Diwali_Sales_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0979edbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import python libraries\n",
    "\n",
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt # visualizing data\n",
    "%matplotlib inline\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7fbfb436",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import csv file\n",
    "df = pd.read_csv('Diwali Sales Data.csv', encoding= 'unicode_escape')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b8db4a5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11251, 15)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "662c471c",
   "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>Cust_name</th>\n",
       "      <th>Product_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age Group</th>\n",
       "      <th>Age</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>State</th>\n",
       "      <th>Zone</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Product_Category</th>\n",
       "      <th>Orders</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Status</th>\n",
       "      <th>unnamed1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1002903</td>\n",
       "      <td>Sanskriti</td>\n",
       "      <td>P00125942</td>\n",
       "      <td>F</td>\n",
       "      <td>26-35</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>Western</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Auto</td>\n",
       "      <td>1</td>\n",
       "      <td>23952.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000732</td>\n",
       "      <td>Kartik</td>\n",
       "      <td>P00110942</td>\n",
       "      <td>F</td>\n",
       "      <td>26-35</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>Southern</td>\n",
       "      <td>Govt</td>\n",
       "      <td>Auto</td>\n",
       "      <td>3</td>\n",
       "      <td>23934.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1001990</td>\n",
       "      <td>Bindu</td>\n",
       "      <td>P00118542</td>\n",
       "      <td>F</td>\n",
       "      <td>26-35</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>Uttar Pradesh</td>\n",
       "      <td>Central</td>\n",
       "      <td>Automobile</td>\n",
       "      <td>Auto</td>\n",
       "      <td>3</td>\n",
       "      <td>23924.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1001425</td>\n",
       "      <td>Sudevi</td>\n",
       "      <td>P00237842</td>\n",
       "      <td>M</td>\n",
       "      <td>0-17</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>Karnataka</td>\n",
       "      <td>Southern</td>\n",
       "      <td>Construction</td>\n",
       "      <td>Auto</td>\n",
       "      <td>2</td>\n",
       "      <td>23912.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000588</td>\n",
       "      <td>Joni</td>\n",
       "      <td>P00057942</td>\n",
       "      <td>M</td>\n",
       "      <td>26-35</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>Gujarat</td>\n",
       "      <td>Western</td>\n",
       "      <td>Food Processing</td>\n",
       "      <td>Auto</td>\n",
       "      <td>2</td>\n",
       "      <td>23877.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   User_ID  Cust_name Product_ID Gender Age Group  Age  Marital_Status  \\\n",
       "0  1002903  Sanskriti  P00125942      F     26-35   28               0   \n",
       "1  1000732     Kartik  P00110942      F     26-35   35               1   \n",
       "2  1001990      Bindu  P00118542      F     26-35   35               1   \n",
       "3  1001425     Sudevi  P00237842      M      0-17   16               0   \n",
       "4  1000588       Joni  P00057942      M     26-35   28               1   \n",
       "\n",
       "            State      Zone       Occupation Product_Category  Orders  \\\n",
       "0     Maharashtra   Western       Healthcare             Auto       1   \n",
       "1  Andhra Pradesh  Southern             Govt             Auto       3   \n",
       "2   Uttar Pradesh   Central       Automobile             Auto       3   \n",
       "3       Karnataka  Southern     Construction             Auto       2   \n",
       "4         Gujarat   Western  Food Processing             Auto       2   \n",
       "\n",
       "    Amount  Status  unnamed1  \n",
       "0  23952.0     NaN       NaN  \n",
       "1  23934.0     NaN       NaN  \n",
       "2  23924.0     NaN       NaN  \n",
       "3  23912.0     NaN       NaN  \n",
       "4  23877.0     NaN       NaN  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0814c45b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 11251 entries, 0 to 11250\n",
      "Data columns (total 15 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   User_ID           11251 non-null  int64  \n",
      " 1   Cust_name         11251 non-null  object \n",
      " 2   Product_ID        11251 non-null  object \n",
      " 3   Gender            11251 non-null  object \n",
      " 4   Age Group         11251 non-null  object \n",
      " 5   Age               11251 non-null  int64  \n",
      " 6   Marital_Status    11251 non-null  int64  \n",
      " 7   State             11251 non-null  object \n",
      " 8   Zone              11251 non-null  object \n",
      " 9   Occupation        11251 non-null  object \n",
      " 10  Product_Category  11251 non-null  object \n",
      " 11  Orders            11251 non-null  int64  \n",
      " 12  Amount            11239 non-null  float64\n",
      " 13  Status            0 non-null      float64\n",
      " 14  unnamed1          0 non-null      float64\n",
      "dtypes: float64(3), int64(4), object(8)\n",
      "memory usage: 1.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "29afa95b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop unrelated/blank columns\n",
    "df.drop(['Status', 'unnamed1'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5cee58eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "User_ID              0\n",
       "Cust_name            0\n",
       "Product_ID           0\n",
       "Gender               0\n",
       "Age Group            0\n",
       "Age                  0\n",
       "Marital_Status       0\n",
       "State                0\n",
       "Zone                 0\n",
       "Occupation           0\n",
       "Product_Category     0\n",
       "Orders               0\n",
       "Amount              12\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for null values\n",
    "pd.isnull(df).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "65de74e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop null values\n",
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "14e4ff48",
   "metadata": {},
   "outputs": [],
   "source": [
    "# change data type\n",
    "df['Amount'] = df['Amount'].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "aa6752dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int32')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Amount'].dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5f723a99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['User_ID', 'Cust_name', 'Product_ID', 'Gender', 'Age Group', 'Age',\n",
       "       'Marital_Status', 'State', 'Zone', 'Occupation', 'Product_Category',\n",
       "       'Orders', 'Amount'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a85bb3c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .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>Cust_name</th>\n",
       "      <th>Product_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age Group</th>\n",
       "      <th>Age</th>\n",
       "      <th>Shaadi</th>\n",
       "      <th>State</th>\n",
       "      <th>Zone</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Product_Category</th>\n",
       "      <th>Orders</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1002903</td>\n",
       "      <td>Sanskriti</td>\n",
       "      <td>P00125942</td>\n",
       "      <td>F</td>\n",
       "      <td>26-35</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>Western</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Auto</td>\n",
       "      <td>1</td>\n",
       "      <td>23952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000732</td>\n",
       "      <td>Kartik</td>\n",
       "      <td>P00110942</td>\n",
       "      <td>F</td>\n",
       "      <td>26-35</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>Southern</td>\n",
       "      <td>Govt</td>\n",
       "      <td>Auto</td>\n",
       "      <td>3</td>\n",
       "      <td>23934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1001990</td>\n",
       "      <td>Bindu</td>\n",
       "      <td>P00118542</td>\n",
       "      <td>F</td>\n",
       "      <td>26-35</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>Uttar Pradesh</td>\n",
       "      <td>Central</td>\n",
       "      <td>Automobile</td>\n",
       "      <td>Auto</td>\n",
       "      <td>3</td>\n",
       "      <td>23924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1001425</td>\n",
       "      <td>Sudevi</td>\n",
       "      <td>P00237842</td>\n",
       "      <td>M</td>\n",
       "      <td>0-17</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>Karnataka</td>\n",
       "      <td>Southern</td>\n",
       "      <td>Construction</td>\n",
       "      <td>Auto</td>\n",
       "      <td>2</td>\n",
       "      <td>23912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000588</td>\n",
       "      <td>Joni</td>\n",
       "      <td>P00057942</td>\n",
       "      <td>M</td>\n",
       "      <td>26-35</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>Gujarat</td>\n",
       "      <td>Western</td>\n",
       "      <td>Food Processing</td>\n",
       "      <td>Auto</td>\n",
       "      <td>2</td>\n",
       "      <td>23877</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>11246</th>\n",
       "      <td>1000695</td>\n",
       "      <td>Manning</td>\n",
       "      <td>P00296942</td>\n",
       "      <td>M</td>\n",
       "      <td>18-25</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>Western</td>\n",
       "      <td>Chemical</td>\n",
       "      <td>Office</td>\n",
       "      <td>4</td>\n",
       "      <td>370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11247</th>\n",
       "      <td>1004089</td>\n",
       "      <td>Reichenbach</td>\n",
       "      <td>P00171342</td>\n",
       "      <td>M</td>\n",
       "      <td>26-35</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>Haryana</td>\n",
       "      <td>Northern</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Veterinary</td>\n",
       "      <td>3</td>\n",
       "      <td>367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11248</th>\n",
       "      <td>1001209</td>\n",
       "      <td>Oshin</td>\n",
       "      <td>P00201342</td>\n",
       "      <td>F</td>\n",
       "      <td>36-45</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>Madhya Pradesh</td>\n",
       "      <td>Central</td>\n",
       "      <td>Textile</td>\n",
       "      <td>Office</td>\n",
       "      <td>4</td>\n",
       "      <td>213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11249</th>\n",
       "      <td>1004023</td>\n",
       "      <td>Noonan</td>\n",
       "      <td>P00059442</td>\n",
       "      <td>M</td>\n",
       "      <td>36-45</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>Karnataka</td>\n",
       "      <td>Southern</td>\n",
       "      <td>Agriculture</td>\n",
       "      <td>Office</td>\n",
       "      <td>3</td>\n",
       "      <td>206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11250</th>\n",
       "      <td>1002744</td>\n",
       "      <td>Brumley</td>\n",
       "      <td>P00281742</td>\n",
       "      <td>F</td>\n",
       "      <td>18-25</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>Western</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Office</td>\n",
       "      <td>3</td>\n",
       "      <td>188</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11239 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       User_ID    Cust_name Product_ID Gender Age Group  Age  Shaadi  \\\n",
       "0      1002903    Sanskriti  P00125942      F     26-35   28       0   \n",
       "1      1000732       Kartik  P00110942      F     26-35   35       1   \n",
       "2      1001990        Bindu  P00118542      F     26-35   35       1   \n",
       "3      1001425       Sudevi  P00237842      M      0-17   16       0   \n",
       "4      1000588         Joni  P00057942      M     26-35   28       1   \n",
       "...        ...          ...        ...    ...       ...  ...     ...   \n",
       "11246  1000695      Manning  P00296942      M     18-25   19       1   \n",
       "11247  1004089  Reichenbach  P00171342      M     26-35   33       0   \n",
       "11248  1001209        Oshin  P00201342      F     36-45   40       0   \n",
       "11249  1004023       Noonan  P00059442      M     36-45   37       0   \n",
       "11250  1002744      Brumley  P00281742      F     18-25   19       0   \n",
       "\n",
       "                State      Zone       Occupation Product_Category  Orders  \\\n",
       "0         Maharashtra   Western       Healthcare             Auto       1   \n",
       "1      Andhra Pradesh  Southern             Govt             Auto       3   \n",
       "2       Uttar Pradesh   Central       Automobile             Auto       3   \n",
       "3           Karnataka  Southern     Construction             Auto       2   \n",
       "4             Gujarat   Western  Food Processing             Auto       2   \n",
       "...               ...       ...              ...              ...     ...   \n",
       "11246     Maharashtra   Western         Chemical           Office       4   \n",
       "11247         Haryana  Northern       Healthcare       Veterinary       3   \n",
       "11248  Madhya Pradesh   Central          Textile           Office       4   \n",
       "11249       Karnataka  Southern      Agriculture           Office       3   \n",
       "11250     Maharashtra   Western       Healthcare           Office       3   \n",
       "\n",
       "       Amount  \n",
       "0       23952  \n",
       "1       23934  \n",
       "2       23924  \n",
       "3       23912  \n",
       "4       23877  \n",
       "...       ...  \n",
       "11246     370  \n",
       "11247     367  \n",
       "11248     213  \n",
       "11249     206  \n",
       "11250     188  \n",
       "\n",
       "[11239 rows x 13 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#rename column\n",
    "df.rename(columns= {'Marital_Status':'Shaadi'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a2484863",
   "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>Age</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Orders</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.123900e+04</td>\n",
       "      <td>11239.000000</td>\n",
       "      <td>11239.000000</td>\n",
       "      <td>11239.000000</td>\n",
       "      <td>11239.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.003004e+06</td>\n",
       "      <td>35.410357</td>\n",
       "      <td>0.420055</td>\n",
       "      <td>2.489634</td>\n",
       "      <td>9453.610553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.716039e+03</td>\n",
       "      <td>12.753866</td>\n",
       "      <td>0.493589</td>\n",
       "      <td>1.114967</td>\n",
       "      <td>5222.355168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000001e+06</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>188.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.001492e+06</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5443.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.003064e+06</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>8109.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.004426e+06</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>12675.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.006040e+06</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>23952.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            User_ID           Age  Marital_Status        Orders        Amount\n",
       "count  1.123900e+04  11239.000000    11239.000000  11239.000000  11239.000000\n",
       "mean   1.003004e+06     35.410357        0.420055      2.489634   9453.610553\n",
       "std    1.716039e+03     12.753866        0.493589      1.114967   5222.355168\n",
       "min    1.000001e+06     12.000000        0.000000      1.000000    188.000000\n",
       "25%    1.001492e+06     27.000000        0.000000      2.000000   5443.000000\n",
       "50%    1.003064e+06     33.000000        0.000000      2.000000   8109.000000\n",
       "75%    1.004426e+06     43.000000        1.000000      3.000000  12675.000000\n",
       "max    1.006040e+06     92.000000        1.000000      4.000000  23952.000000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# describe() method returns description of the data in the DataFrame (i.e. count, mean, std, etc)\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2c380030",
   "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>Orders</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>11239.000000</td>\n",
       "      <td>11239.000000</td>\n",
       "      <td>11239.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>35.410357</td>\n",
       "      <td>2.489634</td>\n",
       "      <td>9453.610553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>12.753866</td>\n",
       "      <td>1.114967</td>\n",
       "      <td>5222.355168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>188.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5443.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>33.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>8109.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>43.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>12675.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>92.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>23952.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Age        Orders        Amount\n",
       "count  11239.000000  11239.000000  11239.000000\n",
       "mean      35.410357      2.489634   9453.610553\n",
       "std       12.753866      1.114967   5222.355168\n",
       "min       12.000000      1.000000    188.000000\n",
       "25%       27.000000      2.000000   5443.000000\n",
       "50%       33.000000      2.000000   8109.000000\n",
       "75%       43.000000      3.000000  12675.000000\n",
       "max       92.000000      4.000000  23952.000000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use describe() for specific columns\n",
    "df[['Age', 'Orders', 'Amount']].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc649558",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "504b419c",
   "metadata": {},
   "source": [
    "### Gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "84c7918d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting a bar chart for Gender and it's count\n",
    "\n",
    "ax = sns.countplot(x = 'Gender',data = df)\n",
    "\n",
    "for bars in ax.containers:\n",
    "    ax.bar_label(bars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c5fd4566",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Gender', ylabel='Amount'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting a bar chart for gender vs total amount\n",
    "\n",
    "sales_gen = df.groupby(['Gender'], as_index=False)['Amount'].sum().sort_values(by='Amount', ascending=False)\n",
    "\n",
    "sns.barplot(x = 'Gender',y= 'Amount' ,data = sales_gen)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e108cec",
   "metadata": {},
   "source": [
    "*From above graphs we can see that most of the buyers are females and even the purchasing power of females are greater than men*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcc927f9",
   "metadata": {},
   "source": [
    "### Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "88563dec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.countplot(data = df, x = 'Age Group', hue = 'Gender')\n",
    "\n",
    "for bars in ax.containers:\n",
    "    ax.bar_label(bars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bfbe3937",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Age Group', ylabel='Amount'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Total Amount vs Age Group\n",
    "sales_age = df.groupby(['Age Group'], as_index=False)['Amount'].sum().sort_values(by='Amount', ascending=False)\n",
    "\n",
    "sns.barplot(x = 'Age Group',y= 'Amount' ,data = sales_age)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abaa6c07",
   "metadata": {},
   "source": [
    "*From above graphs we can see that most of the buyers are of age group between 26-35 yrs female*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83a49098",
   "metadata": {},
   "source": [
    "### State"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "dfac4f2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='State', ylabel='Orders'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# total number of orders from top 10 states\n",
    "\n",
    "sales_state = df.groupby(['State'], as_index=False)['Orders'].sum().sort_values(by='Orders', ascending=False).head(10)\n",
    "\n",
    "sns.set(rc={'figure.figsize':(15,5)})\n",
    "sns.barplot(data = sales_state, x = 'State',y= 'Orders')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a66617bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='State', ylabel='Amount'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# total amount/sales from top 10 states\n",
    "\n",
    "sales_state = df.groupby(['State'], as_index=False)['Amount'].sum().sort_values(by='Amount', ascending=False).head(10)\n",
    "\n",
    "sns.set(rc={'figure.figsize':(15,5)})\n",
    "sns.barplot(data = sales_state, x = 'State',y= 'Amount')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39d46e9e",
   "metadata": {},
   "source": [
    "*From above graphs we can see that most of the orders & total sales/amount are from Uttar Pradesh, Maharashtra and Karnataka respectively*\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89f865a5",
   "metadata": {},
   "source": [
    "### Marital Status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "77e6635e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.countplot(data = df, x = 'Marital_Status')\n",
    "\n",
    "sns.set(rc={'figure.figsize':(7,5)})\n",
    "for bars in ax.containers:\n",
    "    ax.bar_label(bars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2d8c7b27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Marital_Status', ylabel='Amount'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3kpLS+GCsfCIeAAClkq//ArcSF0wCAAAjxAMAADBCPAAAACPEAwCgxHO7ubZBKrzngXgAAJRYDodDkpSZmWHxJN7h2vPgcBTs/RK82wIAUGLZ7Q4FBgYrNTVJkuTvX1Y2mzVvz7SS2+1WZmaGUlOTFBgYLLu9YMcOiAcAQIkWGlpBkjwBUZoFBgZ7no+CIB4AACWazWZTWFhFhYSEy+nMsnocyzgcfgU+4nAN8QAAKBXsdrvsdn+rxygRuGASAAAYIR4AAIAR4gEAABghHgAAgBHiAQAAGCEeAACAEeIBAAAYIR4AAIAR4gEAABghHgAAgBHiAQAAGCEeAACAEeIBAAAYIR4AAIAR4gEAABghHgAAgBHiAQAAGCEeAACAEeIBAAAYIR4AAIAR4gEAABghHgAAgBHiAQAAGCEeAACAEeIBAAAYIR4AAIAR4gEAABghHgAAgBHiAQAAGCEeAACAEeIBAAAYIR4AAIAR4gEAABghHgAAgBHiAQAAGCEeAACAEeIBAAAYIR4AAIAR4gEAABghHgAAgBHiAQAAGCEeAACAEeIBAAAYIR4AAIAR4gEAABghHgAAgBHiAQAAGPGzeoDz589r6tSp2rFjhzIyMtS8eXONGTNGt956q9WjAfAxdrtNdrvN6jFKFZfLLZfLbfUYKGaWx8OwYcPkcrn0zjvvKCgoSG+88Yb69eunjRs3KjAw0OrxAPgIu92m8uXLyeHggGpxcjpdSk6+TECUMpbGw8WLF1WjRg3FxsbqtttukyQNHTpUf/rTn/T999+rUaNGVo4HwIfY7TY5HHa9+X9f6FTiRavHKRVqVA7TsJ73yG63EQ+ljKXxEBYWpunTp3u+vnDhgt577z1VrVpV9erVs3AyAL7qVOJF/XQqyeoxgBLN8tMW1zz33HP68MMP5e/vr7feekvlypWzeiQAAJALr4mHvn37qnv37lq6dKmGDRumZcuW6fbbb8/Xtvz8iuacJ+dSrcNzjxvhz4h1eO5LH6+Jh2unKV5++WX9+9//1pIlSzRlyhTj7djtNoWHBxX2eLBYaCgXzwLeitdn6WNpPFy4cEG7d+/WfffdJz+/q6PY7XbVq1dPiYmJ+dqmy+VWSsrlwhzTw+Gw8yKxSEpKmpxOl9VjwIvx+rQOr8+SITQ0MM9HkSyNh3Pnzumpp57S/Pnz1bZtW0nSlStXdPjwYUVHR+d7u1lZ/CEuaZxOF/9eAS/F67P0sfRE1W233aZ27drppZde0tdff63//Oc/Gjt2rFJSUtSvXz8rRwMAANdh+VUuM2bMUKtWrTRq1Ch169ZNycnJWrp0qapXr271aAAAIBeWXzAZEhKiF154QS+88ILVowAAgDyw/MgDAADwLcQDAAAwQjwAAAAjxAMAADBCPAAAACPEAwAAMEI8AAAAI8QDAAAwQjwAAAAjxAMAADBCPAAAACPEAwAAMEI8AAAAI8QDAAAwQjwAAAAjxAMAADBCPAAAACPEAwAAMEI8AAAAI8QDAAAwQjwAAAAjxAMAADBCPAAAACPEAwAAMGIcD6dPn9aVK1dyfSwjI0N79+4t8FAAAMB7GcfDvffeqyNHjuT62IEDB9S/f/8CDwUAALyXX15WeuWVV5ScnCxJcrvdmjt3rsLDw3Osd+TIEYWEhBTqgAAAwLvkKR7q1q2rt956S5Jks9n07bffyt/fP9s6DodDISEhGjduXOFPCQAAvEae4qFbt27q1q2bJCk6Olpz585VZGRkkQ4GAAC8U57i4be2bNlSFHMAAAAfYRwPbrdbH330kbZu3aq0tDS5XK5sj9tsNr3//vuFNiAAAPAuxvEwffp0zZ8/XzVr1lTVqlVls9myPe52uwttOAAA4H2M42H16tXq37+/xowZUxTzAAAAL2d8n4fU1FRFRUUVwSgAAMAXGMfD3XffzV0kAQAoxYxPWwwcOFB///vflZWVpcaNGyswMDDHOs2bNy+U4QAAgPcxjodrt59+8803JSnbBZNut1s2m+26t68GAAC+zzgeFi1aVBRzAAAAH2EcDy1atCiKOQAAgI/I11s1b+SRRx7JxygAAMAXGMfD2LFjc11us9nkcDjkcDiIBwAASjDjeNi8eXOOZZcvX9aePXv07rvvei6kBAAAJZNxPNSoUSPX5fXr19eVK1c0adIkLVu2rMCDAQAA72R8k6g/EhERoUOHDhXmJgEAgJcptHjIzMzUihUrVLFixcLaJAAA8ELGpy2io6NzfJKmy+VSUlKSMjIy+MAsAABKuHzd5+H38SBJwcHB6tChg1q3bl0ogwEAAO9kHA9Tp04tijkAAICPMI4H6er1DStXrtRXX32llJQUhYeHq1mzZnrkkUcUEBBQ2DMCAAAvYhwPKSkp6tOnj44eParq1avrpptuUnx8vNatW6elS5dq2bJlCgkJKYpZAQCAFzB+t8X06dOVkJCgJUuWaMuWLVq+fLm2bNmiJUuW6Pz583rjjTeKYk4AAOAljONh8+bNevLJJ9WsWbNsy5s1a6aRI0dq48aNhTYcAADwPsbxcOnSJdWqVSvXx2rVqqXk5OSCzgQAALyYcTzUrVtXW7duzfWxrVu3qnbt2gUeCgAAeC/jCyYHDBigp59+Wk6nU507d1alSpV07tw5rVu3Th9++KEmTJhQFHMCAAAvYRwPDz74oH766SfNmzdPH3zwgSTJ7XbL399fQ4cOVffu3Qt9SAAA4D3ydZ+HoUOHqlevXtq3b59SUlIUFhamxo0bKywsrLDnAwAAXiZf8SBJoaGhat++fWHOAgAAfIBxPJw+fVovvvii9u7dq19//TXH4zabTYcPHy6U4QAAgPcxjod//OMf2r9/vx599FGVL1++CEYCAADezDge9u/fr5deekmdO3cuinkAAICXM77Pw0033aTAwMCimAUAAPgA43iIjY3V7NmzderUqaKYBwAAeDnj0xZRUVGaP3++OnbsqPDw8BxHIWw2mz777LNCGxAAAHgX43gYN26cTp48qTZt2qhSpUpFMRMAAPBixvHw1VdfacKECerWrVtRzAMAALyc8TUPoaGhqlatWlHMAgAAfIBxPPTs2VPvvPOOUlNTi2IeAADg5YxPW5w5c0aHDh1SmzZtVLduXQUHB+dYZ9GiRYUyHAAA8D7G8RAfH6+GDRt6vna73dke//3XAACgZDGOh8WLF+e6/OzZs/roo4+0YsWKAg8FAAC8V74/VfOaHTt26IMPPtDnn3+urKws1apVqzDmAgAAXipf8XDhwgWtWLFCH374oU6dOqXg4GB16dJFf/rTn9SsWTOjbSUnJ2vGjBnatm2bUlNTFRERoaefftp4OwAAoHgYxcOXX36p5cuX67PPPpPT6dTdd9+tU6dO6c0331SLFi3yNcBTTz2lX375RTNmzFDFihW1ePFiDRgwQB9//LHq1q2br20CAICik6d4eO+997R8+XLFx8erdu3aGjp0qLp06aJy5cqpRYsWstls+dr58ePH9cUXX2jZsmW6++67JUnPPfecduzYobVr1+qJJ57I13YBAEDRyVM8TJ06VREREVq0aFG2Iwy//vprgXYeHh6ud955R3feeadnmc1mk81mU0pKSoG2DQAAikae4qFz587avHmzYmNj1apVK3Xp0kUdOnQo8M5DQ0PVvn37bMs2bNig48eP69lnn833dv38jO99lScOR9FsFzfGc48b4c+IdXjuS588xcP06dOVmpqqtWvXatWqVRoxYoTCw8PVsWNHz5GCwrB3716NGzdOMTExioqKytc27HabwsODCmUeeI/Q0MAbrwTAErw+S588XzAZHBysnj17qmfPnvr++++1cuVKrV27Vm63W88++6w6d+6szp07q169evka5LPPPtPo0aN11113adq0afnahiS5XG6lpFzO9/f/EYfDzovEIikpaXI6XVaPAS/G69M6vD5LhtDQwDwfRcrXWzXr16+vsWPHavTo0dq6datWrlypd999V/PmzVP9+vW1Zs0ao+0tWbJEL7/8su6//3698sor8vf3z89YHllZ/CEuaZxOF/9eAS/F67P0KdBNovz8/NSpUyd16tRJ586d08cff6yPP/7YaBvLli3TpEmT1Lt3b/3jH/8otFMgAACgaBT4DpPXVKpUSYMGDdKgQYPy/D3x8fGaPHmyOnXqpNjYWJ07d87zWEBAgEJCQgprPAAAUEgKLR7yY8OGDbpy5Yo2bdqkTZs2ZXusS5cumjp1qkWTAQCA67E0Hh5//HE9/vjjVo4AAAAM8eZcAABghHgAAABGiAcAAGCEeAAAAEaIBwAAYIR4AAAARix9qyaQV3xqX/FyudxyudxWjwHASxEP8GphIQFyu1x84FExc7mcSkpKIyAA5Ip4gFcLCvCXzW5X/Lp3lXb+jNXjlAqBFavplocGyW63EQ8AckU8wCeknT+jtLMnrB4DACAumAQAAIaIBwAAYIR4AAAARogHAABghHgAAABGiAcAAGCEeAAAAEaIBwAAYIR4AAAARogHAABghHgAAABGiAcAAGCEeAAAAEaIBwAAYIR4AAAARogHAABghHgAAABGiAcAAGCEeAAAAEaIBwAAYIR4AAAARogHAABghHgAAABGiAcAAGCEeAAAAEaIBwAAYIR4AAAARogHAABghHgAAABGiAcAAGCEeAAAAEaIBwAAYIR4AAAARogHAABghHgAAABGiAcAAGDEz+oBAAC+zeHg76HFyeVyy+VyWzoD8QAAyJewkAC5XS6FhgZaPUqp4nI5lZSUZmlAEA8AgHwJCvCXzW5X/Lp3lXb+jNXjlAqBFavplocGyW63EQ8AAN+Vdv6M0s6esHoMFCNOVAEAACPEAwAAMEI8AAAAI8QDAAAwQjwAAAAjxAMAADBCPAAAACPEAwAAMEI8AAAAI8QDAAAwQjwAAAAjxAMAADBCPAAAACPEAwAAMEI8AAAAI8QDAAAwQjwAAAAjxAMAADBCPAAAACPEAwAAMEI8AAAAI8QDAAAw4lXx8Pbbb6t3795WjwEAAP6A18TD0qVLNXPmTKvHAAAAN+Bn9QBnz57VhAkTFBcXpzp16lg9DgAAuAHLjzwcOnRIZcqU0Zo1a9S4cWOrxwEAADdg+ZGH6OhoRUdHF+o2/fyKpokcDstbCyg2vvbn3dfmBQrC6j/vlsdDYbPbbQoPD7J6DMDnhYYGWj0CgOuw+vVZ4uLB5XIrJeVykWzb4bBb/i8MKC4pKWlyOl1Wj5FnvD5RmhTF6zM0NDDPRzRKXDxIUlaW7/wHD/BWTqeL1xLgpax+fXKSEAAAGCEeAACAEeIBAAAY8aprHqZOnWr1CAAA4AY48gAAAIwQDwAAwAjxAAAAjBAPAADACPEAAACMEA8AAMAI8QAAAIwQDwAAwAjxAAAAjBAPAADACPEAAACMEA8AAMAI8QAAAIwQDwAAwAjxAAAAjBAPAADACPEAAACMEA8AAMAI8QAAAIwQDwAAwAjxAAAAjBAPAADACPEAAACMEA8AAMAI8QAAAIwQDwAAwAjxAAAAjBAPAADACPEAAACMEA8AAMAI8QAAAIwQDwAAwAjxAAAAjBAPAADACPEAAACMEA8AAMAI8QAAAIwQDwAAwAjxAAAAjBAPAADACPEAAACMEA8AAMAI8QAAAIwQDwAAwAjxAAAAjBAPAADACPEAAACMEA8AAMAI8QAAAIwQDwAAwAjxAAAAjBAPAADACPEAAACMEA8AAMAI8QAAAIwQDwAAwAjxAAAAjBAPAADACPEAAACMEA8AAMAI8QAAAIwQDwAAwAjxAAAAjBAPAADACPEAAACMEA8AAMAI8QAAAIwQDwAAwAjxAAAAjBAPAADACPEAAACMEA8AAMCI5fHgcrk0a9YstW3bVk2aNNGgQYN08uRJq8cCAADXYXk8zJ07V8uWLdOkSZP0wQcfyOVyaeDAgcrMzLR6NAAAkAtL4yEzM1MLFy7UyJEjFRUVpcjISL3++utKSEjQxo0brRwNAABch6XxcPToUV26dEmtWrXyLAsNDVXDhg319ddfWzgZAAC4Hj8rd56QkCBJqlatWrbllStX9jxmym63qUKFoALPlhub7er/jxkQLafTVST7QHb+ZRySpPpdn5Tb5bR4mtLBZr/6nIeFBcrttngYA7w+ix+vz+JXlK9Pu92W53UtjYe0tDRJkr+/f7blZcuW1cWLF/O1TZvNJocj709AfoQFBxTp9pFTmaBQq0codex2yy+Jyhden8WP12fxs/r1aeneAwKuvsh/f3FkRkaGAgMDrRgJAADcgKXxcO10RWJiYrbliYmJqlKlihUjAQCAG7A0HiIjIxUcHKy4uDjPspSUFB0+fFjNmze3cDIAAHA9ll7z4O/vr169emnatGmqUKGCatSooddee01Vq1ZVTEyMlaMBAIDrsDQeJGnkyJHKysrS+PHjlZ6erubNm2vBggUqU6aM1aMBAIBc2NxuX3ozFgAAsJpvvhcLAABYhngAAABGiAcAAGCEeAAAAEaIBwAAYIR4AAAARogHeCWXy6VZs2apbdu2atKkiQYNGqSTJ09aPRaA33n77bfVu3dvq8dAMSMe4JXmzp2rZcuWadKkSfrggw/kcrk0cODAHB+iBsA6S5cu1cyZM60eAxYgHuB1MjMztXDhQo0cOVJRUVGKjIzU66+/roSEBG3cuNHq8YBS7+zZs3r88cc1bdo01alTx+pxYAHiAV7n6NGjunTpklq1auVZFhoaqoYNG+rrr7+2cDIAknTo0CGVKVNGa9asUePGja0eBxaw/LMtgN9LSEiQ9N+PbL+mcuXKnscAWCc6OlrR0dFWjwELceQBXictLU3S1U9d/a2yZcsqIyPDipEAAL9BPMDrBAQESFKOiyMzMjIUGBhoxUgAgN8gHuB1rp2uSExMzLY8MTFRVapUsWIkAMBvEA/wOpGRkQoODlZcXJxnWUpKig4fPqzmzZtbOBkAQOKCSXghf39/9erVS9OmTVOFChVUo0YNvfbaa6patapiYmKsHg8ASj3iAV5p5MiRysrK0vjx45Wenq7mzZtrwYIFKlOmjNWjAUCpZ3O73W6rhwAAAL6Dax4AAIAR4gEAABghHgAAgBHiAQAAGCEeAACAEeIBAAAYIR4AAIAR4gHADXnr7WDyM5e3/iyALyEeAC/Xu3dvRUREqEePHtddZ9SoUYqIiNDYsWMLtK+ff/5ZERERWrVqlaSrnynyzDPPaM+ePUbbGTt2rKKjo433n5SUpClTpqhjx46644471KJFC/Xt21ebNm3Ktl5CQoIGDx6sU6dOGW3/+++/V8+ePY3nApAd8QD4ALvdrv379yshISHHY5cvX9bWrVsLZT+VK1fW8uXLFRUVJUk6cuSI/vnPf8rlchXK9v9Ienq6HnvsMW3btk2DBw/WggULNHnyZFWqVEnDhw/X+++/71l3165d+vzzz433sX79eu3bt68wxwZKJT7bAvABDRs21A8//KD169erX79+2R7bunWrAgMDFRoaWuD9+Pv7q0mTJgXeTn6sX79eP/74ozZs2KA6dep4lnfs2FHp6emaNWuWevXqJYfDYcl8AP6LIw+ADyhXrpzat2+v9evX53jsk08+0X333Sc/v//+XeDChQuaOHGiOnTo4Dn8P2zYMP3888+edXr37q3Ro0dr5MiRatKkifr375/ttEVcXJz69OkjSerTp4969+4tSXI6nXrnnXf00EMPqVGjRmrSpIl69OihL7/8skA/47lz5yQp16McsbGxGjp0qDIzM7Vq1SqNGzdOknTvvfd6TtWkp6dr+vTpiomJ0R133KG77rpL/fv315EjRyRJs2fP1pw5cyRJERERmj17do7TNNf8/rTLiRMn9Pjjj6tly5Zq3Lixunfvnq8jH0BJQTwAPuLBBx/MceoiNTVV27dv10MPPeRZ5na7FRsbqy+++EKjR4/WggULNHz4cO3evVsTJkzIts1PP/1UQUFBeuuttzRw4MBsj91+++16/vnnJUnPP/+853unTZumuXPnqnv37po/f74mTZqk5ORkPfHEE0pLS8v3z9e2bVv5+fmpb9++mjNnjvbv368rV65Ikho1aqQBAwYoMDBQUVFRGjJkiCRpzpw5Gjp0qCTpmWee0cqVKzV48GAtXLhQ48aN0/fff6+nn35abrdb3bp1U9euXSVJy5cvV7du3fI0l8vlUmxsrNLS0vTqq69q7ty5Kl++vIYMGaLjx4/n++cFfBmnLQAfERUVpcDAwGynLjZt2qSKFSvq7rvv9qyXmJiowMBAjRkzRs2aNZMktWzZUidOnNDy5cuzbbNMmTKaOHGi/P39JSnbkYng4GDVq1dPklSvXj3PPycmJmrUqFGeIxGSVLZsWY0YMULfffddvk97RERE6PXXX9fEiRM1e/ZszZ49WwEBAWrWrJm6du2qBx54QJJUoUIF3XzzzZKkBg0aqGbNmsrMzNSlS5c0fvx4Pfjgg5KkFi1aKDU1VVOnTtW5c+dUtWpVVa1aVZI8M/72572e8+fP69ixYxo6dKjat28v6WrMzJkzR5mZmfn6WQFfRzwAPiIgIEDR0dHZ4uFf//qXHnjgAdlsNs96VapU0aJFi+R2u/Xzzz/r+PHjOnbsmPbu3Zvjl13dunU94ZBX06dPl3T11MixY8d0/PhxzwWbBf1lGhMTow4dOujLL7/Url27FBcXp127dmnnzp369NNP9cYbb2T7Wa/x9/fXggULJElnz55VfHy8fvrpp0KZq1KlSqpXr56ee+457dy5U23atFG7du08p06A0oh4AHzIAw88oOHDhyshIUFly5bV7t279eSTT+ZYb82aNZoxY4bOnDmj8uXLq0GDBgoICMixXlBQkPEMBw8e1MSJE3Xw4EEFBgaqXr16ql69uqTCuYdCmTJl1LZtW7Vt21bS1Rh46aWXtGHDBm3btk0dOnTI9ft27NihyZMn69ixYwoKClJkZKTKlStX4LlsNpsWLlyot956S5s2bdLq1atVpkwZdezYURMnTlRYWFi+tw34Kq55AHxIu3btFBQUpPXr12vTpk2qWbOm7rjjjmzr7NmzR2PGjFFMTIy2b9+uuLg4vffee4XyLorU1FQNHDhQ5cqV07/+9S/t3btXK1as0KOPPlrgbffo0SPXv81XqVJFL7/8siTphx9+yPV7T5w4oWHDhqlBgwbatGmTvvnmGy1btuy6oXHNtaMYTqcz2/LLly/nmOGFF17Qzp07tXr1ag0YMEAbN27UzJkz8/rjASUK8QD4EH9/f3Xs2FEbNmzQp59+qs6dO+dYZ9++fXK5XBoxYoSqVKki6eovx127dknK/d0M1/P7t0UeO3ZMycnJ6tOnj+rVqye7/ep/QrZv32687d+rUaOG1q9fr5MnT+Z4LD4+XpJ02223SZJnv9d8++23ysjI0ODBg3XzzTd7omDHjh2S/nvk4fffFxwcLOnq0Y1rrly5ogMHDni+3rdvn1q3bq0DBw7IZrOpQYMGGjVqlG677TadPn063z8v4Ms4bQH4mAcffFCxsbGy2+0aP358jscbNWokSXrxxRf16KOP6uLFi1q6dKmOHj0q6erfqq/90ryRkJAQSdK2bdsUFhamW265RcHBwZo3b578/Pzk5+enDRs2aMWKFZJUoHdbjBo1SnFxceratav69Omjpk2bym636+DBg1q4cKHatWundu3aSZLnnhabNm1Su3btdPvtt8vPz0+vvfaa/va3v3ne0rlt2zbPz/zb71u3bp0aN26sWrVqqWnTplq8eLFq166tsLAwLVq0SOnp6Z5THg0bNlRAQICeeeYZjRgxQpUqVdKuXbt05MgRz1tZgdKGIw+Aj2ndurVCQ0NVv3593XrrrTkeb9mypZ5//nnt27dPgwYN0tSpU1W9enXPPQ6++eabPO+rfv36euihh7R06VKNHj1aISEhmjt3rtxut5544gk988wzOn36tJYsWaKgoCDj21j/Vs2aNfXxxx/r4Ycf1tq1azV06FDFxsZq7dq1GjBggN58803PEYWWLVuqdevWmj59ul555RXVrl1b06dP19mzZzVkyBDPW0wXL14sm83mmSsmJkZ33nmnxo4d67nAcurUqbrjjjs0fvx4jRs3Trfffrv69u3rmats2bJauHCh6tevr5dfflkDBgzQ5s2b9eKLL+rPf/5zvn9ewJfZ3HxKDAAAMMBpCwBFxu1257gYMTcOhyPXt2AC8E7EA4Ai89VXX+XpuoApU6ZwCgDwIZy2AFBkUlNTPe+U+CM1a9ZUeHh4MUwEoDAQDwAAwAjvtgAAAEaIBwAAYIR4AAAARogHAABghHgAAABGiAcAAGCEeAAAAEaIBwAAYOT/Af3jBJ8FilIIAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sales_state = df.groupby(['Marital_Status', 'Gender'], as_index=False)['Amount'].sum().sort_values(by='Amount', ascending=False)\n",
    "\n",
    "sns.set(rc={'figure.figsize':(6,5)})\n",
    "sns.barplot(data = sales_state, x = 'Marital_Status',y= 'Amount', hue='Gender')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37afbb83",
   "metadata": {},
   "source": [
    "*From above graphs we can see that most of the buyers are married (women) and they have high purchasing power*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "230cfe66",
   "metadata": {},
   "source": [
    "### Occupation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4deb41f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(20,5)})\n",
    "ax = sns.countplot(data = df, x = 'Occupation')\n",
    "\n",
    "for bars in ax.containers:\n",
    "    ax.bar_label(bars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4eb620f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Occupation', ylabel='Amount'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sales_state = df.groupby(['Occupation'], as_index=False)['Amount'].sum().sort_values(by='Amount', ascending=False)\n",
    "\n",
    "sns.set(rc={'figure.figsize':(20,5)})\n",
    "sns.barplot(data = sales_state, x = 'Occupation',y= 'Amount')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26c22532",
   "metadata": {},
   "source": [
    "*From above graphs we can see that most of the buyers are working in IT, Healthcare and Aviation sector*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40518579",
   "metadata": {},
   "source": [
    "### Product Category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3e7e4d39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(20,5)})\n",
    "ax = sns.countplot(data = df, x = 'Product_Category')\n",
    "\n",
    "for bars in ax.containers:\n",
    "    ax.bar_label(bars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "99f5c6f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Product_Category', ylabel='Amount'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sales_state = df.groupby(['Product_Category'], as_index=False)['Amount'].sum().sort_values(by='Amount', ascending=False).head(10)\n",
    "\n",
    "sns.set(rc={'figure.figsize':(20,5)})\n",
    "sns.barplot(data = sales_state, x = 'Product_Category',y= 'Amount')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5be54973",
   "metadata": {},
   "source": [
    "*From above graphs we can see that most of the sold products are from Food, Clothing and Electronics category*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f33d23c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Product_ID', ylabel='Orders'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sales_state = df.groupby(['Product_ID'], as_index=False)['Orders'].sum().sort_values(by='Orders', ascending=False).head(10)\n",
    "\n",
    "sns.set(rc={'figure.figsize':(20,5)})\n",
    "sns.barplot(data = sales_state, x = 'Product_ID',y= 'Orders')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "17d2bb43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Product_ID'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# top 10 most sold products (same thing as above)\n",
    "\n",
    "fig1, ax1 = plt.subplots(figsize=(12,7))\n",
    "df.groupby('Product_ID')['Orders'].sum().nlargest(10).sort_values(ascending=False).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31f88f63",
   "metadata": {},
   "source": [
    "## Conclusion:\n",
    "\n",
    "### "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56e52bca",
   "metadata": {},
   "source": [
    "*Married women age group 26-35 yrs from UP,  Maharastra and Karnataka working in IT, Healthcare and Aviation are more likely to buy products from Food, Clothing and Electronics category*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e91055b",
   "metadata": {},
   "source": [
    "complete project on YouTube: https://www.youtube.com/@RishabhMishraOfficial\n",
    "    \n",
    "complete project on GitHub: https://github.com/rishabhnmishra/Python_Diwali_Sales_Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7bfbc58",
   "metadata": {},
   "source": [
    "Thank you!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}