db8f    77  f19421c1d4aa40978ebb69ca19b0e20d   \n",
       "9  0009655768c64bdeb2e877511632db8f    85  fafdcd668e3743c1bb461111dcafc2a4   \n",
       "\n",
       "   max_offer_day  offer_received  offer_viewed  transaction  offer_completed  \n",
       "0          32.00              29             0            0                0  \n",
       "1          32.00               0            33            0                0  \n",
       "2          32.00               0             0           39                0  \n",
       "3          61.00              57             0            0                0  \n",
       "4          61.00               0            63            0                0  \n",
       "5          74.00              69             0            0                0  \n",
       "6          74.00               0             0           70                0  \n",
       "7          74.00               0             0            0               70  \n",
       "8          74.00               0            77            0                0  \n",
       "9          95.00              85             0            0                0  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 4. I modified the aboved dataframe: trans_per_pp_days_offer for convenient calculation as follows \n",
    "# 4-1. Replace the 0s in 'max_offer_day' column with np. NaNs. \n",
    "trans_per_pp_days_offer_v1  = trans_per_pp_days_offer.replace( {'max_offer_day': 0.00, 'offer_id':''}, np.NaN)\n",
    "# 4-2. Fill the NaNs using the method = 'ffill': to propagate last valid observation forward to next valid backfill \n",
    "trans_per_pp_days_offer_v1.fillna(method='ffill',inplace= True)\n",
    "# 4-3. Finally I properly edit the max_offer_day column. \n",
    "print(trans_per_pp_days_offer_v1.shape)\n",
    "trans_per_pp_days_offer_v1.head(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "trans_per_event = trans_per_pp_days_offer_v1.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 4. Filtering if events occurred prior to max_offer_day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To receive an offer always occur before max_offer_day \n",
    "trans_per_event = trans_per_event.query('offer_received ==0 ')\n",
    "trans_per_event['event_sum'] = trans_per_event[event_cols].sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# I create a \"desirable\" column: meaning that it is a desirable event, when the event occur before the max_offer_day\n",
    "trans_per_event['desirable'] = trans_per_event['max_offer_day'] - trans_per_event.event_sum\n",
    "\n",
    "# A desirable event must have positive value. \n",
    "desirable_event = trans_per_event.query('desirable >=0 ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>offer_id</th>\n",
       "      <th>max_offer_day</th>\n",
       "      <th>days</th>\n",
       "      <th>offer_received</th>\n",
       "      <th>offer_viewed</th>\n",
       "      <th>transaction</th>\n",
       "      <th>offer_completed</th>\n",
       "      <th>event_sum</th>\n",
       "      <th>desirable</th>\n",
       "      <th>offer_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0009655768c64bdeb2e877511632db8f</td>\n",
       "      <td>fafdcd668e3743c1bb461111dcafc2a4</td>\n",
       "      <td>95.00</td>\n",
       "      <td>93</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>93</td>\n",
       "      <td>89</td>\n",
       "      <td>93</td>\n",
       "      <td>6.00</td>\n",
       "      <td>discount</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0020c2b971eb4e9188eac86d93036a77</td>\n",
       "      <td>fafdcd668e3743c1bb461111dcafc2a4</td>\n",
       "      <td>11.00</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>8.00</td>\n",
       "      <td>discount</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>003d66b6608740288d6cc97a6903f4f0</td>\n",
       "      <td>fafdcd668e3743c1bb461111dcafc2a4</td>\n",
       "      <td>79.00</td>\n",
       "      <td>79</td>\n",
       "      <td>0</td>\n",
       "      <td>71</td>\n",
       "      <td>79</td>\n",
       "      <td>0</td>\n",
       "      <td>79</td>\n",
       "      <td>8.00</td>\n",
       "      <td>discount</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00426fe3ffde4c6b9cb9ad6d077a13ea</td>\n",
       "      <td>fafdcd668e3743c1bb461111dcafc2a4</td>\n",
       "      <td>39.00</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>7.00</td>\n",
       "      <td>discount</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>004b041fbfe44859945daa2c7f79ee64</td>\n",
       "      <td>f19421c1d4aa40978ebb69ca19b0e20d</td>\n",
       "      <td>90.00</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>86</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>4.00</td>\n",
       "      <td>bogo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13020</th>\n",
       "      <td>fff7576017104bcc8677a8d63322b5e1</td>\n",
       "      <td>fafdcd668e3743c1bb461111dcafc2a4</td>\n",
       "      <td>95.00</td>\n",
       "      <td>94</td>\n",
       "      <td>0</td>\n",
       "      <td>88</td>\n",
       "      <td>94</td>\n",
       "      <td>0</td>\n",
       "      <td>94</td>\n",
       "      <td>7.00</td>\n",
       "      <td>discount</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13021</th>\n",
       "      <td>fff8957ea8b240a6b5e634b6ee8eafcf</td>\n",
       "      <td>fafdcd668e3743c1bb461111dcafc2a4</td>\n",
       "      <td>79.00</td>\n",
       "      <td>76</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>76</td>\n",
       "      <td>0</td>\n",
       "      <td>76</td>\n",
       "      <td>6.00</td>\n",
       "      <td>discount</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13022</th>\n",
       "      <td>ffff82501cea40309d5fdd7edcca4a07</td>\n",
       "      <td>0b1e1539f2cc45b7b9fa7c272da2e1d7</td>\n",
       "      <td>39.00</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>35</td>\n",
       "      <td>34</td>\n",
       "      <td>35</td>\n",
       "      <td>9.00</td>\n",
       "      <td>discount</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13023</th>\n",
       "      <td>ffff82501cea40309d5fdd7edcca4a07</td>\n",
       "      <td>2906b810c7d4411798c6938adc9daaa5</td>\n",
       "      <td>104.00</td>\n",
       "      <td>102</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>102</td>\n",
       "      <td>0</td>\n",
       "      <td>102</td>\n",
       "      <td>6.00</td>\n",
       "      <td>discount</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13024</th>\n",
       "      <td>ffff82501cea40309d5fdd7edcca4a07</td>\n",
       "      <td>fafdcd668e3743c1bb461111dcafc2a4</td>\n",
       "      <td>11.00</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>9.00</td>\n",
       "      <td>discount</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13025 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            customer_id                          offer_id  \\\n",
       "0      0009655768c64bdeb2e877511632db8f  fafdcd668e3743c1bb461111dcafc2a4   \n",
       "1      0020c2b971eb4e9188eac86d93036a77  fafdcd668e3743c1bb461111dcafc2a4   \n",
       "2      003d66b6608740288d6cc97a6903f4f0  fafdcd668e3743c1bb461111dcafc2a4   \n",
       "3      00426fe3ffde4c6b9cb9ad6d077a13ea  fafdcd668e3743c1bb461111dcafc2a4   \n",
       "4      004b041fbfe44859945daa2c7f79ee64  f19421c1d4aa40978ebb69ca19b0e20d   \n",
       "...                                 ...                               ...   \n",
       "13020  fff7576017104bcc8677a8d63322b5e1  fafdcd668e3743c1bb461111dcafc2a4   \n",
       "13021  fff8957ea8b240a6b5e634b6ee8eafcf  fafdcd668e3743c1bb461111dcafc2a4   \n",
       "13022  ffff82501cea40309d5fdd7edcca4a07  0b1e1539f2cc45b7b9fa7c272da2e1d7   \n",
       "13023  ffff82501cea40309d5fdd7edcca4a07  2906b810c7d4411798c6938adc9daaa5   \n",
       "13024  ffff82501cea40309d5fdd7edcca4a07  fafdcd668e3743c1bb461111dcafc2a4   \n",
       "\n",
       "       max_offer_day  days  offer_received  offer_viewed  transaction  \\\n",
       "0              95.00    93               0            91           93   \n",
       "1              11.00    10               0             3           10   \n",
       "2              79.00    79               0            71           79   \n",
       "3              39.00    37               0            32           37   \n",
       "4              90.00    90               0            86           90   \n",
       "...              ...   ...             ...           ...          ...   \n",
       "13020          95.00    94               0            88           94   \n",
       "13021          79.00    76               0            73           76   \n",
       "13022          39.00    35               0            30           35   \n",
       "13023         104.00   102               0            98          102   \n",
       "13024          11.00    11               0             2           11   \n",
       "\n",
       "       offer_completed  event_sum  desirable offer_type  \n",
       "0                   89         93       6.00   discount  \n",
       "1                   10         10       8.00   discount  \n",
       "2                    0         79       8.00   discount  \n",
       "3                    0         37       7.00   discount  \n",
       "4                   90         90       4.00       bogo  \n",
       "...                ...        ...        ...        ...  \n",
       "13020                0         94       7.00   discount  \n",
       "13021                0         76       6.00   discount  \n",
       "13022               34         35       9.00   discount  \n",
       "13023                0        102       6.00   discount  \n",
       "13024               11         11       9.00   discount  \n",
       "\n",
       "[13025 rows x 11 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# I groupped all transactions by customers, offer_id, and max_offer_days\n",
    "desirable_event_per_offer = desirable_event.groupby(['customer_id','offer_id','max_offer_day'], as_index = False).max()\n",
    "\n",
    "# One more condition to be desirable is that a customer should view the offer and purchase the product after received the offer. \n",
    "desirable_event_per_offer_mod = desirable_event_per_offer.query('offer_viewed > 0 & transaction > 0')\n",
    "\n",
    "# Finally, I combined the dataframe with each offer name from offer information dataframe. \n",
    "event_name_df = desirable_event_per_offer_mod.merge(offer_info_df[['offer_id', 'offer_type']],\n",
    "                                                    left_on = 'offer_id', right_on = 'offer_id', how='left')\n",
    "\n",
    "# This dataframe shows the desirably-used offers only.\n",
    "event_name_df "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "discount         6468\n",
       "bogo             5370\n",
       "informational    1187\n",
       "Name: offer_type, dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The number of desirably-used offer types\n",
    "event_name_df.offer_type.value_counts() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9042"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The unique number of customers who used offers in a desirable way\n",
    "event_name_df.customer_id.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 5. Comparison\n",
    "The dataframe (event_name_df) is incomplete, since it shows only  desirably-used offers. Now I create a dataframe with all provided offers and then classify whether individual offers are used in a desirable way. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>days</th>\n",
       "      <th>offer_id</th>\n",
       "      <th>max_offer_day</th>\n",
       "      <th>offer_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0009655768c64bdeb2e877511632db8f</td>\n",
       "      <td>29</td>\n",
       "      <td>5a8bc65990b245e5a138643cd4eb9837</td>\n",
       "      <td>32.00</td>\n",
       "      <td>informational</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0009655768c64bdeb2e877511632db8f</td>\n",
       "      <td>57</td>\n",
       "      <td>3f207df678b143eea3cee63160fa8bed</td>\n",
       "      <td>61.00</td>\n",
       "      <td>informational</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0009655768c64bdeb2e877511632db8f</td>\n",
       "      <td>69</td>\n",
       "      <td>f19421c1d4aa40978ebb69ca19b0e20d</td>\n",
       "      <td>74.00</td>\n",
       "      <td>bogo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0009655768c64bdeb2e877511632db8f</td>\n",
       "      <td>85</td>\n",
       "      <td>fafdcd668e3743c1bb461111dcafc2a4</td>\n",
       "      <td>95.00</td>\n",
       "      <td>discount</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0009655768c64bdeb2e877511632db8f</td>\n",
       "      <td>97</td>\n",
       "      <td>2906b810c7d4411798c6938adc9daaa5</td>\n",
       "      <td>104.00</td>\n",
       "      <td>discount</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76272</th>\n",
       "      <td>ffff82501cea40309d5fdd7edcca4a07</td>\n",
       "      <td>29</td>\n",
       "      <td>0b1e1539f2cc45b7b9fa7c272da2e1d7</td>\n",
       "      <td>39.00</td>\n",
       "      <td>discount</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76273</th>\n",
       "      <td>ffff82501cea40309d5fdd7edcca4a07</td>\n",
       "      <td>57</td>\n",
       "      <td>2906b810c7d4411798c6938adc9daaa5</td>\n",
       "      <td>64.00</td>\n",
       "      <td>discount</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76274</th>\n",
       "      <td>ffff82501cea40309d5fdd7edcca4a07</td>\n",
       "      <td>69</td>\n",
       "      <td>2906b810c7d4411798c6938adc9daaa5</td>\n",
       "      <td>76.00</td>\n",
       "      <td>discount</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76275</th>\n",
       "      <td>ffff82501cea40309d5fdd7edcca4a07</td>\n",
       "      <td>85</td>\n",
       "      <td>9b98b8c7a33c4b65b9aebfe6a799e6d9</td>\n",
       "      <td>92.00</td>\n",
       "      <td>bogo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76276</th>\n",
       "      <td>ffff82501cea40309d5fdd7edcca4a07</td>\n",
       "      <td>97</td>\n",
       "      <td>2906b810c7d4411798c6938adc9daaa5</td>\n",
       "      <td>104.00</td>\n",
       "      <td>discount</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>76277 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            customer_id  days  \\\n",
       "0      0009655768c64bdeb2e877511632db8f    29   \n",
       "1      0009655768c64bdeb2e877511632db8f    57   \n",
       "2      0009655768c64bdeb2e877511632db8f    69   \n",
       "3      0009655768c64bdeb2e877511632db8f    85   \n",
       "4      0009655768c64bdeb2e877511632db8f    97   \n",
       "...                                 ...   ...   \n",
       "76272  ffff82501cea40309d5fdd7edcca4a07    29   \n",
       "76273  ffff82501cea40309d5fdd7edcca4a07    57   \n",
       "76274  ffff82501cea40309d5fdd7edcca4a07    69   \n",
       "76275  ffff82501cea40309d5fdd7edcca4a07    85   \n",
       "76276  ffff82501cea40309d5fdd7edcca4a07    97   \n",
       "\n",
       "                               offer_id  max_offer_day     offer_type  \n",
       "0      5a8bc65990b245e5a138643cd4eb9837          32.00  informational  \n",
       "1      3f207df678b143eea3cee63160fa8bed          61.00  informational  \n",
       "2      f19421c1d4aa40978ebb69ca19b0e20d          74.00           bogo  \n",
       "3      fafdcd668e3743c1bb461111dcafc2a4          95.00       discount  \n",
       "4      2906b810c7d4411798c6938adc9daaa5         104.00       discount  \n",
       "...                                 ...            ...            ...  \n",
       "76272  0b1e1539f2cc45b7b9fa7c272da2e1d7          39.00       discount  \n",
       "76273  2906b810c7d4411798c6938adc9daaa5          64.00       discount  \n",
       "76274  2906b810c7d4411798c6938adc9daaa5          76.00       discount  \n",
       "76275  9b98b8c7a33c4b65b9aebfe6a799e6d9          92.00           bogo  \n",
       "76276  2906b810c7d4411798c6938adc9daaa5         104.00       discount  \n",
       "\n",
       "[76277 rows x 5 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# From a previous dataframe, I filtered the data points with 'offer_received' event\n",
    "offers_received_mod = trans_per_pp_days_offer_v1.query('offer_received > 0')[['customer_id','days','offer_id','max_offer_day']]\n",
    "\n",
    "# Then I combined\n",
    "total_offers = offers_received_mod.merge(offers_received[['customer_id','days','offer_id','offer_type']], \n",
    "                                        left_on = ['customer_id','days','offer_id'],\n",
    "                                        right_on = ['customer_id','days','offer_id'],\n",
    "                                        how='left')\n",
    "\n",
    "total_offers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_issued_bogo = total_offers.query('offer_type ==\"bogo\"')\n",
    "total_issued_disc = total_offers.query('offer_type ==\"discount\"')\n",
    "total_issued_info = total_offers.query('offer_type ==\"informational\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((30499, 5), (30543, 5), (15235, 5))"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_issued_bogo.shape, total_issued_disc.shape, total_issued_info.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Classification_of_offers(total_offers, desirable_offer_df): \n",
    "    \n",
    "    desirable_offer_df['desirable_use'] = 'desirable'\n",
    "    total_offers_final = total_offers.merge(desirable_offer_df[['customer_id','offer_id','max_offer_day','desirable_use']],\n",
    "                      left_on = ['customer_id','max_offer_day','offer_id'],\n",
    "                      right_on =['customer_id','max_offer_day','offer_id'],\n",
    "                      how = 'left')\n",
    "    total_offers_final= total_offers_final.fillna('non-desirable')\n",
    "    \n",
    "    return total_offers_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>days</th>\n",
       "      <th>offer_id</th>\n",
       "      <th>max_offer_day</th>\n",
       "      <th>offer_type</th>\n",
       "      <th>desirable_use</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0009655768c64bdeb2e877511632db8f</td>\n",
       "      <td>29</td>\n",
       "      <td>5a8bc65990b245e5a138643cd4eb9837</td>\n",
       "      <td>32.00</td>\n",
       "      <td>informational</td>\n",
       "      <td>non-desirable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0009655768c64bdeb2e877511632db8f</td>\n",
       "      <td>57</td>\n",
       "      <td>3f207df678b143eea3cee63160fa8bed</td>\n",
       "      <td>61.00</td>\n",
       "      <td>informational</td>\n",
       "      <td>non-desirable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0009655768c64bdeb2e877511632db8f</td>\n",
       "      <td>69</td>\n",
       "      <td>f19421c1d4aa40978ebb69ca19b0e20d</td>\n",
       "      <td>74.00</td>\n",
       "      <td>bogo</td>\n",
       "      <td>non-desirable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0009655768c64bdeb2e877511632db8f</td>\n",
       "      <td>85</td>\n",
       "      <td>fafdcd668e3743c1bb461111dcafc2a4</td>\n",
       "      <td>95.00</td>\n",
       "      <td>discount</td>\n",
       "      <td>desirable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0009655768c64bdeb2e877511632db8f</td>\n",
       "      <td>97</td>\n",
       "      <td>2906b810c7d4411798c6938adc9daaa5</td>\n",
       "      <td>104.00</td>\n",
       "      <td>discount</td>\n",
       "      <td>non-desirable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76272</th>\n",
       "      <td>ffff82501cea40309d5fdd7edcca4a07</td>\n",
       "      <td>29</td>\n",
       "      <td>0b1e1539f2cc45b7b9fa7c272da2e1d7</td>\n",
       "      <td>39.00</td>\n",
       "      <td>discount</td>\n",
       "      <td>desirable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76273</th>\n",
       "      <td>ffff82501cea40309d5fdd7edcca4a07</td>\n",
       "      <td>57</td>\n",
       "      <td>2906b810c7d4411798c6938adc9daaa5</td>\n",
       "      <td>64.00</td>\n",
       "      <td>discount</td>\n",
       "      <td>non-desirable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76274</th>\n",
       "      <td>ffff82501cea40309d5fdd7edcca4a07</td>\n",
       "      <td>69</td>\n",
       "      <td>2906b810c7d4411798c6938adc9daaa5</td>\n",
       "      <td>76.00</td>\n",
       "      <td>discount</td>\n",
       "      <td>non-desirable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76275</th>\n",
       "      <td>ffff82501cea40309d5fdd7edcca4a07</td>\n",
       "      <td>85</td>\n",
       "      <td>9b98b8c7a33c4b65b9aebfe6a799e6d9</td>\n",
       "      <td>92.00</td>\n",
       "      <td>bogo</td>\n",
       "      <td>non-desirable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76276</th>\n",
       "      <td>ffff82501cea40309d5fdd7edcca4a07</td>\n",
       "      <td>97</td>\n",
       "      <td>2906b810c7d4411798c6938adc9daaa5</td>\n",
       "      <td>104.00</td>\n",
       "      <td>discount</td>\n",
       "      <td>desirable</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>76277 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            customer_id  days  \\\n",
       "0      0009655768c64bdeb2e877511632db8f    29   \n",
       "1      0009655768c64bdeb2e877511632db8f    57   \n",
       "2      0009655768c64bdeb2e877511632db8f    69   \n",
       "3      0009655768c64bdeb2e877511632db8f    85   \n",
       "4      0009655768c64bdeb2e877511632db8f    97   \n",
       "...                                 ...   ...   \n",
       "76272  ffff82501cea40309d5fdd7edcca4a07    29   \n",
       "76273  ffff82501cea40309d5fdd7edcca4a07    57   \n",
       "76274  ffff82501cea40309d5fdd7edcca4a07    69   \n",
       "76275  ffff82501cea40309d5fdd7edcca4a07    85   \n",
       "76276  ffff82501cea40309d5fdd7edcca4a07    97   \n",
       "\n",
       "                               offer_id  max_offer_day     offer_type  \\\n",
       "0      5a8bc65990b245e5a138643cd4eb9837          32.00  informational   \n",
       "1      3f207df678b143eea3cee63160fa8bed          61.00  informational   \n",
       "2      f19421c1d4aa40978ebb69ca19b0e20d          74.00           bogo   \n",
       "3      fafdcd668e3743c1bb461111dcafc2a4          95.00       discount   \n",
       "4      2906b810c7d4411798c6938adc9daaa5         104.00       discount   \n",
       "...                                 ...            ...            ...   \n",
       "76272  0b1e1539f2cc45b7b9fa7c272da2e1d7          39.00       discount   \n",
       "76273  2906b810c7d4411798c6938adc9daaa5          64.00       discount   \n",
       "76274  2906b810c7d4411798c6938adc9daaa5          76.00       discount   \n",
       "76275  9b98b8c7a33c4b65b9aebfe6a799e6d9          92.00           bogo   \n",
       "76276  2906b810c7d4411798c6938adc9daaa5         104.00       discount   \n",
       "\n",
       "       desirable_use  \n",
       "0      non-desirable  \n",
       "1      non-desirable  \n",
       "2      non-desirable  \n",
       "3          desirable  \n",
       "4      non-desirable  \n",
       "...              ...  \n",
       "76272      desirable  \n",
       "76273  non-desirable  \n",
       "76274  non-desirable  \n",
       "76275  non-desirable  \n",
       "76276      desirable  \n",
       "\n",
       "[76277 rows x 6 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Classification_of_offers(total_offers, event_name_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_issued_bogo = Classification_of_offers(total_offers, event_name_df).query('offer_type ==\"bogo\"')\n",
    "total_issued_disc = Classification_of_offers(total_offers, event_name_df).query('offer_type ==\"discount\"')\n",
    "total_issued_info = Classification_of_offers(total_offers, event_name_df).query('offer_type ==\"informational\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BOGO Offer:\n",
      "non-desirable    25129\n",
      "desirable         5370\n",
      "Name: desirable_use, dtype: int64\n",
      " \n",
      "Discount Offer:\n",
      "non-desirable    24075\n",
      "desirable         6468\n",
      "Name: desirable_use, dtype: int64\n",
      " \n",
      "Informational Offer:\n",
      "non-desirable    14048\n",
      "desirable         1187\n",
      "Name: desirable_use, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print('BOGO Offer:' )\n",
    "print(total_issued_bogo.desirable_use.value_counts())\n",
    "print(' ')\n",
    "print('Discount Offer:' )\n",
    "print(total_issued_disc.desirable_use.value_counts())\n",
    "print(' ')\n",
    "print('Informational Offer:' )\n",
    "print(total_issued_info.desirable_use.value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Exportation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export the datasets for the second part of analysis\n",
    "Classification_of_offers(total_offers, event_name_df).to_csv('total_issued_offer.csv', index=False)\n",
    "rfm_df.to_csv('rfm_score.csv', index=False)\n",
    "customer_info_df.to_csv('customer_df.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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