notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "aac443ec-491c-446c-8c10-9eca7bc94323",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "# Cleaning Phase"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa4d2e8c-4fee-48f1-86c0-bbac5d29a9f8",
   "metadata": {},
   "source": [
    "# import important packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "063f7630-3180-4fa1-bef5-33cf78746097",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np  \n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc2f3839-0110-46ce-bdd2-88e4d7b4f67f",
   "metadata": {},
   "source": [
    "# Read the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d4f5519e-29b7-4fca-a252-6bed738702f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9948a67a-ecbc-42be-b728-e3b5819b564e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Person Description</th>\n",
       "      <th>Place Code</th>\n",
       "      <th>Customer Order</th>\n",
       "      <th>Additional Features in market</th>\n",
       "      <th>Promotion Name</th>\n",
       "      <th>Store Kind</th>\n",
       "      <th>Store Sales</th>\n",
       "      <th>Store Cost</th>\n",
       "      <th>Gross Weight</th>\n",
       "      <th>Net Weight</th>\n",
       "      <th>Package Weight</th>\n",
       "      <th>Is Recyclable?</th>\n",
       "      <th>Yearly Income</th>\n",
       "      <th>Store Area</th>\n",
       "      <th>Grocery Area</th>\n",
       "      <th>Frozen Area</th>\n",
       "      <th>Meat Area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Single Male with four children, education: par...</td>\n",
       "      <td>B06ls_CA</td>\n",
       "      <td>Candy from Snacks department, Ordered Brand : ...</td>\n",
       "      <td>['Florist', 'Ready Food', 'Video Store', 'Bar ...</td>\n",
       "      <td>Fantastic Discounts</td>\n",
       "      <td>Gourmet</td>\n",
       "      <td>11.76 Millions</td>\n",
       "      <td>4.704 Millions</td>\n",
       "      <td>31.8337</td>\n",
       "      <td>28.7811</td>\n",
       "      <td>3.0525</td>\n",
       "      <td>yes</td>\n",
       "      <td>50K+</td>\n",
       "      <td>2201.06</td>\n",
       "      <td>1424.85</td>\n",
       "      <td>465.54</td>\n",
       "      <td>308.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Married Female with three children, education:...</td>\n",
       "      <td>S01em_OR</td>\n",
       "      <td>Vegetables from Frozen Foods department, Order...</td>\n",
       "      <td>['Florist', 'Coffee Bar', 'Ready Food', 'Bar F...</td>\n",
       "      <td>Bag Stuffer</td>\n",
       "      <td>Deluxe</td>\n",
       "      <td>2.16 Millions</td>\n",
       "      <td>0.6696 Millions</td>\n",
       "      <td>29.9440</td>\n",
       "      <td>27.0368</td>\n",
       "      <td>2.9072</td>\n",
       "      <td>no</td>\n",
       "      <td>70K+</td>\n",
       "      <td>2577.16</td>\n",
       "      <td>1735.17</td>\n",
       "      <td>505.07</td>\n",
       "      <td>336.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Single Female with three children, education: ...</td>\n",
       "      <td>H11go_ZA</td>\n",
       "      <td>Dairy from Dairy department, Ordered Brand : Club</td>\n",
       "      <td>['Bar For Salad', 'Video Store', 'Ready Food',...</td>\n",
       "      <td>Pick Your Savings</td>\n",
       "      <td>Deluxe</td>\n",
       "      <td>1.83 Millions</td>\n",
       "      <td>0.8235 Millions</td>\n",
       "      <td>29.2172</td>\n",
       "      <td>26.3100</td>\n",
       "      <td>2.9072</td>\n",
       "      <td>no</td>\n",
       "      <td>130K+</td>\n",
       "      <td>2837.58</td>\n",
       "      <td>2038.11</td>\n",
       "      <td>481.98</td>\n",
       "      <td>321.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Single Male with three children, education: pa...</td>\n",
       "      <td>M10da_YU</td>\n",
       "      <td>Frozen Desserts from Frozen Foods department, ...</td>\n",
       "      <td>['Coffee Bar', 'Video Store', 'Bar For Salad',...</td>\n",
       "      <td>Price Winners</td>\n",
       "      <td>Deluxe</td>\n",
       "      <td>8.82 Millions</td>\n",
       "      <td>4.41 Millions</td>\n",
       "      <td>28.0543</td>\n",
       "      <td>25.0018</td>\n",
       "      <td>3.0525</td>\n",
       "      <td>no</td>\n",
       "      <td>10K+</td>\n",
       "      <td>2859.04</td>\n",
       "      <td>1871.16</td>\n",
       "      <td>593.93</td>\n",
       "      <td>394.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Single Male with two children, education: high...</td>\n",
       "      <td>L05es_CA</td>\n",
       "      <td>Hot Beverages from Beverages department, Order...</td>\n",
       "      <td>['Florist']</td>\n",
       "      <td>Dollar Days</td>\n",
       "      <td>Supermarket</td>\n",
       "      <td>4.32 Millions</td>\n",
       "      <td>1.9872 Millions</td>\n",
       "      <td>23.5482</td>\n",
       "      <td>20.6410</td>\n",
       "      <td>2.9072</td>\n",
       "      <td>no</td>\n",
       "      <td>30K+</td>\n",
       "      <td>2193.97</td>\n",
       "      <td>1320.15</td>\n",
       "      <td>523.32</td>\n",
       "      <td>348.85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                                 Person Description Place Code  \\\n",
       "0           0  Single Male with four children, education: par...   B06ls_CA   \n",
       "1           1  Married Female with three children, education:...   S01em_OR   \n",
       "2           2  Single Female with three children, education: ...   H11go_ZA   \n",
       "3           3  Single Male with three children, education: pa...   M10da_YU   \n",
       "4           4  Single Male with two children, education: high...   L05es_CA   \n",
       "\n",
       "                                      Customer Order  \\\n",
       "0  Candy from Snacks department, Ordered Brand : ...   \n",
       "1  Vegetables from Frozen Foods department, Order...   \n",
       "2  Dairy from Dairy department, Ordered Brand : Club   \n",
       "3  Frozen Desserts from Frozen Foods department, ...   \n",
       "4  Hot Beverages from Beverages department, Order...   \n",
       "\n",
       "                       Additional Features in market       Promotion Name  \\\n",
       "0  ['Florist', 'Ready Food', 'Video Store', 'Bar ...  Fantastic Discounts   \n",
       "1  ['Florist', 'Coffee Bar', 'Ready Food', 'Bar F...          Bag Stuffer   \n",
       "2  ['Bar For Salad', 'Video Store', 'Ready Food',...    Pick Your Savings   \n",
       "3  ['Coffee Bar', 'Video Store', 'Bar For Salad',...        Price Winners   \n",
       "4                                        ['Florist']          Dollar Days   \n",
       "\n",
       "    Store Kind     Store Sales       Store Cost  Gross Weight  Net Weight  \\\n",
       "0      Gourmet  11.76 Millions   4.704 Millions       31.8337     28.7811   \n",
       "1       Deluxe   2.16 Millions  0.6696 Millions       29.9440     27.0368   \n",
       "2       Deluxe   1.83 Millions  0.8235 Millions       29.2172     26.3100   \n",
       "3       Deluxe   8.82 Millions    4.41 Millions       28.0543     25.0018   \n",
       "4  Supermarket   4.32 Millions  1.9872 Millions       23.5482     20.6410   \n",
       "\n",
       "   Package Weight Is Recyclable? Yearly Income  Store Area  Grocery Area  \\\n",
       "0          3.0525            yes          50K+     2201.06       1424.85   \n",
       "1          2.9072             no          70K+     2577.16       1735.17   \n",
       "2          2.9072             no         130K+     2837.58       2038.11   \n",
       "3          3.0525             no          10K+     2859.04       1871.16   \n",
       "4          2.9072             no          30K+     2193.97       1320.15   \n",
       "\n",
       "   Frozen Area  Meat Area  \n",
       "0       465.54     308.73  \n",
       "1       505.07     336.59  \n",
       "2       481.98     321.26  \n",
       "3       593.93     394.58  \n",
       "4       523.32     348.85  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0240093-eb2b-4b35-830d-f8bc2dc6ea1f",
   "metadata": {},
   "source": [
    "# information about data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6f46b2d8-f0d6-42bd-a2cb-ad4483720bb5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 19942 entries, 0 to 19941\n",
      "Data columns (total 18 columns):\n",
      " #   Column                         Non-Null Count  Dtype  \n",
      "---  ------                         --------------  -----  \n",
      " 0   Unnamed: 0                     19942 non-null  int64  \n",
      " 1   Person Description             19942 non-null  object \n",
      " 2   Place Code                     19942 non-null  object \n",
      " 3   Customer Order                 19942 non-null  object \n",
      " 4   Additional Features in market  19942 non-null  object \n",
      " 5   Promotion Name                 19916 non-null  object \n",
      " 6   Store Kind                     19914 non-null  object \n",
      " 7   Store Sales                    19942 non-null  object \n",
      " 8   Store Cost                     19942 non-null  object \n",
      " 9   Gross Weight                   18959 non-null  float64\n",
      " 10  Net Weight                     18924 non-null  float64\n",
      " 11  Package Weight                 19942 non-null  float64\n",
      " 12  Is Recyclable?                 19942 non-null  object \n",
      " 13  Yearly Income                  19942 non-null  object \n",
      " 14  Store Area                     18946 non-null  float64\n",
      " 15  Grocery Area                   18900 non-null  float64\n",
      " 16  Frozen Area                    18981 non-null  float64\n",
      " 17  Meat Area                      18967 non-null  float64\n",
      "dtypes: float64(7), int64(1), object(10)\n",
      "memory usage: 2.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ec4b2dd-695a-4b7a-8850-8ce20cb194ac",
   "metadata": {},
   "source": [
    "# the descption of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "089ab9c8-30e0-445a-b473-13689e2b10bd",
   "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>Unnamed: 0</th>\n",
       "      <th>Gross Weight</th>\n",
       "      <th>Net Weight</th>\n",
       "      <th>Package Weight</th>\n",
       "      <th>Store Area</th>\n",
       "      <th>Grocery Area</th>\n",
       "      <th>Frozen Area</th>\n",
       "      <th>Meat Area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>19942.00000</td>\n",
       "      <td>18959.000000</td>\n",
       "      <td>18924.000000</td>\n",
       "      <td>19942.000000</td>\n",
       "      <td>18946.000000</td>\n",
       "      <td>18900.000000</td>\n",
       "      <td>18981.000000</td>\n",
       "      <td>18967.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9970.50000</td>\n",
       "      <td>20.081932</td>\n",
       "      <td>17.185442</td>\n",
       "      <td>2.921236</td>\n",
       "      <td>2602.295714</td>\n",
       "      <td>1780.441371</td>\n",
       "      <td>494.490102</td>\n",
       "      <td>329.654561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5756.90387</td>\n",
       "      <td>6.712775</td>\n",
       "      <td>6.800127</td>\n",
       "      <td>1.014127</td>\n",
       "      <td>532.042036</td>\n",
       "      <td>371.638284</td>\n",
       "      <td>147.539135</td>\n",
       "      <td>98.284822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>8.721600</td>\n",
       "      <td>4.433500</td>\n",
       "      <td>1.453600</td>\n",
       "      <td>1882.700000</td>\n",
       "      <td>1233.080000</td>\n",
       "      <td>227.800000</td>\n",
       "      <td>149.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4985.25000</td>\n",
       "      <td>14.114400</td>\n",
       "      <td>11.294400</td>\n",
       "      <td>1.599000</td>\n",
       "      <td>2189.290000</td>\n",
       "      <td>1508.000000</td>\n",
       "      <td>440.920000</td>\n",
       "      <td>293.405000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>9970.50000</td>\n",
       "      <td>19.768900</td>\n",
       "      <td>16.861700</td>\n",
       "      <td>2.907200</td>\n",
       "      <td>2572.860000</td>\n",
       "      <td>1734.500000</td>\n",
       "      <td>470.270000</td>\n",
       "      <td>313.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>14955.75000</td>\n",
       "      <td>25.874000</td>\n",
       "      <td>23.257500</td>\n",
       "      <td>3.052500</td>\n",
       "      <td>2865.170000</td>\n",
       "      <td>2054.085000</td>\n",
       "      <td>593.930000</td>\n",
       "      <td>394.215000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>19941.00000</td>\n",
       "      <td>31.833700</td>\n",
       "      <td>30.234700</td>\n",
       "      <td>4.506100</td>\n",
       "      <td>3692.880000</td>\n",
       "      <td>2822.670000</td>\n",
       "      <td>855.220000</td>\n",
       "      <td>570.750000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0  Gross Weight    Net Weight  Package Weight    Store Area  \\\n",
       "count  19942.00000  18959.000000  18924.000000    19942.000000  18946.000000   \n",
       "mean    9970.50000     20.081932     17.185442        2.921236   2602.295714   \n",
       "std     5756.90387      6.712775      6.800127        1.014127    532.042036   \n",
       "min        0.00000      8.721600      4.433500        1.453600   1882.700000   \n",
       "25%     4985.25000     14.114400     11.294400        1.599000   2189.290000   \n",
       "50%     9970.50000     19.768900     16.861700        2.907200   2572.860000   \n",
       "75%    14955.75000     25.874000     23.257500        3.052500   2865.170000   \n",
       "max    19941.00000     31.833700     30.234700        4.506100   3692.880000   \n",
       "\n",
       "       Grocery Area   Frozen Area     Meat Area  \n",
       "count  18900.000000  18981.000000  18967.000000  \n",
       "mean    1780.441371    494.490102    329.654561  \n",
       "std      371.638284    147.539135     98.284822  \n",
       "min     1233.080000    227.800000    149.900000  \n",
       "25%     1508.000000    440.920000    293.405000  \n",
       "50%     1734.500000    470.270000    313.550000  \n",
       "75%     2054.085000    593.930000    394.215000  \n",
       "max     2822.670000    855.220000    570.750000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f9da1c7-b2e9-4793-92c9-ac277502b638",
   "metadata": {},
   "source": [
    "# check missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "71c4b8db-074d-4728-8db6-3be8c17b6b31",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unnamed: 0                          0\n",
      "Person Description                  0\n",
      "Place Code                          0\n",
      "Customer Order                      0\n",
      "Additional Features in market       0\n",
      "Promotion Name                     26\n",
      "Store Kind                         28\n",
      "Store Sales                         0\n",
      "Store Cost                          0\n",
      "Gross Weight                      983\n",
      "Net Weight                       1018\n",
      "Package Weight                      0\n",
      "Is Recyclable?                      0\n",
      "Yearly Income                       0\n",
      "Store Area                        996\n",
      "Grocery Area                     1042\n",
      "Frozen Area                       961\n",
      "Meat Area                         975\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "missing_values = df.isnull().sum()\n",
    "print(missing_values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24e99d3b-3c8c-401f-8b18-71ec4dffb642",
   "metadata": {},
   "source": [
    "# check duplicates and remove it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6603319c-094a-4d9e-81ef-5fc2e41c3aba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duplicates = df.duplicated().sum()\n",
    "df = df.drop_duplicates()\n",
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f728752a-1425-4303-8899-76cf6ae10561",
   "metadata": {},
   "source": [
    "# convert sales to numeric "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "130ebf27-4cd6-4bcd-ae56-59141c56c096",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        11760.0\n",
       "1         2160.0\n",
       "2         1830.0\n",
       "3         8820.0\n",
       "4         4320.0\n",
       "          ...   \n",
       "19937     4460.0\n",
       "19938     3810.0\n",
       "19939     2140.0\n",
       "19940     3520.0\n",
       "19941     4620.0\n",
       "Name: Store Sales, Length: 19942, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Store Sales'] = df['Store Sales'].str.replace(' Millions', '', regex=False).astype(float)*1000\n",
    "df['Store Sales']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c657da2-1730-40cb-8520-8ceab99275ec",
   "metadata": {},
   "source": [
    "# fill nan values with best values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4f3c7aa8-2613-477d-9ebe-364442bd684c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Eng Abd Allah\\AppData\\Local\\Temp\\ipykernel_10696\\284123370.py:1: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  df['Store Area'].fillna(df['Store Area'].mean(),inplace=True)\n",
      "C:\\Users\\Eng Abd Allah\\AppData\\Local\\Temp\\ipykernel_10696\\284123370.py:2: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  df['Grocery Area'].fillna(df['Grocery Area'].mean(),inplace=True)\n",
      "C:\\Users\\Eng Abd Allah\\AppData\\Local\\Temp\\ipykernel_10696\\284123370.py:3: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  df['Meat Area'].fillna(df['Meat Area'].mean(),inplace=True)\n",
      "C:\\Users\\Eng Abd Allah\\AppData\\Local\\Temp\\ipykernel_10696\\284123370.py:4: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  df['Frozen Area'].fillna(df['Frozen Area'].mean(),inplace=True)\n",
      "C:\\Users\\Eng Abd Allah\\AppData\\Local\\Temp\\ipykernel_10696\\284123370.py:5: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  df['Net Weight'].fillna(df['Net Weight'].mean(),inplace=True)\n",
      "C:\\Users\\Eng Abd Allah\\AppData\\Local\\Temp\\ipykernel_10696\\284123370.py:6: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  df['Gross Weight'].fillna(df['Gross Weight'].mean(),inplace=True)\n"
     ]
    }
   ],
   "source": [
    "df['Store Area'].fillna(df['Store Area'].mean(),inplace=True)\n",
    "df['Grocery Area'].fillna(df['Grocery Area'].mean(),inplace=True)\n",
    "df['Meat Area'].fillna(df['Meat Area'].mean(),inplace=True)\n",
    "df['Frozen Area'].fillna(df['Frozen Area'].mean(),inplace=True)\n",
    "df['Net Weight'].fillna(df['Net Weight'].mean(),inplace=True)\n",
    "df['Gross Weight'].fillna(df['Gross Weight'].mean(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c8907e5c-d248-47c5-ab75-040ff7441d2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Gourmet', 'Deluxe', 'Supermarket', 'Small Grocery', 'Mid-Size',\n",
       "       nan], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Store Kind'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8c016d6d-21bb-4836-b99c-e65439f45975",
   "metadata": {},
   "outputs": [],
   "source": [
    "o= ['Supermarket','Deluxe','Small Grocery','Gourmet']\n",
    "nan_kind = df['Store Kind'].isnull()\n",
    "df.loc[nan_kind , 'Store Kind'] = np.random.choice(o)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9369198d-1909-483d-b398-bdccc2b11452",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unnamed: 0                        0\n",
      "Person Description                0\n",
      "Place Code                        0\n",
      "Customer Order                    0\n",
      "Additional Features in market     0\n",
      "Promotion Name                   26\n",
      "Store Kind                        0\n",
      "Store Sales                       0\n",
      "Store Cost                        0\n",
      "Gross Weight                      0\n",
      "Net Weight                        0\n",
      "Package Weight                    0\n",
      "Is Recyclable?                    0\n",
      "Yearly Income                     0\n",
      "Store Area                        0\n",
      "Grocery Area                      0\n",
      "Frozen Area                       0\n",
      "Meat Area                         0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "missing_values = df.isnull().sum()\n",
    "print(missing_values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61d6574b-3baf-4dd7-a513-1d7dabebb6be",
   "metadata": {},
   "source": [
    "# convert column is recycable into boolean "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "dd20e987-bb8d-4ec9-bf5f-3c9dbc7e0509",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         true\n",
       "1        False\n",
       "2        False\n",
       "3        False\n",
       "4        False\n",
       "         ...  \n",
       "19937    False\n",
       "19938    False\n",
       "19939     true\n",
       "19940     true\n",
       "19941    False\n",
       "Name: Is Recyclable?, Length: 19942, dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Is Recyclable?\"] = df[\"Is Recyclable?\"].replace({'yes':'true','no':'False'})\n",
    "df[\"Is Recyclable?\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45449243-673f-49bc-906a-c8c11dadf765",
   "metadata": {},
   "source": [
    "# convert yearly income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b2151022-7274-4b03-aecd-3aba433344c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         50\n",
       "1         70\n",
       "2        130\n",
       "3         10\n",
       "4         30\n",
       "        ... \n",
       "19937     10\n",
       "19938     10\n",
       "19939     50\n",
       "19940     70\n",
       "19941     30\n",
       "Name: Yearly Income, Length: 19942, dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Yearly Income'] = df['Yearly Income'].str.replace('K+', '', regex=False)\n",
    "df['Yearly Income']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8749ddca-9741-49c8-8102-0e69698199f0",
   "metadata": {},
   "source": [
    "# drop columns with not useful"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7b5e6cf6-45b2-4c0b-ad9b-b48b6fd00d4d",
   "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>Place Code</th>\n",
       "      <th>Promotion Name</th>\n",
       "      <th>Store Kind</th>\n",
       "      <th>Store Sales</th>\n",
       "      <th>Store Cost</th>\n",
       "      <th>Gross Weight</th>\n",
       "      <th>Net Weight</th>\n",
       "      <th>Package Weight</th>\n",
       "      <th>Is Recyclable?</th>\n",
       "      <th>Yearly Income</th>\n",
       "      <th>Store Area</th>\n",
       "      <th>Grocery Area</th>\n",
       "      <th>Frozen Area</th>\n",
       "      <th>Meat Area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>B06ls_CA</td>\n",
       "      <td>Fantastic Discounts</td>\n",
       "      <td>Gourmet</td>\n",
       "      <td>11760.0</td>\n",
       "      <td>4.704 Millions</td>\n",
       "      <td>31.8337</td>\n",
       "      <td>28.7811</td>\n",
       "      <td>3.0525</td>\n",
       "      <td>true</td>\n",
       "      <td>50</td>\n",
       "      <td>2201.06</td>\n",
       "      <td>1424.850000</td>\n",
       "      <td>465.54</td>\n",
       "      <td>308.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>S01em_OR</td>\n",
       "      <td>Bag Stuffer</td>\n",
       "      <td>Deluxe</td>\n",
       "      <td>2160.0</td>\n",
       "      <td>0.6696 Millions</td>\n",
       "      <td>29.9440</td>\n",
       "      <td>27.0368</td>\n",
       "      <td>2.9072</td>\n",
       "      <td>False</td>\n",
       "      <td>70</td>\n",
       "      <td>2577.16</td>\n",
       "      <td>1735.170000</td>\n",
       "      <td>505.07</td>\n",
       "      <td>336.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>H11go_ZA</td>\n",
       "      <td>Pick Your Savings</td>\n",
       "      <td>Deluxe</td>\n",
       "      <td>1830.0</td>\n",
       "      <td>0.8235 Millions</td>\n",
       "      <td>29.2172</td>\n",
       "      <td>26.3100</td>\n",
       "      <td>2.9072</td>\n",
       "      <td>False</td>\n",
       "      <td>130</td>\n",
       "      <td>2837.58</td>\n",
       "      <td>2038.110000</td>\n",
       "      <td>481.98</td>\n",
       "      <td>321.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M10da_YU</td>\n",
       "      <td>Price Winners</td>\n",
       "      <td>Deluxe</td>\n",
       "      <td>8820.0</td>\n",
       "      <td>4.41 Millions</td>\n",
       "      <td>28.0543</td>\n",
       "      <td>25.0018</td>\n",
       "      <td>3.0525</td>\n",
       "      <td>False</td>\n",
       "      <td>10</td>\n",
       "      <td>2859.04</td>\n",
       "      <td>1871.160000</td>\n",
       "      <td>593.93</td>\n",
       "      <td>394.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>L05es_CA</td>\n",
       "      <td>Dollar Days</td>\n",
       "      <td>Supermarket</td>\n",
       "      <td>4320.0</td>\n",
       "      <td>1.9872 Millions</td>\n",
       "      <td>23.5482</td>\n",
       "      <td>20.6410</td>\n",
       "      <td>2.9072</td>\n",
       "      <td>False</td>\n",
       "      <td>30</td>\n",
       "      <td>2193.97</td>\n",
       "      <td>1320.150000</td>\n",
       "      <td>523.32</td>\n",
       "      <td>348.85</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19937</th>\n",
       "      <td>H11go_ZA</td>\n",
       "      <td>Two for One</td>\n",
       "      <td>Deluxe</td>\n",
       "      <td>4460.0</td>\n",
       "      <td>1.9624 Millions</td>\n",
       "      <td>11.1200</td>\n",
       "      <td>8.2128</td>\n",
       "      <td>2.9072</td>\n",
       "      <td>False</td>\n",
       "      <td>10</td>\n",
       "      <td>2839.33</td>\n",
       "      <td>2038.110000</td>\n",
       "      <td>481.98</td>\n",
       "      <td>321.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19938</th>\n",
       "      <td>S03le_WA</td>\n",
       "      <td>Best Price Savers</td>\n",
       "      <td>Supermarket</td>\n",
       "      <td>3810.0</td>\n",
       "      <td>1.3335 Millions</td>\n",
       "      <td>20.6410</td>\n",
       "      <td>17.5885</td>\n",
       "      <td>3.0525</td>\n",
       "      <td>False</td>\n",
       "      <td>10</td>\n",
       "      <td>1970.94</td>\n",
       "      <td>1780.441371</td>\n",
       "      <td>440.92</td>\n",
       "      <td>295.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19939</th>\n",
       "      <td>G16ra_JA</td>\n",
       "      <td>Best Savings</td>\n",
       "      <td>Small Grocery</td>\n",
       "      <td>2140.0</td>\n",
       "      <td>0.749 Millions</td>\n",
       "      <td>23.1121</td>\n",
       "      <td>20.0596</td>\n",
       "      <td>3.0525</td>\n",
       "      <td>true</td>\n",
       "      <td>50</td>\n",
       "      <td>2288.65</td>\n",
       "      <td>1394.660000</td>\n",
       "      <td>534.29</td>\n",
       "      <td>355.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19940</th>\n",
       "      <td>S01em_OR</td>\n",
       "      <td>Bag Stuffer</td>\n",
       "      <td>Deluxe</td>\n",
       "      <td>3520.0</td>\n",
       "      <td>1.1616 Millions</td>\n",
       "      <td>16.2802</td>\n",
       "      <td>13.3585</td>\n",
       "      <td>2.9217</td>\n",
       "      <td>true</td>\n",
       "      <td>70</td>\n",
       "      <td>2572.86</td>\n",
       "      <td>1734.500000</td>\n",
       "      <td>503.07</td>\n",
       "      <td>335.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19941</th>\n",
       "      <td>T02ma_WA</td>\n",
       "      <td>Super Duper Savers</td>\n",
       "      <td>Deluxe</td>\n",
       "      <td>4620.0</td>\n",
       "      <td>1.6632 Millions</td>\n",
       "      <td>30.2347</td>\n",
       "      <td>27.1822</td>\n",
       "      <td>3.0525</td>\n",
       "      <td>False</td>\n",
       "      <td>30</td>\n",
       "      <td>3145.51</td>\n",
       "      <td>2054.280000</td>\n",
       "      <td>654.13</td>\n",
       "      <td>436.09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19942 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Place Code       Promotion Name     Store Kind  Store Sales  \\\n",
       "0       B06ls_CA  Fantastic Discounts        Gourmet      11760.0   \n",
       "1       S01em_OR          Bag Stuffer         Deluxe       2160.0   \n",
       "2       H11go_ZA    Pick Your Savings         Deluxe       1830.0   \n",
       "3       M10da_YU        Price Winners         Deluxe       8820.0   \n",
       "4       L05es_CA          Dollar Days    Supermarket       4320.0   \n",
       "...          ...                  ...            ...          ...   \n",
       "19937   H11go_ZA          Two for One         Deluxe       4460.0   \n",
       "19938   S03le_WA    Best Price Savers    Supermarket       3810.0   \n",
       "19939   G16ra_JA         Best Savings  Small Grocery       2140.0   \n",
       "19940   S01em_OR          Bag Stuffer         Deluxe       3520.0   \n",
       "19941   T02ma_WA   Super Duper Savers         Deluxe       4620.0   \n",
       "\n",
       "            Store Cost  Gross Weight  Net Weight  Package Weight  \\\n",
       "0       4.704 Millions       31.8337     28.7811          3.0525   \n",
       "1      0.6696 Millions       29.9440     27.0368          2.9072   \n",
       "2      0.8235 Millions       29.2172     26.3100          2.9072   \n",
       "3        4.41 Millions       28.0543     25.0018          3.0525   \n",
       "4      1.9872 Millions       23.5482     20.6410          2.9072   \n",
       "...                ...           ...         ...             ...   \n",
       "19937  1.9624 Millions       11.1200      8.2128          2.9072   \n",
       "19938  1.3335 Millions       20.6410     17.5885          3.0525   \n",
       "19939   0.749 Millions       23.1121     20.0596          3.0525   \n",
       "19940  1.1616 Millions       16.2802     13.3585          2.9217   \n",
       "19941  1.6632 Millions       30.2347     27.1822          3.0525   \n",
       "\n",
       "      Is Recyclable? Yearly Income  Store Area  Grocery Area  Frozen Area  \\\n",
       "0               true            50     2201.06   1424.850000       465.54   \n",
       "1              False            70     2577.16   1735.170000       505.07   \n",
       "2              False           130     2837.58   2038.110000       481.98   \n",
       "3              False            10     2859.04   1871.160000       593.93   \n",
       "4              False            30     2193.97   1320.150000       523.32   \n",
       "...              ...           ...         ...           ...          ...   \n",
       "19937          False            10     2839.33   2038.110000       481.98   \n",
       "19938          False            10     1970.94   1780.441371       440.92   \n",
       "19939           true            50     2288.65   1394.660000       534.29   \n",
       "19940           true            70     2572.86   1734.500000       503.07   \n",
       "19941          False            30     3145.51   2054.280000       654.13   \n",
       "\n",
       "       Meat Area  \n",
       "0         308.73  \n",
       "1         336.59  \n",
       "2         321.26  \n",
       "3         394.58  \n",
       "4         348.85  \n",
       "...          ...  \n",
       "19937     321.26  \n",
       "19938     295.66  \n",
       "19939     355.98  \n",
       "19940     335.38  \n",
       "19941     436.09  \n",
       "\n",
       "[19942 rows x 14 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(columns=['Unnamed: 0', 'Person Description', 'Customer Order', 'Additional Features in market'])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7cd888e-ff54-4452-9dd7-929a420354e7",
   "metadata": {},
   "source": [
    "# Now Visualization Phase"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95b25140-183d-4adb-bbee-ba4ec2d09193",
   "metadata": {},
   "source": [
    "# create Count Plot for Store Kind with palette and hue adjustments (using scatter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1d48682d-ebef-4d34-9784-cebc90e27e1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "store_kind_counts = df['Store Kind'].value_counts().reset_index()\n",
    "store_kind_counts.columns = ['Store Kind', 'Count']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4d21d013-160c-4609-8737-a0e7df26e46c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.scatterplot(data=store_kind_counts,x='Store Kind', y='Count',hue='Store Kind',legend=True)\n",
    "plt.title(\"Count of Each Store Kind\")\n",
    "plt.xlabel(\"Store Kind\")\n",
    "plt.ylabel(\"Count\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a167346-c797-45eb-916e-8afa3b8d3ca7",
   "metadata": {},
   "source": [
    "# Lineplot of Store Sales vs Yearly Income by Store Kind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3d4e3c4b-32ae-4a6e-8a08-e1fb5887ca90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 8))\n",
    "sns.lineplot(data=df,x='Yearly Income', y='Store Sales',hue='Store Kind',marker=\"o\")\n",
    "plt.title(\"Store Sales vs Yearly Income by Store Kind\")\n",
    "plt.xlabel(\"Yearly Income\")\n",
    "plt.ylabel(\"Store Sales\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88c89b6f-2266-46dc-9eb7-c10e4eecfad2",
   "metadata": {},
   "source": [
    "# Histogram for Store Sales, Yearly Income, and Store Cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9c440d2a-d87f-4441-9db9-f534600f6f75",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Store Cost'] = pd.to_numeric(df['Store Cost'].str.replace(' Millions', '', regex=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fe5e3305-6c77-42d3-b04c-f5f57172b306",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(df['Store Sales'], bins=30,color='blue')\n",
    "plt.title('Histogram of Store Sales')\n",
    "plt.xlabel('Store Sales')\n",
    "plt.ylabel('Frequency')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5c3507b8-4a5c-4dba-8039-f873efd93062",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(df['Yearly Income'], bins=30,color='orange')\n",
    "plt.title('Histogram of Yearly Income')\n",
    "plt.xlabel('Yearly Income')\n",
    "plt.ylabel('Frequency')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "372a8317-53b2-4566-b726-995ea6e01f8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(df['Store Cost'], bins=30,color='green')\n",
    "plt.title('Histogram of Store Cost')\n",
    "plt.xlabel('Store Cost')\n",
    "plt.ylabel('Frequency')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9acb67e5-3f87-4df8-b361-50fe408348ba",
   "metadata": {},
   "source": [
    "# using bar plot , represent store kind , store sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8bc74bfb-0c0b-4879-bc24-27991bfc1798",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(data=df, x='Store Kind', y='Store Sales' )\n",
    "\n",
    "plt.title('Store Sales by Store Kind and Marital Status')\n",
    "plt.xlabel('Store Kind')\n",
    "plt.ylabel('Store Sales')\n",
    "plt.legend(title='Marital Status')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08c728b2-b7fc-4fc1-9030-76513e60e7fc",
   "metadata": {},
   "source": [
    "# make a Distribution of Store Sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d4da918f-74b3-4113-8d12-261924bd3514",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0f/9+Pf/884qIiNDu3bvP2WNiYqI++eQTde3aVU8++aTatm2rkpIS7du3TytWrND48ePVqVMnTZo0SQcOHFDPnj3VqFEjHT9+XH/+85/l4+Ojbt26Xei3BgCqnmfn5wAAlM7YV/rl6+trQkNDTbdu3czUqVNNdnZ2mW3OniEwLS3N3HXXXSYqKsr4+fmZBg0amG7dupWZGW7lypXmxhtvNH5+fkaSa+a30v0dOXLkvMcy5pdZ426//Xbz8ccfm9atWxtfX1/TtGlTM2PGjDLb//vf/zbx8fGmXr16pmHDhmbMmDHmyy+/LDMb4c8//2zuuecec9VVVxmbzeZ2TJUzi+L27dvNHXfcYRwOh/H19TXt2rUz8+bNc6spnY3wo48+clteOnvg2fXl+frrr81tt91mAgMDjd1uN507dzaff/55ufu7kNkIN27caB5//HHTrl07ExwcbOrUqWMaNmxo+vTp45pdr1RhYaF5+OGHTcOGDV3XpHTGwoKCAvP000+bqKgo4+PjYyIiIszvf/97k5OT47aP0u9VeY4cOWLGjh1roqOjjY+PjwkODjYdOnQwzz77rDl58mSF55CTk2NeeOEFc9ttt5mrr77a+Pr6msDAQHPDDTeYF154weTn57vVv/TSS6Zp06bGz8/PtGrVyrzzzjsVjqtfz0ZojDEnT540f/zjH03Lli2Nr6+vcTgcpk2bNubJJ580WVlZxhhjvvjiC9O3b19XL6GhoaZfv37m66+/rvAcAKA62Iw5zxRXAAAAAICLxme2AAAAAMAChC0AAAAAsABhCwAAAAAsQNgCAAAAAAsQtgAAAADAAoQtAAAAALAAv9T4ApWUlOjQoUMKCgqq1C+xBAAAAHBlMMboxIkTioyMlJdXxfevCFsX6NChQ2rcuLGn2wAAAABQQ+zfv1+NGjWqcD1h6wIFBQVJ+uWC1qtX75L25XQ6tWLFCsXHx8vHx6cq2gPOiTGH6saYgycw7lDdGHO1V15enho3buzKCBUhbF2g0kcH69WrVyVhKyAgQPXq1eMvJqoFYw7VjTEHT2Dcobox5nC+jxcxQQYAAAAAWICwBQAAAAAWIGwBAAAAgAUIWwAAAABgAcIWAAAAAFiAsAUAAAAAFiBsAQAAAIAFCFsAAAAAYAHCFgAAAABYgLAFAAAAABYgbAEAAACABQhbAAAAAGABwhYAAAAAWICwBQAAAAAWIGwBAAAAgAU8GraSkpJks9ncvsLDw13rjTFKSkpSZGSk7Ha7unfvrp07d7rto7CwUGPGjFFISIgCAwM1YMAAHThwwK0mJydHCQkJcjgccjgcSkhI0PHjx6vjFAEAAADUUh6/s9W6dWtlZma6vrZv3+5aN336dM2YMUOzZs3Spk2bFB4erri4OJ04ccJVk5iYqOXLl2vJkiXasGGDTp48qf79++vMmTOumqFDhyo9PV3JyclKTk5Wenq6EhISqvU8AQAAANQu3h5vwNvb7W5WKWOMXn/9dT377LMaNGiQJGnBggUKCwvTokWLNGrUKOXm5mru3Ll6//331atXL0nSwoUL1bhxY61cuVK9e/dWRkaGkpOTtXHjRnXq1EmS9M477yg2Nla7du1Sy5Yty+2rsLBQhYWFrtd5eXmSJKfTKafTeUnnXLr9pe4HuFCMOVQ3xhw8gXGH6saYq70u9Hvu8bC1e/duRUZGys/PT506ddLUqVPVrFkz7dmzR1lZWYqPj3fV+vn5qVu3bkpNTdWoUaO0ZcsWOZ1Ot5rIyEjFxMQoNTVVvXv3VlpamhwOhytoSVLnzp3lcDiUmppaYdiaNm2apkyZUmb5ihUrFBAQUCXnnpKSUiX7AS4UYw7VjTEHT2Dcobox5mqf/Pz8C6rzaNjq1KmT3nvvPV177bU6fPiwXnjhBXXp0kU7d+5UVlaWJCksLMxtm7CwMO3du1eSlJWVJV9fX9WvX79MTen2WVlZCg0NLXPs0NBQV015Jk6cqHHjxrle5+XlqXHjxoqPj1e9evUqd8L/5XQ6lZKSori4OPn4+FzSvoALsXfvXu3cuVMPPfSQCgoKPN1OjWK3B2jz5k1q1KiRp1u5ovA+B09g3KG6MeZqr9Kn3s7Ho2Grb9++rj+3adNGsbGxat68uRYsWKDOnTtLkmw2m9s2xpgyy852dk159efbj5+fn/z8/Mos9/HxqbK/TFW5L+BcSieEaX3PE/Jv2MSzzdQgeZk/6Zt3pygnJ0fR0dGebueKxPscPIFxh+rGmKt9LvT77fHHCH8tMDBQbdq00e7du3XnnXdK+uXOVEREhKsmOzvbdbcrPDxcRUVFysnJcbu7lZ2drS5durhqDh8+XOZYR44cKXPXDLjSBYU1UVCj8h+dBQAAQNXy+GyEv1ZYWKiMjAxFREQoOjpa4eHhbs/AFhUVad26da4g1aFDB/n4+LjVZGZmaseOHa6a2NhY5ebm6ttvv3XVfPPNN8rNzXXVAAAAAEBV8+idrQkTJuiOO+5QkyZNlJ2drRdeeEF5eXkaNmyYbDabEhMTNXXqVLVo0UItWrTQ1KlTFRAQoKFDh0qSHA6HRowYofHjx6tBgwYKDg7WhAkT1KZNG9fshK1atVKfPn00cuRIzZkzR5L0yCOPqH///hVOjgEAAAAAl8qjYevAgQP63e9+p6NHj6phw4bq3LmzNm7cqKioKEnSU089pYKCAj322GPKyclRp06dtGLFCgUFBbn28dprr8nb21uDBw9WQUGBevbsqfnz56tOnTqumg8++EBjx451zVo4YMAAzZo1q3pPFgAAAECt4tGwtWTJknOut9lsSkpKUlJSUoU1/v7+mjlzpmbOnFlhTXBwsBYuXFjZNgEAAADgotWoz2wBAAAAwJWCsAUAAAAAFiBsAQAAAIAFCFsAAAAAYAHCFgAAAABYgLAFAAAAABYgbAEAAACABQhbAAAAAGABwhYAAAAAWICwBQAAAAAWIGwBAAAAgAUIWwAAAABgAcIWAAAAAFiAsAUAAAAAFiBsAQAAAIAFCFsAAAAAYAHCFgAAAABYgLAFAAAAABYgbAEAAACABQhbAAAAAGABwhYAAAAAWICwBQAAAAAWIGwBAAAAgAUIWwAAAABgAcIWAAAAAFiAsAUAAAAAFiBsAQAAAIAFCFsAAAAAYAFvTzcAVKV9+/bp6NGjnm6jxtm1a5fq1q3r6TYAAABqFcIWrhj79u3Tdde1UkFBvqdbqXHsdrsWL16s4iKnp1sBAACoNQhbuGIcPXpUBQX56vTQZNWLaOrpdmqUn/+1SZJUXFzs4U4AAABqD8IWrjj1IpoquElLT7dRo5w+ss/TLQAAANQ6TJABAAAAABYgbAEAAACABQhbAAAAAGABwhYAAAAAWICwBQAAAAAWIGwBAAAAgAUIWwAAAABgAcIWAAAAAFiAsAUAAAAAFiBsAQAAAIAFCFsAAAAAYAHCFgAAAABYgLAFAAAAABYgbAEAAACABQhbAAAAAGABwhYAAAAAWICwBQAAAAAWIGwBAAAAgAUIWwAAAABgAcIWAAAAAFiAsAUAAAAAFiBsAQAAAIAFCFsAAAAAYAHCFgAAAABYgLAFAAAAABYgbAEAAACABQhbAAAAAGABwhYAAAAAWICwBQAAAAAWIGwBAAAAgAUIWwAAAABgAcIWAAAAAFiAsAUAAAAAFiBsAQAAAIAFCFsAAAAAYAHCFgAAAABYgLAFAAAAABYgbAEAAACABQhbAAAAAGABwhYAAAAAWICwBQAAAAAWIGwBAAAAgAUIWwAAAABgAcIWAAAAAFiAsAUAAAAAFqgxYWvatGmy2WxKTEx0LTPGKCkpSZGRkbLb7erevbt27tzptl1hYaHGjBmjkJAQBQYGasCAATpw4IBbTU5OjhISEuRwOORwOJSQkKDjx49Xw1kBAAAAqK1qRNjatGmT3n77bbVt29Zt+fTp0zVjxgzNmjVLmzZtUnh4uOLi4nTixAlXTWJiopYvX64lS5Zow4YNOnnypPr3768zZ864aoYOHar09HQlJycrOTlZ6enpSkhIqLbzAwAAAFD7eDxsnTx5Uvfdd5/eeecd1a9f37XcGKPXX39dzz77rAYNGqSYmBgtWLBA+fn5WrRokSQpNzdXc+fO1auvvqpevXrpxhtv1MKFC7V9+3atXLlSkpSRkaHk5GT99a9/VWxsrGJjY/XOO+/oiy++0K5duzxyzgAAAACufN6ebuDxxx/X7bffrl69eumFF15wLd+zZ4+ysrIUHx/vWubn56du3bopNTVVo0aN0pYtW+R0Ot1qIiMjFRMTo9TUVPXu3VtpaWlyOBzq1KmTq6Zz585yOBxKTU1Vy5Yty+2rsLBQhYWFrtd5eXmSJKfTKafTeUnnXLr9pe4H7kpKSmS32+XjJXnbSjzdTo3i6/3L/6v41rFxbX7Fx0uy2+0qKSnh72MV430OnsC4Q3VjzNVeF/o992jYWrJkib777jtt2rSpzLqsrCxJUlhYmNvysLAw7d2711Xj6+vrdkestKZ0+6ysLIWGhpbZf2hoqKumPNOmTdOUKVPKLF+xYoUCAgLOc2YXJiUlpUr2g/+zePHi//7pqEf7qHHi20iSHu0SIa7Nr4TW1yOLF+vgwYM6ePCgp7u5IvE+B09g3KG6MeZqn/z8/Auq81jY2r9/v5544gmtWLFC/v7+FdbZbDa318aYMsvOdnZNefXn28/EiRM1btw41+u8vDw1btxY8fHxqlev3jmPfz5Op1MpKSmKi4uTj4/PJe0L/2fbtm3q2rWrekyYrfqNW3i6nRolK32NxsS30VupmWpwTRtPt1Nj5OzfrTV/ekzr169Xu3btPN3OFYX3OXgC4w7VjTFXe5U+9XY+HgtbW7ZsUXZ2tjp06OBadubMGa1fv16zZs1yfZ4qKytLERERrprs7GzX3a7w8HAVFRUpJyfH7e5Wdna2unTp4qo5fPhwmeMfOXKkzF2zX/Pz85Ofn1+Z5T4+PlX2l6kq9wXJy8tLBQUFcpZIxcbjH0esUYqKf3l0sOiM4dr8irNEKigokJeXF38XLcL7HDyBcYfqxpirfS70++2xf3X17NlT27dvV3p6uuurY8eOuu+++5Senq5mzZopPDzc7bZsUVGR1q1b5wpSHTp0kI+Pj1tNZmamduzY4aqJjY1Vbm6uvv32W1fNN998o9zcXFcNAAAAAFQ1j93ZCgoKUkxMjNuywMBANWjQwLU8MTFRU6dOVYsWLdSiRQtNnTpVAQEBGjp0qCTJ4XBoxIgRGj9+vBo0aKDg4GBNmDBBbdq0Ua9evSRJrVq1Up8+fTRy5EjNmTNHkvTII4+of//+FU6OAQAAAACXyuOzEZ7LU089pYKCAj322GPKyclRp06dtGLFCgUFBblqXnvtNXl7e2vw4MEqKChQz549NX/+fNWpU8dV88EHH2js2LGuWQsHDBigWbNmVfv5AAAAAKg9alTYWrt2rdtrm82mpKQkJSUlVbiNv7+/Zs6cqZkzZ1ZYExwcrIULF1ZRlwAAAABwfnxSHgAAAAAsQNgCAAAAAAsQtgAAAADAAoQtAAAAALAAYQsAAAAALEDYAgAAAAALELYAAAAAwAKELQAAAACwAGELAAAAACxA2AIAAAAACxC2AAAAAMAChC0AAAAAsABhCwAAAAAsQNgCAAAAAAsQtgAAAADAAoQtAAAAALAAYQsAAAAALEDYAgAAAAALELYAAAAAwAKELQAAAACwAGELAAAAACxA2AIAAAAACxC2AAAAAMAChC0AAAAAsABhCwAAAAAsQNgCAAAAAAsQtgAAAADAAoQtAAAAALAAYQsAAAAALODt6QYAwNMyMjI83UKNFBISoiZNmni6DQAALluELQC1VkHuMUk23X///Z5upUay2wP0r39lELgAAKgkwhaAWsuZf0KS0Q1Dn1bD6Os83U6Nkpf5k755d4qOHj1K2AIAoJIIWwBqvbqhTRTcpKWn2wAAAFcYJsgAAAAAAAsQtgAAAADAAoQtAAAAALAAYQsAAAAALEDYAgAAAAALELYAAAAAwAKELQAAAACwAGELAAAAACxA2AIAAAAACxC2AAAAAMAChC0AAAAAsABhCwAAAAAsQNgCAAAAAAsQtgAAAADAAoQtAAAAALAAYQsAAAAALEDYAgAAAAALELYAAAAAwAKELQAAAACwAGELAAAAACxA2AIAAAAACxC2AAAAAMAChC0AAAAAsABhCwAAAAAsQNgCAAAAAAsQtgAAAADAAoQtAAAAALAAYQsAAAAALEDYAgAAAAALELYAAAAAwAKELQAAAACwAGELAAAAACxA2AIAAAAACxC2AAAAAMAChC0AAAAAsABhCwAAAAAsQNgCAAAAAAsQtgAAAADAAoQtAAAAALAAYQsAAAAALEDYAgAAAAALELYAAAAAwAKELQAAAACwAGELAAAAACzg0bD15ptvqm3btqpXr57q1aun2NhY/f3vf3etN8YoKSlJkZGRstvt6t69u3bu3Om2j8LCQo0ZM0YhISEKDAzUgAEDdODAAbeanJwcJSQkyOFwyOFwKCEhQcePH6+OUwQAAABQS3k0bDVq1EgvvfSSNm/erM2bN+u2227TwIEDXYFq+vTpmjFjhmbNmqVNmzYpPDxccXFxOnHihGsfiYmJWr58uZYsWaINGzbo5MmT6t+/v86cOeOqGTp0qNLT05WcnKzk5GSlp6crISGh2s8XAAAAQO3h7cmD33HHHW6vX3zxRb355pvauHGjrr/+er3++ut69tlnNWjQIEnSggULFBYWpkWLFmnUqFHKzc3V3Llz9f7776tXr16SpIULF6px48ZauXKlevfurYyMDCUnJ2vjxo3q1KmTJOmdd95RbGysdu3apZYtW1bvSQMAAACoFTwatn7tzJkz+uijj3Tq1CnFxsZqz549ysrKUnx8vKvGz89P3bp1U2pqqkaNGqUtW7bI6XS61URGRiomJkapqanq3bu30tLS5HA4XEFLkjp37iyHw6HU1NQKw1ZhYaEKCwtdr/Py8iRJTqdTTqfzks61dPtL3Q/clZSUyG63y8dL8raVeLqdGsXX+5eb2L51bFybX/H19pLdbue6lMPHS7Lb7SopKanUexXvc/AExh2qG2Ou9rrQ73mlwtaePXsUHR1dmU3L2L59u2JjY3X69GnVrVtXy5cv1/XXX6/U1FRJUlhYmFt9WFiY9u7dK0nKysqSr6+v6tevX6YmKyvLVRMaGlrmuKGhoa6a8kybNk1Tpkwps3zFihUKCAi4uJOsQEpKSpXsB/9n8eLF//3TUY/2UePEt5EkPdolQlybX+nXTurHmClXaH09snixDh48qIMHD1Z6N7zPwRMYd6hujLnaJz8//4LqKhW2rrnmGnXt2lUjRozQPffcI39//8rsRpLUsmVLpaen6/jx4/rkk080bNgwrVu3zrXeZrO51Rtjyiw729k15dWfbz8TJ07UuHHjXK/z8vLUuHFjxcfHq169euc9r3NxOp1KSUlRXFycfHx8Lmlf+D/btm1T165d1WPCbNVv3MLT7dQoWelrNCa+jd5KzVSDa9p4up0aY9/mVdr8/kuKfexVRbRs6+l2apSc/bu15k+Paf369WrXrt1Fb8/7HDyBcYfqxpirvUqfejufSoWtbdu26d1339X48eM1evRoDRkyRCNGjNBNN9100fvy9fXVNddcI0nq2LGjNm3apD//+c96+umnJf1yZyoiIsJVn52d7brbFR4erqKiIuXk5Ljd3crOzlaXLl1cNYcPHy5z3CNHjpS5a/Zrfn5+8vPzK7Pcx8enyv4yVeW+IHl5eamgoEDOEqnY8FsNfq2o+JdH5IrOGK7NrxQVl6igoIDrUg5niVRQUCAvL69Lep/ifQ6ewLhDdWPM1T4X+v2u1L8uYmJiNGPGDB08eFDz5s1TVlaWbrnlFrVu3VozZszQkSNHKrNbSb/ccSosLFR0dLTCw8PdbssWFRVp3bp1riDVoUMH+fj4uNVkZmZqx44drprY2Fjl5ubq22+/ddV88803ys3NddUAAAAAQFW7pP/K9fb21l133aUPP/xQL7/8sv7zn/9owoQJatSokR544AFlZmaec/tnnnlGX3/9tX766Sdt375dzz77rNauXav77rtPNptNiYmJmjp1qpYvX64dO3Zo+PDhCggI0NChQyVJDodDI0aM0Pjx47Vq1Spt3bpV999/v9q0aeOanbBVq1bq06ePRo4cqY0bN2rjxo0aOXKk+vfvz0yEAAAAACxzSbMRbt68We+++66WLFmiwMBATZgwQSNGjNChQ4c0adIkDRw40O2O0tkOHz6shIQEZWZmyuFwqG3btkpOTlZcXJwk6amnnlJBQYEee+wx5eTkqFOnTlqxYoWCgoJc+3jttdfk7e2twYMHq6CgQD179tT8+fNVp04dV80HH3ygsWPHumYtHDBggGbNmnUppw4AAAAA51SpsDVjxgzNmzdPu3btUr9+/fTee++pX79+8vL65UZZdHS05syZo+uuu+6c+5k7d+4519tsNiUlJSkpKanCGn9/f82cOVMzZ86ssCY4OFgLFy4857EAAAAAoCpVKmy9+eabeuihh/Tggw8qPDy83JomTZqcN0wBAAAAwJWqUmFr9+7d563x9fXVsGHDKrN7AAAAALjsVWqCjHnz5umjjz4qs/yjjz7SggULLrkpAAAAALjcVSpsvfTSSwoJCSmzPDQ0VFOnTr3kpgAAAADgclepsLV3715FR0eXWR4VFaV9+/ZdclMAAAAAcLmrVNgKDQ3V999/X2b5tm3b1KBBg0tuCgAAAAAud5UKW/fee6/Gjh2rNWvW6MyZMzpz5oxWr16tJ554Qvfee29V9wgAAAAAl51KzUb4wgsvaO/everZs6e8vX/ZRUlJiR544AE+swUAAAAAqmTY8vX11dKlS/X8889r27ZtstvtatOmjaKioqq6PwAAAAC4LFUqbJW69tprde2111ZVLwAAAABwxahU2Dpz5ozmz5+vVatWKTs7WyUlJW7rV69eXSXNoWL79u3T0aNHPd1GjZKRkeHpFgAAAACXSoWtJ554QvPnz9ftt9+umJgY2Wy2qu4L57Bv3z5dd10rFRTke7qVGslZWOTpFgAAAIDKha0lS5boww8/VL9+/aq6H1yAo0ePqqAgX50emqx6EU093U6Nkbk9TTs+e1vFxcWebgUAAACo/AQZ11xzTVX3gotUL6Kpgpu09HQbNUZe5k+ebgEAAABwqdTv2Ro/frz+/Oc/yxhT1f0AAAAAwBWhUne2NmzYoDVr1ujvf/+7WrduLR8fH7f1y5Ytq5LmAAAAAOByVamwddVVV+muu+6q6l4AAAAA4IpRqbA1b968qu4DAAAAAK4olfrMliQVFxdr5cqVmjNnjk6cOCFJOnTokE6ePFllzQEAAADA5apSd7b27t2rPn36aN++fSosLFRcXJyCgoI0ffp0nT59Wm+99VZV9wkAAAAAl5VK3dl64okn1LFjR+Xk5Mhut7uW33XXXVq1alWVNQcAAAAAl6tKz0b4z3/+U76+vm7Lo6KidPDgwSppDAAAAAAuZ5W6s1VSUqIzZ86UWX7gwAEFBQVdclMAAAAAcLmrVNiKi4vT66+/7npts9l08uRJTZ48Wf369auq3gAAAADgslWpxwhfe+019ejRQ9dff71Onz6toUOHavfu3QoJCdHixYurukcAAAAAuOxUKmxFRkYqPT1dixcv1nfffaeSkhKNGDFC9913n9uEGQAAAABQW1UqbEmS3W7XQw89pIceeqgq+wEAAACAK0KlwtZ77713zvUPPPBApZoBAAAAgCtFpcLWE0884fba6XQqPz9fvr6+CggIIGwBAAAAqPUqNRthTk6O29fJkye1a9cu3XLLLUyQAQAAAACqZNgqT4sWLfTSSy+VuesFAAAAALVRlYUtSapTp44OHTpUlbsEAAAAgMtSpT6z9dlnn7m9NsYoMzNTs2bN0s0331wljQEAAADA5axSYevOO+90e22z2dSwYUPddtt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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.histplot(df['Store Sales'], bins=10)\n",
    "\n",
    "plt.title('Distribution of Store Sales')\n",
    "plt.xlabel('Store Sales')\n",
    "plt.ylabel('Frequency')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e880459-0411-44b4-94bc-0290198713c2",
   "metadata": {},
   "source": [
    "# Scatter plot of Yearly Income vs Store Sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2e7d7288-2bbc-4247-bdca-703d62be06f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.scatterplot(data=df, x='Yearly Income', y='Store Sales')\n",
    "\n",
    "plt.title('Scatter Plot of Yearly Income vs Store Sales')\n",
    "plt.xlabel('Yearly Income')\n",
    "plt.ylabel('Store Sales')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e351b9a-cc64-4b1f-a5a7-56b8487bb787",
   "metadata": {},
   "source": [
    "# Now Machine learning phase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0dd2ec1b-ad8d-4256-8288-540bfee302bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.metrics import accuracy_score, classification_report , mean_squared_error"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae9a85ce-4eca-4845-b6b7-afff5a27f8fc",
   "metadata": {},
   "source": [
    "### Select Features and Target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d162e930-7514-4daa-a5d5-9821fc70bf57",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Eng Abd Allah\\AppData\\Local\\Temp\\ipykernel_10696\\1971861602.py:1: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  df[\"Is Recyclable?\"] = df[\"Is Recyclable?\"].replace({'true':1,'False':0})\n"
     ]
    }
   ],
   "source": [
    "df[\"Is Recyclable?\"] = df[\"Is Recyclable?\"].replace({'true':1,'False':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "86c0c295-6344-4c98-82e3-94da871a39d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['Store Cost']\n",
    "x = df[['Store Sales', 'Package Weight', 'Yearly Income', 'Store Area', 'Is Recyclable?']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0351c1c1-b0ae-48be-b7e6-a763c4eb932f",
   "metadata": {},
   "source": [
    "###  Split the Data into Training and Testing Sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "18d375c8-6401-4441-beaa-4fd773b0426e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape: (15953, 5)\n",
      "X_test shape: (3989, 5)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(x, y, test_size=0.2, random_state=0)\n",
    "print(\"X_train shape:\", X_train.shape)\n",
    "print(\"X_test shape:\", X_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efa501a4-70ab-40bf-b922-9404dc2bc30b",
   "metadata": {},
   "source": [
    "### Building the Linear Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "05f23718-ee0c-4689-af1b-192019236315",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "model.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e85dde66-bec5-4080-a6aa-4679221214ed",
   "metadata": {},
   "source": [
    "## Make Predictions and Evaluate the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "4b720efe-33dd-4618-b760-dc0d3d13f0c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 0.18952967979576207\n",
      "R Squared Score: 0.913459330857974\n"
     ]
    }
   ],
   "source": [
    "Y_pred = model.predict(X_test)\n",
    "\n",
    "mse = mean_squared_error(Y_test, Y_pred)\n",
    "r2 = r2_score(Y_test, Y_pred)\n",
    "\n",
    "print(\"Mean Squared Error:\", mse)\n",
    "print(\"R Squared Score:\", r2)"
   ]
  }
 ],
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