Sales_Analysis_with_python.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0979edbb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: numpy in c:\\users\\hemku\\anaconda3\\lib\\site-packages (1.24.3)\n"
     ]
    }
   ],
   "source": [
    "# import python libraries\n",
    "! pip install numpy\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": 4,
   "id": "7fbfb436",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import csv file\n",
    "df = pd.read_csv('Sales Data.csv', encoding= 'unicode_escape')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b8db4a5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11251, 15)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "662c471c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": 8,
   "id": "29afa95b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop unrelated/blank columns\n",
    "df.drop(['Status', 'unnamed1'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for null values\n",
    "pd.isnull(df).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "65de74e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop null values\n",
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "14e4ff48",
   "metadata": {},
   "outputs": [],
   "source": [
    "# change data type\n",
    "df['Amount'] = df['Amount'].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "aa6752dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int32')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Amount'].dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a85bb3c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#rename column\n",
    "df.rename(columns= {'Marital_Status':'Shaadi'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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": 15,
     "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": 16,
   "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": 16,
     "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": 31,
   "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",
    "# ax = sns.countplot(x=\"Gender\", data=df, palette=\"hls\")\n",
    "\n",
    "sns.set_theme(style=\"whitegrid\")                          # Set a visually appealing background grid\n",
    "ax = sns.countplot(x=\"Gender\", data=df, palette=\"husl\")  # Use a vibrant color palette\n",
    "\n",
    "# Add clear and informative labels\n",
    "plt.xlabel(\"Gender\", fontsize=10)\n",
    "plt.ylabel(\"Count\", fontsize=10)\n",
    "plt.title(\"Gender Distribution\", fontsize=15, fontweight=\"bold\")\n",
    "\n",
    "for bars in ax.containers:\n",
    "    ax.bar_label(bars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "c5fd4566",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Gender Distribution based on purchasing power')"
      ]
     },
     "execution_count": 85,
     "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": [
    "# 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, palette=\"husl\")\n",
    "plt.title(\"Gender Distribution based on purchasing power\", fontsize=\"15\", fontweight=\"bold\")"
   ]
  },
  {
   "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",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "dcc927f9",
   "metadata": {},
   "source": [
    "### Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "88563dec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.countplot(data = df, x = 'Age Group', hue = 'Gender', palette=\"husl\")\n",
    "plt.title(\" Gender Distribution based on Age-Groups \", fontsize=\"15\", fontweight=\"bold\")\n",
    "\n",
    "for bars in ax.containers:\n",
    "    ax.bar_label(bars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "bfbe3937",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Age Distribution based on purchasing power')"
      ]
     },
     "execution_count": 87,
     "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": [
    "# 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, palette=\"husl\")\n",
    "plt.title(\"Age Distribution based on purchasing power\", fontsize=\"15\", fontweight=\"bold\")\n"
   ]
  },
  {
   "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": 92,
   "id": "dfac4f2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Total number of Orders from Top-10 States')"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vzz77TK1bt1alSpXum58tWzatXLlSgwYNMs7x7t+/32aZpL/39x4/prTPlZ7zvMmdU8uMCGWeYaNHj5avr68xrFiPHj3Uo0cPFSxYUHv27FGhQoX00Ucf6auvvtKkSZP0zTff6MMPP9TevXs1b948m7I2b96sZcuWadasWXJzc0vV9iMjI7Vw4UKdPHlSzZs3N6bv2LFDdevW1YYNG9S6dWstW7ZMCxYs0LBhw/TNN99o7ty5OnXqlKZMmSIpYSiQ119/XeHh4Vq5cqUCAgK0aNEim2398MMPeuutt9SpUyd99dVXGjt2rLZt26Zhw4ZJkq5du6b+/furQ4cO2rp1q+bMmaMDBw7cN/TG8uXLNWbMGH355ZcqWbKkBg0aZHMgiwS+vr42J6JiYmL07bff2uzA7tixQ2PHjlW3bt20bds2LVu2TNHR0Ro9erRNWZ9//rm8vb315ZdfatiwYVq6dKk2btyYpjJWr16tMWPGaPHixcYJiD/++EMff/yxvvnmG40cOVJBQUFau3atpISd3vnz52v8+PHavn273n77bc2bN8/mAOjChQs6ePCgFixYoJUrV+rKlSvGdmfPni0vLy/5+vradOtMaz2QMT788EPjipCmTZvazOvTp4+uXbumwMBAff3113r99dc1f/587dq1y1jmwoULunz5sjZu3KihQ4ca06dOnarRo0crKChIxYoV09tvv218H6T03iYerI0aNUqjR4/W5cuX1aNHD3l6eiooKEhffPGFqlSpopEjRybbjXjatGkKCgrSkiVLVL16dUVFRal79+5ydXXVqlWrtGXLFvn6+mry5Mn6+++/M/w1hbnKlSuns2fPKjIyUr169dLp06e1YMECrVu3TtWrV9crr7yiI0eOPHD9lStXqk2bNtqyZYu6dOmiGTNmaN++fSY+g8wpNb+FUup+ywYNGqQ//vhD8+fP15IlS/Tdd9/pwoUL921zzZo1mj59ujZs2CBnZ2cNGjRIktS6dWvdvXvX5orun376SdeuXVOrVq10+PBhvfnmm2rWrJk2b96s9evXy8PDQ2+//Xaa7ksSExOj77//Xl988YXNvt2936Op2d7EiRO1detWTZkyRatXr9bFixdt7t9w+fJldenSRcWKFdPnn3+u+fPnKzIyUp07dzZOyA4ZMkT58+fXhg0btH79elksFvXv39+mzs/S5+Pzzz+Xq6ur/vOf/6hJkyZydnbW6tWrbZZZsWKF8ubNq/Xr1xuvcWLId+7cOfXo0UMVKlTQxo0b1a9fP33yySf3bWf79u3KmzevgoKC9NFHH2nr1q337Xt///33Wrt2rSZPnqyrV6+m6TfuQWJjYxUcHKzly5erYcOGypYtW7q3t3DhQi1evFjDhw9XUFCQcuTIoa1btxrl3b59W/7+/oqLi9PKlSu1YsUKubu767///a8x7vq4ceMUHR2tlStX6ssvv1SpUqX0xhtv2AQCD9uPfRZ9+eWXunnzptatW6eRI0dq5cqV6tixo5o3b66goCA999xzeuedd4zAIKX9sPPnz8vf31958uTR2rVrNWfOHO3fv1/vvfeesc2HtceM+m671+XLlzVhwgTj85Yorfv6qfnMBQQEaNWqVcYxaNeuXTVu3DjjPrCPetyClD3sWGLkyJH68ccfNXXqVG3cuFG+vr7q27evzcWb6fkNTOpxtWM8mVJ7XHfo0CF5eHjoiy++0GuvvZZsL+UvvvhCrVu3VpYsWVJ97uFRjnHxZDp//rwuXryounXrPnCZIkWKGBcZpEVqz2Ok9Tzvg86pZTZO9q4AHp+cOXPKxcXFGFZMSuia7ejoaPxfpUoVNWvWTM8//7ykhA9q/fr177uCp0uXLipTpsxDtxccHCwvLy9JCUltVFSUsTPRrFkzY7lcuXLp9ddfN/4vXry4pkyZYqSjRYoUka+vr9EbZt++ffrnn3+0Y8cOFS9eXFLCWK9JuxTPnz9fHTt21CuvvGKUOX78eL322ms6f/68bt68qZiYGBUuXFhFihRRkSJFNH/+/PuuZBs1apRq1aolSXrzzTf17bff6uTJk6patWpKL3em4uvrq8DAQF26dEmFChXS3r175e7urooVKxrL5M6dWxMnTjTepyJFiqhTp04aO3asTVlNmzbVa6+9JkkqVqyYli9frsOHD6tjx46pLqNhw4Y2P0D16tWTt7e3PD09JUlFixbVypUrjXZ99uxZZc2aVUWLFlXhwoVVuHBh5c+fX4ULFzbKcHJy0tSpU5UzZ05JUufOnRUQEGA8tyxZssjFxcWmW2da64FHt27dOkVERKhGjRr67LPP1KlTJxUrVkySFB0drTZt2uill15SkSJFJCVckbFw4UIdO3ZMTZo0Mcp54403jPUSrxoZNGiQ6tSpYzxu06aNjh8/Li8vrxTf28Tv2Jw5cypnzpy6fv26+vfvr549e8piSbgeok+fPgoKCtLp06eVN29eoy4zZ87U+vXrtXTpUuMzFRUVpa5du6pLly7KkSOHJKl///5asGCBjh07pgoVKjyGVxdmSbzYYdeuXTp06JD27dtnfLcMGTJEv/76q5YvX27sxN7rlVdeMb4n33jjDS1ZskSHDx822i8ej9T8Fkop/x7++++/2rNnj5YuXSpvb29J0vTp0/XCCy/ct83Jkycb+2M9e/ZU//79FRYWJg8PDzVu3FibN29WmzZtJMm48ixPnjy6fPmyxowZo1dffdUoq2vXrurRo4fCwsIeOtxa4r6dlPBdlDVrVrVo0UJDhgyxWS7p9+jff//90O3lzJlTQUFBxtWYic8t6VV7q1evVv78+W1Ozs6cOVO1a9fW119/rfbt2+vs2bOqV6+eihYtKicnJ02ePFn//vuvrFar8V37rHw+YmNjjfs1JoYVDRs21BdffKGhQ4ca00qXLm28N6VKldKWLVv066+/Skr4zcybN6/Gjh0rR0dHlS5dWpcuXdIHH3xgs60qVaoYZRQvXlz16tXT4cOHbZbp0aOHSpYsKSnhxHNqf+Pu1apVK6NnWXR0tBwdHdWwYUNNmDAh3dvz8PDQihUr1LVrV7Vq1UpSwonVpO1ry5YtCg8P1/Tp05UlSxZJ0qRJk7R//36tW7dOAwYM0NmzZ1WuXDkVL15cWbNm1ejRo/Xyyy/L0dHRKOdh+7FPsi+//FLffPPNfdNjYmJUo0aNB67n5uam0aNHy9HRUSVKlND06dNVu3Zt4zPWpUsXfffdd7p69apy5syZ4n7YunXrlCtXLk2ZMsV4HyZOnGjTI+lh7dHR0THd321JLViwwBjWJTY2VjExMSpdurRmzpxpc3yQ1n39lD5zt2/f1tKlS/XRRx8Z3/nFixfXhQsXFBgYqFdfffWRj1vwcA87ljhz5oy++uorff7556pSpYokqXv37jp69KgCAwPVqFEjo5y0/Abe2y4zqh3jyZDS92tajusGDhxofK47duyopUuX6tChQ/Ly8tLp06d16NAhjR8/XlLqzz08yjEunkyJ4ci9Q44ljgKSqHDhwmnuMR4TE5Oqfby0nud90Dm1zIZQ5ink5OR03/AOiaxWq9HVOjXatGmjffv2acaMGTp9+rROnjypf//91zgpkKhEiRIpllW5cmVNmzZNkmSxWOTq6ioPD4/7lru3rMaNG+v333/Xxx9/rDNnzujkyZP6559/VKBAAUnS8ePHlStXLiOQkaQKFSrYXD135MgR/fHHHzZXpiVeoXXy5Ek1bNhQrVq1Ut++fVWoUCHVrVtXjRo1uq+bXKlSpYzHiSfJ6Kp5v8qVK6tYsWL6+uuv1b17d23dutU46E3k4+OjPHnyaO7cuTpz5oxOnTqlv//++762m/Q1lxK+zBOHl0htGfe2qS5dumjXrl364osvdPbsWR0/flznzp0zDuZbt26tDRs2qFmzZipfvrzq1aunpk2b2hzc5M2b19gBkhLaQ0ptIa31wKNL7JHn6empl19+WUOGDNGqVauMH3g/Pz99/fXXWrZsmc6cOaOjR48qNDT0vjaU3Hvy3HPPGY8Td5jv3r0rKe3vbbFixdShQwetXLlSJ06c0OnTp40roZKGw7/99psOHDigPHnyqGDBgsb0PHnyqEuXLtq6dauOHj2qM2fOGOs/6PcAT4/E+yYkdud+8cUXbebHxMTcN+xOUvd+j7q5uT10eWSM1PwWSin/lh0/flySjBM+UsJvUOLJnaQetp/SoUMH9e3bV5cvX1b27Nn17bffatasWZIS9pty5cqlRYsW6dSpUw/8DkrOpk2bJEkODg7KmjWr8ubNa3NSOlHS77+Utnfq1CndvXvX5jlnzZrV5kTEkSNHdPLkSZtQSJLu3LljDNk1ePBgTZ48WatXr1bt2rXVoEED+fr6GgeN975mia/b0/j52L17t65cuWJz0/UWLVpox44d2rJlixEClC5d2ma9nDlzGkMPHz9+XBUrVrR5/5I7AZ/cvtm9PbeSvt+p/Y1LzsKFC419fmdnZ3l4eCR7JWdathceHq4rV67YtC9Jql69utF2jhw5osjISOPCtERJ21f//v01bNgw7dixQ97e3qpfv75atGihrFmzPvS1ehraV+PGjfX222/fN33atGm6fv36A9crXry4TfvJli2bzXdV4mtz584d5cuXL8X9sGPHjqlSpUpGICMlfGf6+PgY/z+sPT7Kd1tSnTt3lr+/v6SE49jcuXPbHAckSuu+fkqfuRMnTujOnTsaMWKERo4caUxPDIaio6Mf23ELEjzsWCKxl3LXrl1t1rl79+59o4ek5TfwXhnVjvFkSOn7NbXHdR4eHjaf67Jly6pKlSratGmTvLy8tHHjRlWuXNkYNju1x6cZeYyLJ4O7u7sk3ff7PX78eOO3YMWKFTajhaRWavfx0nqeFwkIZZ5CuXLleuB9Xa5fv65cuXKluqxx48Zp69atatu2rRo1aqR+/fopMDDQ6LafKDU3eHRxcUlVeHNvWYsWLdLs2bPVvn17Pf/88/L399fOnTtt7htz75i5kmzCJ6vVqtdff13t2rW7b7nEK9anT5+uN998Uz/88IN++uknY3zOxLHWJdkcxD9s2/j/YVu6dOminTt3av369Tbzt2zZouHDh6tVq1aqWrWqOnbsqOPHj9935WNyJ3YSX/PUlpG0TcXHx6tv3746duyYXn75Zb300ksaMmSI3n33XWOZPHny6IsvvtChQ4e0d+9e7dmzR0uWLNGAAQOMYU+Sq1dK0loPPLquXbsavdumTJmiHj16aObMmRo2bJiioqL06quvKioqSr6+vmrTpo3effddm6vQEiX3HZfcSaH4+Ph0vbcnT57UK6+8oooVK6pevXp68cUX5e7urk6dOtks5+rqqvnz52vo0KF6//33jascr169qv/+979yd3fXiy++qDp16qhKlSrGFeZ4uv31118qWbKksmTJohw5chjj+Sb1sO7mD/sexeOV0m+hlPrfsnsD1uQusnnYe12/fn3ly5dPW7ZsMU4oNmjQQJJ04MAB9ejRQw0bNpS3t7datmypqKgovfnmmyk+x9Ts20m236Pp3d69+3a1a9e+r3esJOMkxauvvqrmzZtr9+7dxkVGs2fP1qZNm4wr956Vz0fi98LAgQPvm7dmzRojlElpaIp7n3tq29m9kgYTqf2NS07hwoVVtGjRx7K9hz1Xq9WqUqVK3Tdks5TwWywl9IL58ccf9eOPP+qnn37S4sWLNWvWLK1bt05ly5aV9PS2r+zZsyf72c6ePftDQ5mk4Umi5I6fJKVqP8zJyem+e3Dd62Ht8VG+25LKlStXmo9jU7s/+LB2mDhv5syZNidKEzk7O8vFxeWxHLcgwcOOJRLfn88++0zZs2e3We/edv8ov4EZ1Y7xZEjp+zW1x3XJHZ926NBBAQEBGj16tL788kv17NlTUtrOPWTkMS6eDMWKFVO+fPn0yy+/2AyjnDQAufc8cdLfpsRQLjmp3edKz3leEMo8lapUqaIlS5bozp07Ngco8fHxCg4Otrmy6N6d3KT/h4eHa/Xq1QoICLC56u7ff/81DkbMMG/ePPXv31+9e/c2pgUGBhpfEhUrVlRERIT++ecf4wDo1KlTxpXFUsJVA//++6/Nj98vv/yiZcuWady4cTp+/Li2bt2qUaNG6bnnnlO3bt20efNmDRs2TGFhYSY902eLr6+vFi5cqM8//1zFihW778rMxCHlErvTStLOnTslJbTVlA7A0lvGkSNHtHv3bq1bt07VqlWTlPAjc/bsWeNKvi+++EKRkZF69dVXVbNmTQ0cOFBjxozR1q1b7xuLPr1SUw88uqQHtnXr1pW/v78CAwNVt25d3bp1S3/99Zf27t1rnJy7fv26wsLCHumESXre29WrV8vDw8Pm5t2JV6okrUu5cuXk5eWl8ePH6/XXX5evr6+aNWumL7/8UtevX9c333xjnBBJ7Eb+NJz8wYOFhIRo586d6tWrl8qVK6fIyEjFxMQYv3eSNGbMGHl6esrPz8+ONUVyUvotlFL+LUsc7uzXX381hkKJiIjQ2bNn01QXR0dHtW3bVtu3b1fu3LnVpk0b40RdYGCgatWqZXOD2BUrVhh1yGgpba906dLKmjWrDh48aAyRERsbq6NHjxonx8qWLautW7eqUKFCxgmE69eva8SIEerevbvKlCmjuXPnqnfv3mrfvr3at2+vy5cv6z//+Y9++eUXm33bp921a9e0e/dutW/fXt27d7eZt2zZMn3++ef666+/UiynQoUKCgoKUkxMjPGa/vnnn49cv9T+xmWUlLaXJ08eFSpUSAcPHrQZqvTw4cPGb2i5cuX0xRdfKGfOnMawGbGxsRoyZIiaN2+uJk2aaPr06WrTpo1atGihFi1aKCoqSvXr19f3339v8x2N5P34448p7oeVKVNGX375peLi4ozvqx07duj9999Pdvife5n93ZZUavYHU/rMPffcc3JyctLFixdthqxcvny5Tpw4oQkTJphy3JKZPexYIvFzHhoaajNUWUBAgBwcHIz7ut0rre3Snu0Y5nuU47pWrVppypQpWrp0qa5cuWL00M6Icw+cv3h6OTo6qmvXrvrkk0/0yiuvGPvWSV26dEnS/19cERkZacx72DFHevfxUjrPiwTJX9aCJ1qHDh3k4OCgN998UwcPHtSFCxf066+/aujQobp06ZLNwZqrq6tCQ0ONIVFcXV1148YNnTp1Sjly5FDOnDm1c+dOnTlzRseOHdO7776rv/76y9QbyiWOw37ixAn9+++/CggI0Pbt24061KpVS9WqVdPw4cP122+/6c8//9Q777xjc3VKr169tH37ds2ePVunTp3Svn37NHLkSEVERChfvnzKkSOHVq1apalTpxrPdcuWLSpZsqTR1Q9pU6FCBZUoUUIzZsy476bGUsL7+uuvv+qvv/7S2bNntXTpUq1cuVKSUt2+0lNG3rx55eTkpG3btuncuXP6888/NWjQIF25csVY586dO/rwww+1adMmnT9/XsHBwfrll1/uGyLlYbJnz64LFy4oJCQk3fVAxnv77bdVpkwZDR8+3LgyZPPmzbpw4YKCg4P1xhtv6O7du4/0HqT2vXV1ddXJkycVHh6uggULKiQkRLt379aFCxe0fft2jRs3TlLybblBgwZq3bq1xo8fb6wfFRWlbdu26eLFi9qzZ48xvjrt6elx+/ZtXblyRVeuXNG5c+f07bff6vXXX1fRokXVvXt3NWjQQBUqVNCgQYO0b98+nTlzRh9++KE2bNiQ7Ml+2F9Kv4VSyr9lxYsXV/PmzTVhwgT99NNPOn78uIYPH56uz3aHDh30+++/66efflL79u1t6nDs2DEFBwfr/Pnz2rBhgzG02eP4Dklpe66urvLz89PHH3+s7du36+TJkxo7dqxNT+0uXbro5s2bGjJkiP7++28dPXpUQ4cO1R9//KGyZcsqd+7c+v777zVmzBj9/fffOnfunDHkTOXKlTP8OdnTF198odjYWL3++usqV66czV/fvn3l6Oio1atXp1jOK6+8oqioKI0aNUonT57Ud999Z3MSML3S+htnxvZ69eqlzz77TOvXr9epU6c0c+ZM/fHHH0YZrVu3Vq5cudS/f3/99ttvOnnypEaOHKndu3erbNmycnZ21u+//653331Xv/32m86fP6+goCDdunUrTfuLmVniMKwP2w/r0qWLwsPDNXbsWJ08eVLBwcGaNm2a6tWrZzNU9YOY/d2WVGr2B1P6zOXMmVOdO3fWzJkztWnTJp07d04bN27U1KlTjSArI45bkHpJjyXc3d31wgsvaOzYsdq5c6fOnTunwMBALViw4KEnqdPaLu3ZjmG+Rzmuy5kzp5o2bapPPvlETZo0MXo/ZMS5B85fPN1ef/11vfDCC+rSpYvmz5+vo0eP6vz589q1a5d69OihDRs2qHbt2sqfP7+KFSumTz/9VCdOnNCff/6pd99994E9rdO7j5fSeV4p5XNqmQGhzFMod+7cWrt2rfLkyaNBgwbppZde0oABA2S1WrV27VqbHYS2bdsqKipKrVq1UmhoqJo1a6Z8+fKpdevWOnLkiGbNmqXjx4/r5Zdf1uuvv66oqCgNGTJE//zzj27fvm3K8/noo48UHR2tDh06yM/PT8ePH9f48eMVFham8+fPy2KxaMGCBXruuefUo0cP9enTRy1atLC5GVTz5s0VEBCgnTt36uWXX9bbb7+tOnXqGDu9ZcqU0ezZs/Xzzz+rbdu26tKli5ycnLRo0aIHdrlHynx9fRUZGZns1ajvvvuu8ubNKz8/P3Xq1EnfffedPvroI0nS77//nqry01NGgQIFNGXKFO3atUstWrTQW2+9pQIFCqhbt276888/FR8fr//+978aMGCA5s6dK19fXw0aNEgNGjTQmDFjUv3cO3furOPHj6t169bJjvWbmnog42XNmlVTp07VjRs39PHHH2vkyJFavny5fH19NXLkSPn4+KhVq1apboPJSe1726NHD61cuVKjRo1S165d5evrawxhNG/ePA0ZMkRFihSxOUmU1KhRo2S1WjVx4kQ1b95cPXv21IcffihfX19NnjxZHTt2lI+PzwPXx5NnyZIlql+/vurXr6/WrVtr+vTpevHFF7Vq1Splz55djo6OWrJkiapWrarBgwerdevW2r9/v2bPnv3U3ZQ8M3nYb6GUut+yDz/8UI0aNdLgwYP16quvqkyZMukKFkqUKKHq1aurQoUKNkHewIEDVb16dfXt21dt27bV+vXrNXnyZLm4uDyW75DUbG/o0KHq0qWLJkyYoI4dOyo+Pt7mXn/FihXTypUrFRUVpS5dusjPz08ODg5atmyZPDw8bPbjunXrppYtW+rnn3/WwoULbe5D+CwICgpS3bp1kw1nixUrpqZNm2rLli02Vz0mp0CBAlq2bJlCQkLUrl07TZkyRf369Xvk+qXnN+5xb+/VV1/VsGHDNG/ePLVp00b//POPMcSblHBia+XKlfLw8NDrr7+ujh07GjdXT7w6ftasWSpWrJj69eun5s2ba+3atZo+ffp9995E8qpWrZrifliBAgW0ZMkSnTp1Su3atdOgQYPUsGHDZIctTI7Z321JpWZ/MDWfuZEjR6pbt276+OOP5evrq08++UT9+/fXgAEDJClDjluQekmPJUaMGKEZM2bopZde0tixY9WiRQtt2LBB77//vjp06PDAMtLaLu3ZjmG+Rz2ua9++vaKjo20uvsmIcw+cv3i6WSwWzZw5U5MmTVJwcLB69OhhXPSVJ08erVy5UpMnT5aDg4OmTp2qO3fuqG3btsb+eNL72SaV3n28lM7zSimfU8sMHOL5ZAEAAABPvfj4eDVr1ky9e/dO1f08AAAA8PTYtGmTZs6cqV27dnGBMR6LgIAAff3116kaRhSPhnvKAAAAAE+xu3fvateuXfr5558VGRn5wKHUAAAA8PT566+/9O+//2rmzJny8/MjkMFjcfjwYR06dOiBPWeQsQhlAAAAgKdYlixZNHHiREnS1KlT5erqaucaAQA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      "text/plain": [
       "<Figure size 2000x500 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':(20,5)})\n",
    "sns.barplot(data = sales_state, x = 'State',y= 'Orders', palette=\"husl\")\n",
    "plt.title(\"Total number of Orders from Top-10 States\", fontsize=\"15\", fontweight=\"bold\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "a66617bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Total Amount/Sales from Top-10 States')"
      ]
     },
     "execution_count": 93,
     "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": [
    "# 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':(20,5)})\n",
    "sns.barplot(data = sales_state, x = 'State',y= 'Amount', palette=\"husl\")\n",
    "plt.title(\"Total Amount/Sales from Top-10 States\", fontsize=\"15\", fontweight=\"bold\")"
   ]
  },
  {
   "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": 94,
   "id": "77e6635e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Marital Status')"
      ]
     },
     "execution_count": 94,
     "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": [
    "ax = sns.countplot(data = df, x = 'Marital_Status', palette=\"hls\")\n",
    "\n",
    "sns.set(rc={'figure.figsize':(7,5)})\n",
    "for bars in ax.containers:\n",
    "    ax.bar_label(bars)\n",
    "plt.title(\"Marital Status\", fontsize=\"15\", fontweight=\"bold\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "2d8c7b27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Marital Status based on Gender')"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "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', palette=\"hls\")\n",
    "plt.title(\"Marital Status based on Gender\", fontsize=\"15\", fontweight=\"bold\")"
   ]
  },
  {
   "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": 96,
   "id": "4deb41f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Occupation Distribution')"
      ]
     },
     "execution_count": 96,
     "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": [
    "sns.set(rc={'figure.figsize':(20,5)})\n",
    "ax = sns.countplot(data = df, x = 'Occupation', palette=\"hls\")\n",
    "\n",
    "for bars in ax.containers:\n",
    "    ax.bar_label(bars)\n",
    "plt.title(\"Occupation Distribution\", fontsize=\"15\", fontweight=\"bold\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "4eb620f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Top-10 Buyers (based on Occupation)')"
      ]
     },
     "execution_count": 102,
     "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')\n",
    "plt.title(\"Top-10 Buyers (based on Occupation)\", fontsize=\"15\",fontweight=\"bold\")"
   ]
  },
  {
   "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": 108,
   "id": "3e7e4d39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Product Category')"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(25,8)})\n",
    "ax = sns.countplot(data = df, x = 'Product_Category')\n",
    "\n",
    "for bars in ax.containers:\n",
    "    ax.bar_label(bars)\n",
    "plt.title(\"Product Category\", fontsize=\"15\", fontweight=\"bold\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "99f5c6f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Top-10 Product_Category')"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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M8OHDjbe3t/VZOHPmjKlQoYIZNmyYOXLkiNm/f7/p3LmzqVmzprlx44a5fv26GT16tKlWrZo5d+6ciY6ONu+//77p1KmTCQ4ONsePHzcDBgww5cqVMzdu3Ej2uiUlOe3zQY9Jd6/biRMnjJeXl/n777+NMcbs37/feHt7m9KlS5vIyEhjjDFTp041DRo0MMYYs2DBAhMQEGBWrFhhTp48af773/+awMBA8/nnn1vb1s/Pz4wePdocP37cHDlyxAwYMMD4+vpa7aV///7Gy8vLTJ061Rw9etT8+++/91zf1atXGy8vL1O8eHEzevRoExUVleR23L17tylVqpQpUaKEadeunfnmm2/Mtm3bEkzbtm1b4+XlZb788ktz4sQJs3btWlOqVCkzfvz4ZG1LY4z58ssvzcsvv2zWr19vTpw4YX766SdTpkwZM3fuXGOMMevWrTPly5c3mzZtMqdPnzYrV640JUuWNMuWLUtyPZ5kSbX1xMb379/f+gwnth81Julj8IQJE0yNGjWMMcZcvHjRVKtWzYwePdpcvnzZmj5uHxon/vEg7pjfvXt3c/LkSRMcHJzk/gQpc/LkSePl5WWWL1+erPLJ3eeULFnSDBs2zBw/ftw6lwsMDDRLly41R44cMV26dDGVKlWyjuu9e/c2jRo1Mlu3bjXHjh0zM2bMMCVLljQbNmwwxhgze/ZsU7NmTbNr1y5z6tQpM2fOHIe2di/ff/+98fLyMn5+fua7775L0bbZtm2bw7HOmKSPCbNmzTJlypQxN2/etKbp3bu3de6bFse2R8V///tf4+PjY/z8/EynTp3MlClTzN69e633PE6NGjWMr6+v+eabb0xISIj5/vvvjY+Pj1m5cqUxxpi9e/da7enw4cNm27ZtpkGDBqZZs2YmJibGYR6bN282+/btM+Hh4Q98XhF//xXn5s2bZvHixaZ48eJmxowZVrm7z7mTU+cOHTqYV1991fz111/m4MGD5q233jJeXl4J9n/x3d0mp02bZvz8/MyPP/5oTpw4YebOnWuKFy9uNm3alGCf6+y5KO4vse9Oe/fuNZUqVTILFiwwxiR97rJ//37j4+Njpk+fbk6ePGn++ecf07lzZ1O5cmVz+/ZtY0zSx+m73+9Dhw4l2EcuWLDAVK5cOVnndnh8jR492pQuXdpERESY8PBw4+vra0aMGGGNT2lbSs73Zjwekjouh4eHm549e5pWrVqZc+fOGWOSvlZwr2sv27ZtM8Ykfd4Ud1xr0KCB2bJliwkKCjJdunQxZcuWNdevXzfGJL0PjZtHmzZtzLFjx8zBgwfN2rVrjbe3tzl9+rS1/l9++aVp2rRp2mxsFyBkSafatm1rSpQoYUqXLu3w75133jHXrl0zJUuWNHPmzHGYZtSoUeall14ysbGx5osvvjBVq1a1TjiMMebChQvGz8/PzJs3z5rHwoULrfG3bt0ygYGBhCxO2L17t/Hy8jKbN29OVvn4F0yaN29uunfv7jB+3bp1xsvLyxw5csQYk/AgX6NGDdOjRw+HaRo1amSGDBlijEk8ZPn0008dygcEBJjJkycbY4x58803zQcffOAwftSoUfcNWa5fv266detmypUrZ15++WVTp04dc/XqVRMdHe3wpfluQUFBxsvLy/zwww/GGGOioqJMpUqVTIcOHRzKeXl5OZzoGGNMy5YtrXpOmDDB+Pr6mjNnzljjN27caLy8vExISIi5ePGimTJlinXR0Rhjbt++bby8vMzSpUuteXh5eZnw8HCrTN++fU3nzp0dlht3cSXuIJgaIUtin/fSpUs7vC/xl5tUPeM+43HbOc64ceOsC9fx22HcgX7WrFlW2d9//914eXmZoKAga1hsbKxp2rSp6dmzpzHmzjb09va2LgYaY8zMmTNNyZIlrdfxl9OqVasEbW3hwoVmxYoV5uDBg8bLy8usX7/eGhccHGz++eefe267kJAQ07BhQ1O7dm3j5+dn1evmzZsmOjr6ntPVqVPH9O7d22HYypUrE2z/uC9dc+bMSVCPVq1amYEDBxpjjBk7dqypUaOGwzLjLlqFhoaaiIgI4+fnl+D9aNq0qXWy/tVXX5mGDRs6jI+bLm773X0xo3HjxqZv377m1q1bxhhjrl27ZjZv3my9fhjudzyKW+aDHpMSW7eGDRuaiRMnGmOMmTJliuncubMpWbKk2blzp1WvcePGGWOMqVatmpk2bZrD8n/66SdTqlQpc+vWLXPy5Enz3XffWRdtjDHm2LFjDl+s+/fvbwICApLcHmvWrDEVK1Y0jRo1Ml5eXtaJa/x9zb0cOXLEDB482FSrVs14eXkZLy8vU6VKFbNmzRqrTNu2bc3rr7/uMF3Xrl3Nu+++a4wxSW7LGzdumFKlSplff/3VYR5ff/21tX2///57ExgYaI4fP26N/+uvvxxOsNOjpNp6ckOW+PtRY5I+Bt/d9u8+xiQ3ZIm/j0rO/gTJF3eet2nTJofhXbp0cWgr9evXN8aYZO9zKleu7HDcqFSpkunTp4/1Ou685ty5c+b48ePGy8srwcWafv36WW3w008/NQ0bNrS+9BtjzObNm82FCxfuuW4//PCDKV26tGnYsKHx8fGxjsHJ2acZk/CCdnKOCZcvXza+vr5mxYoV1jR+fn7WstPi2PYo2bt3r+nbt6+pVKmSdWyoU6eOdbwz5s5nvkuXLg7T9erVy7Rs2dIYY0zPnj1Ns2bNHMbHXUD+/fffrXl069bNocyDnlfEnQfG/xzEBcujR4+2PgOJnXMnVee4H7Ft2bLFGn/+/Hnj6+ubopClSpUqZuzYsQ5lpk2bZjZu3Ghtl7j5OXMuiqTVqFHDlCxZ0mojJUuWNF5eXqZFixYmPDw8WecuBw8etM654mzatMl4eXlZP4xJ6jgdV5f4x9imTZuawYMHW69btWplRo8e/XBWHI+kqKgoExgY6PCdtFu3bqZ8+fImIiLCGJPytpSc7814fCR1XL67LSR1rcCYxK+9xL9uc7/zprjjWvzvjP/884/x8vIyu3fvTtY+NG4ea9eutcbHfRbivpPExMSY6tWrJ6jLk4RnsqRjNWvWVN++fR2GeXp66ujRo4qKilK5cuUcxpUvX17ff/+9Ll68qODgYPn6+lq3aEt3Hp5dqFAhBQUF6dixY4qKilKpUqWs8RkzZlTx4sVTd6WeUHF9U1+5ciXF0wYHByfo/zMgIECSFBQUpCJFiiQ6XaFChRxe58iRw+piLDF3zydr1qyKioqSdOfZKnXq1HEYH9ee7qVfv346cuSIVqxYoZs3b6ply5bq06ePWrVqpR49eujnn39WsWLFEky3ePFiubu7W8tzd3dX3bp1tWDBAp08eVLPP/+8VbZChQoO0/r7+zvcEv7iiy/qmWeesV6XKVNG0p1t+uqrr6pNmzZatWqVDh06pBMnTuiff/6RJIcuhHLnzu3wUPaDBw/qxIkT1rziCwkJUcWKFe+5TR5EYp93SQ5dCMWXVD0zZ86sqKgolS5d2mHcBx98cN96vPDCC9bfwcHBypYtm7y8vKxhNptN5cuX159//mkNy5Mnj5566inrdbZs2ay2dbegoCDVq1fPYVj8B8Y1bNhQXbt2Vf78+fXSSy/p5ZdfdugmL76IiAi988478vX11bhx47R27Vr17t1bxYsX1+XLl7Vq1Sr9/vvvstsT9ryZM2fOBJ/X6tWra9myZZKks2fPql27dlZ3VW3atNH69ev1888/6+TJkwoODlZoaKhefPFFa1uVKFHC4fktZcuWtf4OCQnRrVu3Erwf5cqVs27FPXjwoEJCQhK8p7dv33a4fT2+999/Xx9++KHWrFmj8uXLq0qVKqpfv74yZsyYaHln3et4JOmhHJPutcwtW7aoW7du2rp1q1555RWFh4dr27Zt8vLy0u7du/Xhhx/q0qVLOnPmjL7++mtNnDjRmj42Nla3b9/WqVOnVKRIEb3++uuaO3eujhw5ouPHj1v7g7j3WHJs/4k5cOCAevTooV69eqlz584aMmSIRo0aJS8vL40aNUovvvjifZ/PUqRIEQ0fPlySdPz4cW3atEmzZ89Wz549Hbo8SWz/fvr0aUlKclseOXJEt2/fVv/+/R2644mOjlZkZKRu3bqlxo0ba/HixapTp468vb0VGBio2rVr69lnn73v+qcH92vryZVYO7rfMfhhidsfSc7tT3BvOXPmlJTwPG/YsGFWVxBz5szR+vXrJd151kBy9jnPP/+8w3EjU6ZMKliwoPU6bl9++/ZtHTx4UJISdOsQFRVlPc/pzTff1Nq1a1WtWjX5+voqMDBQ9erVU+7cuRNdr7Vr12rYsGEaM2aM6tevr65du6pv375asGCB3n77bb3++uvq06dPirZVco4JefLkUc2aNbVs2TI1bNhQv/76q7Jly6aqVatKSrtj26PCz89PY8aMkTFGwcHB2rhxo2bPnq1OnTppzZo11vt39zlo6dKl9fvvv0u6c2wIDAx0GO/t7a3s2bMrKCjIel5iUsc5Z7b9008/rTlz5ki6c54Y92wrm83mUO7uc+6k6nzz5k1Jcvi+midPHofPSFIuXbqkc+fOyd/f32F4/O5H4ytevHiKzkWRfG+88YbVrVJ0dLSOHz+ur776Sm3atNGnn36a5LlL8eLFlSNHDk2dOlXHjh275341pV5//XWNHz9eH3/8scLCwrRnzx7rXA1Ppo0bN+r8+fOqX7++Nax+/fpas2aNVq5cqebNm6d4nsn93ozHQ1LH5bslda3gfpJz3hSncOHC1t9Zs2aVdOc8MDnf/+LEr5O7u7saN26sn3/+WV26dNG2bdt04cIFNWzYMMl6P64IWdKxLFmyJHoiHNef+90nrnEXjt3d3WWMSTBeunMCkiFDhnsu092dJueMggULKk+ePNqzZ4/DwTrO8ePHNXz4cPXv3z9Bv8GJvVdxJ4r3ez8Sexi3uU9/+vEvyN1d3t3dPUXP47l+/brWrl2rr776Svnz55ckffXVV+rSpYv27t2rF198MdGAJSoqSsuXL1d0dLSqVKniUA9jjBYsWODwwNi71z82NtbhgvndbTluHdzc3HThwgW1bNlSOXPmVK1atVS5cmWVKlXK+pIZ5+6LZ7GxsWrUqJHVp31893uQ+oO61+f9XpKqZ9wF2ZSKvz3utR+JjY11eG8Sa1v34u7unug843z55Zfq1q2b9ayfuL67Z8+enaDstm3bdObMGS1ZskQZM2ZUgwYNFBwcrPHjxytLlixq2LBhogGLdCfc+PHHHxUZGWnVP/7Dp+N/vowx6tq1q4KCgtSoUSPVrVtXvXv31uDBgx3meffnL/42ivv7fp/R2NhYVapUSUOGDEkwLv5Fifhq166tP//8U3/++ae2bNmiadOm6euvv9bChQsT/Qw6637tM26dHvYxqUaNGpo+fbouXbqkXbt2afDgwbp48aK2b9+uQoUKKXfu3CpVqpR14jlw4EC99NJLCeaTP39+hYSEqHXr1ipRooQCAwNVq1Yt5cyZ0yHgk5K+mL5ixQq9+OKL1sPrP/74Yx09elTdunXT1atX9e677yY6XUREhL766iu9/vrr8vHxkXTnBPfFF19Uw4YNVa1aNW3atMk6PiS2f4+T1LaMez/Gjx/vcCIex8PDQ56envr555+1Z88ebd68WZs2bdKMGTPUvXv3VH/I8qMuqX3x3Z/hxIKSxNrR/Y7ByZHS5TqzP8G9FSxYUHnz5tWOHTscfhgT/4cecQ+Al5TsfU5i+797Hbfi2sC8efOsY9Xd07z44otavXq1duzYoc2bN2vdunWaPHmyRo0apaZNmyaY588//6wqVapYX6bHjRunVq1a6c0339TVq1eT9Zy9e9XzfscE6c6Fza5du+rChQtavny5GjdubI1Lq2Obq505c0ZTp05V586d9cwzz8hms1nPlatVq5bq16+vv/76S6+++qqkhOfFxhjrvb/fOVv8dpbUcc6Zbe/u7p6sc9i7l53cOt/9HSWx70fx5xX/OY9x+977nXveLSXnoki+HDlyOLSTIkWKKEeOHHrzzTeti9D3O3f566+/1KFDB1WvXl3ly5dXgwYNdPPmTXXr1s2hbHKOl/E1atRIn3/+uTZs2KDg4GCVKlXK4UI5njxxz7nr0aNHgnELFiywQpaUtKXkfm/Goy25x+X4knut4F6Se94k3fv7RHK+/8W5+0cTr7/+uqZPn67//e9/Wr58uWrVquXwA9onDQ++RwKFCxeWu7t7godo7dy5U3nz5lWOHDnk5eWlffv2KTIy0hp/4cIFnThxQkWKFFGRIkWUMWNG7dq1yxofHR395D7cKJXZ7XY1b95cS5Ys0dmzZxOMnzZtmv7++28999xzCcZ5eXk5vA+SrPf2XnexPGw+Pj7au3evw7C7X8fn4eEhd3d3XbhwwRpWtWpVtW7dWlevXr3nr71+//13Xbp0SUOGDNGyZcusfz///LP1YN/4bXb//v0O0//9998OD5w9duyYrl27Zr3es2ePpDu/QluxYoWuXLmiBQsW6L333lPt2rWth5fd78JWsWLFdPjwYb3wwgvWv5iYGI0aNUphYWH3nC6tJVXPF154QRkyZEiwDZs3b65p06Ylaxne3t4KDw9XcHCww/Bdu3apaNGiTtW7SJEiCer02Wef6b333tPff/+tzz77TIULF1b79u313Xff6bPPPtP27dsdfsERJ+5hmfEf5NyrVy8VLVpU165du++vDt944w1FR0fr22+/TXR8/IeTHzx4UBs3btSECRPUt29fNW7cWM8//7xOnjxptaXixYtr//7992y/L7zwgjw9PfX33387LGffvn3W38WKFVNISIjy589vvac5cuTQZ599Zr0H8U++IiMjNWrUKIWGhqp+/fr69NNPtWbNGtntduuXrWnhYRyTpIQnlv7+/sqRI4cmT56sHDlyqHDhwnrppZf0999/67ffflPNmjVls9mUO3du5c6dWydPnnT4PBw4cEDjx4+XJP3www/KnTu3Zs6cqU6dOql69erW/islF7o9PT0VHh5urUeGDBn05ZdfKiIiQp6enomGPHHTLV++XAsWLEgwLlOmTHJ3d7/nL83vltS2jHs//v33X4ftsXHjRk2fPl12u10///yzfvjhB5UrV049evTQwoUL1aJFC61atSrZ2yI9ypAhg8MxR5JOnjyZ5su9fv26Ll26dN9pkrM/QfK5ubnprbfe0rJly+55rhz/HOFh7XPii7vAfe7cOYfP9pIlS7R48WJJ0uzZs7V69WoFBgaqX79+WrFihSpXrnzPz7anp6fD8TVr1qwaPXq0rl69qvz58zt1h3tyjgmSVKVKFeXNm1eLFi3Srl271KxZM0mPzrEtLXh4eOjHH39M8HBv6f//hWqePHmsYXefP+3evVslSpSQdOfYcPc2P3TokK5fv37f7xOuPK9Iqs5x67Z7925rfHh4uMN+Ny6Mib+PPHHihPV31qxZ9fTTTyfYdj169NCnn36aoE4pPRfFw+Hj45Pkucv06dNVsWJFTZw4Ue3bt1dgYKC1343brzpznM6ePbtq166t1atXa/Xq1da+CE+mS5cuaePGjWrWrJnD9Yhly5apefPm2r9/vw4cOJDitpQa35uR9pJ7XI5/7EzOtYL7Se55U3Lmcb996L0UKVJEZcqU0apVq7Rhw4Ynfh9IyIIEsmXLppYtW2rChAlasWKFTpw4oXnz5mn+/Pnq0KGDbDabWrdurevXr6tv3746dOiQ9u3bp549eypnzpxq0KCBMmfOrLZt22rChAlavXq1QkJCNGTIkEQDAiRP165d9cILL+iNN97QsmXLdPLkSe3fv1+DBg3S4sWLNWLECGvHHF/Hjh21evVqffvttzp27Jg2bNigESNGqEaNGtaXoixZsuj06dMOF34fpk6dOum3337T999/rxMnTmjp0qXWbf+J8fDwUMuWLTVx4kT99ttvOnnypGbNmqXFixerdOnSmjFjhvWFP77FixcrX758atmypby8vBz+dejQQZcvX9Z///tfq/ysWbO0YsUKHTt2TJ9//rkOHTqkt99+2xofERGhfv36KTg4WFu3btXw4cNVv359FShQQPny5dPNmzf166+/6t9//9WmTZvUu3dvSXK4OHm3Dh066J9//tEnn3yiI0eOaO/everbt6+OHTuWrNs9nXXr1i2dP38+0X+JdQOXVD0zZcqktm3b6uuvv9a6det08uRJffXVVzpy5Ihq1Kgh6U5XZCEhIbp8+XKidQoMDJS3t7f69Omj7du3KyQkRMOGDVNwcLDD+5ASnTt31qpVqzR79mydPHlSK1eu1IIFC1S7dm1lzZpV8+fP15gxY3TixAkFBQVp5cqVevHFF62uWuILCAiQj4+P+vfvr927d+vIkSMaPny4Tp48qeLFi2vgwIEJvlTHKViwoEaNGqWZM2eqZ8+e2rp1q06fPq39+/friy++UMeOHVWwYEE999xzypMnj9zd3fXrr78qNDRU+/fvV69evXT+/HmrLbVu3Vo3b97URx99pJCQEG3YsMGh66pMmTKpXbt2mjBhgtauXatjx45p7NixDqFLmzZtdO3aNfXu3Vv//POPDh06pD59+mjfvn3WxbXMmTPr6tWrOnbsmGw2m/bu3avBgwfr77//1qlTp7RkyRLduHEj0W7kUsvDOCbdvW5RUVGy2WyqXr26fvjhB1WqVEnSneDFzc1Nq1evtn5hbbPZ9O6772rOnDmaM2eOTp48aXWB4+HhIQ8PD+XLl09nzpzRxo0bdfr0aa1evVpDhw6VdP/9wd2aN2+uGzduaODAgTpy5Ih2796tvn376umnn1a2bNnUpUuXRC/C2O129e3bVz/88IOGDBmiffv26dSpU1Z3aPny5bN+qZyUpLZltmzZ9MYbb2j8+PFatmyZQkNDtXTpUo0ZM8a6WHf79m19/vnnWrZsmU6dOqWdO3dqx44dadpuHkdly5bVli1btH79eoWGhmrChAmpElhkyZJFx48fty7KlylTRj/++KMOHDig4OBg9evXL8lfRSZnf4KUeffdd1WjRg21adNGkydP1qFDh3Tq1CmtX79eHTp00OLFi6191cPa58RXrFgx1ahRQ0OGDNG6desUGhqq6dOna8qUKVb3SRcvXtTw4cO1bt06nT59Wn/88YcOHjx4z8/2m2++qX/++UejRo3SsWPHtGXLFn388cfy8vLS1atX1bNnT0VERKSonsk5Jkh39otNmjTR5MmT5evra12E8vDweCSObWkhV65cevfddzV+/Hh99dVX+ueffxQaGqoNGzbo/fffV8WKFVW+fHmr/MqVKzV37lwdP35c06ZN05o1a6w7KNu3b69Dhw5p+PDhCgkJ0Y4dO9S3b1+VKFFClStXvmcdXHlekVSdn3/+eb366qsaPny4tmzZYu3/4n+GSpcuLbvdrvHjxys0NFS///67ZsyY4bCczp07a9asWdb3s3nz5mndunXWeUT8fW5Kz0WRfBEREdZ3m3Pnzmnnzp367LPP9PTTTyswMDDJc5f8+fMrKChIO3fu1KlTp7R48WJ9/fXXkv7//WpyjtN3H2OlO7/kXrt2rU6cOPFEd5ODO3dwRkdH6913301wPaJr165yc3PTDz/8kOK2lBrfm5H2kntczpw5s86dO6fQ0NBkXSuQ7n3tJbnnTfeTnO9/9/P6669r3rx58vDwcOhx5knEfWVI1KBBg5QzZ059+eWXunDhgl544QV98sknatmypaQ7FxDnzJmjsWPHqlWrVvLw8FBgYKDGjBlj9dvcp08fZcyYUcOHD9eNGzdUr149+pt9AJkyZdLcuXM1Y8YMTZ06Vf/++68yZsyokiVLatasWQmeLxKnXr16iomJ0ZQpU/R///d/ypUrlxo2bOhw++obb7yh/v37q3Hjxtq6detDr3u1atU0bNgwTZkyRV9++aV8fX31xhtvaO7cufec5qOPPlLmzJk1YsQIXblyRUWLFtXIkSPVoEEDjRkzRn/++adef/11q/yFCxf0559/qnv37oleHGrQoIHGjRunBQsWqHHjxpKkVq1a6fvvv9fhw4fl4+Oj6dOnW13tSHdOtr28vNSmTRu5u7urUaNGVl/6r776qg4cOKDPP/9c169f13PPPacWLVpo3bp12rdvn1q3bp3oepUuXdrqGqFZs2bKlCmTKlWqpP79+6eoW6yU+vXXX/Xrr78mOm7cuHEJntuTnHr27t1b7u7uGjp0qMLDw+Xt7a3vvvvOCu86dOigadOm6ejRoxo0aFCC5bq7u+v777/X559/ru7duysyMlIlS5bUzJkzEzxbJLlq1qypESNGaOrUqfriiy/03HPP6aOPPrK6Mfnmm280ceJEzZ8/X3a7XZUqVdLUqVMT/fWFu7u7pk6dqs8++0ydOnVSTEyMypYtq3nz5unFF19U9+7dtW/fPoe+vOOrV6+evLy8NHv27P+vvXuPqqpM4zj+BSVBMZGwkQmUUueUiB7y1pg5CN51XIBTuiwccnnD4aImBoIyZEKoiIACk6SjAs2YQTC51MhSnEaj1AnXLCYbS0fESyiQkTrc5g+XezriBdHSsd9nrfPHfve73/3ufc7aZ5/9nPd5Wbx4MadOncLW1tYI3Pj7+xupLV577TVSU1PJzs6mU6dOeHl5ERgYyM6dO2lsbORnP/sZGzZsIC4uDj8/P5ydnQkKCiI2NtbYX1hYGLW1tURHR3PhwgWGDh2Kj4+PEURzdXUlKyuLxMREJk+eTKtWrTCbzWzYsMEY5TBixAg2b97M+PHjycrKIjk5mfj4eIKCgjh//jyPPfYYiYmJFg9lfgx34jvp6mPr06cP3t7eFg8ubWxs6NevHwcOHLC4pk6dOpU2bdqwadMmEhISeOihh/D39zfmIJoyZQpffvml8XDGzc2NefPmkZKSQklJCUOGDGnWcbq4uLB+/XqWLVuGn58fbdu2xdvbm5UrV1JVVcXcuXM5ceLENUelPPvsszg5ORn5fGtqanBycsLHx4dly5Y1e96P5pzLyMhIHB0dSUlJ4cyZM3Tu3Jng4GAjzdlzzz1HdXU1aWlpnDx5kg4dOjBy5Mhrzgsl/xMYGMjx48cJDw/HysqKMWPGEBgYaPEv6zshICCAhIQEvvjiCwoKCvj9739PbGwskyZNwtHRkRdffPGmD76bcz2RW3PlQe62bdt4++232bhxI9988w1OTk7069ePrKwsY069O3XNuVpSUhJJSUnExMRQXV2Nq6srS5YsMe63goODqaurY8mSJVRUVNCpUycmT57MzJkzr9me2WwmPT2d5ORksrOzcXBwYMyYMYSGhlJaWkpsbCxnz5697vxw13Oz74Qr/P39ycjIaPKvyXvlu+3HMGfOHNzc3Ni8eTPZ2dlcvHgRZ2dnxowZ0+R98/X15b333iMhIQE3NzdWrVplpMH19PRk7dq1JCcn4+vri729PcOGDeOll166Yarou3lf0Zw+JyQksGzZMubOnUtDQwMTJ060GMnn6urKK6+8QkZGBps3b8bd3Z2FCxcSFBRk1HnhhRe4dOkSKSkpfP3117i5uZGUlGTcW1x9zb2Ve1FpvnXr1hkBMGtrazp27Ejfvn1ZsWIFdnZ2N713CQ0NpaKiwkiV3L17d+Li4ggPD6ekpIRu3bo163v66vcb4Je//CUdO3bkySefNO6l5P6Um5vLoEGDrjnCz9XVleHDh7N161aKiopu+bN0p383y93RnO9lX19fCgsLGTduHIWFhTd9VmBlZXXDZy/NvW+6kZtdQ29k9OjRvPrqq/j6+t4wbfX9wKqxpWPKRUSaqbi4GCcnJ4v8jRkZGWzZsoX333+/RW1eLy9pc5lMJuLj4687XDE1NZW8vDxjklmRq93uZ/BOKywspG/fvhZz+0ydOpXOnTsTFxd3F3smd8q99pkTEbkdP/Q17ZNPPmH69Ons2bNHcwXdhLe3N35+foSEhNztrojcd7777jsGDx7M6tWrr5v6VUTkflVWVsbw4cPZtm3bD5q95V6gkSwi8oP76KOPKCgoID4+ni5dulBaWsqGDRuYPHlyi9vUg0a52+61z+Abb7xBTk4OCxYswN7enp07d7Jv374maS3k/9e99pkTEbkdP9Q17ciRIxw+fJiMjAz8/PwUYBGRu6K6upp9+/axbds2fv7zn98wtZ6IyP3m5MmTlJSUkJOTwzPPPHPfB1hAQRYR+RH87ne/o6amhgULFnDu3DmcnZ0JDAw08jyLyO1bsWIFr732GoGBgVy8eJHu3buTnJxspKsQERH5KTh69CiRkZH07t3bSOsoIvJjq6urIyoqCkdHR1atWqU/y4jIT0plZSURERG4ublZzCd7P1O6MBERERERERERERERkRbQ7GoiIiIiIiIiIiIiIiItoCCLiIiIiIiIiIiIiIhICyjIIiIiIiIiIiIiIiIi0gIKsoiIiIiIyH3pXpt+8l7rj4iIiIiI3D4FWUREREREhICAAEwmk8WrV69eeHl5ERsbS3V19Q+y39zcXEwmE2VlZXe03Z07d/Lyyy+3ePtDhw4RHh6Ol5cXvXv3xsfHh+joaI4fP37LbZ06dYqZM2dy4sSJFvdHRERERETuTa3vdgdEREREROTe0LNnT2JiYozl2tpa/vGPf7By5UpKS0t58803sbKyuos9bL4//vGPLd42OzubuLg4Bg4cyEsvvcTDDz/Mv//9bzIzM3nvvfdYv3497u7uzW7vb3/7G7t27WLRokUt7pOIiIiIiNybFGQREREREREA7O3tMZvNFmX9+/enpqaGlJQUPvvssybr7zf79+9n6dKlPP/880RFRRnlAwcOxMfHB39/fyIjIykoKLiLvRQRERERkXuF0oWJiIiIiMgN9erVC4Dy8nICAgKYP38+oaGhPPnkk8yYMQOA8+fPEx8fz7Bhw/Dw8GDcuHFs2bLFop2GhgbS0tLw8vKiT58+zJ49u0kastTUVEwmU5M+mEwmUlNTjeWamhri4+MZMmQIZrMZf39/PvjgA+By6rPi4mKKi4sxmUx8/PHHzT7WN954g/bt2zNv3rwm6xwdHYmIiGDEiBF8++23ANTX1/P6668zbtw4evfujdlsZtKkSezduxe4nA4tMjISAB8fHyIiIoz23nrrLcaOHWukZUtNTaWurs5in3l5eYwZMwYPDw/Gjx/P3r176dmzJ7m5uUado0ePEhoaytNPP43ZbCYgIID9+/cb68vKyjCZTKxfv57Ro0czYMAAsrOzMZlM/PnPf7bY3+nTp3niiSfIy8tr9jkTEREREfkp00gWERERERG5oa+++goAV1dXALZt28aoUaNYs2YN9fX1XLx4kcmTJ1NRUUFISAiurq68//77REVFUVFRwaxZswBYvnw5GzduZNasWZjNZrZv305iYuIt96ehoYFp06Zx5MgRQkND6datG/n5+QQHB7N+/XpiYmIIDw8HICYmhu7duzer3cbGRv7617/i7e2NnZ3dNeuMGjXKYnnFihXk5OQwf/58TCYTp06dYs2aNYSFhbFr1y68vLwICgoiPT2d1atXGwGkP/zhDyQlJfHCCy8QGRlJaWkpqampnDx5kri4OADeeecdIiIiePbZZ4mMjKSkpITZs2dTX19v7P9f//oXzz33HF27diU6OhobGxs2btzIb3/7W9atW8eAAQOMuklJSSxevJgHH3yQXr16kZ+fT35+PhMnTjTq5OfnY2try8iRI5t1zkREREREfuoUZBEREREREeBykOH7Iymqq6spLi4mPT0ds9lsjGixtrZmyZIltG3bFoCcnBwOHz5MTk4Offv2BeCZZ56hrq6OtLQ0Jk2ahLW1NZs2bWLKlCmEhIQYdU6fPs2ePXtuqZ9FRUUcOHCAtLQ0fHx8AHjqqac4duwY+/btIywsDHt7e4BbSm9WWVnJpUuXcHFxafY2Z86cYe7cuQQEBBhltra2hISE8Pnnn+Pp6UmXLl0AeOKJJ3BxceH8+fOkp6czceJEoqOjARg8eDAODg5ER0fz4osv0qNHD5KTkxk6dCivvvoqcPl82djYWASmVq9ebQRW2rdvD4CXlxfjxo1j+fLlvPXWW0bdESNG8Jvf/MZYnjBhAosXL+b48eNGAO2dd95h9OjRxnsrIiIiIiI3pnRhIiIiIiICwCeffIK7u7vxGjRoEPPmzcPd3Z2VK1cak967uLhYPIQvLi7mkUceMQIsV4wfP55Lly7x2Wef8fe//53a2lojKHLF6NGjb7mfn376KTY2NgwdOtQos7Ky4s033yQsLOyW27vC2vryz6PvjxS5mcTERAIDAzl37hwHDx4kNzfXmK+ltrb2mtscPHiQCxcu4O3tTV1dnfHy9vYG4KOPPuLYsWOUl5c3GTkzduxYi+Xi4mKGDh1qBFgAWrduzdixYzl06BA1NTVG+S9+8YsmbdnZ2ZGfnw9ASUkJR44cwd/fv9nHLyIiIiLyU6eRLCIiIiIiAoC7uzuxsbHA5aBFmzZtcHZ2NkaFXOHk5GSxXF1d3aTs+/W++eYbo8zR0dGiTqdOnW65n1VVVTg4OBhBkTvFwcGBdu3aUV5eft063333Hf/5z39wcHAA4NChQ8TGxnLo0CFsbW3p3r07jzzyCHB5ZND1+g8Y89lc7cyZM5w7dw6Ahx56yGLd1efrRue+sbHRmDvmStn32dvbM2rUKAoKCggODiYvL4+uXbvSr1+/6xy9iIiIiIhcTUEWEREREREBoF27dnh4eNzydh06dODYsWNNyr/++msAOnbsaJSdPXuWxx57zFi+EnC44spomfr6elq1agVgMRoDoH379lRVVdHQ0GARaCktLaWurq5Fx3DF4MGD+fjjj7l06RJt2rRpsj43N5elS5eSk5NDjx49mDZtGiaTiXfffZdu3bphbW3N7t272bFjx3X38eCDDwKX53Nxc3Nrsv77wZCzZ89arLt6uUOHDlRUVDRp4/vn/syZM9fty4QJE8jLy6OkpIQdO3ZYpD0TEREREZGbU7owERERERG5Lf379+fEiRPs37/forygoAAbGxt69+6Np6cntra2bN++3aLOhx9+aLF8ZdTMyZMnjbIDBw5Y1OnXrx+1tbXs3r3bKGtsbCQqKor09HSAFo9ymTp1KlVVVSQlJTVZd/bsWTIzM+natStms5kvv/ySqqoqpkyZQo8ePYx9FhUVAdDQ0HDNvvTp0wcbGxtOnz6Nh4eH8boy30pZWRmdO3emS5cuFBYWWmx7dfCmf//+fPjhh5w/f94oq6+vZ+vWrXh4ePDAAw/c8Hj79++Pm5sby5cvp7KyEl9f3+adKBERERERATSSRUREREREbpO/vz85OTkEBwcTGhqKq6srH3zwAW+//TbBwcHGyI3Zs2ezatUq7OzseOqpp9i9e3eTIMuvfvUr4uPjWbRoEdOnT+fUqVOsXr2adu3aGXW8vLzw9PQkMjKSsLAwunbtyl/+8hcOHz7MokWLgMujRQ4ePMjevXvp2bMnHTp0aNaxmM1mwsLCWLVqFUeOHMHPz4+OHTvyxRdfsG7dOmpqanj99dexsrLi0Ucfxd7enoyMDFq3bk3r1q3ZsWMHW7ZsAeDChQtGXwAKCwsZMmQI3bp1Y9q0aSQnJ/Ptt98ycOBATp8+TXJyMlZWVjz++ONYWVkRGhrK/PnziYmJYfjw4fzzn/9kzZo1wP8CN8HBwRQVFTFlyhRmzJjBAw88QFZWFsePHyczM7NZxzxhwgQSExN5+umncXZ2btY2IiIiIiJymUayiIiIiIjIbbGzs2PTpk14e3uTkpJCUFAQ+/fvZ+nSpYSEhBj1Zs6cycKFC9m+fTtBQUF8/vnnvPzyyxZtPfrooyQkJFBeXs6MGTPYsGEDS5Ys4eGHHzbqtGrVirVr1zJq1ChSU1OZPXs2X331FZmZmXh6egLw/PPPY2Njw/Tp042RJc0VFBRkBFLi4+OZMWMGmzZtYsiQIeTn5xsTyLdv3560tDQaGxsJCwtjwYIFlJeXk5WVRbt27fj0008BGDhwIIMGDSIxMZGEhAQA5syZQ0REBIWFhUyfPp3ly5fTt29fsrKyjEnsf/3rX/PKK6+wd+9eZs2axdatW4mKigKgbdu2APTo0YOcnBycnJxYuHAh4eHhNDY2snHjRgYNGtSs4/Xy8gLQhPciIiIiIi1g1Xi92RhFRERERETkrnn33Xfp2bOnxRw2u3btYubMmeTn5/P444/fkf2sXbuWzMxM9uzZc9P0YiIiIiIiYknpwkRERERE5L7W0NBgzI9yI61b31s/jwoKCkhKSmLOnDk4Oztz9OhRUlJSGDBgwB0JsOTl5XH48GFycnKMVGMiIiIiInJrNJJFRERERETuaxEREeTl5d203s6dO3FxcfkRetQ8lZWVJCYmUlRUxLlz53BycmLkyJGEhoZazFHTUvHx8fzpT39i2LBhxMfHK8giIiIiItICCrKIiIiIiMh9raysjMrKypvWM5lMCjSIiIiIiMgtUZBFRERERERERERERESkBazvdgdERERERERERERERET+HynIIiIiIiIiIiIiIiIi0gIKsoiIiIiIiIiIiIiIiLSAgiwiIiIiIiIiIiIiIiItoCCLiIiIiIiIiIiIiIhICyjIIiIiIiIiIiIiIiIi0gIKsoiIiIiIiIiIiIiIiLSAgiwiIiIiIiIiIiIiIiIt8F90GOL2o3NpsgAAAABJRU5ErkJggg==",
      "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', palette=\"hls\")\n",
    "plt.title(\"Top-10 Product_Category\", fontsize=\"15\", fontweight=\"bold\")"
   ]
  },
  {
   "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": 117,
   "id": "f33d23c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Top-10 Most sold-out Products')"
      ]
     },
     "execution_count": 117,
     "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', palette=\"hls\")\n",
    "plt.title(\"Top-10 Most sold-out Products\", fontsize=\"15\", fontweight=\"bold\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "17d2bb43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Product_ID'>"
      ]
     },
     "execution_count": 116,
     "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": "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}