Analysis_&_predict_Black_friday_sale/Black_friday_sale_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem Statement #"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Analyze and predict the sales on Black Friday sale"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Variable Statement #"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# User_ID\n",
    "# Product_ID\n",
    "# Gender\n",
    "# Age\n",
    "# Occupation\n",
    "# City_Category(A,B,C)\n",
    "# Stay_In_Current_City_Years\n",
    "# Marital_Status\n",
    "# Product_Category_1\n",
    "# Product_Category_2\n",
    "# Product_Category_3\n",
    "# Purchase : Purchase Amount Target Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns                   #import seabone library\n",
    "import numpy as np                      #import numpy library\n",
    "import pandas as pd                     #import pandas library\n",
    "import matplotlib.pyplot as plt         #import matplotlib library\n",
    "df = pd.read_csv('train.csv')           #take a variable df and read the training data set\n",
    "df1 = pd.read_csv('test.csv')           #take a variable df and read the testing data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>User_ID</th>\n",
       "      <th>Product_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>City_Category</th>\n",
       "      <th>Stay_In_Current_City_Years</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Product_Category_1</th>\n",
       "      <th>Product_Category_2</th>\n",
       "      <th>Product_Category_3</th>\n",
       "      <th>Purchase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000001</td>\n",
       "      <td>P00069042</td>\n",
       "      <td>F</td>\n",
       "      <td>0-17</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000001</td>\n",
       "      <td>P00248942</td>\n",
       "      <td>F</td>\n",
       "      <td>0-17</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000001</td>\n",
       "      <td>P00087842</td>\n",
       "      <td>F</td>\n",
       "      <td>0-17</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000001</td>\n",
       "      <td>P00085442</td>\n",
       "      <td>F</td>\n",
       "      <td>0-17</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000002</td>\n",
       "      <td>P00285442</td>\n",
       "      <td>M</td>\n",
       "      <td>55+</td>\n",
       "      <td>16</td>\n",
       "      <td>C</td>\n",
       "      <td>4+</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1000003</td>\n",
       "      <td>P00193542</td>\n",
       "      <td>M</td>\n",
       "      <td>26-35</td>\n",
       "      <td>15</td>\n",
       "      <td>A</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1000004</td>\n",
       "      <td>P00184942</td>\n",
       "      <td>M</td>\n",
       "      <td>46-50</td>\n",
       "      <td>7</td>\n",
       "      <td>B</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>19215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1000004</td>\n",
       "      <td>P00346142</td>\n",
       "      <td>M</td>\n",
       "      <td>46-50</td>\n",
       "      <td>7</td>\n",
       "      <td>B</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1000004</td>\n",
       "      <td>P0097242</td>\n",
       "      <td>M</td>\n",
       "      <td>46-50</td>\n",
       "      <td>7</td>\n",
       "      <td>B</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>16.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1000005</td>\n",
       "      <td>P00274942</td>\n",
       "      <td>M</td>\n",
       "      <td>26-35</td>\n",
       "      <td>20</td>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7871</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   User_ID Product_ID Gender    Age  Occupation City_Category  \\\n",
       "0  1000001  P00069042      F   0-17          10             A   \n",
       "1  1000001  P00248942      F   0-17          10             A   \n",
       "2  1000001  P00087842      F   0-17          10             A   \n",
       "3  1000001  P00085442      F   0-17          10             A   \n",
       "4  1000002  P00285442      M    55+          16             C   \n",
       "5  1000003  P00193542      M  26-35          15             A   \n",
       "6  1000004  P00184942      M  46-50           7             B   \n",
       "7  1000004  P00346142      M  46-50           7             B   \n",
       "8  1000004   P0097242      M  46-50           7             B   \n",
       "9  1000005  P00274942      M  26-35          20             A   \n",
       "\n",
       "  Stay_In_Current_City_Years  Marital_Status  Product_Category_1  \\\n",
       "0                          2               0                   3   \n",
       "1                          2               0                   1   \n",
       "2                          2               0                  12   \n",
       "3                          2               0                  12   \n",
       "4                         4+               0                   8   \n",
       "5                          3               0                   1   \n",
       "6                          2               1                   1   \n",
       "7                          2               1                   1   \n",
       "8                          2               1                   1   \n",
       "9                          1               1                   8   \n",
       "\n",
       "   Product_Category_2  Product_Category_3  Purchase  \n",
       "0                 NaN                 NaN      8370  \n",
       "1                 6.0                14.0     15200  \n",
       "2                 NaN                 NaN      1422  \n",
       "3                14.0                 NaN      1057  \n",
       "4                 NaN                 NaN      7969  \n",
       "5                 2.0                 NaN     15227  \n",
       "6                 8.0                17.0     19215  \n",
       "7                15.0                 NaN     15854  \n",
       "8                16.0                 NaN     15686  \n",
       "9                 NaN                 NaN      7871  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)  # print first ten rows from the traing data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(550068, 12)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape     #print the number of rows & coloums of training dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(233599, 11)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.shape   #print the number of rows & coloums of testing dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>User_ID</th>\n",
       "      <th>Product_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>City_Category</th>\n",
       "      <th>Stay_In_Current_City_Years</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Product_Category_1</th>\n",
       "      <th>Product_Category_2</th>\n",
       "      <th>Product_Category_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000004</td>\n",
       "      <td>P00128942</td>\n",
       "      <td>M</td>\n",
       "      <td>46-50</td>\n",
       "      <td>7</td>\n",
       "      <td>B</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000009</td>\n",
       "      <td>P00113442</td>\n",
       "      <td>M</td>\n",
       "      <td>26-35</td>\n",
       "      <td>17</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000010</td>\n",
       "      <td>P00288442</td>\n",
       "      <td>F</td>\n",
       "      <td>36-45</td>\n",
       "      <td>1</td>\n",
       "      <td>B</td>\n",
       "      <td>4+</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000010</td>\n",
       "      <td>P00145342</td>\n",
       "      <td>F</td>\n",
       "      <td>36-45</td>\n",
       "      <td>1</td>\n",
       "      <td>B</td>\n",
       "      <td>4+</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000011</td>\n",
       "      <td>P00053842</td>\n",
       "      <td>F</td>\n",
       "      <td>26-35</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1000013</td>\n",
       "      <td>P00350442</td>\n",
       "      <td>M</td>\n",
       "      <td>46-50</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1000013</td>\n",
       "      <td>P00155442</td>\n",
       "      <td>M</td>\n",
       "      <td>46-50</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1000013</td>\n",
       "      <td>P0094542</td>\n",
       "      <td>M</td>\n",
       "      <td>46-50</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1000015</td>\n",
       "      <td>P00161842</td>\n",
       "      <td>M</td>\n",
       "      <td>26-35</td>\n",
       "      <td>7</td>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>13.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1000022</td>\n",
       "      <td>P00067942</td>\n",
       "      <td>M</td>\n",
       "      <td>18-25</td>\n",
       "      <td>15</td>\n",
       "      <td>A</td>\n",
       "      <td>4+</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   User_ID Product_ID Gender    Age  Occupation City_Category  \\\n",
       "0  1000004  P00128942      M  46-50           7             B   \n",
       "1  1000009  P00113442      M  26-35          17             C   \n",
       "2  1000010  P00288442      F  36-45           1             B   \n",
       "3  1000010  P00145342      F  36-45           1             B   \n",
       "4  1000011  P00053842      F  26-35           1             C   \n",
       "5  1000013  P00350442      M  46-50           1             C   \n",
       "6  1000013  P00155442      M  46-50           1             C   \n",
       "7  1000013   P0094542      M  46-50           1             C   \n",
       "8  1000015  P00161842      M  26-35           7             A   \n",
       "9  1000022  P00067942      M  18-25          15             A   \n",
       "\n",
       "  Stay_In_Current_City_Years  Marital_Status  Product_Category_1  \\\n",
       "0                          2               1                   1   \n",
       "1                          0               0                   3   \n",
       "2                         4+               1                   5   \n",
       "3                         4+               1                   4   \n",
       "4                          1               0                   4   \n",
       "5                          3               1                   2   \n",
       "6                          3               1                   1   \n",
       "7                          3               1                   2   \n",
       "8                          1               0                  10   \n",
       "9                         4+               0                   5   \n",
       "\n",
       "   Product_Category_2  Product_Category_3  \n",
       "0                11.0                 NaN  \n",
       "1                 5.0                 NaN  \n",
       "2                14.0                 NaN  \n",
       "3                 9.0                 NaN  \n",
       "4                 5.0                12.0  \n",
       "5                 3.0                15.0  \n",
       "6                11.0                15.0  \n",
       "7                 4.0                 9.0  \n",
       "8                13.0                16.0  \n",
       "9                14.0                 NaN  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head(10)  # print first ten rows from the testing data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 550068 entries, 0 to 550067\n",
      "Data columns (total 12 columns):\n",
      " #   Column                      Non-Null Count   Dtype  \n",
      "---  ------                      --------------   -----  \n",
      " 0   User_ID                     550068 non-null  int64  \n",
      " 1   Product_ID                  550068 non-null  object \n",
      " 2   Gender                      550068 non-null  object \n",
      " 3   Age                         550068 non-null  object \n",
      " 4   Occupation                  550068 non-null  int64  \n",
      " 5   City_Category               550068 non-null  object \n",
      " 6   Stay_In_Current_City_Years  550068 non-null  object \n",
      " 7   Marital_Status              550068 non-null  int64  \n",
      " 8   Product_Category_1          550068 non-null  int64  \n",
      " 9   Product_Category_2          376430 non-null  float64\n",
      " 10  Product_Category_3          166821 non-null  float64\n",
      " 11  Purchase                    550068 non-null  int64  \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 39.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()  # it gives us the summary about the entire dataset that we use in our project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 233599 entries, 0 to 233598\n",
      "Data columns (total 11 columns):\n",
      " #   Column                      Non-Null Count   Dtype  \n",
      "---  ------                      --------------   -----  \n",
      " 0   User_ID                     233599 non-null  int64  \n",
      " 1   Product_ID                  233599 non-null  object \n",
      " 2   Gender                      233599 non-null  object \n",
      " 3   Age                         233599 non-null  object \n",
      " 4   Occupation                  233599 non-null  int64  \n",
      " 5   City_Category               233599 non-null  object \n",
      " 6   Stay_In_Current_City_Years  233599 non-null  object \n",
      " 7   Marital_Status              233599 non-null  int64  \n",
      " 8   Product_Category_1          233599 non-null  int64  \n",
      " 9   Product_Category_2          161255 non-null  float64\n",
      " 10  Product_Category_3          71037 non-null   float64\n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 15.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df1.info()  # it gives us the summary about the entire dataset that we use in our project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "User_ID                            0\n",
       "Product_ID                         0\n",
       "Gender                             0\n",
       "Age                                0\n",
       "Occupation                         0\n",
       "City_Category                      0\n",
       "Stay_In_Current_City_Years         0\n",
       "Marital_Status                     0\n",
       "Product_Category_1                 0\n",
       "Product_Category_2            173638\n",
       "Product_Category_3            383247\n",
       "Purchase                           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum() # gives the sum of missing values with respect to columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "User_ID                            0\n",
       "Product_ID                         0\n",
       "Gender                             0\n",
       "Age                                0\n",
       "Occupation                         0\n",
       "City_Category                      0\n",
       "Stay_In_Current_City_Years         0\n",
       "Marital_Status                     0\n",
       "Product_Category_1                 0\n",
       "Product_Category_2             72344\n",
       "Product_Category_3            162562\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.isnull().sum() # gives the sum of missing values with respect to columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculate Null Value in  our dataset#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "User_ID                        0.000000\n",
       "Product_ID                     0.000000\n",
       "Gender                         0.000000\n",
       "Age                            0.000000\n",
       "Occupation                     0.000000\n",
       "City_Category                  0.000000\n",
       "Stay_In_Current_City_Years     0.000000\n",
       "Marital_Status                 0.000000\n",
       "Product_Category_1             0.000000\n",
       "Product_Category_2            31.566643\n",
       "Product_Category_3            69.672659\n",
       "Purchase                       0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()/df.shape[0]*100   #here we can calculate the not null values present in our training dataset or not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "User_ID                        0.000000\n",
       "Product_ID                     0.000000\n",
       "Gender                         0.000000\n",
       "Age                            0.000000\n",
       "Occupation                     0.000000\n",
       "City_Category                  0.000000\n",
       "Stay_In_Current_City_Years     0.000000\n",
       "Marital_Status                 0.000000\n",
       "Product_Category_1             0.000000\n",
       "Product_Category_2            30.969311\n",
       "Product_Category_3            69.590195\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.isnull().sum()/df1.shape[0]*100   #here we can calculate the not null values present in our testing dataset or not"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Show Unique elements in each attributes #"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "User_ID                        5891\n",
       "Product_ID                     3631\n",
       "Gender                            2\n",
       "Age                               7\n",
       "Occupation                       21\n",
       "City_Category                     3\n",
       "Stay_In_Current_City_Years        5\n",
       "Marital_Status                    2\n",
       "Product_Category_1               20\n",
       "Product_Category_2               17\n",
       "Product_Category_3               15\n",
       "Purchase                      18105\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nunique() #The unique() function is used to find the unique elements of an array.Returns the sorted unique elements of an array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "User_ID                       5891\n",
       "Product_ID                    3491\n",
       "Gender                           2\n",
       "Age                              7\n",
       "Occupation                      21\n",
       "City_Category                    3\n",
       "Stay_In_Current_City_Years       5\n",
       "Marital_Status                   2\n",
       "Product_Category_1              18\n",
       "Product_Category_2              17\n",
       "Product_Category_3              15\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.nunique() #The unique() function is used to find the unique elements of an array.Returns the sorted unique elements of an array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan,  6., 14.,  2.,  8., 15., 16., 11.,  5.,  3.,  4., 12.,  9.,\n",
       "       10., 17., 13.,  7., 18.])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Product_Category_2'].unique()  ## gives different values in that column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([11.,  5., 14.,  9.,  3.,  4., 13.,  2., nan,  8., 15., 17., 12.,\n",
       "       16.,  6., 10., 18.,  7.])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['Product_Category_2'].unique()  ## gives different values in that column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.fillna(0,inplace=True) # replace nan with 0 in that same dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.fillna(0,inplace=True) # replace nan with 0 in that same dataframe1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.Product_Category_3 = df.Product_Category_3.astype('int64')\n",
    "df.Product_Category_2 = df.Product_Category_2.astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.Product_Category_3 = df.Product_Category_3.astype('int64')\n",
    "df1.Product_Category_2 = df.Product_Category_2.astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2', '4+', '3', '1', '0'], dtype=object)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Stay_In_Current_City_Years\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2', '0', '4+', '1', '3'], dtype=object)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[\"Stay_In_Current_City_Years\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(df.duplicated())  #shows there is any duplicate values present in training dataset or not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(df1.duplicated())  #shows there is any duplicate values present in testing dataset or not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#in the below figure we can see the purchase cost is repeating due to discounted products. customers buy discounted products on that day.\n",
    "sns.distplot(df[\"Purchase\"],color='m',vertical=True)        \n",
    "plt.title(\"Purchase Distribution\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#most of the purchase rate is between 5000 to 15000 shown by box plot.\n",
    "sns.boxplot(df[\"Purchase\"],color='c')\n",
    "plt.title(\"Boxplot of Purchase\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Here we can basically visualize which age of customers purchase which amount of product.\n",
    "\n",
    "\n",
    "\n",
    "data = df.groupby('Age')['Purchase'].mean()\n",
    "data  = pd.DataFrame({'Age':data.index, 'Average_purchase':data.values})\n",
    "plt.figure(figsize = (16,5))\n",
    "plt.plot('Age','Average_purchase','bs-',data = data);\n",
    "plt.grid();\n",
    "plt.xlabel('Age group');\n",
    "plt.ylabel('Average_Purchase amount in $');\n",
    "plt.title('Age group');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x1008 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#left graph represent which age group invest how much amount of cost \n",
    "#right graph represent how much amount of cost spend by which gender\n",
    "data_Age = df.groupby('Age')['Age'].count()\n",
    "data_Sex = df.groupby('Gender')['Gender'].count()\n",
    "data_Age = pd.DataFrame({'Age':data_Age.index, 'Count':data_Age.values})\n",
    "data_Sex = pd.DataFrame({'Sex':data_Sex.index, 'Count':data_Sex.values})\n",
    "plt.figure(figsize = (14,14))\n",
    "plt.subplot(121)\n",
    "\n",
    "plt.pie(data_Age['Count'],labels = data_Age['Age'],autopct='%1.3f%%',shadow=True);\n",
    "plt.title('which age group spend in which amount');\n",
    "plt.subplot(122)\n",
    "plt.pie(data_Sex['Count'],labels = data_Sex['Sex'],autopct='%1.4f%%',shadow=True,colors = ['#ff9999','#66b3ff']);\n",
    "plt.title('Gender');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#graph to count total number of males and total number of female\n",
    "sns.countplot(df['Gender'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "M    75.310507\n",
       "F    24.689493\n",
       "Name: Gender, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gender'].value_counts(normalize=True)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender\n",
       "F    8734.565765\n",
       "M    9437.526040\n",
       "Name: Purchase, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"Gender\").mean()[\"Purchase\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#graph for occupation and purchase rate\n",
    "\n",
    "plt.figure(figsize=(14,4))\n",
    "sns.countplot(df['Occupation'],color='c')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Purchase</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Occupation</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9124.428588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8953.193270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8952.481683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9178.593088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9213.980251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9333.149298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9256.535691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9425.728223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9532.592497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8637.743761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>8959.355375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>9213.845848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>9796.640239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>9306.351061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>9500.702772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>9778.891163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>9394.464349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>9821.478236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>9169.655844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>8710.627231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>8836.494905</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Purchase\n",
       "Occupation             \n",
       "0           9124.428588\n",
       "1           8953.193270\n",
       "2           8952.481683\n",
       "3           9178.593088\n",
       "4           9213.980251\n",
       "5           9333.149298\n",
       "6           9256.535691\n",
       "7           9425.728223\n",
       "8           9532.592497\n",
       "9           8637.743761\n",
       "10          8959.355375\n",
       "11          9213.845848\n",
       "12          9796.640239\n",
       "13          9306.351061\n",
       "14          9500.702772\n",
       "15          9778.891163\n",
       "16          9394.464349\n",
       "17          9821.478236\n",
       "18          9169.655844\n",
       "19          8710.627231\n",
       "20          8836.494905"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "occup = pd.DataFrame(df.groupby(\"Occupation\").mean()[\"Purchase\"])\n",
    "occup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "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": [
    "#purchase rate in 10000 according to occupation\n",
    "\n",
    "occup.plot(kind='bar',figsize=(15,5),color='c')\n",
    "plt.title(\"Occupation and Purchase Analysis\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#purchase rate accoring to the city category\n",
    "sns.countplot(df['City_Category'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#here we visualize how many people involve in sales\n",
    "\n",
    "data = df['Stay_In_Current_City_Years'].value_counts();\n",
    "plt.figure(figsize=(16,5));\n",
    "plt.bar(data.index,data.values,width=0.3,color = 'green');\n",
    "plt.xlabel('Stay in current city Years');\n",
    "plt.ylabel('count of people attended sale');\n",
    "plt.title('count of people attended sale vs different stay in coutry');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6001400037087128"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The skew() method skews an element into a given angle. Skewness gives the direction of the outliers if it is right-skewed, most of the outliers are present on the right side of the distribution while if it is left-skewed, most of the outliers will present on the left side of the distribution.\n",
    "\n",
    "df[\"Purchase\"].skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.3383775655851702"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Kurtosis is a statistical measure used to describe the degree to which scores cluster in the tails or the peak of a frequency distribution. The peak is the tallest part of the distribution, and the tails are the ends of the distribution. There are three types of kurtosis: mesokurtic, leptokurtic, and platykurtic\n",
    "\n",
    "df[\"Purchase\"].kurtosis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    550068.000000\n",
       "mean       9263.968713\n",
       "std        5023.065394\n",
       "min          12.000000\n",
       "25%        5823.000000\n",
       "50%        8047.000000\n",
       "75%       12054.000000\n",
       "max       23961.000000\n",
       "Name: Purchase, dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Purchase\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#here we can see which category of city spend money in purchase products.\n",
    "\n",
    "data = df.groupby('Stay_In_Current_City_Years')['Purchase'].mean()\n",
    "plt.figure(figsize=(16,5));\n",
    "plt.grid();\n",
    "plt.plot(data.index,data.values,'go-');\n",
    "plt.xlabel('Stay in current city Years');\n",
    "plt.ylabel('Average purchase amount in $');\n",
    "plt.title('Average amount of purchase for different stay in coutry');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Product_ID</th>\n",
       "      <th>Purchase</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>P00025442</td>\n",
       "      <td>27995166</td>\n",
       "      <td>1615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1016</th>\n",
       "      <td>P00110742</td>\n",
       "      <td>26722309</td>\n",
       "      <td>1612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2443</th>\n",
       "      <td>P00255842</td>\n",
       "      <td>25168963</td>\n",
       "      <td>1383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>582</th>\n",
       "      <td>P00059442</td>\n",
       "      <td>24338343</td>\n",
       "      <td>1406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1745</th>\n",
       "      <td>P00184942</td>\n",
       "      <td>24334887</td>\n",
       "      <td>1440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1030</th>\n",
       "      <td>P00112142</td>\n",
       "      <td>24216006</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1018</th>\n",
       "      <td>P00110942</td>\n",
       "      <td>23639564</td>\n",
       "      <td>1360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2263</th>\n",
       "      <td>P00237542</td>\n",
       "      <td>23425576</td>\n",
       "      <td>1394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>P00057642</td>\n",
       "      <td>23102780</td>\n",
       "      <td>1470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>P00010742</td>\n",
       "      <td>22164153</td>\n",
       "      <td>1350</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Product_ID  Purchase  Count\n",
       "249   P00025442  27995166   1615\n",
       "1016  P00110742  26722309   1612\n",
       "2443  P00255842  25168963   1383\n",
       "582   P00059442  24338343   1406\n",
       "1745  P00184942  24334887   1440\n",
       "1030  P00112142  24216006   1562\n",
       "1018  P00110942  23639564   1360\n",
       "2263  P00237542  23425576   1394\n",
       "565   P00057642  23102780   1470\n",
       "104   P00010742  22164153   1350"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Top 10 products which made highest sales in the store\n",
    "#After sorting the values by descending order of their Purchase values, some random samples from the data were used to get more information. The random samples suggested that most of the Product_IDs had multiple categories of Product associated with them.\n",
    "\n",
    "\n",
    "\n",
    "data1 = df.groupby('Product_ID').agg({'Purchase':'sum'}).reset_index()\n",
    "data2 = df['Product_ID'].value_counts()\n",
    "data2 = pd.DataFrame({'Product_ID':data2.index, 'Count':data2.values})\n",
    "data = pd.merge(data1,data2,left_on='Product_ID',right_on='Product_ID',how = 'left');\n",
    "\n",
    "data = data.sort_values(['Purchase'],ascending=False)[0:10];\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#graph for top ten product accoring to their sale and purchase\n",
    "\n",
    "plt.figure(figsize=(16,6));\n",
    "plt.grid();\n",
    "plt.plot(data['Product_ID'],data['Purchase'],'o-');\n",
    "plt.xlabel('product IDs');\n",
    "plt.ylabel('Total amount it was purchased in 10\\'s of Million $');\n",
    "plt.title('Top 10 Products with highest sales and Count displayed');\n",
    "for a,b,c in zip(data['Product_ID'], data['Purchase'], data['Count']): \n",
    "    plt.text(a, b+100000, str(c))  \n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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": [
    "#here unmarrid female represent as ('F',0)\n",
    "##here marrid female represent as ('F',1)\n",
    "##here unmarrid male represent as ('M',0)\n",
    "##here marrid male represent as ('M',1)\n",
    "\n",
    "\n",
    "data = df.groupby(['Gender','Marital_Status'])['Gender'].count();\n",
    "plt.figure(figsize=(16,16));\n",
    "plt.subplot(211)\n",
    "plt.pie(data.values,labels = data.index,autopct='%1.1f%%',shadow=True, colors = ['#ff9999','#66b3ff','#99ff99','#ffcc99']);\n",
    "plt.title('Plot of split of gender and marital status in the data');\n",
    "data = df.groupby(['Gender','Marital_Status'])['Purchase'].mean()\n",
    "\n",
    "data.unstack(level=1).plot(kind='bar');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#overall marital status\n",
    "\n",
    "sns.countplot(df['Marital_Status'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Marital_Status\n",
       "0    9265.907619\n",
       "1    9261.174574\n",
       "Name: Purchase, dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"Marital_Status\").mean()[\"Purchase\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Product_ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0-17</td>\n",
       "      <td>P00255842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18-25</td>\n",
       "      <td>P00265242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26-35</td>\n",
       "      <td>P00265242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36-45</td>\n",
       "      <td>P00025442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>46-50</td>\n",
       "      <td>P00265242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>51-55</td>\n",
       "      <td>P00265242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>55+</td>\n",
       "      <td>P00265242</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age Product_ID\n",
       "0   0-17  P00255842\n",
       "1  18-25  P00265242\n",
       "2  26-35  P00265242\n",
       "3  36-45  P00025442\n",
       "4  46-50  P00265242\n",
       "5  51-55  P00265242\n",
       "6    55+  P00265242"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1 = df.groupby('Age')['Product_ID'].apply(lambda x: x.value_counts().index[0]).reset_index()\n",
    "data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#the plot shows which age group spend how much amount of money.\n",
    "sns.countplot(df['Age'])\n",
    "plt.title('Distribution of Age')\n",
    "plt.xlabel('Different Categories of Age')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xa891e10>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(\"Age\").mean()[\"Purchase\"].plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(\"Age\").sum()['Purchase'].plot(kind=\"bar\")\n",
    "plt.title(\"Age and Purchase Analysis\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#analysis between product category1 and purchase mean.\n",
    "\n",
    "df.groupby('Product_Category_1').mean()['Purchase'].plot(kind='bar',figsize=(16,4),color='c')\n",
    "plt.title(\"Product_Category_1 and Purchase Mean Analysis\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('Product_Category_1').sum()['Purchase'].plot(kind='bar',figsize=(18,5),color='c')\n",
    "plt.title(\"Product_Category_1 and Purchase Analysis\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualize and count the product category2\n",
    "plt.figure(figsize=(16,4))\n",
    "sns.countplot(df['Product_Category_2'],color='c')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualize and count the product category2\n",
    "plt.figure(figsize=(16,4))\n",
    "sns.countplot(df['Product_Category_3'],color='c')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>User_ID</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Product_Category_1</th>\n",
       "      <th>Product_Category_2</th>\n",
       "      <th>Product_Category_3</th>\n",
       "      <th>Purchase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>User_ID</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.023971</td>\n",
       "      <td>0.020443</td>\n",
       "      <td>0.003825</td>\n",
       "      <td>0.003896</td>\n",
       "      <td>0.003605</td>\n",
       "      <td>0.004716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Occupation</th>\n",
       "      <td>-0.023971</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.024280</td>\n",
       "      <td>-0.007618</td>\n",
       "      <td>0.006712</td>\n",
       "      <td>0.012269</td>\n",
       "      <td>0.020833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marital_Status</th>\n",
       "      <td>0.020443</td>\n",
       "      <td>0.024280</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.019888</td>\n",
       "      <td>0.001099</td>\n",
       "      <td>-0.004629</td>\n",
       "      <td>-0.000463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product_Category_1</th>\n",
       "      <td>0.003825</td>\n",
       "      <td>-0.007618</td>\n",
       "      <td>0.019888</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.067877</td>\n",
       "      <td>-0.385534</td>\n",
       "      <td>-0.343703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product_Category_2</th>\n",
       "      <td>0.003896</td>\n",
       "      <td>0.006712</td>\n",
       "      <td>0.001099</td>\n",
       "      <td>-0.067877</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.094750</td>\n",
       "      <td>0.052288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product_Category_3</th>\n",
       "      <td>0.003605</td>\n",
       "      <td>0.012269</td>\n",
       "      <td>-0.004629</td>\n",
       "      <td>-0.385534</td>\n",
       "      <td>0.094750</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.288501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Purchase</th>\n",
       "      <td>0.004716</td>\n",
       "      <td>0.020833</td>\n",
       "      <td>-0.000463</td>\n",
       "      <td>-0.343703</td>\n",
       "      <td>0.052288</td>\n",
       "      <td>0.288501</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     User_ID  Occupation  Marital_Status  Product_Category_1  \\\n",
       "User_ID             1.000000   -0.023971        0.020443            0.003825   \n",
       "Occupation         -0.023971    1.000000        0.024280           -0.007618   \n",
       "Marital_Status      0.020443    0.024280        1.000000            0.019888   \n",
       "Product_Category_1  0.003825   -0.007618        0.019888            1.000000   \n",
       "Product_Category_2  0.003896    0.006712        0.001099           -0.067877   \n",
       "Product_Category_3  0.003605    0.012269       -0.004629           -0.385534   \n",
       "Purchase            0.004716    0.020833       -0.000463           -0.343703   \n",
       "\n",
       "                    Product_Category_2  Product_Category_3  Purchase  \n",
       "User_ID                       0.003896            0.003605  0.004716  \n",
       "Occupation                    0.006712            0.012269  0.020833  \n",
       "Marital_Status                0.001099           -0.004629 -0.000463  \n",
       "Product_Category_1           -0.067877           -0.385534 -0.343703  \n",
       "Product_Category_2            1.000000            0.094750  0.052288  \n",
       "Product_Category_3            0.094750            1.000000  0.288501  \n",
       "Purchase                      0.052288            0.288501  1.000000  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#define the pairwise correlation of all columns in the dataframe.\n",
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x360 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot the data corellation heatmap\n",
    "sns.heatmap(df.corr(),cmap=\"YlGnBu\", annot=True, annot_kws={\"size\": 10})\n",
    "plt.figure(figsize=(20,5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['User_ID', 'Product_ID', 'Gender', 'Age', 'Occupation', 'City_Category',\n",
       "       'Stay_In_Current_City_Years', 'Marital_Status', 'Product_Category_1',\n",
       "       'Product_Category_2', 'Product_Category_3', 'Purchase'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User_ID</th>\n",
       "      <th>Product_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>City_Category</th>\n",
       "      <th>Stay_In_Current_City_Years</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Product_Category_1</th>\n",
       "      <th>Product_Category_2</th>\n",
       "      <th>Product_Category_3</th>\n",
       "      <th>Purchase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000001</td>\n",
       "      <td>P00069042</td>\n",
       "      <td>F</td>\n",
       "      <td>0-17</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000001</td>\n",
       "      <td>P00248942</td>\n",
       "      <td>F</td>\n",
       "      <td>0-17</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>15200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000001</td>\n",
       "      <td>P00087842</td>\n",
       "      <td>F</td>\n",
       "      <td>0-17</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000001</td>\n",
       "      <td>P00085442</td>\n",
       "      <td>F</td>\n",
       "      <td>0-17</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>1057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000002</td>\n",
       "      <td>P00285442</td>\n",
       "      <td>M</td>\n",
       "      <td>55+</td>\n",
       "      <td>16</td>\n",
       "      <td>C</td>\n",
       "      <td>4+</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7969</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   User_ID Product_ID Gender   Age  Occupation City_Category  \\\n",
       "0  1000001  P00069042      F  0-17          10             A   \n",
       "1  1000001  P00248942      F  0-17          10             A   \n",
       "2  1000001  P00087842      F  0-17          10             A   \n",
       "3  1000001  P00085442      F  0-17          10             A   \n",
       "4  1000002  P00285442      M   55+          16             C   \n",
       "\n",
       "  Stay_In_Current_City_Years  Marital_Status  Product_Category_1  \\\n",
       "0                          2               0                   3   \n",
       "1                          2               0                   1   \n",
       "2                          2               0                  12   \n",
       "3                          2               0                  12   \n",
       "4                         4+               0                   8   \n",
       "\n",
       "   Product_Category_2  Product_Category_3  Purchase  \n",
       "0                   0                   0      8370  \n",
       "1                   6                  14     15200  \n",
       "2                   0                   0      1422  \n",
       "3                  14                   0      1057  \n",
       "4                   0                   0      7969  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dummy variables #\n",
    "\n",
    "df = pd.get_dummies(df, columns=['Stay_In_Current_City_Years'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "lr = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Gender'] = lr.fit_transform(df['Gender'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Age'] = lr.fit_transform(df['Age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['City_Category'] = lr.fit_transform(df['City_Category'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>User_ID</th>\n",
       "      <th>Product_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>City_Category</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Product_Category_1</th>\n",
       "      <th>Product_Category_2</th>\n",
       "      <th>Product_Category_3</th>\n",
       "      <th>Purchase</th>\n",
       "      <th>Stay_In_Current_City_Years_0</th>\n",
       "      <th>Stay_In_Current_City_Years_1</th>\n",
       "      <th>Stay_In_Current_City_Years_2</th>\n",
       "      <th>Stay_In_Current_City_Years_3</th>\n",
       "      <th>Stay_In_Current_City_Years_4+</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000001</td>\n",
       "      <td>P00069042</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8370</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000001</td>\n",
       "      <td>P00248942</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>15200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000001</td>\n",
       "      <td>P00087842</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1422</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000001</td>\n",
       "      <td>P00085442</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>1057</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000002</td>\n",
       "      <td>P00285442</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7969</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   User_ID Product_ID  Gender  Age  Occupation  City_Category  Marital_Status  \\\n",
       "0  1000001  P00069042       0    0          10              0               0   \n",
       "1  1000001  P00248942       0    0          10              0               0   \n",
       "2  1000001  P00087842       0    0          10              0               0   \n",
       "3  1000001  P00085442       0    0          10              0               0   \n",
       "4  1000002  P00285442       1    6          16              2               0   \n",
       "\n",
       "   Product_Category_1  Product_Category_2  Product_Category_3  Purchase  \\\n",
       "0                   3                   0                   0      8370   \n",
       "1                   1                   6                  14     15200   \n",
       "2                  12                   0                   0      1422   \n",
       "3                  12                  14                   0      1057   \n",
       "4                   8                   0                   0      7969   \n",
       "\n",
       "   Stay_In_Current_City_Years_0  Stay_In_Current_City_Years_1  \\\n",
       "0                             0                             0   \n",
       "1                             0                             0   \n",
       "2                             0                             0   \n",
       "3                             0                             0   \n",
       "4                             0                             0   \n",
       "\n",
       "   Stay_In_Current_City_Years_2  Stay_In_Current_City_Years_3  \\\n",
       "0                             1                             0   \n",
       "1                             1                             0   \n",
       "2                             1                             0   \n",
       "3                             1                             0   \n",
       "4                             0                             0   \n",
       "\n",
       "   Stay_In_Current_City_Years_4+  \n",
       "0                              0  \n",
       "1                              0  \n",
       "2                              0  \n",
       "3                              0  \n",
       "4                              1  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "#replaces the NULL values with a specified value.\n",
    "\n",
    "df['Product_Category_2'] =df['Product_Category_2'].fillna(0).astype('int64')\n",
    "df['Product_Category_3'] =df['Product_Category_3'].fillna(0).astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "User_ID                          0\n",
       "Product_ID                       0\n",
       "Gender                           0\n",
       "Age                              0\n",
       "Occupation                       0\n",
       "City_Category                    0\n",
       "Marital_Status                   0\n",
       "Product_Category_1               0\n",
       "Product_Category_2               0\n",
       "Product_Category_3               0\n",
       "Purchase                         0\n",
       "Stay_In_Current_City_Years_0     0\n",
       "Stay_In_Current_City_Years_1     0\n",
       "Stay_In_Current_City_Years_2     0\n",
       "Stay_In_Current_City_Years_3     0\n",
       "Stay_In_Current_City_Years_4+    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# again check gives the sum of missing values with respect to columns present or not.\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 550068 entries, 0 to 550067\n",
      "Data columns (total 16 columns):\n",
      " #   Column                         Non-Null Count   Dtype \n",
      "---  ------                         --------------   ----- \n",
      " 0   User_ID                        550068 non-null  int64 \n",
      " 1   Product_ID                     550068 non-null  object\n",
      " 2   Gender                         550068 non-null  int32 \n",
      " 3   Age                            550068 non-null  int32 \n",
      " 4   Occupation                     550068 non-null  int64 \n",
      " 5   City_Category                  550068 non-null  int32 \n",
      " 6   Marital_Status                 550068 non-null  int64 \n",
      " 7   Product_Category_1             550068 non-null  int64 \n",
      " 8   Product_Category_2             550068 non-null  int64 \n",
      " 9   Product_Category_3             550068 non-null  int64 \n",
      " 10  Purchase                       550068 non-null  int64 \n",
      " 11  Stay_In_Current_City_Years_0   550068 non-null  uint8 \n",
      " 12  Stay_In_Current_City_Years_1   550068 non-null  uint8 \n",
      " 13  Stay_In_Current_City_Years_2   550068 non-null  uint8 \n",
      " 14  Stay_In_Current_City_Years_3   550068 non-null  uint8 \n",
      " 15  Stay_In_Current_City_Years_4+  550068 non-null  uint8 \n",
      "dtypes: int32(3), int64(7), object(1), uint8(5)\n",
      "memory usage: 40.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Remove the unnecessary columns #\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop([\"User_ID\",\"Product_ID\"],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Splitting data into independent and dependent variables #\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(\"Purchase\",axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=df['Purchase']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=123)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Regression Model that use to predict accuracy#"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Regression #"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "# LinearRegression fits a linear model with coefficients w = (w1, …, wp) to minimize the residual sum of squares between the observed targets in the dataset, and the targets predicted by the linear approximation. Parameters fit_interceptbool, default=True. Whether to calculate the intercept for this model.\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Splitting the data into training and testing data\n",
    "lreg = LinearRegression()\n",
    "lreg.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "106950653872138.52"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#False sets the y-intercept to 0. If fit_intercept=True , the y-intercept will be determined by the line of best fit\n",
    "lreg.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4.72919543e+02,  1.09724619e+02,  5.05430053e+00,  3.13443453e+02,\n",
       "       -5.89679972e+01, -3.48447529e+02,  1.29894498e+01,  1.43499471e+02,\n",
       "       -1.06950654e+14, -1.06950654e+14, -1.06950654e+14, -1.06950654e+14,\n",
       "       -1.06950654e+14])"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# It gives gives the coefficient of the features of your dataset and The coef_ contain the coefficients for the prediction of each of the targets. It is also the same as if you trained a model to predict each of the targets separately.\n",
    "lreg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "#predict() function enables us to predict the labels of the data values on the basis of the trained model. The predict() function accepts only a single argument which is usually the data to be tested.\n",
    "\n",
    "y_pred = lreg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mean absolute error regression loss, mean_squared_error, r2_score\n",
    "from sklearn.metrics import mean_absolute_error,mean_squared_error, r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3532.032807188176"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate the accuracy of mean absolute error.\n",
    "mean_absolute_error(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21397687.441979032"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## calculate the accuracy of mean squared error.\n",
    "mean_squared_error(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1519360175264175"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# r2 tells us how well a model can predict the value of the response variable in percentage terms.\n",
    "r2_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE of Linear Regression Model is  4625.76344423048\n"
     ]
    }
   ],
   "source": [
    "# The Root Mean Square Error tells us how well a regression model can predict the value of the response variable in absolute terms.\n",
    "from math import sqrt\n",
    "print(\"Root Mean Square Error of Linear Regression Model is \",sqrt(mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# I did use several regression techniques but linear regression produced the best results. It gave me an RMSE (Root Mean Square Error )score of 4625.76344423048 initially. But, there are still other ways of implementing this such as : Extracting more exciting features, trying other ML models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}