Data Analytics Consulting Virtual Internship - Forage.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4bb9394d",
   "metadata": {},
   "source": [
    "# SPROCKET CENTRAL PVT LTD."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b54a0d17",
   "metadata": {},
   "source": [
    "# TABLE OF CONTENTS\n",
    "\n",
    "1. **[Getting Data](#load)**\n",
    "2. **[Data Quality Assessment](#assess)**   <br>\n",
    "    2a. **[SHEET1](#sh1)**                  <br>\n",
    "    2b. **[SHEET2](#sh2)**                  <br>\n",
    "    2c. **[SHEET3](#sh3)**                  <br>\n",
    "    2d. **[SHEET4](#sh4)**                  <br>\n",
    "3. **[Exploratory Data Analysis](#eda)**\n",
    "4. **[RFM Analysis](#rfm)**\n",
    "5. **[Data Visualization](#viz)**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f2e7c21",
   "metadata": {},
   "source": [
    "<a id='load'></a>\n",
    "## Getting Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6eab1c8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dd24abd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['Title Sheet', 'Transactions', 'NewCustomerList', 'CustomerDemographic', 'CustomerAddress'])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#getting list of sheet names\n",
    "df_dict = pd.read_excel(r\"C:\\Users\\91978\\OneDrive\\Desktop\\KPMG Consulting Virtual Internship\\Task-2\\Sprocket Central Pty Ltd.xlsx\", sheet_name=None, header = 0)\n",
    "df_dict.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cb1bd0e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating separate dataframes for each sheet for easier analysis\n",
    "transactions = df_dict.get('Transactions')\n",
    "newcust = df_dict.get('NewCustomerList')\n",
    "custdemo = df_dict.get('CustomerDemographic')\n",
    "address = df_dict.get('CustomerAddress')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "260e6e83",
   "metadata": {},
   "source": [
    "<a id='assess'></a>\n",
    "# Data Quality Assessment"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc631b50",
   "metadata": {},
   "source": [
    "<a id='sh1'></a>\n",
    "### SHEET1 - Transactions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d6af4417",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>transaction_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>transaction_date</th>\n",
       "      <th>online_order</th>\n",
       "      <th>order_status</th>\n",
       "      <th>brand</th>\n",
       "      <th>product_line</th>\n",
       "      <th>product_class</th>\n",
       "      <th>product_size</th>\n",
       "      <th>list_price</th>\n",
       "      <th>standard_cost</th>\n",
       "      <th>product_first_sold_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2950</td>\n",
       "      <td>2017-02-25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Solex</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>71.49</td>\n",
       "      <td>53.62</td>\n",
       "      <td>41245.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3120</td>\n",
       "      <td>2017-05-21</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Trek Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>large</td>\n",
       "      <td>2091.47</td>\n",
       "      <td>388.92</td>\n",
       "      <td>41701.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>37</td>\n",
       "      <td>402</td>\n",
       "      <td>2017-10-16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Approved</td>\n",
       "      <td>OHM Cycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>low</td>\n",
       "      <td>medium</td>\n",
       "      <td>1793.43</td>\n",
       "      <td>248.82</td>\n",
       "      <td>36361.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>88</td>\n",
       "      <td>3135</td>\n",
       "      <td>2017-08-31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Norco Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>1198.46</td>\n",
       "      <td>381.10</td>\n",
       "      <td>36145.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>78</td>\n",
       "      <td>787</td>\n",
       "      <td>2017-10-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Giant Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>large</td>\n",
       "      <td>1765.30</td>\n",
       "      <td>709.48</td>\n",
       "      <td>42226.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   transaction_id  product_id  customer_id transaction_date  online_order  \\\n",
       "0               1           2         2950       2017-02-25           0.0   \n",
       "1               2           3         3120       2017-05-21           1.0   \n",
       "2               3          37          402       2017-10-16           0.0   \n",
       "3               4          88         3135       2017-08-31           0.0   \n",
       "4               5          78          787       2017-10-01           1.0   \n",
       "\n",
       "  order_status           brand product_line product_class product_size  \\\n",
       "0     Approved           Solex     Standard        medium       medium   \n",
       "1     Approved   Trek Bicycles     Standard        medium        large   \n",
       "2     Approved      OHM Cycles     Standard           low       medium   \n",
       "3     Approved  Norco Bicycles     Standard        medium       medium   \n",
       "4     Approved  Giant Bicycles     Standard        medium        large   \n",
       "\n",
       "   list_price  standard_cost  product_first_sold_date  \n",
       "0       71.49          53.62                  41245.0  \n",
       "1     2091.47         388.92                  41701.0  \n",
       "2     1793.43         248.82                  36361.0  \n",
       "3     1198.46         381.10                  36145.0  \n",
       "4     1765.30         709.48                  42226.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ffa473b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20000, 13)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "66fa3c60",
   "metadata": {
    "scrolled": false
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20000 entries, 0 to 19999\n",
      "Data columns (total 13 columns):\n",
      " #   Column                   Non-Null Count  Dtype         \n",
      "---  ------                   --------------  -----         \n",
      " 0   transaction_id           20000 non-null  int64         \n",
      " 1   product_id               20000 non-null  int64         \n",
      " 2   customer_id              20000 non-null  int64         \n",
      " 3   transaction_date         20000 non-null  datetime64[ns]\n",
      " 4   online_order             19640 non-null  float64       \n",
      " 5   order_status             20000 non-null  object        \n",
      " 6   brand                    19803 non-null  object        \n",
      " 7   product_line             19803 non-null  object        \n",
      " 8   product_class            19803 non-null  object        \n",
      " 9   product_size             19803 non-null  object        \n",
      " 10  list_price               20000 non-null  float64       \n",
      " 11  standard_cost            19803 non-null  float64       \n",
      " 12  product_first_sold_date  19803 non-null  float64       \n",
      "dtypes: datetime64[ns](1), float64(4), int64(3), object(5)\n",
      "memory usage: 2.0+ MB\n"
     ]
    }
   ],
   "source": [
    "transactions.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d05370a",
   "metadata": {},
   "source": [
    "**Observation**\n",
    "- **NULL VALUES**: We can see there are some null values in columns `online_order`, `brand`, `product_line`, `product_class`, `product_size`, `standard_cost`, `product_first_sold_date`. \n",
    "\n",
    "- **CONSISTENCY**:  \n",
    "`product_first_sold_date` looks like it is a number of days since a certain time. We need to change the datatype from float64 to date format.\n",
    "\n",
    "- **DATA TYPE**\n",
    "`online_order` is actually a categorical column. So we change it from float64 to category and also change the values to more meaningful labels where '0' and '1' represent respective their categories.  <br>\n",
    "`order_status`, `brand`, `product_line`, `product_class` and `product_size` are objects, we can change them into categories.\n",
    "Coverting \"objects\" to \"category\" reduces the space required to store the dataframe. It also helps in analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27045059",
   "metadata": {},
   "source": [
    "#### DISTINCT CUSTOMER IDs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "78eeca60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3494"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions.customer_id.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9c9597e",
   "metadata": {},
   "source": [
    "There are transaction records for 3494 customers only."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b7da0f0",
   "metadata": {},
   "source": [
    "#### MISSING VALUES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "96555f0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "transaction_id               0\n",
       "product_id                   0\n",
       "customer_id                  0\n",
       "transaction_date             0\n",
       "online_order               360\n",
       "order_status                 0\n",
       "brand                      197\n",
       "product_line               197\n",
       "product_class              197\n",
       "product_size               197\n",
       "list_price                   0\n",
       "standard_cost              197\n",
       "product_first_sold_date    197\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "75694903",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "transaction_id             0.000\n",
       "product_id                 0.000\n",
       "customer_id                0.000\n",
       "transaction_date           0.000\n",
       "online_order               1.800\n",
       "order_status               0.000\n",
       "brand                      0.985\n",
       "product_line               0.985\n",
       "product_class              0.985\n",
       "product_size               0.985\n",
       "list_price                 0.000\n",
       "standard_cost              0.985\n",
       "product_first_sold_date    0.985\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#percentage missing values in each column\n",
    "transactions.isnull().sum()/len(transactions) *100"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1dc41ff2",
   "metadata": {},
   "source": [
    "**Observation** - We can see there are less than 2% missing values in any any column, if any. We will remove them from the training dataset since it is not a key column and we have sufficient data to make analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "470aa87a",
   "metadata": {},
   "source": [
    "#### Duplicates?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c10683bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to find duplicates\n",
    "transactions.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ed69c2e",
   "metadata": {},
   "source": [
    "**Observation** - Data does not have any duplicates!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2dc38874",
   "metadata": {},
   "source": [
    "### Checking for consistency and validity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cd722bdd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  1., nan])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions['online_order'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4be3e6d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Solex', 'Trek Bicycles', 'OHM Cycles', 'Norco Bicycles',\n",
       "       'Giant Bicycles', 'WeareA2B', nan], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions['brand'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "77afce99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Standard', 'Road', 'Mountain', 'Touring', nan], dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions['product_line'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "df26c620",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['medium', 'low', 'high', nan], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions['product_class'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "381ed60f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['medium', 'large', 'small', nan], dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions['product_size'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67c5da5e",
   "metadata": {},
   "source": [
    "**Observation** - Our data is consistent and does not have any invalid values besides null records. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2591d18",
   "metadata": {},
   "source": [
    "## DATA CLEANING"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a85b2bd",
   "metadata": {},
   "source": [
    "#### DATA TYPE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b37a967f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert float64 column to datetime with date format\n",
    "transactions['product_first_sold_date'] = pd.to_datetime(transactions['product_first_sold_date'].apply(lambda x: pd.Timestamp(x, unit='D').date()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ba3b99dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# coverting \"objects\" to \"category\" reduces the space required to store the dataframe. It also helps in analysis\n",
    "\n",
    "transactions['order_status'] = transactions['order_status'].astype(\"category\")\n",
    "transactions['brand'] = transactions['brand'].astype(\"category\")\n",
    "transactions['product_line'] = transactions['product_line'].astype(\"category\")\n",
    "transactions['product_class'] = transactions['product_class'].astype(\"category\")\n",
    "transactions['product_size'] = transactions['product_size'].astype(\"category\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "73d7ae9e",
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>transaction_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>transaction_date</th>\n",
       "      <th>online_order</th>\n",
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       "      <td>Approved</td>\n",
       "      <td>Solex</td>\n",
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       "      <td>2017-05-21</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Trek Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>large</td>\n",
       "      <td>2091.47</td>\n",
       "      <td>388.92</td>\n",
       "      <td>2084-03-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>37</td>\n",
       "      <td>402</td>\n",
       "      <td>2017-10-16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Approved</td>\n",
       "      <td>OHM Cycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>low</td>\n",
       "      <td>medium</td>\n",
       "      <td>1793.43</td>\n",
       "      <td>248.82</td>\n",
       "      <td>2069-07-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>88</td>\n",
       "      <td>3135</td>\n",
       "      <td>2017-08-31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Norco Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>1198.46</td>\n",
       "      <td>381.10</td>\n",
       "      <td>2068-12-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>78</td>\n",
       "      <td>787</td>\n",
       "      <td>2017-10-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Giant Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>large</td>\n",
       "      <td>1765.30</td>\n",
       "      <td>709.48</td>\n",
       "      <td>2085-08-11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   transaction_id  product_id  customer_id transaction_date  online_order  \\\n",
       "0               1           2         2950       2017-02-25           0.0   \n",
       "1               2           3         3120       2017-05-21           1.0   \n",
       "2               3          37          402       2017-10-16           0.0   \n",
       "3               4          88         3135       2017-08-31           0.0   \n",
       "4               5          78          787       2017-10-01           1.0   \n",
       "\n",
       "  order_status           brand product_line product_class product_size  \\\n",
       "0     Approved           Solex     Standard        medium       medium   \n",
       "1     Approved   Trek Bicycles     Standard        medium        large   \n",
       "2     Approved      OHM Cycles     Standard           low       medium   \n",
       "3     Approved  Norco Bicycles     Standard        medium       medium   \n",
       "4     Approved  Giant Bicycles     Standard        medium        large   \n",
       "\n",
       "   list_price  standard_cost product_first_sold_date  \n",
       "0       71.49          53.62              2082-12-04  \n",
       "1     2091.47         388.92              2084-03-04  \n",
       "2     1793.43         248.82              2069-07-21  \n",
       "3     1198.46         381.10              2068-12-17  \n",
       "4     1765.30         709.48              2085-08-11  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "cc18b2da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('<M8[ns]')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions['product_first_sold_date'].dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9f30c9b",
   "metadata": {},
   "source": [
    "### MISSING VALUES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9a5f38c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "transactions.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "51812239",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 19445 entries, 0 to 19999\n",
      "Data columns (total 13 columns):\n",
      " #   Column                   Non-Null Count  Dtype         \n",
      "---  ------                   --------------  -----         \n",
      " 0   transaction_id           19445 non-null  int64         \n",
      " 1   product_id               19445 non-null  int64         \n",
      " 2   customer_id              19445 non-null  int64         \n",
      " 3   transaction_date         19445 non-null  datetime64[ns]\n",
      " 4   online_order             19445 non-null  float64       \n",
      " 5   order_status             19445 non-null  category      \n",
      " 6   brand                    19445 non-null  category      \n",
      " 7   product_line             19445 non-null  category      \n",
      " 8   product_class            19445 non-null  category      \n",
      " 9   product_size             19445 non-null  category      \n",
      " 10  list_price               19445 non-null  float64       \n",
      " 11  standard_cost            19445 non-null  float64       \n",
      " 12  product_first_sold_date  19445 non-null  datetime64[ns]\n",
      "dtypes: category(5), datetime64[ns](2), float64(3), int64(3)\n",
      "memory usage: 1.4 MB\n"
     ]
    }
   ],
   "source": [
    "transactions.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "29eb34a8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "transaction_id             0.0\n",
       "product_id                 0.0\n",
       "customer_id                0.0\n",
       "transaction_date           0.0\n",
       "online_order               0.0\n",
       "order_status               0.0\n",
       "brand                      0.0\n",
       "product_line               0.0\n",
       "product_class              0.0\n",
       "product_size               0.0\n",
       "list_price                 0.0\n",
       "standard_cost              0.0\n",
       "product_first_sold_date    0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#percentage missing values in each column\n",
    "transactions.isnull().sum()/len(transactions) *100"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e203623c",
   "metadata": {},
   "source": [
    "**We now have no missing values!**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da6c4424",
   "metadata": {},
   "source": [
    "###  data types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4cc1c1d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "transactions['online_order'].replace( {0.0:'False', 1.0:'True'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6c3912ad",
   "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>transaction_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>transaction_date</th>\n",
       "      <th>online_order</th>\n",
       "      <th>order_status</th>\n",
       "      <th>brand</th>\n",
       "      <th>product_line</th>\n",
       "      <th>product_class</th>\n",
       "      <th>product_size</th>\n",
       "      <th>list_price</th>\n",
       "      <th>standard_cost</th>\n",
       "      <th>product_first_sold_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2950</td>\n",
       "      <td>2017-02-25</td>\n",
       "      <td>False</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Solex</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>71.49</td>\n",
       "      <td>53.62</td>\n",
       "      <td>2082-12-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3120</td>\n",
       "      <td>2017-05-21</td>\n",
       "      <td>True</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Trek Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>large</td>\n",
       "      <td>2091.47</td>\n",
       "      <td>388.92</td>\n",
       "      <td>2084-03-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>37</td>\n",
       "      <td>402</td>\n",
       "      <td>2017-10-16</td>\n",
       "      <td>False</td>\n",
       "      <td>Approved</td>\n",
       "      <td>OHM Cycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>low</td>\n",
       "      <td>medium</td>\n",
       "      <td>1793.43</td>\n",
       "      <td>248.82</td>\n",
       "      <td>2069-07-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>88</td>\n",
       "      <td>3135</td>\n",
       "      <td>2017-08-31</td>\n",
       "      <td>False</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Norco Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>1198.46</td>\n",
       "      <td>381.10</td>\n",
       "      <td>2068-12-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>78</td>\n",
       "      <td>787</td>\n",
       "      <td>2017-10-01</td>\n",
       "      <td>True</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Giant Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>large</td>\n",
       "      <td>1765.30</td>\n",
       "      <td>709.48</td>\n",
       "      <td>2085-08-11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   transaction_id  product_id  customer_id transaction_date online_order  \\\n",
       "0               1           2         2950       2017-02-25        False   \n",
       "1               2           3         3120       2017-05-21         True   \n",
       "2               3          37          402       2017-10-16        False   \n",
       "3               4          88         3135       2017-08-31        False   \n",
       "4               5          78          787       2017-10-01         True   \n",
       "\n",
       "  order_status           brand product_line product_class product_size  \\\n",
       "0     Approved           Solex     Standard        medium       medium   \n",
       "1     Approved   Trek Bicycles     Standard        medium        large   \n",
       "2     Approved      OHM Cycles     Standard           low       medium   \n",
       "3     Approved  Norco Bicycles     Standard        medium       medium   \n",
       "4     Approved  Giant Bicycles     Standard        medium        large   \n",
       "\n",
       "   list_price  standard_cost product_first_sold_date  \n",
       "0       71.49          53.62              2082-12-04  \n",
       "1     2091.47         388.92              2084-03-04  \n",
       "2     1793.43         248.82              2069-07-21  \n",
       "3     1198.46         381.10              2068-12-17  \n",
       "4     1765.30         709.48              2085-08-11  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ec4c225",
   "metadata": {},
   "source": [
    "## We do the same process for the other datasets provided"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8f19e2fb",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_status</th>\n",
       "      <th>brand</th>\n",
       "      <th>product_line</th>\n",
       "      <th>product_class</th>\n",
       "      <th>product_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>19445</td>\n",
       "      <td>19445</td>\n",
       "      <td>19445</td>\n",
       "      <td>19445</td>\n",
       "      <td>19445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Approved</td>\n",
       "      <td>Solex</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>19273</td>\n",
       "      <td>4169</td>\n",
       "      <td>13920</td>\n",
       "      <td>13587</td>\n",
       "      <td>12767</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       order_status  brand product_line product_class product_size\n",
       "count         19445  19445        19445         19445        19445\n",
       "unique            2      6            4             3            3\n",
       "top        Approved  Solex     Standard        medium       medium\n",
       "freq          19273   4169        13920         13587        12767"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions.describe(include=['category'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "305d307d",
   "metadata": {},
   "source": [
    "<a id='sh2'></a>\n",
    "### SHEET2 - CUSTOMER DEMOGRAPHIC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d277ae88",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>gender</th>\n",
       "      <th>past_3_years_bike_related_purchases</th>\n",
       "      <th>DOB</th>\n",
       "      <th>job_title</th>\n",
       "      <th>job_industry_category</th>\n",
       "      <th>wealth_segment</th>\n",
       "      <th>deceased_indicator</th>\n",
       "      <th>default</th>\n",
       "      <th>owns_car</th>\n",
       "      <th>tenure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Laraine</td>\n",
       "      <td>Medendorp</td>\n",
       "      <td>F</td>\n",
       "      <td>93</td>\n",
       "      <td>1953-10-12</td>\n",
       "      <td>Executive Secretary</td>\n",
       "      <td>Health</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>\"'</td>\n",
       "      <td>Yes</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Eli</td>\n",
       "      <td>Bockman</td>\n",
       "      <td>Male</td>\n",
       "      <td>81</td>\n",
       "      <td>1980-12-16</td>\n",
       "      <td>Administrative Officer</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>&lt;script&gt;alert('hi')&lt;/script&gt;</td>\n",
       "      <td>Yes</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Arlin</td>\n",
       "      <td>Dearle</td>\n",
       "      <td>Male</td>\n",
       "      <td>61</td>\n",
       "      <td>1954-01-20</td>\n",
       "      <td>Recruiting Manager</td>\n",
       "      <td>Property</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>2018-02-01 00:00:00</td>\n",
       "      <td>Yes</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Talbot</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>33</td>\n",
       "      <td>1961-10-03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IT</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>() { _; } &gt;_[$($())] { touch /tmp/blns.shellsh...</td>\n",
       "      <td>No</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Sheila-kathryn</td>\n",
       "      <td>Calton</td>\n",
       "      <td>Female</td>\n",
       "      <td>56</td>\n",
       "      <td>1977-05-13</td>\n",
       "      <td>Senior Editor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>NIL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   customer_id      first_name  last_name  gender  \\\n",
       "0            1         Laraine  Medendorp       F   \n",
       "1            2             Eli    Bockman    Male   \n",
       "2            3           Arlin     Dearle    Male   \n",
       "3            4          Talbot        NaN    Male   \n",
       "4            5  Sheila-kathryn     Calton  Female   \n",
       "\n",
       "   past_3_years_bike_related_purchases        DOB               job_title  \\\n",
       "0                                   93 1953-10-12     Executive Secretary   \n",
       "1                                   81 1980-12-16  Administrative Officer   \n",
       "2                                   61 1954-01-20      Recruiting Manager   \n",
       "3                                   33 1961-10-03                     NaN   \n",
       "4                                   56 1977-05-13           Senior Editor   \n",
       "\n",
       "  job_industry_category     wealth_segment deceased_indicator  \\\n",
       "0                Health      Mass Customer                  N   \n",
       "1    Financial Services      Mass Customer                  N   \n",
       "2              Property      Mass Customer                  N   \n",
       "3                    IT      Mass Customer                  N   \n",
       "4                   NaN  Affluent Customer                  N   \n",
       "\n",
       "                                             default owns_car  tenure  \n",
       "0                                                 \"'      Yes    11.0  \n",
       "1                       <script>alert('hi')</script>      Yes    16.0  \n",
       "2                                2018-02-01 00:00:00      Yes    15.0  \n",
       "3  () { _; } >_[$($())] { touch /tmp/blns.shellsh...       No     7.0  \n",
       "4                                                NIL      Yes     8.0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "custdemo.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "54e6e25e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4000, 13)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "custdemo.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7e19f6df",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4000 entries, 0 to 3999\n",
      "Data columns (total 13 columns):\n",
      " #   Column                               Non-Null Count  Dtype         \n",
      "---  ------                               --------------  -----         \n",
      " 0   customer_id                          4000 non-null   int64         \n",
      " 1   first_name                           4000 non-null   object        \n",
      " 2   last_name                            3875 non-null   object        \n",
      " 3   gender                               4000 non-null   object        \n",
      " 4   past_3_years_bike_related_purchases  4000 non-null   int64         \n",
      " 5   DOB                                  3913 non-null   datetime64[ns]\n",
      " 6   job_title                            3494 non-null   object        \n",
      " 7   job_industry_category                3344 non-null   object        \n",
      " 8   wealth_segment                       4000 non-null   object        \n",
      " 9   deceased_indicator                   4000 non-null   object        \n",
      " 10  default                              3698 non-null   object        \n",
      " 11  owns_car                             4000 non-null   object        \n",
      " 12  tenure                               3913 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(1), int64(2), object(9)\n",
      "memory usage: 406.4+ KB\n"
     ]
    }
   ],
   "source": [
    "custdemo.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2153c0a",
   "metadata": {},
   "source": [
    "**Observation**\n",
    "- **NULL VALUES**: We can see there are some null values in columns `last_name`, `DOB`, `job_title`, `job_industry_category`, `default`, `tenure`.\n",
    "\n",
    "- **RELEVANCE**:  \n",
    "`default` column seems to contain some metadata which is unnecessary for us.\n",
    "\n",
    "- **DATA TYPE**:\n",
    "`first_name`, `last_name`, `gender`, `job_title`, `job_industry_category`, `wealth_Segment`, `deceased_indiactor`, and `owns_car` are objects, we can change them into categories.\n",
    "Coverting \"objects\" to \"category\" reduces the space required to store the dataframe. It also helps in analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed2d8716",
   "metadata": {},
   "source": [
    "### DISTINCT CUSTOMER IDs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "13e1dffb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4000"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "custdemo.customer_id.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c792929c",
   "metadata": {},
   "source": [
    "There are total 4000 unique customers the organisation has."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5d92d62",
   "metadata": {},
   "source": [
    "### Checking for consistency and validity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a78be52c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['F', 'Male', 'Female', 'U', 'Femal', 'M'], dtype=object)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "custdemo.gender.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d157ccbb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Health', 'Financial Services', 'Property', 'IT', nan, 'Retail',\n",
       "       'Argiculture', 'Manufacturing', 'Telecommunications',\n",
       "       'Entertainment'], dtype=object)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "custdemo['job_industry_category'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "44b3fb1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Mass Customer', 'Affluent Customer', 'High Net Worth'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "custdemo['wealth_segment'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7764c88f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['N', 'Y'], dtype=object)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "custdemo['deceased_indicator'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "64c9af8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Yes', 'No'], dtype=object)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "custdemo['owns_car'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "1df30e66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([11., 16., 15.,  7.,  8., 13., 20.,  9.,  6.,  1., 18., 21., 12.,\n",
       "       19., 14.,  4., 22.,  5., 17.,  2.,  3., 10., nan])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "custdemo['tenure'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b50245c1",
   "metadata": {},
   "source": [
    "**Observation**\n",
    "- **CONSISTENCY**: There is inconsistent data in `gender`.\n",
    "\n",
    "- **RELEVANCE**:We can also remove values corresponding to `deceased indiactor` = `Y`, since we would be analysing the data only for customers who are alive."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a471718f",
   "metadata": {},
   "source": [
    "### Checking for duplicate rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "5dcd2e30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to find duplicates\n",
    "custdemo.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e37e9a9",
   "metadata": {},
   "source": [
    "**Observation** - Data does not have any duplicate rows!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31180941",
   "metadata": {},
   "source": [
    "### DATA CLEANING\n",
    "\n",
    "- drop unnecessary column `default`\n",
    "- values corrsponding to `Y` in deceased indicator\n",
    "- changing data types\n",
    "- column `gender`\n",
    "- null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "7a93f3b5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# drop 'default'\n",
    "custdemo = custdemo.drop('default', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b3acee33",
   "metadata": {},
   "outputs": [],
   "source": [
    "#custdemo = custdemo['deceased_indicator'].drop()\n",
    "custdemo = custdemo.drop(custdemo.index[custdemo['deceased_indicator'] == 'Y'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "21d077ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['N'], dtype=object)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "custdemo['deceased_indicator'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6306f4f",
   "metadata": {},
   "source": [
    "**Data Type**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "63ca8935",
   "metadata": {},
   "outputs": [],
   "source": [
    "# coverting \"objects\" to \"category\" reduces the space required to store the dataframe. It also helps in analysis\n",
    "\n",
    "custdemo['first_name'] = custdemo['first_name'].astype(\"category\")\n",
    "custdemo['last_name'] = custdemo['last_name'].astype(\"category\")\n",
    "custdemo['gender'] = custdemo['gender'].astype(\"category\")\n",
    "custdemo['job_title'] = custdemo['job_title'].astype(\"category\")\n",
    "custdemo['job_industry_category'] = custdemo['job_industry_category'].astype(\"category\")\n",
    "custdemo['wealth_segment'] = custdemo['wealth_segment'].astype(\"category\")\n",
    "custdemo['deceased_indicator'] = custdemo['deceased_indicator'].astype(\"category\")\n",
    "custdemo['owns_car'] = custdemo['owns_car'].astype(\"category\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d560c8a",
   "metadata": {},
   "source": [
    "#### Inconsistent data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "45b272c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['F', 'Male', 'Female', 'U', 'Femal', 'M']\n",
       "Categories (6, object): ['F', 'Femal', 'Female', 'M', 'Male', 'U']"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "custdemo.gender.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "d1638e78",
   "metadata": {},
   "outputs": [],
   "source": [
    "custdemo.replace({'F':'Female', 'M':'Male', 'Femal':'Female'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "c4c14a43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Female', 'Male', 'U']\n",
       "Categories (3, object): ['Female', 'Male', 'U']"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "custdemo.gender.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec1559c9",
   "metadata": {},
   "source": [
    "#### Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "1f7c25c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customer_id                              0\n",
       "first_name                               0\n",
       "last_name                              125\n",
       "gender                                   0\n",
       "past_3_years_bike_related_purchases      0\n",
       "DOB                                     87\n",
       "job_title                              506\n",
       "job_industry_category                  656\n",
       "wealth_segment                           0\n",
       "deceased_indicator                       0\n",
       "owns_car                                 0\n",
       "tenure                                  87\n",
       "dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "custdemo.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "aba043dd",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customer_id                            0.000000\n",
       "first_name                             0.000000\n",
       "last_name                              0.642839\n",
       "gender                                 0.000000\n",
       "past_3_years_bike_related_purchases    0.000000\n",
       "DOB                                    0.447416\n",
       "job_title                              2.602211\n",
       "job_industry_category                  3.373618\n",
       "wealth_segment                         0.000000\n",
       "deceased_indicator                     0.000000\n",
       "owns_car                               0.000000\n",
       "tenure                                 0.447416\n",
       "dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#percentage missing values in each column\n",
    "custdemo.isnull().sum()/len(transactions) *100"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7be68d4",
   "metadata": {},
   "source": [
    "**Observation** - For 'job_title' and 'job_industry_segment' we can impute the values using `mode`. `last_name` and `tenure`do not look like a key columns for our analysis, so we can leave it like that for now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "ebaa9066",
   "metadata": {},
   "outputs": [],
   "source": [
    "# using mode\n",
    "\n",
    "custdemo['job_title'].fillna(custdemo['job_title'].mode()[0], inplace = True)\n",
    "custdemo['job_industry_category'].fillna(custdemo['job_industry_category'].mode()[0], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "fff4baa1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customer_id                            0.000000\n",
       "first_name                             0.000000\n",
       "last_name                              3.126563\n",
       "gender                                 0.000000\n",
       "past_3_years_bike_related_purchases    0.000000\n",
       "DOB                                    2.176088\n",
       "job_title                              0.000000\n",
       "job_industry_category                  0.000000\n",
       "wealth_segment                         0.000000\n",
       "deceased_indicator                     0.000000\n",
       "owns_car                               0.000000\n",
       "tenure                                 2.176088\n",
       "dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "custdemo.isnull().sum()/ len(custdemo) * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38559c21",
   "metadata": {},
   "source": [
    "<a id='sh3'></a>\n",
    "### SHEET3 - CUSTOMER ADDRESS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "ab0fb5f0",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>address</th>\n",
       "      <th>postcode</th>\n",
       "      <th>state</th>\n",
       "      <th>country</th>\n",
       "      <th>property_valuation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>060 Morning Avenue</td>\n",
       "      <td>2016</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>6 Meadow Vale Court</td>\n",
       "      <td>2153</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>0 Holy Cross Court</td>\n",
       "      <td>4211</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>17979 Del Mar Point</td>\n",
       "      <td>2448</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>9 Oakridge Court</td>\n",
       "      <td>3216</td>\n",
       "      <td>VIC</td>\n",
       "      <td>Australia</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   customer_id              address  postcode            state    country  \\\n",
       "0            1   060 Morning Avenue      2016  New South Wales  Australia   \n",
       "1            2  6 Meadow Vale Court      2153  New South Wales  Australia   \n",
       "2            4   0 Holy Cross Court      4211              QLD  Australia   \n",
       "3            5  17979 Del Mar Point      2448  New South Wales  Australia   \n",
       "4            6     9 Oakridge Court      3216              VIC  Australia   \n",
       "\n",
       "   property_valuation  \n",
       "0                  10  \n",
       "1                  10  \n",
       "2                   9  \n",
       "3                   4  \n",
       "4                   9  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "address.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "2fa66855",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3999, 6)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "address.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "5f7e5875",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3999 entries, 0 to 3998\n",
      "Data columns (total 6 columns):\n",
      " #   Column              Non-Null Count  Dtype \n",
      "---  ------              --------------  ----- \n",
      " 0   customer_id         3999 non-null   int64 \n",
      " 1   address             3999 non-null   object\n",
      " 2   postcode            3999 non-null   int64 \n",
      " 3   state               3999 non-null   object\n",
      " 4   country             3999 non-null   object\n",
      " 5   property_valuation  3999 non-null   int64 \n",
      "dtypes: int64(3), object(3)\n",
      "memory usage: 187.6+ KB\n"
     ]
    }
   ],
   "source": [
    "address.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c8940fe",
   "metadata": {},
   "source": [
    "### DISTINCT CUSTOMER IDs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "412eded0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3999"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "address.customer_id.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbc3bcd6",
   "metadata": {},
   "source": [
    "### Checking for consistency and validity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "a5423a38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['New South Wales', 'QLD', 'VIC', 'NSW', 'Victoria'], dtype=object)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "address.state.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "1612cf68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Australia'], dtype=object)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "address.country.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdbf12a8",
   "metadata": {},
   "source": [
    "#### Checking for duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "35268b47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "address.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f30c043",
   "metadata": {},
   "source": [
    "**Observation**\n",
    "- **CONSISTENCY**: There is inconsistent data in `state` column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "66f4dedb",
   "metadata": {},
   "outputs": [],
   "source": [
    "address.state.replace({'NSW':'New South Wales', 'VIC':'Victoria'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "b23afd74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['New South Wales', 'QLD', 'Victoria'], dtype=object)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "address.state.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03028b20",
   "metadata": {},
   "source": [
    "**Changing data type from object to category**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "5858e6f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "address['address'] = address['address'].astype('category')\n",
    "address['state'] = address['state'].astype('category')\n",
    "address['country'] = address['country'].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "35221a84",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3999 entries, 0 to 3998\n",
      "Data columns (total 6 columns):\n",
      " #   Column              Non-Null Count  Dtype   \n",
      "---  ------              --------------  -----   \n",
      " 0   customer_id         3999 non-null   int64   \n",
      " 1   address             3999 non-null   category\n",
      " 2   postcode            3999 non-null   int64   \n",
      " 3   state               3999 non-null   category\n",
      " 4   country             3999 non-null   category\n",
      " 5   property_valuation  3999 non-null   int64   \n",
      "dtypes: category(3), int64(3)\n",
      "memory usage: 270.0 KB\n"
     ]
    }
   ],
   "source": [
    "address.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09e1a06f",
   "metadata": {},
   "source": [
    "<a id='sh4'></a>\n",
    "### SHEET4 - NewCustomersList"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "ff2a76cc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>gender</th>\n",
       "      <th>past_3_years_bike_related_purchases</th>\n",
       "      <th>DOB</th>\n",
       "      <th>job_title</th>\n",
       "      <th>job_industry_category</th>\n",
       "      <th>wealth_segment</th>\n",
       "      <th>deceased_indicator</th>\n",
       "      <th>owns_car</th>\n",
       "      <th>tenure</th>\n",
       "      <th>address</th>\n",
       "      <th>postcode</th>\n",
       "      <th>state</th>\n",
       "      <th>country</th>\n",
       "      <th>property_valuation</th>\n",
       "      <th>Rank</th>\n",
       "      <th>Value</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chickie</td>\n",
       "      <td>Brister</td>\n",
       "      <td>Male</td>\n",
       "      <td>86</td>\n",
       "      <td>1957-07-12</td>\n",
       "      <td>General Manager</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>14</td>\n",
       "      <td>45 Shopko Center</td>\n",
       "      <td>4500</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1.718750</td>\n",
       "      <td>65.852055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Morly</td>\n",
       "      <td>Genery</td>\n",
       "      <td>Male</td>\n",
       "      <td>69</td>\n",
       "      <td>1970-03-22</td>\n",
       "      <td>Structural Engineer</td>\n",
       "      <td>Property</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>16</td>\n",
       "      <td>14 Mccormick Park</td>\n",
       "      <td>2113</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>1.718750</td>\n",
       "      <td>53.150685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ardelis</td>\n",
       "      <td>Forrester</td>\n",
       "      <td>Female</td>\n",
       "      <td>10</td>\n",
       "      <td>1974-08-28</td>\n",
       "      <td>Senior Cost Accountant</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>10</td>\n",
       "      <td>5 Colorado Crossing</td>\n",
       "      <td>3505</td>\n",
       "      <td>VIC</td>\n",
       "      <td>Australia</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1.718750</td>\n",
       "      <td>48.712329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lucine</td>\n",
       "      <td>Stutt</td>\n",
       "      <td>Female</td>\n",
       "      <td>64</td>\n",
       "      <td>1979-01-28</td>\n",
       "      <td>Account Representative III</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>5</td>\n",
       "      <td>207 Annamark Plaza</td>\n",
       "      <td>4814</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1.703125</td>\n",
       "      <td>44.290411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Melinda</td>\n",
       "      <td>Hadlee</td>\n",
       "      <td>Female</td>\n",
       "      <td>34</td>\n",
       "      <td>1965-09-21</td>\n",
       "      <td>Financial Analyst</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>19</td>\n",
       "      <td>115 Montana Place</td>\n",
       "      <td>2093</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>1.703125</td>\n",
       "      <td>57.652055</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  first_name  last_name  gender  past_3_years_bike_related_purchases  \\\n",
       "0    Chickie    Brister    Male                                   86   \n",
       "1      Morly     Genery    Male                                   69   \n",
       "2    Ardelis  Forrester  Female                                   10   \n",
       "3     Lucine      Stutt  Female                                   64   \n",
       "4    Melinda     Hadlee  Female                                   34   \n",
       "\n",
       "         DOB                   job_title job_industry_category  \\\n",
       "0 1957-07-12             General Manager         Manufacturing   \n",
       "1 1970-03-22         Structural Engineer              Property   \n",
       "2 1974-08-28      Senior Cost Accountant    Financial Services   \n",
       "3 1979-01-28  Account Representative III         Manufacturing   \n",
       "4 1965-09-21           Financial Analyst    Financial Services   \n",
       "\n",
       "      wealth_segment deceased_indicator owns_car  tenure              address  \\\n",
       "0      Mass Customer                  N      Yes      14     45 Shopko Center   \n",
       "1      Mass Customer                  N       No      16    14 Mccormick Park   \n",
       "2  Affluent Customer                  N       No      10  5 Colorado Crossing   \n",
       "3  Affluent Customer                  N      Yes       5   207 Annamark Plaza   \n",
       "4  Affluent Customer                  N       No      19    115 Montana Place   \n",
       "\n",
       "   postcode state    country  property_valuation  Rank     Value        Age  \n",
       "0      4500   QLD  Australia                   6     1  1.718750  65.852055  \n",
       "1      2113   NSW  Australia                  11     1  1.718750  53.150685  \n",
       "2      3505   VIC  Australia                   5     1  1.718750  48.712329  \n",
       "3      4814   QLD  Australia                   1     4  1.703125  44.290411  \n",
       "4      2093   NSW  Australia                   9     4  1.703125  57.652055  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newcust.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "9ade3224",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 19)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newcust.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "d2325cff",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000 entries, 0 to 999\n",
      "Data columns (total 19 columns):\n",
      " #   Column                               Non-Null Count  Dtype         \n",
      "---  ------                               --------------  -----         \n",
      " 0   first_name                           1000 non-null   object        \n",
      " 1   last_name                            971 non-null    object        \n",
      " 2   gender                               1000 non-null   object        \n",
      " 3   past_3_years_bike_related_purchases  1000 non-null   int64         \n",
      " 4   DOB                                  983 non-null    datetime64[ns]\n",
      " 5   job_title                            894 non-null    object        \n",
      " 6   job_industry_category                835 non-null    object        \n",
      " 7   wealth_segment                       1000 non-null   object        \n",
      " 8   deceased_indicator                   1000 non-null   object        \n",
      " 9   owns_car                             1000 non-null   object        \n",
      " 10  tenure                               1000 non-null   int64         \n",
      " 11  address                              1000 non-null   object        \n",
      " 12  postcode                             1000 non-null   int64         \n",
      " 13  state                                1000 non-null   object        \n",
      " 14  country                              1000 non-null   object        \n",
      " 15  property_valuation                   1000 non-null   int64         \n",
      " 16  Rank                                 1000 non-null   int64         \n",
      " 17  Value                                1000 non-null   float64       \n",
      " 18  Age                                  1000 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(2), int64(5), object(11)\n",
      "memory usage: 148.6+ KB\n"
     ]
    }
   ],
   "source": [
    "newcust.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "920181fc",
   "metadata": {},
   "source": [
    "### MISSING VALUES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "d73673d6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "first_name                               0\n",
       "last_name                               29\n",
       "gender                                   0\n",
       "past_3_years_bike_related_purchases      0\n",
       "DOB                                     17\n",
       "job_title                              106\n",
       "job_industry_category                  165\n",
       "wealth_segment                           0\n",
       "deceased_indicator                       0\n",
       "owns_car                                 0\n",
       "tenure                                   0\n",
       "address                                  0\n",
       "postcode                                 0\n",
       "state                                    0\n",
       "country                                  0\n",
       "property_valuation                       0\n",
       "Rank                                     0\n",
       "Value                                    0\n",
       "Age                                      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#missing values in each col\n",
    "newcust.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7c6d3ba",
   "metadata": {},
   "source": [
    "#### Duplicates?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "30daee8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to find duplicates\n",
    "newcust.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a61239b7",
   "metadata": {},
   "source": [
    "**Observation** - Data does not have any duplicates!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fb65199",
   "metadata": {},
   "source": [
    "### Checking for consistency and validity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "22822134",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Male', 'Female', 'U'], dtype=object)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newcust['gender'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "d16d0dfe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Manufacturing', 'Property', 'Financial Services', 'Entertainment',\n",
       "       'Retail', 'IT', 'Telecommunications', 'Health', nan, 'Argiculture'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newcust['job_industry_category'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "d7f1acb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Mass Customer', 'Affluent Customer', 'High Net Worth'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newcust['wealth_segment'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "3f530274",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['N'], dtype=object)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newcust['deceased_indicator'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "c54cd216",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Yes', 'No'], dtype=object)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newcust['owns_car'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "f892fda0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([14, 16, 10,  5, 19, 22,  8, 17,  3,  9,  4, 11, 12, 13,  7, 20, 15,\n",
       "        6, 18, 21,  2,  1,  0], dtype=int64)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newcust['tenure'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "0b950509",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['QLD', 'NSW', 'VIC'], dtype=object)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newcust['state'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "27d3f2c0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Australia'], dtype=object)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newcust['country'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "dbb8749e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 6, 11,  5,  1,  9,  7, 10,  8,  4,  2, 12,  3], dtype=int64)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newcust['property_valuation'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "272657ea",
   "metadata": {},
   "source": [
    "**Observation** - Our data is consistent and does not have any invalid values besides null values. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ac0dae3",
   "metadata": {},
   "source": [
    "**Changing data type from object to category**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "ac039d19",
   "metadata": {},
   "outputs": [],
   "source": [
    "newcust['first_name'] = newcust['first_name'].astype(\"category\")\n",
    "newcust['last_name'] = newcust['last_name'].astype(\"category\")\n",
    "newcust['gender'] = newcust['gender'].astype(\"category\")\n",
    "newcust['job_title'] = newcust['job_title'].astype(\"category\")\n",
    "newcust['job_industry_category'] = newcust['job_industry_category'].astype(\"category\")\n",
    "newcust['wealth_segment'] = newcust['wealth_segment'].astype(\"category\")\n",
    "newcust['deceased_indicator'] = newcust['deceased_indicator'].astype(\"category\")\n",
    "newcust['owns_car'] = newcust['owns_car'].astype(\"category\")\n",
    "newcust['address'] = newcust['address'].astype('category')\n",
    "newcust['state'] = newcust['state'].astype('category')\n",
    "newcust['country'] = newcust['country'].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "6b4adc15",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000 entries, 0 to 999\n",
      "Data columns (total 19 columns):\n",
      " #   Column                               Non-Null Count  Dtype         \n",
      "---  ------                               --------------  -----         \n",
      " 0   first_name                           1000 non-null   category      \n",
      " 1   last_name                            971 non-null    category      \n",
      " 2   gender                               1000 non-null   category      \n",
      " 3   past_3_years_bike_related_purchases  1000 non-null   int64         \n",
      " 4   DOB                                  983 non-null    datetime64[ns]\n",
      " 5   job_title                            894 non-null    category      \n",
      " 6   job_industry_category                835 non-null    category      \n",
      " 7   wealth_segment                       1000 non-null   category      \n",
      " 8   deceased_indicator                   1000 non-null   category      \n",
      " 9   owns_car                             1000 non-null   category      \n",
      " 10  tenure                               1000 non-null   int64         \n",
      " 11  address                              1000 non-null   category      \n",
      " 12  postcode                             1000 non-null   int64         \n",
      " 13  state                                1000 non-null   category      \n",
      " 14  country                              1000 non-null   category      \n",
      " 15  property_valuation                   1000 non-null   int64         \n",
      " 16  Rank                                 1000 non-null   int64         \n",
      " 17  Value                                1000 non-null   float64       \n",
      " 18  Age                                  1000 non-null   float64       \n",
      "dtypes: category(11), datetime64[ns](1), float64(2), int64(5)\n",
      "memory usage: 203.4 KB\n"
     ]
    }
   ],
   "source": [
    "newcust.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5313eec2",
   "metadata": {},
   "source": [
    "****"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c803751b",
   "metadata": {},
   "source": [
    "<a id='eda'></a>\n",
    "# EXPLORATORY DATA ANALYSIS"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4308590f",
   "metadata": {},
   "source": [
    "#### Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "2935f9bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>transaction_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>transaction_date</th>\n",
       "      <th>online_order</th>\n",
       "      <th>order_status</th>\n",
       "      <th>brand</th>\n",
       "      <th>product_line</th>\n",
       "      <th>product_class</th>\n",
       "      <th>product_size</th>\n",
       "      <th>list_price</th>\n",
       "      <th>standard_cost</th>\n",
       "      <th>product_first_sold_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2950</td>\n",
       "      <td>2017-02-25</td>\n",
       "      <td>False</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Solex</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>71.49</td>\n",
       "      <td>53.62</td>\n",
       "      <td>2082-12-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3120</td>\n",
       "      <td>2017-05-21</td>\n",
       "      <td>True</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Trek Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>large</td>\n",
       "      <td>2091.47</td>\n",
       "      <td>388.92</td>\n",
       "      <td>2084-03-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>37</td>\n",
       "      <td>402</td>\n",
       "      <td>2017-10-16</td>\n",
       "      <td>False</td>\n",
       "      <td>Approved</td>\n",
       "      <td>OHM Cycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>low</td>\n",
       "      <td>medium</td>\n",
       "      <td>1793.43</td>\n",
       "      <td>248.82</td>\n",
       "      <td>2069-07-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>88</td>\n",
       "      <td>3135</td>\n",
       "      <td>2017-08-31</td>\n",
       "      <td>False</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Norco Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>1198.46</td>\n",
       "      <td>381.10</td>\n",
       "      <td>2068-12-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>78</td>\n",
       "      <td>787</td>\n",
       "      <td>2017-10-01</td>\n",
       "      <td>True</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Giant Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>large</td>\n",
       "      <td>1765.30</td>\n",
       "      <td>709.48</td>\n",
       "      <td>2085-08-11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   transaction_id  product_id  customer_id transaction_date online_order  \\\n",
       "0               1           2         2950       2017-02-25        False   \n",
       "1               2           3         3120       2017-05-21         True   \n",
       "2               3          37          402       2017-10-16        False   \n",
       "3               4          88         3135       2017-08-31        False   \n",
       "4               5          78          787       2017-10-01         True   \n",
       "\n",
       "  order_status           brand product_line product_class product_size  \\\n",
       "0     Approved           Solex     Standard        medium       medium   \n",
       "1     Approved   Trek Bicycles     Standard        medium        large   \n",
       "2     Approved      OHM Cycles     Standard           low       medium   \n",
       "3     Approved  Norco Bicycles     Standard        medium       medium   \n",
       "4     Approved  Giant Bicycles     Standard        medium        large   \n",
       "\n",
       "   list_price  standard_cost product_first_sold_date  \n",
       "0       71.49          53.62              2082-12-04  \n",
       "1     2091.47         388.92              2084-03-04  \n",
       "2     1793.43         248.82              2069-07-21  \n",
       "3     1198.46         381.10              2068-12-17  \n",
       "4     1765.30         709.48              2085-08-11  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13d69ed2",
   "metadata": {},
   "source": [
    "#### created new column 'profit'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "97751541",
   "metadata": {},
   "outputs": [],
   "source": [
    "transactions['Profit'] = transactions['list_price'] - transactions['standard_cost']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "12248d07",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>transaction_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>transaction_date</th>\n",
       "      <th>online_order</th>\n",
       "      <th>order_status</th>\n",
       "      <th>brand</th>\n",
       "      <th>product_line</th>\n",
       "      <th>product_class</th>\n",
       "      <th>product_size</th>\n",
       "      <th>list_price</th>\n",
       "      <th>standard_cost</th>\n",
       "      <th>product_first_sold_date</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2950</td>\n",
       "      <td>2017-02-25</td>\n",
       "      <td>False</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Solex</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>71.49</td>\n",
       "      <td>53.62</td>\n",
       "      <td>2082-12-04</td>\n",
       "      <td>17.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3120</td>\n",
       "      <td>2017-05-21</td>\n",
       "      <td>True</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Trek Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>large</td>\n",
       "      <td>2091.47</td>\n",
       "      <td>388.92</td>\n",
       "      <td>2084-03-04</td>\n",
       "      <td>1702.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>37</td>\n",
       "      <td>402</td>\n",
       "      <td>2017-10-16</td>\n",
       "      <td>False</td>\n",
       "      <td>Approved</td>\n",
       "      <td>OHM Cycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>low</td>\n",
       "      <td>medium</td>\n",
       "      <td>1793.43</td>\n",
       "      <td>248.82</td>\n",
       "      <td>2069-07-21</td>\n",
       "      <td>1544.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>88</td>\n",
       "      <td>3135</td>\n",
       "      <td>2017-08-31</td>\n",
       "      <td>False</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Norco Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>1198.46</td>\n",
       "      <td>381.10</td>\n",
       "      <td>2068-12-17</td>\n",
       "      <td>817.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>78</td>\n",
       "      <td>787</td>\n",
       "      <td>2017-10-01</td>\n",
       "      <td>True</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Giant Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>large</td>\n",
       "      <td>1765.30</td>\n",
       "      <td>709.48</td>\n",
       "      <td>2085-08-11</td>\n",
       "      <td>1055.82</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   transaction_id  product_id  customer_id transaction_date online_order  \\\n",
       "0               1           2         2950       2017-02-25        False   \n",
       "1               2           3         3120       2017-05-21         True   \n",
       "2               3          37          402       2017-10-16        False   \n",
       "3               4          88         3135       2017-08-31        False   \n",
       "4               5          78          787       2017-10-01         True   \n",
       "\n",
       "  order_status           brand product_line product_class product_size  \\\n",
       "0     Approved           Solex     Standard        medium       medium   \n",
       "1     Approved   Trek Bicycles     Standard        medium        large   \n",
       "2     Approved      OHM Cycles     Standard           low       medium   \n",
       "3     Approved  Norco Bicycles     Standard        medium       medium   \n",
       "4     Approved  Giant Bicycles     Standard        medium        large   \n",
       "\n",
       "   list_price  standard_cost product_first_sold_date   Profit  \n",
       "0       71.49          53.62              2082-12-04    17.87  \n",
       "1     2091.47         388.92              2084-03-04  1702.55  \n",
       "2     1793.43         248.82              2069-07-21  1544.61  \n",
       "3     1198.46         381.10              2068-12-17   817.36  \n",
       "4     1765.30         709.48              2085-08-11  1055.82  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af4942ef",
   "metadata": {},
   "source": [
    "#### calculating 'age' from DOB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "e3669edb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>gender</th>\n",
       "      <th>past_3_years_bike_related_purchases</th>\n",
       "      <th>DOB</th>\n",
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       "      <th>wealth_segment</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Laraine</td>\n",
       "      <td>Medendorp</td>\n",
       "      <td>Female</td>\n",
       "      <td>93</td>\n",
       "      <td>1953-10-12</td>\n",
       "      <td>Executive Secretary</td>\n",
       "      <td>Health</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Eli</td>\n",
       "      <td>Bockman</td>\n",
       "      <td>Male</td>\n",
       "      <td>81</td>\n",
       "      <td>1980-12-16</td>\n",
       "      <td>Administrative Officer</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Arlin</td>\n",
       "      <td>Dearle</td>\n",
       "      <td>Male</td>\n",
       "      <td>61</td>\n",
       "      <td>1954-01-20</td>\n",
       "      <td>Recruiting Manager</td>\n",
       "      <td>Property</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Talbot</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>33</td>\n",
       "      <td>1961-10-03</td>\n",
       "      <td>Business Systems Development Analyst</td>\n",
       "      <td>IT</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Sheila-kathryn</td>\n",
       "      <td>Calton</td>\n",
       "      <td>Female</td>\n",
       "      <td>56</td>\n",
       "      <td>1977-05-13</td>\n",
       "      <td>Senior Editor</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   customer_id      first_name  last_name  gender  \\\n",
       "0            1         Laraine  Medendorp  Female   \n",
       "1            2             Eli    Bockman    Male   \n",
       "2            3           Arlin     Dearle    Male   \n",
       "3            4          Talbot        NaN    Male   \n",
       "4            5  Sheila-kathryn     Calton  Female   \n",
       "\n",
       "   past_3_years_bike_related_purchases        DOB  \\\n",
       "0                                   93 1953-10-12   \n",
       "1                                   81 1980-12-16   \n",
       "2                                   61 1954-01-20   \n",
       "3                                   33 1961-10-03   \n",
       "4                                   56 1977-05-13   \n",
       "\n",
       "                              job_title job_industry_category  \\\n",
       "0                   Executive Secretary                Health   \n",
       "1                Administrative Officer    Financial Services   \n",
       "2                    Recruiting Manager              Property   \n",
       "3  Business Systems Development Analyst                    IT   \n",
       "4                         Senior Editor         Manufacturing   \n",
       "\n",
       "      wealth_segment deceased_indicator owns_car  tenure  \n",
       "0      Mass Customer                  N      Yes    11.0  \n",
       "1      Mass Customer                  N      Yes    16.0  \n",
       "2      Mass Customer                  N      Yes    15.0  \n",
       "3      Mass Customer                  N       No     7.0  \n",
       "4  Affluent Customer                  N      Yes     8.0  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "custdemo.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "1e65221e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('<M8[ns]')"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "custdemo.DOB.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "662f6ecc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>gender</th>\n",
       "      <th>past_3_years_bike_related_purchases</th>\n",
       "      <th>DOB</th>\n",
       "      <th>job_title</th>\n",
       "      <th>job_industry_category</th>\n",
       "      <th>wealth_segment</th>\n",
       "      <th>deceased_indicator</th>\n",
       "      <th>owns_car</th>\n",
       "      <th>tenure</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Laraine</td>\n",
       "      <td>Medendorp</td>\n",
       "      <td>Female</td>\n",
       "      <td>93</td>\n",
       "      <td>1953-10-12</td>\n",
       "      <td>Executive Secretary</td>\n",
       "      <td>Health</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>11.0</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Eli</td>\n",
       "      <td>Bockman</td>\n",
       "      <td>Male</td>\n",
       "      <td>81</td>\n",
       "      <td>1980-12-16</td>\n",
       "      <td>Administrative Officer</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>16.0</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Arlin</td>\n",
       "      <td>Dearle</td>\n",
       "      <td>Male</td>\n",
       "      <td>61</td>\n",
       "      <td>1954-01-20</td>\n",
       "      <td>Recruiting Manager</td>\n",
       "      <td>Property</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>15.0</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Talbot</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>33</td>\n",
       "      <td>1961-10-03</td>\n",
       "      <td>Business Systems Development Analyst</td>\n",
       "      <td>IT</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>7.0</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Sheila-kathryn</td>\n",
       "      <td>Calton</td>\n",
       "      <td>Female</td>\n",
       "      <td>56</td>\n",
       "      <td>1977-05-13</td>\n",
       "      <td>Senior Editor</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>8.0</td>\n",
       "      <td>46</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>3995</th>\n",
       "      <td>3996</td>\n",
       "      <td>Rosalia</td>\n",
       "      <td>Halgarth</td>\n",
       "      <td>Female</td>\n",
       "      <td>8</td>\n",
       "      <td>1975-08-09</td>\n",
       "      <td>VP Product Management</td>\n",
       "      <td>Health</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>19.0</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3996</th>\n",
       "      <td>3997</td>\n",
       "      <td>Blanch</td>\n",
       "      <td>Nisuis</td>\n",
       "      <td>Female</td>\n",
       "      <td>87</td>\n",
       "      <td>2001-07-13</td>\n",
       "      <td>Statistician II</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3997</th>\n",
       "      <td>3998</td>\n",
       "      <td>Sarene</td>\n",
       "      <td>Woolley</td>\n",
       "      <td>U</td>\n",
       "      <td>60</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Assistant Manager</td>\n",
       "      <td>IT</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3998</th>\n",
       "      <td>3999</td>\n",
       "      <td>Patrizius</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>11</td>\n",
       "      <td>1973-10-24</td>\n",
       "      <td>Business Systems Development Analyst</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>10.0</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3999</th>\n",
       "      <td>4000</td>\n",
       "      <td>Kippy</td>\n",
       "      <td>Oldland</td>\n",
       "      <td>Male</td>\n",
       "      <td>76</td>\n",
       "      <td>1991-11-05</td>\n",
       "      <td>Software Engineer IV</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>11.0</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3998 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      customer_id      first_name  last_name  gender  \\\n",
       "0               1         Laraine  Medendorp  Female   \n",
       "1               2             Eli    Bockman    Male   \n",
       "2               3           Arlin     Dearle    Male   \n",
       "3               4          Talbot        NaN    Male   \n",
       "4               5  Sheila-kathryn     Calton  Female   \n",
       "...           ...             ...        ...     ...   \n",
       "3995         3996         Rosalia   Halgarth  Female   \n",
       "3996         3997          Blanch     Nisuis  Female   \n",
       "3997         3998          Sarene    Woolley       U   \n",
       "3998         3999       Patrizius        NaN    Male   \n",
       "3999         4000           Kippy    Oldland    Male   \n",
       "\n",
       "      past_3_years_bike_related_purchases        DOB  \\\n",
       "0                                      93 1953-10-12   \n",
       "1                                      81 1980-12-16   \n",
       "2                                      61 1954-01-20   \n",
       "3                                      33 1961-10-03   \n",
       "4                                      56 1977-05-13   \n",
       "...                                   ...        ...   \n",
       "3995                                    8 1975-08-09   \n",
       "3996                                   87 2001-07-13   \n",
       "3997                                   60        NaT   \n",
       "3998                                   11 1973-10-24   \n",
       "3999                                   76 1991-11-05   \n",
       "\n",
       "                                 job_title job_industry_category  \\\n",
       "0                      Executive Secretary                Health   \n",
       "1                   Administrative Officer    Financial Services   \n",
       "2                       Recruiting Manager              Property   \n",
       "3     Business Systems Development Analyst                    IT   \n",
       "4                            Senior Editor         Manufacturing   \n",
       "...                                    ...                   ...   \n",
       "3995                 VP Product Management                Health   \n",
       "3996                       Statistician II         Manufacturing   \n",
       "3997                     Assistant Manager                    IT   \n",
       "3998  Business Systems Development Analyst         Manufacturing   \n",
       "3999                  Software Engineer IV         Manufacturing   \n",
       "\n",
       "         wealth_segment deceased_indicator owns_car  tenure   Age  \n",
       "0         Mass Customer                  N      Yes    11.0    70  \n",
       "1         Mass Customer                  N      Yes    16.0    42  \n",
       "2         Mass Customer                  N      Yes    15.0    69  \n",
       "3         Mass Customer                  N       No     7.0    62  \n",
       "4     Affluent Customer                  N      Yes     8.0    46  \n",
       "...                 ...                ...      ...     ...   ...  \n",
       "3995      Mass Customer                  N       No    19.0    48  \n",
       "3996     High Net Worth                  N      Yes     1.0    22  \n",
       "3997     High Net Worth                  N       No     NaN  <NA>  \n",
       "3998  Affluent Customer                  N      Yes    10.0    50  \n",
       "3999  Affluent Customer                  N       No    11.0    32  \n",
       "\n",
       "[3998 rows x 13 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import date\n",
    "\n",
    "# Convert the DOB column to datetime format\n",
    "#custdemo['DOB'] = pd.to_datetime(custdemo['DOB'])\n",
    "\n",
    "# Calculate the age using today's date and the DOB\n",
    "today = pd.Timestamp(date.today())\n",
    "age = (today - custdemo['DOB']) / pd.Timedelta(days=365)\n",
    "\n",
    "custdemo['Age'] = age.round().astype('Int64')\n",
    "\n",
    "# Print the resulting DataFrame\n",
    "custdemo"
   ]
  },
  {
   "cell_type": "raw",
   "id": "05e2542c",
   "metadata": {},
   "source": [
    "pd.Timedelta(days=365) creates a time duration object representing one year. This is used to divide the time difference between today and the customer's date of birth, which is in days, by the number of days in a year to get the age of the customer in years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "adbf65db",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    3911.000000\n",
       "mean       45.830222\n",
       "std        12.801247\n",
       "min        21.000000\n",
       "25%        36.000000\n",
       "50%        46.000000\n",
       "75%        55.000000\n",
       "max       179.000000\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "custdemo.Age.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "2544a3ab",
   "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>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>gender</th>\n",
       "      <th>past_3_years_bike_related_purchases</th>\n",
       "      <th>DOB</th>\n",
       "      <th>job_title</th>\n",
       "      <th>job_industry_category</th>\n",
       "      <th>wealth_segment</th>\n",
       "      <th>deceased_indicator</th>\n",
       "      <th>owns_car</th>\n",
       "      <th>tenure</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>34</td>\n",
       "      <td>Jephthah</td>\n",
       "      <td>Bachmann</td>\n",
       "      <td>U</td>\n",
       "      <td>59</td>\n",
       "      <td>1843-12-21</td>\n",
       "      <td>Legal Assistant</td>\n",
       "      <td>IT</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>20.0</td>\n",
       "      <td>179</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    customer_id first_name last_name gender  \\\n",
       "33           34   Jephthah  Bachmann      U   \n",
       "\n",
       "    past_3_years_bike_related_purchases        DOB        job_title  \\\n",
       "33                                   59 1843-12-21  Legal Assistant   \n",
       "\n",
       "   job_industry_category     wealth_segment deceased_indicator owns_car  \\\n",
       "33                    IT  Affluent Customer                  N       No   \n",
       "\n",
       "    tenure  Age  \n",
       "33    20.0  179  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered = custdemo[custdemo['Age'] > 100]\n",
    "filtered"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d5109a8",
   "metadata": {},
   "source": [
    "**Observation**- We see here that the maximum age is 179 which we know cannot be possible. So, we change the DOB for this customer from `1843-12-21` to `1943-12-21` and also calculate the age again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "455b97c5",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Change the DOB for the first customer in the filtered dataframe\n",
    "custdemo.at[custdemo.index[33], 'DOB'] = pd.to_datetime('1943-12-21')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "aa1f2cf2",
   "metadata": {},
   "outputs": [],
   "source": [
    "age = (today - custdemo['DOB']) / pd.Timedelta(days=365)\n",
    "\n",
    "custdemo['Age'] = age.round().astype('Int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "6e2e3fec",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    3911.000000\n",
       "mean       45.804654\n",
       "std        12.633965\n",
       "min        21.000000\n",
       "25%        36.000000\n",
       "50%        46.000000\n",
       "75%        55.000000\n",
       "max        92.000000\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "custdemo.Age.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "4036c1dc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axs = plt.subplots(nrows=1,ncols=2, figsize=(10,5))\n",
    "sns.histplot(custdemo.Age, kde=True, ax=axs[0])\n",
    "sns.boxplot(x='Age',data=custdemo, ax=axs[1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cf88278",
   "metadata": {},
   "source": [
    "#### Since is a key variable for our analysis, we will try to deal with the missing values and impute it.\n",
    "**We can see that the variable Age is almost normally distributed. So we can use either of the central tendency values(mean/median) to deal with missing values.**   <br>\n",
    "Here we will use `median` to impute the values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "cc9ddef0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>gender</th>\n",
       "      <th>past_3_years_bike_related_purchases</th>\n",
       "      <th>DOB</th>\n",
       "      <th>job_title</th>\n",
       "      <th>job_industry_category</th>\n",
       "      <th>wealth_segment</th>\n",
       "      <th>deceased_indicator</th>\n",
       "      <th>owns_car</th>\n",
       "      <th>tenure</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [customer_id, first_name, last_name, gender, past_3_years_bike_related_purchases, DOB, job_title, job_industry_category, wealth_segment, deceased_indicator, owns_car, tenure, Age]\n",
       "Index: []"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "custdemo.Age = custdemo.Age.fillna(custdemo.Age.median())\n",
    "custdemo[custdemo.Age.isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2db13905",
   "metadata": {},
   "source": [
    "We can also drop the DOB column now is it is not needed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "f9d04dc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "custdemo.drop('DOB',axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "9cdbecff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 3998 entries, 0 to 3999\n",
      "Data columns (total 12 columns):\n",
      " #   Column                               Non-Null Count  Dtype   \n",
      "---  ------                               --------------  -----   \n",
      " 0   customer_id                          3998 non-null   int64   \n",
      " 1   first_name                           3998 non-null   category\n",
      " 2   last_name                            3873 non-null   category\n",
      " 3   gender                               3998 non-null   category\n",
      " 4   past_3_years_bike_related_purchases  3998 non-null   int64   \n",
      " 5   job_title                            3998 non-null   category\n",
      " 6   job_industry_category                3998 non-null   category\n",
      " 7   wealth_segment                       3998 non-null   category\n",
      " 8   deceased_indicator                   3998 non-null   category\n",
      " 9   owns_car                             3998 non-null   category\n",
      " 10  tenure                               3911 non-null   float64 \n",
      " 11  Age                                  3998 non-null   Int64   \n",
      "dtypes: Int64(1), category(8), float64(1), int64(2)\n",
      "memory usage: 585.7 KB\n"
     ]
    }
   ],
   "source": [
    "custdemo.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5642234b",
   "metadata": {},
   "source": [
    "<a id='rfm'></a>\n",
    "## RFM Analysis \n",
    "It is a customer segmentation technique that is commonly used in marketing to identify and target specific groups of customers. RFM stands for Recency, Frequency, and Monetary value.\n",
    "\n",
    "Using RFM analysis, customers can be segmented into groups based on their recency, frequency, and monetary value scores. For example, customers who have made a purchase recently, make frequent purchases, and have spent a lot of money would be considered the most valuable customers and would be targeted with special promotions and offers."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20b651d2",
   "metadata": {},
   "source": [
    "### Merge all the 3 tables\n",
    "\n",
    "i.e. Transactions, CustomerDemographic and CustomerAddress"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "f58dc2eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_table = address.merge(pd.merge(left=transactions, right=custdemo, how='inner', on='customer_id'), on='customer_id',suffixes= ('_x', '_y'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "792d35fb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>address</th>\n",
       "      <th>postcode</th>\n",
       "      <th>state</th>\n",
       "      <th>country</th>\n",
       "      <th>property_valuation</th>\n",
       "      <th>transaction_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>transaction_date</th>\n",
       "      <th>online_order</th>\n",
       "      <th>...</th>\n",
       "      <th>last_name</th>\n",
       "      <th>gender</th>\n",
       "      <th>past_3_years_bike_related_purchases</th>\n",
       "      <th>job_title</th>\n",
       "      <th>job_industry_category</th>\n",
       "      <th>wealth_segment</th>\n",
       "      <th>deceased_indicator</th>\n",
       "      <th>owns_car</th>\n",
       "      <th>tenure</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>060 Morning Avenue</td>\n",
       "      <td>2016</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10</td>\n",
       "      <td>94</td>\n",
       "      <td>86</td>\n",
       "      <td>2017-12-23</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>Medendorp</td>\n",
       "      <td>Female</td>\n",
       "      <td>93</td>\n",
       "      <td>Executive Secretary</td>\n",
       "      <td>Health</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>11.0</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>060 Morning Avenue</td>\n",
       "      <td>2016</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10</td>\n",
       "      <td>3765</td>\n",
       "      <td>38</td>\n",
       "      <td>2017-04-06</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>Medendorp</td>\n",
       "      <td>Female</td>\n",
       "      <td>93</td>\n",
       "      <td>Executive Secretary</td>\n",
       "      <td>Health</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>11.0</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>060 Morning Avenue</td>\n",
       "      <td>2016</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10</td>\n",
       "      <td>5157</td>\n",
       "      <td>47</td>\n",
       "      <td>2017-05-11</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>Medendorp</td>\n",
       "      <td>Female</td>\n",
       "      <td>93</td>\n",
       "      <td>Executive Secretary</td>\n",
       "      <td>Health</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>11.0</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>060 Morning Avenue</td>\n",
       "      <td>2016</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10</td>\n",
       "      <td>9785</td>\n",
       "      <td>72</td>\n",
       "      <td>2017-01-05</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>Medendorp</td>\n",
       "      <td>Female</td>\n",
       "      <td>93</td>\n",
       "      <td>Executive Secretary</td>\n",
       "      <td>Health</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>11.0</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>060 Morning Avenue</td>\n",
       "      <td>2016</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10</td>\n",
       "      <td>13424</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-02-21</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>Medendorp</td>\n",
       "      <td>Female</td>\n",
       "      <td>93</td>\n",
       "      <td>Executive Secretary</td>\n",
       "      <td>Health</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>11.0</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   customer_id             address  postcode            state    country  \\\n",
       "0            1  060 Morning Avenue      2016  New South Wales  Australia   \n",
       "1            1  060 Morning Avenue      2016  New South Wales  Australia   \n",
       "2            1  060 Morning Avenue      2016  New South Wales  Australia   \n",
       "3            1  060 Morning Avenue      2016  New South Wales  Australia   \n",
       "4            1  060 Morning Avenue      2016  New South Wales  Australia   \n",
       "\n",
       "   property_valuation  transaction_id  product_id transaction_date  \\\n",
       "0                  10              94          86       2017-12-23   \n",
       "1                  10            3765          38       2017-04-06   \n",
       "2                  10            5157          47       2017-05-11   \n",
       "3                  10            9785          72       2017-01-05   \n",
       "4                  10           13424           2       2017-02-21   \n",
       "\n",
       "  online_order  ...  last_name  gender past_3_years_bike_related_purchases  \\\n",
       "0        False  ...  Medendorp  Female                                  93   \n",
       "1         True  ...  Medendorp  Female                                  93   \n",
       "2         True  ...  Medendorp  Female                                  93   \n",
       "3        False  ...  Medendorp  Female                                  93   \n",
       "4        False  ...  Medendorp  Female                                  93   \n",
       "\n",
       "             job_title job_industry_category  wealth_segment  \\\n",
       "0  Executive Secretary                Health   Mass Customer   \n",
       "1  Executive Secretary                Health   Mass Customer   \n",
       "2  Executive Secretary                Health   Mass Customer   \n",
       "3  Executive Secretary                Health   Mass Customer   \n",
       "4  Executive Secretary                Health   Mass Customer   \n",
       "\n",
       "   deceased_indicator owns_car  tenure Age  \n",
       "0                   N      Yes    11.0  70  \n",
       "1                   N      Yes    11.0  70  \n",
       "2                   N      Yes    11.0  70  \n",
       "3                   N      Yes    11.0  70  \n",
       "4                   N      Yes    11.0  70  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_table.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "a1cf7088",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 19408 entries, 0 to 19407\n",
      "Data columns (total 30 columns):\n",
      " #   Column                               Non-Null Count  Dtype         \n",
      "---  ------                               --------------  -----         \n",
      " 0   customer_id                          19408 non-null  int64         \n",
      " 1   address                              19408 non-null  category      \n",
      " 2   postcode                             19408 non-null  int64         \n",
      " 3   state                                19408 non-null  category      \n",
      " 4   country                              19408 non-null  category      \n",
      " 5   property_valuation                   19408 non-null  int64         \n",
      " 6   transaction_id                       19408 non-null  int64         \n",
      " 7   product_id                           19408 non-null  int64         \n",
      " 8   transaction_date                     19408 non-null  datetime64[ns]\n",
      " 9   online_order                         19408 non-null  object        \n",
      " 10  order_status                         19408 non-null  category      \n",
      " 11  brand                                19408 non-null  category      \n",
      " 12  product_line                         19408 non-null  category      \n",
      " 13  product_class                        19408 non-null  category      \n",
      " 14  product_size                         19408 non-null  category      \n",
      " 15  list_price                           19408 non-null  float64       \n",
      " 16  standard_cost                        19408 non-null  float64       \n",
      " 17  product_first_sold_date              19408 non-null  datetime64[ns]\n",
      " 18  Profit                               19408 non-null  float64       \n",
      " 19  first_name                           19408 non-null  category      \n",
      " 20  last_name                            18782 non-null  category      \n",
      " 21  gender                               19408 non-null  category      \n",
      " 22  past_3_years_bike_related_purchases  19408 non-null  int64         \n",
      " 23  job_title                            19408 non-null  category      \n",
      " 24  job_industry_category                19408 non-null  category      \n",
      " 25  wealth_segment                       19408 non-null  category      \n",
      " 26  deceased_indicator                   19408 non-null  category      \n",
      " 27  owns_car                             19408 non-null  category      \n",
      " 28  tenure                               18978 non-null  float64       \n",
      " 29  Age                                  19408 non-null  Int64         \n",
      "dtypes: Int64(1), category(16), datetime64[ns](2), float64(4), int64(6), object(1)\n",
      "memory usage: 3.0+ MB\n"
     ]
    }
   ],
   "source": [
    "new_table.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "b2f7bdfc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3486"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_table.customer_id.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb03ca76",
   "metadata": {},
   "source": [
    "#### **`Recency`** refers to how recently a customer has made a purchase. Customers who have made a purchase more recently are considered to be more valuable than those who have not made a purchase in a while."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "9091e3b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2017-01-01 00:00:00')"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_table = new_table.sort_values(by='transaction_date')\n",
    "comparison_date = sorted_table['transaction_date'].iloc[0]\n",
    "comparison_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "a3994260",
   "metadata": {},
   "outputs": [],
   "source": [
    "# check recency\n",
    "\n",
    "new_table['recency'] = new_table['transaction_date'] - comparison_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "3487d07a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>address</th>\n",
       "      <th>postcode</th>\n",
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       "      <th>job_title</th>\n",
       "      <th>job_industry_category</th>\n",
       "      <th>wealth_segment</th>\n",
       "      <th>deceased_indicator</th>\n",
       "      <th>owns_car</th>\n",
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       "      <th>Age</th>\n",
       "      <th>recency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
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       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>Female</td>\n",
       "      <td>93</td>\n",
       "      <td>Executive Secretary</td>\n",
       "      <td>Health</td>\n",
       "      <td>Mass Customer</td>\n",
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       "      <td>Yes</td>\n",
       "      <td>11.0</td>\n",
       "      <td>70</td>\n",
       "      <td>356 days</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   customer_id             address  postcode            state    country  \\\n",
       "0            1  060 Morning Avenue      2016  New South Wales  Australia   \n",
       "\n",
       "   property_valuation  transaction_id  product_id transaction_date  \\\n",
       "0                  10              94          86       2017-12-23   \n",
       "\n",
       "  online_order  ...  gender past_3_years_bike_related_purchases  \\\n",
       "0        False  ...  Female                                  93   \n",
       "\n",
       "             job_title job_industry_category wealth_segment  \\\n",
       "0  Executive Secretary                Health  Mass Customer   \n",
       "\n",
       "   deceased_indicator  owns_car tenure  Age  recency  \n",
       "0                   N       Yes   11.0   70 356 days  \n",
       "\n",
       "[1 rows x 31 columns]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_table.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "1d39b2c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "rec_values = new_table.recency.astype(str).str.split(' ')\n",
    "new_table['recency'] = rec_values.str[0]\n",
    "new_table['recency'] = new_table['recency'].astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "fe5092a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<p>1 rows × 31 columns</p>\n",
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      ],
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       "   customer_id             address  postcode            state    country  \\\n",
       "0            1  060 Morning Avenue      2016  New South Wales  Australia   \n",
       "\n",
       "   property_valuation  transaction_id  product_id transaction_date  \\\n",
       "0                  10              94          86       2017-12-23   \n",
       "\n",
       "  online_order  ...  gender past_3_years_bike_related_purchases  \\\n",
       "0        False  ...  Female                                  93   \n",
       "\n",
       "             job_title job_industry_category wealth_segment  \\\n",
       "0  Executive Secretary                Health  Mass Customer   \n",
       "\n",
       "   deceased_indicator  owns_car tenure  Age recency  \n",
       "0                   N       Yes   11.0   70     356  \n",
       "\n",
       "[1 rows x 31 columns]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_table.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48e05eb1",
   "metadata": {},
   "source": [
    "#### `Frequency` refers to how often a customer makes purchases. Customers who make more frequent purchases are considered to be more valuable than those who make purchases less frequently."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52e941bd",
   "metadata": {},
   "source": [
    "#### `Monetary value` refers to the amount of money a customer has spent on purchases. Customers who have spent more money are considered to be more valuable than those who have spent less money."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "56e50dfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
       "      <td>3018.09</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>123</td>\n",
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       "      <td>92</td>\n",
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       "      <td>2394.94</td>\n",
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       "      <td>61</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3946.55</td>\n",
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       "      <td>27</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>3496</th>\n",
       "      <td>2045.84</td>\n",
       "      <td>4</td>\n",
       "      <td>65</td>\n",
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       "    <tr>\n",
       "      <th>3497</th>\n",
       "      <td>1648.32</td>\n",
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       "      <th>3498</th>\n",
       "      <td>3147.33</td>\n",
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       "      <td>33</td>\n",
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       "    <tr>\n",
       "      <th>3499</th>\n",
       "      <td>4955.25</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
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       "    <tr>\n",
       "      <th>3500</th>\n",
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       "      <td>6</td>\n",
       "      <td>8</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>3486 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             monetary value  frequency  recency\n",
       "customer_id                                    \n",
       "1                   3018.09         11        4\n",
       "2                   2226.26          3      123\n",
       "4                    220.57          2       92\n",
       "5                   2394.94          6       61\n",
       "6                   3946.55          5       27\n",
       "...                     ...        ...      ...\n",
       "3496                2045.84          4       65\n",
       "3497                1648.32          3      243\n",
       "3498                3147.33          6       33\n",
       "3499                4955.25          7       11\n",
       "3500                1785.86          6        8\n",
       "\n",
       "[3486 rows x 3 columns]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot_table = pd.pivot_table(new_table, index='customer_id',values=['product_id','recency','Profit'],\n",
    "               aggfunc={'product_id': 'count','recency': 'min','Profit': 'sum'})\n",
    "\n",
    "pivot_table = pivot_table.rename(columns={'product_id': 'frequency',\n",
    "                                          'Profit': 'monetary value'})\n",
    "\n",
    "pivot_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "c3202291",
   "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>monetary value</th>\n",
       "      <th>frequency</th>\n",
       "      <th>recency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3486.000000</td>\n",
       "      <td>3486.000000</td>\n",
       "      <td>3486.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3070.750201</td>\n",
       "      <td>5.567413</td>\n",
       "      <td>63.109868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1747.707801</td>\n",
       "      <td>2.292002</td>\n",
       "      <td>61.119426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>15.080000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1796.890000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>18.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2795.780000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>44.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4120.250000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>90.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>11668.950000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>356.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       monetary value    frequency      recency\n",
       "count     3486.000000  3486.000000  3486.000000\n",
       "mean      3070.750201     5.567413    63.109868\n",
       "std       1747.707801     2.292002    61.119426\n",
       "min         15.080000     1.000000     0.000000\n",
       "25%       1796.890000     4.000000    18.000000\n",
       "50%       2795.780000     5.000000    44.000000\n",
       "75%       4120.250000     7.000000    90.000000\n",
       "max      11668.950000    14.000000   356.000000"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot_table.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "5210014e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculating M Score, where a greater value indicates customers who have spent more money. Hence are considered to be more \n",
    "# valuable than those who have spent less money\n",
    "\n",
    "for index,row in pivot_table.iterrows():\n",
    "    if row['monetary value'] > 4120.25:\n",
    "        pivot_table.loc[index,'M_score'] = 4\n",
    "    elif row['monetary value'] > 2795.78:\n",
    "        pivot_table.loc[index, 'M_score'] = 3\n",
    "    elif row['monetary value'] > 1796.89:\n",
    "        pivot_table.loc[index, 'M_score'] = 2\n",
    "    elif row['monetary value'] > 15.08: \n",
    "        pivot_table.loc[index, 'M_score'] = 1\n",
    "    else:\n",
    "        pivot_table.loc[index, 'M_score'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "ddfd48bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculating F Score, where a greater value indicates customers who have purchased products most frequently.\n",
    "\n",
    "for index, row in pivot_table.iterrows():\n",
    "    if row['frequency']  > 7:\n",
    "        pivot_table.loc[index,'F_score'] = 4\n",
    "    elif row['frequency']  > 5:\n",
    "        pivot_table.loc[index,'F_score'] = 3\n",
    "    elif row['frequency'] > 4:\n",
    "        pivot_table.loc[index, 'F_score'] = 2\n",
    "    elif row['frequency'] > 1: \n",
    "        pivot_table.loc[index,'F_score'] = 1\n",
    "    else:\n",
    "        pivot_table.loc[index,'F_score'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "a211aa54",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculating R Score, where a greater value indicates the product was purchased recently\n",
    "\n",
    "for index, row in pivot_table.iterrows():\n",
    "    if row['recency'] > 90:\n",
    "        pivot_table.loc[index, 'R_score'] = 4\n",
    "    elif row['recency'] > 44:\n",
    "        pivot_table.loc[index, 'R_score'] = 3\n",
    "    elif row['recency'] > 18:\n",
    "        pivot_table.loc[index, 'R_score'] = 2\n",
    "    elif row['recency'] > 0: \n",
    "        pivot_table.loc[index, 'R_score'] = 1\n",
    "    else:\n",
    "        pivot_table.loc[index, 'R_score'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "5077e006",
   "metadata": {},
   "outputs": [],
   "source": [
    "pivot_table['RFM_value'] = (pivot_table['R_score']*100) + (pivot_table['F_score']*10) + pivot_table['M_score']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "818ba9af",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>monetary value</th>\n",
       "      <th>frequency</th>\n",
       "      <th>recency</th>\n",
       "      <th>M_score</th>\n",
       "      <th>F_score</th>\n",
       "      <th>R_score</th>\n",
       "      <th>RFM_value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_id</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>1</th>\n",
       "      <td>3018.09</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>143.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2226.26</td>\n",
       "      <td>3</td>\n",
       "      <td>123</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>412.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>220.57</td>\n",
       "      <td>2</td>\n",
       "      <td>92</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>411.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2394.94</td>\n",
       "      <td>6</td>\n",
       "      <td>61</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>332.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3946.55</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>223.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             monetary value  frequency  recency  M_score  F_score  R_score  \\\n",
       "customer_id                                                                  \n",
       "1                   3018.09         11        4      3.0      4.0      1.0   \n",
       "2                   2226.26          3      123      2.0      1.0      4.0   \n",
       "4                    220.57          2       92      1.0      1.0      4.0   \n",
       "5                   2394.94          6       61      2.0      3.0      3.0   \n",
       "6                   3946.55          5       27      3.0      2.0      2.0   \n",
       "\n",
       "             RFM_value  \n",
       "customer_id             \n",
       "1                143.0  \n",
       "2                412.0  \n",
       "4                411.0  \n",
       "5                332.0  \n",
       "6                223.0  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot_table.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "9a911767",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    3486.000000\n",
       "mean      272.594664\n",
       "std       111.510483\n",
       "min        11.000000\n",
       "25%       144.000000\n",
       "50%       244.000000\n",
       "75%       344.000000\n",
       "max       444.000000\n",
       "Name: RFM_value, dtype: float64"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot_table['RFM_value'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2311f951",
   "metadata": {},
   "source": [
    "### Segmenting"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53923256",
   "metadata": {},
   "source": [
    "#### Now, we can classify customers into different segments on the basis of RFM value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "2f9a81f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "for index,row in pivot_table.iterrows():\n",
    "    if row['RFM_value'] > 344:\n",
    "        pivot_table.loc[index,'Customer Profile'] = 'Platinum Customer'\n",
    "    elif row['RFM_value'] > 244:\n",
    "        pivot_table.loc[index, 'Customer Profile'] = 'Gold Customer'\n",
    "    elif row['RFM_value'] > 144:\n",
    "        pivot_table.loc[index, 'Customer Profile'] = 'Silver Customer'\n",
    "    else:\n",
    "        pivot_table.loc[index, 'Customer Profile'] = 'Bronze Customer'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "004bf68d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>monetary value</th>\n",
       "      <th>frequency</th>\n",
       "      <th>recency</th>\n",
       "      <th>M_score</th>\n",
       "      <th>F_score</th>\n",
       "      <th>R_score</th>\n",
       "      <th>RFM_value</th>\n",
       "      <th>Customer Profile</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3018.09</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>Bronze Customer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2226.26</td>\n",
       "      <td>3</td>\n",
       "      <td>123</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>412.0</td>\n",
       "      <td>Platinum Customer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>220.57</td>\n",
       "      <td>2</td>\n",
       "      <td>92</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>Platinum Customer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2394.94</td>\n",
       "      <td>6</td>\n",
       "      <td>61</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>332.0</td>\n",
       "      <td>Gold Customer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3946.55</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>223.0</td>\n",
       "      <td>Silver Customer</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             monetary value  frequency  recency  M_score  F_score  R_score  \\\n",
       "customer_id                                                                  \n",
       "1                   3018.09         11        4      3.0      4.0      1.0   \n",
       "2                   2226.26          3      123      2.0      1.0      4.0   \n",
       "4                    220.57          2       92      1.0      1.0      4.0   \n",
       "5                   2394.94          6       61      2.0      3.0      3.0   \n",
       "6                   3946.55          5       27      3.0      2.0      2.0   \n",
       "\n",
       "             RFM_value   Customer Profile  \n",
       "customer_id                                \n",
       "1                143.0    Bronze Customer  \n",
       "2                412.0  Platinum Customer  \n",
       "4                411.0  Platinum Customer  \n",
       "5                332.0      Gold Customer  \n",
       "6                223.0    Silver Customer  "
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot_table.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "d27cf850",
   "metadata": {},
   "outputs": [],
   "source": [
    "pivot_table.to_excel('RFM.xlsx', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60e3786f",
   "metadata": {},
   "source": [
    "#### Assumptions to be clarified:\n",
    "\n",
    "- Younger customers might be buying more bikes than the older customers, due to various reasons like wishing to save more in the younger years, ability to drive more and not really bending towards comfort as much as the elders would like.\n",
    "- Males might we willing to make more bike purchases than females. They might really care about comfort or other discomforts related to outfits etc as much as females might.\n",
    "- Are people from any specific industry likely to make more purchases than the other?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "394cf163",
   "metadata": {},
   "source": [
    "<a id='viz'></a>\n",
    "## Data Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8c8f07e",
   "metadata": {},
   "source": [
    "### Customer Segments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "154235b2",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RFM_value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Customer Profile</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bronze Customer</th>\n",
       "      <td>114275.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gold Customer</th>\n",
       "      <td>278505.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Platinum Customer</th>\n",
       "      <td>349162.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Silver Customer</th>\n",
       "      <td>202576.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   RFM_value\n",
       "Customer Profile            \n",
       "Bronze Customer     114275.0\n",
       "Gold Customer       278505.0\n",
       "Platinum Customer   349162.0\n",
       "Silver Customer     202576.0"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cust_seg = pd.pivot_table(pivot_table, index='Customer Profile', values='RFM_value',aggfunc='sum')\n",
    "cust_seg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "e41c1454",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cust_seg.plot(kind='bar', figsize=(8,3))\n",
    "plt.xlabel('Customer Profile')\n",
    "plt.ylabel('Profits Generated')\n",
    "plt.title('Customer Segmentation')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "17f956e9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Customer Profile\n",
       "Platinum Customer    832\n",
       "Gold Customer        853\n",
       "Silver Customer      887\n",
       "Bronze Customer      916\n",
       "dtype: int64"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot_table.groupby(by='Customer Profile').size().sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc730d8c",
   "metadata": {},
   "source": [
    "**Inference** We can see that Platinum Customers have the highest RFM value implying they bring maximum revenue for the business. So, we should target such customers.            <br>\n",
    "Also, as of now most of our customers are actually Bronze Customers so we can see there is a huge potential to change them into Platinum Customers."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9932aa2",
   "metadata": {},
   "source": [
    "### Which industry generates highest profit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "0b5cecab",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job_industry_category</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Argiculture</th>\n",
       "      <td>302991.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Entertainment</th>\n",
       "      <td>386109.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Financial Services</th>\n",
       "      <td>2150216.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Health</th>\n",
       "      <td>1659918.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IT</th>\n",
       "      <td>601378.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Manufacturing</th>\n",
       "      <td>3935864.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Property</th>\n",
       "      <td>700139.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Retail</th>\n",
       "      <td>982011.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Telecommunications</th>\n",
       "      <td>187400.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Profit\n",
       "job_industry_category            \n",
       "Argiculture             302991.35\n",
       "Entertainment           386109.45\n",
       "Financial Services     2150216.90\n",
       "Health                 1659918.36\n",
       "IT                      601378.58\n",
       "Manufacturing          3935864.97\n",
       "Property                700139.25\n",
       "Retail                  982011.36\n",
       "Telecommunications      187400.01"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ind_profit = pd.pivot_table(new_table, index='job_industry_category', values='Profit', aggfunc='sum')\n",
    "ind_profit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "5ab9986e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "sns.barplot(data=ind_profit, x=ind_profit.index,y='Profit')\n",
    "plt.xlabel('Job Industry')\n",
    "plt.ylabel('Profits')\n",
    "plt.title('Profits Based on Industry')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70361fb6",
   "metadata": {},
   "source": [
    "**Insights**  <br>\n",
    "Very clearly Manufacturing, Financial Servies and Healthcare are the industries which drive maximum profits for us. And, industries like agriculture, entertainment make the least profits. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ed11880",
   "metadata": {},
   "source": [
    "#### Bike related purchases Based on Industry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "171275d0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>past_3_years_bike_related_purchases</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job_industry_category</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Argiculture</th>\n",
       "      <td>578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Entertainment</th>\n",
       "      <td>698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Financial Services</th>\n",
       "      <td>3880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Health</th>\n",
       "      <td>3091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IT</th>\n",
       "      <td>1076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Manufacturing</th>\n",
       "      <td>7236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Property</th>\n",
       "      <td>1289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Retail</th>\n",
       "      <td>1758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Telecommunications</th>\n",
       "      <td>354</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       past_3_years_bike_related_purchases\n",
       "job_industry_category                                     \n",
       "Argiculture                                            578\n",
       "Entertainment                                          698\n",
       "Financial Services                                    3880\n",
       "Health                                                3091\n",
       "IT                                                    1076\n",
       "Manufacturing                                         7236\n",
       "Property                                              1289\n",
       "Retail                                                1758\n",
       "Telecommunications                                     354"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bike_industry = pd.pivot_table(new_table, index='job_industry_category', values='past_3_years_bike_related_purchases',aggfunc='count')\n",
    "bike_industry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "60419f50",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,4))\n",
    "sns.barplot(data=bike_industry, x=bike_industry.index,y='past_3_years_bike_related_purchases')\n",
    "plt.xlabel('Job Industry')\n",
    "plt.ylabel('Bike Related Purchases')\n",
    "plt.title('Bike Related Purchases Based on Industry')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47bcdc4b",
   "metadata": {},
   "source": [
    "From the above, have seen that our sales to turn into revenues. So, there seems to be no problem with our costs, seeling prices etc. If we sell more, we are likely to generate more profits also."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d65b6257",
   "metadata": {},
   "source": [
    "### Wealth Segments and Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "dcc3f512",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>569.121017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>579.120548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>554.496940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>577.270514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>554.812430</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Profit\n",
       "Age            \n",
       "21   569.121017\n",
       "22   579.120548\n",
       "23   554.496940\n",
       "24   577.270514\n",
       "25   554.812430"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_demo = pd.pivot_table(new_table, index = 'Age', values = ['Profit'])\n",
    "age_demo.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "00871064",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "bins = np.arange(18,100,10)\n",
    "\n",
    "age_demo['age_bin'] = pd.cut(age_demo.index, bins=bins)\n",
    "profit_by_age = age_demo.groupby('age_bin')['Profit'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "2f4bb3b4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1368x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(19,6))\n",
    "sns.barplot(data = age_demo, x=profit_by_age.index.astype(str), y=profit_by_age)\n",
    "\n",
    "plt.xlabel('Age Intervals')\n",
    "plt.ylabel('Total Profit')\n",
    "plt.title('Total Profit by Age')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "2de9e943",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "wealth_segment\n",
       "Affluent Customer     4850\n",
       "High Net Worth        5081\n",
       "Mass Customer        10029\n",
       "dtype: int64"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wealth_seg = new_table.groupby(by='wealth_segment').size().sort_values()\n",
    "wealth_seg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "bb581753",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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T0cCcwi7m5rLRebpjuZmZmdWhjOaAtUhn9+OA9YHVJR3U3SadlEU35Z0d83BJ0yRNW7BgQW9DNjMza0llNAfsDsyOiAUR8TJwGbAD8FSu4ic/P53XnwtsUNh+DKn5YG6e7li+gog4OyImRsTEkSNHDuiLMTMzq6oykoAngO0krZZ78+8GPAxcCRyc1zkYuCJPXwkcIGkVSeNIHQCn5iaDRZK2y/v5eGEbMzMz68GQZh8wIu6UdClwN7AEuAc4GxgGXCJpEilR2D+v/6CkS4CH8vpHRcTSvLsjgfOBocC1+WFmZmZ16DEJkHQ0cB6wCPgZ8Dbg2NyZr08i4gTghA7Fi0m1Ap2tfypwaifl04DN+hqHmZlZO6unOeATEbEQeDcwEjgUOK2hUZmZmVnD1ZME1Hrh7wWcFxEz6LxnvpmZmVVIPUnAdEnXk5KA6yQNB15pbFhmZmbWaN32Cci97r9KagZ4NCL+Kel1pCYBMzMzq7Buk4CICEm/jYitC2XPAs82PDIzMzNrqHqaA/4kaZuGR2JmZmZNVc84AbsAR0h6DHiR1CkwImKLRgZmZmZmjVVPEvCehkdhZmZmTddjc0BEPE4au3/XPP3PerYzMzOz17Yef8wlnQAcAxyXi1YCftHIoMzMzKzx6jmj/wCwN6k/ABExDxjeyKDMzMys8epJAl6KiAACQNLqjQ3JzMzMmqGeJOASST8B1pR0GHAj8NPGhmVmZmaN1uPVARHxbUnvAhYCbwK+GhE3NDwyMzMza6h6LhEkIm6QdGdtfUlrR8RzDY3MzMzMGqrHJEDSp4CvAf8i3ThIpP4BGzU2NDMzM2ukemoCvghMiIhnGh2MmZmZNU89HQP/ShogyMzMzFpIPTUBxwG35z4Bi2uFEfGZhkVlZmZmDVdPEvAT4PfA/aQ+AWZmZtYC6kkClkTE5xseiZmZmTVVPX0CbpJ0uKRRktauPRoemZmZmTVUPTUBH8nPxxXKfImgmZlZxdUzYuC4ZgRiZmZmzVXPYEErAUcC78hFNwM/iYiXGxiXmZmZNVg9zQFnASsBZ+b5j+WyTzYqKDMzM2u8epKAbSLirYX530ua0aiAzMzMrDnquTpgqaSNazOSNgKWNi4kMzMza4Z6agL+m3SZ4KOkmwdtCHyioVGZmZlZw9WTBPwRGA+8iZQE/LmhEZmZmVlT1NMccEdELI6I+yJiRkQsBu5odGBmZmbWWF3WBEh6PTAaGCrpbaRaAIARwGpNiM3MzMwaqLvmgD2AQ4AxwOksSwIWAcc3NiwzMzNrtC6bAyJickTsAhwSEbtGxC75sXdEXNafg0paU9Klkv4s6WFJ2+d7Etwg6ZH8vFZh/eMkzZI0U9IehfKtJd2fl50hSZ0f0czMzDqqp0/AGEkjlPxM0t2S3t3P434f+L+IeDPwVuBh4FhgSkSMB6bkeSRtChwATAD2BM6UNDjv5yzgcFLHxfF5uZmZmdWhniTgExGxEHg3sC5wKHBaXw8oaQRpCOJzACLipYj4B7APMDmvNhnYN0/vA1ycOyfOBmYB20oaBYyIiDsiIoALCtuYmZlZD+pJAmpV7HsB50XEjEJZX2wELADOk3RPrl1YHVgvIuYD5Od18/qjgTmF7efmstF5umP5ii8g3Qp5mqRpCxYs6EfoZmZmraOeJGC6pOtJScB1koYDr/TjmEOArYCzIuJtwIvkqv8udJZwRDflKxZGnB0REyNi4siRI3sbr5mZWUuqJwmYRPqR3iYi/gmsTGoS6Ku5wNyIuDPPX0pKCp7KVfzk56cL629Q2H4MMC+Xj+mk3MzMzOpQTxKwEzAM2ELSO0gd9Nbs6wEj4klgjqQ35aLdgIeAK4GDc9nBwBV5+krgAEmrSBpH6gA4NTcZLJK0Xb4q4OOFbczMzKwH9d47oGZVYFtgOrBrP477aeBCSSsDj5JqFgYBl0iaBDwB7A8QEQ9KuoSUKCwBjoqI2g2MjgTOB4YC1+aHmZmZ1aHHJCAi3l+cl7QB8M3+HDQi7gUmdrJoty7WPxU4tZPyacBm/YnFzMysXdXTHNDRXPzDa2ZmVnk91gRI+gHLet0PArYEZjQwJjMzM2uCevoETCtMLwEuiojbGhSPmZmZNUl3dxEcCYyMiMkdyidIGhkRHnXHzMyswrrrE/ADoLORdcaQxv43MzOzCusuCdg8Im7pWBgR1wFbNC4kMzMza4bukoCV+rjMzMzMKqC7JOARSXt1LJT0HtIAP2ZmZlZh3V0d8DngakkfIo0QCGmAn+2B9zU6MDMzM2usLmsCIuIvwObALcDY/LgF2CIvMzMzswrrdpyAiFgMnNekWMzMzKyJ+jJssJmZmbUAJwFmZmZtqrsRA6dExG6SvhERxzQzKDPrn7HHXlN2CJXw2GnvLTsEs1J11ydglKSdgb0lXQyouDAi7m5oZGZmZtZQ3SUBXwWOJQ0T/J0OywLYtVFBmZmZWeN1mQRExKXApZK+EhEnNzEmMzMza4IebyUcESdL2ht4Ry66OSKubmxYZmZm1mg9JgGSvg5sC1yYi46WtGNEHNfQyNqIO3HVzx25zMwGTo9JAPBeYMuIeAVA0mTgHsBJgJmZWYXVO07AmoXpNRoQh5mZmTVZPTUBXwfukXQT6TLBd+BaADMzs8qrp2PgRZJuBrYhJQHHRMSTjQ7MzMzMGquemgAiYj5wZYNjMTMzsybyvQPMzMzalJMAMzOzNtVtEiBpkKQHmhWMmZmZNU+3SUAeG2CGpDc0KR4zMzNrkno6Bo4CHpQ0FXixVhgRezcsKjMzM2u4epKAkxoehZmZmTVdPeME3CJpQ2B8RNwoaTVgcONDMzMzs0bq8eoASYcBlwI/yUWjgd82MCYzMzNrgnouETwK2BFYCBARjwDr9vfAkgZLukfS1Xl+bUk3SHokP69VWPc4SbMkzZS0R6F8a0n352VnSFJ/4zIzM2sX9SQBiyPipdqMpCFADMCxjwYeLswfC0yJiPHAlDyPpE2BA4AJwJ7AmZJqzRFnAYcD4/NjzwGIy8zMrC3UkwTcIul4YKikdwG/Bq7qz0EljSHdovhnheJ9gMl5ejKwb6H84ohYHBGzgVnAtpJGASMi4o6ICOCCwjZmZmbWg3qSgGOBBcD9wKeA3wFf7udxvwf8D/BKoWy9fI+C2r0Kak0Oo4E5hfXm5rLRebpj+QokHS5pmqRpCxYs6GfoZmZmraGeqwNekTQZuJPUDDAzn3n3iaT3AU9HxHRJ76xnk87C6qZ8xcKIs4GzASZOnDgQTRlmZmaV12MSIOm9wI+Bv5J+eMdJ+lREXNvHY+4I7C1pL2BVYISkXwBPSRoVEfNzVf/Tef25wAaF7ccA83L5mE7KzczMrA71NAecDuwSEe+MiJ2BXYDv9vWAEXFcRIyJiLGkDn+/j4iDSLcqPjivdjBwRZ6+EjhA0iqSxpE6AE7NTQaLJG2Xrwr4eGEbMzMz60E9IwY+HRGzCvOPsuwsfSCdBlwiaRLwBLA/QEQ8KOkS4CFgCXBURCzN2xwJnA8MBa7NDzMzM6tDl0mApA/myQcl/Q64hNTmvj9w10AcPCJuBm7O088Cu3Wx3qnAqZ2UTwM2G4hYzMzM2k13NQHvL0w/BeycpxcAa624upmZmVVJl0lARBzazEDMzMysueq5OmAc8GlgbHF930rYzMys2urpGPhb4BzSKIGvdL+qmZmZVUU9ScC/I+KMhkdiZmZmTVVPEvB9SScA1wOLa4URcXfDojIzM7OGqycJ2Bz4GLAry5oDIs+bmZlZRdWTBHwA2Kh4O2EzMzOrvnqGDZ4BrNngOMzMzKzJ6qkJWA/4s6S7WL5PgC8RNDMzq7B6koATGh6FmZmZNV2PSUBE3NKMQMzMzKy56hkxcBHpagCAlYGVgBcjYkQjAzMzM7PGqqcmYHhxXtK+wLaNCsjMzMyao56rA5YTEb/FYwSYmZlVXj3NAR8szA4CJrKsecDMzMwqqp6rA95fmF4CPAbs05BozMzMrGnq6RNwaDMCMTMzs+bqMgmQ9NVutouIOLkB8ZiZmVmTdFcT8GInZasDk4DXAU4CzMzMKqzLJCAiTq9NSxoOHA0cClwMnN7VdmZmZlYN3fYJkLQ28Hngo8BkYKuI+HszAjMzM7PG6q5PwLeADwJnA5tHxAtNi8rMzMwarrvBgr4ArA98GZgnaWF+LJK0sDnhmZmZWaN01yeg16MJmpmZWXX4h97MzKxNOQkwMzNrU04CzMzM2pSTADMzszblJMDMzKxNOQkwMzNrU04CzMzM2lTTkwBJG0i6SdLDkh6UdHQuX1vSDZIeyc9rFbY5TtIsSTMl7VEo31rS/XnZGZLU7NdjZmZWVWXUBCwBvhARbwG2A46StClwLDAlIsYDU/I8edkBwARgT+BMSYPzvs4CDgfG58eezXwhZmZmVdb0JCAi5kfE3Xl6EfAwMBrYh3STIvLzvnl6H+DiiFgcEbOBWcC2kkYBIyLijogI4ILCNmZmZtaDUvsESBoLvA24E1gvIuZDShSAdfNqo4E5hc3m5rLRebpjuZmZmdWhtCRA0jDgN8BnI6K7GxJ11s4f3ZR3dqzDJU2TNG3BggW9D9bMzKwFlZIESFqJlABcGBGX5eKnchU/+fnpXD4X2KCw+RhgXi4f00n5CiLi7IiYGBETR44cOXAvxMzMrMLKuDpAwDnAwxHxncKiK4GD8/TBwBWF8gMkrSJpHKkD4NTcZLBI0nZ5nx8vbGNmZmY96PJWwg20I/Ax4H5J9+ay44HTgEskTQKeAPYHiIgHJV0CPES6suCoiFiatzsSOB8YClybH2ZmZlaHpicBEfFHOm/PB9iti21OBU7tpHwasNnARWdmZtY+PGKgmZlZm3ISYGZm1qacBJiZmbUpJwFmZmZtykmAmZlZm3ISYGZm1qacBJiZmbUpJwFmZmZtykmAmZlZm3ISYGZm1qacBJiZmbUpJwFmZmZtykmAmZlZm3ISYGZm1qacBJiZmbUpJwFmZmZtykmAmZlZm3ISYGZm1qacBJiZmbUpJwFmZmZtykmAmZlZm3ISYGZm1qacBJiZmbUpJwFmZmZtykmAmZlZm3ISYGZm1qacBJiZmbUpJwFmZmZtykmAmZlZm3ISYGZm1qacBJiZmbUpJwFmZmZtykmAmZlZm6p8EiBpT0kzJc2SdGzZ8ZiZmVVFpZMASYOBHwHvATYFDpS0ablRmZmZVUOlkwBgW2BWRDwaES8BFwP7lByTmZlZJQwpO4B+Gg3MKczPBd7ecSVJhwOH59kXJM1sQmxVtw7wTNlBdKRvlB2B9cNr7jPlz1Pl+TNVnw27WlD1JECdlMUKBRFnA2c3PpzWIWlaREwsOw5rHf5M2UDzZ6r/qt4cMBfYoDA/BphXUixmZmaVUvUk4C5gvKRxklYGDgCuLDkmMzOzSqh0c0BELJH0X8B1wGDg3Ih4sOSwWoWbT2yg+TNlA82fqX5SxApN6GZmZtYGqt4cYGZmZn3kJMDMzKxNOQkwJA2W9Iuy4zAz60r+nvpW2XG0mkp3DLSBERFLJY2UtHIeedGs3ySNBA4DxlL4romIT5QVk1VX/p7aWpLCndkGjJMAq3kMuE3SlcCLtcKI+E5pEVnVXQHcCtwILC05FmsN9wBXSPo1y39PXVZeSNXmJMBq5uXHIGB4ybFYa1gtIo4pOwhrKWsDzwK7FsoCcBLQR75E0JYjafWIeLHnNc26J+kU4PaI+F3ZsZhZ59wx0ACQtL2kh4CH8/xbJZ1ZclhWQZIWSVoIHA1cLelfkhYWys36RNIbJU2R9ECe30LSl8uOq8qcBFjN94A9SFVtRMQM4B1lBmTVFBHDI2JEfh4UEUML8yPKjs8q7afAccDLABFxH2m4eOsjJwH2qoiY06HInbmszyRNqafMrBdWi4ipHcqWlBJJi3DHQKuZI2kHIPLNmD5Dbhow6w1JqwKrA+tIWotlt/weAaxfWmDWCp6RtDH5lvGS9gPmlxtStTkJsJojgO8Do0m3aL4eOKrUiKyqPgV8lvSDP51lScBC4EclxWSt4SjSTYPeLOlvwGzgoHJDqjZfHWBmA07SYOD4iDi57Fis9UhaHRgUEYvKjqXqnAQYAJLGAZ9mxdHd9i4rJqs2SXdExPZlx2GtQ9KawMdZ8XvqMyWFVHluDrCa3wLnAFcBr5QbirWI6yX9B3CZh3m1AfI74E/A/fh7akC4JsAAkHRnRLy97DisdUhaROoguBT4F6lvQPgyQesrSXdHxFZlx9FKnAQYAJI+AowndQhcXCuPiLtLC8rMrEDS54AXgKtZ/nvqudKCqjg3B1jN5sDHSGNy16rZguXH6DbrFUl7s2zQqZsj4uoy47HKewn4FvAl8mWC+Xmj0iKqONcEGACS/gxs4VsJ20CRdBqwDXBhLjoQmB4Rx5YXlVWZpL8Cb4+IZ8qOpVW4JsBqZgBrAk+XHIe1jr2ALSPiFQBJk0m3gnUSYH31IPDPsoNoJU4CrGY94M+S7mL5tjZfImj9sSZQa69do8Q4rDUsBe6VdBPLf0/5EsE+chJgNSeUHYC1nK8D9+QvbJH6BhxXbkhWcb/NDxsg7hNgr5K0HqkNF2BqRLhpwHpN0r7A7RHxtKRRpM+UgDsj4slSg7PKy/c2eWOenRkRL5cZT9X5LoIGgKQPAVOB/YEPAXfmm3OY9dZBpBqAR0i1Aa8HZjkBsP6S9E7gEdI9KM4E/iLJtzzvB9cEGACSZgDvqp39SxoJ3BgRby03MqsqSWOBHfJje+ANwF0RsVeZcVl1SZoOfCQiZub5NwIXRcTW5UZWXe4TYDWDOlT/P4triqwfIuKxfFvhoflRmzbrq5VqCQBARPxF0kplBlR1TgKs5v8kXQdclOc/DFxbYjxWUZKOJ535jwRmksZ6/yFweEQsLTM2q7xpks4Bfp7nP0q6XbX1kZsD7FWSPgjsROrE9YeIuLzkkKyC8sBTtaFdbyd1CHy+3KisFUhaBTiKwvcU8CMPctZ3TgIMAEnfiIhjeiozq4ektVnWH2A7YBhpQKrbI+K8MmOz6pJ0dER8v6cyq5+TAAM6vzuXpPsiYouyYrLqkzQE2Jo0RsCngHERMbjcqKyquvieuici3lZWTFXnPgFtTtKRwH8CG0m6r7BoOHBbOVFZleWbBu0A7AhMIA31ejvwhfxs1iuSDgQ+AoyTdGVh0QhSJ2brI9cEtDlJawBrka7nLo7pvsi357S+kHQZ6cf+NtINg9xea/0iaUNgHJ18TwH3RcSSUgJrAU4CDABJGwNzI2JxHpBjC+CCiPhHmXGZmdVIWh34V0S8kscIeDNwrUcN7DtfB241vwGWStoEOIeUdf+y3JDMzJbzB2BVSaOBKcChwPmlRlRxTgKs5pVcpfZB4HsR8TlgVMkxmZkVKSL+Sfqe+kFEfADYtOSYKs1JgNW8nDvffJx0fTeAR+KyPpN0dD1lZr0gSduTBgm6Jpe5g3s/OAmwmkNJo7ydGhGzJY0DflFyTFZtB3dSdkizg7CW8lnS7agvj4gHJW0E3FRuSNXmjoFmNqAKl3PtBNxaWDQcWBoRu5cSmJmtwNUoBoCk2cAKGWFEbFRCOFZttwPzgXWA0wvli4D7Ot3CrA6SbqLz76ldSwinJTgJsJqJhelVgf2BtUuKxSosIh4HHge2z9d3j4+IGyXV7ia4qNQArcq+WJheFfgPwGME9IObA6xLkv4YETuVHYdVk6TDgMOBtSNiY0njgR9HxG4lh2YtRNItEbFz2XFUlWsCDABJxfG4B5FqBoaXFI61hqOAbYE7ASLiEUnrlhuSVVm+MVXNINJ9KV5fUjgtwUmA1RTbbpcAjwEfKicUaxGLI+IlScCrNxNy1aP1x3TSZ0ik76nZwKRSI6o4JwEGQETsUnYM1nJukXQ8MFTSu0g3qrqq5JiswiJiXNkxtBr3CWhzkj4PPB8R53Qo/zQwOCK+V0pgVnmSBpHO0t5NOnO7DvhZ+EvHeknSQaTfq593KD8MeDEiPMR5HzkJaHOSHgC26ninN0mrAHdFxBblRGZmlki6B3hHRCzqUD4CuCkiti4nsupzc4BFZ7d6zXcTVBkBWbV1dS13Fr46wPpgcMcEACAiFkry8Ob94CTAkLReRDzVsayseKzyvthJ2XbA/wBPNzkWaw0rSVo9Il4sFkoaDqxcUkwtwfcOsG8B10jaWdLw/HgnqQPXt0uNzCopIqbXHsAw4BvAAcAREbFNudFZRZ0DXCppbK0gT1+cl1kfuU+AIek9wLHAZqRq3AeB0yLi2lIDs8qStAfwFeDfpJtS+SYv1i+SjiDdPGgY6XvqRdL31FmlBlZxTgLMbEBJugsYSapluqPj8oi4u+lBWcuQNIz02+XhpweAkwAzG1CSbmZZx8DawC414Zu9mL12OAkwMzNrU+4YaABIWmEkrs7KzMysdTgJsJrfdFJ2adOjMDPrgqT982WBSPqypMs63PzMesnjBLQ5SW8GJgBrSPpgYdEI0v26zcxeK74SEb+WtBOwB+ky5rOAt5cbVnU5CbA3Ae8D1gTeXyhfBBxWRkDWOiSNBjak8F0TEX8oLyKruKX5+b3AWRFxhaQTS4yn8twx0ACQtH1ErHA5l1lfSfoG8GHgIZZ9eUdE7F1eVFZlkq4G/gbsDmwN/AuYGhFvLTWwCnMSYABIGkk68x/L8mdtnygrJqs2STOBLSJicdmxWGuQtBqwJ3B/RDwiaRSweURcX3JoleXmAKu5ArgVuJFlZ21m/fEosBLgJMAGyijgmnyDs3cCWwAXlBpRxbkmwACQdG9EbFl2HFZ9kn5AGiRoNPBWYAqFRCAiPlNSaFZxku4FJpJqLK8DrgTeFBF7lRhWpbkmwGqulrRXRPyu7ECs8qbl5+mkL2mzgfJKRCzJVzJ9LyJ+IOmesoOqMtcEGACSFgGrAy/lh0iduEaUGpiZWSbpTuB7wJeA90fEbEkPRMRm5UZWXa4JMAAiYnjZMVhrkXQ/y+4hUPM8qabglIh4tvlRWcUdChxBujPl7Dyq6S9KjqnSXBNgAEgS8FFgXEScLGkDYFRETC05NKsoSd8kdTL9ZS46gFTD9DywU0S8v6ttzaw5nAQYAJLOAl4Bdo2It0haC7g+IrYpOTSrKEm3RcSOnZVJuj8iNi8rNqsmSeOBrwObUhjRNCI2Ki2oivO9A6zm7RFxFPBvgIj4O7ByuSFZxQ2T9OpwrpK2BYbl2SXlhGQVdx5pmOAlwC6kywN/XmpEFec+AVbzsqTB5DbcPHjQK+WGZBX3SeBcScNIzQALgU9KWp10NmfWW0MjYookRcTjwImSbgVOKDuwqnISYDVnAJcD60o6FdgP+HK5IVmVRcRdwOaS1iA1Pf6jsPiScqKyivu3pEHAI5L+izSE8Lolx1Rp7hNgr8p3FNyNdNY2JSIeLjkkqyBJB0XELyR9vrPlEfGdZsdkrUHSNsDDpBuenQysAXwzIv5UZlxV5poAA0DSG4B/AlcVyyLiifKisopaPT/7slMbULl2CeAF0uWC1k+uCTBguWu6Rep1Ow6YGRETSg3MzNqepG5HnvSdKfvONQEGQMfLtSRtBXyqpHCswiSd0d1y3zvA+mB7YA5wEXAn6WTFBoCTAOtURNyd29/Memt6Yfok3HPb+u/1wLuAA4GPANcAF0XEg6VG1QLcHGAAdOjENQjYCnhdROxRUkjWAiTdExFvKzsOax2SViElA98CvhYRPyg5pEpzTYDVFDtxLSFl2r8pKRZrHT7LsAGRf/zfS0oAxpIua76szJhagWsCzKxhJN0dEVuVHYdVm6TJwGbAtcDFEfFAySG1DCcBbU7SVXRztuZet9Zb+bbUtc/UaqRLT8G3p7Y+kvQK8GKeLX5f+TPVT04C2pyknbtbHhG3NCsWMzNrLicBbU7SlIjYTdI3IuKYsuMxM7PmccdAG5VrA/aWdDEdrr+NiLvLCcvMzBrNNQFtTtJ+wCRgJ+Aulk8CIiJ2LSUwMzNrONcE2PyIeI+kr0bE18oOxszMmmdQ2QFY6WpDvO5bZhBmZtZ8rgmwlyWdB4zubMx3j/NuZta6nATY+4DdgV1Zfsx38GhvZmYtzR0DDQBJb42IGYX5nYADI+KoEsMyM7MGck2AARARMyRtSbpD14eA2fjeAWZmLc1JQJuT9EbgANJNOZ4FfkWqIdql1MDMzKzh3BzQ5vKY3LcCkyJiVi57NCI2KjcyMzNrNF8iaP8BPAncJOmnknajw6iBZmbWmlwTYABIWp00VsCBpCsFJgOXR8T1ZcZlZmaN4yTAViBpbWB/4MMeNtjMrHU5CTAzM2tT7hNgZmbWppwEmJmZtSknAWYtQNJ3JX22MH+dpJ8V5k+X9Pk+7PdESV/M04dIWr+w7DFJ6/Sw/WqSLpR0v6QHJP1R0rDextFIkt4paYey4zArg5MAs9ZwO7ADgKRBwDrAhMLyHYDb+nmMQ4D1e1qpg6OBpyJi84jYDJgEvNzPOAbaO8nvnVm7cRJg1hpuY9kP2QTgAWCRpLUkrQK8BbhH0taSbpE0PdcWjAKQdJikuyTNkPQbSasVdy5pP2AicKGkeyUNzYs+LenufKb/5k7iGgX8rTYTETMjYnHe50GSpub9/UTS4Fw+SdJfJN2cx674YS4/X9JZkm6S9KiknSWdK+lhSecXYn23pDtyXL+u1TzkmouTivFKGgscAXwux/H/+vE3MKscJwFmLSAi5gFLJL2BlAzcAdwJbE/68b6PdFfIHwD7RcTWwLnAqXkXl0XENhHxVuBh0hl7cf+XAtOAj0bElhHxr7zomYjYCjgL+GInoZ0LHJN/lE+RNB5A0luADwM7RsSWwFLgo7m54SvAdsC7gI6JxVqkcSw+B1wFfJeU9GwuacvcPPFlYPcc1zSg2AyyXLwR8RjwY+C7+XXd2vW7bNZ6fO8As9ZRqw3YAfgOMDpPP09qLngTsBlwgySAwcD8vO1mkk4B1gSGAdfVeczL8vN04IMdF0bEvZI2At5NumX1XZK2B3YDts7zAEOBp4FtgVsi4jkASb8G3ljY5VUREZLuJzUz3J/XexAYC4wBNgVuy/tdmZQQ1RWvWbtxEmDWOmr9AjYnNQfMAb4ALCSdkQt4MCK272Tb84F9890kDyG1k9djcX5eShffJxHxAunH97J8r4q9gJeAyRFxXHFdSR+o83ivFKZr80NyHDdExIF9jdesnbg5wKx13Aa8D3guIpbms+k1SU0CdwAzgZH5TBxJK0mqdR4cDsyXtBLw0S72vyivVzdJO0paK0+vTDpLfxyYAuwnad28bG1JGwJTgZ1zX4YhpHtb9MafgB0lbZL3u1q+U2Z3ev26zFqFkwCz1nE/6aqAP3Uoez4inomIl4D9gG9ImgHcy7LOhF8h9SG4AfhzF/s/H/hxh46BPdkYuCVX399DaqP/TUQ8RGq7v17Sffm4oyLib8D/5lhuBB4iNWfUJSIWkK5iuCjv90+s2K+go6uAD7hjoLUjDxtsZq8pkoZFxAu5JuBy4NyIuLzsuMxakWsCzOy15kRJ95L6NcwGfltqNGYtzDUBZmZmbco1AWZmZm3KSYCZmVmbchJgZmbWppwEmJmZtSknAWZmZm3KSYCZmVmb+v/asE2hKFOITgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,4))\n",
    "wealth_seg.plot(kind='bar')\n",
    "plt.xlabel('Wealth Segment')\n",
    "plt.ylabel('Number of Customers')\n",
    "plt.title('Number of Customers from each Wealth Segment')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "593c35c5",
   "metadata": {},
   "source": [
    "#### helps us understand what kind of wealth segment should we be targeting, and the age demographichs to be targeted\n",
    "\n",
    "- We can see that maximum of our customers are Mass Customers.\n",
    "- Also, customers in the age range of 27-63years bring highst business. \n",
    "So, we should focus on suh customers more."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a26725ce",
   "metadata": {},
   "source": [
    "#### Car owned in diffeerent states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "d029384c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>owns_car</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>New South Wales</th>\n",
       "      <td>5201</td>\n",
       "      <td>5476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QLD</th>\n",
       "      <td>2129</td>\n",
       "      <td>2133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Victoria</th>\n",
       "      <td>2596</td>\n",
       "      <td>2425</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "owns_car           No   Yes\n",
       "state                      \n",
       "New South Wales  5201  5476\n",
       "QLD              2129  2133\n",
       "Victoria         2596  2425"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of cars in each state\n",
    "car = pd.pivot_table(new_table, index=['state'],columns='owns_car',values=['customer_id'],aggfunc='count')\n",
    "car.columns = car.columns.droplevel()\n",
    "car = car.rename(columns={'no':'owns_no', 'yes':'owns_yes'})\n",
    "car"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "b87bcd17",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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lu+66K3fccQcAt99+O7vvvnvBr+v57M3MrKLkc6tcMZ177rn84he/WP1+zJgxfOtb3+Kqq66iW7dujB8/vuDXdLI3MzMrsqVLl65e33zzzfnvf/+7+n1VVRWPPfZYUa/vZnwzM7My52RvZmZW5pzszczMypyTvZmZWZlzsjczMytzTvZmZmZlzrfemZlZZRm1cYHP936jmyOCPfbYg4suuogDDjgAgAkTJjBu3Dj+/Oc/FzaWBjjZm5mZFZEkbrzxRo444gj23ntvVq1axUUXXdRsiR6c7M3MzIqub9++fPOb3+SKK65g2bJlHH/88YwePZoXX3yRlStXMmrUKA4++GBmz57NySefzEcffcQnn3zCPffcQ58+fdb7+k72ZmZmzeCSSy5hxx13pE2bNhx44IHss88+jBs3jvfee4+BAwey7777cuONN3LWWWdx3HHH8dFHH7Fq1aqCXLvJZC/pCODPEbFE0g+BHYGfRMRzBYnAzMysAnTo0IGjjjqKjh07MmHCBB544AGuvvpqAJYvX868efMYPHgwo0ePpqamhsMOO6wgtXrIbzT+j9JEvzswFLgVuKEgVzczM6sgLVq0oEWLFkQE99xzDzNnzmTmzJnMmzePrbfemmOPPZaJEyfSvn17hg4dWrBn5ueT7GvbEL4B3BAR9wNt8r2ApJaSnpc0KX3fRdIjkl5LXz+Xs+9ISXMkvSppaE75TpJeTLeNUTEm+zUzM2smQ4cO5frrryciAHj++ecBeOONN/jCF77AmWeeyUEHHcSsWbMKcr18+uz/JenXwL7AFZLasnb3558FvAJ0Tt+PACZHxOWSRqTvL5C0DXA0sC2wBfCopC9HxCqSloThwNPAn4D9gYfWIgYzM7NEE7fKNYcf/ehHnH322fTv35+IoKqqikmTJnHnnXfyu9/9jtatW/P5z3+eiy++uCDXyyfZH0mSXK+OiPckdQfOz+fkknqQtAiMBs5Jiw8G9krXbwWeAC5Iy++IiBXAm5LmAAMlzQU6R8S09Jy3AYfgZG9mZiVm1KhRq9d//etfr7F95MiRjBw5suDXbTTZS2oBTI+IvrVlEbEQWJjn+X8O/ADolFO2eXoOImKhpM3S8i1Jau61atKyj9P1uuX1xTucpAWAXr165RmimZlZeWu0OT4iPgFekLTWmVPSgcDbETEj30PqC6GR8jULI8ZGxICIGNCtW7c8L2tmZlbe8mnG7w7MljQdWFZbGBEHNXHcbsBBkr4OtAM6S/od8Jak7mmtvjvwdrp/DdAz5/gewIK0vEc95WZmZk2KCMppXHftoL61kU+yv3TtQ4GIGAmMBJC0F3BeRBwv6SrgJODy9PX+9JCJwO8lXUsyQK8PSRfCKklLJA0CngFOBK5fl5jMzKyytGvXjkWLFtG1a9eySPgRwaJFi2jXrt1aHddkso+IKescVf0uByZIOgWYBxyRXme2pAnAy8BK4PR0JD7Ad4FbgPYkA/M8OM/MzJrUo0cPampqeOedd7IOpWDatWtHjx49mt4xRz5P0BtEUpPemuT++pbAsojo3OiBOSLiCZJR90TEImBIA/uNJhm5X7e8Gui75hFmZmYNa926Nb179846jMzlc7/8L4BjgNdIatanpmVmZmZWAvKaCCci5khqmTarj5f0tyLHZWZmZgWST7L/r6Q2wExJV5LcY9+huGGZmZlZoeTTjH9Cut8ZJLfe9QT+t5hBmZmZWeE0WLOX1A3oFhEvp0XLgUsl9QWyf7CwmZmZ5aWxmv31QH2PodsSuK444ZiZmVmhNZbs+9V3j31EPAz0L15IZmZmVkiNJfvW67jNzMzMNiCNJfvX0ufaf4akA4A3iheSmZmZFVJjt959H5gk6Uigdua6AcBg4MBiB2ZmZmaF0WDNPiL+AfQDpgBV6TIF6J9uMzMzsxLQ6EN1ImIFML6ZYjEzM7MiyOehOmZmZlbCnOzNzMzKXIPJXtLk9PWK5gvHzMzMCq2xPvvukr4KHCTpDkC5GyPiuaJGZmZmZgXRWLK/GBgB9ACurbMtgH2KFZSZmZkVToPJPiLuBu6W9KOI+HEzxmRmZmYF1OR89hHxY0kHAXumRU9ExKTihmVmZmaF0uRofEk/Bc4CXk6Xs9Kypo5rJ2m6pBckzZZ0aVreRdIjkl5LXz+Xc8xISXMkvSppaE75TpJeTLeNkaT6rmlmZmZryufWu28AX4uIcRExDtg/LWvKCmCfiNgO2B7YX9IgknEAkyOiDzA5fY+kbYCjgW3Ta/xKUsv0XDcAw4E+6bJ/fh/PzMzM8r3PfpOc9Y3zOSASS9O3rdMlgIOBW9PyW4FD0vWDgTsiYkVEvAnMAQZK6g50johpERHAbTnHmJmZWROa7LMHfgo8L+lxktvv9gRG5nPytGY+A/gS8MuIeEbS5hGxECAiFkraLN19S+DpnMNr0rKP0/W65fVdbzhJCwC9evXKJ0QzM7Oy12TNPiL+AAwC7k2XwRFxRz4nj4hVEbE9ye17AyX1bWT3+vrho5Hy+q43NiIGRMSAbt265ROimZlZ2cunZk9aE5+4rheJiPckPUHS1/6WpO5prb478Ha6Ww3QM+ewHsCCtLxHPeVmZmaWh6I9G19SN0mbpOvtgX2Bv5P80XBSuttJwP3p+kTgaEltJfUmGYg3Pf1DY4mkQeko/BNzjjEzM7Mm5FWzX0fdgVvTfvsWwISImCRpGjBB0inAPOAIgIiYLWkCye19K4HTI2JVeq7vArcA7YGH0sXMzMzy0Giyl9QCmBURjfW11ysiZgE71FO+CBjSwDGjgdH1lFcDax2DmZmZNdGMHxGfAC9I8tB2MzOzEpVPM353YLak6cCy2sKIOKhoUZmZmVnB5JPsLy16FGZmZlY0+UyEM0XSVkCfiHhU0kZAy6aOMzMzsw1DPhPhnAbcDfw6LdoS+GMRYzIzM7MCyuc++9OB3YAPACLiNWCzRo8wMzOzDUY+ffYrIuKj2lllJbWigcfVmplZaasa8WDWIaxh7uX5TLRqjcmnZj9F0oVAe0lfA+4CHihuWGZmZlYo+ST7EcA7wIvAt4E/AT8sZlBmZmZWOPmMxv9E0q3AMyTN96+m88qbmZlZCWgy2Uv6BnAj8DrJdLO9JX07Ivx8ejMzsxKQzwC9a4C9I2IOgKQvAg/iyWjMzKw5jNo46wjWNOr9rCNYK/n02b9dm+hTb/DpHPRmZma2gWuwZi/psHR1tqQ/ARNI+uyPAJ5thtjMzMysABprxv9mzvpbwFfT9XeAzxUtIjMzMyuoBpN9RJzcnIGYmZlZceQzGr838H9AVe7+nuLWzMysNOQzGv+PwM0kT837pKjRmJmZWcHlk+yXR8SYokdiZmZmRZFPsr9O0iXAX4AVtYUR8VzRojKzvGyQk5a0OzbrENZUYvdEmxVaPsm+H3ACsA+fNuNH+r5BknoCtwGfT48bGxHXSeoC3EkyBmAucGRE/Cc9ZiRwCrAKODMiHk7LdwJuAdqTPJv/LD+y18zMLD/5JPtDgS9ExEdree6VwLkR8ZykTsAMSY8Aw4DJEXG5pBEkE+1cIGkb4GhgW2AL4FFJX46IVcANwHDgaZJkvz9+gp+ZmVle8nmC3gvAJmt74ohYWNvUHxFLgFeALYGDgVvT3W4FDknXDwbuiIgVEfEmMAcYKKk70DkipqW1+dtyjjEzM7Mm5FOz3xz4u6Rn+Wyffd633kmqAnYgmTlv84hYmJ5joaTN0t22JKm516pJyz5O1+uW13ed4SQtAPTq1Svf8MzMzMpaPsn+kvW5gKSOwD3A2RHxgaQGd62nLBopX7MwYiwwFmDAgAHu0zczMyO/+eynrOvJJbUmSfS3R8S9afFbkrqntfrufDqpTg3QM+fwHsCCtLxHPeVmZmaWhyb77CUtkfRBuiyXtErSB3kcJ5KH8bwSEdfmbJoInJSunwTcn1N+tKS26VP7+gDT0yb/JZIGpec8MecYMzMza0I+NftOue8lHQIMzOPcu5HcsveipJlp2YXA5cAESacA80hm0SMiZkuaALxMMpL/9HQkPsB3+fTWu4fwSHwzM7O85dNn/xkR8cf0lrmm9ptK/f3tAEMaOGY0MLqe8mqg79rEaWZmZol8JsI5LOdtC2AADQyQMzMzsw1PPjX73HntV5I89e7gokRjZmZmBZdPn73ntTczMythDSZ7SRc3clxExI+LEI+ZmZkVWGM1+2X1lHUgmaimK+Bkb2ZmVgIaTPYRcU3tejqRzVnAycAdwDUNHWdmZmYblkb77NPpaM8BjiOZtGbH2ulozczMrDQ01md/FXAYybPm+0XE0maLyszMzAqmscflnksyr/wPgQU5j8xdks/jcs3MzGzD0FiffT5z3ZuZmdkGzgndzMyszDnZm5mZlTknezMzszLnZG9mZlbmnOzNzMzKnJO9mZlZmXOyNzMzK3NO9mZmZmXOyd7MzKzMFS3ZSxon6W1JL+WUdZH0iKTX0tfP5WwbKWmOpFclDc0p30nSi+m2MZJUrJjNzMzKUTFr9rcA+9cpGwFMjog+wOT0PZK2AY4Gtk2P+ZWklukxNwDDgT7pUvecZmZm1oiiJfuI+CuwuE7xwSRT5ZK+HpJTfkdErIiIN4E5wEBJ3YHOETEtIgK4LecYMzMzy0Nz99lvHhELAdLXzdLyLYH5OfvVpGVbput1y+slabikaknV77zzTkEDNzMzK1UbygC9+vrho5HyekXE2IgYEBEDunXrVrDgzMzMSllzJ/u30qZ50te30/IaoGfOfj2ABWl5j3rKzczMLE/NnewnAiel6ycB9+eUHy2praTeJAPxpqdN/UskDUpH4Z+Yc4yZmZnloVWxTizpD8BewKaSaoBLgMuBCZJOAeYBRwBExGxJE4CXgZXA6RGxKj3Vd0lG9rcHHkoXMzMzy1PRkn1EHNPApiEN7D8aGF1PeTXQt4ChmZmZVZQNZYCemZmZFYmTvZmZWZlzsjczMytzTvZmZmZlzsnezMyszDnZm5mZlTknezMzszLnZG9mZlbmnOzNzMzKnJO9mZlZmXOyNzMzK3NO9mZmZmXOyd7MzKzMOdmbmZmVOSd7MzOzMudkb2ZmVuac7M3MzMqck72ZmVmZc7I3MzMrcyWT7CXtL+lVSXMkjcg6HjMzs1JREsleUkvgl8ABwDbAMZK2yTYqMzOz0lASyR4YCMyJiDci4iPgDuDgjGMyMzMrCa2yDiBPWwLzc97XALvU3UnScGB4+nappFebIbaSJtgUeDfrOD7jUmUdga0Hf6es0PydyttWDW0olWRf30811iiIGAuMLX445UNSdUQMyDoOKx/+Tlmh+Tu1/kqlGb8G6JnzvgewIKNYzMzMSkqpJPtngT6SektqAxwNTMw4JjMzs5JQEs34EbFS0hnAw0BLYFxEzM44rHLhbg8rNH+nrND8nVpPilij69vMzMzKSKk045uZmdk6crI3MzMrc072ZmZmZc7J3szMrMw52VcYSVdK6iyptaTJkt6VdHzWcVnpktRP0hHp0jfreKx8SNpMUq/aJet4SpmTfeXZLyI+AA4keVjRl4Hzsw3JSpGkjSU9AfwROBY4Drhf0uOSOmcZm5U2SQdJeg14E5gCzAUeyjSoEudkX3lap69fB/4QEYuzDMZK2o+BaqBPRBwaEYcAfUgegjU6y8Cs5P0YGAT8IyJ6A0OAp7INqbSVxEN1rKAekPR34EPge5K6AcszjslK075A/4j4pLYgIj6RdCHwYnZhWRn4OCIWSWohqUVEPC7piqyDKmVO9hUmIkak/2k+iIhVkv6Lpwu2dfNRRKysW5g+8XJFFgFZ2XhPUkfgr8Dtkt4G1viuWf6c7CuMpI2A04FeJNMBbwF8BZiUZVxWktpJ2oE1Z6UU0DaDeKx8HEzS4vh9krEgGwOXZRpRifPjciuMpDuBGcCJEdFXUntgWkRsn21kVmokPd7I5oiIfZotGDNrlGv2leeLEXGUpGMAIuJDSXVrZmZNioi9G9omaZfmjMXKg6SpEbG7pCVAbk1UJH9A+i6PdeRkX3k+SmvzASDpi4D7V63Q7iLpKjLLW0Tsnr52yjqWcuNb7yrPJcCfgZ6SbgcmAz/INiQrQ24tsnWSjsB/Kes4yo1r9hUmIh6R9BzJPawCzoqIdzMOy8qPBwPZOklv33xBUq+ImJd1POXCyb5CSNqxTtHC9LVX+p/queaOyUqbpAeoP6kL6NrM4Vh56Q7MljQdWFZbGBEHZRdSafNo/ArhkdNWaJK+mq52IHlyXgCvkj6kKSKmZBSalbic79Zn+Du17pzszWydSGoDXAmcSPLscgGbAddHxOWSdoiI5zMM0UqYpM2BndO30yPi7SzjKXVO9hUonZlsG6BdbVlE3JZdRFaKJI0BNgK+HxFL0rLOwNXAKmD/9LnmZmtF0pHAVcATJH9E7gGcHxF3ZxlXKXOyrzCSLgH2Ikn2fwIOAKZGxOFZxmWlR9Ickklwok55S+Bd4ICIeDqT4KykSXoB+FptbT6dw+PRiNgu28hKl2+9qzyHk8wg9e+IOBnYDj/a1NbNJ3UTPUBErALecaK39dCiTrP9Ipyv1ot/eJXnw3SWspVpk+vbwBcyjslK08uSTqxbKOl44JUM4rHy8WdJD0saJmkY8CCez369+Na7ylMtaRPgJpJn5C8FpmcakZWq04F7JX2L5LsUJAOq2gOHZhmYlbaIOF/SYcDuJH32YyPivozDKmnus68Qkg4B/pbbNCapCugcEbOyistKn6R9gG1JfinPjojJGYdkJU7SFRFxQVNllj8n+woh6W5gMPBf4Cngb8BTETE708DMzOqQ9FxE7FinbFZE9M8qplLnZF9hJPUmSfq7pq+9gGcj4uuZBmZmFU/Sd4HvAV8E5uRs6kTSMnlcJoGVAffZV5iIeFNSW5J+1fYk99q3zzYqMzMAfk8yEO+nwIic8iURsTibkMqDa/YVQtKFJDX5biSPNH06XWalt0qZmW0QJA0iGf9R+7CmTsA2EfFMtpGVLif7CiHp7yQj7yeR9Nc/ExHvZxuVmdmaJD0P7Fj7HAdJLYDquv34lj8341eIiPgfSV1I+ur3AkZI6gi8QNIXNj7L+MzMcij3gU3ptLfOV+vBNfsKlP6n2QnYE/g20DsiWmYblZlZQtK9JM/FvyEt+h6wd0QcklVMpc7JvkJIOoikVr8byT3Rs0ma8/9GUrN/J8PwzMxWk7QZMAbYh+RhTZOBsz3z3bpzsq8Q6V/KfyO5x35GRHyUcUhmZtZMnOzNzGyDIOkHEXGlpOtJavSfERFnZhBWWfCABzMz21B8W9JTQHXWgZQb1+zNzGyDIOls4CigO3An8IeImJllTOXCyd7MzDYokrYCjk6XdsAfSBL/a5kGVsKc7CuMpN2AUcBWJN04AiIiPKe9mW1wJO0AjAP6+xbhdec++8pzM/B9kvnH/ZhcM9vgSGoN7E9Ssx8CTAEuzTSoEudkX3nej4iHsg7CzKwuSV8DjgG+AUwH7gCGR8SyTAMrA27GrxCSap8pfSTQErgXWFG7PSKeyyIuM7Nakh4nmfnuHs9yV1hO9hUi/U/UkIiIfZotGDMza1ZO9hVG0hci4o2myszMrHy0yDoAa3Z311N2V7NHYWZmzcYD9CqEpP8hmQBnY0mH5WzqTHIfq5mZlSkn+8rxFeBAYBPgmznlS4DTsgjIzMyah/vsK4ykwRExLes4zMys+TjZVxhJ46l/NqlvZRCOmZk1AzfjV55JOevtgEOBBRnFYmZmzcA1+wonqQXwqO+zNzMrX771zvoAvbIOwszMisfN+BVG0hKSPnulr/8GLsg0KDMzKyo345uZmZU51+wrkKSDgD3Tt09ExKTG9jczs9Lmmn2FkXQ5sDNwe1p0DFAdESOzi8rMzIrJyb7CSJoFbB8Rn6TvWwLPR0T/bCMzM7Ni8Wj8yrRJzvrGWQVhZmbNw332leenwPPp/PYi6bt3E76ZWRlzM34FktSdpN8eYHpE/DvLeMzMrLjcjF8hJG0laWOAiFhIMtvdEOBYSW0yDc7MzIrKyb5yTAA6AEjaHrgLmAdsB/wqu7DMzKzY3GdfOdpHRO2EN8cD4yLimvTZ+DOzC8vMzIrNNfvKoZz1fYDJALW34JmZWflyzb5yPCZpArAQ+BzwGKwerPdRloGZmVlxeTR+hZAk4CigOzAhIv6Vlu8AbBYRD2cZn5mZFY+TvZmZWZlzn72ZmVmZc7I3MzMrc072FUbSPpI2yjoOMzNrPu6zrzCSbgMGAYuAJ9NlakT8J9PAzMysaJzsK5SkLYDDgfOALSLCt2GamZUp/4KvMJKOB/YA+gHvAr8gqd2bmVmZcs2+wkh6F3gduBF4PCLmZhuRmZkVm5N9BZK0Lck89rsDfYBXI+KEbKMyM7Ni8Wj8CiOpM9AL2AqoAjYG/Hx8M7My5pp9hZE0C5iaLn+NiJqMQzIzsyJzsq9QkjpExLKs4zAzs+JzM36FkTRY0svAK+n77ST9KuOwzMysiJzsK8/PgaEkD9UhIl4gGaxnZmZlysm+AkXE/DpFqzIJxMzMmoUfqlN55kvaFQhJbYAzSZv0zcysPHmAXoWRtClwHbAvIOAvwFkRsSjTwMzMrGic7M3MzMqcm/ErhKSLG9kcEfHjZgvGzMyalWv2FULSufUUdwBOAbpGRMdmDsnMzJqJk30FktQJOIsk0U8AromIt7ONyszMisXN+BVEUhfgHOA44FZgx4j4T7ZRmZlZsTnZVwhJVwGHAWOBfhGxNOOQzMysmbgZv0JI+gRYAawEcv/RRTJAr3MmgZmZWdE52ZuZmZU5Py7XzMyszDnZm5mZlTknezMzszLnZG9mjZJ0kaTZkmZJmilpF0lnS9ooj2Pz2s/MissD9MysQZIGA9cCe0XEinQipTbA34ABEfFuE8fPzWc/Mysu1+zNrDHdgXcjYgVAmrQPB7YAHpf0OICkGyRVpy0Al6ZlZ9az336Spkl6TtJdkvyYZrNm4Jq9mTUoTcZTgY2AR4E7I2JK3Rq7pC4RsVhSS2AycGZEzMrdL20VuBc4ICKWSboAaBsRl2Xw0cwqip+gZ2YNioilknYC9gD2Bu6UNKKeXY+UNJzkd0p3YBtgVp19BqXlT0mCpDtgWrFiN7NPOdmbWaMiYhXwBPCEpBeBk3K3S+oNnAfsHBH/kXQL0K6eUwl4JCKOKW7EZlaX++zNrEGSviKpT07R9sA/gSVAp7SsM7AMeF/S5sABOfvn7vc0sJukL6Xn3kjSl4sYvpmlXLM3s8Z0BK6XtAnJvApzgOHAMcBDkhZGxN6SngdmA28AT+UcP7bOfsOAP0hqm27/IfCP5vkoZpXLA/TMzMzKnJvxzczMypyTvZmZWZlzsjczMytzTvZmZmZlzsnezMyszDnZm5mZlTknezMzszL3/wEbx85TpK+FaAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "car.plot(kind='bar', figsize=(8,3))\n",
    "plt.xlabel('State')\n",
    "plt.ylabel('Number of Cars')\n",
    "plt.title('Number of Cars in Each State')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5e3de06",
   "metadata": {},
   "source": [
    "We can see that New South Wales is has a potential market."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9920367",
   "metadata": {},
   "source": [
    "#### Bike related purchases based on state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "3ebb81ed",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>past_3_years_bike_related_purchases</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>New South Wales</th>\n",
       "      <td>10677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QLD</th>\n",
       "      <td>4262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Victoria</th>\n",
       "      <td>5021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 past_3_years_bike_related_purchases\n",
       "state                                               \n",
       "New South Wales                                10677\n",
       "QLD                                             4262\n",
       "Victoria                                        5021"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bike_state = pd.pivot_table(new_table, index='state', values='past_3_years_bike_related_purchases',aggfunc='count')\n",
    "bike_state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "12e66317",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,4))\n",
    "sns.barplot(data = bike_state, x=bike_state.index, y='past_3_years_bike_related_purchases')\n",
    "plt.title('Purchases by State')\n",
    "plt.xlabel('State')\n",
    "plt.ylabel('past_3_years_purchases')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8562e6ec",
   "metadata": {},
   "source": [
    "#### Bike related purchases based on gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "28ad355d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>past_3_years_bike_related_purchases</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>9989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>9516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U</th>\n",
       "      <td>455</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        past_3_years_bike_related_purchases\n",
       "gender                                     \n",
       "Female                                 9989\n",
       "Male                                   9516\n",
       "U                                       455"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bike_gender = pd.pivot_table(new_table, index='gender', values='past_3_years_bike_related_purchases',aggfunc='count')\n",
    "bike_gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "440be6b0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data = bike_gender, x=bike_gender.index, y='past_3_years_bike_related_purchases')\n",
    "plt.title('Purchases by Gender')\n",
    "plt.xlabel('Gender')\n",
    "plt.ylabel('past_3_years_purchases')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8826bc49",
   "metadata": {},
   "source": [
    "### Summary Observations\n",
    "\n",
    "- Customers between the age of 28 and 68 made the most purchases. This was not exactly what we assumed in the beggining.\n",
    "- Gender did not have any relation to making purchases. Our cutomers were noth Males and Females alike withought any significant difference.\n",
    "- People belonging to Manufacturing, Financial Services and Healthcare make the most purchases and hence account for maximum number of our customers.\n",
    "\n",
    "Overall we can say that for the firm to now expand it should focus on people between the age of 28-68, irrespective of the gender. Also, people belonging to Manufacturing, Financial Services and Healthcare sectors are most likely to convert to our customers.\n",
    "\n",
    "But, at the same time we also say that New South Wales offers a wide potential for new market but we need to analyze this further before taking any action. Also, the reasons why the other industries do not make that many purchases should also be studied in order to try and turn them into our potential customers."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "722c48bf",
   "metadata": {},
   "source": [
    "# Customers that the firm should Target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "eac943a1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>gender</th>\n",
       "      <th>past_3_years_bike_related_purchases</th>\n",
       "      <th>DOB</th>\n",
       "      <th>job_title</th>\n",
       "      <th>job_industry_category</th>\n",
       "      <th>wealth_segment</th>\n",
       "      <th>deceased_indicator</th>\n",
       "      <th>owns_car</th>\n",
       "      <th>tenure</th>\n",
       "      <th>address</th>\n",
       "      <th>postcode</th>\n",
       "      <th>state</th>\n",
       "      <th>country</th>\n",
       "      <th>property_valuation</th>\n",
       "      <th>Rank</th>\n",
       "      <th>Value</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chickie</td>\n",
       "      <td>Brister</td>\n",
       "      <td>Male</td>\n",
       "      <td>86</td>\n",
       "      <td>1957-07-12</td>\n",
       "      <td>General Manager</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>14</td>\n",
       "      <td>45 Shopko Center</td>\n",
       "      <td>4500</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1.718750</td>\n",
       "      <td>65.852055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Morly</td>\n",
       "      <td>Genery</td>\n",
       "      <td>Male</td>\n",
       "      <td>69</td>\n",
       "      <td>1970-03-22</td>\n",
       "      <td>Structural Engineer</td>\n",
       "      <td>Property</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>16</td>\n",
       "      <td>14 Mccormick Park</td>\n",
       "      <td>2113</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>1.718750</td>\n",
       "      <td>53.150685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ardelis</td>\n",
       "      <td>Forrester</td>\n",
       "      <td>Female</td>\n",
       "      <td>10</td>\n",
       "      <td>1974-08-28</td>\n",
       "      <td>Senior Cost Accountant</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>10</td>\n",
       "      <td>5 Colorado Crossing</td>\n",
       "      <td>3505</td>\n",
       "      <td>VIC</td>\n",
       "      <td>Australia</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1.718750</td>\n",
       "      <td>48.712329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lucine</td>\n",
       "      <td>Stutt</td>\n",
       "      <td>Female</td>\n",
       "      <td>64</td>\n",
       "      <td>1979-01-28</td>\n",
       "      <td>Account Representative III</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>5</td>\n",
       "      <td>207 Annamark Plaza</td>\n",
       "      <td>4814</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1.703125</td>\n",
       "      <td>44.290411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Melinda</td>\n",
       "      <td>Hadlee</td>\n",
       "      <td>Female</td>\n",
       "      <td>34</td>\n",
       "      <td>1965-09-21</td>\n",
       "      <td>Financial Analyst</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>19</td>\n",
       "      <td>115 Montana Place</td>\n",
       "      <td>2093</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>1.703125</td>\n",
       "      <td>57.652055</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  first_name  last_name  gender  past_3_years_bike_related_purchases  \\\n",
       "0    Chickie    Brister    Male                                   86   \n",
       "1      Morly     Genery    Male                                   69   \n",
       "2    Ardelis  Forrester  Female                                   10   \n",
       "3     Lucine      Stutt  Female                                   64   \n",
       "4    Melinda     Hadlee  Female                                   34   \n",
       "\n",
       "         DOB                   job_title job_industry_category  \\\n",
       "0 1957-07-12             General Manager         Manufacturing   \n",
       "1 1970-03-22         Structural Engineer              Property   \n",
       "2 1974-08-28      Senior Cost Accountant    Financial Services   \n",
       "3 1979-01-28  Account Representative III         Manufacturing   \n",
       "4 1965-09-21           Financial Analyst    Financial Services   \n",
       "\n",
       "      wealth_segment deceased_indicator owns_car  tenure              address  \\\n",
       "0      Mass Customer                  N      Yes      14     45 Shopko Center   \n",
       "1      Mass Customer                  N       No      16    14 Mccormick Park   \n",
       "2  Affluent Customer                  N       No      10  5 Colorado Crossing   \n",
       "3  Affluent Customer                  N      Yes       5   207 Annamark Plaza   \n",
       "4  Affluent Customer                  N       No      19    115 Montana Place   \n",
       "\n",
       "   postcode state    country  property_valuation  Rank     Value        Age  \n",
       "0      4500   QLD  Australia                   6     1  1.718750  65.852055  \n",
       "1      2113   NSW  Australia                  11     1  1.718750  53.150685  \n",
       "2      3505   VIC  Australia                   5     1  1.718750  48.712329  \n",
       "3      4814   QLD  Australia                   1     4  1.703125  44.290411  \n",
       "4      2093   NSW  Australia                   9     4  1.703125  57.652055  "
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newcust.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "b164cea6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>past_3_years_bike_related_purchases</th>\n",
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       "      <th>address</th>\n",
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       "      <th>Value</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chickie</td>\n",
       "      <td>Brister</td>\n",
       "      <td>Male</td>\n",
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       "      <td>General Manager</td>\n",
       "      <td>Manufacturing</td>\n",
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       "      <td>N</td>\n",
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       "      <td>14</td>\n",
       "      <td>45 Shopko Center</td>\n",
       "      <td>4500</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1.718750</td>\n",
       "      <td>66</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Morly</td>\n",
       "      <td>Genery</td>\n",
       "      <td>Male</td>\n",
       "      <td>69</td>\n",
       "      <td>1970-03-22</td>\n",
       "      <td>Structural Engineer</td>\n",
       "      <td>Property</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>16</td>\n",
       "      <td>14 Mccormick Park</td>\n",
       "      <td>2113</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>1.718750</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ardelis</td>\n",
       "      <td>Forrester</td>\n",
       "      <td>Female</td>\n",
       "      <td>10</td>\n",
       "      <td>1974-08-28</td>\n",
       "      <td>Senior Cost Accountant</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>10</td>\n",
       "      <td>5 Colorado Crossing</td>\n",
       "      <td>3505</td>\n",
       "      <td>VIC</td>\n",
       "      <td>Australia</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1.718750</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lucine</td>\n",
       "      <td>Stutt</td>\n",
       "      <td>Female</td>\n",
       "      <td>64</td>\n",
       "      <td>1979-01-28</td>\n",
       "      <td>Account Representative III</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>5</td>\n",
       "      <td>207 Annamark Plaza</td>\n",
       "      <td>4814</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1.703125</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Melinda</td>\n",
       "      <td>Hadlee</td>\n",
       "      <td>Female</td>\n",
       "      <td>34</td>\n",
       "      <td>1965-09-21</td>\n",
       "      <td>Financial Analyst</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>19</td>\n",
       "      <td>115 Montana Place</td>\n",
       "      <td>2093</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>1.703125</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  first_name  last_name  gender  past_3_years_bike_related_purchases  \\\n",
       "0    Chickie    Brister    Male                                   86   \n",
       "1      Morly     Genery    Male                                   69   \n",
       "2    Ardelis  Forrester  Female                                   10   \n",
       "3     Lucine      Stutt  Female                                   64   \n",
       "4    Melinda     Hadlee  Female                                   34   \n",
       "\n",
       "         DOB                   job_title job_industry_category  \\\n",
       "0 1957-07-12             General Manager         Manufacturing   \n",
       "1 1970-03-22         Structural Engineer              Property   \n",
       "2 1974-08-28      Senior Cost Accountant    Financial Services   \n",
       "3 1979-01-28  Account Representative III         Manufacturing   \n",
       "4 1965-09-21           Financial Analyst    Financial Services   \n",
       "\n",
       "      wealth_segment deceased_indicator owns_car  tenure              address  \\\n",
       "0      Mass Customer                  N      Yes      14     45 Shopko Center   \n",
       "1      Mass Customer                  N       No      16    14 Mccormick Park   \n",
       "2  Affluent Customer                  N       No      10  5 Colorado Crossing   \n",
       "3  Affluent Customer                  N      Yes       5   207 Annamark Plaza   \n",
       "4  Affluent Customer                  N       No      19    115 Montana Place   \n",
       "\n",
       "   postcode state    country  property_valuation  Rank     Value  Age  \n",
       "0      4500   QLD  Australia                   6     1  1.718750   66  \n",
       "1      2113   NSW  Australia                  11     1  1.718750   53  \n",
       "2      3505   VIC  Australia                   5     1  1.718750   49  \n",
       "3      4814   QLD  Australia                   1     4  1.703125   44  \n",
       "4      2093   NSW  Australia                   9     4  1.703125   58  "
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert the DOB column to datetime format\n",
    "newcust['DOB'] = pd.to_datetime(newcust['DOB'])\n",
    "\n",
    "# Calculate the age using today's date and the DOB\n",
    "today = pd.Timestamp(date.today())\n",
    "age1 = (today - newcust['DOB']) / pd.Timedelta(days=365)\n",
    "\n",
    "newcust['Age'] = age1.round().astype('Int64')\n",
    "\n",
    "# Print the resulting DataFrame\n",
    "newcust.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "3a449d3d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Rutledge</td>\n",
       "      <td>Hallt</td>\n",
       "      <td>Male</td>\n",
       "      <td>23</td>\n",
       "      <td>1976-10-06</td>\n",
       "      <td>Compensation Analyst</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>8</td>\n",
       "      <td>7 Nevada Crossing</td>\n",
       "      <td>2620</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>1.671875</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Duff</td>\n",
       "      <td>Karlowicz</td>\n",
       "      <td>Male</td>\n",
       "      <td>50</td>\n",
       "      <td>1972-04-28</td>\n",
       "      <td>Speech Pathologist</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>5</td>\n",
       "      <td>717 West Drive</td>\n",
       "      <td>2200</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>1.656250</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Melba</td>\n",
       "      <td>Spellacy</td>\n",
       "      <td>Female</td>\n",
       "      <td>38</td>\n",
       "      <td>1976-12-09</td>\n",
       "      <td>VP Marketing</td>\n",
       "      <td>Health</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>0591 Anzinger Circle</td>\n",
       "      <td>2232</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>1.593750</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Teddie</td>\n",
       "      <td>Burchill</td>\n",
       "      <td>Male</td>\n",
       "      <td>11</td>\n",
       "      <td>1968-12-21</td>\n",
       "      <td>Programmer I</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>13</td>\n",
       "      <td>321 Raven Plaza</td>\n",
       "      <td>2161</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>9</td>\n",
       "      <td>21</td>\n",
       "      <td>1.515625</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Gaston</td>\n",
       "      <td>Dallaghan</td>\n",
       "      <td>Male</td>\n",
       "      <td>44</td>\n",
       "      <td>1993-09-29</td>\n",
       "      <td>Financial Analyst</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>8</td>\n",
       "      <td>656 Fuller Street</td>\n",
       "      <td>2783</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "      <td>1.515625</td>\n",
       "      <td>30</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>924</th>\n",
       "      <td>Lezlie</td>\n",
       "      <td>Clemits</td>\n",
       "      <td>Female</td>\n",
       "      <td>2</td>\n",
       "      <td>1973-05-30</td>\n",
       "      <td>Senior Cost Accountant</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>15</td>\n",
       "      <td>0504 Nevada Drive</td>\n",
       "      <td>2155</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10</td>\n",
       "      <td>924</td>\n",
       "      <td>0.488750</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>953</th>\n",
       "      <td>Noami</td>\n",
       "      <td>Cokly</td>\n",
       "      <td>Female</td>\n",
       "      <td>74</td>\n",
       "      <td>1962-09-17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>15</td>\n",
       "      <td>2886 Buena Vista Terrace</td>\n",
       "      <td>2038</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>11</td>\n",
       "      <td>954</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>960</th>\n",
       "      <td>Sonia</td>\n",
       "      <td>Dunstall</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>1975-07-30</td>\n",
       "      <td>Accountant III</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>10</td>\n",
       "      <td>99 Park Meadow Hill</td>\n",
       "      <td>2570</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>9</td>\n",
       "      <td>960</td>\n",
       "      <td>0.442000</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>964</th>\n",
       "      <td>Jammie</td>\n",
       "      <td>Seldner</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>1975-02-25</td>\n",
       "      <td>Human Resources Manager</td>\n",
       "      <td>Health</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>15</td>\n",
       "      <td>8 Saint Paul Junction</td>\n",
       "      <td>2066</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10</td>\n",
       "      <td>963</td>\n",
       "      <td>0.440000</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>993</th>\n",
       "      <td>Weidar</td>\n",
       "      <td>Etheridge</td>\n",
       "      <td>Male</td>\n",
       "      <td>38</td>\n",
       "      <td>1959-07-13</td>\n",
       "      <td>Compensation Analyst</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>6</td>\n",
       "      <td>0535 Jay Point</td>\n",
       "      <td>2422</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>4</td>\n",
       "      <td>994</td>\n",
       "      <td>0.382500</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>98 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    first_name  last_name  gender  past_3_years_bike_related_purchases  \\\n",
       "6     Rutledge      Hallt    Male                                   23   \n",
       "8         Duff  Karlowicz    Male                                   50   \n",
       "13       Melba   Spellacy  Female                                   38   \n",
       "20      Teddie   Burchill    Male                                   11   \n",
       "21      Gaston  Dallaghan    Male                                   44   \n",
       "..         ...        ...     ...                                  ...   \n",
       "924     Lezlie    Clemits  Female                                    2   \n",
       "953      Noami      Cokly  Female                                   74   \n",
       "960      Sonia   Dunstall  Female                                    0   \n",
       "964     Jammie    Seldner  Female                                   42   \n",
       "993     Weidar  Etheridge    Male                                   38   \n",
       "\n",
       "           DOB                job_title job_industry_category wealth_segment  \\\n",
       "6   1976-10-06     Compensation Analyst    Financial Services  Mass Customer   \n",
       "8   1972-04-28       Speech Pathologist         Manufacturing  Mass Customer   \n",
       "13  1976-12-09             VP Marketing                Health  Mass Customer   \n",
       "20  1968-12-21             Programmer I         Manufacturing  Mass Customer   \n",
       "21  1993-09-29        Financial Analyst    Financial Services  Mass Customer   \n",
       "..         ...                      ...                   ...            ...   \n",
       "924 1973-05-30   Senior Cost Accountant    Financial Services  Mass Customer   \n",
       "953 1962-09-17                      NaN         Manufacturing  Mass Customer   \n",
       "960 1975-07-30           Accountant III    Financial Services  Mass Customer   \n",
       "964 1975-02-25  Human Resources Manager                Health  Mass Customer   \n",
       "993 1959-07-13     Compensation Analyst    Financial Services  Mass Customer   \n",
       "\n",
       "    deceased_indicator owns_car  tenure                   address  postcode  \\\n",
       "6                    N       No       8         7 Nevada Crossing      2620   \n",
       "8                    N      Yes       5            717 West Drive      2200   \n",
       "13                   N       No       4      0591 Anzinger Circle      2232   \n",
       "20                   N      Yes      13           321 Raven Plaza      2161   \n",
       "21                   N      Yes       8         656 Fuller Street      2783   \n",
       "..                 ...      ...     ...                       ...       ...   \n",
       "924                  N      Yes      15         0504 Nevada Drive      2155   \n",
       "953                  N      Yes      15  2886 Buena Vista Terrace      2038   \n",
       "960                  N       No      10       99 Park Meadow Hill      2570   \n",
       "964                  N      Yes      15     8 Saint Paul Junction      2066   \n",
       "993                  N      Yes       6            0535 Jay Point      2422   \n",
       "\n",
       "    state    country  property_valuation  Rank     Value  Age  \n",
       "6     NSW  Australia                   7     6  1.671875   47  \n",
       "8     NSW  Australia                  10     8  1.656250   51  \n",
       "13    NSW  Australia                  10    14  1.593750   46  \n",
       "20    NSW  Australia                   9    21  1.515625   54  \n",
       "21    NSW  Australia                   7    21  1.515625   30  \n",
       "..    ...        ...                 ...   ...       ...  ...  \n",
       "924   NSW  Australia                  10   924  0.488750   50  \n",
       "953   NSW  Australia                  11   954  0.450000   61  \n",
       "960   NSW  Australia                   9   960  0.442000   48  \n",
       "964   NSW  Australia                  10   963  0.440000   48  \n",
       "993   NSW  Australia                   4   994  0.382500   64  \n",
       "\n",
       "[98 rows x 19 columns]"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newcust[(newcust['wealth_segment']=='Mass Customer') & (newcust['Age'] >27) & (newcust['Age'] <69) &\n",
    "       (newcust['job_industry_category'].isin(['Manufacturing','Financial Services','Health']) &\n",
    "        (newcust['state']=='NSW'))]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0016e0b7",
   "metadata": {},
   "source": [
    "We have found out these 98 customers from 1000 new customers that the firm should target in order to derive most value for the organization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfb633c6",
   "metadata": {},
   "source": [
    "#### Saving all our updated files to excel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "372facba",
   "metadata": {},
   "outputs": [],
   "source": [
    "transactions.to_excel(\"Transactions.xlsx\", index=False)\n",
    "newcust.to_excel('NewCustomerList.xlsx', index=False)\n",
    "custdemo.to_excel('CustomerDemographic.xlsx', index=False)\n",
    "address.to_excel('CustomerAddress.xlsx', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0757ee5e",
   "metadata": {},
   "source": [
    "****"
   ]
  }
 ],
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