Diwali-sales.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fdadaf9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c24e825d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('Diwali Sales Data.csv', encoding='unicode_escape')\n",
    "# to avoid encoding error use unicode_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e89bc618",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11251, 15)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape # 11251 rows 15 columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a2e96ebd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>23952.00</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>23934.00</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>23924.00</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>23912.00</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>23877.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</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>Himachal Pradesh</td>\n",
       "      <td>Northern</td>\n",
       "      <td>Food Processing</td>\n",
       "      <td>Auto</td>\n",
       "      <td>1</td>\n",
       "      <td>23877.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1001132</td>\n",
       "      <td>Balk</td>\n",
       "      <td>P00018042</td>\n",
       "      <td>F</td>\n",
       "      <td>18-25</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>Uttar Pradesh</td>\n",
       "      <td>Central</td>\n",
       "      <td>Lawyer</td>\n",
       "      <td>Auto</td>\n",
       "      <td>4</td>\n",
       "      <td>23841.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1002092</td>\n",
       "      <td>Shivangi</td>\n",
       "      <td>P00273442</td>\n",
       "      <td>F</td>\n",
       "      <td>55+</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>Western</td>\n",
       "      <td>IT Sector</td>\n",
       "      <td>Auto</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1003224</td>\n",
       "      <td>Kushal</td>\n",
       "      <td>P00205642</td>\n",
       "      <td>M</td>\n",
       "      <td>26-35</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>Uttar Pradesh</td>\n",
       "      <td>Central</td>\n",
       "      <td>Govt</td>\n",
       "      <td>Auto</td>\n",
       "      <td>2</td>\n",
       "      <td>23809.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1003650</td>\n",
       "      <td>Ginny</td>\n",
       "      <td>P00031142</td>\n",
       "      <td>F</td>\n",
       "      <td>26-35</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>Southern</td>\n",
       "      <td>Media</td>\n",
       "      <td>Auto</td>\n",
       "      <td>4</td>\n",
       "      <td>23799.99</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",
       "5  1000588       Joni  P00057942      M     26-35   28               1   \n",
       "6  1001132       Balk  P00018042      F     18-25   25               1   \n",
       "7  1002092   Shivangi  P00273442      F       55+   61               0   \n",
       "8  1003224     Kushal  P00205642      M     26-35   35               0   \n",
       "9  1003650      Ginny  P00031142      F     26-35   26               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",
       "5  Himachal Pradesh  Northern  Food Processing             Auto       1   \n",
       "6     Uttar Pradesh   Central           Lawyer             Auto       4   \n",
       "7       Maharashtra   Western        IT Sector             Auto       1   \n",
       "8     Uttar Pradesh   Central             Govt             Auto       2   \n",
       "9    Andhra Pradesh  Southern            Media             Auto       4   \n",
       "\n",
       "     Amount  Status  unnamed1  \n",
       "0  23952.00     NaN       NaN  \n",
       "1  23934.00     NaN       NaN  \n",
       "2  23924.00     NaN       NaN  \n",
       "3  23912.00     NaN       NaN  \n",
       "4  23877.00     NaN       NaN  \n",
       "5  23877.00     NaN       NaN  \n",
       "6  23841.00     NaN       NaN  \n",
       "7       NaN     NaN       NaN  \n",
       "8  23809.00     NaN       NaN  \n",
       "9  23799.99     NaN       NaN  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "95085a88",
   "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": "6492e329",
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping unused columns\n",
    "df.drop(['Status','unnamed1'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "eb6af27f",
   "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": [
    "# checking for null values\n",
    "pd.isnull(df).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "06eb79d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11251, 13)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4efa7491",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e4fe3d9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11239, 13)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "acd73a64",
   "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              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isnull(df).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8cc72415",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Amount']=df['Amount'].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9fdd0ab4",
   "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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "47f9f465",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "60ed6308",
   "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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Age','Orders','Amount']].describe()\n",
    "# describe() on particular columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c0b4102",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3e63c8b4",
   "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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9847e603",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=sns.countplot(x='Gender', data=df)\n",
    "\n",
    "for bars in ax.containers:\n",
    "    ax.bar_label(bars)\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c8be63e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Gender', ylabel='Amount'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sales_gen=df.groupby(['Gender'], as_index=False)['Amount'].sum().sort_values(by='Amount', ascending=False)\n",
    "sns.barplot(x='Gender',y='Amount',data=sales_gen)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a87d32c1",
   "metadata": {},
   "source": [
    "*From the above graphs we can see that the most of the buyers are females and evn the purchashing power of female is more than male*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91869678",
   "metadata": {},
   "source": [
    "# Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "aa8cc7a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=sns.countplot(x='Age Group', data=df, hue='Gender')\n",
    "for bars in ax.containers:\n",
    "    ax.bar_label(bars)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64d8121c",
   "metadata": {},
   "source": [
    "*From the graph we can see the most of buyers are of group between 26-35yrs female*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8798ef6a",
   "metadata": {},
   "source": [
    "# State"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "61b2853d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='State', ylabel='Orders'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sales_state=df.groupby(['State'], as_index=False)['Orders'].sum().sort_values(by='Orders', ascending=False).head(10)\n",
    "sns.set(rc={'figure.figsize':(15,5)}) # width, height\n",
    "sns.barplot(data=sales_state, x='State', y='Orders')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "5146ed7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='State', ylabel='Amount'>"
      ]
     },
     "execution_count": 30,
     "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",
    "sales_state=df.groupby(['State'], as_index=False)['Amount'].sum().sort_values(by='Amount', ascending=False).head(10)\n",
    "sns.set(rc={'figure.figsize':(15,5)}) # width, height\n",
    "sns.barplot(data=sales_state, x='State', y='Amount')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a7f4d5b",
   "metadata": {},
   "source": [
    "*From the above graphs we can see that most orders and totalsales/ amount are from Uttar Pradesh, Maharashtra and Karnataka*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66630023",
   "metadata": {},
   "source": [
    "# Marital Status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f4612348",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=sns.countplot(data=df, x='Marital_Status')\n",
    "sns.set(rc={'figure.figsize':(5,5)})\n",
    "for bars in ax.containers:\n",
    "    ax.bar_label(bars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "0517fef8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Marital_Status', ylabel='Amount'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "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('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": "27d68a66",
   "metadata": {},
   "source": [
    "*Married women are the most buyers* "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c25365b1",
   "metadata": {},
   "source": [
    "# Occupation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "0621aa55",
   "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",
    "for bars in ax.containers:\n",
    "    ax.bar_label(bars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "5d3ce187",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Occupation', ylabel='Amount'>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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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('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": "a900e8c6",
   "metadata": {},
   "source": [
    "*max purchases are done by people in IT sector, Healthcare and Aviation*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8f064bc",
   "metadata": {},
   "source": [
    "# Product Category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "06fdf87a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 3000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=sns.countplot(data=df, x='Product_Category')\n",
    "sns.set(rc={'figure.figsize':(20,5)})\n",
    "\n",
    "\n",
    "for bars in ax.containers:\n",
    "    ax.bar_label(bars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "3c7e725b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Product_Category', ylabel='Amount'>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "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('Amount', ascending=False)\n",
    "\n",
    "sns.set(rc={'figure.figsize':(20,5)})\n",
    "sns.barplot(data=sales_state, x='Product_Category',y='Amount')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "7a76f86f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Product_ID', ylabel='Orders'>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "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('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": "markdown",
   "id": "69f39cf9",
   "metadata": {},
   "source": [
    "*Top selling products*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85201b88",
   "metadata": {},
   "source": [
    "# Conclusion:"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "*Married women age group 26-35yrs from UP, Maharastra and Karnataka working in IT, Heathcare and Aviation are more likely to buy products from Food , clothing and electronics category*"
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