Boot Camp Use Case-crosstabs.ipynb

{
    "cells": [
        {
            "cell_type": "code",
            "execution_count": 1,
            "metadata": {},
            "outputs": [],
            "source": [
                "# @hidden_cell\n",
                "\n",
                "Watson_Studio_Env = False\n",
                "\n",
                "if(Watson_Studio_Env == True):\n",
                "    # The project token is an authorization token that is used to access project resources like data sources, connections, and used by platform APIs.\n",
                "    from project_lib import Project\n",
                "    project = Project(project_id='01cb8ab3-b48d-441d-8d33-edc6caae425d', project_access_token='p-d36f9300425b2c63be8cd6c20d68a07ef91ec977')\n",
                "    pc = project.project_context\n"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 2,
            "metadata": {},
            "outputs": [],
            "source": [
                "import pandas as pd\n",
                "import numpy as np\n",
                "import matplotlib.pyplot as plt\n",
                "from sklearn import svm\n",
                "from sklearn.model_selection import train_test_split\n",
                "from sklearn import preprocessing\n",
                "from sklearn.metrics import confusion_matrix\n",
                "import itertools\n",
                "from sklearn.feature_selection import mutual_info_classif"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 3,
            "metadata": {},
            "outputs": [],
            "source": [
                "product_col=['PRODUCT CODE','PRODUCT CATEGORY','UNIT LIST PRICE']\n",
                "transactions_col=['CUSTOMER NUM','PRODUCT NUM','QUANTITY PURCHASED','DISCOUNT TAKEN','TRANSACTION DATE','STOCKOUT']\n",
                "\n",
                "if(Watson_Studio_Env == True):\n",
                "    my_file = project.get_file(\"Product Data Set - Student 2 of 3.csv\")\n",
                "    my_file.seek(0)\n",
                "else:\n",
                "    my_file = \"Product Data Set - Student 2 of 3.csv\"\n",
                "product_data=pd.read_csv(my_file,header=0,sep='|',usecols=product_col)\n",
                "\n",
                "if(Watson_Studio_Env == True):\n",
                "    my_file = project.get_file(\"Transaction Data Set - Student 3 of 3.csv\")\n",
                "    my_file.seek(0)\n",
                "else:\n",
                "    my_file = \"Transaction Data Set - Student 3 of 3.csv\"\n",
                "transactions_data=pd.read_csv(my_file,header=0,sep='|',usecols=transactions_col)\n",
                "\n",
                "# Fetch the Customer file\n",
                "if(Watson_Studio_Env == True):\n",
                "    my_file = project.get_file(\"Customer Data Set - Student 1 of 3.xlsx\")\n",
                "    my_file.seek(0)\n",
                "else:\n",
                "    my_file = \"Customer Data Set - Student 1 of 3.xlsx\"\n",
                "customer_data=pd.read_excel(my_file)"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# perform checks on the input data for any null values"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 4,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "null values for transactoins ? False\n",
                        "null values for products ? False\n",
                        "null values for customers ? True\n"
                    ]
                }
            ],
            "source": [
                "print('null values for transactoins ?',transactions_data.isnull().values.any())\n",
                "print('null values for products ?',product_data.isnull().values.any())\n",
                "print('null values for customers ?',customer_data.isnull().values.any())"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# Check which columns have null values and why ?"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 5,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": [
                            "<div>\n",
                            "<style scoped>\n",
                            "    .dataframe tbody tr th:only-of-type {\n",
                            "        vertical-align: middle;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe tbody tr th {\n",
                            "        vertical-align: top;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe thead th {\n",
                            "        text-align: right;\n",
                            "    }\n",
                            "</style>\n",
                            "<table border=\"1\" class=\"dataframe\">\n",
                            "  <thead>\n",
                            "    <tr style=\"text-align: right;\">\n",
                            "      <th></th>\n",
                            "      <th>CUSTOMER ID</th>\n",
                            "      <th>GENDER</th>\n",
                            "      <th>AGE</th>\n",
                            "      <th>INCOME</th>\n",
                            "      <th>EXPERIENCE SCORE</th>\n",
                            "      <th>LOYALTY GROUP</th>\n",
                            "      <th>ENROLLMENT DATE</th>\n",
                            "      <th>HOUSEHOLD SIZE</th>\n",
                            "      <th>MARITAL STATUS</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>0</th>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1</th>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "      <td>True</td>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2</th>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>3</th>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>4</th>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "      <td>True</td>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>...</th>\n",
                            "      <td>...</td>\n",
                            "      <td>...</td>\n",
                            "      <td>...</td>\n",
                            "      <td>...</td>\n",
                            "      <td>...</td>\n",
                            "      <td>...</td>\n",
                            "      <td>...</td>\n",
                            "      <td>...</td>\n",
                            "      <td>...</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>495</th>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>496</th>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>497</th>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>498</th>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "      <td>True</td>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>499</th>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "      <td>True</td>\n",
                            "      <td>False</td>\n",
                            "      <td>False</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "<p>500 rows × 9 columns</p>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "     CUSTOMER ID  GENDER    AGE  INCOME  EXPERIENCE SCORE  LOYALTY GROUP  \\\n",
                            "0          False   False  False   False             False          False   \n",
                            "1          False   False  False   False             False          False   \n",
                            "2          False   False  False   False             False          False   \n",
                            "3          False   False  False   False             False          False   \n",
                            "4          False   False  False   False             False          False   \n",
                            "..           ...     ...    ...     ...               ...            ...   \n",
                            "495        False   False  False   False             False          False   \n",
                            "496        False   False  False   False             False          False   \n",
                            "497        False   False  False   False             False          False   \n",
                            "498        False   False  False   False             False          False   \n",
                            "499        False   False  False   False             False          False   \n",
                            "\n",
                            "     ENROLLMENT DATE  HOUSEHOLD SIZE  MARITAL STATUS  \n",
                            "0              False           False           False  \n",
                            "1               True           False           False  \n",
                            "2              False           False           False  \n",
                            "3              False           False           False  \n",
                            "4               True           False           False  \n",
                            "..               ...             ...             ...  \n",
                            "495            False           False           False  \n",
                            "496            False           False           False  \n",
                            "497            False           False           False  \n",
                            "498             True           False           False  \n",
                            "499             True           False           False  \n",
                            "\n",
                            "[500 rows x 9 columns]"
                        ]
                    },
                    "execution_count": 5,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "customer_data.isnull()  # (it turned out that it is ENROLLMENT DATE of customers who are not enrolled yet into \n",
                "#Loyalty program ) , this column can be eliminated later since it is not important in the analysis"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 6,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "(30, 3)"
                        ]
                    },
                    "execution_count": 6,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "product_data.shape"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 7,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "(10000, 6)"
                        ]
                    },
                    "execution_count": 7,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "transactions_data.shape"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 8,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "Index(['PRODUCT CODE', 'PRODUCT CATEGORY', 'UNIT LIST PRICE'], dtype='object')"
                        ]
                    },
                    "execution_count": 8,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "product_data.columns"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# collect and integrate data all together into a single view (transactional based)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 9,
            "metadata": {},
            "outputs": [],
            "source": [
                "partial_customer_TX_data=pd.merge(transactions_data,customer_data,how='left', left_on=['CUSTOMER NUM'], right_on=['CUSTOMER ID'])\n",
                "drop_cols=['CUSTOMER ID','PRODUCT CODE']\n",
                "full_customer_TX_data=pd.merge(partial_customer_TX_data,product_data,how='left',left_on=['PRODUCT NUM'],right_on=['PRODUCT CODE']).drop(drop_cols,axis=1)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 10,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "(10000, 16)"
                        ]
                    },
                    "execution_count": 10,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "full_customer_TX_data.shape"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# perform checks on the final merged table for null values"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 11,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "False"
                        ]
                    },
                    "execution_count": 11,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "full_customer_TX_data=full_customer_TX_data.drop('ENROLLMENT DATE',axis=1)  ### DO Not Run this CELL more than once O.W you will recalculate the full_customer_TX_data again\n",
                "full_customer_TX_data.isnull().values.any()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 12,
            "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",
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                            "\n",
                            "    .dataframe thead th {\n",
                            "        text-align: right;\n",
                            "    }\n",
                            "</style>\n",
                            "<table border=\"1\" class=\"dataframe\">\n",
                            "  <thead>\n",
                            "    <tr style=\"text-align: right;\">\n",
                            "      <th></th>\n",
                            "      <th>CUSTOMER NUM</th>\n",
                            "      <th>PRODUCT NUM</th>\n",
                            "      <th>QUANTITY PURCHASED</th>\n",
                            "      <th>DISCOUNT TAKEN</th>\n",
                            "      <th>TRANSACTION DATE</th>\n",
                            "      <th>STOCKOUT</th>\n",
                            "      <th>GENDER</th>\n",
                            "      <th>AGE</th>\n",
                            "      <th>INCOME</th>\n",
                            "      <th>EXPERIENCE SCORE</th>\n",
                            "      <th>LOYALTY GROUP</th>\n",
                            "      <th>HOUSEHOLD SIZE</th>\n",
                            "      <th>MARITAL STATUS</th>\n",
                            "      <th>PRODUCT CATEGORY</th>\n",
                            "      <th>UNIT LIST PRICE</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>0</th>\n",
                            "      <td>10114</td>\n",
                            "      <td>30011</td>\n",
                            "      <td>4</td>\n",
                            "      <td>0.0</td>\n",
                            "      <td>1/2/2015</td>\n",
                            "      <td>0</td>\n",
                            "      <td>1</td>\n",
                            "      <td>26</td>\n",
                            "      <td>41777</td>\n",
                            "      <td>10</td>\n",
                            "      <td>not enrolled</td>\n",
                            "      <td>1</td>\n",
                            "      <td>Single</td>\n",
                            "      <td>APPAREL</td>\n",
                            "      <td>$25.46</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1</th>\n",
                            "      <td>10217</td>\n",
                            "      <td>30016</td>\n",
                            "      <td>3</td>\n",
                            "      <td>0.0</td>\n",
                            "      <td>1/2/2015</td>\n",
                            "      <td>0</td>\n",
                            "      <td>1</td>\n",
                            "      <td>31</td>\n",
                            "      <td>58025</td>\n",
                            "      <td>1</td>\n",
                            "      <td>not enrolled</td>\n",
                            "      <td>6</td>\n",
                            "      <td>Single</td>\n",
                            "      <td>APPAREL</td>\n",
                            "      <td>$29.31</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2</th>\n",
                            "      <td>10224</td>\n",
                            "      <td>30013</td>\n",
                            "      <td>4</td>\n",
                            "      <td>0.0</td>\n",
                            "      <td>1/2/2015</td>\n",
                            "      <td>0</td>\n",
                            "      <td>1</td>\n",
                            "      <td>28</td>\n",
                            "      <td>47145</td>\n",
                            "      <td>2</td>\n",
                            "      <td>not enrolled</td>\n",
                            "      <td>2</td>\n",
                            "      <td>Single</td>\n",
                            "      <td>APPAREL</td>\n",
                            "      <td>$26.85</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>3</th>\n",
                            "      <td>10103</td>\n",
                            "      <td>30012</td>\n",
                            "      <td>3</td>\n",
                            "      <td>0.2</td>\n",
                            "      <td>1/2/2015</td>\n",
                            "      <td>0</td>\n",
                            "      <td>1</td>\n",
                            "      <td>30</td>\n",
                            "      <td>51978</td>\n",
                            "      <td>7</td>\n",
                            "      <td>not enrolled</td>\n",
                            "      <td>4</td>\n",
                            "      <td>Married</td>\n",
                            "      <td>APPAREL</td>\n",
                            "      <td>$28.51</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>4</th>\n",
                            "      <td>10037</td>\n",
                            "      <td>30010</td>\n",
                            "      <td>8</td>\n",
                            "      <td>0.0</td>\n",
                            "      <td>1/2/2015</td>\n",
                            "      <td>0</td>\n",
                            "      <td>1</td>\n",
                            "      <td>31</td>\n",
                            "      <td>57724</td>\n",
                            "      <td>9</td>\n",
                            "      <td>not enrolled</td>\n",
                            "      <td>3</td>\n",
                            "      <td>Married</td>\n",
                            "      <td>APPAREL</td>\n",
                            "      <td>$25.27</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>...</th>\n",
                            "      <td>...</td>\n",
                            "      <td>...</td>\n",
                            "      <td>...</td>\n",
                            "      <td>...</td>\n",
                            "      <td>...</td>\n",
                            "      <td>...</td>\n",
                            "      <td>...</td>\n",
                            "      <td>...</td>\n",
                            "      <td>...</td>\n",
                            "      <td>...</td>\n",
                            "      <td>...</td>\n",
                            "      <td>...</td>\n",
                            "      <td>...</td>\n",
                            "      <td>...</td>\n",
                            "      <td>...</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>9995</th>\n",
                            "      <td>10154</td>\n",
                            "      <td>30024</td>\n",
                            "      <td>14</td>\n",
                            "      <td>0.0</td>\n",
                            "      <td>12/31/2015</td>\n",
                            "      <td>0</td>\n",
                            "      <td>0</td>\n",
                            "      <td>51</td>\n",
                            "      <td>118393</td>\n",
                            "      <td>7</td>\n",
                            "      <td>enrolled</td>\n",
                            "      <td>4</td>\n",
                            "      <td>Married</td>\n",
                            "      <td>FOOD</td>\n",
                            "      <td>$5.03</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>9996</th>\n",
                            "      <td>10090</td>\n",
                            "      <td>30003</td>\n",
                            "      <td>7</td>\n",
                            "      <td>0.1</td>\n",
                            "      <td>12/31/2015</td>\n",
                            "      <td>0</td>\n",
                            "      <td>0</td>\n",
                            "      <td>20</td>\n",
                            "      <td>24369</td>\n",
                            "      <td>8</td>\n",
                            "      <td>enrolled</td>\n",
                            "      <td>1</td>\n",
                            "      <td>Single</td>\n",
                            "      <td>HEALTH &amp; BEAUTY</td>\n",
                            "      <td>$5.49</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>9997</th>\n",
                            "      <td>10212</td>\n",
                            "      <td>30004</td>\n",
                            "      <td>7</td>\n",
                            "      <td>0.0</td>\n",
                            "      <td>12/31/2015</td>\n",
                            "      <td>0</td>\n",
                            "      <td>0</td>\n",
                            "      <td>21</td>\n",
                            "      <td>28729</td>\n",
                            "      <td>10</td>\n",
                            "      <td>enrolled</td>\n",
                            "      <td>2</td>\n",
                            "      <td>Married</td>\n",
                            "      <td>HEALTH &amp; BEAUTY</td>\n",
                            "      <td>$6.46</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>9998</th>\n",
                            "      <td>10102</td>\n",
                            "      <td>30006</td>\n",
                            "      <td>8</td>\n",
                            "      <td>0.2</td>\n",
                            "      <td>12/31/2015</td>\n",
                            "      <td>0</td>\n",
                            "      <td>1</td>\n",
                            "      <td>24</td>\n",
                            "      <td>36793</td>\n",
                            "      <td>7</td>\n",
                            "      <td>enrolled</td>\n",
                            "      <td>3</td>\n",
                            "      <td>Married</td>\n",
                            "      <td>HEALTH &amp; BEAUTY</td>\n",
                            "      <td>$6.27</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>9999</th>\n",
                            "      <td>10315</td>\n",
                            "      <td>30007</td>\n",
                            "      <td>10</td>\n",
                            "      <td>0.0</td>\n",
                            "      <td>12/31/2015</td>\n",
                            "      <td>0</td>\n",
                            "      <td>0</td>\n",
                            "      <td>40</td>\n",
                            "      <td>91000</td>\n",
                            "      <td>8</td>\n",
                            "      <td>enrolled</td>\n",
                            "      <td>6</td>\n",
                            "      <td>Married</td>\n",
                            "      <td>HEALTH &amp; BEAUTY</td>\n",
                            "      <td>$6.50</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "<p>10000 rows × 15 columns</p>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "      CUSTOMER NUM  PRODUCT NUM  QUANTITY PURCHASED  DISCOUNT TAKEN  \\\n",
                            "0            10114        30011                   4             0.0   \n",
                            "1            10217        30016                   3             0.0   \n",
                            "2            10224        30013                   4             0.0   \n",
                            "3            10103        30012                   3             0.2   \n",
                            "4            10037        30010                   8             0.0   \n",
                            "...            ...          ...                 ...             ...   \n",
                            "9995         10154        30024                  14             0.0   \n",
                            "9996         10090        30003                   7             0.1   \n",
                            "9997         10212        30004                   7             0.0   \n",
                            "9998         10102        30006                   8             0.2   \n",
                            "9999         10315        30007                  10             0.0   \n",
                            "\n",
                            "     TRANSACTION DATE  STOCKOUT  GENDER  AGE  INCOME  EXPERIENCE SCORE  \\\n",
                            "0            1/2/2015         0       1   26   41777                10   \n",
                            "1            1/2/2015         0       1   31   58025                 1   \n",
                            "2            1/2/2015         0       1   28   47145                 2   \n",
                            "3            1/2/2015         0       1   30   51978                 7   \n",
                            "4            1/2/2015         0       1   31   57724                 9   \n",
                            "...               ...       ...     ...  ...     ...               ...   \n",
                            "9995       12/31/2015         0       0   51  118393                 7   \n",
                            "9996       12/31/2015         0       0   20   24369                 8   \n",
                            "9997       12/31/2015         0       0   21   28729                10   \n",
                            "9998       12/31/2015         0       1   24   36793                 7   \n",
                            "9999       12/31/2015         0       0   40   91000                 8   \n",
                            "\n",
                            "     LOYALTY GROUP  HOUSEHOLD SIZE MARITAL STATUS PRODUCT CATEGORY  \\\n",
                            "0     not enrolled               1         Single          APPAREL   \n",
                            "1     not enrolled               6         Single          APPAREL   \n",
                            "2     not enrolled               2         Single          APPAREL   \n",
                            "3     not enrolled               4        Married          APPAREL   \n",
                            "4     not enrolled               3        Married          APPAREL   \n",
                            "...            ...             ...            ...              ...   \n",
                            "9995      enrolled               4        Married             FOOD   \n",
                            "9996      enrolled               1         Single  HEALTH & BEAUTY   \n",
                            "9997      enrolled               2        Married  HEALTH & BEAUTY   \n",
                            "9998      enrolled               3        Married  HEALTH & BEAUTY   \n",
                            "9999      enrolled               6        Married  HEALTH & BEAUTY   \n",
                            "\n",
                            "     UNIT LIST PRICE  \n",
                            "0            $25.46   \n",
                            "1            $29.31   \n",
                            "2            $26.85   \n",
                            "3            $28.51   \n",
                            "4            $25.27   \n",
                            "...              ...  \n",
                            "9995          $5.03   \n",
                            "9996          $5.49   \n",
                            "9997          $6.46   \n",
                            "9998          $6.27   \n",
                            "9999          $6.50   \n",
                            "\n",
                            "[10000 rows x 15 columns]"
                        ]
                    },
                    "execution_count": 12,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "full_customer_TX_data"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# Now perform some aggregation functions and derive new features"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# let's make the grouping based on the customers (customer NUM) and then derive multiple grouping funcitons"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 13,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "DISCOUNT TAKEN    0.0\n",
                        "dtype: float64\n",
                        "DISCOUNT TAKEN    0.7\n",
                        "dtype: float64\n"
                    ]
                }
            ],
            "source": [
                "print(full_customer_TX_data.agg({'DISCOUNT TAKEN':'min'})) # this is a check on the values prior to define the following function\n",
                "print(full_customer_TX_data.agg({'DISCOUNT TAKEN':'max'}))"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# Construct new functions to derived features"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 14,
            "metadata": {},
            "outputs": [],
            "source": [
                "def calculate_total_spending(unit_price,quantity,discount):\n",
                "    total_spent=(1-discount)*float(unit_price.replace('$', ''))*quantity\n",
                "    return total_spent\n",
                "\n",
                "def calculate_pcntg_spent_to_income(INCOME,total_spent):\n",
                "    pcntg = (total_spent/INCOME)*100\n",
                "    return pcntg\n",
                " "
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 15,
            "metadata": {},
            "outputs": [],
            "source": [
                "full_customer_TX_data['sub_spent'] = full_customer_TX_data.apply(lambda row:calculate_total_spending(row['UNIT LIST PRICE'],row['QUANTITY PURCHASED'],row['DISCOUNT TAKEN']),axis=1)\n"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 16,
            "metadata": {},
            "outputs": [],
            "source": [
                "customer_view = pd.DataFrame(full_customer_TX_data.groupby(['CUSTOMER NUM','GENDER','AGE','INCOME',\\\n",
                "                               'EXPERIENCE SCORE','LOYALTY GROUP','HOUSEHOLD SIZE','MARITAL STATUS'])\\\n",
                ".agg({'sub_spent':'sum','QUANTITY PURCHASED':'sum','CUSTOMER NUM':'count'})\\\n",
                "                             .rename(columns={'QUANTITY PURCHASED':'purchase_count','sub_spent':\\\n",
                "                                              'total_spent','CUSTOMER NUM':'tx_count'}).to_records())\\\n",
                "  # this flattened the output pivot table of the groupby and agg funcitons into a normal dataframe \n",
                "#with column  names equals to the names of the complex index of that pivot table."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 17,
            "metadata": {},
            "outputs": [],
            "source": [
                "customer_view['spent_pcntg_income'] = customer_view.apply(lambda row:calculate_pcntg_spent_to_income(row['INCOME'],row['total_spent']),axis=1)\n"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# Let's do cross tabulation of each feature against the Loyalty Group attribute"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 18,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "Index(['CUSTOMER NUM', 'GENDER', 'AGE', 'INCOME', 'EXPERIENCE SCORE',\n",
                            "       'LOYALTY GROUP', 'HOUSEHOLD SIZE', 'MARITAL STATUS', 'total_spent',\n",
                            "       'purchase_count', 'tx_count', 'spent_pcntg_income'],\n",
                            "      dtype='object')"
                        ]
                    },
                    "execution_count": 18,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "customer_view.columns"
            ]
        },
        {
            "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>CUSTOMER NUM</th>\n",
                            "      <th>GENDER</th>\n",
                            "      <th>AGE</th>\n",
                            "      <th>INCOME</th>\n",
                            "      <th>EXPERIENCE SCORE</th>\n",
                            "      <th>LOYALTY GROUP</th>\n",
                            "      <th>HOUSEHOLD SIZE</th>\n",
                            "      <th>MARITAL STATUS</th>\n",
                            "      <th>total_spent</th>\n",
                            "      <th>purchase_count</th>\n",
                            "      <th>tx_count</th>\n",
                            "      <th>spent_pcntg_income</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>0</th>\n",
                            "      <td>10001</td>\n",
                            "      <td>0</td>\n",
                            "      <td>64</td>\n",
                            "      <td>133498</td>\n",
                            "      <td>5</td>\n",
                            "      <td>enrolled</td>\n",
                            "      <td>4</td>\n",
                            "      <td>Single</td>\n",
                            "      <td>6826.770</td>\n",
                            "      <td>396</td>\n",
                            "      <td>27</td>\n",
                            "      <td>5.113762</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1</th>\n",
                            "      <td>10002</td>\n",
                            "      <td>0</td>\n",
                            "      <td>42</td>\n",
                            "      <td>94475</td>\n",
                            "      <td>9</td>\n",
                            "      <td>not enrolled</td>\n",
                            "      <td>6</td>\n",
                            "      <td>Married</td>\n",
                            "      <td>5062.451</td>\n",
                            "      <td>202</td>\n",
                            "      <td>24</td>\n",
                            "      <td>5.358509</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2</th>\n",
                            "      <td>10003</td>\n",
                            "      <td>0</td>\n",
                            "      <td>40</td>\n",
                            "      <td>88610</td>\n",
                            "      <td>9</td>\n",
                            "      <td>enrolled</td>\n",
                            "      <td>5</td>\n",
                            "      <td>Married</td>\n",
                            "      <td>8562.440</td>\n",
                            "      <td>265</td>\n",
                            "      <td>23</td>\n",
                            "      <td>9.663063</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>3</th>\n",
                            "      <td>10004</td>\n",
                            "      <td>0</td>\n",
                            "      <td>38</td>\n",
                            "      <td>84313</td>\n",
                            "      <td>8</td>\n",
                            "      <td>enrolled</td>\n",
                            "      <td>1</td>\n",
                            "      <td>Single</td>\n",
                            "      <td>5522.694</td>\n",
                            "      <td>197</td>\n",
                            "      <td>19</td>\n",
                            "      <td>6.550228</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>4</th>\n",
                            "      <td>10005</td>\n",
                            "      <td>0</td>\n",
                            "      <td>30</td>\n",
                            "      <td>51498</td>\n",
                            "      <td>3</td>\n",
                            "      <td>not enrolled</td>\n",
                            "      <td>1</td>\n",
                            "      <td>Single</td>\n",
                            "      <td>213.512</td>\n",
                            "      <td>12</td>\n",
                            "      <td>2</td>\n",
                            "      <td>0.414603</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>...</th>\n",
                            "      <td>...</td>\n",
                            "      <td>...</td>\n",
                            "      <td>...</td>\n",
                            "      <td>...</td>\n",
                            "      <td>...</td>\n",
                            "      <td>...</td>\n",
                            "      <td>...</td>\n",
                            "      <td>...</td>\n",
                            "      <td>...</td>\n",
                            "      <td>...</td>\n",
                            "      <td>...</td>\n",
                            "      <td>...</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>495</th>\n",
                            "      <td>10496</td>\n",
                            "      <td>0</td>\n",
                            "      <td>47</td>\n",
                            "      <td>113125</td>\n",
                            "      <td>7</td>\n",
                            "      <td>enrolled</td>\n",
                            "      <td>2</td>\n",
                            "      <td>Single</td>\n",
                            "      <td>8501.464</td>\n",
                            "      <td>309</td>\n",
                            "      <td>23</td>\n",
                            "      <td>7.515106</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>496</th>\n",
                            "      <td>10497</td>\n",
                            "      <td>0</td>\n",
                            "      <td>49</td>\n",
                            "      <td>118108</td>\n",
                            "      <td>7</td>\n",
                            "      <td>enrolled</td>\n",
                            "      <td>1</td>\n",
                            "      <td>Single</td>\n",
                            "      <td>9903.658</td>\n",
                            "      <td>360</td>\n",
                            "      <td>27</td>\n",
                            "      <td>8.385256</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>497</th>\n",
                            "      <td>10498</td>\n",
                            "      <td>0</td>\n",
                            "      <td>42</td>\n",
                            "      <td>97353</td>\n",
                            "      <td>8</td>\n",
                            "      <td>enrolled</td>\n",
                            "      <td>5</td>\n",
                            "      <td>Married</td>\n",
                            "      <td>6251.466</td>\n",
                            "      <td>337</td>\n",
                            "      <td>29</td>\n",
                            "      <td>6.421442</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>498</th>\n",
                            "      <td>10499</td>\n",
                            "      <td>1</td>\n",
                            "      <td>22</td>\n",
                            "      <td>34424</td>\n",
                            "      <td>1</td>\n",
                            "      <td>not enrolled</td>\n",
                            "      <td>2</td>\n",
                            "      <td>Single</td>\n",
                            "      <td>20.010</td>\n",
                            "      <td>3</td>\n",
                            "      <td>2</td>\n",
                            "      <td>0.058128</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>499</th>\n",
                            "      <td>10500</td>\n",
                            "      <td>1</td>\n",
                            "      <td>34</td>\n",
                            "      <td>75114</td>\n",
                            "      <td>9</td>\n",
                            "      <td>not enrolled</td>\n",
                            "      <td>3</td>\n",
                            "      <td>Divorced</td>\n",
                            "      <td>4182.032</td>\n",
                            "      <td>136</td>\n",
                            "      <td>20</td>\n",
                            "      <td>5.567580</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "<p>500 rows × 12 columns</p>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "     CUSTOMER NUM  GENDER  AGE  INCOME  EXPERIENCE SCORE LOYALTY GROUP  \\\n",
                            "0           10001       0   64  133498                 5      enrolled   \n",
                            "1           10002       0   42   94475                 9  not enrolled   \n",
                            "2           10003       0   40   88610                 9      enrolled   \n",
                            "3           10004       0   38   84313                 8      enrolled   \n",
                            "4           10005       0   30   51498                 3  not enrolled   \n",
                            "..            ...     ...  ...     ...               ...           ...   \n",
                            "495         10496       0   47  113125                 7      enrolled   \n",
                            "496         10497       0   49  118108                 7      enrolled   \n",
                            "497         10498       0   42   97353                 8      enrolled   \n",
                            "498         10499       1   22   34424                 1  not enrolled   \n",
                            "499         10500       1   34   75114                 9  not enrolled   \n",
                            "\n",
                            "     HOUSEHOLD SIZE MARITAL STATUS  total_spent  purchase_count  tx_count  \\\n",
                            "0                 4         Single     6826.770             396        27   \n",
                            "1                 6        Married     5062.451             202        24   \n",
                            "2                 5        Married     8562.440             265        23   \n",
                            "3                 1         Single     5522.694             197        19   \n",
                            "4                 1         Single      213.512              12         2   \n",
                            "..              ...            ...          ...             ...       ...   \n",
                            "495               2         Single     8501.464             309        23   \n",
                            "496               1         Single     9903.658             360        27   \n",
                            "497               5        Married     6251.466             337        29   \n",
                            "498               2         Single       20.010               3         2   \n",
                            "499               3       Divorced     4182.032             136        20   \n",
                            "\n",
                            "     spent_pcntg_income  \n",
                            "0              5.113762  \n",
                            "1              5.358509  \n",
                            "2              9.663063  \n",
                            "3              6.550228  \n",
                            "4              0.414603  \n",
                            "..                  ...  \n",
                            "495            7.515106  \n",
                            "496            8.385256  \n",
                            "497            6.421442  \n",
                            "498            0.058128  \n",
                            "499            5.567580  \n",
                            "\n",
                            "[500 rows x 12 columns]"
                        ]
                    },
                    "execution_count": 19,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "customer_view"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# Now lets derive some features with respect to product categories"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 20,
            "metadata": {},
            "outputs": [],
            "source": [
                "product_stats = pd.DataFrame(full_customer_TX_data.groupby(['PRODUCT CATEGORY']).agg({'sub_spent':'sum','QUANTITY PURCHASED':'sum','DISCOUNT TAKEN':'mean'}).to_records()).rename(columns={'sub_spent':'total_spent','DISCOUNT TAKEN':'average_discount'})\n",
                "product_view = pd.DataFrame(full_customer_TX_data.groupby(['PRODUCT CATEGORY','PRODUCT NUM']).agg({'sub_spent':'sum','QUANTITY PURCHASED':'sum','DISCOUNT TAKEN':'mean'}).to_records()).rename(columns={'sub_spent':'total_spent','DISCOUNT TAKEN':'average_discount'})"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 21,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": [
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                            "    <tr style=\"text-align: right;\">\n",
                            "      <th></th>\n",
                            "      <th>PRODUCT CATEGORY</th>\n",
                            "      <th>PRODUCT NUM</th>\n",
                            "      <th>total_spent</th>\n",
                            "      <th>QUANTITY PURCHASED</th>\n",
                            "      <th>average_discount</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>9</th>\n",
                            "      <td>ELECTRONICS</td>\n",
                            "      <td>30019</td>\n",
                            "      <td>287032.326</td>\n",
                            "      <td>5721</td>\n",
                            "      <td>0.063383</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>12</th>\n",
                            "      <td>ELECTRONICS</td>\n",
                            "      <td>30022</td>\n",
                            "      <td>271710.684</td>\n",
                            "      <td>5294</td>\n",
                            "      <td>0.053772</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>10</th>\n",
                            "      <td>ELECTRONICS</td>\n",
                            "      <td>30020</td>\n",
                            "      <td>270412.876</td>\n",
                            "      <td>5712</td>\n",
                            "      <td>0.067970</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>7</th>\n",
                            "      <td>ELECTRONICS</td>\n",
                            "      <td>30017</td>\n",
                            "      <td>267335.302</td>\n",
                            "      <td>5645</td>\n",
                            "      <td>0.065677</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>8</th>\n",
                            "      <td>ELECTRONICS</td>\n",
                            "      <td>30018</td>\n",
                            "      <td>257041.100</td>\n",
                            "      <td>5377</td>\n",
                            "      <td>0.072780</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>11</th>\n",
                            "      <td>ELECTRONICS</td>\n",
                            "      <td>30021</td>\n",
                            "      <td>253660.134</td>\n",
                            "      <td>5065</td>\n",
                            "      <td>0.063485</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>5</th>\n",
                            "      <td>APPAREL</td>\n",
                            "      <td>30015</td>\n",
                            "      <td>179377.057</td>\n",
                            "      <td>6526</td>\n",
                            "      <td>0.069552</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2</th>\n",
                            "      <td>APPAREL</td>\n",
                            "      <td>30012</td>\n",
                            "      <td>142589.914</td>\n",
                            "      <td>5376</td>\n",
                            "      <td>0.067130</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>6</th>\n",
                            "      <td>APPAREL</td>\n",
                            "      <td>30016</td>\n",
                            "      <td>135338.925</td>\n",
                            "      <td>4953</td>\n",
                            "      <td>0.066049</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1</th>\n",
                            "      <td>APPAREL</td>\n",
                            "      <td>30011</td>\n",
                            "      <td>124695.442</td>\n",
                            "      <td>5244</td>\n",
                            "      <td>0.063918</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>4</th>\n",
                            "      <td>APPAREL</td>\n",
                            "      <td>30014</td>\n",
                            "      <td>122099.423</td>\n",
                            "      <td>4863</td>\n",
                            "      <td>0.059041</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>3</th>\n",
                            "      <td>APPAREL</td>\n",
                            "      <td>30013</td>\n",
                            "      <td>117643.275</td>\n",
                            "      <td>4725</td>\n",
                            "      <td>0.067939</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>0</th>\n",
                            "      <td>APPAREL</td>\n",
                            "      <td>30010</td>\n",
                            "      <td>115013.878</td>\n",
                            "      <td>4881</td>\n",
                            "      <td>0.067850</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>13</th>\n",
                            "      <td>FOOD</td>\n",
                            "      <td>30023</td>\n",
                            "      <td>32036.592</td>\n",
                            "      <td>5457</td>\n",
                            "      <td>0.072159</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>14</th>\n",
                            "      <td>FOOD</td>\n",
                            "      <td>30024</td>\n",
                            "      <td>25503.106</td>\n",
                            "      <td>5393</td>\n",
                            "      <td>0.054600</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>18</th>\n",
                            "      <td>FOOD</td>\n",
                            "      <td>30028</td>\n",
                            "      <td>12300.500</td>\n",
                            "      <td>1825</td>\n",
                            "      <td>0.075912</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>16</th>\n",
                            "      <td>FOOD</td>\n",
                            "      <td>30026</td>\n",
                            "      <td>7590.162</td>\n",
                            "      <td>1733</td>\n",
                            "      <td>0.071429</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>27</th>\n",
                            "      <td>HEALTH &amp; BEAUTY</td>\n",
                            "      <td>30007</td>\n",
                            "      <td>7567.950</td>\n",
                            "      <td>1262</td>\n",
                            "      <td>0.077439</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>28</th>\n",
                            "      <td>HEALTH &amp; BEAUTY</td>\n",
                            "      <td>30008</td>\n",
                            "      <td>6932.670</td>\n",
                            "      <td>809</td>\n",
                            "      <td>0.046429</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>15</th>\n",
                            "      <td>FOOD</td>\n",
                            "      <td>30025</td>\n",
                            "      <td>6709.978</td>\n",
                            "      <td>1590</td>\n",
                            "      <td>0.060000</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>20</th>\n",
                            "      <td>FOOD</td>\n",
                            "      <td>30030</td>\n",
                            "      <td>6657.268</td>\n",
                            "      <td>908</td>\n",
                            "      <td>0.077941</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>29</th>\n",
                            "      <td>HEALTH &amp; BEAUTY</td>\n",
                            "      <td>30009</td>\n",
                            "      <td>6508.176</td>\n",
                            "      <td>1057</td>\n",
                            "      <td>0.060284</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>21</th>\n",
                            "      <td>HEALTH &amp; BEAUTY</td>\n",
                            "      <td>30001</td>\n",
                            "      <td>6366.025</td>\n",
                            "      <td>927</td>\n",
                            "      <td>0.080153</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>25</th>\n",
                            "      <td>HEALTH &amp; BEAUTY</td>\n",
                            "      <td>30005</td>\n",
                            "      <td>6156.467</td>\n",
                            "      <td>938</td>\n",
                            "      <td>0.081679</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>26</th>\n",
                            "      <td>HEALTH &amp; BEAUTY</td>\n",
                            "      <td>30006</td>\n",
                            "      <td>5917.626</td>\n",
                            "      <td>1046</td>\n",
                            "      <td>0.091216</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>24</th>\n",
                            "      <td>HEALTH &amp; BEAUTY</td>\n",
                            "      <td>30004</td>\n",
                            "      <td>5850.822</td>\n",
                            "      <td>967</td>\n",
                            "      <td>0.070000</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>22</th>\n",
                            "      <td>HEALTH &amp; BEAUTY</td>\n",
                            "      <td>30002</td>\n",
                            "      <td>5129.045</td>\n",
                            "      <td>1028</td>\n",
                            "      <td>0.061268</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>23</th>\n",
                            "      <td>HEALTH &amp; BEAUTY</td>\n",
                            "      <td>30003</td>\n",
                            "      <td>4347.531</td>\n",
                            "      <td>838</td>\n",
                            "      <td>0.052066</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>17</th>\n",
                            "      <td>FOOD</td>\n",
                            "      <td>30027</td>\n",
                            "      <td>2986.452</td>\n",
                            "      <td>1627</td>\n",
                            "      <td>0.059350</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>19</th>\n",
                            "      <td>FOOD</td>\n",
                            "      <td>30029</td>\n",
                            "      <td>2260.552</td>\n",
                            "      <td>1611</td>\n",
                            "      <td>0.048760</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "   PRODUCT CATEGORY  PRODUCT NUM  total_spent  QUANTITY PURCHASED  \\\n",
                            "9       ELECTRONICS        30019   287032.326                5721   \n",
                            "12      ELECTRONICS        30022   271710.684                5294   \n",
                            "10      ELECTRONICS        30020   270412.876                5712   \n",
                            "7       ELECTRONICS        30017   267335.302                5645   \n",
                            "8       ELECTRONICS        30018   257041.100                5377   \n",
                            "11      ELECTRONICS        30021   253660.134                5065   \n",
                            "5           APPAREL        30015   179377.057                6526   \n",
                            "2           APPAREL        30012   142589.914                5376   \n",
                            "6           APPAREL        30016   135338.925                4953   \n",
                            "1           APPAREL        30011   124695.442                5244   \n",
                            "4           APPAREL        30014   122099.423                4863   \n",
                            "3           APPAREL        30013   117643.275                4725   \n",
                            "0           APPAREL        30010   115013.878                4881   \n",
                            "13             FOOD        30023    32036.592                5457   \n",
                            "14             FOOD        30024    25503.106                5393   \n",
                            "18             FOOD        30028    12300.500                1825   \n",
                            "16             FOOD        30026     7590.162                1733   \n",
                            "27  HEALTH & BEAUTY        30007     7567.950                1262   \n",
                            "28  HEALTH & BEAUTY        30008     6932.670                 809   \n",
                            "15             FOOD        30025     6709.978                1590   \n",
                            "20             FOOD        30030     6657.268                 908   \n",
                            "29  HEALTH & BEAUTY        30009     6508.176                1057   \n",
                            "21  HEALTH & BEAUTY        30001     6366.025                 927   \n",
                            "25  HEALTH & BEAUTY        30005     6156.467                 938   \n",
                            "26  HEALTH & BEAUTY        30006     5917.626                1046   \n",
                            "24  HEALTH & BEAUTY        30004     5850.822                 967   \n",
                            "22  HEALTH & BEAUTY        30002     5129.045                1028   \n",
                            "23  HEALTH & BEAUTY        30003     4347.531                 838   \n",
                            "17             FOOD        30027     2986.452                1627   \n",
                            "19             FOOD        30029     2260.552                1611   \n",
                            "\n",
                            "    average_discount  \n",
                            "9           0.063383  \n",
                            "12          0.053772  \n",
                            "10          0.067970  \n",
                            "7           0.065677  \n",
                            "8           0.072780  \n",
                            "11          0.063485  \n",
                            "5           0.069552  \n",
                            "2           0.067130  \n",
                            "6           0.066049  \n",
                            "1           0.063918  \n",
                            "4           0.059041  \n",
                            "3           0.067939  \n",
                            "0           0.067850  \n",
                            "13          0.072159  \n",
                            "14          0.054600  \n",
                            "18          0.075912  \n",
                            "16          0.071429  \n",
                            "27          0.077439  \n",
                            "28          0.046429  \n",
                            "15          0.060000  \n",
                            "20          0.077941  \n",
                            "29          0.060284  \n",
                            "21          0.080153  \n",
                            "25          0.081679  \n",
                            "26          0.091216  \n",
                            "24          0.070000  \n",
                            "22          0.061268  \n",
                            "23          0.052066  \n",
                            "17          0.059350  \n",
                            "19          0.048760  "
                        ]
                    },
                    "execution_count": 21,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "product_view.sort_values(['total_spent','QUANTITY PURCHASED'],ascending=False)"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# Let's generate here some visualization for the data to understand it."
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# How Many males and females are enrolled in Loyalty"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 22,
            "metadata": {},
            "outputs": [],
            "source": [
                "table= pd.crosstab(customer_view['GENDER'],customer_view['LOYALTY GROUP'])"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 23,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": "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",
                        "text/plain": [
                            "<Figure size 432x288 with 1 Axes>"
                        ]
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": [
                "table.div(table.sum(1).astype(float), axis=0).plot(kind='bar', stacked=True)\n",
                "plt.show()\n",
                "# Comment: the Gender field is not a strong predictor as the class label is 50% split among each gender type"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 24,
            "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>LOYALTY GROUP</th>\n",
                            "      <th>enrolled</th>\n",
                            "      <th>not enrolled</th>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>GENDER</th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>0</th>\n",
                            "      <td>131</td>\n",
                            "      <td>120</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1</th>\n",
                            "      <td>133</td>\n",
                            "      <td>116</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "LOYALTY GROUP  enrolled  not enrolled\n",
                            "GENDER                               \n",
                            "0                   131           120\n",
                            "1                   133           116"
                        ]
                    },
                    "execution_count": 24,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "table"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# How many w.r.t.o marital status are enrolled into loyalty"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 25,
            "metadata": {},
            "outputs": [],
            "source": [
                "table= pd.crosstab(customer_view['MARITAL STATUS'],customer_view['LOYALTY GROUP'])"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 26,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 432x288 with 1 Axes>"
                        ]
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": [
                "table.div(table.sum(1).astype(float), axis=0).plot(kind='bar', stacked=True)\n",
                "plt.show()\n",
                "#Comment: the Marital Status is not a strong predictor as the class labels are nearly half split among each state"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 27,
            "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>LOYALTY GROUP</th>\n",
                            "      <th>enrolled</th>\n",
                            "      <th>not enrolled</th>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>MARITAL STATUS</th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>Divorced</th>\n",
                            "      <td>15</td>\n",
                            "      <td>22</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>Married</th>\n",
                            "      <td>144</td>\n",
                            "      <td>123</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>Single</th>\n",
                            "      <td>96</td>\n",
                            "      <td>84</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>Widow/Widower</th>\n",
                            "      <td>9</td>\n",
                            "      <td>7</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "LOYALTY GROUP   enrolled  not enrolled\n",
                            "MARITAL STATUS                        \n",
                            "Divorced              15            22\n",
                            "Married              144           123\n",
                            "Single                96            84\n",
                            "Widow/Widower          9             7"
                        ]
                    },
                    "execution_count": 27,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "table"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# Perform Binning of Age in the Customer View dataset"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 28,
            "metadata": {},
            "outputs": [],
            "source": [
                "customer_view['AGE_BINNED'] = pd.cut(customer_view['AGE'],20) # 10 bins of age"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 29,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": [
                            "<div>\n",
                            "<style scoped>\n",
                            "    .dataframe tbody tr th:only-of-type {\n",
                            "        vertical-align: middle;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe tbody tr th {\n",
                            "        vertical-align: top;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe thead th {\n",
                            "        text-align: right;\n",
                            "    }\n",
                            "</style>\n",
                            "<table border=\"1\" class=\"dataframe\">\n",
                            "  <thead>\n",
                            "    <tr style=\"text-align: right;\">\n",
                            "      <th></th>\n",
                            "      <th>CUSTOMER NUM</th>\n",
                            "      <th>GENDER</th>\n",
                            "      <th>AGE</th>\n",
                            "      <th>INCOME</th>\n",
                            "      <th>EXPERIENCE SCORE</th>\n",
                            "      <th>LOYALTY GROUP</th>\n",
                            "      <th>HOUSEHOLD SIZE</th>\n",
                            "      <th>MARITAL STATUS</th>\n",
                            "      <th>total_spent</th>\n",
                            "      <th>purchase_count</th>\n",
                            "      <th>tx_count</th>\n",
                            "      <th>spent_pcntg_income</th>\n",
                            "      <th>AGE_BINNED</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>0</th>\n",
                            "      <td>10001</td>\n",
                            "      <td>0</td>\n",
                            "      <td>64</td>\n",
                            "      <td>133498</td>\n",
                            "      <td>5</td>\n",
                            "      <td>enrolled</td>\n",
                            "      <td>4</td>\n",
                            "      <td>Single</td>\n",
                            "      <td>6826.770</td>\n",
                            "      <td>396</td>\n",
                            "      <td>27</td>\n",
                            "      <td>5.113762</td>\n",
                            "      <td>(61.2, 64.8]</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1</th>\n",
                            "      <td>10002</td>\n",
                            "      <td>0</td>\n",
                            "      <td>42</td>\n",
                            "      <td>94475</td>\n",
                            "      <td>9</td>\n",
                            "      <td>not enrolled</td>\n",
                            "      <td>6</td>\n",
                            "      <td>Married</td>\n",
                            "      <td>5062.451</td>\n",
                            "      <td>202</td>\n",
                            "      <td>24</td>\n",
                            "      <td>5.358509</td>\n",
                            "      <td>(39.6, 43.2]</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2</th>\n",
                            "      <td>10003</td>\n",
                            "      <td>0</td>\n",
                            "      <td>40</td>\n",
                            "      <td>88610</td>\n",
                            "      <td>9</td>\n",
                            "      <td>enrolled</td>\n",
                            "      <td>5</td>\n",
                            "      <td>Married</td>\n",
                            "      <td>8562.440</td>\n",
                            "      <td>265</td>\n",
                            "      <td>23</td>\n",
                            "      <td>9.663063</td>\n",
                            "      <td>(39.6, 43.2]</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>3</th>\n",
                            "      <td>10004</td>\n",
                            "      <td>0</td>\n",
                            "      <td>38</td>\n",
                            "      <td>84313</td>\n",
                            "      <td>8</td>\n",
                            "      <td>enrolled</td>\n",
                            "      <td>1</td>\n",
                            "      <td>Single</td>\n",
                            "      <td>5522.694</td>\n",
                            "      <td>197</td>\n",
                            "      <td>19</td>\n",
                            "      <td>6.550228</td>\n",
                            "      <td>(36.0, 39.6]</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>4</th>\n",
                            "      <td>10005</td>\n",
                            "      <td>0</td>\n",
                            "      <td>30</td>\n",
                            "      <td>51498</td>\n",
                            "      <td>3</td>\n",
                            "      <td>not enrolled</td>\n",
                            "      <td>1</td>\n",
                            "      <td>Single</td>\n",
                            "      <td>213.512</td>\n",
                            "      <td>12</td>\n",
                            "      <td>2</td>\n",
                            "      <td>0.414603</td>\n",
                            "      <td>(28.8, 32.4]</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>...</th>\n",
                            "      <td>...</td>\n",
                            "      <td>...</td>\n",
                            "      <td>...</td>\n",
                            "      <td>...</td>\n",
                            "      <td>...</td>\n",
                            "      <td>...</td>\n",
                            "      <td>...</td>\n",
                            "      <td>...</td>\n",
                            "      <td>...</td>\n",
                            "      <td>...</td>\n",
                            "      <td>...</td>\n",
                            "      <td>...</td>\n",
                            "      <td>...</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>495</th>\n",
                            "      <td>10496</td>\n",
                            "      <td>0</td>\n",
                            "      <td>47</td>\n",
                            "      <td>113125</td>\n",
                            "      <td>7</td>\n",
                            "      <td>enrolled</td>\n",
                            "      <td>2</td>\n",
                            "      <td>Single</td>\n",
                            "      <td>8501.464</td>\n",
                            "      <td>309</td>\n",
                            "      <td>23</td>\n",
                            "      <td>7.515106</td>\n",
                            "      <td>(46.8, 50.4]</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>496</th>\n",
                            "      <td>10497</td>\n",
                            "      <td>0</td>\n",
                            "      <td>49</td>\n",
                            "      <td>118108</td>\n",
                            "      <td>7</td>\n",
                            "      <td>enrolled</td>\n",
                            "      <td>1</td>\n",
                            "      <td>Single</td>\n",
                            "      <td>9903.658</td>\n",
                            "      <td>360</td>\n",
                            "      <td>27</td>\n",
                            "      <td>8.385256</td>\n",
                            "      <td>(46.8, 50.4]</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>497</th>\n",
                            "      <td>10498</td>\n",
                            "      <td>0</td>\n",
                            "      <td>42</td>\n",
                            "      <td>97353</td>\n",
                            "      <td>8</td>\n",
                            "      <td>enrolled</td>\n",
                            "      <td>5</td>\n",
                            "      <td>Married</td>\n",
                            "      <td>6251.466</td>\n",
                            "      <td>337</td>\n",
                            "      <td>29</td>\n",
                            "      <td>6.421442</td>\n",
                            "      <td>(39.6, 43.2]</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>498</th>\n",
                            "      <td>10499</td>\n",
                            "      <td>1</td>\n",
                            "      <td>22</td>\n",
                            "      <td>34424</td>\n",
                            "      <td>1</td>\n",
                            "      <td>not enrolled</td>\n",
                            "      <td>2</td>\n",
                            "      <td>Single</td>\n",
                            "      <td>20.010</td>\n",
                            "      <td>3</td>\n",
                            "      <td>2</td>\n",
                            "      <td>0.058128</td>\n",
                            "      <td>(21.6, 25.2]</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>499</th>\n",
                            "      <td>10500</td>\n",
                            "      <td>1</td>\n",
                            "      <td>34</td>\n",
                            "      <td>75114</td>\n",
                            "      <td>9</td>\n",
                            "      <td>not enrolled</td>\n",
                            "      <td>3</td>\n",
                            "      <td>Divorced</td>\n",
                            "      <td>4182.032</td>\n",
                            "      <td>136</td>\n",
                            "      <td>20</td>\n",
                            "      <td>5.567580</td>\n",
                            "      <td>(32.4, 36.0]</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "<p>500 rows × 13 columns</p>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "     CUSTOMER NUM  GENDER  AGE  INCOME  EXPERIENCE SCORE LOYALTY GROUP  \\\n",
                            "0           10001       0   64  133498                 5      enrolled   \n",
                            "1           10002       0   42   94475                 9  not enrolled   \n",
                            "2           10003       0   40   88610                 9      enrolled   \n",
                            "3           10004       0   38   84313                 8      enrolled   \n",
                            "4           10005       0   30   51498                 3  not enrolled   \n",
                            "..            ...     ...  ...     ...               ...           ...   \n",
                            "495         10496       0   47  113125                 7      enrolled   \n",
                            "496         10497       0   49  118108                 7      enrolled   \n",
                            "497         10498       0   42   97353                 8      enrolled   \n",
                            "498         10499       1   22   34424                 1  not enrolled   \n",
                            "499         10500       1   34   75114                 9  not enrolled   \n",
                            "\n",
                            "     HOUSEHOLD SIZE MARITAL STATUS  total_spent  purchase_count  tx_count  \\\n",
                            "0                 4         Single     6826.770             396        27   \n",
                            "1                 6        Married     5062.451             202        24   \n",
                            "2                 5        Married     8562.440             265        23   \n",
                            "3                 1         Single     5522.694             197        19   \n",
                            "4                 1         Single      213.512              12         2   \n",
                            "..              ...            ...          ...             ...       ...   \n",
                            "495               2         Single     8501.464             309        23   \n",
                            "496               1         Single     9903.658             360        27   \n",
                            "497               5        Married     6251.466             337        29   \n",
                            "498               2         Single       20.010               3         2   \n",
                            "499               3       Divorced     4182.032             136        20   \n",
                            "\n",
                            "     spent_pcntg_income    AGE_BINNED  \n",
                            "0              5.113762  (61.2, 64.8]  \n",
                            "1              5.358509  (39.6, 43.2]  \n",
                            "2              9.663063  (39.6, 43.2]  \n",
                            "3              6.550228  (36.0, 39.6]  \n",
                            "4              0.414603  (28.8, 32.4]  \n",
                            "..                  ...           ...  \n",
                            "495            7.515106  (46.8, 50.4]  \n",
                            "496            8.385256  (46.8, 50.4]  \n",
                            "497            6.421442  (39.6, 43.2]  \n",
                            "498            0.058128  (21.6, 25.2]  \n",
                            "499            5.567580  (32.4, 36.0]  \n",
                            "\n",
                            "[500 rows x 13 columns]"
                        ]
                    },
                    "execution_count": 29,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "customer_view"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 30,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 432x288 with 1 Axes>"
                        ]
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": [
                "table= pd.crosstab(customer_view['AGE_BINNED'],customer_view['LOYALTY GROUP'])\n",
                "table.div(table.sum(1).astype(float), axis=0).plot(kind='bar', stacked=True)\n",
                "plt.show() \n",
                "### the figure below indicates that the Age could be a good contributor to classification"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 31,
            "metadata": {},
            "outputs": [],
            "source": [
                "#customer_view['INCOME'].describe()\n",
                "customer_view['INCOME_BINNED'] =pd.cut(customer_view['INCOME'],15)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 32,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 432x288 with 1 Axes>"
                        ]
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": [
                "table= pd.crosstab(customer_view['INCOME_BINNED'],customer_view['LOYALTY GROUP'])\n",
                "table.div(table.sum(1).astype(float), axis=0).plot(kind='bar', stacked=True)\n",
                "plt.show() \n",
                "#the variation seen below i dicates that income could be a better contributor in classification"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 33,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 432x288 with 1 Axes>"
                        ]
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": [
                "table= pd.crosstab(customer_view['EXPERIENCE SCORE'],customer_view['LOYALTY GROUP'])\n",
                "table.div(table.sum(1).astype(float), axis=0).plot(kind='bar', stacked=True)\n",
                "plt.show()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 34,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAEDCAYAAAAlRP8qAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAAAc+0lEQVR4nO3dfZyNdf7H8dfHIKTS0q3JzrCjjLuJyU1J/LRSKu1upJslu5FNN1uPLL/tt6pN27bp163yU4ldwqY7tbrf2kTJTSOGyF2alJBIRsLn98c5puM4Y85wZsZ8vZ+Ph4dzXdf3XN/PdfB2zXW+1/cyd0dERCq/KhVdgIiIpIYCXUQkEAp0EZFAKNBFRAKhQBcRCYQCXUQkEFUrquN69ep5RkZGRXUvIlIpzZ07d727H5NoW4UFekZGBnPmzKmo7kVEKiUz+7S4bbrkIiISCAW6iEggFOgiIoGosGvoInLw++GHHygoKGDbtm0VXcohp0aNGqSnp1OtWrWk36NAF5FiFRQUcMQRR5CRkYGZVXQ5hwx3Z8OGDRQUFJCZmZn0+0q85GJmY8zsKzNbWMx2M7MHzWyZmX1kZq1KUbeIHMS2bdtG3bp1FeblzMyoW7duqX8ySuYa+lig2z62nwtkRX8NAB4tVQUiclBTmFeM/fncSwx0d38H+HofTXoAf/eI94E6ZnZCqSsRkUqpdu3ae63btGkTffr0oVGjRjRq1Ig+ffqwadMmRo8ezSWXXFLUbvPmzTRq1IiVK1cC0KNHD9q3b7/Hvm677TZGjBgBwJNPPklOTg45OTlUr16d5s2bk5OTQ8+ePWncuDGFhYVF7+vevTuTJk3aq7YPPviATp06kZWVRatWrejevTsLFiwo6qt+/frk5OSQnZ3NxIkTi97n7gwfPpysrCwaN25M586dyc/PL/ZzGDt2LNdee+1e+23WrBlTp05N7sMtpVRcQ68PfBazXBBd90V8QzMbQOQsngYNGuxfb7cdtX/v21+3bSrn/nR8qe0v4OMr72Nb82Hi9b5rr22/7T+YZqc04u/TpwBw64hHueryi/nn/93NuMce4Y1Jozi7Y1uGDbuH31zcjczDvuGbxe8wb/Ysah9ek5Xvv0Rmu/P36qpfv37069cPiNyc+NZbb1GvXj0Ahg4dyp133snw4cN5/vnn+eGHH+jdu/ce71+7di29evXiqaee4vTTTwfg3XffZfny5TSvuwO+/YIbf9uLmwf24ZMVq2l97uVcfHojqlWrxsgnJzHzrRnMf2UctWrW5LX/vMeF3buR/++nqVHjsL0/h42fwnfrIuti9rv4kxWc+Yu+fLV+A1WqpHagYSr2lujngoSPQXL30e6e6+65xxyT8M5VEanklq1czdwFi/nT7/sXrRt24wDmfLSIFZ8W8Ohd/83vbxvBnPmLeHPGbAb/ri8Az0x7kwt+3pHePc5h0guvlrrfYcOG8fTTT5OXl8fQoUMZOXLkXm0efvhh+vbtWxTmAB06dOCiiy7aq21WwwbUqlmDjZu+BeDukeN46I4/UKtmTQC6ntWe01u3YMJzL5eqziZZDalaNY3169eX6n3JSEWgFwAnxSynA2tSsF8RqYQWfbKSnKYnk5aWVrQuLS2NnKYnk790BS2yG3POWe3pcslAHvzzYKpXjwzLm/j8K1x60Tlc2qMbE194pdT91qpVixEjRtCxY0d69+5NVlbWXm3y8/Np1Sq5cRvzFiwmK/Mkjq33EzZ/u4XvCgtplHHSHm1yW2aTv2R5qeqcNW8BVaoYZXFSm4pLLlOBa81sEtAW2OTue11uEZFDg7uT6Ps8dy/6cX7Qlb14+a0ZdD7jNADWrtvAslWf0aHNqZgZVdOqsnDhQpo1a1aqvi+44ALq1KnDNddck1T7tm3bsnnzZrp27coDQ64E4L7HJvDYhOdYsfpzXhn/0D7fHznW4r+8jN1232MTGP/MNI6oXYvJj/61TL5sTmbY4kTgPeBkMysws9+a2UAzGxhtMg1YASwDHgOS+yRFJEhNGzfkw4VL2LVrV9G6Xbt2MX/RUppkRcZUV6lSZY/rx5OnvsbGTd+S2e58Mtp2Z1XBmoRfaCYjft971Na0KfPmzStanjVrFnfccQebNv34fcSN/S9nyfTnmPzoXfT5/TC2bfueI4+ozeE1a7Li04I99jdvwcdkN24IQM0ah7F9+w9F277+ZjP1flJnj/3mvT6J6c+N4cy2ZTO6O5lRLpe6+wnuXs3d0939CXcf5e6jotvd3Qe5eyN3b+7umkJR5BD2s8wGnNrsZIY/8HjRuuEPPE6r5qfws8zEgyEmPv8Kr4x/iFWz/sWqWf9i7ssT9jvQ92XQoEGMHTuWmTNnFq3bunVrwra/PK8LuS2yGff0iwAM/l0frv/T3ygsjIwNf+OdWbw7O4/LLoqM6j6rXWvGP/svAAoLt/HPF1+j8+m5KT+GfdGdoiJyQLYWbiO99Y+3qtw04AqeGHEr1/3pbn52xoW4Q/vWzXlixK0J37/qszWsXvMl7Vq3KFqX2aA+Rx55JLNmzQJg+PDh3H///UXbCwoK4neTlOOPP57JkyczZMgQPv/8c4499ljq1avHsGHDErYfdmN/Lht0C/0v/yXX/aY3GzdtpvnZvUirksbxx9blhTH/S82aNQB44M+DuXrIcB58YhLuTp+Lz6dju9b7Vef+MveEA1LKXG5uru/XfOga9pbi/nR8qe0vrGGLixcvpkmTJpGF4oYtlpUTTy3f/g7C49vj848ys7nunvDUX7MtiogEQoEuIhIIBbqISCAU6CIigVCgi4gEQoEuIhIIBbqIHHKuvPJKpkyJzATZqVMnSjOE+u2Zczi/z/VlVdoB0Y1FIpK0jAdTO+/equtPTOn+Yu3YsYOqVQ+tiNMZuogc1MaPH0+bNm3Iycnh6quvZufOndSuXZtbbrmFli1b0q5dO9auXQtEzrxvuukmOnfuzJAhQ8jLy6Ndu3a0aNGCX/ziF2zcuHGffb32n/dof0FfWp1zGT0H/IEt30WmBXjlrRmc0vGXdLjoNzz78r/L/Jj3lwJdRA5aixcvZvLkycyYMYO8vDzS0tKYMGEC3333He3atWP+/Pl07NiRxx57rOg9S5cu5Y033uDee++lT58+3H333Xz00Uc0b96c22+/vdi+1n+9keEPPM4bk0cx79WnyG2Zzf+OHs+2bd/Tf/BwXhx7P9Ofe4Ivv9pQHoe+Xw6tn0dEpFJ58803mTt3LqedFplmt7CwkGOPPZbq1atz/vmRJxq1bt2a119/veg9PXv2JC0tjU2bNvHNN99w1llnAdC3b1969uxZbF/vz13AoqUrOaNH5IlI23/4gfatW/DxslVkNjiRrIaRicWu+NV5jB7/TJkc74FSoIvIQcvd6du3L3fdddce60eMGFE0n3haWho7duwo2nb44Yfvd18/79iWiY/s2VfewiWV5kHZuuQiIgetLl26MGXKFL766isAvv76az799NOk3nvUUUdx9NFHM336dAD+8Y9/FJ2tJ9KudQtmzJ7PspWrAdhaWMjS5Z9yys8yWLl6DctXRR6dPPH50j9NqbzoDF1EDlrZ2dkMHz6crl27smvXrsjDmhM8K7Q448aNY+DAgWzdupWGDRvy5JNPFtv2mLpHM/a+27h00B/5fvt2AIb/YRCNG/2U0X+7he59bqDeT+rQoU0OCz9edsDHVhY0fW6J/QU8/Sro+FLen6bPTRlNn6vpc0VEDlUKdBGRQCjQRUQCoUAXEQmEAl1EJBAKdBGRQCjQRSQoY8eOZc2a1M4KWZLYKXXHTp7Ktbf8tVTvz8jIYP369Qdch24sEpHkje6U2v0NeDu1+yMS6M2aNePEE1M7Ne/OnTtJS0tL6T5TTWfoInLQWrVqFU2aNKF///40bdqUrl27UlhYCJBwatwpU6YwZ84cLr/8cnJycora7rZ8+XK6detG69atOfPMM/n444+ByLS71//pb5x+4ZU0bH8BU156A4iceXe+eACXDfojzbv0Ytu27+l3460079KLU7teylszZu+z/nUbNvKr/jdz2nlXcNp5VzBjdh4AG77+hq5du3Lqqady9dVXk6obPBXoInJQ++STTxg0aBD5+fnUqVOHZ56JzHSYaGrciy++mNzcXCZMmEBeXh41a9bcY18DBgzgoYceYu7cuYwYMYJrrrmmaNsXa9fz7vNjeGncAwy968Gi9R/kLeTOIYNY9PYzjBz7TwAWvPlPJj7yF/r+/la2bfu+2NpvGHYPN/a/nNnTxvPMY/dw1c1/BuD2+0bToUMHPvzwQy688EJWr16dks9Kl1xE5KCWmZlJTk4OEJkqd9WqVaWeGhdgy5YtzJw5c49233//Yxhf1K0TVapUIbtxQ9au+7pofZucZmQ2qA/Au7PzuK7fJQCc8rNMfpp+PEtXFD9Z2BvTZ7Fo6Yqi5c1bvuPbLd/xzvvzePaWOwHo3r07Rx99dDIfRYkU6CJyUDvssMOKXqelpe11GSVZu3btok6dOuTl5SXup3r1otexl0AOr1Uj4frk+nTemzqWmjVr7LWtLKbk1SUXEal09jU17hFHHMG3336713uOPPJIMjMzefrpp4FIOM+fP79U/XZs24oJz70MwNLln7L68y85uVFGse27ntWOh8dOLlrOW7gksp92rZgwYQIAL7/8comPxkuWAl1EKqVx48YxePBgWrRoQV5eHsOGDQMiX3AOHDgw4ZeiEyZM4IknnqBly5Y0bdqUF154oVR9XtO3Jzt37qJ5l15c8ruhjL3vdg47rHqx7R+8YzBz5i+ixdm9yO70K0b9YwoAt944gHfeeYdWrVrx2muv0aBBg1IefWKaPrfE/gKefhV0fCnvT9Pnpoymzy2b6XPNrJuZLTGzZWY2NMH2o8zsRTObb2b5ZtYvmf2KiEjqlBjoZpYGjATOBbKBS80sO67ZIGCRu7cEOgH3mlnxP4eIiEjKJXOG3gZY5u4r3H07MAnoEdfGgSMs8rVtbeBrYAciIlJukgn0+sBnMcsF0XWxHgaaAGuABcAN7r4rJRWKSIWqqO/ZDnX787knE+iJBkvG93QOkAecCOQAD5vZkXvtyGyAmc0xsznr1q0rZakiUt5q1KjBhg0bFOrlzN3ZsGEDNWrsPX59X5K5sagAOClmOZ3ImXisfsBfPfKnvszMVgKnAB/EFTkaGA2RUS6lqlREyl16ejoFBQWsW7cOvvmqfDvftLh8+zvIjq9GjRqkp6eXapfJBPpsIMvMMoHPgd7AZXFtVgNdgOlmdhxwMrACEanUqlWrRmZmZmThtnbl23m5Dzmt/MdXYqC7+w4zuxZ4FUgDxrh7vpkNjG4fBdwBjDWzBUQu0Qxx9wOf3FdERJKW1Fwu7j4NmBa3blTM6zVA19SWJiIipaFb/0VEAqFAFxEJhAJdRCQQCnQRkUAo0EVEAqFAFxEJhAJdRCQQCnQRkUAo0EVEAqFAFxEJhAJdRCQQCnQRkUAo0EVEAqFAFxEJhAJdRCQQCnQRkUAo0EVEAqFAFxEJhAJdRCQQCnQRkUAo0EVEAqFAFxEJhAJdRCQQCnQRkUAo0EVEAqFAFxEJhAJdRCQQCnQRkUAo0EVEAqFAFxEJhAJdRCQQCnQRkUAkFehm1s3MlpjZMjMbWkybTmaWZ2b5Zvaf1JYpIiIlqVpSAzNLA0YCPwcKgNlmNtXdF8W0qQM8AnRz99VmdmwZ1SsiIsVI5gy9DbDM3Ve4+3ZgEtAjrs1lwLPuvhrA3b9KbZkiIlKSZAK9PvBZzHJBdF2sxsDRZva2mc01sz6pKlBERJJT4iUXwBKs8wT7aQ10AWoC75nZ++6+dI8dmQ0ABgA0aNCg9NWKiEixkjlDLwBOillOB9YkaPOKu3/n7uuBd4CW8Tty99Hunuvuucccc8z+1iwiIgkkE+izgSwzyzSz6kBvYGpcmxeAM82sqpnVAtoCi1NbqoiI7EuJl1zcfYeZXQu8CqQBY9w938wGRrePcvfFZvYK8BGwC3jc3ReWZeEiIrKnZK6h4+7TgGlx60bFLd8D3JO60kREpDR0p6iISCAU6CIigUjqkouUn4xtT5Vrf6vKtbfwj0+kIukMXUQkEAp0EZFAKNBFRAKhQBcRCYQCXUQkEAp0EZFAKNBFRAKhcegiKVSe4+xXlVtPUlnoDF1EJBAKdBGRQCjQRUQCoUAXEQmEAl1EJBAa5SIiQhgzgVa6QA/hQxcRKQu65CIiEggFuohIIBToIiKBUKCLiARCgS4iEggFuohIIBToIiKBUKCLiASi0t1YJCIVQzf1Hfx0hi4iEggFuohIIBToIiKBUKCLiARCgS4iEggFuohIIJIKdDPrZmZLzGyZmQ3dR7vTzGynmV2cuhJFRCQZJQa6maUBI4FzgWzgUjPLLqbd3cCrqS5SRERKlswZehtgmbuvcPftwCSgR4J21wHPAF+lsD4REUlSMoFeH/gsZrkguq6ImdUHfgGMSl1pIiJSGskEuiVY53HL9wND3H3nPndkNsDM5pjZnHXr1iVZooiIJCOZuVwKgJNiltOBNXFtcoFJZgZQDzjPzHa4+/Oxjdx9NDAaIDc3N/4/BREROQDJBPpsIMvMMoHPgd7AZbEN3D1z92szGwu8FB/mIiJStkoMdHffYWbXEhm9kgaMcfd8MxsY3a7r5iIiB4Gkps9192nAtLh1CYPc3a888LJERKS0dKeoiEggFOgiIoFQoIuIBEKBLiISCAW6iEggFOgiIoFQoIuIBEKBLiISCAW6iEggFOgiIoFQoIuIBEKBLiISCAW6iEggFOgiIoFQoIuIBEKBLiISCAW6iEggFOgiIoFQoIuIBEKBLiISCAW6iEggFOgiIoFQoIuIBEKBLiISCAW6iEggFOgiIoFQoIuIBEKBLiISCAW6iEggFOgiIoFQoIuIBEKBLiISiKQC3cy6mdkSM1tmZkMTbL/czD6K/pppZi1TX6qIiOxLiYFuZmnASOBcIBu41Myy45qtBM5y9xbAHcDoVBcqIiL7lswZehtgmbuvcPftwCSgR2wDd5/p7huji+8D6aktU0RESpJMoNcHPotZLoiuK85vgZcPpCgRESm9qkm0sQTrPGFDs85EAr1DMdsHAAMAGjRokGSJIiKSjGTO0AuAk2KW04E18Y3MrAXwONDD3Tck2pG7j3b3XHfPPeaYY/anXhERKUYygT4byDKzTDOrDvQGpsY2MLMGwLPAr919aerLFBGRkpR4ycXdd5jZtcCrQBowxt3zzWxgdPsoYBhQF3jEzAB2uHtu2ZUtIiLxkrmGjrtPA6bFrRsV8/oq4KrUliYiIqWhO0VFRAKhQBcRCYQCXUQkEAp0EZFAKNBFRAKhQBcRCYQCXUQkEAp0EZFAKNBFRAKhQBcRCYQCXUQkEAp0EZFAKNBFRAKhQBcRCYQCXUQkEAp0EZFAKNBFRAKhQBcRCYQCXUQkEAp0EZFAKNBFRAKhQBcRCYQCXUQkEAp0EZFAKNBFRAKhQBcRCYQCXUQkEAp0EZFAKNBFRAKhQBcRCYQCXUQkEAp0EZFAKNBFRAKRVKCbWTczW2Jmy8xsaILtZmYPRrd/ZGatUl+qiIjsS4mBbmZpwEjgXCAbuNTMsuOanQtkRX8NAB5NcZ0iIlKCZM7Q2wDL3H2Fu28HJgE94tr0AP7uEe8DdczshBTXKiIi+1A1iTb1gc9ilguAtkm0qQ98EdvIzAYQOYMH2GJmS0pV7YGpB6wv7Zvs7jKopGzo+BKoJMcX8rGBji+hAzi+nxa3IZlAtwTrfD/a4O6jgdFJ9JlyZjbH3XMrou/yoOOrvEI+NtDxladkLrkUACfFLKcDa/ajjYiIlKFkAn02kGVmmWZWHegNTI1rMxXoEx3t0g7Y5O5fxO9IRETKTomXXNx9h5ldC7wKpAFj3D3fzAZGt48CpgHnAcuArUC/sit5v1XIpZ5ypOOrvEI+NtDxlRtz3+tSt4iIVEK6U1REJBAKdBGRQCjQRUQCoUCvpMzsFDPrYma149Z3q6iaUsXM2pjZadHX2WZ2k5mdV9F1lRUz+3tF11BWzKxD9M+va0XXkgpm1tbMjoy+rmlmt5vZi2Z2t5kdVeH1HWpfippZP3d/sqLrOBBmdj0wCFgM5AA3uPsL0W3z3L3STo5mZrcSmRuoKvA6kbuS3wbOBl519zsrrroDZ2bxQ34N6Az8G8DdLyz3olLIzD5w9zbR1/2J/D19DugKvOjuf63I+g6UmeUDLaOj/0YTGdU3BegSXf/LCq3vEAz01e7eoKLrOBBmtgBo7+5bzCyDyF+of7j7A2b2obufWrEV7r/oseUAhwFfAunuvtnMagKz3L1FRdZ3oMxsHrAIeJzI3dQGTCRyfwfu/p+Kq+7Axf79M7PZwHnuvs7MDgfed/fmFVvhgTGzxe7eJPp6j5MnM8tz95wKK47kbv2vdMzso+I2AceVZy1lJM3dtwC4+yoz6wRMMbOfkngahspkh7vvBLaa2XJ33wzg7oVmtquCa0uFXOAG4BZgsLvnmVlhZQ/yGFXM7Ggil3PN3dcBuPt3ZrajYktLiYUxP+XPN7Ncd59jZo2BHyq6uCADnUhonwNsjFtvwMzyLyflvjSzHHfPA4ieqZ8PjAEq9RkQsN3Marn7VqD17pXR65OVPtDdfRdwn5k9Hf19LWH9OzwKmEvk35qb2fHu/mX0u57KfrIBcBXwgJn9D5EJud4zs8+ITE54VYVWRqCXXMzsCeBJd383wban3P2yCigrZcwsnciZ7JcJtp3h7jMqoKyUMLPD3P37BOvrASe4+4IKKKvMmFl34Ax3/2NF11KWzKwWcJy7r6zoWlLBzI4AGhL5z7jA3ddWcElAoIEuInIo0rBFEZFAKNBFRAKhQJdyYWZb4pavNLOHY5YHmNnH0V8fmFmHmG2rotfQdy93MrOXoq+PM7OXzGy+mS0ys2nR9RlmVmhmeTG/+pS0v+jyRdGHnX9sZgvM7KKYbWPN7OK4Y9nd14dmtjhaf99iPodaZjYhut+FZvbu7pvDdn9GZjYoru6FZuZm1iRa66a47WeX6g9DghXSt+tSSUVH6FwNdHD39WbWCnjezNok+uI3zp+B1939gei+YsepLy/tuGAzawmMAH7u7ivNLBN43cxWuHtxw2F397V7/HVD4Fkzq5LgJrYbgLW7x2Ob2cnEDXdz95FEHsy+u6a/AHnuvtjMjgOmu/v5pTkuOTToDF0OBkOIjMleD+Du84BxRO4yLMkJRJ6YRfS9+wrdZNwM/GX3aIzo73cBg5PdgbuvAG4Crk+w+QTg85i2SxKN6tnNzDoCvYBrku1fDl06Q5fyUtPM8mKWf8KPT75qSmTscqw5QMLLFnFGApMt8hCWN4gMV939+MNGcX1e5+7To6/fMrOd0de1gY9jahmRoJZk/nOJNQ84JcH6McBr0cs2bwLj3P2TRDswszrAk0Cf3TdYRZ0Zd1y/cvflpaxPAqRAl/JSGHv5w8yuJHLXZHGMHx80nmhsrQO4+6vRSxzdiMwB86GZNYu22dcll867fyKI3ml7c4J+E9WSrIQ30UTvDG1IZG6Ts4HZZtbe3RcnaP4oMD7BfQW65CIJ6ZKLHAwWEXNXaFSr6HqADcDRMdt+QuQuPQDc/Wt3f8rdf03kGbgdD6CWfPb+jya2lmSdSmTytL24+xZ3f9bdrwHGE3l84x6iX6pmAHeUsl85hCnQ5WDwN+BuM6sLYGY5wJXAI9HtbwO/jm5LA64A3oou/1f0LsTdd+81AlYfQC0jgP+2yKRnRH//I3BvsjuIvmcE8FCCbWdE5zrBIg9dzwY+jWvTELgTuNzdQ5j/RMqJLrlIhXP3qWZWH5hpZg58C1zh7l9Em9wBPGpm84lcyniFyJktRM7sH7bIxE9VgMfdfXY0VOOvoY9x9wdLqCXPzIYAL5pZNSIjUP6we96cqP8zs/ujrz8DLo329SFQI1r/Q8VM09woeiwWrfdfwDNxbYYAhxMZKRO7/rro7/HX0Ie7+5R9HZccGnTrv4hIIHTJRUQkEAp0EZFAKNBFRAKhQBcRCYQCXUQkEAp0EZFAKNBFRAKhQBcRCcT/A/bPMWbVqBHwAAAAAElFTkSuQmCC",
                        "text/plain": [
                            "<Figure size 432x288 with 1 Axes>"
                        ]
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": [
                "table= pd.crosstab(customer_view['HOUSEHOLD SIZE'],customer_view['LOYALTY GROUP'])\n",
                "table.div(table.sum(1).astype(float), axis=0).plot(kind='bar', stacked=True)\n",
                "plt.show()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 35,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 432x288 with 1 Axes>"
                        ]
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": [
                "total_spent_binned=pd.cut(customer_view['total_spent'],20)\n",
                "table= pd.crosstab(total_spent_binned,customer_view['LOYALTY GROUP'])\n",
                "table.div(table.sum(1).astype(float), axis=0).plot(kind='bar', stacked=True)\n",
                "plt.show()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 36,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 432x288 with 1 Axes>"
                        ]
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": [
                "Purchase_count_binned=pd.cut(customer_view['purchase_count'],10)\n",
                "table= pd.crosstab(Purchase_count_binned,customer_view['LOYALTY GROUP'])\n",
                "table.div(table.sum(1).astype(float), axis=0).plot(kind='bar', stacked=True)\n",
                "plt.show()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 37,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 432x288 with 1 Axes>"
                        ]
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": [
                "spent_pcntg_income_binned=pd.cut(customer_view['spent_pcntg_income'],15)\n",
                "table= pd.crosstab(spent_pcntg_income_binned,customer_view['LOYALTY GROUP'])\n",
                "table.div(table.sum(1).astype(float), axis=0).plot(kind='bar', stacked=True)\n",
                "plt.show()"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# Relation between Age and product category sales"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 38,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 720x360 with 1 Axes>"
                        ]
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": [
                "#full_customer_TX_data.groupby(['AGE','INCOME']).agg({'PRODUCT CATEGORY':'count'})\n",
                "table= pd.crosstab(pd.cut(full_customer_TX_data['AGE'],20),full_customer_TX_data['PRODUCT CATEGORY'])\n",
                "\n",
                "table.div(table.sum(1).astype(float), axis=0).plot(kind='bar', stacked=True,figsize=(10,5))\n",
                "plt.show() \n",
                "\n",
                "#there is deficiency in sales of health and beauty products among ages>25\n",
                "#there is deficiency in sales of FOOD in ages between 18 years, 25 years"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 39,
            "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>PRODUCT CATEGORY</th>\n",
                            "      <th>APPAREL</th>\n",
                            "      <th>ELECTRONICS</th>\n",
                            "      <th>FOOD</th>\n",
                            "      <th>HEALTH &amp; BEAUTY</th>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>AGE</th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>(17.928, 21.6]</th>\n",
                            "      <td>266</td>\n",
                            "      <td>8</td>\n",
                            "      <td>5</td>\n",
                            "      <td>426</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>(21.6, 25.2]</th>\n",
                            "      <td>280</td>\n",
                            "      <td>23</td>\n",
                            "      <td>17</td>\n",
                            "      <td>379</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>(25.2, 28.8]</th>\n",
                            "      <td>264</td>\n",
                            "      <td>129</td>\n",
                            "      <td>66</td>\n",
                            "      <td>171</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>(28.8, 32.4]</th>\n",
                            "      <td>439</td>\n",
                            "      <td>424</td>\n",
                            "      <td>130</td>\n",
                            "      <td>18</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>(32.4, 36.0]</th>\n",
                            "      <td>519</td>\n",
                            "      <td>449</td>\n",
                            "      <td>174</td>\n",
                            "      <td>15</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>(36.0, 39.6]</th>\n",
                            "      <td>339</td>\n",
                            "      <td>254</td>\n",
                            "      <td>90</td>\n",
                            "      <td>25</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>(39.6, 43.2]</th>\n",
                            "      <td>534</td>\n",
                            "      <td>413</td>\n",
                            "      <td>162</td>\n",
                            "      <td>14</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>(43.2, 46.8]</th>\n",
                            "      <td>354</td>\n",
                            "      <td>291</td>\n",
                            "      <td>89</td>\n",
                            "      <td>24</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>(46.8, 50.4]</th>\n",
                            "      <td>250</td>\n",
                            "      <td>203</td>\n",
                            "      <td>74</td>\n",
                            "      <td>33</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>(50.4, 54.0]</th>\n",
                            "      <td>258</td>\n",
                            "      <td>210</td>\n",
                            "      <td>75</td>\n",
                            "      <td>13</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>(54.0, 57.6]</th>\n",
                            "      <td>73</td>\n",
                            "      <td>110</td>\n",
                            "      <td>76</td>\n",
                            "      <td>18</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>(57.6, 61.2]</th>\n",
                            "      <td>35</td>\n",
                            "      <td>73</td>\n",
                            "      <td>95</td>\n",
                            "      <td>7</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>(61.2, 64.8]</th>\n",
                            "      <td>50</td>\n",
                            "      <td>63</td>\n",
                            "      <td>105</td>\n",
                            "      <td>27</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>(64.8, 68.4]</th>\n",
                            "      <td>47</td>\n",
                            "      <td>101</td>\n",
                            "      <td>104</td>\n",
                            "      <td>10</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>(68.4, 72.0]</th>\n",
                            "      <td>29</td>\n",
                            "      <td>49</td>\n",
                            "      <td>75</td>\n",
                            "      <td>5</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>(72.0, 75.6]</th>\n",
                            "      <td>24</td>\n",
                            "      <td>42</td>\n",
                            "      <td>62</td>\n",
                            "      <td>3</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>(75.6, 79.2]</th>\n",
                            "      <td>41</td>\n",
                            "      <td>76</td>\n",
                            "      <td>86</td>\n",
                            "      <td>8</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>(79.2, 82.8]</th>\n",
                            "      <td>23</td>\n",
                            "      <td>50</td>\n",
                            "      <td>51</td>\n",
                            "      <td>6</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>(82.8, 86.4]</th>\n",
                            "      <td>40</td>\n",
                            "      <td>83</td>\n",
                            "      <td>89</td>\n",
                            "      <td>2</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>(86.4, 90.0]</th>\n",
                            "      <td>49</td>\n",
                            "      <td>80</td>\n",
                            "      <td>110</td>\n",
                            "      <td>16</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "PRODUCT CATEGORY  APPAREL  ELECTRONICS  FOOD  HEALTH & BEAUTY\n",
                            "AGE                                                          \n",
                            "(17.928, 21.6]        266            8     5              426\n",
                            "(21.6, 25.2]          280           23    17              379\n",
                            "(25.2, 28.8]          264          129    66              171\n",
                            "(28.8, 32.4]          439          424   130               18\n",
                            "(32.4, 36.0]          519          449   174               15\n",
                            "(36.0, 39.6]          339          254    90               25\n",
                            "(39.6, 43.2]          534          413   162               14\n",
                            "(43.2, 46.8]          354          291    89               24\n",
                            "(46.8, 50.4]          250          203    74               33\n",
                            "(50.4, 54.0]          258          210    75               13\n",
                            "(54.0, 57.6]           73          110    76               18\n",
                            "(57.6, 61.2]           35           73    95                7\n",
                            "(61.2, 64.8]           50           63   105               27\n",
                            "(64.8, 68.4]           47          101   104               10\n",
                            "(68.4, 72.0]           29           49    75                5\n",
                            "(72.0, 75.6]           24           42    62                3\n",
                            "(75.6, 79.2]           41           76    86                8\n",
                            "(79.2, 82.8]           23           50    51                6\n",
                            "(82.8, 86.4]           40           83    89                2\n",
                            "(86.4, 90.0]           49           80   110               16"
                        ]
                    },
                    "execution_count": 39,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "table"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# Relation between income and product category sales"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 40,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 1440x360 with 1 Axes>"
                        ]
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": [
                "table= pd.crosstab(pd.cut(full_customer_TX_data['INCOME'],15),full_customer_TX_data['PRODUCT CATEGORY'])\n",
                "\n",
                "table.div(table.sum(1).astype(float), axis=0).plot(kind='bar', stacked=True,figsize=(20,5))\n",
                "plt.show() \n",
                "\n",
                "#there is a deficiency in the sales of Electronics devices in Incomes between 20k-46K -> make discounts and more offers."
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# Relation between marital status and product category sales"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 41,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 1440x360 with 1 Axes>"
                        ]
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": [
                "table= pd.crosstab(full_customer_TX_data['MARITAL STATUS'],full_customer_TX_data['PRODUCT CATEGORY'])\n",
                "\n",
                "table.div(table.sum(1).astype(float), axis=0).plot(kind='bar', stacked=True,figsize=(20,5))\n",
                "plt.show() \n"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# check input data for balance against target field (see if data is balanced with class labels, we have 2 class labels, it is idealy that the input train data for classification is balanced w.r.t.o count of records for each class labels.)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 42,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "count          500\n",
                            "unique           2\n",
                            "top       enrolled\n",
                            "freq           264\n",
                            "Name: LOYALTY GROUP, dtype: object"
                        ]
                    },
                    "execution_count": 42,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "customer_view['LOYALTY GROUP'].describe()\n",
                "# the data is slightly biased toward the class label (enrolled) however, we will leave it as it is and not remove  those e\n",
                "#any since the data amount is very small."
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# Perform classification of end users with respect to loyalty group using"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 43,
            "metadata": {},
            "outputs": [],
            "source": [
                "drop_columns=['INCOME_BINNED','AGE_BINNED'] # drop the binned fields as their information gain is less than their unbinned parents\n",
                "customer_view = customer_view.drop(drop_columns,axis=1)# drop the Customer ID as it is a useless column"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# Calculate Entropy for input features with respect to class label"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 44,
            "metadata": {},
            "outputs": [],
            "source": [
                "#mutual_info_classif\n",
                "class_label=customer_view['LOYALTY GROUP']\n",
                "cols=['GENDER','AGE','INCOME','EXPERIENCE SCORE','HOUSEHOLD SIZE','MARITAL STATUS','total_spent','purchase_count','tx_count','spent_pcntg_income']\n",
                "input_fields=pd.get_dummies(customer_view[cols])\n",
                "gain_list=mutual_info_classif(input_fields,class_label, discrete_features=False)\n"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 45,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "Index(['GENDER', 'AGE', 'INCOME', 'EXPERIENCE SCORE', 'HOUSEHOLD SIZE',\n",
                            "       'total_spent', 'purchase_count', 'tx_count', 'spent_pcntg_income',\n",
                            "       'MARITAL STATUS_Divorced', 'MARITAL STATUS_Married',\n",
                            "       'MARITAL STATUS_Single', 'MARITAL STATUS_Widow/Widower'],\n",
                            "      dtype='object')"
                        ]
                    },
                    "execution_count": 45,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "input_fields.columns"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 50,
            "metadata": {},
            "outputs": [],
            "source": [
                "list(gain_list)\n",
                "a= list(range(0,len(gain_list)))\n",
                "col1=[0,1,2,3,4,5,6,7,8,9,10,11,12]"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 51,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "0.05802739710550952"
                        ]
                    },
                    "execution_count": 51,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "gain_list.mean()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 52,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "array([0.        , 0.03186066, 0.06862284, 0.13749541, 0.00769034,\n",
                            "       0.12306398, 0.16830155, 0.07298358, 0.13605482, 0.00690847,\n",
                            "       0.        , 0.00137451, 0.        ])"
                        ]
                    },
                    "execution_count": 52,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "gain_list"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 53,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 2160x288 with 1 Axes>"
                        ]
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": [
                "plt.xticks(col1,input_fields.columns,fontsize =10)\n",
                "plt.ylabel('Information Gain')\n",
                "plt.title('calculate_info_gain_for_input_features')\n",
                "plt.bar(a,list(gain_list),align=\"center\",alpha=0.5)\n",
                "fig = plt.gcf()\n",
                "fig.set_size_inches(30, 4)\n",
                "plt.show()\n",
                "\n"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 54,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 14400x1440 with 1 Axes>"
                        ]
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": [
                "table= pd.crosstab(customer_view['total_spent'],customer_view['LOYALTY GROUP'])\n",
                "table.div(table.sum(1).astype(float), axis=0).plot(kind='bar', stacked=True,figsize=(200,20))\n",
                "plt.show() # the figure below confirms that the total_spent derived field has high info gain"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# split the input data into train-test splits (80%-20% split)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 55,
            "metadata": {},
            "outputs": [],
            "source": [
                "#train, test = train_test_split(pd.get_dummies(customer_view), test_size=0.2)\n",
                "cols=['INCOME','EXPERIENCE SCORE','LOYALTY GROUP','total_spent','purchase_count','tx_count','spent_pcntg_income']\n",
                "train, test = train_test_split(customer_view[cols], test_size=0.2)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 56,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "Index(['CUSTOMER NUM', 'GENDER', 'AGE', 'INCOME', 'EXPERIENCE SCORE',\n",
                            "       'LOYALTY GROUP', 'HOUSEHOLD SIZE', 'MARITAL STATUS', 'total_spent',\n",
                            "       'purchase_count', 'tx_count', 'spent_pcntg_income'],\n",
                            "      dtype='object')"
                        ]
                    },
                    "execution_count": 56,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "customer_view.columns"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 57,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "Index(['INCOME', 'EXPERIENCE SCORE', 'LOYALTY GROUP', 'total_spent',\n",
                            "       'purchase_count', 'tx_count', 'spent_pcntg_income'],\n",
                            "      dtype='object')"
                        ]
                    },
                    "execution_count": 57,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "train.columns"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# separate the class labels from the test and train data"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 58,
            "metadata": {},
            "outputs": [],
            "source": [
                "train_class_label=train[['LOYALTY GROUP']].values \n",
                "train_class_label = train_class_label.ravel()\n",
                "train_features = train.drop('LOYALTY GROUP',axis=1)\n",
                "test_class_label=test[['LOYALTY GROUP']].values\n",
                "test_class_label = test_class_label.ravel()\n",
                "test_features = test.drop('LOYALTY GROUP',axis=1)\n"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 59,
            "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>0</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>count</th>\n",
                            "      <td>100</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>unique</th>\n",
                            "      <td>2</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>top</th>\n",
                            "      <td>not enrolled</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>freq</th>\n",
                            "      <td>57</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "                   0\n",
                            "count            100\n",
                            "unique             2\n",
                            "top     not enrolled\n",
                            "freq              57"
                        ]
                    },
                    "execution_count": 59,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "pd.DataFrame(test_class_label).describe()\n"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# perform One Hot Encoding for categorical features and convert them into continious values"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 60,
            "metadata": {},
            "outputs": [],
            "source": [
                "#train_features=pd.get_dummies(train_features)\n",
                "#test_features=pd.get_dummies(test_features)"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# Perform Normalization"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 61,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "numpy.ndarray"
                        ]
                    },
                    "execution_count": 61,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "type(train_class_label)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 62,
            "metadata": {},
            "outputs": [],
            "source": [
                "X_train_np=preprocessing.minmax_scale(train_features)\n",
                "X_test_np=preprocessing.minmax_scale(test_features)\n",
                "#train_features=pd.DataFrame(X_train_np)\n",
                "#test_features=pd.DataFrame(X_test_np)"
            ]
        },
        {
            "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>LOYALTY GROUP</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>count</th>\n",
                            "      <td>400</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>unique</th>\n",
                            "      <td>2</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>top</th>\n",
                            "      <td>enrolled</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>freq</th>\n",
                            "      <td>221</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "       LOYALTY GROUP\n",
                            "count            400\n",
                            "unique             2\n",
                            "top         enrolled\n",
                            "freq             221"
                        ]
                    },
                    "execution_count": 63,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "train[['LOYALTY GROUP']].describe()"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# Classify using SVM "
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 66,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Wall time: 103 ms\n"
                    ]
                },
                {
                    "name": "stderr",
                    "output_type": "stream",
                    "text": [
                        "C:\\IDE\\Anaconda3\\envs\\python38\\lib\\site-packages\\sklearn\\svm\\_base.py:246: ConvergenceWarning: Solver terminated early (max_iter=200).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
                        "  warnings.warn('Solver terminated early (max_iter=%i).'\n"
                    ]
                },
                {
                    "data": {
                        "text/plain": [
                            "SVC(max_iter=200)"
                        ]
                    },
                    "execution_count": 66,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "clf = svm.SVC(kernel='rbf',max_iter=200)\n",
                "%time clf.fit(X_train_np, train_class_label)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 67,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "0.77"
                        ]
                    },
                    "execution_count": 67,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "clf.score(X_test_np,test_class_label)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 70,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Wall time: 4 ms\n"
                    ]
                },
                {
                    "name": "stderr",
                    "output_type": "stream",
                    "text": [
                        "C:\\IDE\\Anaconda3\\envs\\python38\\lib\\site-packages\\sklearn\\svm\\_base.py:246: ConvergenceWarning: Solver terminated early (max_iter=200).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
                        "  warnings.warn('Solver terminated early (max_iter=%i).'\n"
                    ]
                },
                {
                    "data": {
                        "text/plain": [
                            "SVC(kernel='linear', max_iter=200)"
                        ]
                    },
                    "execution_count": 70,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "clf_lin = svm.SVC(kernel='linear',max_iter=200)\n",
                "%time clf_lin.fit(X_train_np, train_class_label)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 71,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "0.67"
                        ]
                    },
                    "execution_count": 71,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "clf_lin.score(X_test_np,test_class_label)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 72,
            "metadata": {},
            "outputs": [],
            "source": [
                "predicted_class_label=clf.predict(X_test_np)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 73,
            "metadata": {},
            "outputs": [],
            "source": [
                "cnf_mtrx=confusion_matrix(predicted_class_label,test_class_label,labels=['not enrolled','enrolled'])"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 74,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "100"
                        ]
                    },
                    "execution_count": 74,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "len(predicted_class_label)"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# Plotting Confusion Matrix"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 75,
            "metadata": {},
            "outputs": [],
            "source": [
                "def plot_confusion_matrix(cm, classes,\n",
                "                          normalize=False,\n",
                "                          title='Confusion matrix',\n",
                "                          cmap=plt.cm.Blues):\n",
                "    \"\"\"\n",
                "    This function prints and plots the confusion matrix.\n",
                "    Normalization can be applied by setting `normalize=True`.\n",
                "    \"\"\"\n",
                "    if normalize:\n",
                "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
                "        print(\"Normalized confusion matrix\")\n",
                "    else:\n",
                "        print('Confusion matrix, without normalization')\n",
                "\n",
                "    print(cm)\n",
                "\n",
                "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
                "    plt.title(title)\n",
                "    plt.colorbar()\n",
                "    tick_marks = np.arange(len(classes))\n",
                "    plt.xticks(tick_marks, classes, rotation=45)\n",
                "    plt.yticks(tick_marks, classes)\n",
                "\n",
                "    fmt = '.2f' if normalize else 'd'\n",
                "    thresh = cm.max() / 2.\n",
                "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
                "        plt.text(j, i, format(cm[i, j], fmt),\n",
                "                 horizontalalignment=\"center\",\n",
                "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
                "\n",
                "    plt.tight_layout()\n",
                "    plt.ylabel('True label')\n",
                "    plt.xlabel('Predicted label')"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 76,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Confusion matrix, without normalization\n",
                        "[[34  0]\n",
                        " [23 43]]\n"
                    ]
                },
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 432x288 with 2 Axes>"
                        ]
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": [
                "class_labels=['enrolled','not enrolled']\n",
                "plot_confusion_matrix(cnf_mtrx, classes=class_labels, normalize=False,title='Normalized confusion matrix')"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# Perform Segmentation for all customer base"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# 1- Using KMeans"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 77,
            "metadata": {},
            "outputs": [],
            "source": [
                "from sklearn.cluster import KMeans"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# Transform encode categorical features and convert continious variables to Normal scale and "
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 78,
            "metadata": {},
            "outputs": [],
            "source": [
                "cols=['INCOME','EXPERIENCE SCORE','HOUSEHOLD SIZE','total_spent',\\\n",
                "      'purchase_count', 'tx_count', 'spent_pcntg_income']\n",
                "continious_cust_view = pd.get_dummies(customer_view[cols])\n",
                "continious_cust_view=preprocessing.minmax_scale(continious_cust_view)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 79,
            "metadata": {},
            "outputs": [],
            "source": [
                "#new_derived_data_to_apply_clustering for\n",
                "custo_product = pd.DataFrame(full_customer_TX_data.groupby(['CUSTOMER NUM','PRODUCT CATEGORY']).agg({'QUANTITY PURCHASED':'sum'}).to_records())\n",
                "custo_product = pd.pivot_table(custo_product,values='QUANTITY PURCHASED',index='CUSTOMER NUM',columns='PRODUCT CATEGORY',aggfunc=np.sum).fillna(0)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": []
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# Loop to get the best number of clusters"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 80,
            "metadata": {},
            "outputs": [],
            "source": [
                "#getting the best K \n",
                "# getting the K against the last abrubt change in the curve\n",
                "cost_list=[]\n",
                "for k in range(1,10):\n",
                "    kmeans = KMeans(n_clusters=k)\n",
                "    kmeans.fit(custo_product)\n",
                "    cost_list.append(kmeans.inertia_)\n",
                "diff_list=[]\n",
                "for i in (range(0,len(cost_list)-1)):\n",
                "    diff_list.append(cost_list[i+1]-cost_list[i])\n",
                "    \n",
                "    "
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 81,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "[4143289.3199999942,\n",
                            " 2797558.484574289,\n",
                            " 1791962.0683097914,\n",
                            " 1325687.5394773579,\n",
                            " 1160516.958983408,\n",
                            " 1035333.429521675,\n",
                            " 915050.2874248615,\n",
                            " 824586.9326995689,\n",
                            " 763270.77647588]"
                        ]
                    },
                    "execution_count": 81,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "cost_list"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 82,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "[-1345730.8354257052,\n",
                            " -1005596.4162644977,\n",
                            " -466274.5288324335,\n",
                            " -165170.58049394982,\n",
                            " -125183.52946173307,\n",
                            " -120283.14209681354,\n",
                            " -90463.35472529253,\n",
                            " -61316.15622368897]"
                        ]
                    },
                    "execution_count": 82,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "diff_list"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 83,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 432x288 with 1 Axes>"
                        ]
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": [
                "index = list(range(0,len(cost_list)))\n",
                "plt.plot(index,cost_list)\n",
                "plt.xticks(index)\n",
                "plt.show()\n",
                "#Best K from the diff_list is 3 and from the graph is 3 as at K=3 the graph experiences the last abrubt change.\n",
                "#(elbow graph)\n"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "\n"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# segmenting the customers according to the best K"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 84,
            "metadata": {},
            "outputs": [],
            "source": [
                "#Best K=3\n",
                "kmeans = KMeans(n_clusters=3)\n",
                "kmeans.fit(custo_product)\n",
                "class_labels_kmeans=kmeans.predict(custo_product)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 85,
            "metadata": {},
            "outputs": [],
            "source": [
                "\n",
                "customer_view['customer_segment']=class_labels_kmeans"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 86,
            "metadata": {},
            "outputs": [],
            "source": [
                "custo_product['customer_segment']=class_labels_kmeans"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 87,
            "metadata": {},
            "outputs": [],
            "source": [
                "cluster_profile=custo_product.groupby('customer_segment').agg({'APPAREL':'mean', 'ELECTRONICS':'mean', 'FOOD':'mean','HEALTH & BEAUTY':'mean'})\n"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 88,
            "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>APPAREL</th>\n",
                            "      <th>ELECTRONICS</th>\n",
                            "      <th>FOOD</th>\n",
                            "      <th>HEALTH &amp; BEAUTY</th>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>customer_segment</th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>0</th>\n",
                            "      <td>96.751799</td>\n",
                            "      <td>81.881295</td>\n",
                            "      <td>25.964029</td>\n",
                            "      <td>2.697842</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1</th>\n",
                            "      <td>41.204545</td>\n",
                            "      <td>5.916667</td>\n",
                            "      <td>3.969697</td>\n",
                            "      <td>54.325758</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2</th>\n",
                            "      <td>47.022222</td>\n",
                            "      <td>103.000000</td>\n",
                            "      <td>137.800000</td>\n",
                            "      <td>10.566667</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "                    APPAREL  ELECTRONICS        FOOD  HEALTH & BEAUTY\n",
                            "customer_segment                                                     \n",
                            "0                 96.751799    81.881295   25.964029         2.697842\n",
                            "1                 41.204545     5.916667    3.969697        54.325758\n",
                            "2                 47.022222   103.000000  137.800000        10.566667"
                        ]
                    },
                    "execution_count": 88,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "cluster_profile"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 89,
            "metadata": {},
            "outputs": [],
            "source": [
                "cluster_profile['FOOD']=preprocessing.minmax_scale(cluster_profile['FOOD'])\n",
                "cluster_profile['ELECTRONICS']=preprocessing.minmax_scale(cluster_profile['ELECTRONICS'])\n",
                "cluster_profile['HEALTH & BEAUTY']=preprocessing.minmax_scale(cluster_profile['HEALTH & BEAUTY'])\n",
                "cluster_profile['APPAREL']=preprocessing.minmax_scale(cluster_profile['APPAREL'])\n"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 90,
            "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>APPAREL</th>\n",
                            "      <th>ELECTRONICS</th>\n",
                            "      <th>FOOD</th>\n",
                            "      <th>HEALTH &amp; BEAUTY</th>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>customer_segment</th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>0</th>\n",
                            "      <td>1.000000</td>\n",
                            "      <td>0.782468</td>\n",
                            "      <td>0.164345</td>\n",
                            "      <td>0.000000</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1</th>\n",
                            "      <td>0.000000</td>\n",
                            "      <td>0.000000</td>\n",
                            "      <td>0.000000</td>\n",
                            "      <td>1.000000</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2</th>\n",
                            "      <td>0.104734</td>\n",
                            "      <td>1.000000</td>\n",
                            "      <td>1.000000</td>\n",
                            "      <td>0.152414</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "                   APPAREL  ELECTRONICS      FOOD  HEALTH & BEAUTY\n",
                            "customer_segment                                                  \n",
                            "0                 1.000000     0.782468  0.164345         0.000000\n",
                            "1                 0.000000     0.000000  0.000000         1.000000\n",
                            "2                 0.104734     1.000000  1.000000         0.152414"
                        ]
                    },
                    "execution_count": 90,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "cluster_profile"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 91,
            "metadata": {},
            "outputs": [],
            "source": [
                "#customer profiles:\n",
                "#people who buy the same categories are grouped together as:\n",
                "#0- Apparel and Electronics and Food Products and Health and Beauty\n",
                "#1- Apparell and Electronics and Food Products\n",
                "#2- Health and Beauty"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 92,
            "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>AGE</th>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>customer_segment</th>\n",
                            "      <th></th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>0</th>\n",
                            "      <td>278</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1</th>\n",
                            "      <td>132</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2</th>\n",
                            "      <td>90</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "                  AGE\n",
                            "customer_segment     \n",
                            "0                 278\n",
                            "1                 132\n",
                            "2                  90"
                        ]
                    },
                    "execution_count": 92,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "customer_view.groupby(['customer_segment']).agg({'AGE':'count'})"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 93,
            "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>total_spent</th>\n",
                            "      <th>AGE</th>\n",
                            "      <th>INCOME</th>\n",
                            "      <th>HOUSEHOLD SIZE</th>\n",
                            "      <th>spent_pcntg_income</th>\n",
                            "      <th>purchase_count</th>\n",
                            "      <th>GENDER</th>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>customer_segment</th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>0</th>\n",
                            "      <td>6637.829266</td>\n",
                            "      <td>41.071942</td>\n",
                            "      <td>89846.503597</td>\n",
                            "      <td>3.316547</td>\n",
                            "      <td>7.625865</td>\n",
                            "      <td>207.294964</td>\n",
                            "      <td>0.489209</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1</th>\n",
                            "      <td>1700.825008</td>\n",
                            "      <td>26.795455</td>\n",
                            "      <td>45217.416667</td>\n",
                            "      <td>2.181818</td>\n",
                            "      <td>4.119866</td>\n",
                            "      <td>105.416667</td>\n",
                            "      <td>0.462121</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2</th>\n",
                            "      <td>6943.842456</td>\n",
                            "      <td>68.922222</td>\n",
                            "      <td>132780.155556</td>\n",
                            "      <td>2.633333</td>\n",
                            "      <td>5.285519</td>\n",
                            "      <td>298.388889</td>\n",
                            "      <td>0.577778</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "                  total_spent        AGE         INCOME  HOUSEHOLD SIZE  \\\n",
                            "customer_segment                                                          \n",
                            "0                 6637.829266  41.071942   89846.503597        3.316547   \n",
                            "1                 1700.825008  26.795455   45217.416667        2.181818   \n",
                            "2                 6943.842456  68.922222  132780.155556        2.633333   \n",
                            "\n",
                            "                  spent_pcntg_income  purchase_count    GENDER  \n",
                            "customer_segment                                                \n",
                            "0                           7.625865      207.294964  0.489209  \n",
                            "1                           4.119866      105.416667  0.462121  \n",
                            "2                           5.285519      298.388889  0.577778  "
                        ]
                    },
                    "execution_count": 93,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "#customer_view['GENDER']=customer_view['GENDER'].replace('Male',0).replace('Female',1)\n",
                "customer_view.groupby(['customer_segment']).agg({'total_spent':'mean','AGE':'mean','INCOME':'mean','HOUSEHOLD SIZE':'mean','spent_pcntg_income':'mean','purchase_count':'mean','GENDER':'mean'})\n"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 94,
            "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>AGE</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>count</th>\n",
                            "      <td>500.000000</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>mean</th>\n",
                            "      <td>42.316000</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>std</th>\n",
                            "      <td>17.567509</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>min</th>\n",
                            "      <td>18.000000</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>25%</th>\n",
                            "      <td>30.000000</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>50%</th>\n",
                            "      <td>39.000000</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>75%</th>\n",
                            "      <td>50.250000</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>max</th>\n",
                            "      <td>90.000000</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "              AGE\n",
                            "count  500.000000\n",
                            "mean    42.316000\n",
                            "std     17.567509\n",
                            "min     18.000000\n",
                            "25%     30.000000\n",
                            "50%     39.000000\n",
                            "75%     50.250000\n",
                            "max     90.000000"
                        ]
                    },
                    "execution_count": 94,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "customer_view[['AGE']].describe()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 95,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": [
                            "<div>\n",
                            "<style scoped>\n",
                            "    .dataframe tbody tr th:only-of-type {\n",
                            "        vertical-align: middle;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe tbody tr th {\n",
                            "        vertical-align: top;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe thead th {\n",
                            "        text-align: right;\n",
                            "    }\n",
                            "</style>\n",
                            "<table border=\"1\" class=\"dataframe\">\n",
                            "  <thead>\n",
                            "    <tr style=\"text-align: right;\">\n",
                            "      <th></th>\n",
                            "      <th>INCOME</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>count</th>\n",
                            "      <td>500.000000</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>mean</th>\n",
                            "      <td>85792.482000</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>std</th>\n",
                            "      <td>37157.766304</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>min</th>\n",
                            "      <td>20256.000000</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>25%</th>\n",
                            "      <td>52429.000000</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>50%</th>\n",
                            "      <td>86846.500000</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>75%</th>\n",
                            "      <td>118381.000000</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>max</th>\n",
                            "      <td>149999.000000</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "              INCOME\n",
                            "count     500.000000\n",
                            "mean    85792.482000\n",
                            "std     37157.766304\n",
                            "min     20256.000000\n",
                            "25%     52429.000000\n",
                            "50%     86846.500000\n",
                            "75%    118381.000000\n",
                            "max    149999.000000"
                        ]
                    },
                    "execution_count": 95,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "customer_view[['INCOME']].describe()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 96,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": [
                            "<div>\n",
                            "<style scoped>\n",
                            "    .dataframe tbody tr th:only-of-type {\n",
                            "        vertical-align: middle;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe tbody tr th {\n",
                            "        vertical-align: top;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe thead th {\n",
                            "        text-align: right;\n",
                            "    }\n",
                            "</style>\n",
                            "<table border=\"1\" class=\"dataframe\">\n",
                            "  <thead>\n",
                            "    <tr style=\"text-align: right;\">\n",
                            "      <th></th>\n",
                            "      <th>spent_pcntg_income</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>count</th>\n",
                            "      <td>500.000000</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>mean</th>\n",
                            "      <td>6.279019</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>std</th>\n",
                            "      <td>2.807923</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>min</th>\n",
                            "      <td>0.058128</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>25%</th>\n",
                            "      <td>4.526334</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>50%</th>\n",
                            "      <td>6.069121</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>75%</th>\n",
                            "      <td>7.961431</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>max</th>\n",
                            "      <td>17.710494</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "       spent_pcntg_income\n",
                            "count          500.000000\n",
                            "mean             6.279019\n",
                            "std              2.807923\n",
                            "min              0.058128\n",
                            "25%              4.526334\n",
                            "50%              6.069121\n",
                            "75%              7.961431\n",
                            "max             17.710494"
                        ]
                    },
                    "execution_count": 96,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "customer_view[['spent_pcntg_income']].describe()\n"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 97,
            "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>purchase_count</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>count</th>\n",
                            "      <td>500.000000</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>mean</th>\n",
                            "      <td>196.796000</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>std</th>\n",
                            "      <td>96.740187</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>min</th>\n",
                            "      <td>3.000000</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>25%</th>\n",
                            "      <td>126.000000</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>50%</th>\n",
                            "      <td>184.500000</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>75%</th>\n",
                            "      <td>264.250000</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>max</th>\n",
                            "      <td>555.000000</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "       purchase_count\n",
                            "count      500.000000\n",
                            "mean       196.796000\n",
                            "std         96.740187\n",
                            "min          3.000000\n",
                            "25%        126.000000\n",
                            "50%        184.500000\n",
                            "75%        264.250000\n",
                            "max        555.000000"
                        ]
                    },
                    "execution_count": 97,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "customer_view[['purchase_count']].describe()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 98,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "array(['Divorced', 'Married', 'Single', 'Widow/Widower'], dtype=object)"
                        ]
                    },
                    "execution_count": 98,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "np.unique(customer_view[['MARITAL STATUS']].values)"
            ]
        }
    ],
    "metadata": {
        "kernelspec": {
            "display_name": "Python 3.5",
            "language": "python",
            "name": "python3"
        },
        "language_info": {
            "codemirror_mode": {
                "name": "ipython",
                "version": 3
            },
            "file_extension": ".py",
            "mimetype": "text/x-python",
            "name": "python",
            "nbconvert_exporter": "python",
            "pygments_lexer": "ipython3",
            "version": "3.8.5"
        }
    },
    "nbformat": 4,
    "nbformat_minor": 2
}