Customer_segmentation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Customer segmentation based on RFM method and K-Means clustering\n",
    "\n",
    "\n",
    "In this project I have performed customer segmentation by combining the RFM method and K-Means grouping. I have used RFM scoring (assigned RFM score) and K-means clustering to determine segments of clients. The dataset includes sample sales data based on retail analytics and contains three years of sales.\n",
    "\n",
    "\n",
    "**Customer segmentation**\n",
    "\n",
    "The customer segmentation is an effective method that enables us to get better know our clients and to better correspond, their various needs. Almost every company that sells products or services stores data of shopping. This type of data can be used to execute customer segmentation thus, the results of the analysis can be translated into marketing campaigns to increase sales. One of the most widely used techniques is RFM analysis, which allows us to create personalized special offers to improve sales and decrease customer retention.\n",
    "\n",
    "**RFM** stands for Recency, Frequency, Monetary Value and it is the technique of customer segmentation based on their transaction history. The RFM analysis is based on three criterias, which measure different customer characteristics:\n",
    "-  Recency: Days since last purchase/order of the client;\n",
    "- Frequency:  Total number of purchases the customer were made;\n",
    "- Monetary Value: Total money the customer spent per order.\n",
    "\n",
    "Customer segmentation can be performed using the classic RFM method, assigning them ratings from 1 to 4. Four is the best / highest value and one is the lowest / worst value. The final RFM score is calculated by combining the individual score numbers into one column to create RFM value classes for our customers. \n",
    "\n",
    "\n",
    "**Dataset**\n",
    "\n",
    "The dataset comes from https://www.kaggle.com/kyanyoga/sample-sales-data.\n",
    "\n",
    "The whole project is divided into 6 steps:\n",
    "- Importing libraries and loading data set\n",
    "- Data cleaning and preparation\n",
    "- Data analysis and visualization\n",
    "- RFM Analysis\n",
    "- Model: RFM scoring + K-Means clustering\n",
    "- Description of the results\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import libriaries and data"
   ]
  },
  {
   "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",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "\n",
    "import datetime as dt\n",
    "import scipy.stats\n",
    "import statsmodels.formula.api as sm\n",
    "\n",
    "from sklearn.cluster import KMeans\n",
    "from mpl_toolkits.mplot3d import Axes3D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ORDERNUMBER</th>\n",
       "      <th>QUANTITYORDERED</th>\n",
       "      <th>PRICEEACH</th>\n",
       "      <th>ORDERLINENUMBER</th>\n",
       "      <th>SALES</th>\n",
       "      <th>ORDERDATE</th>\n",
       "      <th>STATUS</th>\n",
       "      <th>QTR_ID</th>\n",
       "      <th>MONTH_ID</th>\n",
       "      <th>YEAR_ID</th>\n",
       "      <th>...</th>\n",
       "      <th>ADDRESSLINE1</th>\n",
       "      <th>ADDRESSLINE2</th>\n",
       "      <th>CITY</th>\n",
       "      <th>STATE</th>\n",
       "      <th>POSTALCODE</th>\n",
       "      <th>COUNTRY</th>\n",
       "      <th>TERRITORY</th>\n",
       "      <th>CONTACTLASTNAME</th>\n",
       "      <th>CONTACTFIRSTNAME</th>\n",
       "      <th>DEALSIZE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10107</td>\n",
       "      <td>30</td>\n",
       "      <td>95.70</td>\n",
       "      <td>2</td>\n",
       "      <td>2871.00</td>\n",
       "      <td>2/24/2003 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2003</td>\n",
       "      <td>...</td>\n",
       "      <td>897 Long Airport Avenue</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NYC</td>\n",
       "      <td>NY</td>\n",
       "      <td>10022</td>\n",
       "      <td>USA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yu</td>\n",
       "      <td>Kwai</td>\n",
       "      <td>Small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10121</td>\n",
       "      <td>34</td>\n",
       "      <td>81.35</td>\n",
       "      <td>5</td>\n",
       "      <td>2765.90</td>\n",
       "      <td>5/7/2003 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2003</td>\n",
       "      <td>...</td>\n",
       "      <td>59 rue de l'Abbaye</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reims</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51100</td>\n",
       "      <td>France</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Henriot</td>\n",
       "      <td>Paul</td>\n",
       "      <td>Small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10134</td>\n",
       "      <td>41</td>\n",
       "      <td>94.74</td>\n",
       "      <td>2</td>\n",
       "      <td>3884.34</td>\n",
       "      <td>7/1/2003 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2003</td>\n",
       "      <td>...</td>\n",
       "      <td>27 rue du Colonel Pierre Avia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Paris</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75508</td>\n",
       "      <td>France</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Da Cunha</td>\n",
       "      <td>Daniel</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10145</td>\n",
       "      <td>45</td>\n",
       "      <td>83.26</td>\n",
       "      <td>6</td>\n",
       "      <td>3746.70</td>\n",
       "      <td>8/25/2003 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>2003</td>\n",
       "      <td>...</td>\n",
       "      <td>78934 Hillside Dr.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pasadena</td>\n",
       "      <td>CA</td>\n",
       "      <td>90003</td>\n",
       "      <td>USA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Young</td>\n",
       "      <td>Julie</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10159</td>\n",
       "      <td>49</td>\n",
       "      <td>100.00</td>\n",
       "      <td>14</td>\n",
       "      <td>5205.27</td>\n",
       "      <td>10/10/2003 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>2003</td>\n",
       "      <td>...</td>\n",
       "      <td>7734 Strong St.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>USA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brown</td>\n",
       "      <td>Julie</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ORDERNUMBER  QUANTITYORDERED  PRICEEACH  ORDERLINENUMBER    SALES  \\\n",
       "0        10107               30      95.70                2  2871.00   \n",
       "1        10121               34      81.35                5  2765.90   \n",
       "2        10134               41      94.74                2  3884.34   \n",
       "3        10145               45      83.26                6  3746.70   \n",
       "4        10159               49     100.00               14  5205.27   \n",
       "\n",
       "         ORDERDATE   STATUS  QTR_ID  MONTH_ID  YEAR_ID  ...  \\\n",
       "0   2/24/2003 0:00  Shipped       1         2     2003  ...   \n",
       "1    5/7/2003 0:00  Shipped       2         5     2003  ...   \n",
       "2    7/1/2003 0:00  Shipped       3         7     2003  ...   \n",
       "3   8/25/2003 0:00  Shipped       3         8     2003  ...   \n",
       "4  10/10/2003 0:00  Shipped       4        10     2003  ...   \n",
       "\n",
       "                    ADDRESSLINE1  ADDRESSLINE2           CITY STATE  \\\n",
       "0        897 Long Airport Avenue           NaN            NYC    NY   \n",
       "1             59 rue de l'Abbaye           NaN          Reims   NaN   \n",
       "2  27 rue du Colonel Pierre Avia           NaN          Paris   NaN   \n",
       "3             78934 Hillside Dr.           NaN       Pasadena    CA   \n",
       "4                7734 Strong St.           NaN  San Francisco    CA   \n",
       "\n",
       "  POSTALCODE COUNTRY TERRITORY CONTACTLASTNAME CONTACTFIRSTNAME DEALSIZE  \n",
       "0      10022     USA       NaN              Yu             Kwai    Small  \n",
       "1      51100  France      EMEA         Henriot             Paul    Small  \n",
       "2      75508  France      EMEA        Da Cunha           Daniel   Medium  \n",
       "3      90003     USA       NaN           Young            Julie   Medium  \n",
       "4        NaN     USA       NaN           Brown            Julie   Medium  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(r'C:\\Python Scripts\\Datasets\\sales_data_sample.csv', encoding = 'unicode_escape')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**First observations:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ORDERNUMBER', 'QUANTITYORDERED', 'PRICEEACH', 'ORDERLINENUMBER',\n",
       "       'SALES', 'ORDERDATE', 'STATUS', 'QTR_ID', 'MONTH_ID', 'YEAR_ID',\n",
       "       'PRODUCTLINE', 'MSRP', 'PRODUCTCODE', 'CUSTOMERNAME', 'PHONE',\n",
       "       'ADDRESSLINE1', 'ADDRESSLINE2', 'CITY', 'STATE', 'POSTALCODE',\n",
       "       'COUNTRY', 'TERRITORY', 'CONTACTLASTNAME', 'CONTACTFIRSTNAME',\n",
       "       'DEALSIZE'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2823 entries, 0 to 2822\n",
      "Data columns (total 25 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   ORDERNUMBER       2823 non-null   int64  \n",
      " 1   QUANTITYORDERED   2823 non-null   int64  \n",
      " 2   PRICEEACH         2823 non-null   float64\n",
      " 3   ORDERLINENUMBER   2823 non-null   int64  \n",
      " 4   SALES             2823 non-null   float64\n",
      " 5   ORDERDATE         2823 non-null   object \n",
      " 6   STATUS            2823 non-null   object \n",
      " 7   QTR_ID            2823 non-null   int64  \n",
      " 8   MONTH_ID          2823 non-null   int64  \n",
      " 9   YEAR_ID           2823 non-null   int64  \n",
      " 10  PRODUCTLINE       2823 non-null   object \n",
      " 11  MSRP              2823 non-null   int64  \n",
      " 12  PRODUCTCODE       2823 non-null   object \n",
      " 13  CUSTOMERNAME      2823 non-null   object \n",
      " 14  PHONE             2823 non-null   object \n",
      " 15  ADDRESSLINE1      2823 non-null   object \n",
      " 16  ADDRESSLINE2      302 non-null    object \n",
      " 17  CITY              2823 non-null   object \n",
      " 18  STATE             1337 non-null   object \n",
      " 19  POSTALCODE        2747 non-null   object \n",
      " 20  COUNTRY           2823 non-null   object \n",
      " 21  TERRITORY         1749 non-null   object \n",
      " 22  CONTACTLASTNAME   2823 non-null   object \n",
      " 23  CONTACTFIRSTNAME  2823 non-null   object \n",
      " 24  DEALSIZE          2823 non-null   object \n",
      "dtypes: float64(2), int64(7), object(16)\n",
      "memory usage: 551.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Shape of data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2823, 25)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are in data some unnecessary columns such as „ADDRESSLINE1”, „ADDRESSLINE2”, „POSTALCODE”, „TERRITORY”, „PHONE”. \n",
    "\n",
    "In the first step I remove them. Next I regroup the column 'PRODUCTINITIAL' use only three first signs. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove columns\n",
    "data=['ADDRESSLINE1','ADDRESSLINE2','POSTALCODE', 'TERRITORY', 'PHONE', 'CITY' , 'STATE','CONTACTFIRSTNAME', 'CONTACTLASTNAME', 'MONTH_ID']\n",
    "df.drop(data,axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# regrouping product code\n",
    "df['PRODUCTINITIAL'] = df['PRODUCTCODE'].str[:3]\n",
    "df.drop('PRODUCTCODE',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ORDERNUMBER</th>\n",
       "      <th>QUANTITYORDERED</th>\n",
       "      <th>PRICEEACH</th>\n",
       "      <th>ORDERLINENUMBER</th>\n",
       "      <th>SALES</th>\n",
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       "      <th>COUNTRY</th>\n",
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       "      <th>PRODUCTINITIAL</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10107</td>\n",
       "      <td>30</td>\n",
       "      <td>95.70</td>\n",
       "      <td>2</td>\n",
       "      <td>2871.00</td>\n",
       "      <td>2/24/2003 0:00</td>\n",
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       "      <td>1</td>\n",
       "      <td>2003</td>\n",
       "      <td>Motorcycles</td>\n",
       "      <td>95</td>\n",
       "      <td>Land of Toys Inc.</td>\n",
       "      <td>USA</td>\n",
       "      <td>Small</td>\n",
       "      <td>S10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10121</td>\n",
       "      <td>34</td>\n",
       "      <td>81.35</td>\n",
       "      <td>5</td>\n",
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       "      <td>5/7/2003 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>2</td>\n",
       "      <td>2003</td>\n",
       "      <td>Motorcycles</td>\n",
       "      <td>95</td>\n",
       "      <td>Reims Collectables</td>\n",
       "      <td>France</td>\n",
       "      <td>Small</td>\n",
       "      <td>S10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10134</td>\n",
       "      <td>41</td>\n",
       "      <td>94.74</td>\n",
       "      <td>2</td>\n",
       "      <td>3884.34</td>\n",
       "      <td>7/1/2003 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>3</td>\n",
       "      <td>2003</td>\n",
       "      <td>Motorcycles</td>\n",
       "      <td>95</td>\n",
       "      <td>Lyon Souveniers</td>\n",
       "      <td>France</td>\n",
       "      <td>Medium</td>\n",
       "      <td>S10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10145</td>\n",
       "      <td>45</td>\n",
       "      <td>83.26</td>\n",
       "      <td>6</td>\n",
       "      <td>3746.70</td>\n",
       "      <td>8/25/2003 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>3</td>\n",
       "      <td>2003</td>\n",
       "      <td>Motorcycles</td>\n",
       "      <td>95</td>\n",
       "      <td>Toys4GrownUps.com</td>\n",
       "      <td>USA</td>\n",
       "      <td>Medium</td>\n",
       "      <td>S10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10159</td>\n",
       "      <td>49</td>\n",
       "      <td>100.00</td>\n",
       "      <td>14</td>\n",
       "      <td>5205.27</td>\n",
       "      <td>10/10/2003 0:00</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>4</td>\n",
       "      <td>2003</td>\n",
       "      <td>Motorcycles</td>\n",
       "      <td>95</td>\n",
       "      <td>Corporate Gift Ideas Co.</td>\n",
       "      <td>USA</td>\n",
       "      <td>Medium</td>\n",
       "      <td>S10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ORDERNUMBER  QUANTITYORDERED  PRICEEACH  ORDERLINENUMBER    SALES  \\\n",
       "0        10107               30      95.70                2  2871.00   \n",
       "1        10121               34      81.35                5  2765.90   \n",
       "2        10134               41      94.74                2  3884.34   \n",
       "3        10145               45      83.26                6  3746.70   \n",
       "4        10159               49     100.00               14  5205.27   \n",
       "\n",
       "         ORDERDATE   STATUS  QTR_ID  YEAR_ID  PRODUCTLINE  MSRP  \\\n",
       "0   2/24/2003 0:00  Shipped       1     2003  Motorcycles    95   \n",
       "1    5/7/2003 0:00  Shipped       2     2003  Motorcycles    95   \n",
       "2    7/1/2003 0:00  Shipped       3     2003  Motorcycles    95   \n",
       "3   8/25/2003 0:00  Shipped       3     2003  Motorcycles    95   \n",
       "4  10/10/2003 0:00  Shipped       4     2003  Motorcycles    95   \n",
       "\n",
       "               CUSTOMERNAME COUNTRY DEALSIZE PRODUCTINITIAL  \n",
       "0         Land of Toys Inc.     USA    Small            S10  \n",
       "1        Reims Collectables  France    Small            S10  \n",
       "2           Lyon Souveniers  France   Medium            S10  \n",
       "3         Toys4GrownUps.com     USA   Medium            S10  \n",
       "4  Corporate Gift Ideas Co.     USA   Medium            S10  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic data analysis\n",
    "\n",
    "Checking unique values in each column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique values in ORDERNUMBER: 307\n",
      "Number of unique values in QUANTITYORDERED: 58\n",
      "Number of unique values in PRICEEACH: 1016\n",
      "Number of unique values in ORDERLINENUMBER: 18\n",
      "Number of unique values in SALES: 2763\n",
      "Number of unique values in ORDERDATE: 252\n",
      "Number of unique values in STATUS: 6\n",
      "Number of unique values in QTR_ID: 4\n",
      "Number of unique values in YEAR_ID: 3\n",
      "Number of unique values in PRODUCTLINE: 7\n",
      "Number of unique values in MSRP: 80\n",
      "Number of unique values in CUSTOMERNAME: 92\n",
      "Number of unique values in COUNTRY: 19\n",
      "Number of unique values in DEALSIZE: 3\n",
      "Number of unique values in PRODUCTINITIAL: 8\n"
     ]
    }
   ],
   "source": [
    "for column in df:\n",
    "    print(f'Number of unique values in {column}:', df[column].nunique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking the missing values in data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ORDERNUMBER        0\n",
       "QUANTITYORDERED    0\n",
       "PRICEEACH          0\n",
       "ORDERLINENUMBER    0\n",
       "SALES              0\n",
       "ORDERDATE          0\n",
       "STATUS             0\n",
       "QTR_ID             0\n",
       "YEAR_ID            0\n",
       "PRODUCTLINE        0\n",
       "MSRP               0\n",
       "CUSTOMERNAME       0\n",
       "COUNTRY            0\n",
       "DEALSIZE           0\n",
       "PRODUCTINITIAL     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking unique values in YEAR_ID:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2003, 2004, 2005], dtype=int64)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['YEAR_ID'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After first observations one can see that:\n",
    "- there are no duplicates;\n",
    "- no missing values;\n",
    "- in data are 92 unique clients for whom I will perform RFM analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data visualization\n",
    "\n",
    "Firstly I plots the data to get more insight:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1152 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = [18, 16]\n",
    "df.plot(kind=\"density\", subplots=True, layout=(4,4), sharex=False, sharey=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Majority of the data comes from 2003 and 2004 years (YEAR_ID). According to the data most of the sales are within a certain price range. But there are a few outliers for \"SALES\" and \"QUANTITYORDERED\". In plots one can see skewed variables like \"PRICEEACH\", \"ORDERLINENUMBER\" and also variables with a large discrepancy such as \"PRICEEACH\", \"ORDERLINENUMBER\", \"MSRP\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking unique values in STATUS:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Shipped       2617\n",
       "Cancelled       60\n",
       "Resolved        47\n",
       "On Hold         44\n",
       "In Process      41\n",
       "Disputed        14\n",
       "Name: STATUS, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['STATUS'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The variable shows type of order status. Most of them is shipped.\n",
    "\n",
    "Below plot of the distribution of Sales status:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = [8, 5]\n",
    "sns.countplot(y='STATUS',data=df,hue='YEAR_ID', )\n",
    "plt.title('Distribution of Sales status');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Comparing Sales for each year:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "YEAR_ID\n",
       "2003    3516979.54\n",
       "2004    4724162.60\n",
       "2005    1791486.71\n",
       "Name: SALES, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['YEAR_ID'])['SALES'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": [
    "df.groupby(['YEAR_ID'])['SALES'].sum().plot(kind='bar', figsize=(6,4), title='Sales by year');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The most sales were recorded in 2004 compared to 2003. There are not all the data for 2005."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparing sales for each year (Quarter Wise):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>SALES</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YEAR_ID</th>\n",
       "      <th>QTR_ID</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">2003</th>\n",
       "      <th>1</th>\n",
       "      <td>445094.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>562365.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>649514.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1860005.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">2004</th>\n",
       "      <th>1</th>\n",
       "      <td>833730.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>766260.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1109396.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014774.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2005</th>\n",
       "      <th>1</th>\n",
       "      <td>1071992.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>719494.35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     SALES\n",
       "YEAR_ID QTR_ID            \n",
       "2003    1        445094.69\n",
       "        2        562365.22\n",
       "        3        649514.54\n",
       "        4       1860005.09\n",
       "2004    1        833730.68\n",
       "        2        766260.73\n",
       "        3       1109396.27\n",
       "        4       2014774.92\n",
       "2005    1       1071992.36\n",
       "        2        719494.35"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales = df.groupby(['YEAR_ID','QTR_ID']).agg({'SALES': lambda x: x.sum()})\n",
    "sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>YEAR_ID</th>\n",
       "      <th>QTR_ID</th>\n",
       "      <th>SALES</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2003</td>\n",
       "      <td>1</td>\n",
       "      <td>445094.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2003</td>\n",
       "      <td>2</td>\n",
       "      <td>562365.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2003</td>\n",
       "      <td>3</td>\n",
       "      <td>649514.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2003</td>\n",
       "      <td>4</td>\n",
       "      <td>1860005.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2004</td>\n",
       "      <td>1</td>\n",
       "      <td>833730.68</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   YEAR_ID  QTR_ID       SALES\n",
       "0     2003       1   445094.69\n",
       "1     2003       2   562365.22\n",
       "2     2003       3   649514.54\n",
       "3     2003       4  1860005.09\n",
       "4     2004       1   833730.68"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales.reset_index(inplace=True)\n",
    "sales.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\PC\\Anaconda3\\lib\\site-packages\\seaborn\\categorical.py:3717: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "data": {
      "image/png": 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S0rnAw6nclNI2zMwskx5LLBFxbBvx42vEbqQYflyrfAuwb434C8BhNeIBnNJGW1OBqe3128zMusdX3puZWVZOLGZmlpUTi5mZZeXEYmZmWTmxmJlZVk4sZmaWlROLmZll5cRiZmZZObGYmVlWTixmZpaVE4uZmWXlxGJmZlk5sZiZWVZOLGZmlpUTi5mZZeXEYmZmWTmxmJlZVk4sZmaWlROLmZll5cRiZmZZObGYmVlWTixmZpaVE4uZmWXlxGJmZlk5sZiZWVZOLGZmlpUTi5mZZdVjiUXSVEnLJD1Rip0j6TlJc9LjyNK6r0lqlfSUpMNL8bEp1ippcim+m6QHJc2XdJ2kLVJ8y/S6Na0fXm8bZmaWT0/usVwDjK0RvyQiRqbHrQCS9gbGA/ukOpdL6iepH3AZcASwN3BsKgtwYWprBLAKODHFTwRWRcQewCWpXJvbyPyezcz6vB5LLBFxH7Cyg8XHATMi4o2IeAZoBQ5Mj9aIWBARbwIzgHGSBBwK3JDqTwOOKrU1LS3fAByWyre1DTMzy6gR51hOlfR4OlQ2KMUGA4tKZRanWFvx7YEXI2J1VXydttL6l1L5ttoyM7OMejuxXAHsDowElgIXpbhqlI0uxLvS1nokTZLUIqll+fLltYqYmVkbejWxRMTzEbEmIt4GrmLtoajFwNBS0SHAknbiK4CBkvpXxddpK63fluKQXFtt1ernlRHRHBHNTU1NXXmrZmZ9Vq8mFkk7l15+CqiMGJsJjE8junYDRgAPAQ8DI9IIsC0oTr7PjIgA7gGOTvUnAreU2pqYlo8G7k7l29qGmZll1L9+ka6RdC1wCLCDpMXA2cAhkkZSHIJaCHwJICLmSboe+B2wGjglItakdk4F7gD6AVMjYl7axFnADEnnAY8BV6f41cBPJLVS7KmMr7cNMzPLR8Uf89aW5ubmaGlpaXQ3zDY6B5w5PUs7N2/93SztDPvW3CzttKPWedw+yVfem5lZVk4sZmaWlROLmZll5cRiZmZZObGYmVlWTixmZpaVE4uZmWXlxGJmZlk5sZiZWVZOLGZmlpUTi5mZZeXEYmZmWTmxmJlZVk4sZmaWlROLmZll5cRiZmZZObGYmVlWTixmZpaVE4uZmWXlxGJmZlk5sZiZWVZOLGZmlpUTi5mZZeXEYmZmWTmxmJlZVk4sZmaWlROLmZll5cRiZmZZ9VhikTRV0jJJT5Ri35X0e0mPS7pZ0sAUHy7pdUlz0uNHpToHSJorqVXSpZKU4ttJmiVpfnoelOJK5VrTdj5UamtiKj9f0sSeeu9mZn1ZT+6xXAOMrYrNAvaNiA8AfwC+Vlr3dESMTI+TSvErgEnAiPSotDkZuCsiRgB3pdcAR5TKTkr1kbQdcDZwEHAgcHYlGZmZWT49llgi4j5gZVXszohYnV7OBoa014aknYFtIuKBiAhgOnBUWj0OmJaWp1XFp0dhNjAwtXM4MCsiVkbEKookV534zMysmxp5juVvgdtKr3eT9JikeyUdnGKDgcWlMotTDGCniFgKkJ53LNVZVKNOW/H1SJokqUVSy/Llyzv/zszM+rCGJBZJ3wBWAz9LoaXAsIjYH/gK8HNJ2wCqUT3qNd9GnQ63FRFXRkRzRDQ3NTXV2ZyZmZX1emJJJ80/CXw+Hd4iIt6IiBfS8iPA08D7KPYqyofLhgBL0vLz6RBX5ZDZshRfDAytUaetuJmZZdSriUXSWOAs4K8j4rVSvElSv7T8XooT7wvSIa5XJI1Ko8EmALekajOBysiuiVXxCWl02CjgpdTOHcAYSYPSSfsxKWZmZhn176mGJV0LHALsIGkxxYisrwFbArPSqOHZaQTYR4EpklYDa4CTIqJy4v9kihFmAyjOyVTOy1wAXC/pROBZ4JgUvxU4EmgFXgNOAIiIlZLOBR5O5aaUtmFmZpn0WGKJiGNrhK9uo+yNwI1trGsB9q0RfwE4rEY8gFPaaGsqMLXtXpuZWXf5ynszM8vKicXMzLJyYjEzs6ycWMzMelkatfpbSUeUYp+RdLukNaV5E+dImlwq0yTpLUlfqmpvYZpT8fF0kfmudbb/anquzNP4mKQnJT2UYx7FHjt5b2ZmtUVESDoJ+IWke4B+wPkU00z9d0SMbKPqMRTTYR0L/Lhq3ccjYoWkbwPfBL7Ywe48nS5Or1zucZOkzSLi3zr3rtbyHouZWQNExBPAv1Nc23c2xRyHT9epdizwVWCIpJpTUgEP0MZ0VR3o0wKK2U++3JX6Fd5jMTNrnG8DjwJvAs0pNkDSnFKZ70TEdZKGAu+JiIckXQ98Fri4RptjgV92o0+PAu/vRn0nFjOzRomI/5F0HfBqRLyRwq+3cShsPHB9Wp5BcV1gObHcI2kniumtvtmNbtWaV7FTfCjMzKyx3k6Peo4Fjpe0kGLqqg9KGlFa/3FgV2AeMKUb/dkfeLIb9dtPLJJ2lbRt6fXHJf2LpK9I2qI7GzYzs46RtCfw7ogYHBHDI2I48B2KvZh3RMTrwBkU8yVu14XtDAe+B/ygO/2tt8dyPfDutMGRwC8o5uX6IHB5dzZsZmY1DagabnwBxd7KzVXlbkzxdaRJd6+ljamtati9MtyY4jf/B90ZEQb1z7EMiIjK1PJfAKZGxEWSNgPmtFPPzMw6ICLOqXrdr4P1Hgf2TsvDq9adVqfuVul5IcUEv1nV22Mpn8Q5lOLe8kRER44HmplZH1Rvj+XuNKxtKTAIuBveubHWmz3cNzMz6yJJ25N2BqocVrmxYk+pl1jOoBgrvTPwkYh4K8XfA3yjJztm1pc9O2W/LO0M+9bcLO3Yxiclj7au4O9R9RLLnhExA0DSlpVgRDyW7s5oZma2jnrnWH5eWn6gap1HhZmZ2Xo6c/K++mrMbl+daWZmm556iSXaWK712szMGkzSUEn3pGnw50k6PcW3kzRL0vz0PCjFJelSSa1p2v0Ppfiukh5J19LMS7Mxd0i9cyxDJF1KsXdSWSa97tLsmWZmfckBZ07P+kf4I9+dUO9o0WrgqxHxqKStgUckzQKOB+6KiAvSPV4mU8ysfAQwIj0OAq5Iz0uBv4yINyRtBTwhaWbp2sY21UssZ5aWW6rWVb+2Bhv9g9HdbuP+0+7P0BMza5R05f3StPxKuqJ+MDAOOCQVmwb8miKxjKOYsj+A2ZIGSto5tVOxJZ2YW7LdxBIR09paJ+l7Hd2ImZn1vjT31/7Ag8BOlWQREUsl7ZiKDQYWlaotTrGlaar+/wD2AM7syN4KdG924890o66ZmfWgdPjqRuCMiHi5vaI1YgEQEYsi4gMUiWVimpa/ru4kFo8KMzPbAEnanCKp/Cwibkrh59OsKZXZU5al+GJgaKn6EGCdPZO0pzIPOLgj2683bf52bTy2x4nFzGyDI0kUNwF7MiLKNwKbCUxMyxOBW0rxCWl02CjgpXSobIikAanNQcBo4KmO9KHeyftHKHaJaiWRt2rEzMyssUYDxwFzS7c4/jpwAXC9pBMpbn9yTFp3K3Ak0Aq8BpyQ4nsBF0mq5IDvRUSH5giqd/J+t46/FzMzq9aB4cFZRcRvafuI0mE1ygc17t0SEbOAD3SlD50+xyJpd0nfkPREB8pOlbSsXLazF+mkdRNT+fmSJpbiB0iam+pcmnYBu7QNMzPLo0OJRdLOks6Q9BDFCZz+1LhzWQ3XAGOrYpMpLtIZQTGl8+QUL1+kM4niIh3S7TXPprhg50Dg7EqiSGUmleqN7co2zMwsn3on778o6W7gXmAH4O+ApRHx7Y4ca4uI+4CVVeFxFBfnkJ6PKsWnR2E2MDCNXDgcmBURKyNiFTALGJvWbRMRD6RduelVbXVmG2Zmlkm9k/eXUcxq/LmIaAFIJ3K6o7MX6bQXX1wj3pVtlK8wRdIkij0ahg0b1oW3aGbWd9U7FLYLMAO4WNJTks4FNu+hvrR1kU5n413ZxrqBiCsjojkimpuamuo0aWZmZe0mlohYERFXRMRHKUYTvAQsS7Nm/lMXt9nZi3Taiw+pEe/KNszMLJN651g+LOk9ABGxGFgOPAc8A2zVxW126iId4A5gjKRB6aT9GOCOtO4VSaPSaLAJVW11ZhtmZpsEZZo2v9TeNpKek/TDjvah3jmWHwOfSI1/FPgOcBrFfZT36sAbvJZiNs0dJC2mGN3VqYt0ImJlOgT3cCo3JSIqAwJOphh5NgC4LT3o7DbMzHrKs1P2yzpt/rBvze2tafMrzqUYwNVh9RJLv9KP+GeBKyPiRuDG0hWdbYqItoYkd/ginbRuKjC1RrwF2LdG/IXObsPMbFOQc9p8SQcAOwG3A80d7UO9k/f9JFWSz2HA3aV19ZKSmZk1kNqZNh9od7SspM2Ai1j3vlwdUi85XAvcK2kF8Drwm9TZPShO5JuZ2QZIVdPmp4lJahatEQvg74FbI2JRO3VrqjdX2PmS7gJ2Bu5Mu0pQ7Omc1qktmZlZr1A70+anQ1wdGS37v4CDJf09xWCtLSS9GhGTqaPu4ax0hXp17A/16pmZWe9Lo2Tbmzb/AtYfLXuqpBkUJ+0ro2U/X2rzeKC5I0kFfJ7EzGxTk2va/C5zYjEz60EdGB6cVa5p86vKXENxaUeHdOfWxGZmZutxYjEzs6ycWMzMLCsnFjMzy8qJxczMsnJiMTOzrJxYzMw2ITmnzZe0RtKc9JjZ0T74OhYzsx40+gejs06bf/9p9/fmtPmvR8TIzvbReyxmZpuQiFgaEY+m5VeA8rT501KxacBRafmdafPTFF4DK3fg7SonFjOzTVR3ps1Py38hqUXSbElH0UE+FGZmtgnKMG0+wLCIWCLpvcDdkuZGxNP1tu09FjOzTUx70+an9R2ZNp+IqDwvoLjj5P4d2b73WDYAz07ZL09Dg7bJ046ZbbRyTZufRo29FhFvSNqBYtbkf+5IH5xYzMw2Lbmmzd8L+LGktymObl0QEb/rSAecWMzMelAHhgdnlWva/Ij4L6BLh1N8jsXMzLJyYjEzs6ycWMzMLCsnFjMzy8qJxczMsnJiMTOzrHo9sUjaszQN8xxJL0s6Q9I5kp4rxY8s1flamtL5KUmHl+JjU6w1zdZZie8m6cE0PfR1krZI8S3T69a0fnhvvnczs76g169jiYingJEAkvoBzwE3U1yUc0lEfK9cXtLewHhgH2AX4D8lvS+tvgz43xRTEjwsaWa6gOfC1NYMST8CTqSYCvpEYFVE7CFpfCr32R59w9YpOWYhGPatuRl6YmZd1ehDYYcBT0fEH9spMw6YERFvRMQzFFeHHpgerRGxICLeBGYA49J0BocCN6T61dNDV6aNvgE4TO3MzGZmZp3X6MQyHri29PrUdAezqZW7m9H2lM5txbcHXoyI1VXxddpK619K5dchaVKaKrpl+fLl3Xl/ZmZ9TsMSSzrv8dfAL1LoCmB3isNkS4GLKkVrVI8uxNtra91AxJUR0RwRzU1NTW2+BzMzW18j91iOAB6NiOcBIuL5iFgTEW8DV1Ec6oK2p3RuK76C4g5o/avi67SV1m8LrMz8vszM+rRGJpZjKR0Gq7oV5qeAJ9LyTGB8GtG1G8V9mR8CHgZGpBFgW1AcVpuZJlS7Bzg61a+eHnpiWj4auDuVNzOzTBoyu7Gkd1GM5vpSKfzPkkZSHJpaWFkXEfMkXQ/8DlgNnBIRa1I7pwJ3AP2AqRExL7V1FjBD0nnAYxT3JiA9/0RSK8Weyvgee5NmZn1UQxJLRLxG1UnziDiunfLnA+fXiN9KcS+B6vgC1h5KK8f/zNp7ENgmavQPRmdp5/7T7s/Sjllf0+hRYWZmtolxYjEzs6ycWMzMLCsnFjMzy8qJxczMsnJiMTOzrJxYzMwsKycWMzPLyonFzMyyasiV92abqgPOnJ6lnZu3ztKMWUN4j8XMzLJyYjEzs6ycWMzMLCsnFjMzy8qJxczMsnJiMTOzrJxYzMwsKycWMzPLyonFzMyycmIxM7OsnFjMzCwrzxVmWXiOLDOr8B6LmZll5cRiZmZZObGYmVlWTixmZpaVE4uZmWXVsMQiaaGkuZLmSGpJse0kzZI0Pz0PSnFJulRSq6THJX2o1M7EVH6+pIml+AGp/dZUV+1tw8zM8mj0HsvHI2JkRDSn15OBuyJiBHBXeg1wBFDq8YcAAAloSURBVDAiPSYBV0CRJICzgYOAA4GzS4niilS2Um9snW2YmVkGjU4s1cYB09LyNOCoUnx6FGYDAyXtDBwOzIqIlRGxCpgFjE3rtomIByIigOlVbdXahpmZZdDIxBLAnZIekTQpxXaKiKUA6XnHFB8MLCrVXZxi7cUX14i3t413SJokqUVSy/Lly7vxFs3M+p5GXnk/OiKWSNoRmCXp9+2UVY1YdCHeIRFxJXAlQHNzc4frmZlZA/dYImJJel4G3ExxjuT5dBiL9LwsFV8MDC1VHwIsqRMfUiNOO9swM7MMGpJYJL1b0taVZWAM8AQwE6iM7JoI3JKWZwIT0uiwUcBL6TDWHcAYSYPSSfsxwB1p3SuSRqXRYBOq2qq1DTMzy6BRh8J2Am5OI4D7Az+PiNslPQxcL+lE4FngmFT+VuBIoBV4DTgBICJWSjoXeDiVmxIRK9PyycA1wADgtvQAuKCNbZiZWQYNSSwRsQD4YI34C8BhNeIBnNJGW1OBqTXiLcC+Hd2GmZnlsaENNzYzs42cE4uZmWXlG311g29uZWa2Pu+xmJlZVk4sZmaWlROLmZll5cRiZmZZObGYmVlWTixmZpaVE4uZmWXlxGJmZlk5sZiZWVZOLGZmlpUTi5mZZeXEYmZmWTmxmJlZVk4sZmaWlROLmZll5cRiZmZZObGYmVlWTixmZpaVE4uZmWXlxGJmZlk5sZiZWVZOLGZmlpUTi5mZZeXEYmZmWfV6YpE0VNI9kp6UNE/S6Sl+jqTnJM1JjyNLdb4mqVXSU5IOL8XHplirpMml+G6SHpQ0X9J1krZI8S3T69a0fnjvvXMzs76hEXssq4GvRsRewCjgFEl7p3WXRMTI9LgVIK0bD+wDjAUul9RPUj/gMuAIYG/g2FI7F6a2RgCrgBNT/ERgVUTsAVySypmZWUa9nlgiYmlEPJqWXwGeBAa3U2UcMCMi3oiIZ4BW4MD0aI2IBRHxJjADGCdJwKHADan+NOCoUlvT0vINwGGpvJmZZdLQcyzpUNT+wIMpdKqkxyVNlTQoxQYDi0rVFqdYW/HtgRcjYnVVfJ220vqXUnkzM8ukYYlF0lbAjcAZEfEycAWwOzASWApcVClao3p0Id5eW9V9mySpRVLL8uXL230fZma2roYkFkmbUySVn0XETQAR8XxErImIt4GrKA51QbHHMbRUfQiwpJ34CmCgpP5V8XXaSuu3BVZW9y8iroyI5ohobmpq6u7bNTPrUxoxKkzA1cCTEXFxKb5zqdingCfS8kxgfBrRtRswAngIeBgYkUaAbUFxgn9mRARwD3B0qj8RuKXU1sS0fDRwdypvZmaZ9K9fJLvRwHHAXElzUuzrFKO6RlIcmloIfAkgIuZJuh74HcWIslMiYg2ApFOBO4B+wNSImJfaOwuYIek84DGKREZ6/omkVoo9lfE9+UbNzPqiXk8sEfFbap/ruLWdOucD59eI31qrXkQsYO2htHL8z8AxnemvmZl1jq+8NzOzrJxYzMwsKycWMzPLyonFzMyycmIxM7OsnFjMzCwrJxYzM8vKicXMzLJyYjEzs6ycWMzMLCsnFjMzy8qJxczMsnJiMTOzrJxYzMwsKycWMzPLyonFzMyycmIxM7OsnFjMzCwrJxYzM8vKicXMzLJyYjEzs6ycWMzMLCsnFjMzy8qJxczMsnJiMTOzrJxYzMwsKycWMzPLyonFzMyy6pOJRdJYSU9JapU0udH9MTPblPS5xCKpH3AZcASwN3CspL0b2yszs01Hn0sswIFAa0QsiIg3gRnAuAb3ycxsk6GIaHQfepWko4GxEfF36fVxwEERcWqpzCRgUnq5J/BUr3d0fTsAKxrdiQ2AP4e1/FmstSF8FisiYmyD+7BB6N/oDjSAasTWya4RcSVwZe90p2MktUREc6P70Wj+HNbyZ7GWP4sNS188FLYYGFp6PQRY0qC+mJltcvpiYnkYGCFpN0lbAOOBmQ3uk5nZJqPPHQqLiNWSTgXuAPoBUyNiXoO71REb1KG5BvLnsJY/i7X8WWxA+tzJezMz61l98VCYmZn1ICcWMzPLyollAydpqqRlkp5odF8aSdJQSfdIelLSPEmnN7pPjSLpLyQ9JOm/02fx7Ub3qZEk9ZP0mKRfNbovVnBi2fBdA/iiK1gNfDUi9gJGAaf04al43gAOjYgPAiOBsZJGNbhPjXQ68GSjO2FrObFs4CLiPmBlo/vRaBGxNCIeTcuvUPyQDG5srxojCq+ml5unR58chSNpCPB/gH9tdF9sLScW2+hIGg7sDzzY2J40Tjr8MwdYBsyKiL76WXwf+Efg7UZ3xNZyYrGNiqStgBuBMyLi5Ub3p1EiYk1EjKSYOeJASfs2uk+9TdIngWUR8Uij+2LrcmKxjYakzSmSys8i4qZG92dDEBEvAr+mb56HGw38taSFFLOUHyrpp43tkoETi20kJAm4GngyIi5udH8aSVKTpIFpeQDwCeD3je1V74uIr0XEkIgYTjE1090R8YUGd8twYtngSboWeADYU9JiSSc2uk8NMho4juKv0jnpcWSjO9UgOwP3SHqcYu67WRHhoba2wfCULmZmlpX3WMzMLCsnFjMzy8qJxczMsnJiMTOzrJxYzMwsKycWMzPLyonF+hxJQyTdImm+pAWSfijp8NL1Ma9KeiotT5d0iKSX0tTsv5f0vTrtHy/ph2n5HEnPpbbmS7qpD8/KbH2EE4v1KekK/puAX0bECGAEMAA4MiJGpvm3WoDPp9cTUtXfRMT+FJNfflLS6E5s9pLU1gjgOuBuSU3Z3pTZBsaJxfqaQ4E/R8S/QTGZI/APwIQ0wWW7IuJ1YA5dnLI/Iq4D7gQ+15X6ZhsDJxbra/YB1pkNN82SvBDYo15lSYMo9nLu60YfHgXe3436Zhs0Jxbra0Ttm2KpTr2D09xcfwJ+FRF/6mYfzDZZTizW18wDmssBSdsAOwFPtVPvNxHxAWA/4GRJI7vRh/3xrXRtE+bEYn3NXcC7JE2A4k6MwEXAD9P5k3ZFxB+A7wBndWXjkv4GGANc25X6ZhsDJxbrU6KYzvtTwNGS5gMvAG9HxPmdaOZHwEcl7dbB8v9QGW4MfAE4NCKWd6rjZhsRT5tvfZqkv6TYe/i0b3FrlocTi5mZZdW/0R0w21hJOgE4vSp8f0Sc0oj+mG0ovMdiZmZZ+eS9mZll5cRiZmZZObGYmVlWTixmZpbV/wcCQJS0B0FhtQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 421.25x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = [8, 5]\n",
    "sns.factorplot(y='SALES', x='QTR_ID',data=sales,kind=\"bar\" ,hue='YEAR_ID');\n",
    "plt.title('Comparing sales by quarter wise');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RFM Analysis\n",
    "\n",
    "For making RFM analysis I need only four columns: 'CUSTOMERNAME', 'ORDERNUMBER', 'ORDERDATE' and 'SALES'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>CUSTOMERNAME</th>\n",
       "      <th>ORDERNUMBER</th>\n",
       "      <th>ORDERDATE</th>\n",
       "      <th>SALES</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Land of Toys Inc.</td>\n",
       "      <td>10107</td>\n",
       "      <td>2/24/2003 0:00</td>\n",
       "      <td>2871.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Reims Collectables</td>\n",
       "      <td>10121</td>\n",
       "      <td>5/7/2003 0:00</td>\n",
       "      <td>2765.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lyon Souveniers</td>\n",
       "      <td>10134</td>\n",
       "      <td>7/1/2003 0:00</td>\n",
       "      <td>3884.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Toys4GrownUps.com</td>\n",
       "      <td>10145</td>\n",
       "      <td>8/25/2003 0:00</td>\n",
       "      <td>3746.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Corporate Gift Ideas Co.</td>\n",
       "      <td>10159</td>\n",
       "      <td>10/10/2003 0:00</td>\n",
       "      <td>5205.27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               CUSTOMERNAME  ORDERNUMBER        ORDERDATE    SALES\n",
       "0         Land of Toys Inc.        10107   2/24/2003 0:00  2871.00\n",
       "1        Reims Collectables        10121    5/7/2003 0:00  2765.90\n",
       "2           Lyon Souveniers        10134    7/1/2003 0:00  3884.34\n",
       "3         Toys4GrownUps.com        10145   8/25/2003 0:00  3746.70\n",
       "4  Corporate Gift Ideas Co.        10159  10/10/2003 0:00  5205.27"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col =['CUSTOMERNAME', 'ORDERNUMBER', 'ORDERDATE', 'SALES']\n",
    "RFM = df[col]\n",
    "RFM.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2823, 4)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RFM.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In RFM clustering it is usually assumed that only operations from the last 12 months are taken into account. The reason for this is simple. The older operations can say little about the current situation of the seller, buyer, and the product itself. In order to set them you need to adopt an agreed date for the grouping. In the data for \"today\" I will assume the day of the newest operation in the set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\PC\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "RFM['ORDERDATE'] = pd.to_datetime(RFM['ORDERDATE'], errors='coerce')"
   ]
  },
  {
   "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>CUSTOMERNAME</th>\n",
       "      <th>ORDERNUMBER</th>\n",
       "      <th>ORDERDATE</th>\n",
       "      <th>SALES</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Land of Toys Inc.</td>\n",
       "      <td>10107</td>\n",
       "      <td>2003-02-24</td>\n",
       "      <td>2871.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Reims Collectables</td>\n",
       "      <td>10121</td>\n",
       "      <td>2003-05-07</td>\n",
       "      <td>2765.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lyon Souveniers</td>\n",
       "      <td>10134</td>\n",
       "      <td>2003-07-01</td>\n",
       "      <td>3884.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Toys4GrownUps.com</td>\n",
       "      <td>10145</td>\n",
       "      <td>2003-08-25</td>\n",
       "      <td>3746.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Corporate Gift Ideas Co.</td>\n",
       "      <td>10159</td>\n",
       "      <td>2003-10-10</td>\n",
       "      <td>5205.27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               CUSTOMERNAME  ORDERNUMBER  ORDERDATE    SALES\n",
       "0         Land of Toys Inc.        10107 2003-02-24  2871.00\n",
       "1        Reims Collectables        10121 2003-05-07  2765.90\n",
       "2           Lyon Souveniers        10134 2003-07-01  3884.34\n",
       "3         Toys4GrownUps.com        10145 2003-08-25  3746.70\n",
       "4  Corporate Gift Ideas Co.        10159 2003-10-10  5205.27"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RFM.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2005-05-31 00:00:00')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RFM['ORDERDATE'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "now = dt.datetime(2005,5,31)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Create the RFM Table:**\n",
    "\n",
    "Calculation of the \"Recency\" variable - information about how long ago the customer was shopping in the store:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfm_df = RFM.groupby('CUSTOMERNAME').agg({'ORDERDATE': lambda x: (now - x.max()).days}) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"Frequency\" and \"Monetary value\" I made by the using groupby method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfm_df['Frequency'] = RFM.groupby(by=['CUSTOMERNAME'])['ORDERNUMBER'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfm_df['MonetaryValue'] = RFM.groupby(by='CUSTOMERNAME').agg({'SALES': 'sum'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>ORDERDATE</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>MonetaryValue</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CUSTOMERNAME</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AV Stores, Co.</th>\n",
       "      <td>195</td>\n",
       "      <td>51</td>\n",
       "      <td>157807.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alpha Cognac</th>\n",
       "      <td>64</td>\n",
       "      <td>20</td>\n",
       "      <td>70488.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amica Models &amp; Co.</th>\n",
       "      <td>264</td>\n",
       "      <td>26</td>\n",
       "      <td>94117.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anna's Decorations, Ltd</th>\n",
       "      <td>83</td>\n",
       "      <td>46</td>\n",
       "      <td>153996.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Atelier graphique</th>\n",
       "      <td>187</td>\n",
       "      <td>7</td>\n",
       "      <td>24179.96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         ORDERDATE  Frequency  MonetaryValue\n",
       "CUSTOMERNAME                                                \n",
       "AV Stores, Co.                 195         51      157807.81\n",
       "Alpha Cognac                    64         20       70488.44\n",
       "Amica Models & Co.             264         26       94117.26\n",
       "Anna's Decorations, Ltd         83         46      153996.13\n",
       "Atelier graphique              187          7       24179.96"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I rename 'ORDERDATE' value to 'Recency':"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>Recency</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>MonetaryValue</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CUSTOMERNAME</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AV Stores, Co.</th>\n",
       "      <td>195</td>\n",
       "      <td>51</td>\n",
       "      <td>157807.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alpha Cognac</th>\n",
       "      <td>64</td>\n",
       "      <td>20</td>\n",
       "      <td>70488.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amica Models &amp; Co.</th>\n",
       "      <td>264</td>\n",
       "      <td>26</td>\n",
       "      <td>94117.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anna's Decorations, Ltd</th>\n",
       "      <td>83</td>\n",
       "      <td>46</td>\n",
       "      <td>153996.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Atelier graphique</th>\n",
       "      <td>187</td>\n",
       "      <td>7</td>\n",
       "      <td>24179.96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Recency  Frequency  MonetaryValue\n",
       "CUSTOMERNAME                                              \n",
       "AV Stores, Co.               195         51      157807.81\n",
       "Alpha Cognac                  64         20       70488.44\n",
       "Amica Models & Co.           264         26       94117.26\n",
       "Anna's Decorations, Ltd       83         46      153996.13\n",
       "Atelier graphique            187          7       24179.96"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm_df.rename(columns={'ORDERDATE': 'Recency'}, inplace=True)\n",
    "rfm_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model: RFM scoring + K-Means clustering \n",
    "\n",
    "In this approach I use RFM scoring (assigned RFM score) and K-means clustering.\n",
    "\n",
    "**Transformation of variables:**\n",
    "\n",
    "The next step is to transform all three variables. The RFM method requires ordinal variables and this can be done in several ways,  I will use the percentiles of the distribution of the given variable. Scaling is not required.\n",
    "\n",
    "\n",
    "The labels will be values 1-4. When labeling the values of variables, it is important to remember that the higher label should mean the better situation of the person from the point of view of the seller. In the case of \"Recency\" - the smaller value of the variable, the higher label (clients who are active are prefers). In the case of \"Frequency\" - the greater value of the variable, the higher label (customers who buy more often and for large amounts are prefers)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = pd.qcut(rfm_df.Recency, 4, labels = list(range(0,4)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = pd.qcut(rfm_df.Frequency, 4, labels = list(range(0,4)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "m = pd.qcut(rfm_df.MonetaryValue, 4, labels = list(range(0,4)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>Recency</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>MonetaryValue</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CUSTOMERNAME</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AV Stores, Co.</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alpha Cognac</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amica Models &amp; Co.</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anna's Decorations, Ltd</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Atelier graphique</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vida Sport, Ltd</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vitachrome Inc.</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Volvo Model Replicas, Co</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Coast Collectables Co.</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>giftsbymail.co.uk</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>92 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Recency Frequency MonetaryValue\n",
       "CUSTOMERNAME                                               \n",
       "AV Stores, Co.                    2         3             3\n",
       "Alpha Cognac                      0         1             1\n",
       "Amica Models & Co.                3         1             2\n",
       "Anna's Decorations, Ltd           1         3             3\n",
       "Atelier graphique                 2         0             0\n",
       "...                             ...       ...           ...\n",
       "Vida Sport, Ltd                   3         2             2\n",
       "Vitachrome Inc.                   2         1             2\n",
       "Volvo Model Replicas, Co          2         0             1\n",
       "West Coast Collectables Co.       3         0             0\n",
       "giftsbymail.co.uk                 2         1             1\n",
       "\n",
       "[92 rows x 3 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm_df_cutted = pd.DataFrame({'Recency' : r, 'Frequency' : f, 'MonetaryValue' : m})\n",
    "rfm_df_cutted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfm_df_raw = rfm_df_cutted.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K-Means clustering model\n",
    "\n",
    "K-means is an unsupervised machine learning algorithm, which is used for data clustering. In k-means algorithm number of clusters K is predetermined and the algorithm iteratively assigns each data point to one of the K clusters based on the feature similarity.\n",
    "\n",
    "The optimal number of clusters I will found using the elbow method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "group = []\n",
    "for i in range(1, 15):\n",
    "    kmeans = KMeans(n_clusters=i)\n",
    "    kmeans.fit(rfm_df_raw)\n",
    "    group.append([i, kmeans.inertia_])\n",
    "groups = pd.DataFrame(group, columns = ['number_of_group', 'inertia'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
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       "      <th>1</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>76.595037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>61.403073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>47.539915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>39.174006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>32.320114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>29.945189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>25.533331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>22.123014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>19.483974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>17.904537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>16.106197</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    number_of_group     inertia\n",
       "0                 1  344.304348\n",
       "1                 2  159.730326\n",
       "2                 3  107.023375\n",
       "3                 4   76.595037\n",
       "4                 5   61.403073\n",
       "5                 6   47.539915\n",
       "6                 7   39.174006\n",
       "7                 8   32.320114\n",
       "8                 9   29.945189\n",
       "9                10   25.533331\n",
       "10               11   22.123014\n",
       "11               12   19.483974\n",
       "12               13   17.904537\n",
       "13               14   16.106197"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RWpzK+ee3trbm/DOHAq9b33jd+sbr1jdet77xuvVNPq9b3oJcVpfqBcBRwCdIGh/Kexza/femzP7ubdt6HLOpN58/Y8YMyst7flRubS1Zzr1PzaN+XBMHTarL6We3trbS0tKS088cCrxufeN16xuvW9943frG69Y3A33d2tvb9zj4lOuu1fEhhPdlulcBiDHuAJ4kuW26FJjQ423dfy/L7Gc3x/S83VrwmhuTFR6cGFiSJPVFrp+RmwRcAxzXvSGEUArMJulmvRs4PoSQPVJ4CvBsjHEl8BiwATg56/0jM++/a6CL729j6yoZUVXqxMCSJKlPcn1rdR5Jx+n3QwgfBtYBXwRGAZcBm4HPAj8KIXwDaAE+DXwcIMbYHkL4NvC1EMJKYBHwDeBF4Fc5/i77LZVK0dSQZqGdq5IkqQ9yPf3IDuAdJKsxXA88CNQDJ8QYF8UYXwZOB5pJphS5BLg4xnh11mm+DFwFfB+YC6SAM2KM2c/MDRrTGtO8sHID27Z35rsUSZI0yOS82SHGuJZk4t/d7X8IOHYP+ztJJgi+uP+ry72mhjSdO5IVHnLd8CBJkga3vM0jp0RzQ9Lw4HxykiSptwxyeZY0PJS5VJckSeo1g1yepVIpmhtqHZGTJEm9ZpArAM2NaZas3Ei7DQ+SJKkXDHIFoLm74WG505BIkqR9Z5ArAK82PBjkJEnSvjPIFYAxdZWMrC5zqS5JktQrBrkCkDQ8pF2qS5Ik9YpBrkA0NdSy5CUbHiRJ0r4zyBWIaY1pdtjwIEmSesEgVyCauhsevL0qSZL2kUGuQIxJJw0Pdq5KkqR9ZZArEKlUiubGtCs8SJKkfWaQKyDNDWkbHiRJ0j4zyBWQ5oZaduzoYpEND5IkaR8Y5ApIc0MdAAtteJAkSfvAIFdARqcrqK0p4zmfk5MkSfvAIFdAUqkUTQ1pFtq5KkmS9oFBrsBMyzQ8bN3Wke9SJElSgTPIFZimhu4VHjbkuxRJklTgDHIFprl7hQefk5MkSXthkCswo9MVpGvKDXKSJGmvDHIFJml4qHXNVUmStFcGuQLU3JBmqQ0PkiRpLwxyBai5Mc2OLmx4kCRJe2SQK0DdDQ/PeXtVkiTtgUGuAI2qteFBkiTtnUGuAKVSKZob0yw0yEmSpD0wyBWopobapOGh3YYHSZK0awa5AtXckDQ8LLLhQZIk7YZBrkBNa8w0PCxbm+dKJElSoTLIFaj6kRWkR5SzcNn6fJciSZIKlEGuQKVSKZob0nauSpKk3TLIFbDmhjTLbHiQJEm7YZArYM0NtezogueXe3tVkiS9nkGugDVnGh68vSpJknbFIFfARtVWUjeinAUu1SVJknbBIFfgmhrSLLBzVZIk7YJBrsBNa0zz4ssbabPhQZIk9WCQK3DdKzw8/6KjcpIk6bVKcv2BIYQRwFeBtwOjgWeAr8YYb8zs/zpw8S7eWhpj7MgccyFwETAeeBT4RIxxXg7Kz7mmhloAFi5bxxsOHJXnaiRJUiHJx4jc1cBbgQ8ChwG/Bm4IIZya2T8LuIokpO38yQpx5wP/CXwJaAEicGsIYWzuvkLujKqtpH5kuZ2rkiTpdXI6IhdCGAecA7w1xnhbZvPXQginARcAfwZmAr+LMa7czWm+AHwnxnht5pwXAAuBjwCXDGT9+dLkCg+SJGkXcj0itxk4E7i7x/YuoD6EkAYagad29eYQwgHANOCO7m0xxk7gHuDEgSi4EDQ3pFn28iYbHiRJ0mvkdEQuxrgRuCV7WwjhaOBU4BMko3EA54UQfgiUAXcCF8cYVwATM/uX9Tj1cmDOAJWdd82NaboyDQ8+JydJkrrlvNkhWwjhYOAG4AHgeyTPzQGsB95B8nzc14A7QwiHA1WZ/e09TtUOVPTms+fPn9/HqnNvS1snAHfc9wRb147ot/O2trb227mGE69b33jd+sbr1jdet77xuvVNPq9b3oJcCOFEkhD3AvCWGOP2EMJ3gZ/HGNdmDns8hDAfWAqcTdLYAFDe43TlwKbefP6MGTMoL+95msL1v7ffQntqBC0tLf1yvtbW1n4713Didesbr1vfeN36xuvWN163vhno69be3r7Hwae8zCMXQjgP+BPQCpwUY1wDEGPsygpxZLa9CKwGJgFLMpsn9DjlBF5/u3VIaW6oY6END5IkKUvOg1wI4X3A/wHXkYzEbczad0UI4ZEex08lmW/uyRjjKpJRuZOz9hcDJwB3DXz1+dPcUMuylzexZev2fJciSZIKRK6nH2kgmSPuDuCzwKgQQvfubcD1wMdDCFcA3yYZabsCeBD4Q+a4S4ErQwgxs/2fgZrMeYespkzDw6LlG2x4kCRJQO5H5M4haVg4laTTdEXWz40xxntJJgs+EngE+BXJ7dezYow7AGKMV5FMBnwJ8BDQDJweY3wlt18lt5ob0gDOJydJknbK9fQjVwJX7uWYW4Fb93LMpSQjc8NG/cgK6kdWsGCpQU6SJCXy0uygvml2hQdJkpTFIDeINDemeXGVDQ+SJClhkBtEmhtqd67wIEmSZJAbRF5teDDISZIkg9ygUjeyglG1FU4MLEmSAIPcoNPckOY5O1clSRIGuUGnqSHN8ldseJAkSQa5QWdaZoWHhTY8SJI07BnkBpmmhloAn5OTJEkGucGmbkQFo2srWLDUETlJkoY7g9wg1NSQZsGytfkuQ5Ik5ZlBbhBKVnjYbMODJEnDnEFuEOqeGNiGB0mShjeD3CC0c4UH55OTJGlYM8gNQukR5UnDg52rkiQNawa5Qaq5Me0UJJIkDXMGuUGquSFpeNjcZsODJEnDlUFukGrKPCf3vA0PkiQNWwa5QWpnw4O3VyVJGrYMcoNUekQ5o9OVdq5KkjSMGeQGseaGWkfkJEkaxgxyg1hzY5rlr9jwIEnScGWQG8ReXeHBUTlJkoYjg9wg9uoKD3auSpI0HBnkBrHamnLG1FU6MbAkScOUQW6Qa25I85xBTpKkYckgN8g1NdSywoYHSZKGJYPcIDetoQ6w4UGSpOHIIDfINTXUAjgxsCRJw5BBbpDrbnhYsMzOVUmShhuD3BDQ3JB2hQdJkoYhg9wQ0NyQZsUrm9lkw4MkScOKQW4I2LnCg6NykiQNKwa5IcCGB0mShieD3BBQW1PO2LpKn5OTJGmYMcgNEU0NaRbauSpJ0rBikBsipjWmWbF6M5u2bMt3KZIkKUcMckNE086GB0flJEkaLgxyQ0R356rPyUmSNHwY5IaIkdVljK2v4jmDnCRJw4ZBbghpbqh1LjlJkoaRklx/YAhhBPBV4O3AaOAZ4Ksxxhsz+6cA3wZOBNqAq4Evxhg7ss5xIXARMB54FPhEjHFe7r5FYWpuSDP38RVs2rKNmqqyfJcjSZIGWD5G5K4G3gp8EDgM+DVwQwjh1BBCGfBHoAs4FvgQcAHwb91vDiGcD/wn8CWgBYjArSGEsbn7CoXJ5+QkSRpechrkQgjjgHOAT8UYb4sxLogxfg24kySwvROYDLw/xjg/M0r3OeCTIYTKzGm+AHwnxnhtjPGpzPs2AB/J5XcpRE07g5ydq5IkDQe5HpHbDJwJ3N1jexdQD5wAPBpjXJu1706gGpgdQjgAmAbc0b0zxtgJ3ENyK3ZYG1ldxgH1VY7ISZI0TOT0GbkY40bgluxtIYSjgVOBTwCnA8t6vG155rWR5Jk5dnPMnH4tdpBqbki75qokScNEzpsdsoUQDgZuAB4AvgecDbzc47D2zGsFUNVjW/YxFb357Pnz5/eq1sGiIrWJl9Zs4Z6586gq3/OAa2tra46qGlq8bn3jdesbr1vfeN36xuvWN/m8bv0W5EIIRTHGHb04/kSSEPcC8JYY4/YQQhtQ3uPQ7r838eqI3K6O2dSbemfMmEF5ec/TDH7FI17m9sfuY8ToyRx20O77P1pbW2lpaclhZUOD161vvG5943XrG69b33jd+magr1t7e/seB5/2OciFEFLAucDJJMEpldlVRPIM29Ek04Hsy7nOA34E3AW8I3PLFWApcHiPwydkXpcBS7K2PdHjmJ63W4el7oaH55au22OQkyRJg19vmh2+AvwUeBdJ5+lbgTcD7wP+imQakb0KIbwP+D/gOpKRuI1Zu+8GDgshpLO2nQJsBB6OMa4imW7k5KzzFZM0SdzVi+8yZI2oShoeXHNVkqShrze3Vt8PXAv8LfCvwOQY4/khhCOAm0gC1h6FEBqAq0i6Tj8LjAohdO/eBvwG+HfgFyGEfyaZiuTrwGUxxm2Z4y4FrgwhROBB4J+Bmsx5BTQ32vAgSdJw0JsRuYnANTHGLqCVZMJeYowPAd8A/n4fznEOScPCqSSdpiuyfm6MMW4Fzsgc290A8T2SlSDIfN5VJJMBXwI8BDQDp8cYX+nFdxnSmhvSvLRmCxs2b9v7wZIkadDqzYjcZpL53gCeAw4MIVTGGNtIlsn6yt5OEGO8ErhyL8csILllu6djLiUZmdMuNDfUArBw2ToOd8ELSZKGrN6MyD0InJ/5fQHJrdBTM38fzOunBFGeuFSXJEnDQ2+C3NeAc0IIt8YYtwM/AX4aQvgtydqntw5Egeq9mqoyxo1yhQdJkoa6fQ5yMca7SVZPuCaz6ZPA9STPqP2CZGUGFYimhrRrrkqSNMT1akLgGONjwGOZ39uBDw9EUdp/0xrS/OWx5WzYvI2R1WX5LkeSJA2APQa5EMLfA7+LMa7K/L5HMcYf9Vtl2i/Zz8nNtuFBkqQhaW8jcj8AjgdWZX7fky6S1RpUAJqyOlcNcpIkDU17C3JTSeZ46/5dg0RNVRnjR1Xb8CBJ0hC2xyAXY3wh68+TgJt2NfFuCGEicB5J96oKRFNDLc8uWZvvMiRJ0gDZ2zNy3V2tKeB/gZNCCHN3cegpwL9hkCsozQ1p7n1sOes3tVNbU57vciRJUj/b2/QjdwPbSSb/TZEsTL99Fz8/Bh4fuDLVF82NScPDQqchkSRpSNrbM3IfAt5NEuK+DFwNLOlxTCewDvh5fxen/dOU3bk63YYHSZKGmr09I/c0yS1TQghzgK/FGBfmojDtv5rKUhseJEkawnqzRNfRJA0PGkSaG9MGOUmShqjeBLmtwOqBKkQDo7mhllVr21i/qT3fpUiSpH7WmyW6Pg9cEUI4AJgPrOx5QIzx+f4qTP0ju+HB5+QkSRpaehPkfggUA98lWcVhV4r3uyL1q6aJSZB7btlag5wkSUNMb4LcBwesCg2Y6spSxo+udgoSSZKGoH0OcjHGHw9kIRo40xrSPP3CmnyXIUmS+llvRuS6V3p4F3A6MAH4BHAM8ECMMfZ/eeoPTQ1p7n70RVd4kCRpiNnnrtUQwkiSlR5+BpxBEuZGAB8AHgghHDogFWq/NTfWAjgNiSRJQ0xvph/5BjAdOA6YTLLaAyQrPzwPXNK/pam/dDc8GOQkSRpaehPk3gF8McZ4H1ldqzHG1SQh75h+rk39pLqylAmjq1mw1CAnSdJQ0psgNwJYtpt9m4Dq/S9HA6W5Ic0CO1clSRpSehPkngDev5t9fw08vv/laKA0N6Z5ZV0b6za6woMkSUNFb4LcvwPvDCH8Cfgoye3V00II3wUuAL45APWpnzQ3+JycJElDzT4HuRjj74BzgYOA/yFpdvgmyWjcB2OMNwxIheoXB05MOlcXGuQkSRoyejWPXIzxOuC6EMJBwGhgHfBMjHHHQBSn/lNdWcrEMdWOyEmSNIT0Ksh1izE+Czzbz7VogDU1pHnq+dX5LkOSJPWTfQ5yIYRRwOXA20g6VFM9DumKMfYpGCo3mhvS3P3IizY8SJI0RPQmeH0beDtwHbAU8HbqINPc+GrDQ88ULkmSBp/eBLmzgM/FGK8YqGI0sJomvrpU17T6PBcjSZL2W2+mH+kCnh6oQjTwqioyDQ+u8CBJ0pDQmyB3E8nzcRrEmhvqnIJEkqQhoje3Vq8Hrso0PfwF2NLzgBjjj/qrMA2M5sZa7npkGZvaOvNdiiRJ2k+9CXK/yry+J/PTUxdgkCtwTZkVHpav2ZbnSiRJ0v7qTZCbOmBVKGeaJtZSWlJE68LNvLeri1TK/lVJkgarPQa5EMJPgK/GGBcAl+zlXF3AB/qrMA2MqopS3n/Wwfzwxie57cElvOmoyfkuSZIk9ZKzTroAACAASURBVNHeRuROAEZmfj+RJKztzp72qYC87YQm/vzAAr7/myeY0TSa8aOr812SJEnqgz0GuRjj1Kzfpwx4NcqJoqIUZx9Tx/dvXc2l17byzQuPp7i4Nw3MkiSpEPhf72GqtqqEC99xKPGFtVx3+3P5LkeSJPWBQW4YO+HwiZzc0sDP/xSJL6zJdzmSJKmX8rrIfQjh88BbYozHZ237OnDxLg4vjTF2ZI65ELgIGA88CnwixjgvByUPOR99+yyefH41l177MFd8+mQqy/P6PwlJktQLeRuRCyF8HPiPXeyaBVxFEtJ2/mSFuPOB/wS+BLQAEbg1hDA2B2UPOdWVpXz63NmsXL2ZH944P9/lSJKkXsj58EsIYQLwPeAUkhDW00zgdzHGlbs5xReA78QYr82c7wJgIfAR9j5FinZhRtNozjm5mV/dsYA5Bx/AUTPG57skSZK0D/IxItcCbCQZeXsge0cIIQ00Ak/t6o0hhAOAacAd3dtijJ3APSTTo6iPzjvjYA6cUMuV1z3K2g1b812OJEnaBzkPcjHG38UY3xdjfH4Xu2dmXs8LITwXQnghhPDjEEL3ENHEzOuyHu9bThIA1UelJUVcdN5strZ3cOV1j9LV5bSAkiQVukJ7sn1G5nU98A6S5+O+BtwZQjgcqMrsb+/xvnagojcfNH++z4O1tra+bttph47k5taX+N4v7mbOtJo8VFX4dnXdtHdet77xuvWN161vvG59k8/rVmhB7rvAz2OMazN/Px5CmA8sBc7m1Wfqynu8rxzY1JsPmjFjBuXlPU8zfLS2ttLS0vK67bNnd/HS5vv506Oreeups2kYOyIP1RWu3V037ZnXrW+8bn3jdesbr1vfDPR1a29v3+PgU0HNIxdj7MoKcd3bXgRWA5OAJZnNE3q8dQKvv92qPkilUnzyPYdTXlrMpde00tG5I98lSZKk3SioIBdCuCKE8EiPbVOB0cCTMcZVJKNyJ2ftLyZZE/auHJY6pNWPrOAf3nUoC5at52d/3FVjsSRJKgSFdmv1euDjIYQrgG+TjLRdATwI/CFzzKXAlSGEmNn+z0ANydxz6ifHzprAm46cxC9vf5aW6WM5ZOqofJckSZJ6KKgRuRjjvcBbgSOBR4BfAa3AWTHGHZljriKZDPgS4CGgGTg9xvhKXooewj741zMYW1/FZdc+zJat2/NdjiRJ6iGvI3IxxvN3se1W4Na9vO9SkpE5DaCqilIuel8Ln/v2PXz/N0/wqffOzndJkiQpS0GNyKnwTJ9Sz7veeBC3z1vKXx5bnu9yJElSFoOc9uq9bwpMa0zznV8+yur1bfkuR5IkZRjktFclxUVcdF4L2zp2cPnPH2HHDld9kCSpEBjktE8mjqnhg2+bwaPPruL39+5qdTVJkpRrBjntszcfPZkjDxnH1X94ihdWbsh3OZIkDXsGOe2zVCrFP777MKorSrn0mla2d3TmuyRJkoY1g5x6JT2inH98z2EsWr6Bn978TL7LkSRpWDPIqdeOPGQcZx4zhRvuWsDjC1bluxxJkoYtg5z65O//6g1MGF3Nf//sETa1ueqDJEn5YJBTn1SUl3DReS2s3bCV7/7q8XyXI0nSsGSQU59Na6zj3NMDdz2yjDsfXpbvciRJGnYMctov7zx1GgdPqee7v3qMl9duyXc5kiQNKwY57Zfi4iI+/b7Z7Ojq4r9/9jCdrvogSVLOGOS038aNqubDZ89i/sLV/PauBfkuR5KkYcMgp35x2pxGjp01nv+7+Wmef3F9vsuRJGlYMMipX6RSKS5852GMrC7j0mtbad/uqg+SJA00g5z6zcjqMj75ntksWbmRn/zhqXyXI0nSkGeQU7+aPX0sf3XCgdx4z/M8HF/OdzmSJA1pBjn1uw+85RAaDxjBFT9/mA2bt+W7HEmShiyDnPpdeWkxnzmvhQ2bt/GdXz5KV5dTkkiSNBAMchoQB06s5W/OOJi5j6/gzw8tzXc5kiQNSQY5DZizT25mRtMovnfD46xcvTnf5UiSNOQY5DRgiotS/NO5sylKpbjs2ofp7NyR75IkSRpSDHIaUGPrqvjoOw7l6cVr+OUdz+W7HEmShhSDnAbcybMbOPHwifzs1sizS9bmuxxJkoYMg5xy4mPnzKJuRDmXXdvK1vaOfJcjSdKQYJBTTtRUlfFP75vN8lc286PfP5nvciRJGhIMcsqZWc1jOPukZm6eu5h5T63MdzmSJA16Bjnl1N+eOZ0p40dy5S8eZd3G9nyXI0nSoGaQU06VliSrPmzeup3/uc5VHyRJ2h8GOeXc5PEjOf8th/DgUyu59f4X8l2OJEmDlkFOefHW4w/ksGlj+MGN81m+alO+y5EkaVAyyCkviopSfOrcwyktLuLSa1vpcNUHSZJ6zSCnvBlVW8k/vOswnl2yjl/86dl8lyNJ0qBjkFNeHXfoBE49opHrbos8s3hNvsuRJGlQMcgp7z7y9pmMrqvi0mtb2bJ1e77LkSRp0DDIKe+qKkr59LmzeXnNFn7w2/n5LkeSpEHDIKeC8IYDR/GOU6fxpweXcOv9i/NdjiRJg4JBTgXj3NOnc9i0MXz7+sf44Y3z6dzhZMGSJO2JQU4Fo7SkiK986GjeetxUfnPXQr76w/vZ1OYzc5Ik7U5JPj88hPB54C0xxuOztk0Bvg2cCLQBVwNfjDF2ZB1zIXARMB54FPhEjHFe7irXQCkpLuIj58xi8viRfPfXj/OZK+7mXy44ioljavJdmiRJBSdvI3IhhI8D/9FjWxnwR6ALOBb4EHAB8G9Zx5wP/CfwJaAFiMCtIYSxOSlcOXHGMVP4948ey8Yt27joirt5OL6c75IkSSo4OQ9yIYQJIYTfkYSx2GP3O4HJwPtjjPNjjDcCnwM+GUKozBzzBeA7McZrY4xPkQS9DcBHcvMNlCszmkZz2adOYky6kn+76j5uvHshXV0+NydJUrd8jMi1ABuBWcADPfadADwaY1ybte1OoBqYHUI4AJgG3NG9M8bYCdxDcitWQ8wB9VX85z+ewFEzxnPVb+fzP9c9yvaOznyXJUlSQcj5M3Ixxt8BvwMIIfTcPRFY1mPb8sxrI8kzc+zmmDn9V6UKSWV5CRe/fw7X/vEZfvGnZ1n28iY+f/4c6kZU5Ls0SZLyKq/NDrtQBfR8GKo981qR2Z+9LfuYXv1Xff58J55tbW3Ndwm9cvAYeOdx9fzm/rX8w3/exrknjmJ8fVnO6xhs161QeN36xuvWN163vvG69U0+r1uhBbk2oLzHtu6/N/HqiNyujtnUmw+aMWMG5eU9TzN8tLa20tLSku8yeq2lBY47ch3/8aMHuPrPq/nUew/n+EMn5uzzB+t1yzevW9943frG69Y3Xre+Gejr1t7evsfBp0KbR24pMKHHtu6/lwFLemzLPqbn7VYNUc0NaS77p5OYOn4k3/zJQ1xzyzPscPJgSdIwVGhB7m7gsBBCOmvbKSTNEQ/HGFeRdLqe3L0zhFBM0iRxVw7rVJ7Vjajgax8/jtPmNPLzP0W+8ZN5tLV37P2NkiQNIYUW5H4DvAj8IoQwK4TwV8DXgctijNsyx1wKfCqEcH4I4RDgB0ANcFVeKlbelJYU88n3HM4Fb5vBA/NX8Nn/uYeX1mzJd1mSJOVMQQW5GONW4IzMnw8A38v8fDXrmKtIJgO+BHgIaAZOjzG+kttqVQhSqRRnn9TEVz54DKvWbuHTl9/Fk8+vzndZkiTlRF6bHWKM5+9i2wLgzXt536UkI3MSALOnj+XST53EJT+8ny999y989JxZvPnoKfkuS5KkAVVQI3LS/pg4poZvffIkZjWP4dvXP8b3fv04nZ078l2WJEkDxiCnIaWmspQvX3AUZ5/UxO//soivXHUfG7ds2/sbJUkahAxyGnKKi4u44G0z+OR7DufJ59dw0eV3s2TlhnyXJUlSvzPIach645GT+NrHjqNtWwefufIeHnxqZb5LkiSpXxnkNKQdPLWeyz55EhPGVPPvP3qAX/75Obq6nDxYkjQ0GOQ05I2pq+QbFx7PcbMm8OM/PMVl1z5M+/bOfJclSdJ+M8hpWKgoK+Gzf3sEf3PmdO58eBmf/869rF7ftvc3SpJUwAxyGjZSqRTveWPgC+cfydKXNvLpy+/i2SVr812WJEl9ZpDTsHPMzPH81ydOpKSkmIu/cy93ti7Nd0mSJPWJQU7D0pTxI7nskydy0KQ6Lr32Ya7+/ZN07rAJQpI0uBjkNGzV1pRzyUeO5YxjpvCrOxbwH//7AFu2bs93WZIk7TODnIa10pIiLnznoXz0nFm0PvMyn7nybpa/sinfZUmStE8MchLwluOm8tUPH8O6je1cdPndPPbsqnyXJEnSXhnkpIxDp43h0k+eRN3ICr581X38/t7nnTxYklTQDHJSlvGjq/nWJ07giOkH8L0bnuA7v3yM7R078l2WJEm7ZJCTeqiqKOWLf3ck7zptGrfe/wL/8r25rN/Unu+yJEl6HYOctAtFRSnef9YhXHReC88tWcunL7+LRcvX57ssSZJewyAn7cHJsxv4+oXH09HZxWf/5x7ufWqDo3OSpIJhkJP24qBJdfz3P53EwVPque3RDZz/1T9y6TWtPLVotc0QkqS8Ksl3AdJgUD+ygq9+5Fhu/vP9LFlfxZ8fWsqdDy9jyviRnHnsFE6e3UBVRWm+y5QkDTMGOakXxtaWcuaps/jAWYdw1yPLuGnuYv7frx7n6t8/ycmzGznz2ClMnVCb7zIlScOEQU7qg4ryEt589BROP2oyzy5Zy01zF3P7vCXcfN9iDp5Sz5nHTuG4WRMoKy3Od6mSpCHMICfth1QqRZhcT5hczwf/ekYS5uYu5rJrH+YHv53PG+dM4oxjpjB+dHW+S5UkDUEGOamfjKgq4+yTmnnbCU08vmAVN81dzG/uXsiv71zA7OljOeuYKRxxyDiKi1L5LlWSNEQY5KR+VlSU4rCDxnLYQWNZvb6NP97/Arfc/wL//r8PMjpdyRlHT+b0oyZTN7Ii36VKkgY5g5w0gEbVVnLum6fz7jcexINPreSmuYv56S3P8LM/Ro6eOZ6zjp3CzKbRpFKO0kmSes8gJ+VAcXERx8ycwDEzJ7B81SZuvi9pjvjLY8tpGFvDmcdM4dQ5k6ipdAoTSdK+M8hJOTZhTA0XvG0Gf3PmwfzlsRe5ae5irvrtfH5809OcdPhEzjx2CtMa6/JdpiRpEDDISXlSXlrMqUdM4tQjJrFw2Tpuvm8xdz28jD89uITmxjRnHTOFEw6fSEWZ/5hKknbN/0JIBaCpIc0/vOsw/u6tb+DO1qX8Ye5irrzuUX74uyc57YhGzjhmCo0HjMh3mZKkAmOQkwpIdWUpbzn+QM46bipPPr+am+cu5qa5i7jxnueZ1TyaM4+dwtEzxlNS7DLJkiSDnFSQUqkUM5pGM6NpNGs3buW2B5dwy32L+eZPHqJuRDmnHz2ZNx81hTF1lfkuVZKURwY5qcDVjajgXacdxDmnTOPhZ17iprmLue62Z7n+tmeZc8g4zjp2KocdNIYiJxqWpGHHICcNEsVFKeYcMo45h4zjpTVbuPX+xfzpgSU88ORKxo+q5k1HTeKUlkZGpx2lk6ThwiAnDUIH1Ffx/rMO4dzTp3PfE8u5+b7F/OSmp/m/m5/msGljOG3OJI6eOZ7y0uJ8lypJGkAGOWkQKy0p4sTDGzjx8AZWrt7Mnx9ayu3zlvCta1qpqijhhMMmctoRk5g+pc7VIyRpCDLISUPEuFHVvO/N03nvmwJPPr+a2+Yt4c6Hl3Hr/S8wYXQ1p81Jbr3aICFJQ4dBThpiiopSzGwezczm0Xzk7TOZ+/gKbn9oCf9389P89JanObT71uuMcU42LEmDnP8Wl4awqopS3njkJN545KRXb70+tJRLr2mlsjxz63VOIwdPqffWqyQNQgY5aZh4za3XRau5fd4S7n5kGX984AXGj67mtDmNnNLSyNi6qnyXKknaRwY5aZgpKkoxs2k0M5tG85G3z2Lu48u5fd5SfnrzM1xzyzMc2jyGU+c0cszM8d56laQCV3D/lg4hBOCZXez6UIzxByGEw4DLgTnAauDKGOO3clmjNFRUlpdw2pxJnDYnufV6R+sybp+3hMuufZjK8hKOP3QCp82ZxCFTvfUqSYWo4IIcMAvYAIQe29eHEEYDtwE3AB8DjgT+XwhhfYzxqtyWKQ0t40ZVc+7pgfe88SCeWrSa2+ct5Z5HX+RPDy5Jbr0e0cgpR3jrVZIKSSEGuZnA0zHGlT13hBD+CdgGfCzG2AE8HUJoBi4GDHJSPygqenWd1w+/fSb3PZG59XrLM1xz6zPMah7NqUdM4tiZ46koL8R/hUjS8FGI/xaeBTy1m30nAPdkQly3O4EvhRAmxhhfHOjipOGksryEU4+YxKlHTOKlNVu4ozWZcPi/f/Yw3/11MccfOtFbr5KUR4UY5GYCz4UQ/gI0A88Cl8QY/whM5PUhb3nmtREwyEkD5ID6Kt77pu5br2u4fd4S7n0sc+t1VDWnzmnk1JZGxtZ761WSciXV1dWV7xp2CiFUAxuBB0lul24E/gb4JHA68F3guhjjF7LecyCwEDglxnjn3j6jtbV1CrCov2uXhqNtHTt4emkbjz6/hUUvtQMw5YByDptaxSGTKikrKcpzhZI0ZExtaWlZ3HNjQY3IxRg3hxBGAttijNsym1tDCIcAnwXagPIeb+v+e1NvPmvGjBmUl/c81fDR2tpKS0tLvssYdLxur3fMUcnryztvvS7lN/ev5dZHNnDcrGTC4ba1i5lzxBH5LXQQ8n9vfeN16xuvW98M9HVrb29n/vz5u91fUEEOIMa4q0D2OPBXwPPAhB77uv9eNpB1SdqzsfVVvOdNgXe/5tbrcm6bt4Ty0hRveOQ+Dp5Sz8GT65k2KU1VRWm+S5akQa+gglwI4Rjgj8CpMcZ5WbvmAE8C84ALQwglWQ0PpwDP7qrLVVLupVIp3nDgKN5w4Cg+fPZMHnhyJXc88Ayr12/l2lufoasLilIwefxIpk+pT8LdlHoOqK+yYUKSeqmgghzwEMnza1eFEC4kmfD3Y8CxwFEkzQyfBX4UQvgG0AJ8Gvh4fsqVtCcV5SWcNLuBmq6XaGlpYXPbduKStTyzeA3PLF7Dna3LuHnuYgDSI8qZPrmOg6fUM31KPc0NacpKi/P7BSSpwBVUkIsxbg8hnAF8A/gVkAZagTfFGB8BCCGcDlwJPAysBC6OMV6dn4ol9UZ1ZSmzw1hmh7EAdO7oYulLG3k6E+yeXryG++cng+slxSmaGtJMn1yfCXd1jKqtzGf5klRwCirIAcQYlwPv38P+h0hG6CQNcsVFKaaMH8mU8SM585gpAKzb2E58IQl1z7ywlpvnLuK3dy8EYGxd5c7bsdMn1zN1wkiKi+2MlTR8FVyQkzS8pUeUc9SM8Rw1YzwA2zt2sGj5ep7OjNg9+fxq7n4kmTKyvKyYgxrrmD4luSUbJtczsrosn+VLUk4Z5CQVtNKSIg6aVMdBk+r46xObAFi1ti25FZsZufvVHQvYsSOZE7NhbA3TJ9dnRu7qaBg7gqIimygkDU0GOUmDzpi6SsbUTeSEwycCsLW9g+eWrdv5nN0DT67gtnlLgOS5vOmT65Jg59QnkoYYg5ykQa+ivISZTaOZ2TQagK6uLpa/spmnF63hmcyoXestzwDJ1CdTxtfuvB3b3Jhm/Ogaih21kzQIGeQkDTmpVIqJY2qYOKaGNx45CYBNW7YRl6zl6cVriIvXckfrUm7KTH1SXlbM5HEjmDqhlqkTajlwQi2Tx49w5E5SwTPISRoWaqrKaJl+AC3TDwCSqU+WrNzAwmXrWbR8PYuWb+Dex5Zz6/0v7HzP+FHVTJ04cme4mzJhJGPSlU5cLKlgGOQkDUvFRamdI3Ddurq6WLWujUUvrmfRig1JwHtxA3MfX7HzmJrK0sz7Ru58nTRuBKUlTl4sKfcMcpKUkUqlGFtXxdi6qp3TnwBs2bqdF1Zs5Pnl3aN367nl/hfYtr0TSEJh4wEjmDJhJAdmhbzamvJ8fRVJw4RBTpL2oqqilIOn1nPw1Pqd2zp3dLHilU0senEDi1as5/kX1/P4c69wZ+uyncfUj6xg6oSRHDixlqnja5k6caSNFZL6lUFOkvqguChFw9gRNIwdsXMaFID1m9p3PnP3/PL1LF6+gUefXUVnZp67stJipox/tbFi6oRkZQsbKyT1hUFOkvpRbU05hx00lsMOGrtz2/aOTpas3Mii5Rt2hry/7KGxYur4kUydWMuOrq58fAVJg4hBTpIGWGlJMU0NaZoa0ju3dTdWLM6M3HUHvOzGCoDqG16iuqqMmopSqitLqakqpboi81r52t9rKl/7Wl5abIetNMQZ5CQpD7IbK458w7id27sbKxatWM9TcREj0qPZ3LadTW3b2dy2neWrNu38feu2zj1+RklxiprKMqorSzKvuw58r9mWCYrVlaWUFBcN9GWQtJ8McpJUQLIbKw4oX0NLy6zdHru9Ywdbtr4a8ja1bWfzlu1s2rqdTVu2sbltO5u3duz8feOWbaxcvZnNW7ezacv2nc/t7U5leXFmxK/sdcGvbkQ5Y+qqGFtXyZh0FfW1FTZxSHlgkJOkQaq0pIjamvI+TXPS1dVF+7bO14bAzOumtm1sbuvIvL66/eW1W9i0PAmBbe0drzlfUVGK0bUVjKmrYkxdJWPrqhiTzrzWVTImXUlFuf/Jkfqb/1RJ0jCUSqWoKC+horyE0enKXr+/rb2DVWu38PLaNlata2PV2i2sWtvGy2u38OTzq7l7/Yvs6DHiN6KqjLH1r4a8V0NfMqpXW1PmM31SLxnkJEm9VllewqRxI5k0buQu93d27mD1hq2sWtv2msD38totLHt5E4/El1/3jF9ZSVEyetc9mlf/2lG9UbWVlJb43J6UzSAnSep3xcVFO5s5YNTr9nd1dSW3a9ds2RnwktDXxqp1W5j39AbWbWx/zXtSKagbUZGM4O18Pq+SMVmBr7rS+fg0vBjkJEk5l0qlGFFVxoiqstdMy5Jt2/ZOXlnXtvOWbXbgW7B0Hfc9sYKOzh2veU9VRQlVZTDhwb+QriknPbKcdE05dSMqqOv+fWQFtdVlFNuVqyHAICdJKkhlpcVMGFPDhDE1u9y/Y0cX6za1v3rrNnMbd+GSlWzv2MFzy9axbuNW2tpfP01LKgUjq8uoG1Gxx8CXrilnZHUZRXbkqkAZ5CRJg1JRUYr6kRXUj6wgTH51e2vrdlpaWnb+vbW9g3Wb2lm7oZ11m7aydmP37+2s3bCVdZvaWb5oM+s2bGVbx45dfk5td+jbGfjKSY+ooG5E+WuCX01lqQ0byimDnCRpSKsoL2FceQnjRlXv8biuri7a2jtYu7GddRvbWbtx6+sC39qN7SxZsYF1m9rp6Hz9PHwlxanMCF9FVuBLRvrSI8qprSnbeUu5psrVN7T/DHKSJJE8t1dVUUpVRSkTd3M7t1t3s8aeAt+aDVt5/sV1rNu07XVTsXQrLSnKBLtk4uURVaWvCXojqsoYUf3q9prKMkZUGwD1KoOcJEm9lN2s0XjAiD0eu2NHFxu3bGPtxnY2bG5n45Zk5Y2NW7azcfM2Nm7ZxqbMyhsrXtnMs1vWsXHLNrbv4jZvtyQAdoe/rKC3izA4sjoTAKtKKS8zAA41BjlJkgZQUVGqTytwtG/vfDXobUmC3sZMANwZBDPbVq7ewnNL17Fx87ZdPufXrWcArKnMBL2qMtat3sDSTQsoLyuhoqyYirJiystKKC9Nfq8oL9m5raKs2LV4C4RBTpKkAlReWkx5urLXK2+0b+98bdDbnB3+Xt2+act2XlqzhQXLXg2Adzzx5D5/Tklx6nWhL/m9hPKyYsozv1f0+H1fjvXW8b4zyEmSNISUlxZTXpushNEbD857iDfMOJSt2zpo39ZJ+/ZOtrZ3snVbB1u3ddKeeX3t78mxybbk781t21m9vu0129q3d9K168cEdymVIjMSmAS9ysxoYGVmWbnKzE9FWTGVFSVUlpXsdd9Qva1skJMkSRQXpaiuLB2Q1TG6urpo396ZFfo6XhP0egbF7IDY/Xvb1g62tHewZsNW2to7aGvvpK2943WTQu/O/2/v7qOsqs47jn9HGAe0GlFrFCLS1uZZJmJdoI1RUXxppEYTFUlpJMYgxmAidUUSrS+ACsHa1a5YTXyJNVDUlWgEFd+rgtZYG8WYill5jNYoIBFfEkHehoHpH88+ejmZ18vMPfdkfp+1Zp05+55z7jOby8xz9j5774YGGLB9fwY2RYI4cED/tF+R/DX1ayMprEggc6/Vw5JxSuRERESkVzU0NKSu0/58pIevvallCxuaW1Jy18KGjS1bJXobmqNs3cYWNmRl2X5zC79bs4GVb394zobmli63Hvbv18Do4TtRMW1hzSmRExERkdJq7L8djf1j8EZPaG1tjRbA5iwx3PxBkrhVotjcwvoNLQxqfK9H3rdaSuREREREkoaGhhih29SfQR3PLAPAkiVLej+oDhTfuSsiIiIiVVEiJyIiIlJSSuRERERESkqJnIiIiEhJKZETERERKSklciIiIiIlpUROREREpKSUyImIiIiUlBI5ERERkZJSIiciIiJSUqVcosvMtgOmA5OAQcCTwDnu/nKhgYmIiIjUUFlb5KYBk4GzgEOAFuAhMxtQaFQiIiIiNVS6RM7MmoDzgRnufr+7/y8wHtgTGFdocCIiIiI1VLpEDjgQ+BNgUVbg7quB54AjigpKREREpNbK+IzckLRdnit/A9i7C+f3A2hubu7JmEpp48aNRYdQSqq36qjeqqN6q47qrTqqt+r0Zr1V5Cv92nq9jIncDmmbr7WNQFeekdsL4KWXXurJmEpp6dKlRYdQSqq36qjeqqN6q47qrTqqt+rUqN72Al7JF5YxkVuftk1AZbNaE/B+t6o/DgAADC9JREFUF85/BhgFrAQ292xoIiIiIj2qH5HEPdPWi2VM5Jal7WDAK8oHAy92dvLIkSM3EtOViIiIiJTBH7TEZco42OEXwGpgdFZgZjsDI4DHC4pJREREpOYaWltbi46h28xsFvA1YCLwKnAlsC+wv7trFIOIiIj0CWXsWoWYELgfcCOwI/BfwBglcSIiItKXlLJFTkRERETK+YyciIiIiKBETkRERKS0lMiJiIiIlFRZBztIFcxsJ+By4GRgd+BXwOXufk+hgZWImX2cWNf3PHe/qeh46p2ZnQ5cCPw5MQ/SDHe/o9io6puZbQ9cBpwGDAKeBy5w96cKDayOmdk/Ap9198MryoYB1xJrcK8H5gAXu3tLETHWo3bqbQQxE8RBwCZgMTDV3Ze1eZE+qK16y71+MTATaKzF500tcn3LHOAEYBJwIDAfWGBmRxcZVFmYWSNwKzFSWjphZhOAm4EbgP2B24AfmdlhhQZW/6YTUyudRcyP+SvgQTMb0uFZfZSZnQPMypVtDzwMtAKHEnV5JpEgC+3W297Ao8Ta5YcDnwX2AB42s4E1D7IOtVVvudcPBmbULCDUItdnmNmewCnACe7+SCr+jpkdQ/yCe6yw4MrjMmBN0UGUgZk1AFcA17j71al4lpmNAo4GflpYcPXv88At7v4QgJmdT9x8HQbcXmRg9cTMBhM3CUex9So/AKcC+wCfcvffAUvN7ALgajOb6e7r6aM6qbfTiNbLSVlLUmpVf51I7P6zhqHWlU7qLTtmR+Jm/wni91xNqEWu71gL/C3xAavUCuxa+3DKxcyOAM4Gvlx0LCVhwDCiFe4D7j7G3a8oJKLyWAWcYGbDzKwfkcRtBH5ebFh1ZyRxY3UA8D+510YBz6ckLrOYaE0fUZPo6ldH9XYn8He57sBsjrK+/neio3rLXA28AMyrVVCgFrk+w93XAA9WlpnZIcRdw5RCgioJM9uF+I95rrsvM7OiQyqDj6dtk5ndBxxMrMIy090XFhdWKZxLtLy9CmwGtgDj3P3XhUZVZ9LnaCFAG/8nhwDLc2VvpO3evRtZfeuo3tJnLP85u4hopevTvTadfN4ws5OB44HhwIm1jE0tcn2Ume0HLCDuLG4oOJx6dx3w3+5+W6dHSmbntJ0H/AT4DPAQcLeZ/U1hUZXD/sR60icBhxDPGc5LD6FL1+xAtGJWyvYH1DiW0jKzbwKTicE2bxUdT71K3a43AhPd/Z1av79a5Pqg1E24AHiNGHmzqeCQ6paZfYnophledCwlky2X96/u/sP0/fNmdhAwlT78rE1HzGwokfwe5+6LUvGzZvZJYsT5CYUFVy7rgaZcWbb/fo1jKR0z2w6YDXybmNngmoJDqlvpeeC5wO3u/mBnx/cGtcj1MWZ2GvFHdAlwpLu/W3BI9W4i8FFgmZm9b2bZH4FrzezFAuOqd1m31gu58qXAn9U4ljL5a6AReCZX/jTwl7UPp7SWAYNzZdl+vstVKpjZAKJrfyrwdXefXnBI9W4ocCzwlYq/Eden135vZhf1dgBK5PoQM/sicbd/O9ESpxGYnZsA7EdM15J9QYxgPb6ooErg58SDwQfnyocDL9c+nNLIkowDcuUHAC/VOJYyewI4MD3fmjmK+Ew+V0xI9S+1xN1JtPyOdffvFxxSGawgbrIO4MO/EdPSayP5MKnrNepa7SPM7GPAD4BFRHP5bhUPbDarZa5t7r4iX5bq7S13f632EZWDu683s6uAS83sDaJFaTxwHKBn5Nr3M+BJ4GYzm0wkdqcTd/yjigysZO4iJmT9sZl9i5iKZDbR1d/c4Zl923nEDepk4Ok0bVXmvb48bUt70gjfrW5OzWxV+vYVTQgsPekU4gHgo4nRWysrvrSyg/Q4d59J3JleDvwS+AJxl/9ooYHVMXffAnyOmJR1DtF6dAxwrFZ26Dp33wCMSbvZgK4biM+itG9C2l7H1n8jVhJzzEkdamhtbe38KBERERGpO2qRExERESkpJXIiIiIiJaVETkRERKSklMiJiIiIlJQSOREREZGSUiInIiIiUlKaEFhE/iiY2W+AJ919QieHFs7MhgD/ARxKrAv6KXf/dbFRiUgZKZETEam9bwKjibV8lwOvFhqNiJSWEjkRkdrbFfitu88tOhARKTet7CAiPSJ1bc4BGoEziGTlWeA8d19iZjOA6UBj5fqDZrYceMTdz0j7rcDXgYOAscAW4FZgKnAJMAkYADwCnO3u71S8/9PEEnRnEM8ALwSmuvubFe/3CeCfgCPTMYuB893d0+vDiBayfwDOAv4CuNDd/62L9fARYmmyk4AhwCvAd939BxVx7lNxytzsZ+/i9acC5wB7EfU7G7gPOMrdF6d6ngDcRNTZZmLx7pXAV4l1NPcF3gF+BEzL1tA0sznEcmAfq3i//sAm4DJ3n1FRP6cR6+cem67178AV7r65qz+LiGw7DXYQkZ40BTiESDS+AgwD5ptZv25eZ3bajgPmEYndEuCviERuFnBy2lYaCxyejrmAWAD87uxFM9sXeAoYSiQ1k4hk66dmtnfuWlcB1xOL1t/flaDNbCCx6P2Xge8Saxw/BdxoZtPSYacCDwBvAaPa+Bk6uv60FNf89LM+B/ykjUOHprhPBy5w9+XEWqPXpPceS6yn+Q1goZk1dDWGCtcC64g1dG8BLgX+uYrriMg2UNeqiPSkdcAYd98EYGY7Ei1DB3bzOq+4+8R0jceIZ8l2Asa6ezNwr5mNBg7Lnfce0aK0Op37JrDAzI539/uBGUAL0Xr1bjrmAeBlorXv7Ipr3e3u3+tm3GcA+wOj3f3xVHZ/atW6yMyuc/dnzWwV0OzuT3b1wqkuLwSuc/epFdceSLQcVmokWhkfSOd+AjgTmO7u2cLxD5jZCqIV9UTgnm7+rL9w9/Hp+3tTfOea2cysbkWk96lFTkR60s+yJC5ZlrY7dvM6HyQ4qRv2bWBJSuIybwODcufdlyVxyT1E4nZs2j+G6EpdbWb9U4K1luim/UzuWi90M2aIAQwrKpK4zFygCfh0FdfMfBoYCNyRK7+1neMr4z8ybW9r49zNRNzdNS+3fwfROHBoFdcSkSopkRORnrQut78lbbv7u2ZNG2Vrc/ttPeD728odd9/C1gnf7kS34qbc13iii7Xda3XRru2cl5XtUsU1M3+atm/lyt/MH5h7zyyufFllklxNXCty+6vSNp9ci0gvUteqiNRKlnj1I1rJMjt1cGx37Vq5k1rcdufDJOP3wCLiObPe8C6wXxvlg9P27W249vK0/SjwYkX5Hl2MC2BPohsZADNrJOoni6uV+PeptHM719w9t79n2q7KHygivUctciJSK1mX59CswMxG0H6iUI3jzKypYv9U4oZ1UdpfDHySeL7r2eyLGKTxxR54/8eBIWZ2RK58ApG8Pr0N136eeAZwbK781C7GBX/4M/49kbhlXdmrgd3MbIeKY0a3c81TcvvjgA3E4A4RqRG1yIlIrSwE/gW4ycyuJLoKpxNTV/SU3YC7zexqYoqNWcBjwEPp9cuIZOphM/se0V07kUiGvtQD7z+HGGE7P00D8n/A54gRvLO2ZRCAu68xs6uAK8xsLZGcHgl8LR2ypYNzf2lmc4FpKUlbTIwAngY8QUxfAvFM4RRgnpldT9ThxcD7bVz2JDP7PnAvkexNJgZTtNUtLiK9RC1yIlIT7v4yMffYHsBdxPQg3yZamnrKDcQcZz8mRqjeCnze3VtTDEuJ6UnWAjcTD+jvA3zB3W/Z1jd393VEcrWAmI5jPjFI4avufsm2Xp+YlmU60ZJ2V7r2t9JrbSVblc5M544jErbJxHQkY7K539z9UeA8YASReE8k/s3aeg7vUqLu7iTmzJvi7jOr/cFEpDqaEFhEpATS836nE5Mnv15R/g1izrrd3P29GsQxjEiWz3L3m3r7/USkY+paFRHpRHrurrNJc1vdfWMV1+5P134XNxNrtE4xs+8QAxSGA5cDP6xFEici9UeJnIhI55ytl9Vqy2vEShbddQnR5dmZo4iJe2cTXaK7AK8TqylcWcX7isgfASVyIiKdO5GY0Lcj3W6NS24kBgx0xtNAgvGdHtmL3P03dN46KSI1omfkREREREpKo1ZFRERESkqJnIiIiEhJKZETERERKSklciIiIiIlpUROREREpKT+H/bS9Cab1lYJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,7))\n",
    "sns.set(font_scale=1.4, style=\"whitegrid\")\n",
    "sns.lineplot(data = groups, x = 'number_of_group', y = 'inertia').set(title = \"Elbow Method\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the Elbow plot I could choose 4 and 6 and I will test both of them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function of visualize grouping:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plots_model():\n",
    "    fig = plt.figure(figsize=(10, 8))\n",
    "    ax = fig.add_subplot(111, projection='3d')\n",
    "    # Group data by 'groups'\n",
    "    grouped_data = rfm_df.groupby('groups')\n",
    "    \n",
    "    # Plot each group separately\n",
    "    for i, (group_name, group_data) in enumerate(grouped_data):\n",
    "        xs = group_data['Recency']\n",
    "        ys = group_data['MonetaryValue']\n",
    "        zs = group_data['Frequency']\n",
    "        ax.scatter(xs, ys, zs, s=50, alpha=0.6, edgecolors='w', label=group_name)\n",
    "\n",
    "    ax.set_xlabel('Recency')\n",
    "    ax.set_zlabel('Frequency')\n",
    "    ax.set_ylabel('MonetaryValue')\n",
    "    plt.title('Visualization of created groups')\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**First model - 6 groups:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_1 = KMeans(n_clusters=6, init='k-means++', max_iter=300)\n",
    "groups = model_1.fit_predict(rfm_df_raw)\n",
    "rfm_df_cutted['groups'] = groups\n",
    "rfm_df['groups'] = groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plots_model()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Distribution of the size of individual groups:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2    20.652174\n",
      "1    19.565217\n",
      "5    18.478261\n",
      "4    17.391304\n",
      "3    11.956522\n",
      "0    11.956522\n"
     ]
    }
   ],
   "source": [
    "print((rfm_df.groups.value_counts(normalize = True, sort = True) * 100).to_string())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Statistics for the whole set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Recency</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>MonetaryValue</th>\n",
       "      <th>groups</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>181.826087</td>\n",
       "      <td>30.684783</td>\n",
       "      <td>109050.313587</td>\n",
       "      <td>2.586957</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Recency  Frequency  MonetaryValue    groups\n",
       "mean  181.826087  30.684783  109050.313587  2.586957"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm_df.agg(['mean'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Statistics for individual groups:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Recency</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>MonetaryValue</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>groups</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>103.454545</td>\n",
       "      <td>28.727273</td>\n",
       "      <td>103478.149091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>351.944444</td>\n",
       "      <td>14.944444</td>\n",
       "      <td>52639.097778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>65.157895</td>\n",
       "      <td>60.052632</td>\n",
       "      <td>216240.730526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>242.545455</td>\n",
       "      <td>36.454545</td>\n",
       "      <td>127694.250909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>88.000000</td>\n",
       "      <td>18.312500</td>\n",
       "      <td>62768.398125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>231.823529</td>\n",
       "      <td>23.705882</td>\n",
       "      <td>84080.025882</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Recency  Frequency  MonetaryValue\n",
       "              mean       mean           mean\n",
       "groups                                      \n",
       "0       103.454545  28.727273  103478.149091\n",
       "1       351.944444  14.944444   52639.097778\n",
       "2        65.157895  60.052632  216240.730526\n",
       "3       242.545455  36.454545  127694.250909\n",
       "4        88.000000  18.312500   62768.398125\n",
       "5       231.823529  23.705882   84080.025882"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm_df.groupby('groups').agg(['mean'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The sum of the values for each group:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rfm_df.groups.value_counts().plot(kind='bar', figsize=(6,4), title='The sum of the values of individual groups');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Second model - 4 groups:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_1 = KMeans(n_clusters=4, init='k-means++', max_iter=300)\n",
    "groups = model_1.fit_predict(rfm_df_raw)\n",
    "rfm_df['groups'] = groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plots_model()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Distribution of the size of individual groups:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    34.782609\n",
      "2    26.086957\n",
      "1    20.652174\n",
      "3    18.478261\n"
     ]
    }
   ],
   "source": [
    "print((rfm_df.groups.value_counts(normalize = True, sort = True) * 100).to_string())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Statistics for the whole set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Recency</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>MonetaryValue</th>\n",
       "      <th>groups</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>181.826087</td>\n",
       "      <td>30.684783</td>\n",
       "      <td>109050.313587</td>\n",
       "      <td>1.282609</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Recency  Frequency  MonetaryValue    groups\n",
       "mean  181.826087  30.684783  109050.313587  1.282609"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm_df.agg(['mean'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Distribution of the size of individual groups:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Recency</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>MonetaryValue</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>groups</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>105.156250</td>\n",
       "      <td>50.187500</td>\n",
       "      <td>179045.850625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>343.578947</td>\n",
       "      <td>15.157895</td>\n",
       "      <td>53855.717895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>225.041667</td>\n",
       "      <td>25.458333</td>\n",
       "      <td>90571.409583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84.352941</td>\n",
       "      <td>18.705882</td>\n",
       "      <td>65069.950588</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Recency  Frequency  MonetaryValue\n",
       "              mean       mean           mean\n",
       "groups                                      \n",
       "0       105.156250  50.187500  179045.850625\n",
       "1       343.578947  15.157895   53855.717895\n",
       "2       225.041667  25.458333   90571.409583\n",
       "3        84.352941  18.705882   65069.950588"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm_df.groupby('groups').agg(['mean'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The sum of the values for each group:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rfm_df.groups.value_counts().plot(kind='bar', figsize=(6,4), title='The sum of the values of individual groups');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From above received calculations I choose segmentation for four groups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Recency</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>MonetaryValue</th>\n",
       "      <th>groups</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CUSTOMERNAME</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AV Stores, Co.</th>\n",
       "      <td>195</td>\n",
       "      <td>51</td>\n",
       "      <td>157807.81</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alpha Cognac</th>\n",
       "      <td>64</td>\n",
       "      <td>20</td>\n",
       "      <td>70488.44</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amica Models &amp; Co.</th>\n",
       "      <td>264</td>\n",
       "      <td>26</td>\n",
       "      <td>94117.26</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anna's Decorations, Ltd</th>\n",
       "      <td>83</td>\n",
       "      <td>46</td>\n",
       "      <td>153996.13</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Atelier graphique</th>\n",
       "      <td>187</td>\n",
       "      <td>7</td>\n",
       "      <td>24179.96</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Recency  Frequency  MonetaryValue  groups\n",
       "CUSTOMERNAME                                                      \n",
       "AV Stores, Co.               195         51      157807.81       0\n",
       "Alpha Cognac                  64         20       70488.44       3\n",
       "Amica Models & Co.           264         26       94117.26       2\n",
       "Anna's Decorations, Ltd       83         46      153996.13       0\n",
       "Atelier graphique            187          7       24179.96       1"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Function to assigned segment name to each value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "def name(row):\n",
    "    segment_mapping = {0: 'departing', 1: 'active', 2: 'inactive'}\n",
    "    return segment_mapping.get(row, 'new')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adding column SegmentName to DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfm_df['SegmentName'] = rfm_df['groups'].apply(name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Recency</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>MonetaryValue</th>\n",
       "      <th>groups</th>\n",
       "      <th>SegmentName</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CUSTOMERNAME</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>AV Stores, Co.</th>\n",
       "      <td>195</td>\n",
       "      <td>51</td>\n",
       "      <td>157807.81</td>\n",
       "      <td>0</td>\n",
       "      <td>departing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alpha Cognac</th>\n",
       "      <td>64</td>\n",
       "      <td>20</td>\n",
       "      <td>70488.44</td>\n",
       "      <td>3</td>\n",
       "      <td>new</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amica Models &amp; Co.</th>\n",
       "      <td>264</td>\n",
       "      <td>26</td>\n",
       "      <td>94117.26</td>\n",
       "      <td>2</td>\n",
       "      <td>inactive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anna's Decorations, Ltd</th>\n",
       "      <td>83</td>\n",
       "      <td>46</td>\n",
       "      <td>153996.13</td>\n",
       "      <td>0</td>\n",
       "      <td>departing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Atelier graphique</th>\n",
       "      <td>187</td>\n",
       "      <td>7</td>\n",
       "      <td>24179.96</td>\n",
       "      <td>1</td>\n",
       "      <td>active</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Recency  Frequency  MonetaryValue  groups SegmentName\n",
       "CUSTOMERNAME                                                                  \n",
       "AV Stores, Co.               195         51      157807.81       0   departing\n",
       "Alpha Cognac                  64         20       70488.44       3         new\n",
       "Amica Models & Co.           264         26       94117.26       2    inactive\n",
       "Anna's Decorations, Ltd       83         46      153996.13       0   departing\n",
       "Atelier graphique            187          7       24179.96       1      active"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save recommendations to a CSV file\n",
    "rfm_data.to_csv('Segmentation\\customers_segments.csv', index=False)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Description of the results\n",
    "\n",
    "\n",
    "**Interpretation of the resulting groups:**\n",
    "\n",
    "From performed RFM analysis I chose segmentation for four groups. They can be interpret as the following customer segments:\n",
    "\n",
    "- **Group 0**: Customers who were shopping for a long time, buy moderately often and they shop for high amounts.\n",
    "- **Group 1**: Customers who have been shopping recently, they buy often and make purchases for large amounts.\n",
    "- **Group 2**: Customers who have not been buying anything for a long time, they rarely buy and make purchases for relatively small amounts.\n",
    "- **Group 3**: Customers who have been shopping recently, they rarely buy and make purchases for relatively small amounts.\n",
    "\n",
    "Based on the received groups they can be name as follows: \n",
    "\n",
    "- **Group 0** - former best customers - **departing**,\n",
    "- **Group 1** - best customers - **active**,\n",
    "- **Group 2** - old and random customers - **inactive**,\n",
    "- **Group 3** - new and random customers - **new**.\n",
    "\n",
    "\n",
    "**Marketing strategies for the customer segments**\n",
    "\n",
    "Based on the 4 clusters may could formulate marketing strategies relevant to each group:\n",
    "\n",
    "For the groups 1 and 3 one can apply a typical promotional strategy would focus certain efforts for the high value customers. For the best customers example actions may it be: special offers with discounts, customer cards entitling to benefits (e.g. collecting points redeemed for rewards) or a gift for the next order.  \n",
    "\n",
    "The new clients have not bought from the store before, that's why is worth fighting to transform them into best clients. Example actions for them are: e-mail marketing presenting the advantages of our products, special offers, with discounts for appropriately large purchases.\n",
    "\n",
    "For the groups 0 and 2 i.e. former best customers and old clients  also can adopt certain marketing strategy. \n",
    "In group 0 (former best clients) include a big customers who for some reason have stopped buying our products, then it is worth trying to renew relations with them hrough an conduct marketing campaign. The group 2 is an old and random customers and here is worth to considering whether it can to carry out some marketing activities.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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