(Task -1-A) Retail EDA  Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "36fbf739",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy.stats as stas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "9485b888",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Transaction ID        Date Customer ID  Gender  Age Product Category  \\\n",
      "0                 1  2023-11-24     CUST001    Male   34           Beauty   \n",
      "1                 2  2023-02-27     CUST002  Female   26         Clothing   \n",
      "2                 3  2023-01-13     CUST003    Male   50      Electronics   \n",
      "3                 4  2023-05-21     CUST004    Male   37         Clothing   \n",
      "4                 5  2023-05-06     CUST005    Male   30           Beauty   \n",
      "..              ...         ...         ...     ...  ...              ...   \n",
      "995             996  2023-05-16     CUST996    Male   62         Clothing   \n",
      "996             997  2023-11-17     CUST997    Male   52           Beauty   \n",
      "997             998  2023-10-29     CUST998  Female   23           Beauty   \n",
      "998             999  2023-12-05     CUST999  Female   36      Electronics   \n",
      "999            1000  2023-04-12    CUST1000    Male   47      Electronics   \n",
      "\n",
      "     Quantity  Price per Unit  Total Amount  \n",
      "0           3              50           150  \n",
      "1           2             500          1000  \n",
      "2           1              30            30  \n",
      "3           1             500           500  \n",
      "4           2              50           100  \n",
      "..        ...             ...           ...  \n",
      "995         1              50            50  \n",
      "996         3              30            90  \n",
      "997         4              25           100  \n",
      "998         3              50           150  \n",
      "999         4              30           120  \n",
      "\n",
      "[1000 rows x 9 columns]\n"
     ]
    }
   ],
   "source": [
    "df=pd.read_csv(r\"C:\\Users\\VAMSI\\OneDrive\\Desktop\\Oyasis Infobyte\\retail_sales_dataset (EDA-1).csv\")\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "abe5c808",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000 entries, 0 to 999\n",
      "Data columns (total 9 columns):\n",
      " #   Column            Non-Null Count  Dtype \n",
      "---  ------            --------------  ----- \n",
      " 0   Transaction ID    1000 non-null   int64 \n",
      " 1   Date              1000 non-null   object\n",
      " 2   Customer ID       1000 non-null   object\n",
      " 3   Gender            1000 non-null   object\n",
      " 4   Age               1000 non-null   int64 \n",
      " 5   Product Category  1000 non-null   object\n",
      " 6   Quantity          1000 non-null   int64 \n",
      " 7   Price per Unit    1000 non-null   int64 \n",
      " 8   Total Amount      1000 non-null   int64 \n",
      "dtypes: int64(5), object(4)\n",
      "memory usage: 70.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "91b08062",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Since There are NO  null values But Data types of coloumns are wrong which results in in-accurate results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "2caba2bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date                datetime64[ns]\n",
       "Customer ID                 object\n",
       "Gender                    category\n",
       "Age                          int64\n",
       "Product Category          category\n",
       "Quantity                     int64\n",
       "Price per Unit               int64\n",
       "Total Amount                 int64\n",
       "Month                       object\n",
       "dtype: object"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8928c2ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "#chainging Date, gender, product category data types into convinient necessary data types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "d631449f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    November\n",
       "1    February\n",
       "2     January\n",
       "3         May\n",
       "4         May\n",
       "Name: Month, dtype: object"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Date']=pd.to_datetime(df['Date'])\n",
    "df['Month'] = df['Date'].dt.month_name()\n",
    "df['Gender']=df['Gender'].astype('category')\n",
    "df['Product Category']=df['Product Category'].astype('category')\n",
    "df['Month'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "f3245c97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date                datetime64[ns]\n",
       "Customer ID                 object\n",
       "Gender                    category\n",
       "Age                          int64\n",
       "Product Category          category\n",
       "Quantity                     int64\n",
       "Price per Unit               int64\n",
       "Total Amount                 int64\n",
       "Month                       object\n",
       "dtype: object"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1283460b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#identifying and dropping duplicates.\n",
    "#deleting unwanted coloumns from the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "acff35a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "3d8b11c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop_duplicates(inplace=True)\n",
    "df.drop(['Transaction ID'],axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "51c157c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1000 entries, 0 to 999\n",
      "Data columns (total 9 columns):\n",
      " #   Column            Non-Null Count  Dtype         \n",
      "---  ------            --------------  -----         \n",
      " 0   Date              1000 non-null   datetime64[ns]\n",
      " 1   Customer ID       1000 non-null   object        \n",
      " 2   Gender            1000 non-null   category      \n",
      " 3   Age               1000 non-null   int64         \n",
      " 4   Product Category  1000 non-null   category      \n",
      " 5   Quantity          1000 non-null   int64         \n",
      " 6   Price per Unit    1000 non-null   int64         \n",
      " 7   Total Amount      1000 non-null   int64         \n",
      " 8   Month             1000 non-null   object        \n",
      "dtypes: category(2), datetime64[ns](1), int64(4), object(2)\n",
      "memory usage: 64.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "446b168c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Price per Unit</th>\n",
       "      <th>Total Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1000.00000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>41.39200</td>\n",
       "      <td>2.514000</td>\n",
       "      <td>179.890000</td>\n",
       "      <td>456.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13.68143</td>\n",
       "      <td>1.132734</td>\n",
       "      <td>189.681356</td>\n",
       "      <td>559.997632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>29.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>60.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>42.00000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>135.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>53.00000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>900.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>64.00000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age     Quantity  Price per Unit  Total Amount\n",
       "count  1000.00000  1000.000000     1000.000000   1000.000000\n",
       "mean     41.39200     2.514000      179.890000    456.000000\n",
       "std      13.68143     1.132734      189.681356    559.997632\n",
       "min      18.00000     1.000000       25.000000     25.000000\n",
       "25%      29.00000     1.000000       30.000000     60.000000\n",
       "50%      42.00000     3.000000       50.000000    135.000000\n",
       "75%      53.00000     4.000000      300.000000    900.000000\n",
       "max      64.00000     4.000000      500.000000   2000.000000"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "28899273",
   "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>Date</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Product Category</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Price per Unit</th>\n",
       "      <th>Total Amount</th>\n",
       "      <th>Month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023-11-24</td>\n",
       "      <td>CUST001</td>\n",
       "      <td>Male</td>\n",
       "      <td>34</td>\n",
       "      <td>Beauty</td>\n",
       "      <td>3</td>\n",
       "      <td>50</td>\n",
       "      <td>150</td>\n",
       "      <td>November</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023-02-27</td>\n",
       "      <td>CUST002</td>\n",
       "      <td>Female</td>\n",
       "      <td>26</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>2</td>\n",
       "      <td>500</td>\n",
       "      <td>1000</td>\n",
       "      <td>February</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-01-13</td>\n",
       "      <td>CUST003</td>\n",
       "      <td>Male</td>\n",
       "      <td>50</td>\n",
       "      <td>Electronics</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023-05-21</td>\n",
       "      <td>CUST004</td>\n",
       "      <td>Male</td>\n",
       "      <td>37</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>1</td>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>May</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023-05-06</td>\n",
       "      <td>CUST005</td>\n",
       "      <td>Male</td>\n",
       "      <td>30</td>\n",
       "      <td>Beauty</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>100</td>\n",
       "      <td>May</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>2023-05-16</td>\n",
       "      <td>CUST996</td>\n",
       "      <td>Male</td>\n",
       "      <td>62</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>May</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>2023-11-17</td>\n",
       "      <td>CUST997</td>\n",
       "      <td>Male</td>\n",
       "      <td>52</td>\n",
       "      <td>Beauty</td>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
       "      <td>90</td>\n",
       "      <td>November</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>2023-10-29</td>\n",
       "      <td>CUST998</td>\n",
       "      <td>Female</td>\n",
       "      <td>23</td>\n",
       "      <td>Beauty</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>100</td>\n",
       "      <td>October</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>2023-12-05</td>\n",
       "      <td>CUST999</td>\n",
       "      <td>Female</td>\n",
       "      <td>36</td>\n",
       "      <td>Electronics</td>\n",
       "      <td>3</td>\n",
       "      <td>50</td>\n",
       "      <td>150</td>\n",
       "      <td>December</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>2023-04-12</td>\n",
       "      <td>CUST1000</td>\n",
       "      <td>Male</td>\n",
       "      <td>47</td>\n",
       "      <td>Electronics</td>\n",
       "      <td>4</td>\n",
       "      <td>30</td>\n",
       "      <td>120</td>\n",
       "      <td>April</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date Customer ID  Gender  Age Product Category  Quantity  \\\n",
       "0   2023-11-24     CUST001    Male   34           Beauty         3   \n",
       "1   2023-02-27     CUST002  Female   26         Clothing         2   \n",
       "2   2023-01-13     CUST003    Male   50      Electronics         1   \n",
       "3   2023-05-21     CUST004    Male   37         Clothing         1   \n",
       "4   2023-05-06     CUST005    Male   30           Beauty         2   \n",
       "..         ...         ...     ...  ...              ...       ...   \n",
       "995 2023-05-16     CUST996    Male   62         Clothing         1   \n",
       "996 2023-11-17     CUST997    Male   52           Beauty         3   \n",
       "997 2023-10-29     CUST998  Female   23           Beauty         4   \n",
       "998 2023-12-05     CUST999  Female   36      Electronics         3   \n",
       "999 2023-04-12    CUST1000    Male   47      Electronics         4   \n",
       "\n",
       "     Price per Unit  Total Amount     Month  \n",
       "0                50           150  November  \n",
       "1               500          1000  February  \n",
       "2                30            30   January  \n",
       "3               500           500       May  \n",
       "4                50           100       May  \n",
       "..              ...           ...       ...  \n",
       "995              50            50       May  \n",
       "996              30            90  November  \n",
       "997              25           100   October  \n",
       "998              50           150  December  \n",
       "999              30           120     April  \n",
       "\n",
       "[1000 rows x 9 columns]"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "5e12048d",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Price per Unit</th>\n",
       "      <th>Total Amount</th>\n",
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       "    <tr>\n",
       "      <th>Product Category</th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Beauty</th>\n",
       "      <td>2.511401</td>\n",
       "      <td>184.055375</td>\n",
       "      <td>467.475570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Clothing</th>\n",
       "      <td>2.547009</td>\n",
       "      <td>174.287749</td>\n",
       "      <td>443.247863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Electronics</th>\n",
       "      <td>2.482456</td>\n",
       "      <td>181.900585</td>\n",
       "      <td>458.786550</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Quantity  Price per Unit  Total Amount\n",
       "Product Category                                        \n",
       "Beauty            2.511401      184.055375    467.475570\n",
       "Clothing          2.547009      174.287749    443.247863\n",
       "Electronics       2.482456      181.900585    458.786550"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2=df.groupby(\"Product Category\")[['Gender','Quantity','Price per Unit','Total Amount']].mean()\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "9331882f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df3=df2.transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "4532ff00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df3.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "c763e014",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df3.boxplot()          #using boxplot we can easily identify the outliers. there are no outliers as of now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "9b64577e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Gender'>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Gender'].value_counts().plot(kind='pie',autopct='%2f%%')         \n",
    "\n",
    "#The Gender percentage of the people that shows the difference about who shopped more Frequently."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "a39ff86e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Product Category'].value_counts().plot(kind='bar', color='black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "2f73f980",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1000.000000\n",
       "mean        2.514000\n",
       "std         1.132734\n",
       "min         1.000000\n",
       "25%         1.000000\n",
       "50%         3.000000\n",
       "75%         4.000000\n",
       "max         4.000000\n",
       "Name: Quantity, dtype: float64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Quantity'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "5a8a21b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3]),\n",
       " [Text(0, 0, '4'), Text(1, 0, '1'), Text(2, 0, '2'), Text(3, 0, '3')])"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Quantity'].value_counts().plot(kind='bar')\n",
    "plt.xlabel('Quantity')\n",
    "plt.xticks(rotation = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "5f4aff76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1000.000000\n",
       "mean      179.890000\n",
       "std       189.681356\n",
       "min        25.000000\n",
       "25%        30.000000\n",
       "50%        50.000000\n",
       "75%       300.000000\n",
       "max       500.000000\n",
       "Name: Price per Unit, dtype: float64"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Price per Unit'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "1aae23aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEGCAYAAACKB4k+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAUAUlEQVR4nO3df+xdd33f8ecLJxhKaIkbx/JsZzatB3WiEtC3oU0YggQ1YWV1VjXUXWFu59atmrawrjC7SGunylo2VRXT1DAsoHUL1JiUKC7ZAq5JSisgjk0SEifx4pGQfGUvNmEIKMg06Xt/3OMPN/b3a39j+3zv177Ph3R1zvmcH/f9uV/5vnzOufdzU1VIkgTwglEXIEmaOwwFSVJjKEiSGkNBktQYCpKk5rxRF3A6Lrroolq+fPmoy5Cks8qePXu+WlULp1p3VofC8uXL2b1796jLkKSzSpKvTLfOy0eSpMZQkCQ1hoIkqTEUJEmNoSBJagwFSVJjKEiSml5DIcnLktyS5JEkDyf5iSQLkuxI8mg3vXBo+41J9ifZl+TaPmuTJB2v7zOF/wbcUVWvBF4FPAxsAHZW1UpgZ7dMklXAGuBS4Drg5iTzeq5PkjSkt1BI8v3A64EPAlTVd6vq68BqYEu32Rbg+m5+NbC1qo5U1WPAfuCKvuoDWLLsEpLMymPJskv67IoknRF9DnPxcuAw8CdJXgXsAd4BLKqqgwBVdTDJxd32S4AvDO0/2bU9R5L1wHqASy45vTfaA5NP8nPv/9xpHWOmPvarV87K80jS6ejz8tF5wGuA91XVq4G/p7tUNI1M0Xbcb4VW1eaqmqiqiYULpxzPSZJ0ivoMhUlgsqru7pZvYRASTyVZDNBNDw1tv2xo/6XAgR7rkyQdo7dQqKr/CzyZ5BVd0zXAQ8B2YG3Xtha4rZvfDqxJMj/JCmAlsKuv+iRJx+t76OzfBD6S5IXAl4FfYhBE25KsA54AbgCoqr1JtjEIjmeAG6vq2Z7rkyQN6TUUquo+YGKKVddMs/0mYFOfNUmSpuc3miVJjaEgSWoMBUlSYyhIkhpDQZLUGAqSpMZQkCQ1hoIkqTEUJEmNoSBJagwFSVJjKEiSGkNBktQYCpKkxlCQJDWGgiSpMRQkSY2hIElqDAVJUmMoSJIaQ0GS1BgKkqTGUJAkNYaCJKkxFCRJTa+hkOTxJA8kuS/J7q5tQZIdSR7tphcObb8xyf4k+5Jc22dtkqTjzcaZwhur6vKqmuiWNwA7q2olsLNbJskqYA1wKXAdcHOSebNQnySpM4rLR6uBLd38FuD6ofatVXWkqh4D9gNXzH55kjS++g6FAj6dZE+S9V3boqo6CNBNL+7alwBPDu072bU9R5L1SXYn2X348OEeS5ek8XNez8e/qqoOJLkY2JHkkRNsmyna6riGqs3AZoCJiYnj1kuSTl2vZwpVdaCbHgJuZXA56KkkiwG66aFu80lg2dDuS4EDfdYnSXqu3kIhyUuSvPToPPCTwIPAdmBtt9la4LZufjuwJsn8JCuAlcCuvuqTJB2vz8tHi4Bbkxx9no9W1R1J7gG2JVkHPAHcAFBVe5NsAx4CngFurKpne6xPknSM3kKhqr4MvGqK9qeBa6bZZxOwqa+aJEkn5jeaJUmNoSBJagwFSVJjKEiSGkNBktQYCpKkxlCQJDWGgiSpMRQkSY2hIElqDAVJUmMoSJIaQ0GS1BgKkqTGUJAkNYaCJKkxFCRJjaEgSWoMBUlSYyhIkhpDQZLUGAqSpMZQkCQ1hoIkqTEUJElN76GQZF6Se5N8sltekGRHkke76YVD225Msj/JviTX9l2bJOm5ZuNM4R3Aw0PLG4CdVbUS2Nktk2QVsAa4FLgOuDnJvFmoT5LU6TUUkiwFfgr4wFDzamBLN78FuH6ofWtVHamqx4D9wBV91idJeq6+zxTeC7wb+MehtkVVdRCgm17ctS8BnhzabrJrkyTNkt5CIclbgENVtWemu0zRVlMcd32S3Ul2Hz58+LRqlCQ9V59nClcBP53kcWArcHWSDwNPJVkM0E0PddtPAsuG9l8KHDj2oFW1uaomqmpi4cKFPZYvSeOnt1Coqo1VtbSqljO4gfyZqnobsB1Y2222Fritm98OrEkyP8kKYCWwq6/6JEnHO28Ez3kTsC3JOuAJ4AaAqtqbZBvwEPAMcGNVPTuC+iRpbM1KKFTVXcBd3fzTwDXTbLcJ2DQbNUmSjuc3miVJjaEgSWoMBUlSYyhIkhpDQZLUGAqSpMZQkCQ1hoIkqTEUJEmNoSBJagwFSVIzo1BIctVM2iRJZ7eZnin89xm2SZLOYiccJTXJTwBXAguT/PbQqu8H5vVZmCRp9p1s6OwXAhd02710qP0bwM/2VZQkaTROGApV9TfA3yT506r6yizVJEkakZn+yM78JJuB5cP7VNXVfRQlSRqNmYbCx4H/AXwA8CcyJekcNdNQeKaq3tdrJZKkkZvpR1L/KsmvJ1mcZMHRR6+VSZJm3UzPFNZ203cNtRXw8jNbjiRplGYUClW1ou9CJEmjN6NQSPJvpmqvqj87s+VIkkZpppePfmxo/kXANcAXAUNBks4hM7189JvDy0l+APjzXiqSJI3MqQ6d/W1g5ZksRJI0ejO9p/BXDD5tBIOB8H4E2HaSfV4EfBaY3z3PLVX1e91HWT/G4NvRjwNvrar/1+2zEVjH4Atyv1VVn3qe/ZEknYaZ3lP4w6H5Z4CvVNXkSfY5AlxdVd9Kcj7wd0n+F/AzwM6quinJBmAD8B+SrALWAJcC/wT46yT/rKr8BrUkzZIZXT7qBsZ7hMFIqRcC353BPlVV3+oWz+8eBawGtnTtW4Dru/nVwNaqOlJVjwH7gStm1g1J0pkw019eeyuwC7gBeCtwd5KTDp2dZF6S+4BDwI6quhtYVFUHAbrpxd3mS4Anh3af7NqOPeb6JLuT7D58+PBMypckzdBMLx+9B/ixqjoEkGQh8NfALSfaqbv0c3mSlwG3JrnsBJtnqkNMcczNwGaAiYmJ49ZLkk7dTD999IKjgdB5+nnsS1V9HbgLuA54KsligG569LiTwLKh3ZYCB2b6HJKk0zfTN/Y7knwqyS8m+UXgduB/nmiHJAu7MwSSvBh4E4P7Etv53lhKa4HbuvntwJok85OsYPCR113Poy+SpNN0st9o/mEG9wDeleRngNcxuMzzeeAjJzn2YmBLknkMwmdbVX0yyeeBbUnWAU8wuE9BVe1Nsg14iMEnnG70k0eSNLtOdk/hvcDvAlTVJ4BPACSZ6Nb9y+l2rKovAa+eov1pBsNkTLXPJmDTycuWJPXhZJePlndv7s9RVbsZfPlMknQOOVkovOgE6158JguRJI3eyULhniS/cmxjdz9gTz8lSZJG5WT3FN7J4PsFv8D3QmACeCHwr3qsS5I0AicMhap6CrgyyRuBo188u72qPtN7ZZKkWTfT31O4E7iz51okSSN2qr+nIEk6BxkKkqTGUJAkNYaCJKkxFCRJjaEgSWoMBUlSYyhIkhpDQZLUGAqSpMZQkCQ1hoIkqTEUJEmNoSBJagwFSVJjKEiSGkNBktQYCpKkxlCQJDW9hUKSZUnuTPJwkr1J3tG1L0iyI8mj3fTCoX02JtmfZF+Sa/uqTZI0tT7PFJ4B/n1V/Qjw48CNSVYBG4CdVbUS2Nkt061bA1wKXAfcnGRej/VJko7RWyhU1cGq+mI3/03gYWAJsBrY0m22Bbi+m18NbK2qI1X1GLAfuKKv+iRJx5uVewpJlgOvBu4GFlXVQRgEB3Bxt9kS4Mmh3Sa7tmOPtT7J7iS7Dx8+3GvdkjRueg+FJBcAfwm8s6q+caJNp2ir4xqqNlfVRFVNLFy48EyVKc0ZS5ZdQpJZeSxZdsmou3vOmM2/W59/u/N6OWonyfkMAuEjVfWJrvmpJIur6mCSxcChrn0SWDa0+1LgQJ/1SXPRgckn+bn3f25Wnutjv3rlrDzPOJjNvxv097fr89NHAT4IPFxVfzS0ajuwtptfC9w21L4myfwkK4CVwK6+6pMkHa/PM4WrgLcDDyS5r2v7XeAmYFuSdcATwA0AVbU3yTbgIQafXLqxqp7tsT5J0jF6C4Wq+jumvk8AcM00+2wCNvVVkyTpxPxGsySpMRQkSY2hIElqDAVJUmMoSJIaQ0GS1BgKkqTGUJAkNYaCJKkxFCRJjaEgSWoMBUlSYyhIkhpDQZLUGAqSpMZQkCQ1hoIkqTEUJEmNoSBJagwFSVJjKEiSGkNBktQYCpKkxlCQJDWGgiSp6S0UknwoyaEkDw61LUiyI8mj3fTCoXUbk+xPsi/JtX3VJUmaXp9nCn8KXHdM2wZgZ1WtBHZ2yyRZBawBLu32uTnJvB5rkyRNobdQqKrPAl87pnk1sKWb3wJcP9S+taqOVNVjwH7gir5qkyRNbbbvKSyqqoMA3fTirn0J8OTQdpNdmyRpFs2VG82Zoq2m3DBZn2R3kt2HDx/uuSxJGi+zHQpPJVkM0E0Pde2TwLKh7ZYCB6Y6QFVtrqqJqppYuHBhr8VK0riZ7VDYDqzt5tcCtw21r0kyP8kKYCWwa5Zrk6Sxd15fB07yF8AbgIuSTAK/B9wEbEuyDngCuAGgqvYm2QY8BDwD3FhVz/ZVmyRpar2FQlX9/DSrrplm+03Apr7qkSSd3Fy50SxJmgMMBUlSYyhIkhpDQZLUGAqSpMZQkCQ1hoIkqTEUJEmNoSBJagwFSVJjKEiSGkNBktQYCpKkxlCQJDWGgiSpMRQkSY2hIElqDAVJUmMoSJIaQ0GS1BgKkqTGUJAkNYaCJKkxFCRJjaEgSWoMBUlSM+dCIcl1SfYl2Z9kw6jrkaRxMqdCIck84I+BNwOrgJ9Psmq0VUnS+JhToQBcAeyvqi9X1XeBrcDqEdckSWMjVTXqGpokPwtcV1W/3C2/HXhtVf3G0DbrgfXd4iuAfbNe6NxxEfDVURcxQvbf/tv/U/NPq2rhVCvOO/V6epEp2p6TWlW1Gdg8O+XMbUl2V9XEqOsYFftv/+3/me//XLt8NAksG1peChwYUS2SNHbmWijcA6xMsiLJC4E1wPYR1yRJY2NOXT6qqmeS/AbwKWAe8KGq2jvisuaycb+MZv/Hm/3vwZy60SxJGq25dvlIkjRChoIkqTEU5qgkH0pyKMmDQ20LkuxI8mg3vXBo3cZuaJB9Sa4dTdVnTpJlSe5M8nCSvUne0bWPxWuQ5EVJdiW5v+v/f+rax6L/RyWZl+TeJJ/slsem/0keT/JAkvuS7O7a+u9/VfmYgw/g9cBrgAeH2v4rsKGb3wD8l25+FXA/MB9YAfwfYN6o+3Ca/V8MvKabfynwv7t+jsVrwOA7Oxd08+cDdwM/Pi79H3odfhv4KPDJbnls+g88Dlx0TFvv/fdMYY6qqs8CXzumeTWwpZvfAlw/1L61qo5U1WPAfgZDhpy1qupgVX2xm/8m8DCwhDF5DWrgW93i+d2jGJP+AyRZCvwU8IGh5rHp/zR677+hcHZZVFUHYfCmCVzctS8BnhzabrJrOyckWQ68msH/lsfmNegundwHHAJ2VNVY9R94L/Bu4B+H2sap/wV8OsmebngfmIX+z6nvKeiUnXR4kLNVkguAvwTeWVXfSKbq6mDTKdrO6tegqp4FLk/yMuDWJJedYPNzqv9J3gIcqqo9Sd4wk12maDtr+9+5qqoOJLkY2JHkkRNse8b675nC2eWpJIsBuumhrv2cHB4kyfkMAuEjVfWJrnmsXgOAqvo6cBdwHePT/6uAn07yOIPRkq9O8mHGp/9U1YFuegi4lcHloN77byicXbYDa7v5tcBtQ+1rksxPsgJYCewaQX1nTAanBB8EHq6qPxpaNRavQZKF3RkCSV4MvAl4hDHpf1VtrKqlVbWcwXA3n6mqtzEm/U/ykiQvPToP/CTwILPR/1HfYfcx7ScP/gI4CPwDg/8FrAN+ENgJPNpNFwxt/x4GnzjYB7x51PWfgf6/jsHp75eA+7rHvxiX1wD4UeDerv8PAv+xax+L/h/zWryB7336aCz6D7ycwaeJ7gf2Au+Zrf47zIUkqfHykSSpMRQkSY2hIElqDAVJUmMoSJIaQ0HnpCTPdqNLPpjk40m+b5rtPjfbtZ1MNzrmRUPLbzg6SuhJ9vtcN12e5F/3WaPOXYaCzlXfqarLq+oy4LvArw2vTDIPoKquHEVxQ3UkyRn5dzjUl+WAoaBTYihoHPwt8MPd/7jvTPJR4AGAJEdHIiXJu7vx6+9PclPX9kNJ7ugGJfvbJK889uBJfj/Jnyf5TDfO/a8MrXtXknuSfGnoNxGWZ/A7ETcDX+S5wxOcUPdcH0pyV5IvJ/mtoXVH+3IT8M+7M6V/93xeKMkB8XROS3Ie8Gbgjq7pCuCyGgwvPLzdmxkMQ/zaqvp2kgXdqs3Ar1XVo0leC9wMXD3FU/0og987eAlwb5LbgcsYDDdwBYMBy7YneT3wBPAK4Jeq6tdPoVuvBN7I4Hcm9iV5X1X9w9D6DcDvVNVbTuHYGnOGgs5VL+6GnYbBmcIHgSuBXccGQudNwJ9U1bcBqupr3QitVwIfHxqddf40z3dbVX0H+E6SOxkEwesYjFlzb7fNBQxC4gngK1X1hWmONdUwA8Ntt1fVEeBIkkPAIgZDoUinzVDQueo7VXX5cEP3xv7302wfjn8zfgHw9WOPM41j963umP+5qt5/TB3LT1AHwNPAhcBXu+UFQ/MAR4bmn8V/xzqDvKcgDXwa+LdHP6WUZEFVfQN4LMkNXVuSvGqa/Vdn8LvKP8hgALd7gE91x7yg239JNzb+ydwFvL3bZx7wNuDO59GXbzK4tCQ9b4aCBFTVHQyGH97dXXb6nW7VLwDrkhwdrXL1NIfYBdwOfAH4g6o6UFWfZvD7wp9P8gBwCzN7s/4DBjfG72dw6Wk/8OHn0Z0vAc90N8y90aznxVFSpdOU5PeBb1XVH466Ful0eaYgSWo8U5AkNZ4pSJIaQ0GS1BgKkqTGUJAkNYaCJKn5//GRSxhJGyPGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(df, x='Price per Unit')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "ffd32bbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1000.000000\n",
       "mean      456.000000\n",
       "std       559.997632\n",
       "min        25.000000\n",
       "25%        60.000000\n",
       "50%       135.000000\n",
       "75%       900.000000\n",
       "max      2000.000000\n",
       "Name: Total Amount, dtype: float64"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Total Amount'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "132de99b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(df['Total Amount'])\n",
    "plt.title('Total Amount Distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "31f5fc04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Time Series Analysis\n",
    "\n",
    "plt.figure(figsize=(12,8))\n",
    "sns.lineplot(x='Month', y='Total Amount', data= df)\n",
    "plt.title('Sales Trend Overtime')\n",
    "plt.xlabel('Month')\n",
    "plt.ylabel('Total Sales Amount per month')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "392d51a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Analyzing Sales of Product Categories per month\n",
    "\n",
    "heatmap = df.pivot_table(index='Month', columns='Product Category', values='Total Amount')\n",
    "plt.figure(figsize=(12,8))\n",
    "sns.heatmap(heatmap, annot=True, fmt='.0f')\n",
    "plt.title('Sales Heatmap')\n",
    "plt.xlabel('Product Category')\n",
    "plt.ylabel('Month')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "9b933df9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Customer and Product Analysis\n",
    "#Showing Customer Retention over Time\n",
    "\n",
    "retention = df.groupby('Customer ID')['Date'].min().reset_index()\n",
    "retention['Year Month'] = retention['Date'].dt.to_period('M')\n",
    "\n",
    "plt.figure(figsize=(12,8))\n",
    "sns.countplot(x='Year Month', data= retention)\n",
    "plt.title('Customer Retention over Time')\n",
    "plt.xlabel('Year Month')\n",
    "plt.ylabel('Number of Customers')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "28dff0e1",
   "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>Date</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Product Category</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Price per Unit</th>\n",
       "      <th>Total Amount</th>\n",
       "      <th>Month</th>\n",
       "      <th>Age Group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023-11-24</td>\n",
       "      <td>CUST001</td>\n",
       "      <td>Male</td>\n",
       "      <td>34</td>\n",
       "      <td>Beauty</td>\n",
       "      <td>3</td>\n",
       "      <td>50</td>\n",
       "      <td>150</td>\n",
       "      <td>November</td>\n",
       "      <td>26-35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023-02-27</td>\n",
       "      <td>CUST002</td>\n",
       "      <td>Female</td>\n",
       "      <td>26</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>2</td>\n",
       "      <td>500</td>\n",
       "      <td>1000</td>\n",
       "      <td>February</td>\n",
       "      <td>26-35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-01-13</td>\n",
       "      <td>CUST003</td>\n",
       "      <td>Male</td>\n",
       "      <td>50</td>\n",
       "      <td>Electronics</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>January</td>\n",
       "      <td>36-50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023-05-21</td>\n",
       "      <td>CUST004</td>\n",
       "      <td>Male</td>\n",
       "      <td>37</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>1</td>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>May</td>\n",
       "      <td>36-50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023-05-06</td>\n",
       "      <td>CUST005</td>\n",
       "      <td>Male</td>\n",
       "      <td>30</td>\n",
       "      <td>Beauty</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>100</td>\n",
       "      <td>May</td>\n",
       "      <td>26-35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date Customer ID  Gender  Age Product Category  Quantity  \\\n",
       "0 2023-11-24     CUST001    Male   34           Beauty         3   \n",
       "1 2023-02-27     CUST002  Female   26         Clothing         2   \n",
       "2 2023-01-13     CUST003    Male   50      Electronics         1   \n",
       "3 2023-05-21     CUST004    Male   37         Clothing         1   \n",
       "4 2023-05-06     CUST005    Male   30           Beauty         2   \n",
       "\n",
       "   Price per Unit  Total Amount     Month Age Group  \n",
       "0              50           150  November     26-35  \n",
       "1             500          1000  February     26-35  \n",
       "2              30            30   January     36-50  \n",
       "3             500           500       May     36-50  \n",
       "4              50           100       May     26-35  "
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Analyzing product quantity purchased by diffrent Age Group\n",
    "# Dividing Age Category \n",
    "\n",
    "age_bins = [0, 18, 25, 35, 50, 100]\n",
    "age_group = ['0-18', '19-25', '26-35', '36-50', '50+']\n",
    "df['Age Group'] = pd.cut(df['Age'], bins = age_bins, labels=age_group)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "42abae07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# bar plot \n",
    "\n",
    "plt.figure(figsize=(12,8))\n",
    "sns.barplot(x='Age Group', y='Quantity', hue= 'Product Category', data=df)\n",
    "plt.title('Product Category purchased by Age group')\n",
    "plt.xlabel('Age Group')\n",
    "plt.ylabel('Quantity Purchased')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "bd032837",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Analyzing Customer Spending Behaviour\n",
    "\n",
    "plt.figure(figsize=(12,8))\n",
    "sns.barplot(x='Age Group', y='Total Amount', hue='Product Category', data=df)\n",
    "plt.title('Customer Spending Behaviour')\n",
    "plt.xlabel('Age Group')\n",
    "plt.ylabel('Total Amount')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "06940e08",
   "metadata": {},
   "outputs": [],
   "source": [
    "                                        -------- END OF THE PROJECT-------"
   ]
  }
 ],
 "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}