Predict_CLTV_with_linear_regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predict CLTV with linear regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='table'></a>\n",
    "## Table of Contents\n",
    "\n",
    "<ul>\n",
    "<li><a href=\"#intro\">Introduction</a></li>\n",
    "<li><a href=\"#dw\">Data wrangling</a></li>\n",
    "<li><a href=\"#eda\">Exploratory data analysis</a></li>\n",
    "<li><a href=\"#datacleaning\">Data cleaning and transformation</a></li>\n",
    "<li><a href=\"#dataanalysis\">Data analysis</a></li>\n",
    "<li><a href=\"#predictcltv\">Predict 3-month CLTV</a></li>\n",
    "<li><a href=\"#summary\">Summary</a></li>\n",
    "<li><a href=\"#reference\">References</a></li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='intro'></a>\n",
    "## Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CLTV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Customer Lifetime Value (CLTV)** represents the total amount of money a customer is expected to spend in a business during his/her lifetime. This is an important metric to monitor because it helps to make decisions about how much money to invest in acquiring new customers and retaining existing ones."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this analysis I am using a public dataset from UCI Machine Learning Repositiry, which can found [here](http://archive.ics.uci.edu/ml/index.php). This dataset contains information on transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail. The company mainly sells unique all-occasion gifts. Many customers of the company are wholesalers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Attribute information:**\n",
    "\n",
    "* InvoiceNo: Invoice number. Nominal, a 6-digit integral number uniquely assigned to each transaction. If this code starts with letter 'c', it indicates a cancellation\n",
    "* StockCode: Product (item) code. Nominal, a 5-digit integral number uniquely assigned to each distinct product.\n",
    "* Description: Product (item) name. Nominal\n",
    "* Quantity: The quantities of each product (item) per transaction. Numeric\n",
    "* InvoiceDate: Invice Date and time. Numeric, the day and time when each transaction was generated\n",
    "* UnitPrice: Unit price. Numeric, Product price per unit in sterling\n",
    "* CustomerID: Customer number. Nominal, a 5-digit integral number uniquely assigned to each customer\n",
    "* Country: Country name. Nominal, the name of the country where each customer resides."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='dw'></a>\n",
    "## Data wrangling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel('online_retail_dataset.xlsx', sheet_name='Online Retail')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  InvoiceNo StockCode                          Description  Quantity  \\\n",
       "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1    536365     71053                  WHITE METAL LANTERN         6   \n",
       "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
       "\n",
       "          InvoiceDate  UnitPrice  CustomerID         Country  \n",
       "0 2010-12-01 08:26:00       2.55     17850.0  United Kingdom  \n",
       "1 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
       "2 2010-12-01 08:26:00       2.75     17850.0  United Kingdom  \n",
       "3 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
       "4 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 541909 entries, 0 to 541908\n",
      "Data columns (total 8 columns):\n",
      " #   Column       Non-Null Count   Dtype         \n",
      "---  ------       --------------   -----         \n",
      " 0   InvoiceNo    541909 non-null  object        \n",
      " 1   StockCode    541909 non-null  object        \n",
      " 2   Description  540455 non-null  object        \n",
      " 3   Quantity     541909 non-null  int64         \n",
      " 4   InvoiceDate  541909 non-null  datetime64[ns]\n",
      " 5   UnitPrice    541909 non-null  float64       \n",
      " 6   CustomerID   406829 non-null  float64       \n",
      " 7   Country      541909 non-null  object        \n",
      "dtypes: datetime64[ns](1), float64(2), int64(1), object(4)\n",
      "memory usage: 33.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Description* and *CustomerID* columns have NULL values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(541909, 8)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='eda'></a>\n",
    "## Exploratory data analysis (EDA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us have a look at the data and decide whether we need any data cleaning and data transformation for further analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NULL values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of missing values in the dataset: 136534\n",
      "Percentage of missing values in the dataset: 3.149375633178264\n"
     ]
    }
   ],
   "source": [
    "print('Number of missing values in the dataset:', df.isnull().sum().sum())\n",
    "print('Percentage of missing values in the dataset:', df.isnull().sum().sum()*100/(df.shape[0]*df.shape[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of missing values in 'Description' column: 1454\n",
      "Percentage of missing values in 'Description' column: 0.2683107311375157\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of missing values in 'Description' column:\", df['Description'].isnull().sum())\n",
    "print(\"Percentage of missing values in 'Description' column:\", df['Description'].isnull().sum()*100/df.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of missing values in 'CustomerID' column: 135080\n",
      "Percentage of missing values in 'CustomerID' column: 24.926694334288598\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of missing values in 'CustomerID' column:\", df['CustomerID'].isnull().sum())\n",
    "print(\"Percentage of missing values in 'CustomerID' column:\", df['CustomerID'].isnull().sum()*100/df.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "About 25% of CustomerIDs is missing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5268"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the number of duplicated values\n",
    "df.duplicated().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's get the duplicated rows\n",
    "duplicates = df[df.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>517</th>\n",
       "      <td>536409</td>\n",
       "      <td>21866</td>\n",
       "      <td>UNION JACK FLAG LUGGAGE TAG</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-12-01 11:45:00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>17908.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>527</th>\n",
       "      <td>536409</td>\n",
       "      <td>22866</td>\n",
       "      <td>HAND WARMER SCOTTY DOG DESIGN</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-12-01 11:45:00</td>\n",
       "      <td>2.10</td>\n",
       "      <td>17908.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537</th>\n",
       "      <td>536409</td>\n",
       "      <td>22900</td>\n",
       "      <td>SET 2 TEA TOWELS I LOVE LONDON</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-12-01 11:45:00</td>\n",
       "      <td>2.95</td>\n",
       "      <td>17908.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>539</th>\n",
       "      <td>536409</td>\n",
       "      <td>22111</td>\n",
       "      <td>SCOTTIE DOG HOT WATER BOTTLE</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-12-01 11:45:00</td>\n",
       "      <td>4.95</td>\n",
       "      <td>17908.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>555</th>\n",
       "      <td>536412</td>\n",
       "      <td>22327</td>\n",
       "      <td>ROUND SNACK BOXES SET OF 4 SKULLS</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-12-01 11:49:00</td>\n",
       "      <td>2.95</td>\n",
       "      <td>17920.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>587</th>\n",
       "      <td>536412</td>\n",
       "      <td>22273</td>\n",
       "      <td>FELTCRAFT DOLL MOLLY</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-12-01 11:49:00</td>\n",
       "      <td>2.95</td>\n",
       "      <td>17920.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>536412</td>\n",
       "      <td>22749</td>\n",
       "      <td>FELTCRAFT PRINCESS CHARLOTTE DOLL</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-12-01 11:49:00</td>\n",
       "      <td>3.75</td>\n",
       "      <td>17920.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>594</th>\n",
       "      <td>536412</td>\n",
       "      <td>22141</td>\n",
       "      <td>CHRISTMAS CRAFT TREE TOP ANGEL</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-12-01 11:49:00</td>\n",
       "      <td>2.10</td>\n",
       "      <td>17920.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>598</th>\n",
       "      <td>536412</td>\n",
       "      <td>21448</td>\n",
       "      <td>12 DAISY PEGS IN WOOD BOX</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-12-01 11:49:00</td>\n",
       "      <td>1.65</td>\n",
       "      <td>17920.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600</th>\n",
       "      <td>536412</td>\n",
       "      <td>22569</td>\n",
       "      <td>FELTCRAFT CUSHION BUTTERFLY</td>\n",
       "      <td>2</td>\n",
       "      <td>2010-12-01 11:49:00</td>\n",
       "      <td>3.75</td>\n",
       "      <td>17920.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601</th>\n",
       "      <td>536412</td>\n",
       "      <td>21448</td>\n",
       "      <td>12 DAISY PEGS IN WOOD BOX</td>\n",
       "      <td>2</td>\n",
       "      <td>2010-12-01 11:49:00</td>\n",
       "      <td>1.65</td>\n",
       "      <td>17920.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>604</th>\n",
       "      <td>536412</td>\n",
       "      <td>21448</td>\n",
       "      <td>12 DAISY PEGS IN WOOD BOX</td>\n",
       "      <td>2</td>\n",
       "      <td>2010-12-01 11:49:00</td>\n",
       "      <td>1.65</td>\n",
       "      <td>17920.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>605</th>\n",
       "      <td>536412</td>\n",
       "      <td>22902</td>\n",
       "      <td>TOTE BAG I LOVE LONDON</td>\n",
       "      <td>7</td>\n",
       "      <td>2010-12-01 11:49:00</td>\n",
       "      <td>2.10</td>\n",
       "      <td>17920.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>616</th>\n",
       "      <td>536412</td>\n",
       "      <td>21708</td>\n",
       "      <td>FOLDING UMBRELLA CREAM POLKADOT</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-12-01 11:49:00</td>\n",
       "      <td>4.95</td>\n",
       "      <td>17920.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>617</th>\n",
       "      <td>536412</td>\n",
       "      <td>22900</td>\n",
       "      <td>SET 2 TEA TOWELS I LOVE LONDON</td>\n",
       "      <td>2</td>\n",
       "      <td>2010-12-01 11:49:00</td>\n",
       "      <td>2.95</td>\n",
       "      <td>17920.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618</th>\n",
       "      <td>536412</td>\n",
       "      <td>21706</td>\n",
       "      <td>FOLDING UMBRELLA RED/WHITE POLKADOT</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-12-01 11:49:00</td>\n",
       "      <td>4.95</td>\n",
       "      <td>17920.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>620</th>\n",
       "      <td>536412</td>\n",
       "      <td>85184C</td>\n",
       "      <td>S/4 VALENTINE DECOUPAGE HEART BOX</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-12-01 11:49:00</td>\n",
       "      <td>2.95</td>\n",
       "      <td>17920.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>758</th>\n",
       "      <td>536446</td>\n",
       "      <td>21651</td>\n",
       "      <td>HANGING GLASS ETCHED TEALIGHT</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 12:15:00</td>\n",
       "      <td>1.65</td>\n",
       "      <td>15983.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>831</th>\n",
       "      <td>536464</td>\n",
       "      <td>22866</td>\n",
       "      <td>HAND WARMER SCOTTY DOG DESIGN</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-12-01 12:23:00</td>\n",
       "      <td>2.10</td>\n",
       "      <td>17968.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>835</th>\n",
       "      <td>536464</td>\n",
       "      <td>22945</td>\n",
       "      <td>CHRISTMAS METAL TAGS ASSORTED</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 12:23:00</td>\n",
       "      <td>0.85</td>\n",
       "      <td>17968.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    InvoiceNo StockCode                          Description  Quantity  \\\n",
       "517    536409     21866          UNION JACK FLAG LUGGAGE TAG         1   \n",
       "527    536409     22866        HAND WARMER SCOTTY DOG DESIGN         1   \n",
       "537    536409     22900      SET 2 TEA TOWELS I LOVE LONDON          1   \n",
       "539    536409     22111         SCOTTIE DOG HOT WATER BOTTLE         1   \n",
       "555    536412     22327    ROUND SNACK BOXES SET OF 4 SKULLS         1   \n",
       "587    536412     22273                 FELTCRAFT DOLL MOLLY         1   \n",
       "589    536412     22749    FELTCRAFT PRINCESS CHARLOTTE DOLL         1   \n",
       "594    536412     22141       CHRISTMAS CRAFT TREE TOP ANGEL         1   \n",
       "598    536412     21448            12 DAISY PEGS IN WOOD BOX         1   \n",
       "600    536412     22569          FELTCRAFT CUSHION BUTTERFLY         2   \n",
       "601    536412     21448            12 DAISY PEGS IN WOOD BOX         2   \n",
       "604    536412     21448            12 DAISY PEGS IN WOOD BOX         2   \n",
       "605    536412     22902               TOTE BAG I LOVE LONDON         7   \n",
       "616    536412     21708      FOLDING UMBRELLA CREAM POLKADOT         1   \n",
       "617    536412     22900      SET 2 TEA TOWELS I LOVE LONDON          2   \n",
       "618    536412     21706  FOLDING UMBRELLA RED/WHITE POLKADOT         1   \n",
       "620    536412    85184C    S/4 VALENTINE DECOUPAGE HEART BOX         1   \n",
       "758    536446     21651        HANGING GLASS ETCHED TEALIGHT         6   \n",
       "831    536464     22866        HAND WARMER SCOTTY DOG DESIGN         1   \n",
       "835    536464     22945       CHRISTMAS METAL TAGS ASSORTED          6   \n",
       "\n",
       "            InvoiceDate  UnitPrice  CustomerID         Country  \n",
       "517 2010-12-01 11:45:00       1.25     17908.0  United Kingdom  \n",
       "527 2010-12-01 11:45:00       2.10     17908.0  United Kingdom  \n",
       "537 2010-12-01 11:45:00       2.95     17908.0  United Kingdom  \n",
       "539 2010-12-01 11:45:00       4.95     17908.0  United Kingdom  \n",
       "555 2010-12-01 11:49:00       2.95     17920.0  United Kingdom  \n",
       "587 2010-12-01 11:49:00       2.95     17920.0  United Kingdom  \n",
       "589 2010-12-01 11:49:00       3.75     17920.0  United Kingdom  \n",
       "594 2010-12-01 11:49:00       2.10     17920.0  United Kingdom  \n",
       "598 2010-12-01 11:49:00       1.65     17920.0  United Kingdom  \n",
       "600 2010-12-01 11:49:00       3.75     17920.0  United Kingdom  \n",
       "601 2010-12-01 11:49:00       1.65     17920.0  United Kingdom  \n",
       "604 2010-12-01 11:49:00       1.65     17920.0  United Kingdom  \n",
       "605 2010-12-01 11:49:00       2.10     17920.0  United Kingdom  \n",
       "616 2010-12-01 11:49:00       4.95     17920.0  United Kingdom  \n",
       "617 2010-12-01 11:49:00       2.95     17920.0  United Kingdom  \n",
       "618 2010-12-01 11:49:00       4.95     17920.0  United Kingdom  \n",
       "620 2010-12-01 11:49:00       2.95     17920.0  United Kingdom  \n",
       "758 2010-12-01 12:15:00       1.65     15983.0  United Kingdom  \n",
       "831 2010-12-01 12:23:00       2.10     17968.0  United Kingdom  \n",
       "835 2010-12-01 12:23:00       0.85     17968.0  United Kingdom  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duplicates[:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems that products, which were bought together in one invoice, were considered as duplicates. However, there are no duplicates, if we look at the *Description* column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Number of unique customers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4372"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['CustomerID'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Number of purchases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25900"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['InvoiceNo'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Period of time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2010-12-01 08:26:00')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['InvoiceDate'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-12-09 12:50:00')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['InvoiceDate'].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ttIIgCIJBoL82i9XM7AmA9L1qSl8DeDTL91hKWyP9Lqe3lDGz+cCzwMod6uqFpIMlTZM0be7cuf08pSAIgqAdA23gVkWadUjvb5nWRLNTzWyCmU0YM2ZMo4YGQRAEzemvsHgyqZZI33NS+mPAmlm+scDjKX1sRXpLGUkjgRVxtVe7uoIgCIJBpr/C4jKg8E46CLg0S5+YPJzWxg3ZtyZV1fOStkr2iANLZYq69gGuTXaNq4BdJa2UDNu7prQgCIJgkBlZl0HSecAOwCqSHsM9lL4GXChpEvAIsC+Amd0t6ULgHmA+cKiZvZSq+jjuWbUscGX6AJwOnCNpFj6imJjqmifpOOC2lO9YMysb2oMgCIJBQN6J7x4mTJhg06ZNA2DcCa37Hp48BA0KgiBYDJA03cwmtNsfM7iDIAiCWkJYBEEQBLWEsAiCIAhqCWERBEEQ1BLCIgiCIKglhEUQBEFQSwiLIAiCoJYQFkEQBEEtISyCIAiCWkJYBEEQBLWEsAiCIAhqCWERBEEQ1BLCIgiCIKglhEUQBEFQSwiLIAiCoJYQFkEQBEEtISyCIAiCWkJYBEEQBLWEsAiCIAhqCWERBEEQ1BLCIgiCIKglhEUQBEFQSwiLIAiCoJYQFkEQBEEtISyCIAiCWkJYBEEQBLWEsAiCIAhqCWERBEEQ1BLCIgiCIKglhEUQBEFQSwiLIAiCoJYQFkEQBEEtI4e6AUPJuBN6pz08eeHzlPcHQRAs7iwWIwtJu0u6X9IsSUcPdXuCIAheaQx7YSFpCeAHwB7ARsB+kjYa2lYFQRC8slgc1FBbALPM7M8Aks4H9gbuGdJW9ZEmqqqByDOYqrXBam8QBEOPzGyo29ARSfsAu5vZR9L2B4Atzey/szwHAwenzdcD95eqWQV4quZQdXkGoo7hlmc4taVJnuHUliZ5hlNbmuQZTm0ZqDzDqS1N8gxlW8aZ2Zi2uc1sWH+AfYHTsu0PACf2sY5pC5tnIOoYbnmGU1uivUOfZzi15ZV6TsOpLeXPsLdZAI8Ba2bbY4HHh6gtQRAEr0gWB2FxG7C+pLUlLQVMBC4b4jYFQRC8ohj2Bm4zmy/pv4GrgCWAH5vZ3X2s5tQByDMQdQy3PMOpLU3yDKe2NMkznNrSJM9wastA5RlObWmSZzi1pYVhb+AOgiAIhp7FQQ0VBEEQDDEhLIIgCIJaQlgEQRAMYyQtJ2lEtj1C0qsGux3D3sDdH1KIkLcD48nO0cy+PVRt6jYkvQP4lZm93CHPG83srg77JwNnAM8DpwFvBo42s6sXol0rAWua2R/7W8eiRNI2wDHAOPzeFGBmts4gHX8ZYE8zu0jSphVZngUeNrP5g9GegUbScsA/zexlSa8DNgCuNLP/ZHm+CZzRD0eZoeIaYBfg72n7VcDVwNZ9qST995OANwDLFOlm9uEm5btSWACXA/8CZgKVL7PkYXWumT3TrpIkvY8E1jKzj0paH3i9mV0h6SgzOz7l29fMLsrKfdXMPt+XBqeXyAwz+4ekA4BNgRPM7OFSvjdREoJ1mNnPJX2qJs+3U/1Nb6iJwAmSLsYfvHsrqj0luTufCfzUzP5W2v9hMztB0m7AGOBDuPC4WtLomvbOK35L+i2wF35NZgBzJV1vZp/K8iwNvIfeHYhjJc0E2np6mNmbUh2110bSHmZ2ZV5e0sfM7JS0eTrwSWA68FL5WE3+p6b/ZVbnEsCuwH7AbsDvgIuAk/D77I+40Hpj+r1yavPVqXyn5+DdNW35eaktq9J67R7J9r0OOBlYzczemO71vczsf0t1jAE+i8eKy+vaCbgB+K/UabgGmAa8D9g/q+I+4FRJI/H77Twze7bTeUhaF79+E83sjaU2f4Ye4Z+3pchTed8An+50zOweX8bM/p6l/72fI4tz8HPfDTgWvyZVz20l3SosxhYPeAdeA9wm6Xbgx8BV1ts17Az8oX5L2n4Mf8iuwF+Wx6f0z6X0gt2BBcIiPVBfB1bFH8qiNzkqK3MysLGkjYGj8JfK2cD2WT0/Bt4E3E2PELRUH6n+rYFr0/aOwG+BnwMr1FyPgkY3lJkdIGkU/gCdIcnoefCeT3m2TS+WDwPTJN2KC5apxSml77el9DslFWnTs3NbC3gm/X418AiwdtacFc3sOUkfSfVMkVQeWVyK95qnAy+W9r0jfR+aXQPSub/Qx2vzP5JeNLNrASR9FtgBKITFs2VhUqL4n14PbE7PnKI98RdhnqcjkrYD3o+Psm8FtgHWNrPinB4CJhU97BSg8zPAcfg9U4zwOj0He6a0TvcekvYCvgW8FpiDv1zvxV+gBT9Kx/8hgJn9UdJPgRZhAZwLXJDO62PAQcDc4rTN7AVJk/BID8dLuiMvbGanAadJej3eQfmjpN8DPzKz67LrtzouaN6PP3f/h9/vORfh/+2PqBD+iXb3TX6PlzGgGG3+Q9KmZnZ7atdmwD/LBRq8Z9Yzs30l7W1mZ6Vre1WbNle0qA/TvReXT7pguzbIp/QHng/MAr4KrJvtn5a+78jS7qxIu6NUb3l7FrBhTVtuT99fwh/gBWlZnntq6rgCWD3bXh34eR+v3R3p+4/pe0ng2g75VwGOwF88VwIPAIeV8iyB9+r/gj8k9wHvJo0iUplX4S/B6aWypwBvy7b3AL5VyjMznevVwOZ5+7M8dzU49993SmtybdL1uBn4L+ArwMXAktn+rwHfwF+8mxafiuNeDayQba8A/LoP/+NjwB/w8DgrpLTZpTwzKsrNKO/r9Bw0vfeAO4GVs2u4I3BqqY7bKo5T1cbp5f8YuL4om67tzcAbivujoo4l8ICkv8Bf2p/FNRLnAx/Fhd6fcEH1pvK1K7dlIJ+pivKbAw/iI8Lf4e+TzSrydXzPALem7xvwUeQqwJ+btqNbRxY3A5cko9B/qO7JY2Ym6a/AX4H5wErAzyRNNbOjgH9LWpakokhD0aJXmo9CyiOS8vaTVq2myXle0ueAA4DtkupgyVKemyRtZGbtIu6ON7Mn8uMCr8szNFClFLrdv0l6I35txpcPJGlPfMSwLt5z2sLM5qTh8b3AiUmN8CG8BzgV15XfLum1wE346GAT/IZ9QdLKKX/O5mb2saydV0o6rpTnWLyHdKOZ3SZpHVwA5fxB0v8zs5nlc8lYTtK2ZnZjOsetgeWy/bXXxsyeSr3o3+AvoX0sPaGJLdP3hLwYsBOtrAX8O9v+d/lYNWqbi4F34j3jlyRdSu/78n5JJ+MvSFLePyWV3X+yfJ2eg4K6e+8/ZvZ0Ms6OMLPrJH29VMdTqe7iOPsAT9Cbom1PSHo7Hv5nbEo7Ah/pX2Jmd6d74bq8sKRv4yOia4GvmtmtadfXJd2Pd2RuAt5vZtNSmXZqysslfQK4JL8mlqlJaXPfSNrAzO5rYzvC0kgi3dMb4KNNAfdZZoPJqHvPnJrUc/+Dj1iXxzunzWgqVRanD/BnvDegDnkOxx/mq/BghUum9BHAg+n3W4Hr8SHuuXjveYe07yXgOdw4Oz/9Lrb/UzrWCfiweT/8Rnw38O5SntcAnwL+K22vBRxYyrMdrkq5H9ctz6S1d/X9dD4fxIfmV1IKuogPm4/DeyoH4T3YE7L9H8GF5vbpOs4BPlZx/c4GtmtzbXe2nh7MgcCypf2bAl8k61nTppedzueL+ItyHPAFXGXY13viHvyFW3ntUp7N8B7wQ8Bs3P6xaba/7bVJ//tz2edfuEHyeeC5frT3C6ktxwBTUls+X8pzPR7C/44s7a7st3Ah9CN8VPc88F5g+bR/WdwWcQnew/40PsIbUeSpew6a3nu48FweOBE4D38m/lCqY52U74XU3htxIVS+Nu8AVsR7x9fhz/GepTzLdbi2HwZe1WbfiniP++P4/Xs//rw82ib/7IrPn0t5Ku8b0sgqnUP5cy2wU9r/7qpPRVtq3zML8+nKGdySrgL2sM6eOscCp1vJgJz2bWhJQqfe7lb4g3ezmdWF/a061hkVyWYNvRCyembhAqXFcJ+fQ9Jb/lfavMHMLinVcYeZvVnSH83sTZKWxF++5Z7tIkNS0dNbBn9BFwbWNwG3mNm2Wd7R+Mtyu5R0A/BlazVwn0GFgdpaDc/jqtrS5v8fhXc0Oho9K8oJ98R6pCbf2+k9sju2It9mQHEtbjCzO0r7bzOzzYv/NKXNMLNNKupaErel7YeraFdpeE4jgH1wY3HH56DTvSf3UvpXKr8//lI+18yerqhnOWCEJdtXxf5tzOz3VWmS3oLb+5Y3s7WSDfAQM/tElvcaM9u5VL5XWkofi9sn98MF6SXWR+eV/iLpy+b2t0bvj3b5cJXXT9TGMcIaeol2q7A4E++lXEnr0LDKS2Q1Wr0Ycu+Md+EX+tm0/Wq8R/WLDsd+NXComX2lYVufp7Mnzqgs77UL+1KXdKuZbSHpBuAT+JD4VuBLfbmhJG2F9xI3BJbCdcD/MLNRau9dVKgDC++i84GvWFINpSH6p83sg308p/dkm8sA7wIeN7PDS/m2BdY3szPkHjXLm9nsbP9quN3qtWa2RzL4vgV4sem1kTTdzDbr0NZT8JfOjri78D64LnlSRd66+/NK4L+Bi8xs06S2mWRme7Q59krA33Dvmn+qtxtvcYx1SuVuMLPtWMRI+ipwvCWvudTeI83si6V8t5vZplVpkm7Br+llmQC9y1xNtwx+7a/DnQ4Kw/Io3L12w5r2vQ7Yz8y+nKUtiY9CiuvzW+CHZvYfSQc0uW8kPQh8w3o85pB0hZm9o6pcX5F0iJn9UNKUNu34clV6mW61WRTDwaXSpxdy19ljcN1q7lmUe1FNyXtHZva3dMF/IWlNXPf3WnwI/1N8uHpg+o2Se62kE6nu+R5uZiukvMfiL+5z6Ol9lb1e7pN7MFxOqxAsPE6aeF2101sWuvmmXlPfx3tcF+H69wOB9dK+pjf5BpbZEMzsLkmb5BnSA/pperu87pT9vrhU5jxcnZGnTUntfD1uWF8S+AnuIVRwZtr3hbT9J3xY//203eTa3CxpczO7rc3+rdOI7o9m9mVJ3yJ5DJXaexg+onoSV3mK3vfnoXgwuA0k/QW/5w9I5b8EXGiuE18a+DWwMa4yfT9+fTq68WZMlfRp/Fr8o0gsje4q773su5LSvblH3ms3s2ckvQ1XQ5JGDVsDY0ov4FF4Z6Uo96gWONVBdm6H4DaN1wK3Z/ufw5duLs5lHPC3rJO4I27/eRj3iMo5Gb+XTkrbH0hpH6H5M/UfYEdJW+KjoH8Da7QTMtl5FsKm9j2TvhsJhXZ0pbAoLoqkFXyzx0c54wjcV7zXMDijaoZ7cc3OxvW4F+PD+5txl9b/Z2Z/TXkKY9O0Bs3ezcy2zLZPTr2k47O0ZXEhsWuWZvS8bI7HdbdtjVzmboOktveaCNaXG8rMZklawsxewt1n/5DSe6l22nCvpNPwl7bhL7py2wvXxNPo/ELLWR+3+eS8C5/0VxgNH0/3R84qZnah3NEA84jHL6Ve2RK47eE7NcfeEThE0sP4i7VlNEWPy+MLckP/07S6ARdMpub+NF9qeJc2apv34Z0XcBsC+FyW1wFn4cKizo23oFB3HJqlGa33T8d7r2FnaAlJS5vZi6nMssDS2f6l8M7NyFLZ5/DRBMCjcscEk8/xOZx0T5nZCfjcoMPM7MQO53shfr88mzovF+FCYmNcqHw0y7u5mW2cbV8r6c50vMIFuO6ZesHM3ifpKOB3kt6LX9+mHbdG7xm5sf8EXJ1ouBH/k+k+qqUrhUVSZ5wDjE7bT+HG4nzG5qO4sbgT0+SeEz/AL+5heC8MYLSZHZN+XyXpSfzGyXv8l6fvsxo0+yVJ++OeKYbrSFtejmZW9hQqU+t11aa38iywMx2WYSyrdPCX3VLADEnH414ruedQR1VVyvIhfAg/OW3fgPfKcuabWTmtfE5lVd5fcVfInH+bmSl5taQXbJl/yG1URZ6tSPeImb0k93KqExaVKqCMK5Kq8hu44DLcAF2myf3ZYv8oetPm9o9/W4+OeTfg/CTU75VPRgO4TtI38M5Gft/mvW7MrEqYlam795p0hn4CXKMeG9SHccFWtON64HpJZ3bokHwMfyGugbsPX42rW5G0k/n8l7+oYjKh9UwgXNbMigXWDsCXRfiW3H4zo1TsJUnrmtmD6RjrUHpuk8rzo/QeHRdCWGn7eEmF083oph23Prxnfoq/y96VtifizgZbti2R0ZXCAh+af8rSBBtJO+APZD49/s/AbyX9kvZ2jcNwdc0F+B96NVnvKqlzivHuX4FXFS+h0hC904zTgvfjN/kJ+IPy+5S2ANUbcqdJugBXi/VSUyUmpM/lafvt+AJTW6XvX5Xrb8MH8JHXf+OqjDXxuRQ5nVRVmNm/8JdvpxdwR9dE+RvyDVZjVAYulPRD4NWSPoq/iMov6U/hqrl15ZO0xtDTYwV3v/0+vdUxt2e/H07tapmpnO0vevsXS7oCtx9UCYXa+1Nt7B9p94up0/Rk2p/PFi5m/zZy4+2kl8+y1d17TTpDx8snU+6CP1fHmdmCSWOSLqdHkFPGzPbCR2P5bG3ktpnf495I19IzkbClOD0j9LzynXBXXMxDiJTLfQYXun9O5cbR2/37Unx+xG/K55xY4L5qZtdI2hX3Kiva32hE0OA9IzM7Jyvyk6SOb0S3GrjvLA0Ne6UtrLFH0kO4raNy9qVlRkJJV+MvmE+TzTg1s3Lvt+6YHQ25auA1IfcUe0+hmpO0PPCzVNd0M9uoYVt2wr1iXuiQZ5qZTUj6+cKo/Qcz2zr9rjWwSppNb8rXt6NROcv3VlyFJ9wDbGpFnpH0+LPfb60xha4r509t2SnLUzlT2czekPYvg/d0t8Uf/BuBk5PgzNtRe3+qx6Ot+F4enwi3a9J/n4ULvO9YCpmRbAAfMLPyTOS2JFXhkvT08j8AvGRmH8nydLz3JI3HX3jb0NMZOsLMHiodaxzuhPAb+ZydJQr1mqTtO7XTzK5XB+N3H873BHxS4RN4GJnXmRusVwcuN7MJpfxL0zoH4sXS/koPtVKelXD1af6SvyHtuxkfEZyXdk3EJ75uWaqj8j2D25LAI0P8jR6B/T5g6awD05FuFRaX4EP8QooeAEwws3f2sZ5a42rDeqab2Wall+b1ZrZ9lqduqFpV7wjgN31pj6R7gY3NjWjFjT7DzDaUh0XYlfpREJLOxns6T9Mzs/RGy2JtyT2udsF7vX/FH74PFkJb0n1UGFitsx2p6px+AJxp7Y3KdeV3MrNrq1QTqT2FA8E6Fb25lrSkr94J/1/eLDeO7mdmB6f9F+LzHX6SiuwHrGRm+/aj3YVn2824T/3T+DyL9ZO6sejIWPo8hf9Hs1P5FWl1S74eOLY80mnS+RoI0ojvYFwFs648VMwpVuHSWlG2MH4fQetIdRTwrlJHsW2csLRf+It0ddxJ4C8p/c3AqvloJ6VvXVHX2dn+/8XnlFSO2uVhaibjEwtn4M/VTcUzJ+mWCsFws5ltVUqrfM/g9rvC2aBMS8erE92qhvow8GV8WClcF94yNEwv56Po7e+evxTbGlfVZtZlVk+u9+0047SgbqhaRYshV82CAP4U99i5NG3vCZyX1Gf30DnuTn5+B6ZjvhZXf/wA703n91SVqip/ITcysCZ1Sll4nZ1laWtUVjPX5O1opp74GT5xMOcifK5IQd1M5deXXrLXJQFTPucm9+flam//qDKOjge+IOkYMzsfj4l2Fz5RD/z/OoPW/wia6eU73nsN781D8UmGt6R9DyR1XgtJiPwfrffEMnhMqU7G74JLaR8nDPMe9PnldHw+0MRSW87BoxjMoOeaGO4AUzAZ+LykF6mOKDEZD+lxs5ntKJ+t/WX1BNO8TtLRtI4IflnRvsr3jDWzOdVjAzS7b3H74PaHSbgnwfb4g/P1Up62cV+onnVZfMrxgqpmnO5VyjOjQZuLWcLF959wlVKxv+Ps7CzfZvgNegQ+4up1zlTE3SnlOwB/OP+A6/mPAt5SyjO5otzk7HdtnCS853sdrns/Ax+h/KyUZ1zVp5TnWFz9swLe2/w4cFTeJmDbNtd9A7wn+iCts2g/CNxdyttxpjLunrtVtr0lcFJf709cCG+dbS+NB1Ssu4dG0xOHrNc91yZtZzx442/x0cdDwI6lPHWRAWrvTXxCJvTEUhpJaZZ9Sr8xtemP6b8+Bp+oSfl/b3MNOsYJS/fH53CbW6G2PAx3nb20lPdeaB8posmHnphYM3C1UPF7Nm67ml3x6RXTiWbvmTfinYMDi0/jdi7MSQ63D260vazdp5S39qWYbsJP4MPR0cVnEbX9f8kC5vWzjjvyc6I60N1aVZ9s/83p+yp8dPFmUviTUj1P4T3AD1ERkiHlub1dG9PvJoJ2Jv5iLAI4robrjcv1boq7SR5GdWC+W9ql0RM8r1d7U/reuKB6On0Xn++RvbBT3uVwr6+R+EvxcGDlbP+9uK3rofR5GXe5LoduaXJ/3rSQ98lNZAIStydU1okLozfh7qNL9/Xea3hvHo9Ha74PDzFyCT5ps3ys4trMzNJ+l77H4B2QX+GjxWsrjnMq7uLe7vpcigv1Q3A32qm4kNykIu9FZAEUO9S5Ej5q2q74ZPsuwaMpH4NrQS7F14rpy3+6BG707pSntuPV6dNtaqhvpu9347GWcr3wQ6W8TVRDB6Xvz2RpBqyjPqxnkXkzvAV/OVR5M0ymzVBVDQOO0SwI4C/pUcssi/v4309PqOj/TbrsI/He8ShchVQ+5iqS3oDf+F9JqoH7zewDkvbDPbnWlnRZVmwF/IVb1LFj1fmUKBaymS8PwzGH0vwQ+QS0felRF50h6SJrXQehkzfOvXKHhTFqDW2ez5G4VNJbzOymTo01s3+kNo2ix+MsZ/f6Uwaa3Z9Xy50efm7pbVCH3DHhmbT5MeDs9H+T0nu5Z1cY5X8n6RRrNcrX3XtN7s2j8dHUTPxF/StcBVzmX8le94Dcm+cv+GRA6FGjvoP2atRtgQ/KnSdehF5zYdYxs/+Xzv00vGO0llWHH1kFuEcefj/3Atur+N3OJkHyOjOzwpX1GLkTxYr4JMqi/IEVx8UyVaw1c+3eBxf2d5jZh+QRC6qubzX96ZkM9w8el6ZjGg2GbDXHuL3qd5vtm3F98Mj0OYCKnm6HY3UMOJblaxQEsFT3prgbJDTonWTlRuFzCr6Ge7bcD5yV9o3DwynclNpSfDYFRpbqeTuuwvpS8SntPwnvdX0MjyR7B75mRZ7nXtwFtdheFvdAyvOMx3tsT+Evj1+QjYjwzsWddFBn4T3fUXiv+JpU1wGl4xyC99weokeFUKUyWJWKkV1f7k9cHfkyHiBxgXoy7ZuJq2nyz2O4a+3RFf9lcV7nVbTlQny2947pcyoeYiTP0/Heq9tf+u9eX3PvbY6r+sbiPeSfk1R7NBuR9fqPS/9zx+e5tG/7qk8pz0zcpjIjbW8AXJB+j6BeLXZi9vlRun69RgR4SPzv4/G5eql16QlRPj3936KkRu3YjqYZF6cP/vJYJ9tem9LLow91Ver46Nt6FlUqkJsr0toOVRfx9coF33UNy/wRf5G/Hzei9ee4p+CGwEfxIfJMPLhju/zjgTdVpF8JvDrbfjVwxSK4TjPS97twN9LR9F7X4QF8Jni7OvZKef6BC5KX+/LA9qGt5ZfhWqRIrLgx/OBS/uVwO0Kv618+x3ZpA9DmvfBOx+y0vQkl9XGDOmrVqNSrYouI0oUAnk9JGPexTZU2iWz/uVR0GDrUt2LVdaG+M1nb8er06TY1VMEn8QlNhZpnPN7jW4CktXH99nhaXd7y4eMUvIe8ET4k3gM3rp1Nq4dN/rtqu9KbofB2MLN5dUPV1J4q185ncV1zVUiT4pzyiVz5DO4ReO8jH6bXTjyTh7642swql4Xs4IFU9gJpGyepk7eZslXDEi8Cd0uamo77VuBGSd9LbT9cHdZ+kHShmb1XvQMgltUTxfoib8N74PPUe5LWg7SurlfmOPy/bXGtzc6tMr5PgWUz6VW9FO93zewR6xxyZRfg15KWMbPvJc+rXwHXmNnRFfnvkLSVmd2cjrslPpos309V7f12ytfRXTUxBe8s/TbtmyGfn1Gcb67SrDrWXjRToxaqWOE9/hZVrJktQUNUH6UA4LHktfYLPM7WM7hasWB1/P69ldZnbi+qeQH3hGzBOqh1kzvw/5kHaTxF0q+BUdaHteq7UliY2a+TDn2DlNRrogz+x52O65VfblNVJx3fxpKew2+4ZdNv6LkBc96Xvg/O8oC7+Bqug59MhftcqZ5JuN3jurS9A67i2gp/wJr88blb4Xz8wckD8RWz3POH2MiElrl+9M3tDmApOGIDOsVJ+laHci3twQ2El2Tbv60o8yPaL9k5OeWpC4B4uXxuyD+BT6SX7L9KeT6HC9xbaNVhFy/5OtfaJnHECqqW4j2HbCneKpKQ2wW4Ml33vfGJgd9rU2RL4EBJj6TttXA7z0zcoNwxHEuio7tqYr6ZPVshgAvego9Cz8OdK1oypk7M+mZ2RTpW5cvTkj0iK7cppc5kH6iKUtDyIrcamwS9n/MWlM1cx4XRhrhqsJyvrUA2M5P0C5Kbt5UmQzahK4VFRQ98XUnP4t4Tc1Lavzo8HAVtjatNeh+SNscXTVk7bR+E/5kPAcdY62pa/zKzf0lCHkztPvkawTkv48smPpnqWw1/UNfHvShutPpAd/dYZoxP9exLzxrik6xi4llFPTNST+8iWntDP6/I246qOEmnpXqaGL8Lnsa9R9oJffDFbm4tvYjmp2MVq7GNAJ6wZLiVB7JbrchsZkenF/tzSWD+A3/R5vwQ98BpWXMk42/ymda/A86VNKdoRzpGS3wfdQ6GOT+9BPbG3VBPT/dYR7Ln41Tg27j95bEiveI/7GiUrxnFFIw1szrj/l2S3o8HFFwf9yT7Q7b/NfiosXCg+CU+wlsQ862Bkbeq/benZ7VfWJuAmlmb1so2Z6fv1+DuyJjHvCryrgI8bUlvlPhm9ns+8LCZPVbRlDqBXBcRuSPdOoP7l3gv5Fq897ED3gN/HT5D9Zx0U66P62krg6hJOgl35ZuID2v/jg/7e3mMtGnH7cAuqSe3Ha6GOgzXxW5oZvtkeS/BPVGOwHvNz+Cr970tyzMz7xWloeXMpFa5Aw+r3PElq5pwCG329wqnoQFa0Cmrb2naxElS/QzZn+D/98W4DrZXQDs1WPtB0jRcNVbMbl8KX4N787Rd65WiLJxJm/N8FT2LAB2Aq0nOLXUciomIRTBM4arClmCY8tm5v8bvm+1SnhnlnnNFG6r+u+x0qv9DleJdWevaGp2WeEXSqfjKeTNpQ7o2X6AnqvJVwP9aKRRKyrs0LjS+gT/TJ6b0r+A9905q1CpV7Mpmtlu7tnVo8w10iFKQ8hTqzbLaaxLuIDIPV0+eg3tXjcD/69wj6jW4is5wG0gR2Tpvy11m9sYObb0Hfwf2mrza6Fy7VFhcDnykogf+Edwr6o2S/g/3UHqQbD0L65liL7w39GjaHk8fdXzKQiLIQ1LMtRSpVh3ixchj4KwI/Lp4caX0k3AVQDEKeA/u4fIZ4Aq8F1b5oEjaA9e1vzftLxiF22QOxHW2x9PqKjwK+Iyl2EYDQcXIr4W8Z6s2M2St98JGo/CXx4fwB+oMvNdZxBVaB+9Jb40L4tnA/ta6ymCv/6T0H+ZhrZfBJ4bdXhL6X8EfxpY1R1Ja+WErhjn/wu/DL5jZNameP6Tt69L2Dvh60QsEUXqBvB9/efwu9WB3sNbZ7QuNauJdpTzXk9R8Vlp0KP2+Bw8iOZsKd9WkQrrKzHapacvSuOF6P7wDcRkeFbYIyXFdRbEFz3XKMyXbNx8f6V9cJZTqkMeyehK3V3wSf/5OMrNZHcoUaq/N8M7oivi9uYeZ3SxXQZ+XXceP4J6CRed3e1xA/rhUb0eBrD6sFllZvkuFRcceuLlh8T7cs+bfHeppFKCuQ/m78Ik889PxDrae4GDF6l2jO9VhrdFrhQuIbfCb5kb8Jre0v+2DItdrb4LbIr6U7X8et4Fshy/wshf+AOb7zzez8tB6LG7YKwLD3YjPhK4aHrfQl56tPJbVRtbgRk1D+APw0dm9+Mvpe5atXaAOS3bKDeQnmtllaXtv4HBrE5tIbkg9x1qdImZXZDXrEH8nvSjfiI8wipfroMRjaoJq4l2lPB2XeG3yopKrNT9QNbpM+8/Cr9OV+D1518Cc4cKRRqAb4M/B/Z3eKVmZ2/H7cJO0fa9lK/WVruP9+Ij36bS9Mh4V4PVpuxi5jMS1JX+mev5Ifvzl8Of9/Wb29ibn2ZU2C3zS0BW09sBvSBfobyntTtyNbE6v0j0slI4PN8RdL19P45+4nhpJ69GzVsF0egf5KrYL47cn+gvzZ+nTC+uggjKzO5Pw2tWq495fSsOJZ4kz8DhTRQC8A1LaW+sKWkM1XuIuXL/7RLsMkvbEnQXWxYfyW5jZnKTWuBc4Ub505c34f3ADHgerzMdwO0Kxfslj+IirHS/gw/oFWD/i8Jjruu8sjVz+LOl/aA2GORtAzb3NBoo6ozzAU5LWLdqV1HwL/jOrCd2e+BcwMwntfGRcjCI/kNJfBxyuHvuT8Pk7y6rZ0rcTcHXXOFpVm43UMTnyCZOn4CND4RNRD7Es5lkbtddc3Dmg4J+0kv+/j+GdtoLncUN/QaOVKZNQexs+Gt0dV9ue0qQsdK+wOJTWHvjZ9PTAixfqavgypbfRZuZlyvsx+ezePuv4zOwrkq7BXeOuznrHI3DbBWa2dhoxrGlt1mRo+nJQmzWkzez0dKyXJK0saakOvZ93Sbobv3l/jXuDHWFmPynlG2Nm+QjhTElHdLgcVec1GRcwz+PeSpviE8auzrK1nSGbhO5rcIH1nWzU9l+SVjCzByUVo5SNcK+e/wK+mYb6d1qPpwrmgfK2khugVR59qLlXSkcbSzssrayW+DA9wTAhC4Zpzb3NBorCKH8DFUb5RNslXqG9KoueyAHgBusiQF5xnRdIBDMb0a6BkgpvpibX5lxcZdbOCaEvfAvYsVA7JYH5S3z0U9DOA/EfauZR+RfgFnnwT8OdKm4thJC1rsHTSyDLQ/Pvhy+CdR09Haq+dNq6Uw3VBLWJjW8eE38tM3tkYXV8fWzPQqm8Uh1XktaQNrON5Wsz3FFSyf0QfylfRmvvrfCHn2Fmm0h6Fz5M/SQ+Ua+sEvkNHj+niLG/H/ChdiqbNu29M7VzN/xl8z+4gXrTLE+n/+kK4PNWsiOlnuMUM9szSxuJuyZvj4d7WBmf5XtIlqejsC21ZT7+UO9nZvmCWI1sLB2uyTL4CGc9/GX2Y2tdZGjAUPt5O7nXYKGyKIzy++M69nOtIpR8OzVfJ1VWUveNNbMfpLy34r1uAz5rJe+9hUXSjWa27QDVdYOZbZdtC58xvl2HYn09xpRO+61nGel2AnlDfET9QesJT//nTqrRKrpyZKE2i8dnPfARwA+svefAL/Bp8g9LutjM3jMIzW6k8pK0Le5LfoZcR79CcQPQZg3pUhWPp88IqnthTSaegfd8v4+7KRpuXO+rJ1RR8dtwIXGnSgezzK2wgvFlQZHKTFM2mSvxHP7y/Tbwo6oXHS78zsBVFOBRfS/A5y8UAmoTfBj/Xrz3fHGpjgk0tLG04Sw8jtLv8EmgG+I2mEVBu3k7r5N0rKVV1SzFu8ra1wtJXwWON5/0hXwxnyPN7IspSydV1lG0hv5eCjf+Lo//H42FhRpMtgWmyGM+XUP7FSXrjlMI2rsl/QofYRo+yr2tlHeh1sXJhMFypf+iTLsJnwfg1/c38onK5+Mj4z7RlcKCmsXjzedO3FmMICqy5C+sPknfhaBW5ZV6GBPwVbnOwB+qn+DqNuiwhnRBduO1899vMvGscJtsN8O0KdPlq3utDXwutenl1L4bzWzbChVcLvjb6b7BYwzl7IePKD4BfETubXSDJe+jRKWwTQ/7xFTH06Rldq3aRlRrY6lhI+sJYnc6PcukLgrazdvZElc5nZPSq9Sgz+ITCI80n5ezh2XBM83sGfmqfIWw6KTKWsqS12HiRnPHjnmqXiu9E7+gfrLth3CD9JJZHqNH5deEfO2TJ+mZCDkXD9uT03ZdnCbIF3Y6HReea8mdVQ4xs0+UslYKZDO7Aw/t8Vn5rP/9gKWSJuISMzu1STu6VVjULR4PnafY5w/GYOnp9qjPwrvwWDe3A5jZ4+kFW3AkvdeQblmBTa3++8iN7wv8961m4pk8wms7zBou0ZiYhHto/dnMXkiCrtCj7p8q7KSDvk3SR82sZS1tSZNwx4G8YYUBfwP8Wh+B92hzodJO2N6H9/T3zHTTn2zTptoopDUsUDklYdWwWL8YXwiKxBx8CdF5knLV17fx0ehPcWE9EReI9+PrbOyAT6Rb2lKkBPmExqXldqXV8Hvon7hac39cRXJYqr/l5Wpm+brQuRG4CU0m225sNXNR6uijvn++mTWZ5d6O7+L2hsvSse+Uz9sqU2tbMrPfA7+XdDjujDIRtzXV0pU2C/kauq+h/eLxdbrwl+jp3S9LT6yfReVtUrQpVzGNAZbPVEyoZxnN280nli2Hrz+Qjz7ariGd9nf031fNxDNJR1bsXg5/8a9sZss3OM+2cZ/SsW5X60TBSlVg6glfgkddLYTDBHzE9S7LJi5JuhgXTLPo8Yi61TLf+tSuE3H3zLvwF9U+uA1iIj5H49f4MP40q/B86nRfdTrnrHxx70Hr/Tfg955q5u0UIyd1WNZTPXano/CR5hm4sP0w/nLbjhq7kqRzgd9WCP1D8HkjfVkvvMlk2x/hDhFVHnF9Qp1jjhVu8YfjgviSUpvm9aqw+hi3mNmWanWnrXKtXg4XyCOosS31h24VFo1mF6vD4vCDTa5iMrPXyWP2XGRm22R5Po0/CG/Fl5X8MPBT65m9+iDwDTM7JStzhZm9I9vu6L+vBhPPsnIr4HGVJuE6229ZZhjtcK5V80EKzHxeSP5gLPjdpr4d8Rc8eATXa7N9m+NuhmviI7IDaB9ypaOwVY9v+n64sfYsfBh/damOYXNfdULqPG8ny3cTbpsqXLb3AT6VhEU+l2IP/H4R7v13lTrMKlaaDyX33vkF/iItXuqb4QsuvbM0+qk7p46TbVOee/EOQOUEwb6gDpMR5XNuym7xBWYNDcySfoaP7r6P2yQOx1e4nNihTFXYkIXDBjjM8OLyAT6KG6IeTNvr41E3h6o9M/Cb6o4srWpJybfiIQ6+Cby1tO8+XJ9+Bq4Hht7h0i/BvY7Gp88XgV90aNeK9F5lcDQegG82vrrXSovgerRdL6Sv9ZBWN8R7uY/jL8jj6L086zLAp3Dd9cW4qmqZNvWOxmfhlldhG1b31QD9F+vgNoBiLZDLcW+tZWmzFG1WdlbTfbgAPix9dupnW+8r7v0OecZVffp5vCL8+B1Z2ozyfVVRrvK+anOMVXB33yfxEcpPyFbspCeQ6M9xNfVdeOiROcDuA3YfDPWNOJAfetZUPhFf8rLlU8o7A1dX5H/yzMFoZ5u2FwuTFOsjL0eFsCjdQCqlFWWPwqNyjiu/aHH98PfSS/R2XB/a9mWPGwHvy7a/gffaPouryRbV9SjWFHienvUEiu3GawqQrbsA/AAfTSy4B0p5axf5aXC8YXVf1bT13fi6Bs/259qmOp7P/pv8U6SfB3y0otwk0gJAA3xOFwCrNsi3Er5M7KZUrP3eh+NdiY9SimdvH+DKUp6q5YUXpgO0Eq5KLran4fG09sVD2RQLQW2AG7ZHpe3RVZ+mx+02A3dh1G4S5vlFM/t3YUBM6oeh1MldKJ8D8WpJH8VVTD9KbduKNgHHJOUBxwRgZsdLmo4HYisM2WX//SOtwn9f9RPPjsSH7l8EvqDWWbRmA6RTtz6sKVDDEpJGmtl8XEVycLavfP+/3lpVdNfJ5wf0heF2X3Wio9dgQbKffZTerp8ftpoJgoVdSb6kbS+7Uv+b3pbaybaSjgM+iHd6iv/GaA1735SqyYj7p+O8BlgDn3D3ZnrUUaOAV9VVLGlNXAvwWlwjcB4erudAeuY3gc9evzqVOdbSuiPmkavBHRPeQftoEY3UYd0mLB4BsIpwFpI+Xkq6XtLn8T/yrbhL5eWLvonVmNk3Uzuew3XmXzKzqWn39+kJOHYtpYBj9MTG/1JW3zXyyW4HpaSm/vvlcMgiW5zHOsyiHaY0CblS0HaRnz4wrO6rGpp4DYKHgvkd8Bv66Pppbm/YumRX+qVldqUBZkqDPO8F1rUGMZzqMHcb3kXZZER5JIPv4h5MH8QXNMtnWT+PP891nA1cj6tEd8fnwNyNx7TLo87mLsK9woZYsllaP0LR5HSVgVs+4WRfM5teSv8y3oPKZwaPwIfCC8Ihm1nzxcsHkZIRsVPAsWusNIO6SFMWXDH1dm+1UijyrMwmlCaemdn3B/B8ar2hBupY6Xhb0RNy5R8p7XW4Gi33krkXF9TF3Ju18NHqyzQ0gJbuK+Gju9NsGD5oauA1mPItuP+GK5I2MLP70u8FLrxpe0EHIG1fDHzcGjhj9LMtj5jZWtn2e8ysPHmzST0tziiSnsSXX32xlK+T9+Yy+LyZtjR93rptZLEvcJGk/c3spuTtcTIeeGwHALWGFvhRUvmMATaT9DczqwzSt6hR51nnHXsOScX0KmAV+czZfLj72vS7o/+++jbxbGHpyyp4C03+osjS/lSRtW5xnibHelm+ItkvzGxuXf4hZhT+Ytk1SzN6T067QtLbzOxXg9ayvvNT3PYAvhxx3iE5qbT9f/go8i76NxemDgFIOsA8ptp4VQQ4tFJMp8qKWp/nvwKvSqMYLHny1alsM+/DZXAV4J2pzjfhts1GoU+6SliY2XRJ78R1pIfielZwj4BiyFkXWmBIhAWd9cd1S7gegquUXkuP6yG4SusHpToo1SP8BVGs3tZk4tlCsYgE0EAwEnjMzF6Uzz95E3C2pRAWnUgdkyn4AktKSS/hIc+PXWQtXgis+cSyycDnJb2IdzoW6XyjfqI2v6u2z8I7ZgMRSLCKYhRZzD6vmnvUZKS5Im5nyNtfPN+NbQ3WM1/mfHyZhJlp+414GJJm9NciPxw/9Fj4t8Xd/M7HDcELrP4kV7eszPez3zcPYdt/PwB1HLYQZd+FjyYexQ3rOwOzF/E5vwo3lJ+attcH3jGE/8EMXGCshxs/v4Mv19qk7CeBqcDaWdo6uBrqk0N1TjVtHosbTufgbpkX46PuIW9bP86lrat1xfb1A3C8Tl5g8xuUP2IIrtGMJmntPt1ms5hN79DGhfXfzGwdSbPMbL025R80s3UHoan5MYuAZNvTQH/cpo6dzOxatVmBrkkdWV2NJp4NBJIuwHtOB5pPYloWn5G+yUAfq2F7ipnxR+Hrr5+omgmBWdk78HkvT5XSx+C2kto6Bhv5uhE/pXXNjP3N7K1p/wbmHjWVNiYbYNvSwiAPbXE+/qy/L/0mbb/XzFbL8n4bf8Yuo80s70Fob4tdY5COeR5u2/gJ/l48ALfbNZoh321qqCbW/ltUHU/oEBZt0LZ25AHJmuiPq9ge95Las2Jf0zo8sxuAz8Vjy4zG7UBH4+ETBpp1zex9kvZLx/6nysaUweU/qS0H0nMtl+yQP2fJsqAAMLO5kprWMdjUrUnyKdzVOLcx5b3LAbUtLST5UsBl1/nydiG4t8rSBtxWVsNQ3OcfAj6OqxXBQ940jlnVVSOLJmgAQwsMcLu2MQ/y1TGtm5DHqdoZV8FtKl845jwz22KI2rMRPhflJjM7Tx7u+n1m9rUGZRfEsurLvqFENWuSSNoCeMSSm6akg+gQKiVozlCMLBaWV5ywKJC0Ez2rdLXEExqi9vR6ofT1JaPS0qE2AIHSFiVpHsIX8VXsrsZjFH3QzH47lO3qD2oNANiyCw/tMOxGF5LWwufwvIWeNUkmW88SqLcDu5hHod0OV+0chgdk3NAq4oUtDsjXTp+Ch38Bn8twrLVZ+3shjtNphctlzayxZke+TvtqtE6KrFpeoVMduZp+AdY0RtUrVVgMF+Sx6rfGvZm+k+0ahUdO3biqXJu6lqZn6dBt8On+LUuHDjfkIcG3wh+gm6tUOYPYlm3wWFfj8Idyga1rqNo0lKg1wOQPgLlmdkzanjFUtqWFJc2zuIuehZw+gIctr7T5DTWSDsOF25O0BkfsU+DD9KwVLIOrmEebWadlBxbQVTYL9YQErmSYDpuXwl3rRtK6ct1zeJyZvvAS7tr4En5TFYHHhjPL4PFsRgIbScLSWtpDwOm4V9N0+rFIzeKCPLJw216i9SwB25dQKYsT61pryPsvS5oxVI1pwGQ8FM1ChRqvKP9dSTeSRX7oxOL8h1eRxz5ZC38JCXg1Pit3oaa7LwrM1zm4XtKZ5su41i2d2IkmS4cOG+SLLL0PD2GQr1g2VMLiWTO7coiOPZjkBt8v0z5ERl9CpQwLJK0DnICr1l7GJ+h90jwsR8E/JW1rZjemMtvQe7LrcOJRBuB6l7zaRuAT9DrG9mop341qKEmn4GG1f5W298B1r1UL9wwLlC2daGadlk7sVMfe+ByTLfAFgaqWDh02SLofj3PzYm3mQUDS1/DgiT9niFwqB5s612A1DJUyXJB0Mz4RtTDaT8TnH22Z5dkYj7u0Ykp6BreV9TVo5CIlm/X9BjwMzS9pvS9rZ4CX6rsu25yPOyp808zub1S+S4XFdDPbrJQ2zcwmDFWb6pB0C652usxKi6j0o6586dBVzay8HvWwQL4G8L7Wex3wISF7mPK5OmbZwjndxnD11Oov6rCqX0XeUQBm9lx533BAviBaW8zsy4PVFug+NVTBU5K+SOvkk2GtkgEws0dL0wz6pDdX76VDP8DQzB1pygvADEnX0NpjOrx9kYEn68FdUTQBX+TnRsuWtQ2GL5m98jpJR+OeW4arOX+Z8nwKVzWeDj1CIhmQlzCz7w52uzuxKISBpLfjI5VlsuM0CkfTrcJiP1wPewk9OvDG6/gOEY9K2hoPDLgUvnRik/DRqGfp0K/RunToqrjXx7/alx5SLkufoaZKbzsOX6/jGDM7v2L/YkvJpfNVao0ZZja8Yj41pbxWwyHZPsPXgfkwrQEFC07FVzf87iJsX79JM+33tRSjTB5c8Hwz262P9ZyCh9jZETgN12Q07kx2pRqqQNLyw0XFUYd8zdwTgF3wG/5q3Oe9dkTUrf7wQ03qrf6mm9Q0r2SUhenvy76hpspNuWkYmlKZP5rZm7Lv5YGfm9mutYXp0pFF6qGfhruk9stYPNik+QX797P4Eplb8PvwwHwXAxcPZ5fA4T6vIQnfoQw/EvQDeTTVjWhVtZyd9q1WjtIgX81vOPOSpLWKSXiSxtG/1RcLj68XJL0WV8039hDtSmGBT27bjaTiMLM7U4972CGpk4+zmdlxDapZXP3hh/W8hjTL/5mhbkfQnGQU3gEXFr/CHT1uxL2fvgH8UtKRtIb6OZ7WFSKHG18AbpR0fdrejtZnvClXSHo1fh1uxwXOjzqWyBjOL5KFYmGNxYNI1ZyK5fDV1lbGda11LHb+8IlhMa9B0kx699RGA4/jQQWDxYd9gI2BO8zsQ2nUcBr46ELSXHwd6zfi//ndwJThcB+2w8x+neZIFJEOPtmfSAdZx/NiSVfgYWgavx+6VVj021g82JjZgoieklbAZ2t+CLc7dFpRLq/jK8mjqPCHL158I3DbxXDlOknfYOjnNbyjtG3A0wsxOTIYOv5pvlrh/OQaO4dskaAkFIatYKgiqUJ3B9Yxs2MlrSVpCzNrZJwuHGCsJyDkgbgDzMPJgaNRZIuuNHC3MRYfPkzDfRSG1E/hNouzgBPMrOvVH6VJQgVdPa8hWLRIOgn4PD4Z70jg7/gCP01XBRx2SDoZn42+k5ltmLyhrjazzRuWHxAHmG4VFotNuO/Us3437r73g8XFeysIhhupBz7WzB5N2+OBUWb2xyFt2EKinkW5FnhAKQvy2KD8gASE7FY11In09qeuShsOHImrYL6I+/UX6Yuzz3tjFmaSUBDkmJlJ+gVutMbMHhrSBg0c/5GHKDcA+eqLfVk7fEAcYLpKWKgn3PeYbFYueLjvJYamVZ0xsxFD3YahYmEnCQVBBTdL2tzMbmuXQR7K/z3AeFrXhxiunZTv4ROMV5X0Ffw5+WIfyg+IA0xXqaEkbY+7zX0MOCXb9TxwuZk9MBTtCqpZ2ElCQVBG0j3A64CHcU/DYoT+pizPr/GXZIvLdu5sMtxI8d52xs/nGjPrk8OOBiAgZFcJiwJJ4yyt9hUMX4qgb/JIoe/GJwndZWbrD3HTgsWUNGGtF/n7QP0M0DlUSDodONHMZmRpxxR2h8Giq1Qgkr6bfn5f0mXlz1C2LaikPEnoIdxTIwj6hZk9XHyAp/CQ/SeVsv1B0rAM7dGG3YAzk8trwV6D3YiuGllI2szMpid1VC/MFxoKhiFJj9ynSUJBUCbNq3ob8H58bsLFuGrz8izPPcB6wGzcuaSXqmo4kVxfdwDOxRdxmwzc1tfYUAvdjm4SFsHiR5o8OZ5WQ+PZQ9agYLFE0lvxyNK7AdcBF+Cqm/EVeWtVVcOJksvsMcBbgdUHO4ZaV3lDFQz3AHWBI+kcYF1gBj2GRsPj+ARBX7gK9/LZ1tIaJJJOqMpYCAVJq5K5bA9jFqjQzewYSdPwSbyDSlcKC4Z5gLpgAROAjSyGt8HCsxk+a/s3kv6M274q3eUl7YWH0nktHg5kHB4O6A2D09RmJNfW1cysvGLec8BHB7s9XWXgznjWzK40szlm9nTxGepGBb24C3jNUDciWPwxszvM7LNmti6uVXgzsJSkKyWVI7Qehwfl+5OZrY27pA676A74YkzPV6S/wBAs1NSVNgtJX8N7FUMdoC6oQNLluLppBTw+za20/k+D7ukRdB+SRuD6/Yl5bChJ08xsgqQ7gTenwIO3mtkWQ9bYCjq5+GoIFmvqVjVUsWD7hCzNgAhQNzwYzmsHBF2Cmb2M2zKuKu36W5oA+jvgXElzgPmD3b4GdLKnLDtorUh05cgiGN5kuthysMftgL+Y2YND07LglYCk5fCwFyPwSM8rAucON1W1pPOAa83sR6X0ScCuZva+QW1PNwmLUjwo8NHEU8CNhYdEMPSkhVc+X44GKmkCvhDNnkPTsuCVQnKfXd/MfiPpVfjSxFX2gSEjLdx0CfBv3FkHXFuyFPCuYn2KQWtPlwmLstcA+IpnuwHHmFnMDh4GDDddbLD4k9aEaUu+lo2kj+KRV0eb2bqS1gdOMbOdF3Ez+4WkHfGV/QDuNrNrh6Qd3SQs2pFupN+Y2XAMUf6KQ9IsM1uvr/uCoB2SZuOaBAFr4WunC3g18EjyeiryzgC2AG7JJrtFJ6WGbnWdbSH1KlSbMRgsbku9uxaSLnZ6Rf4g6IiZrZ0m3V4F7Glmq5jZyviSuT8vZX/RzP5dbEgaSe812IMS3eoN1YKknfCeRjA8OAK4RNL+VOhih6pRQVewuZl9rNgwsyslHVfKc72kzwPLpjAhnwAuJ+hIV6mhJM2kdw9hNPA4cKCZ3Tf4rQraMVx0sUH3IKkI+/ET/F1wALCdme2W5RkBTAJ2xTUOVwGnRSSBznSbsCgHCDPg6WKxjyAIuptkn5wCbIc//zcAx+YG7pRvKWCDlOf+XC0VVNNVwiIIggBA0vJm9vc2+96Or6T5ID6yWBs4xMyuHMQmLnaEsAiCoGtIIe9Pw5cLXUvSxrgg+ESW5z7gHWY2K22vC/zSzDYYkkYvJrwivKGCIHjF8B18XtXTAGZ2J66SyplTCIrEn/Hos0EHXhHeUEEQvHIws0elFk/58jIFd0v6FXAhbrPYF3fnfncqX3a1DQhhEQRBd/FoUkVZMmIfjq9VkbMM8CRQLL88F/ea3BMXHiEsKgibRRAEXYOkVYATgF1w4/XVwOFlb6ig74TNIgiCbuL1Zra/ma1mZqua2QHAhnkGSWMlXSJpjqQnJV0saewQtXexIYRFEATdxIkN0s7A17V+LbAGPnv7jEXcrsWesFkEQbDYI+ktwNbAmNJSBaPovRb3GDPLhcOZko5YxE1c7ImRRRAE3cBSwPJ4B3iF7PMcsE8p71OSDpC0RPocQHK1DdoTBu4gCLoGSePM7OGaPGsB3wfegns//QGYXFfulU4IiyAIFnskfdfMjpB0ORXhxs1sr5RvCeCsZPgO+kDYLIIg6AbOSd/f7JTJzF6SNEbSUhE8sG/EyCIIglcUkn4IbIp7RC2ISG1m3x6yRi0GxMgiCIKuQdI2wDHAOPz9JsDSKnoFj6fPCNwIHjQgRhZBEHQNKaLsJ/EVGBfEhDKz8HZaSGJkEQRBN/Fs3boUkq6j2gi+0yJrVRcQwiIIgm7iOknfwIMBvlgkmtntWZ5PZ7+XAd4DzB+c5i2+hBoqCIKuIY0ayljdqEHS9Wa2fac8r3RiZBEEQddgZjvW5UnrdBeMADYDXrPIGtUlhLAIgmCxpxQPCtwm8RRwo5nNLu2bnvYLVz/NBiYt8kYu5oSwCIKgG6hygR0PfEHSMWZ2fpFoZmsPWqu6iLBZBEHQtSSV02/MbFNJR5nZ8Sl9XzO7KMv3VTP7/JA1dDEgos4GQdC1pBXyigW5J2a7PlfKuvvgtGjxJYRFEARdi6SdgGeKzXxXOevgtGjxJWwWQRAs9kiaSe+JdqPxsB4Hpu18fzlv6ONrCJtFEASLPZLGlZIMeNrM/pHleQkPHChgWeCFYhewjJktORhtXVwJYREEQRDUEjaLIAiCoJYQFkEQBEEtISyCIAiCWkJYBEEQBLWEsAiCIAhq+f9X6OgnFgCTigAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Country'].value_counts().plot.bar(color='dodgerblue');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Country'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we have approximately 1 year data record of 25,900 purchases for 4,372 unique users made in 38 countries (mostly in the UK)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quantity and unit price of the products"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's look at the price distribution of the products\n",
    "\n",
    "plt.hist(data=df, x='UnitPrice', color='dodgerblue')\n",
    "plt.xlabel('Unit price', fontsize=14)\n",
    "plt.ylabel('Count', fontsize=14)\n",
    "plt.title(\"Unit price distribution\", fontsize=16);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems that we have negative values and some outliers representing very expensive products. The negative values may be associated with cancelled or refunded orders. Let's remove negative records and outliers and look at the price distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    541909.000000\n",
       "mean          4.611114\n",
       "std          96.759853\n",
       "min      -11062.060000\n",
       "50%           2.080000\n",
       "99%          18.000000\n",
       "max       38970.000000\n",
       "Name: UnitPrice, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['UnitPrice'].describe(percentiles=[.99])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.arange(0, 21, 2)\n",
    "ticks = np.arange(0, 21, 2)\n",
    "plt.hist(data=df, x='UnitPrice', bins=bins, color='dodgerblue')\n",
    "plt.xticks(ticks, ticks)\n",
    "plt.xlabel('Unit price', fontsize=14)\n",
    "plt.ylabel('Count', fontsize=14)\n",
    "plt.title('Unit price distribution', fontsize=16);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The price for most of the products is equal or less than 10£"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the negative values in *UnitPrice*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>299983</th>\n",
       "      <td>A563186</td>\n",
       "      <td>B</td>\n",
       "      <td>Adjust bad debt</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-08-12 14:51:00</td>\n",
       "      <td>-11062.06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299984</th>\n",
       "      <td>A563187</td>\n",
       "      <td>B</td>\n",
       "      <td>Adjust bad debt</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-08-12 14:52:00</td>\n",
       "      <td>-11062.06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       InvoiceNo StockCode      Description  Quantity         InvoiceDate  \\\n",
       "299983   A563186         B  Adjust bad debt         1 2011-08-12 14:51:00   \n",
       "299984   A563187         B  Adjust bad debt         1 2011-08-12 14:52:00   \n",
       "\n",
       "        UnitPrice  CustomerID         Country  \n",
       "299983  -11062.06         NaN  United Kingdom  \n",
       "299984  -11062.06         NaN  United Kingdom  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['UnitPrice'] < 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's look at the quantity of the products\n",
    "\n",
    "plt.hist(data=df, x='Quantity', color='dodgerblue')\n",
    "plt.xlabel('Quantity', fontsize=14)\n",
    "plt.ylabel('Count', fontsize=14)\n",
    "plt.title(\"Distribution of quantity of products\", fontsize=16);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    541909.000000\n",
       "mean          9.552250\n",
       "std         218.081158\n",
       "min      -80995.000000\n",
       "50%           3.000000\n",
       "99%         100.000000\n",
       "max       80995.000000\n",
       "Name: Quantity, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Quantity'].describe(percentiles=[.99])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.arange(0, 110, 10)\n",
    "ticks = np.arange(0, 110, 10)\n",
    "plt.hist(data=df, x='Quantity', bins=bins, color='dodgerblue')\n",
    "plt.xticks(ticks, ticks)\n",
    "plt.xlabel('Quantity', fontsize=14)\n",
    "plt.ylabel('Count', fontsize=14)\n",
    "plt.title('Distribution of quantity of products', fontsize=16);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In most cases customers buy less than 50 items per order."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the negative values in *Quantity*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "neg_quantity = df[df['Quantity']<0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>C536379</td>\n",
       "      <td>D</td>\n",
       "      <td>Discount</td>\n",
       "      <td>-1</td>\n",
       "      <td>2010-12-01 09:41:00</td>\n",
       "      <td>27.50</td>\n",
       "      <td>14527.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>C536383</td>\n",
       "      <td>35004C</td>\n",
       "      <td>SET OF 3 COLOURED  FLYING DUCKS</td>\n",
       "      <td>-1</td>\n",
       "      <td>2010-12-01 09:49:00</td>\n",
       "      <td>4.65</td>\n",
       "      <td>15311.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>C536391</td>\n",
       "      <td>22556</td>\n",
       "      <td>PLASTERS IN TIN CIRCUS PARADE</td>\n",
       "      <td>-12</td>\n",
       "      <td>2010-12-01 10:24:00</td>\n",
       "      <td>1.65</td>\n",
       "      <td>17548.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>C536391</td>\n",
       "      <td>21984</td>\n",
       "      <td>PACK OF 12 PINK PAISLEY TISSUES</td>\n",
       "      <td>-24</td>\n",
       "      <td>2010-12-01 10:24:00</td>\n",
       "      <td>0.29</td>\n",
       "      <td>17548.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>C536391</td>\n",
       "      <td>21983</td>\n",
       "      <td>PACK OF 12 BLUE PAISLEY TISSUES</td>\n",
       "      <td>-24</td>\n",
       "      <td>2010-12-01 10:24:00</td>\n",
       "      <td>0.29</td>\n",
       "      <td>17548.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>C536391</td>\n",
       "      <td>21980</td>\n",
       "      <td>PACK OF 12 RED RETROSPOT TISSUES</td>\n",
       "      <td>-24</td>\n",
       "      <td>2010-12-01 10:24:00</td>\n",
       "      <td>0.29</td>\n",
       "      <td>17548.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>C536391</td>\n",
       "      <td>21484</td>\n",
       "      <td>CHICK GREY HOT WATER BOTTLE</td>\n",
       "      <td>-12</td>\n",
       "      <td>2010-12-01 10:24:00</td>\n",
       "      <td>3.45</td>\n",
       "      <td>17548.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>C536391</td>\n",
       "      <td>22557</td>\n",
       "      <td>PLASTERS IN TIN VINTAGE PAISLEY</td>\n",
       "      <td>-12</td>\n",
       "      <td>2010-12-01 10:24:00</td>\n",
       "      <td>1.65</td>\n",
       "      <td>17548.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>C536391</td>\n",
       "      <td>22553</td>\n",
       "      <td>PLASTERS IN TIN SKULLS</td>\n",
       "      <td>-24</td>\n",
       "      <td>2010-12-01 10:24:00</td>\n",
       "      <td>1.65</td>\n",
       "      <td>17548.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>939</th>\n",
       "      <td>C536506</td>\n",
       "      <td>22960</td>\n",
       "      <td>JAM MAKING SET WITH JARS</td>\n",
       "      <td>-6</td>\n",
       "      <td>2010-12-01 12:38:00</td>\n",
       "      <td>4.25</td>\n",
       "      <td>17897.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    InvoiceNo StockCode                        Description  Quantity  \\\n",
       "141   C536379         D                           Discount        -1   \n",
       "154   C536383    35004C    SET OF 3 COLOURED  FLYING DUCKS        -1   \n",
       "235   C536391     22556     PLASTERS IN TIN CIRCUS PARADE        -12   \n",
       "236   C536391     21984   PACK OF 12 PINK PAISLEY TISSUES        -24   \n",
       "237   C536391     21983   PACK OF 12 BLUE PAISLEY TISSUES        -24   \n",
       "238   C536391     21980  PACK OF 12 RED RETROSPOT TISSUES        -24   \n",
       "239   C536391     21484        CHICK GREY HOT WATER BOTTLE       -12   \n",
       "240   C536391     22557   PLASTERS IN TIN VINTAGE PAISLEY        -12   \n",
       "241   C536391     22553             PLASTERS IN TIN SKULLS       -24   \n",
       "939   C536506     22960           JAM MAKING SET WITH JARS        -6   \n",
       "\n",
       "            InvoiceDate  UnitPrice  CustomerID         Country  \n",
       "141 2010-12-01 09:41:00      27.50     14527.0  United Kingdom  \n",
       "154 2010-12-01 09:49:00       4.65     15311.0  United Kingdom  \n",
       "235 2010-12-01 10:24:00       1.65     17548.0  United Kingdom  \n",
       "236 2010-12-01 10:24:00       0.29     17548.0  United Kingdom  \n",
       "237 2010-12-01 10:24:00       0.29     17548.0  United Kingdom  \n",
       "238 2010-12-01 10:24:00       0.29     17548.0  United Kingdom  \n",
       "239 2010-12-01 10:24:00       3.45     17548.0  United Kingdom  \n",
       "240 2010-12-01 10:24:00       1.65     17548.0  United Kingdom  \n",
       "241 2010-12-01 10:24:00       1.65     17548.0  United Kingdom  \n",
       "939 2010-12-01 12:38:00       4.25     17897.0  United Kingdom  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neg_quantity.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10624, 8)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neg_quantity.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The percentage of records with negative Quantity: 1.9604767590130445\n"
     ]
    }
   ],
   "source": [
    "print(\"The percentage of records with negative Quantity:\", neg_quantity.shape[0]*100/df.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 10,624 raws with negative *Quantity* values, which is about 2% out of the total number of records in the dataframe."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='datacleaning'></a>\n",
    "## Data cleaning and transformation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Drop NULL values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have NULL records in columns *CustomerId* and *Description*. Because we are planning to predict CLTV, we need records for each customer ID to proceed calculations. Let's drop rows containing NULL values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(subset=['CustomerID'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(406829, 8)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's check NULL records in the CustomerId column\n",
    "df['CustomerID'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 406829 entries, 0 to 541908\n",
      "Data columns (total 8 columns):\n",
      " #   Column       Non-Null Count   Dtype         \n",
      "---  ------       --------------   -----         \n",
      " 0   InvoiceNo    406829 non-null  object        \n",
      " 1   StockCode    406829 non-null  object        \n",
      " 2   Description  406829 non-null  object        \n",
      " 3   Quantity     406829 non-null  int64         \n",
      " 4   InvoiceDate  406829 non-null  datetime64[ns]\n",
      " 5   UnitPrice    406829 non-null  float64       \n",
      " 6   CustomerID   406829 non-null  float64       \n",
      " 7   Country      406829 non-null  object        \n",
      "dtypes: datetime64[ns](1), float64(2), int64(1), object(4)\n",
      "memory usage: 27.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Handle negative quantity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.loc[df['Quantity'] > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [InvoiceNo, StockCode, Description, Quantity, InvoiceDate, UnitPrice, CustomerID, Country]\n",
       "Index: []"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's check negative records\n",
    "df[df['Quantity'] < 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove rows with negative price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.loc[df['UnitPrice'] > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [InvoiceNo, StockCode, Description, Quantity, InvoiceDate, UnitPrice, CustomerID, Country]\n",
       "Index: []"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's check negative records\n",
    "df[df['UnitPrice'] < 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Handle incomplete data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Period of time for the purchase records is from December 1, 2010 to December  9, 2011. The data for the last month is incomplete. Because we are planning to predict CLTV for the next 3 months and we will be aggregating data monthly, let us ignore the records for the incomplete month."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.loc[df['InvoiceDate'] < '2011-12-01']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Timestamp('2010-12-01 08:26:00'), Timestamp('2011-11-30 17:37:00'))"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's check the time period\n",
    "df['InvoiceDate'].min(), df['InvoiceDate'].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate total sales"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us create a column for the total sales value for each transaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Sales'] = df['Quantity'] * df['UnitPrice']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_orders = df.groupby(['CustomerID', 'InvoiceNo']).agg({'Sales': sum, 'InvoiceDate': max})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Sales</th>\n",
       "      <th>InvoiceDate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CustomerID</th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12346.0</th>\n",
       "      <th>541431</th>\n",
       "      <td>77183.60</td>\n",
       "      <td>2011-01-18 10:01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">12347.0</th>\n",
       "      <th>537626</th>\n",
       "      <td>711.79</td>\n",
       "      <td>2010-12-07 14:57:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>542237</th>\n",
       "      <td>475.39</td>\n",
       "      <td>2011-01-26 14:30:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>549222</th>\n",
       "      <td>636.25</td>\n",
       "      <td>2011-04-07 10:43:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>556201</th>\n",
       "      <td>382.52</td>\n",
       "      <td>2011-06-09 13:01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>562032</th>\n",
       "      <td>584.91</td>\n",
       "      <td>2011-08-02 08:48:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>573511</th>\n",
       "      <td>1294.32</td>\n",
       "      <td>2011-10-31 12:25:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12348.0</th>\n",
       "      <th>539318</th>\n",
       "      <td>892.80</td>\n",
       "      <td>2010-12-16 19:09:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Sales         InvoiceDate\n",
       "CustomerID InvoiceNo                              \n",
       "12346.0    541431     77183.60 2011-01-18 10:01:00\n",
       "12347.0    537626       711.79 2010-12-07 14:57:00\n",
       "           542237       475.39 2011-01-26 14:30:00\n",
       "           549222       636.25 2011-04-07 10:43:00\n",
       "           556201       382.52 2011-06-09 13:01:00\n",
       "           562032       584.91 2011-08-02 08:48:00\n",
       "           573511      1294.32 2011-10-31 12:25:00\n",
       "12348.0    539318       892.80 2010-12-16 19:09:00"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_orders.head(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Timestamp('2011-11-30 17:37:00'), Timestamp('2010-12-01 08:26:00'))"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_orders['InvoiceDate'].max(), df_orders['InvoiceDate'].min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='dataanalysis'></a>\n",
    "## Data analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to predict and calculate CLTV, we have to estimate the **frequency**, **recency**, and **total amount** of purchases by each customer. We are going to determine basic information about each customer's average and lifetime purchase amount, as well as each customer's duration and frequency of purchase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "def groupby_mean(x):\n",
    "    return x.mean()\n",
    "\n",
    "def groupby_count(x):\n",
    "    return x.count()\n",
    "\n",
    "def purchase_duration(x):\n",
    "    return (x.max() - x.min()).days\n",
    "\n",
    "def avg_frequency(x):\n",
    "    return (x.max() - x.min()).days / x.count()\n",
    "\n",
    "groupby_mean.__name__ = 'avg'\n",
    "groupby_count.__name__ = 'count'\n",
    "purchase_duration.__name__ = 'purchase_duration'\n",
    "avg_frequency.__name__ = 'purchase_frequency'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_summary = df_orders.reset_index().groupby('CustomerID').agg({\n",
    "            'Sales': [min, max, sum, groupby_mean, groupby_count],\n",
    "            'InvoiceDate': [min, max, purchase_duration, avg_frequency]\n",
    "             })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"5\" halign=\"left\">Sales</th>\n",
       "      <th colspan=\"4\" halign=\"left\">InvoiceDate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>sum</th>\n",
       "      <th>avg</th>\n",
       "      <th>count</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>purchase_duration</th>\n",
       "      <th>purchase_frequency</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CustomerID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12346.0</th>\n",
       "      <td>77183.60</td>\n",
       "      <td>77183.60</td>\n",
       "      <td>77183.60</td>\n",
       "      <td>77183.600000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2011-01-18 10:01:00</td>\n",
       "      <td>2011-01-18 10:01:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12347.0</th>\n",
       "      <td>382.52</td>\n",
       "      <td>1294.32</td>\n",
       "      <td>4085.18</td>\n",
       "      <td>680.863333</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2010-12-07 14:57:00</td>\n",
       "      <td>2011-10-31 12:25:00</td>\n",
       "      <td>327</td>\n",
       "      <td>54.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12348.0</th>\n",
       "      <td>227.44</td>\n",
       "      <td>892.80</td>\n",
       "      <td>1797.24</td>\n",
       "      <td>449.310000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2010-12-16 19:09:00</td>\n",
       "      <td>2011-09-25 13:13:00</td>\n",
       "      <td>282</td>\n",
       "      <td>70.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12349.0</th>\n",
       "      <td>1757.55</td>\n",
       "      <td>1757.55</td>\n",
       "      <td>1757.55</td>\n",
       "      <td>1757.550000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2011-11-21 09:51:00</td>\n",
       "      <td>2011-11-21 09:51:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12350.0</th>\n",
       "      <td>334.40</td>\n",
       "      <td>334.40</td>\n",
       "      <td>334.40</td>\n",
       "      <td>334.400000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2011-02-02 16:01:00</td>\n",
       "      <td>2011-02-02 16:01:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Sales                                          \\\n",
       "                 min       max       sum           avg count   \n",
       "CustomerID                                                     \n",
       "12346.0     77183.60  77183.60  77183.60  77183.600000   1.0   \n",
       "12347.0       382.52   1294.32   4085.18    680.863333   6.0   \n",
       "12348.0       227.44    892.80   1797.24    449.310000   4.0   \n",
       "12349.0      1757.55   1757.55   1757.55   1757.550000   1.0   \n",
       "12350.0       334.40    334.40    334.40    334.400000   1.0   \n",
       "\n",
       "                   InvoiceDate                                        \\\n",
       "                           min                 max purchase_duration   \n",
       "CustomerID                                                             \n",
       "12346.0    2011-01-18 10:01:00 2011-01-18 10:01:00                 0   \n",
       "12347.0    2010-12-07 14:57:00 2011-10-31 12:25:00               327   \n",
       "12348.0    2010-12-16 19:09:00 2011-09-25 13:13:00               282   \n",
       "12349.0    2011-11-21 09:51:00 2011-11-21 09:51:00                 0   \n",
       "12350.0    2011-02-02 16:01:00 2011-02-02 16:01:00                 0   \n",
       "\n",
       "                               \n",
       "           purchase_frequency  \n",
       "CustomerID                     \n",
       "12346.0                   0.0  \n",
       "12347.0                  54.5  \n",
       "12348.0                  70.5  \n",
       "12349.0                   0.0  \n",
       "12350.0                   0.0  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_summary.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This data gives us an idea on the purchases each customer made."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us have a closer look at the **repeat customers**. Particularly, we are interested in the **number of purchases** and **frequency of purchases** made.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Repeat customers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Number of purchases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_summary.columns = ['_'.join(col).lower() for col in df_summary.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>sales_min</th>\n",
       "      <th>sales_max</th>\n",
       "      <th>sales_sum</th>\n",
       "      <th>sales_avg</th>\n",
       "      <th>sales_count</th>\n",
       "      <th>invoicedate_min</th>\n",
       "      <th>invoicedate_max</th>\n",
       "      <th>invoicedate_purchase_duration</th>\n",
       "      <th>invoicedate_purchase_frequency</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CustomerID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12346.0</th>\n",
       "      <td>77183.60</td>\n",
       "      <td>77183.60</td>\n",
       "      <td>77183.60</td>\n",
       "      <td>77183.600000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2011-01-18 10:01:00</td>\n",
       "      <td>2011-01-18 10:01:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>12347.0</th>\n",
       "      <td>382.52</td>\n",
       "      <td>1294.32</td>\n",
       "      <td>4085.18</td>\n",
       "      <td>680.863333</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2010-12-07 14:57:00</td>\n",
       "      <td>2011-10-31 12:25:00</td>\n",
       "      <td>327</td>\n",
       "      <td>54.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12348.0</th>\n",
       "      <td>227.44</td>\n",
       "      <td>892.80</td>\n",
       "      <td>1797.24</td>\n",
       "      <td>449.310000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2010-12-16 19:09:00</td>\n",
       "      <td>2011-09-25 13:13:00</td>\n",
       "      <td>282</td>\n",
       "      <td>70.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12349.0</th>\n",
       "      <td>1757.55</td>\n",
       "      <td>1757.55</td>\n",
       "      <td>1757.55</td>\n",
       "      <td>1757.550000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2011-11-21 09:51:00</td>\n",
       "      <td>2011-11-21 09:51:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>12350.0</th>\n",
       "      <td>334.40</td>\n",
       "      <td>334.40</td>\n",
       "      <td>334.40</td>\n",
       "      <td>334.400000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2011-02-02 16:01:00</td>\n",
       "      <td>2011-02-02 16:01:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <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>18280.0</th>\n",
       "      <td>180.60</td>\n",
       "      <td>180.60</td>\n",
       "      <td>180.60</td>\n",
       "      <td>180.600000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2011-03-07 09:52:00</td>\n",
       "      <td>2011-03-07 09:52:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18281.0</th>\n",
       "      <td>80.82</td>\n",
       "      <td>80.82</td>\n",
       "      <td>80.82</td>\n",
       "      <td>80.820000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2011-06-12 10:53:00</td>\n",
       "      <td>2011-06-12 10:53:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>18282.0</th>\n",
       "      <td>100.21</td>\n",
       "      <td>100.21</td>\n",
       "      <td>100.21</td>\n",
       "      <td>100.210000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2011-08-05 13:35:00</td>\n",
       "      <td>2011-08-05 13:35:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18283.0</th>\n",
       "      <td>1.95</td>\n",
       "      <td>313.65</td>\n",
       "      <td>1886.88</td>\n",
       "      <td>125.792000</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2011-01-06 14:14:00</td>\n",
       "      <td>2011-11-30 12:59:00</td>\n",
       "      <td>327</td>\n",
       "      <td>21.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18287.0</th>\n",
       "      <td>70.68</td>\n",
       "      <td>1001.32</td>\n",
       "      <td>1837.28</td>\n",
       "      <td>612.426667</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2011-05-22 10:39:00</td>\n",
       "      <td>2011-10-28 09:29:00</td>\n",
       "      <td>158</td>\n",
       "      <td>52.666667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4297 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            sales_min  sales_max  sales_sum     sales_avg  sales_count  \\\n",
       "CustomerID                                                               \n",
       "12346.0      77183.60   77183.60   77183.60  77183.600000          1.0   \n",
       "12347.0        382.52    1294.32    4085.18    680.863333          6.0   \n",
       "12348.0        227.44     892.80    1797.24    449.310000          4.0   \n",
       "12349.0       1757.55    1757.55    1757.55   1757.550000          1.0   \n",
       "12350.0        334.40     334.40     334.40    334.400000          1.0   \n",
       "...               ...        ...        ...           ...          ...   \n",
       "18280.0        180.60     180.60     180.60    180.600000          1.0   \n",
       "18281.0         80.82      80.82      80.82     80.820000          1.0   \n",
       "18282.0        100.21     100.21     100.21    100.210000          1.0   \n",
       "18283.0          1.95     313.65    1886.88    125.792000         15.0   \n",
       "18287.0         70.68    1001.32    1837.28    612.426667          3.0   \n",
       "\n",
       "               invoicedate_min     invoicedate_max  \\\n",
       "CustomerID                                           \n",
       "12346.0    2011-01-18 10:01:00 2011-01-18 10:01:00   \n",
       "12347.0    2010-12-07 14:57:00 2011-10-31 12:25:00   \n",
       "12348.0    2010-12-16 19:09:00 2011-09-25 13:13:00   \n",
       "12349.0    2011-11-21 09:51:00 2011-11-21 09:51:00   \n",
       "12350.0    2011-02-02 16:01:00 2011-02-02 16:01:00   \n",
       "...                        ...                 ...   \n",
       "18280.0    2011-03-07 09:52:00 2011-03-07 09:52:00   \n",
       "18281.0    2011-06-12 10:53:00 2011-06-12 10:53:00   \n",
       "18282.0    2011-08-05 13:35:00 2011-08-05 13:35:00   \n",
       "18283.0    2011-01-06 14:14:00 2011-11-30 12:59:00   \n",
       "18287.0    2011-05-22 10:39:00 2011-10-28 09:29:00   \n",
       "\n",
       "            invoicedate_purchase_duration  invoicedate_purchase_frequency  \n",
       "CustomerID                                                                 \n",
       "12346.0                                 0                        0.000000  \n",
       "12347.0                               327                       54.500000  \n",
       "12348.0                               282                       70.500000  \n",
       "12349.0                                 0                        0.000000  \n",
       "12350.0                                 0                        0.000000  \n",
       "...                                   ...                             ...  \n",
       "18280.0                                 0                        0.000000  \n",
       "18281.0                                 0                        0.000000  \n",
       "18282.0                                 0                        0.000000  \n",
       "18283.0                               327                       21.800000  \n",
       "18287.0                               158                       52.666667  \n",
       "\n",
       "[4297 rows x 9 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_summary = df_summary.loc[df_summary['invoicedate_purchase_duration'] > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2692, 9)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_summary.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's look at the number of purchases\n",
    "ax = df_summary.groupby('sales_count').count()['sales_avg'][:20].plot(\n",
    "        kind='bar', \n",
    "        color='dodgerblue', \n",
    "        figsize=(10,8), \n",
    "        grid=False)\n",
    "plt.xlabel('Sales count', fontsize=14)\n",
    "plt.ylabel('Number of customers', fontsize=14)\n",
    "plt.title('Number of purchases made by repeat customers', fontsize=16);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In our analysis, we consider repeat customers who made at least 2 purchases. As you can see, repeat customers tend to make about 12 purchases or less."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Average number of days between purchases "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df_summary['invoicedate_purchase_frequency'].hist(\n",
    "        bins=20,\n",
    "        color='dodgerblue',\n",
    "        rwidth=0.7,\n",
    "        figsize=(10,8),\n",
    "        grid=False)\n",
    "plt.xlabel('Avearge number of days between purchases', fontsize=14)\n",
    "plt.ylabel('Number of customers', fontsize=14)\n",
    "plt.title('Frequency of purchases made by repeat customers', fontsize=16);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see from this plot, the majority of repeat customers tend to make a purchase every 12 to 50 days."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='predictcltv'></a>\n",
    "## Predict 3-month CLTV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are going to build a model, which predicts CLTV for 3 months. First, let us slice the data into chunks with 3-month data each and take the last chunk as the target for predictions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "clv_freq = '3M'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_data = df_orders.reset_index().groupby([\n",
    "            'CustomerID',\n",
    "            pd.Grouper(key='InvoiceDate', freq=clv_freq)\n",
    "            ]).agg({'Sales': [sum, groupby_mean, groupby_count],})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_data.columns = ['_'.join(col).lower() for col in df_data.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_data = df_data.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>CustomerID</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>sales_sum</th>\n",
       "      <th>sales_avg</th>\n",
       "      <th>sales_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12346.0</td>\n",
       "      <td>2011-03-31</td>\n",
       "      <td>77183.60</td>\n",
       "      <td>77183.600</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12347.0</td>\n",
       "      <td>2010-12-31</td>\n",
       "      <td>711.79</td>\n",
       "      <td>711.790</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12347.0</td>\n",
       "      <td>2011-03-31</td>\n",
       "      <td>475.39</td>\n",
       "      <td>475.390</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12347.0</td>\n",
       "      <td>2011-06-30</td>\n",
       "      <td>1018.77</td>\n",
       "      <td>509.385</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12347.0</td>\n",
       "      <td>2011-09-30</td>\n",
       "      <td>584.91</td>\n",
       "      <td>584.910</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID InvoiceDate  sales_sum  sales_avg  sales_count\n",
       "0     12346.0  2011-03-31   77183.60  77183.600          1.0\n",
       "1     12347.0  2010-12-31     711.79    711.790          1.0\n",
       "2     12347.0  2011-03-31     475.39    475.390          1.0\n",
       "3     12347.0  2011-06-30    1018.77    509.385          2.0\n",
       "4     12347.0  2011-09-30     584.91    584.910          1.0"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9219 entries, 0 to 9218\n",
      "Data columns (total 5 columns):\n",
      " #   Column       Non-Null Count  Dtype         \n",
      "---  ------       --------------  -----         \n",
      " 0   CustomerID   9219 non-null   float64       \n",
      " 1   InvoiceDate  9219 non-null   datetime64[ns]\n",
      " 2   sales_sum    9219 non-null   float64       \n",
      " 3   sales_avg    9219 non-null   float64       \n",
      " 4   sales_count  9219 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(4)\n",
      "memory usage: 360.2 KB\n"
     ]
    }
   ],
   "source": [
    "df_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For further simplicity, let us encode *InvoiceDate* column and define in which month a purchase was made"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "map_date_month = {str(x)[:10]: 'M_%s' % (i+1) for i, x in enumerate(\n",
    "                    sorted(df_data.reset_index()['InvoiceDate'].unique(), reverse=True))}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'2011-12-31': 'M_1',\n",
       " '2011-09-30': 'M_2',\n",
       " '2011-06-30': 'M_3',\n",
       " '2011-03-31': 'M_4',\n",
       " '2010-12-31': 'M_5'}"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "map_date_month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_data['M'] = df_data['InvoiceDate'].apply(lambda x: map_date_month[str(x)[:10]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>sales_sum</th>\n",
       "      <th>sales_avg</th>\n",
       "      <th>sales_count</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12346.0</td>\n",
       "      <td>2011-03-31</td>\n",
       "      <td>77183.60</td>\n",
       "      <td>77183.600</td>\n",
       "      <td>1.0</td>\n",
       "      <td>M_4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12347.0</td>\n",
       "      <td>2010-12-31</td>\n",
       "      <td>711.79</td>\n",
       "      <td>711.790</td>\n",
       "      <td>1.0</td>\n",
       "      <td>M_5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12347.0</td>\n",
       "      <td>2011-03-31</td>\n",
       "      <td>475.39</td>\n",
       "      <td>475.390</td>\n",
       "      <td>1.0</td>\n",
       "      <td>M_4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12347.0</td>\n",
       "      <td>2011-06-30</td>\n",
       "      <td>1018.77</td>\n",
       "      <td>509.385</td>\n",
       "      <td>2.0</td>\n",
       "      <td>M_3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12347.0</td>\n",
       "      <td>2011-09-30</td>\n",
       "      <td>584.91</td>\n",
       "      <td>584.910</td>\n",
       "      <td>1.0</td>\n",
       "      <td>M_2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID InvoiceDate  sales_sum  sales_avg  sales_count    M\n",
       "0     12346.0  2011-03-31   77183.60  77183.600          1.0  M_4\n",
       "1     12347.0  2010-12-31     711.79    711.790          1.0  M_5\n",
       "2     12347.0  2011-03-31     475.39    475.390          1.0  M_4\n",
       "3     12347.0  2011-06-30    1018.77    509.385          2.0  M_3\n",
       "4     12347.0  2011-09-30     584.91    584.910          1.0  M_2"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "M_1    2406\n",
       "M_2    2161\n",
       "M_3    1991\n",
       "M_4    1776\n",
       "M_5     885\n",
       "Name: M, dtype: int64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of purchases made in each chunk\n",
    "df_data['M'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We marked data with a chunk/time period it belongs to. Now, let us create a set with features and target variables. In order to train a model, we have to transfrom the data into tabular data, where each row represent a costomer and each column represent a feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_features = pd.pivot_table(\n",
    "                df_data.loc[df_data['M'] != 'M_1'], \n",
    "                values=['sales_sum', 'sales_avg', 'sales_count'], \n",
    "                columns='M', \n",
    "                index='CustomerID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th colspan=\"4\" halign=\"left\">sales_avg</th>\n",
       "      <th colspan=\"4\" halign=\"left\">sales_count</th>\n",
       "      <th colspan=\"4\" halign=\"left\">sales_sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <th></th>\n",
       "      <th>M_2</th>\n",
       "      <th>M_3</th>\n",
       "      <th>M_4</th>\n",
       "      <th>M_5</th>\n",
       "      <th>M_2</th>\n",
       "      <th>M_3</th>\n",
       "      <th>M_4</th>\n",
       "      <th>M_5</th>\n",
       "      <th>M_2</th>\n",
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       "      <th>M_4</th>\n",
       "      <th>M_5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
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       "      <td>711.79</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>584.91</td>\n",
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       "      <td>475.39</td>\n",
       "      <td>711.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12348.0</td>\n",
       "      <td>310.000000</td>\n",
       "      <td>367.000</td>\n",
       "      <td>227.440000</td>\n",
       "      <td>892.80</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>310.00</td>\n",
       "      <td>367.00</td>\n",
       "      <td>227.44</td>\n",
       "      <td>892.80</td>\n",
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       "      <th>3</th>\n",
       "      <td>12350.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>334.40</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
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       "      <th>4</th>\n",
       "      <td>12352.0</td>\n",
       "      <td>316.250000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>312.362000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>632.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1561.81</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3611</th>\n",
       "      <td>18280.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>180.600000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>180.60</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>3612</th>\n",
       "      <td>18281.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80.820</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80.82</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3613</th>\n",
       "      <td>18282.0</td>\n",
       "      <td>100.210000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3614</th>\n",
       "      <td>18283.0</td>\n",
       "      <td>92.696667</td>\n",
       "      <td>131.170</td>\n",
       "      <td>105.966667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>278.09</td>\n",
       "      <td>524.68</td>\n",
       "      <td>317.90</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>3615</th>\n",
       "      <td>18287.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>765.28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3616 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     CustomerID   sales_avg                                sales_count       \\\n",
       "M                       M_2      M_3           M_4     M_5         M_2  M_3   \n",
       "0       12346.0         NaN      NaN  77183.600000     NaN         NaN  NaN   \n",
       "1       12347.0  584.910000  509.385    475.390000  711.79         1.0  2.0   \n",
       "2       12348.0  310.000000  367.000    227.440000  892.80         1.0  1.0   \n",
       "3       12350.0         NaN      NaN    334.400000     NaN         NaN  NaN   \n",
       "4       12352.0  316.250000      NaN    312.362000     NaN         2.0  NaN   \n",
       "...         ...         ...      ...           ...     ...         ...  ...   \n",
       "3611    18280.0         NaN      NaN    180.600000     NaN         NaN  NaN   \n",
       "3612    18281.0         NaN   80.820           NaN     NaN         NaN  1.0   \n",
       "3613    18282.0  100.210000      NaN           NaN     NaN         1.0  NaN   \n",
       "3614    18283.0   92.696667  131.170    105.966667     NaN         3.0  4.0   \n",
       "3615    18287.0         NaN  765.280           NaN     NaN         NaN  1.0   \n",
       "\n",
       "               sales_sum                             \n",
       "M     M_4  M_5       M_2      M_3       M_4     M_5  \n",
       "0     1.0  NaN       NaN      NaN  77183.60     NaN  \n",
       "1     1.0  1.0    584.91  1018.77    475.39  711.79  \n",
       "2     1.0  1.0    310.00   367.00    227.44  892.80  \n",
       "3     1.0  NaN       NaN      NaN    334.40     NaN  \n",
       "4     5.0  NaN    632.50      NaN   1561.81     NaN  \n",
       "...   ...  ...       ...      ...       ...     ...  \n",
       "3611  1.0  NaN       NaN      NaN    180.60     NaN  \n",
       "3612  NaN  NaN       NaN    80.82       NaN     NaN  \n",
       "3613  NaN  NaN    100.21      NaN       NaN     NaN  \n",
       "3614  3.0  NaN    278.09   524.68    317.90     NaN  \n",
       "3615  NaN  NaN       NaN   765.28       NaN     NaN  \n",
       "\n",
       "[3616 rows x 13 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_features.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_features.columns = ['_'.join(col) for col in df_features.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>sales_avg_M_3</th>\n",
       "      <th>sales_avg_M_4</th>\n",
       "      <th>sales_avg_M_5</th>\n",
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       "      <th>sales_sum_M_5</th>\n",
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       "    <tr>\n",
       "      <th>CustomerID</th>\n",
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       "      <th>12347.0</th>\n",
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       "      <th>12348.0</th>\n",
       "      <td>310.00</td>\n",
       "      <td>367.000</td>\n",
       "      <td>227.440</td>\n",
       "      <td>892.80</td>\n",
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       "      <td>227.44</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>334.40</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12352.0</th>\n",
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       "      <td>312.362</td>\n",
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       "      <td>5.0</td>\n",
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       "      <td>NaN</td>\n",
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      ],
      "text/plain": [
       "            sales_avg_M_2  sales_avg_M_3  sales_avg_M_4  sales_avg_M_5  \\\n",
       "CustomerID                                                               \n",
       "12346.0               NaN            NaN      77183.600            NaN   \n",
       "12347.0            584.91        509.385        475.390         711.79   \n",
       "12348.0            310.00        367.000        227.440         892.80   \n",
       "12350.0               NaN            NaN        334.400            NaN   \n",
       "12352.0            316.25            NaN        312.362            NaN   \n",
       "\n",
       "            sales_count_M_2  sales_count_M_3  sales_count_M_4  \\\n",
       "CustomerID                                                      \n",
       "12346.0                 NaN              NaN              1.0   \n",
       "12347.0                 1.0              2.0              1.0   \n",
       "12348.0                 1.0              1.0              1.0   \n",
       "12350.0                 NaN              NaN              1.0   \n",
       "12352.0                 2.0              NaN              5.0   \n",
       "\n",
       "            sales_count_M_5  sales_sum_M_2  sales_sum_M_3  sales_sum_M_4  \\\n",
       "CustomerID                                                                 \n",
       "12346.0                 NaN            NaN            NaN       77183.60   \n",
       "12347.0                 1.0         584.91        1018.77         475.39   \n",
       "12348.0                 1.0         310.00         367.00         227.44   \n",
       "12350.0                 NaN            NaN            NaN         334.40   \n",
       "12352.0                 NaN         632.50            NaN        1561.81   \n",
       "\n",
       "            sales_sum_M_5  \n",
       "CustomerID                 \n",
       "12346.0               NaN  \n",
       "12347.0            711.79  \n",
       "12348.0            892.80  \n",
       "12350.0               NaN  \n",
       "12352.0               NaN  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_features.reset_index(level=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>sales_avg_M_2</th>\n",
       "      <th>sales_avg_M_3</th>\n",
       "      <th>sales_avg_M_4</th>\n",
       "      <th>sales_avg_M_5</th>\n",
       "      <th>sales_count_M_2</th>\n",
       "      <th>sales_count_M_3</th>\n",
       "      <th>sales_count_M_4</th>\n",
       "      <th>sales_count_M_5</th>\n",
       "      <th>sales_sum_M_2</th>\n",
       "      <th>sales_sum_M_3</th>\n",
       "      <th>sales_sum_M_4</th>\n",
       "      <th>sales_sum_M_5</th>\n",
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       "      <th>0</th>\n",
       "      <td>12346.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77183.600</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>12347.0</td>\n",
       "      <td>584.91</td>\n",
       "      <td>509.385</td>\n",
       "      <td>475.390</td>\n",
       "      <td>711.79</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>584.91</td>\n",
       "      <td>1018.77</td>\n",
       "      <td>475.39</td>\n",
       "      <td>711.79</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12348.0</td>\n",
       "      <td>310.00</td>\n",
       "      <td>367.000</td>\n",
       "      <td>227.440</td>\n",
       "      <td>892.80</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>310.00</td>\n",
       "      <td>367.00</td>\n",
       "      <td>227.44</td>\n",
       "      <td>892.80</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>334.400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>334.40</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12352.0</td>\n",
       "      <td>316.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>312.362</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>632.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1561.81</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID  sales_avg_M_2  sales_avg_M_3  sales_avg_M_4  sales_avg_M_5  \\\n",
       "0     12346.0            NaN            NaN      77183.600            NaN   \n",
       "1     12347.0         584.91        509.385        475.390         711.79   \n",
       "2     12348.0         310.00        367.000        227.440         892.80   \n",
       "3     12350.0            NaN            NaN        334.400            NaN   \n",
       "4     12352.0         316.25            NaN        312.362            NaN   \n",
       "\n",
       "   sales_count_M_2  sales_count_M_3  sales_count_M_4  sales_count_M_5  \\\n",
       "0              NaN              NaN              1.0              NaN   \n",
       "1              1.0              2.0              1.0              1.0   \n",
       "2              1.0              1.0              1.0              1.0   \n",
       "3              NaN              NaN              1.0              NaN   \n",
       "4              2.0              NaN              5.0              NaN   \n",
       "\n",
       "   sales_sum_M_2  sales_sum_M_3  sales_sum_M_4  sales_sum_M_5  \n",
       "0            NaN            NaN       77183.60            NaN  \n",
       "1         584.91        1018.77         475.39         711.79  \n",
       "2         310.00         367.00         227.44         892.80  \n",
       "3            NaN            NaN         334.40            NaN  \n",
       "4         632.50            NaN        1561.81            NaN  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_features.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We got Null values after pivoting the table. Let us fill in the Null values with 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_features.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's check the Null values\n",
    "df_features.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3616 entries, 0 to 3615\n",
      "Data columns (total 13 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   CustomerID       3616 non-null   float64\n",
      " 1   sales_avg_M_2    3616 non-null   float64\n",
      " 2   sales_avg_M_3    3616 non-null   float64\n",
      " 3   sales_avg_M_4    3616 non-null   float64\n",
      " 4   sales_avg_M_5    3616 non-null   float64\n",
      " 5   sales_count_M_2  3616 non-null   float64\n",
      " 6   sales_count_M_3  3616 non-null   float64\n",
      " 7   sales_count_M_4  3616 non-null   float64\n",
      " 8   sales_count_M_5  3616 non-null   float64\n",
      " 9   sales_sum_M_2    3616 non-null   float64\n",
      " 10  sales_sum_M_3    3616 non-null   float64\n",
      " 11  sales_sum_M_4    3616 non-null   float64\n",
      " 12  sales_sum_M_5    3616 non-null   float64\n",
      "dtypes: float64(13)\n",
      "memory usage: 367.4 KB\n"
     ]
    }
   ],
   "source": [
    "df_features.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>sales_avg_M_2</th>\n",
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       "      <th>sales_avg_M_5</th>\n",
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       "      <th>sales_count_M_5</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12348.0</td>\n",
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       "      <td>367.000</td>\n",
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       "      <td>367.00</td>\n",
       "      <td>227.44</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>334.40</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12352.0</td>\n",
       "      <td>316.25</td>\n",
       "      <td>0.000</td>\n",
       "      <td>312.362</td>\n",
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       "      <td>1561.81</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID  sales_avg_M_2  sales_avg_M_3  sales_avg_M_4  sales_avg_M_5  \\\n",
       "0     12346.0           0.00          0.000      77183.600           0.00   \n",
       "1     12347.0         584.91        509.385        475.390         711.79   \n",
       "2     12348.0         310.00        367.000        227.440         892.80   \n",
       "3     12350.0           0.00          0.000        334.400           0.00   \n",
       "4     12352.0         316.25          0.000        312.362           0.00   \n",
       "\n",
       "   sales_count_M_2  sales_count_M_3  sales_count_M_4  sales_count_M_5  \\\n",
       "0              0.0              0.0              1.0              0.0   \n",
       "1              1.0              2.0              1.0              1.0   \n",
       "2              1.0              1.0              1.0              1.0   \n",
       "3              0.0              0.0              1.0              0.0   \n",
       "4              2.0              0.0              5.0              0.0   \n",
       "\n",
       "   sales_sum_M_2  sales_sum_M_3  sales_sum_M_4  sales_sum_M_5  \n",
       "0           0.00           0.00       77183.60           0.00  \n",
       "1         584.91        1018.77         475.39         711.79  \n",
       "2         310.00         367.00         227.44         892.80  \n",
       "3           0.00           0.00         334.40           0.00  \n",
       "4         632.50           0.00        1561.81           0.00  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_features.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our target variables will be:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_target = df_data.loc[df_data['M'] == 'M_1', ['CustomerID', 'sales_sum']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_target.columns = ['CustomerID', 'CLV_'+clv_freq]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2406, 2)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_target.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>CLV_3M</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>12347.0</td>\n",
       "      <td>1294.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>12349.0</td>\n",
       "      <td>1757.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>12352.0</td>\n",
       "      <td>311.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>12356.0</td>\n",
       "      <td>58.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>12357.0</td>\n",
       "      <td>6207.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    CustomerID   CLV_3M\n",
       "5      12347.0  1294.32\n",
       "10     12349.0  1757.55\n",
       "14     12352.0   311.73\n",
       "20     12356.0    58.35\n",
       "21     12357.0  6207.67"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_target.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sample_set = df_features.merge(\n",
    "                df_target, \n",
    "                left_on='CustomerID', \n",
    "                right_on='CustomerID',\n",
    "                how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>711.79</td>\n",
       "      <td>1294.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12348.0</td>\n",
       "      <td>310.00</td>\n",
       "      <td>367.000</td>\n",
       "      <td>227.440</td>\n",
       "      <td>892.80</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>310.00</td>\n",
       "      <td>367.00</td>\n",
       "      <td>227.44</td>\n",
       "      <td>892.80</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12350.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>334.400</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>334.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12352.0</td>\n",
       "      <td>316.25</td>\n",
       "      <td>0.000</td>\n",
       "      <td>312.362</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>632.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1561.81</td>\n",
       "      <td>0.00</td>\n",
       "      <td>311.73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID  sales_avg_M_2  sales_avg_M_3  sales_avg_M_4  sales_avg_M_5  \\\n",
       "0     12346.0           0.00          0.000      77183.600           0.00   \n",
       "1     12347.0         584.91        509.385        475.390         711.79   \n",
       "2     12348.0         310.00        367.000        227.440         892.80   \n",
       "3     12350.0           0.00          0.000        334.400           0.00   \n",
       "4     12352.0         316.25          0.000        312.362           0.00   \n",
       "\n",
       "   sales_count_M_2  sales_count_M_3  sales_count_M_4  sales_count_M_5  \\\n",
       "0              0.0              0.0              1.0              0.0   \n",
       "1              1.0              2.0              1.0              1.0   \n",
       "2              1.0              1.0              1.0              1.0   \n",
       "3              0.0              0.0              1.0              0.0   \n",
       "4              2.0              0.0              5.0              0.0   \n",
       "\n",
       "   sales_sum_M_2  sales_sum_M_3  sales_sum_M_4  sales_sum_M_5   CLV_3M  \n",
       "0           0.00           0.00       77183.60           0.00      NaN  \n",
       "1         584.91        1018.77         475.39         711.79  1294.32  \n",
       "2         310.00         367.00         227.44         892.80      NaN  \n",
       "3           0.00           0.00         334.40           0.00      NaN  \n",
       "4         632.50           0.00        1561.81           0.00   311.73  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sample_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sample_set.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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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       "        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>CustomerID</th>\n",
       "      <th>sales_avg_M_2</th>\n",
       "      <th>sales_avg_M_3</th>\n",
       "      <th>sales_avg_M_4</th>\n",
       "      <th>sales_avg_M_5</th>\n",
       "      <th>sales_count_M_2</th>\n",
       "      <th>sales_count_M_3</th>\n",
       "      <th>sales_count_M_4</th>\n",
       "      <th>sales_count_M_5</th>\n",
       "      <th>sales_sum_M_2</th>\n",
       "      <th>sales_sum_M_3</th>\n",
       "      <th>sales_sum_M_4</th>\n",
       "      <th>sales_sum_M_5</th>\n",
       "      <th>CLV_3M</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12346.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>77183.600</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>77183.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12347.0</td>\n",
       "      <td>584.91</td>\n",
       "      <td>509.385</td>\n",
       "      <td>475.390</td>\n",
       "      <td>711.79</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>584.91</td>\n",
       "      <td>1018.77</td>\n",
       "      <td>475.39</td>\n",
       "      <td>711.79</td>\n",
       "      <td>1294.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12348.0</td>\n",
       "      <td>310.00</td>\n",
       "      <td>367.000</td>\n",
       "      <td>227.440</td>\n",
       "      <td>892.80</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>310.00</td>\n",
       "      <td>367.00</td>\n",
       "      <td>227.44</td>\n",
       "      <td>892.80</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>0.000</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>334.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12352.0</td>\n",
       "      <td>316.25</td>\n",
       "      <td>0.000</td>\n",
       "      <td>312.362</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>632.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1561.81</td>\n",
       "      <td>0.00</td>\n",
       "      <td>311.73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID  sales_avg_M_2  sales_avg_M_3  sales_avg_M_4  sales_avg_M_5  \\\n",
       "0     12346.0           0.00          0.000      77183.600           0.00   \n",
       "1     12347.0         584.91        509.385        475.390         711.79   \n",
       "2     12348.0         310.00        367.000        227.440         892.80   \n",
       "3     12350.0           0.00          0.000        334.400           0.00   \n",
       "4     12352.0         316.25          0.000        312.362           0.00   \n",
       "\n",
       "   sales_count_M_2  sales_count_M_3  sales_count_M_4  sales_count_M_5  \\\n",
       "0              0.0              0.0              1.0              0.0   \n",
       "1              1.0              2.0              1.0              1.0   \n",
       "2              1.0              1.0              1.0              1.0   \n",
       "3              0.0              0.0              1.0              0.0   \n",
       "4              2.0              0.0              5.0              0.0   \n",
       "\n",
       "   sales_sum_M_2  sales_sum_M_3  sales_sum_M_4  sales_sum_M_5   CLV_3M  \n",
       "0           0.00           0.00       77183.60           0.00     0.00  \n",
       "1         584.91        1018.77         475.39         711.79  1294.32  \n",
       "2         310.00         367.00         227.44         892.80     0.00  \n",
       "3           0.00           0.00         334.40           0.00     0.00  \n",
       "4         632.50           0.00        1561.81           0.00   311.73  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sample_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     3616.000000\n",
       "mean       511.558520\n",
       "std       2371.743293\n",
       "min          0.000000\n",
       "25%          0.000000\n",
       "50%          0.000000\n",
       "75%        458.662500\n",
       "max      68012.350000\n",
       "Name: CLV_3M, dtype: float64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sample_set['CLV_3M'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Build linear regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_var = 'CLV_3M'\n",
    "all_features = [x for x in df_sample_set.columns if x not in ['CustomerID', target_var]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sales_avg_M_2',\n",
       " 'sales_avg_M_3',\n",
       " 'sales_avg_M_4',\n",
       " 'sales_avg_M_5',\n",
       " 'sales_count_M_2',\n",
       " 'sales_count_M_3',\n",
       " 'sales_count_M_4',\n",
       " 'sales_count_M_5',\n",
       " 'sales_sum_M_2',\n",
       " 'sales_sum_M_3',\n",
       " 'sales_sum_M_4',\n",
       " 'sales_sum_M_5']"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>sales_avg_M_2</th>\n",
       "      <th>sales_avg_M_3</th>\n",
       "      <th>sales_avg_M_4</th>\n",
       "      <th>sales_avg_M_5</th>\n",
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       "      <th>sales_count_M_3</th>\n",
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       "      <th>sales_count_M_5</th>\n",
       "      <th>sales_sum_M_2</th>\n",
       "      <th>sales_sum_M_3</th>\n",
       "      <th>sales_sum_M_4</th>\n",
       "      <th>sales_sum_M_5</th>\n",
       "      <th>CLV_3M</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12347.0</td>\n",
       "      <td>584.910000</td>\n",
       "      <td>509.385</td>\n",
       "      <td>475.390000</td>\n",
       "      <td>711.79</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>584.91</td>\n",
       "      <td>1018.77</td>\n",
       "      <td>475.39</td>\n",
       "      <td>711.79</td>\n",
       "      <td>1294.32</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12348.0</td>\n",
       "      <td>310.000000</td>\n",
       "      <td>367.000</td>\n",
       "      <td>227.440000</td>\n",
       "      <td>892.80</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>310.00</td>\n",
       "      <td>367.00</td>\n",
       "      <td>227.44</td>\n",
       "      <td>892.80</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
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       "      <td>0.000</td>\n",
       "      <td>334.400000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>334.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12352.0</td>\n",
       "      <td>316.250000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>312.362000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>632.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1561.81</td>\n",
       "      <td>0.00</td>\n",
       "      <td>311.73</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>3611</th>\n",
       "      <td>18280.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>180.600000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>180.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>3612</th>\n",
       "      <td>18281.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>80.820</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>80.82</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>3613</th>\n",
       "      <td>18282.0</td>\n",
       "      <td>100.210000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>3614</th>\n",
       "      <td>18283.0</td>\n",
       "      <td>92.696667</td>\n",
       "      <td>131.170</td>\n",
       "      <td>105.966667</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>278.09</td>\n",
       "      <td>524.68</td>\n",
       "      <td>317.90</td>\n",
       "      <td>0.00</td>\n",
       "      <td>766.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3615</th>\n",
       "      <td>18287.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>765.280</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>765.28</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1072.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3616 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      CustomerID  sales_avg_M_2  sales_avg_M_3  sales_avg_M_4  sales_avg_M_5  \\\n",
       "0        12346.0       0.000000          0.000   77183.600000           0.00   \n",
       "1        12347.0     584.910000        509.385     475.390000         711.79   \n",
       "2        12348.0     310.000000        367.000     227.440000         892.80   \n",
       "3        12350.0       0.000000          0.000     334.400000           0.00   \n",
       "4        12352.0     316.250000          0.000     312.362000           0.00   \n",
       "...          ...            ...            ...            ...            ...   \n",
       "3611     18280.0       0.000000          0.000     180.600000           0.00   \n",
       "3612     18281.0       0.000000         80.820       0.000000           0.00   \n",
       "3613     18282.0     100.210000          0.000       0.000000           0.00   \n",
       "3614     18283.0      92.696667        131.170     105.966667           0.00   \n",
       "3615     18287.0       0.000000        765.280       0.000000           0.00   \n",
       "\n",
       "      sales_count_M_2  sales_count_M_3  sales_count_M_4  sales_count_M_5  \\\n",
       "0                 0.0              0.0              1.0              0.0   \n",
       "1                 1.0              2.0              1.0              1.0   \n",
       "2                 1.0              1.0              1.0              1.0   \n",
       "3                 0.0              0.0              1.0              0.0   \n",
       "4                 2.0              0.0              5.0              0.0   \n",
       "...               ...              ...              ...              ...   \n",
       "3611              0.0              0.0              1.0              0.0   \n",
       "3612              0.0              1.0              0.0              0.0   \n",
       "3613              1.0              0.0              0.0              0.0   \n",
       "3614              3.0              4.0              3.0              0.0   \n",
       "3615              0.0              1.0              0.0              0.0   \n",
       "\n",
       "      sales_sum_M_2  sales_sum_M_3  sales_sum_M_4  sales_sum_M_5   CLV_3M  \n",
       "0              0.00           0.00       77183.60           0.00     0.00  \n",
       "1            584.91        1018.77         475.39         711.79  1294.32  \n",
       "2            310.00         367.00         227.44         892.80     0.00  \n",
       "3              0.00           0.00         334.40           0.00     0.00  \n",
       "4            632.50           0.00        1561.81           0.00   311.73  \n",
       "...             ...            ...            ...            ...      ...  \n",
       "3611           0.00           0.00         180.60           0.00     0.00  \n",
       "3612           0.00          80.82           0.00           0.00     0.00  \n",
       "3613         100.21           0.00           0.00           0.00     0.00  \n",
       "3614         278.09         524.68         317.90           0.00   766.21  \n",
       "3615           0.00         765.28           0.00           0.00  1072.00  \n",
       "\n",
       "[3616 rows x 14 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sample_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "                                    df_sample_set[all_features], \n",
    "                                    df_sample_set[target_var], \n",
    "                                    random_state=2,\n",
    "                                    test_size=0.35)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "lin_reg = LinearRegression(n_jobs=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(n_jobs=1)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-17.526580313512454"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_reg.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "coef = pd.DataFrame(list(zip(all_features, lin_reg.coef_)))\n",
    "coef.columns = ['feature', 'coef']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "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>feature</th>\n",
       "      <th>coef</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sales_avg_M_2</td>\n",
       "      <td>0.211380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sales_avg_M_3</td>\n",
       "      <td>-0.059222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sales_avg_M_4</td>\n",
       "      <td>0.091413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sales_avg_M_5</td>\n",
       "      <td>-0.581033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sales_count_M_2</td>\n",
       "      <td>128.559625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>sales_count_M_3</td>\n",
       "      <td>-55.323992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>sales_count_M_4</td>\n",
       "      <td>18.092563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>sales_count_M_5</td>\n",
       "      <td>-39.690706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>sales_sum_M_2</td>\n",
       "      <td>0.251125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>sales_sum_M_3</td>\n",
       "      <td>0.377412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>sales_sum_M_4</td>\n",
       "      <td>-0.076224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>sales_sum_M_5</td>\n",
       "      <td>0.907732</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            feature        coef\n",
       "0     sales_avg_M_2    0.211380\n",
       "1     sales_avg_M_3   -0.059222\n",
       "2     sales_avg_M_4    0.091413\n",
       "3     sales_avg_M_5   -0.581033\n",
       "4   sales_count_M_2  128.559625\n",
       "5   sales_count_M_3  -55.323992\n",
       "6   sales_count_M_4   18.092563\n",
       "7   sales_count_M_5  -39.690706\n",
       "8     sales_sum_M_2    0.251125\n",
       "9     sales_sum_M_3    0.377412\n",
       "10    sales_sum_M_4   -0.076224\n",
       "11    sales_sum_M_5    0.907732"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coef"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import r2_score, median_absolute_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_preds =  lin_reg.predict(X_train)\n",
    "test_preds = lin_reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### R-Squared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-Squared for Train set: 0.71\n",
      "R-Squared for Test set: 0.71\n"
     ]
    }
   ],
   "source": [
    "print('R-Squared for Train set: %0.2f' % r2_score(y_true=y_train, y_pred=train_preds))\n",
    "print('R-Squared for Test set: %0.2f' % r2_score(y_true=y_test, y_pred=test_preds))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "R-Squared is the same for the train and test sets. Therefore, we do not have any overfitting or underfitting of the model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Median Absolute Error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MedAE for Train set: 202.41\n",
      "MedAE for Test set: 205.20\n"
     ]
    }
   ],
   "source": [
    "print('MedAE for Train set: %0.2f' % median_absolute_error(y_true=y_train, y_pred=train_preds))\n",
    "print('MedAE for Test set: %0.2f' % median_absolute_error(y_true=y_test, y_pred=test_preds))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MedAE has a small difference between test and train sets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Scatter plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6), dpi=80)\n",
    "plt.scatter(y_train, train_preds, color='dodgerblue')\n",
    "plt.plot([0, max(y_train)], [0, max(train_preds)], color='gray', lw=1, linestyle='--')\n",
    "\n",
    "plt.xlabel('Actual Values', fontsize=14)\n",
    "plt.ylabel('Predicted Values', fontsize=14)\n",
    "plt.title('Actual vs. Predicted for Train Set', fontsize=16)\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6), dpi=80)\n",
    "plt.scatter(y_test, test_preds, color='dodgerblue')\n",
    "plt.plot([0, max(y_test)], [0, max(test_preds)], color='gray', lw=1, linestyle='--')\n",
    "\n",
    "plt.xlabel('Actual Values', fontsize=14)\n",
    "plt.ylabel('Predicted Values', fontsize=14)\n",
    "plt.title('Actual vs. Predicted for Test Set', fontsize=16)\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see on this plot, the x-axis is the actual values and the y-axis represents the predicted values. Closer the dots are located to the straight diagonal line, better predictions are."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='summary'></a>\n",
    "## Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Based on the data analysis, we found that the repeat customers tend to make about 12 purchases or less within a year and the majority of repeat customers tend to make a purchase every 12 to 50 days\n",
    "* We predicted 3-month CLTV for customers of the online retail using linear regression\n",
    "* R-squared value for the test set is 0.71, which is not great but it is a good benchmark to try other regression models such as Epsilon-Support Vector Regression and Random Forest Regressor\n",
    "* By knowing CLTV, we can develop positive ROI strategies and make decisions about how much money to invest in acquiring new customers and retaining existing ones."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='reference'></a>\n",
    "## References"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Hwang, Y. H. (2019). Hands-on data science for marketing: Improve your marketing strategies with machine learning using Python and R. Birmingham, UK: Packt Publishing.\n",
    "2. Müller, A. C., &amp; Guido, S. (2018). Introduction to machine learning with Python: A guide for data scientists. Sebastopol, CA: O'Reilly Media.\n",
    "3. Jeffery, M. (2010). Data-driven marketing the 15 metrics everyone in marketing should know. Hoboken (N.J.), Canada: John Wiley.\n",
    "4. Customer lifetime Value (CLV) definition - what is customer lifetime Value (CLV). (n.d.). Retrieved February 07, 2021, from https://www.shopify.com/encyclopedia/customer-lifetime-value-clv#:~:text=The%20lifetime%20value%20of%20a,your%20products%2C%20during%20their%20lifetime."
   ]
  }
 ],
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   "file_extension": ".py",
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