Bike Sales EDA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9dccc927",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5cd8c4a9",
   "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>Day</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>Customer_Age</th>\n",
       "      <th>Age_Group</th>\n",
       "      <th>Customer_Gender</th>\n",
       "      <th>Country</th>\n",
       "      <th>State</th>\n",
       "      <th>Product_Category</th>\n",
       "      <th>Sub_Category</th>\n",
       "      <th>Product</th>\n",
       "      <th>Order_Quantity</th>\n",
       "      <th>Unit_Cost</th>\n",
       "      <th>Unit_Price</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Cost</th>\n",
       "      <th>Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-11-26</td>\n",
       "      <td>26</td>\n",
       "      <td>November</td>\n",
       "      <td>2013</td>\n",
       "      <td>19</td>\n",
       "      <td>Youth (&lt;25)</td>\n",
       "      <td>M</td>\n",
       "      <td>Canada</td>\n",
       "      <td>British Columbia</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>Bike Racks</td>\n",
       "      <td>Hitch Rack - 4-Bike</td>\n",
       "      <td>8</td>\n",
       "      <td>45</td>\n",
       "      <td>120</td>\n",
       "      <td>590</td>\n",
       "      <td>360</td>\n",
       "      <td>950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-11-26</td>\n",
       "      <td>26</td>\n",
       "      <td>November</td>\n",
       "      <td>2015</td>\n",
       "      <td>19</td>\n",
       "      <td>Youth (&lt;25)</td>\n",
       "      <td>M</td>\n",
       "      <td>Canada</td>\n",
       "      <td>British Columbia</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>Bike Racks</td>\n",
       "      <td>Hitch Rack - 4-Bike</td>\n",
       "      <td>8</td>\n",
       "      <td>45</td>\n",
       "      <td>120</td>\n",
       "      <td>590</td>\n",
       "      <td>360</td>\n",
       "      <td>950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-03-23</td>\n",
       "      <td>23</td>\n",
       "      <td>March</td>\n",
       "      <td>2014</td>\n",
       "      <td>49</td>\n",
       "      <td>Adults (35-64)</td>\n",
       "      <td>M</td>\n",
       "      <td>Australia</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>Bike Racks</td>\n",
       "      <td>Hitch Rack - 4-Bike</td>\n",
       "      <td>23</td>\n",
       "      <td>45</td>\n",
       "      <td>120</td>\n",
       "      <td>1366</td>\n",
       "      <td>1035</td>\n",
       "      <td>2401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-03-23</td>\n",
       "      <td>23</td>\n",
       "      <td>March</td>\n",
       "      <td>2016</td>\n",
       "      <td>49</td>\n",
       "      <td>Adults (35-64)</td>\n",
       "      <td>M</td>\n",
       "      <td>Australia</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>Bike Racks</td>\n",
       "      <td>Hitch Rack - 4-Bike</td>\n",
       "      <td>20</td>\n",
       "      <td>45</td>\n",
       "      <td>120</td>\n",
       "      <td>1188</td>\n",
       "      <td>900</td>\n",
       "      <td>2088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-05-15</td>\n",
       "      <td>15</td>\n",
       "      <td>May</td>\n",
       "      <td>2014</td>\n",
       "      <td>47</td>\n",
       "      <td>Adults (35-64)</td>\n",
       "      <td>F</td>\n",
       "      <td>Australia</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>Bike Racks</td>\n",
       "      <td>Hitch Rack - 4-Bike</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "      <td>120</td>\n",
       "      <td>238</td>\n",
       "      <td>180</td>\n",
       "      <td>418</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  Day     Month  Year  Customer_Age       Age_Group  \\\n",
       "0  2013-11-26   26  November  2013            19     Youth (<25)   \n",
       "1  2015-11-26   26  November  2015            19     Youth (<25)   \n",
       "2  2014-03-23   23     March  2014            49  Adults (35-64)   \n",
       "3  2016-03-23   23     March  2016            49  Adults (35-64)   \n",
       "4  2014-05-15   15       May  2014            47  Adults (35-64)   \n",
       "\n",
       "  Customer_Gender    Country             State Product_Category Sub_Category  \\\n",
       "0               M     Canada  British Columbia      Accessories   Bike Racks   \n",
       "1               M     Canada  British Columbia      Accessories   Bike Racks   \n",
       "2               M  Australia   New South Wales      Accessories   Bike Racks   \n",
       "3               M  Australia   New South Wales      Accessories   Bike Racks   \n",
       "4               F  Australia   New South Wales      Accessories   Bike Racks   \n",
       "\n",
       "               Product  Order_Quantity  Unit_Cost  Unit_Price  Profit  Cost  \\\n",
       "0  Hitch Rack - 4-Bike               8         45         120     590   360   \n",
       "1  Hitch Rack - 4-Bike               8         45         120     590   360   \n",
       "2  Hitch Rack - 4-Bike              23         45         120    1366  1035   \n",
       "3  Hitch Rack - 4-Bike              20         45         120    1188   900   \n",
       "4  Hitch Rack - 4-Bike               4         45         120     238   180   \n",
       "\n",
       "   Revenue  \n",
       "0      950  \n",
       "1      950  \n",
       "2     2401  \n",
       "3     2088  \n",
       "4      418  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"Bike Sales in Europe.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b14db0a9",
   "metadata": {},
   "source": [
    "#### Data-Information:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "92688b87",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 113036 entries, 0 to 113035\n",
      "Data columns (total 18 columns):\n",
      " #   Column            Non-Null Count   Dtype \n",
      "---  ------            --------------   ----- \n",
      " 0   Date              113036 non-null  object\n",
      " 1   Day               113036 non-null  int64 \n",
      " 2   Month             113036 non-null  object\n",
      " 3   Year              113036 non-null  int64 \n",
      " 4   Customer_Age      113036 non-null  int64 \n",
      " 5   Age_Group         113036 non-null  object\n",
      " 6   Customer_Gender   113036 non-null  object\n",
      " 7   Country           113036 non-null  object\n",
      " 8   State             113036 non-null  object\n",
      " 9   Product_Category  113036 non-null  object\n",
      " 10  Sub_Category      113036 non-null  object\n",
      " 11  Product           113036 non-null  object\n",
      " 12  Order_Quantity    113036 non-null  int64 \n",
      " 13  Unit_Cost         113036 non-null  int64 \n",
      " 14  Unit_Price        113036 non-null  int64 \n",
      " 15  Profit            113036 non-null  int64 \n",
      " 16  Cost              113036 non-null  int64 \n",
      " 17  Revenue           113036 non-null  int64 \n",
      "dtypes: int64(9), object(9)\n",
      "memory usage: 15.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4fc76f09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(113036, 18)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "92deefc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date                0\n",
       "Day                 0\n",
       "Month               0\n",
       "Year                0\n",
       "Customer_Age        0\n",
       "Age_Group           0\n",
       "Customer_Gender     0\n",
       "Country             0\n",
       "State               0\n",
       "Product_Category    0\n",
       "Sub_Category        0\n",
       "Product             0\n",
       "Order_Quantity      0\n",
       "Unit_Cost           0\n",
       "Unit_Price          0\n",
       "Profit              0\n",
       "Cost                0\n",
       "Revenue             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5eec75fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Date', 'Day', 'Month', 'Year', 'Customer_Age', 'Age_Group',\n",
       "       'Customer_Gender', 'Country', 'State', 'Product_Category',\n",
       "       'Sub_Category', 'Product', 'Order_Quantity', 'Unit_Cost', 'Unit_Price',\n",
       "       'Profit', 'Cost', 'Revenue'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ad37857f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Accessories    70120\n",
       "Bikes          25982\n",
       "Clothing       16934\n",
       "Name: Product_Category, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Product_Category.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1b1a896a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Customer_Gender  Product_Category\n",
       "F                Accessories         33844\n",
       "                 Bikes               12852\n",
       "                 Clothing             8028\n",
       "M                Accessories         36276\n",
       "                 Bikes               13130\n",
       "                 Clothing             8906\n",
       "Name: Product_Category, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"Customer_Gender\")[\"Product_Category\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c9b6d034",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "United States     39206\n",
       "Australia         23936\n",
       "Canada            14178\n",
       "United Kingdom    13620\n",
       "Germany           11098\n",
       "France            10998\n",
       "Name: Country, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Country.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cdd277df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Customer_Gender  Country       \n",
       "F                United States     18934\n",
       "                 Australia         11506\n",
       "                 United Kingdom     6712\n",
       "                 Canada             6496\n",
       "                 Germany            5572\n",
       "                 France             5504\n",
       "M                United States     20272\n",
       "                 Australia         12430\n",
       "                 Canada             7682\n",
       "                 United Kingdom     6908\n",
       "                 Germany            5526\n",
       "                 France             5494\n",
       "Name: Country, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"Customer_Gender\")[\"Country\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "59cb2e31",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California             22450\n",
       "British Columbia       14116\n",
       "England                13620\n",
       "Washington             11264\n",
       "New South Wales        10412\n",
       "Victoria                6016\n",
       "Oregon                  5286\n",
       "Queensland              5220\n",
       "Saarland                2770\n",
       "Nordrhein-Westfalen     2484\n",
       "Hessen                  2384\n",
       "Seine (Paris)           2328\n",
       "Hamburg                 1836\n",
       "Seine Saint Denis       1684\n",
       "Nord                    1670\n",
       "South Australia         1564\n",
       "Bayern                  1426\n",
       "Hauts de Seine          1084\n",
       "Essonne                  994\n",
       "Yveline                  954\n",
       "Tasmania                 724\n",
       "Seine et Marne           394\n",
       "Moselle                  386\n",
       "Loiret                   382\n",
       "Val d'Oise               264\n",
       "Garonne (Haute)          208\n",
       "Brandenburg              198\n",
       "Val de Marne             158\n",
       "Charente-Maritime        148\n",
       "Somme                    134\n",
       "Loir et Cher             120\n",
       "Pas de Calais             90\n",
       "Alberta                   56\n",
       "Texas                     30\n",
       "Ohio                      28\n",
       "Illinois                  28\n",
       "New York                  20\n",
       "Florida                   14\n",
       "Kentucky                  10\n",
       "South Carolina            10\n",
       "Utah                      10\n",
       "Wyoming                    8\n",
       "Georgia                    8\n",
       "Ontario                    6\n",
       "Missouri                   6\n",
       "Montana                    6\n",
       "Minnesota                  6\n",
       "Virginia                   4\n",
       "North Carolina             4\n",
       "Mississippi                4\n",
       "Arizona                    4\n",
       "Alabama                    4\n",
       "Massachusetts              2\n",
       "Name: State, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.State.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "eca99c5c",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Tires and Tubes      33870\n",
       "Bottles and Cages    15876\n",
       "Road Bikes           13430\n",
       "Helmets              12158\n",
       "Mountain Bikes        8854\n",
       "Jerseys               6010\n",
       "Caps                  4358\n",
       "Fenders               4032\n",
       "Touring Bikes         3698\n",
       "Gloves                2686\n",
       "Cleaners              1802\n",
       "Shorts                1794\n",
       "Hydration Packs       1334\n",
       "Socks                 1122\n",
       "Vests                  964\n",
       "Bike Racks             592\n",
       "Bike Stands            456\n",
       "Name: Sub_Category, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Sub_Category.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "213b602e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Customer_Gender  Sub_Category     \n",
       "F                Tires and Tubes      16338\n",
       "                 Bottles and Cages     7808\n",
       "                 Road Bikes            6538\n",
       "                 Helmets               5794\n",
       "                 Mountain Bikes        4474\n",
       "                 Jerseys               2834\n",
       "                 Caps                  2162\n",
       "                 Fenders               1878\n",
       "                 Touring Bikes         1840\n",
       "                 Gloves                1210\n",
       "                 Cleaners               862\n",
       "                 Shorts                 834\n",
       "                 Hydration Packs        648\n",
       "                 Socks                  536\n",
       "                 Vests                  452\n",
       "                 Bike Racks             268\n",
       "                 Bike Stands            248\n",
       "M                Tires and Tubes      17532\n",
       "                 Bottles and Cages     8068\n",
       "                 Road Bikes            6892\n",
       "                 Helmets               6364\n",
       "                 Mountain Bikes        4380\n",
       "                 Jerseys               3176\n",
       "                 Caps                  2196\n",
       "                 Fenders               2154\n",
       "                 Touring Bikes         1858\n",
       "                 Gloves                1476\n",
       "                 Shorts                 960\n",
       "                 Cleaners               940\n",
       "                 Hydration Packs        686\n",
       "                 Socks                  586\n",
       "                 Vests                  512\n",
       "                 Bike Racks             324\n",
       "                 Bike Stands            208\n",
       "Name: Sub_Category, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"Customer_Gender\")[\"Sub_Category\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "da9fc861",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Water Bottle - 30 oz.      10794\n",
       "Patch Kit/8 Patches        10416\n",
       "Mountain Tire Tube          6816\n",
       "AWC Logo Cap                4358\n",
       "Sport-100 Helmet, Red       4220\n",
       "                           ...  \n",
       "Mountain-100 Silver, 48       58\n",
       "Mountain-100 Silver, 42       54\n",
       "Mountain-500 Silver, 48       40\n",
       "Road-650 Red, 52              32\n",
       "Mountain-500 Black, 52        22\n",
       "Name: Product, Length: 130, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Product.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "93a3c504",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Day</th>\n",
       "      <th>Year</th>\n",
       "      <th>Customer_Age</th>\n",
       "      <th>Order_Quantity</th>\n",
       "      <th>Unit_Cost</th>\n",
       "      <th>Unit_Price</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Cost</th>\n",
       "      <th>Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>113036.000000</td>\n",
       "      <td>113036.000000</td>\n",
       "      <td>113036.000000</td>\n",
       "      <td>113036.000000</td>\n",
       "      <td>113036.000000</td>\n",
       "      <td>113036.000000</td>\n",
       "      <td>113036.000000</td>\n",
       "      <td>113036.000000</td>\n",
       "      <td>113036.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>15.665753</td>\n",
       "      <td>2014.401739</td>\n",
       "      <td>35.919212</td>\n",
       "      <td>11.901660</td>\n",
       "      <td>267.296366</td>\n",
       "      <td>452.938427</td>\n",
       "      <td>285.051665</td>\n",
       "      <td>469.318695</td>\n",
       "      <td>754.370360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.781567</td>\n",
       "      <td>1.272510</td>\n",
       "      <td>11.021936</td>\n",
       "      <td>9.561857</td>\n",
       "      <td>549.835483</td>\n",
       "      <td>922.071219</td>\n",
       "      <td>453.887443</td>\n",
       "      <td>884.866118</td>\n",
       "      <td>1309.094674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2011.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>-30.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>2013.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>63.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>223.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>23.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>358.000000</td>\n",
       "      <td>432.000000</td>\n",
       "      <td>800.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>2171.000000</td>\n",
       "      <td>3578.000000</td>\n",
       "      <td>15096.000000</td>\n",
       "      <td>42978.000000</td>\n",
       "      <td>58074.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Day           Year   Customer_Age  Order_Quantity  \\\n",
       "count  113036.000000  113036.000000  113036.000000   113036.000000   \n",
       "mean       15.665753    2014.401739      35.919212       11.901660   \n",
       "std         8.781567       1.272510      11.021936        9.561857   \n",
       "min         1.000000    2011.000000      17.000000        1.000000   \n",
       "25%         8.000000    2013.000000      28.000000        2.000000   \n",
       "50%        16.000000    2014.000000      35.000000       10.000000   \n",
       "75%        23.000000    2016.000000      43.000000       20.000000   \n",
       "max        31.000000    2016.000000      87.000000       32.000000   \n",
       "\n",
       "           Unit_Cost     Unit_Price         Profit           Cost  \\\n",
       "count  113036.000000  113036.000000  113036.000000  113036.000000   \n",
       "mean      267.296366     452.938427     285.051665     469.318695   \n",
       "std       549.835483     922.071219     453.887443     884.866118   \n",
       "min         1.000000       2.000000     -30.000000       1.000000   \n",
       "25%         2.000000       5.000000      29.000000      28.000000   \n",
       "50%         9.000000      24.000000     101.000000     108.000000   \n",
       "75%        42.000000      70.000000     358.000000     432.000000   \n",
       "max      2171.000000    3578.000000   15096.000000   42978.000000   \n",
       "\n",
       "             Revenue  \n",
       "count  113036.000000  \n",
       "mean      754.370360  \n",
       "std      1309.094674  \n",
       "min         2.000000  \n",
       "25%        63.000000  \n",
       "50%       223.000000  \n",
       "75%       800.000000  \n",
       "max     58074.000000  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4ddd00d2",
   "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>Day</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>Customer_Age</th>\n",
       "      <th>Age_Group</th>\n",
       "      <th>Customer_Gender</th>\n",
       "      <th>Country</th>\n",
       "      <th>State</th>\n",
       "      <th>Product_Category</th>\n",
       "      <th>Sub_Category</th>\n",
       "      <th>Product</th>\n",
       "      <th>Order_Quantity</th>\n",
       "      <th>Unit_Cost</th>\n",
       "      <th>Unit_Price</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Cost</th>\n",
       "      <th>Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-11-26</td>\n",
       "      <td>26</td>\n",
       "      <td>November</td>\n",
       "      <td>2013</td>\n",
       "      <td>19</td>\n",
       "      <td>Youth (&lt;25)</td>\n",
       "      <td>M</td>\n",
       "      <td>Canada</td>\n",
       "      <td>British Columbia</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>Bike Racks</td>\n",
       "      <td>Hitch Rack - 4-Bike</td>\n",
       "      <td>8</td>\n",
       "      <td>45</td>\n",
       "      <td>120</td>\n",
       "      <td>590</td>\n",
       "      <td>360</td>\n",
       "      <td>950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-11-26</td>\n",
       "      <td>26</td>\n",
       "      <td>November</td>\n",
       "      <td>2015</td>\n",
       "      <td>19</td>\n",
       "      <td>Youth (&lt;25)</td>\n",
       "      <td>M</td>\n",
       "      <td>Canada</td>\n",
       "      <td>British Columbia</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>Bike Racks</td>\n",
       "      <td>Hitch Rack - 4-Bike</td>\n",
       "      <td>8</td>\n",
       "      <td>45</td>\n",
       "      <td>120</td>\n",
       "      <td>590</td>\n",
       "      <td>360</td>\n",
       "      <td>950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-03-23</td>\n",
       "      <td>23</td>\n",
       "      <td>March</td>\n",
       "      <td>2014</td>\n",
       "      <td>49</td>\n",
       "      <td>Adults (35-64)</td>\n",
       "      <td>M</td>\n",
       "      <td>Australia</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>Bike Racks</td>\n",
       "      <td>Hitch Rack - 4-Bike</td>\n",
       "      <td>23</td>\n",
       "      <td>45</td>\n",
       "      <td>120</td>\n",
       "      <td>1366</td>\n",
       "      <td>1035</td>\n",
       "      <td>2401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-03-23</td>\n",
       "      <td>23</td>\n",
       "      <td>March</td>\n",
       "      <td>2016</td>\n",
       "      <td>49</td>\n",
       "      <td>Adults (35-64)</td>\n",
       "      <td>M</td>\n",
       "      <td>Australia</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>Bike Racks</td>\n",
       "      <td>Hitch Rack - 4-Bike</td>\n",
       "      <td>20</td>\n",
       "      <td>45</td>\n",
       "      <td>120</td>\n",
       "      <td>1188</td>\n",
       "      <td>900</td>\n",
       "      <td>2088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-05-15</td>\n",
       "      <td>15</td>\n",
       "      <td>May</td>\n",
       "      <td>2014</td>\n",
       "      <td>47</td>\n",
       "      <td>Adults (35-64)</td>\n",
       "      <td>F</td>\n",
       "      <td>Australia</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>Bike Racks</td>\n",
       "      <td>Hitch Rack - 4-Bike</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "      <td>120</td>\n",
       "      <td>238</td>\n",
       "      <td>180</td>\n",
       "      <td>418</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  Day     Month  Year  Customer_Age       Age_Group  \\\n",
       "0  2013-11-26   26  November  2013            19     Youth (<25)   \n",
       "1  2015-11-26   26  November  2015            19     Youth (<25)   \n",
       "2  2014-03-23   23     March  2014            49  Adults (35-64)   \n",
       "3  2016-03-23   23     March  2016            49  Adults (35-64)   \n",
       "4  2014-05-15   15       May  2014            47  Adults (35-64)   \n",
       "\n",
       "  Customer_Gender    Country             State Product_Category Sub_Category  \\\n",
       "0               M     Canada  British Columbia      Accessories   Bike Racks   \n",
       "1               M     Canada  British Columbia      Accessories   Bike Racks   \n",
       "2               M  Australia   New South Wales      Accessories   Bike Racks   \n",
       "3               M  Australia   New South Wales      Accessories   Bike Racks   \n",
       "4               F  Australia   New South Wales      Accessories   Bike Racks   \n",
       "\n",
       "               Product  Order_Quantity  Unit_Cost  Unit_Price  Profit  Cost  \\\n",
       "0  Hitch Rack - 4-Bike               8         45         120     590   360   \n",
       "1  Hitch Rack - 4-Bike               8         45         120     590   360   \n",
       "2  Hitch Rack - 4-Bike              23         45         120    1366  1035   \n",
       "3  Hitch Rack - 4-Bike              20         45         120    1188   900   \n",
       "4  Hitch Rack - 4-Bike               4         45         120     238   180   \n",
       "\n",
       "   Revenue  \n",
       "0      950  \n",
       "1      950  \n",
       "2     2401  \n",
       "3     2088  \n",
       "4      418  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fbe9f3e",
   "metadata": {},
   "source": [
    "### Data Visulization:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3c4b9a9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bf5b3078",
   "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": [
    "(df\n",
    " .groupby(\"Country\")[\"Cost\"]\n",
    " .mean()\n",
    " .sort_values(ascending=False)\n",
    " .plot(kind=\"bar\",xlabel=\"Country\", ylabel=\"Cost\",title =\"Costs in Countries\")\n",
    ");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3232436c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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sv7hcIBgRER1SFxbjbd/Qu7G0TR7KG9q+kupA9bXADaWG44F/BA6XtJDqmMQJZZUTgI1L++HA7KG8b0REDF3dMYuxAyxbd6hvavso4KhezXdQnWXVu+/TVKfsRkREQ+pGFvMlfax3o6S/pjqDKSIiRoC6kcVngLMkHchL4TANWAv4QBvrioiIYWTAsLD9ILCzpN2ArUvzObYvbntlERExbAzqOgvbl1CdrRQRESPQkK7CjoiIkSVhERERtRIWERFRK2ERERG1EhYREVErYREREbUSFhERUSthERERtRIWERFRK2ERERG1EhYREVErYREREbUSFhERUauRsJA0VtJPJN0q6RZJfyppI0kXSrq9/Nyw9JWk70haKGmBpO2bqDkiYiRramTxbeA821sC2wK3UD1b+yLbU4GLeOlZ23sCU8trFnBc58uNiBjZOh4WksYA7wJOALD9rO3HgBnASaXbScA+ZXoGMNeVK4Cxkl7X0aIjIka4JkYWU4ClwI8k/VbSDyWtD4y3fX/p8wAwvkxPAO5tWX9RaYuIiA5pIixGA9sDx9l+K/AHXtrlBIBtA16RjUqaJWm+pPlLly5dZcVGREQzYbEIWGT7yjL/E6rweLBn91L5uaQsXwxMbFl/09L2MraPtz3N9rRx48a1rfiIiJGo42Fh+wHgXklvLE3TgZuBecDM0jYT+HmZngccXM6K2glY1rK7KiIiOmB0Q+/7KeDHktYC7gAOoQquMyQdCtwN7Ff6ngvsBSwEnix9IyKigxoJC9vXAdP6WDS9j74GDmt3TRER0b9cwR0REbUSFhERUSthERERtRIWERFRK2ERERG1EhYREVErYREREbUSFhERUSthERERtRIWERFRK2ERERG1EhYREVErYREREbUSFhERUSthERERtRIWERFRK2ERERG1GgsLSaMk/VbS2WV+iqQrJS2UdHp55CqS1i7zC8vyyU3VHBExUjU5svg0cEvL/NeAY2xvDjwKHFraDwUeLe3HlH4REdFBjYSFpE2B9wE/LPMC3g38pHQ5CdinTM8o85Tl00v/iIjokKZGFt8CPge8WOY3Bh6z/XyZXwRMKNMTgHsByvJlpX9ERHRIx8NC0t7AEtvXrOLtzpI0X9L8pUuXrspNR0SMeE2MLHYB3i/pLuA0qt1P3wbGShpd+mwKLC7Ti4GJAGX5GODh3hu1fbztabanjRs3rr2fICJihOl4WNg+0vamticD+wMX2z4QuATYt3SbCfy8TM8r85TlF9t2B0uOiBjxhtN1Fv8IHC5pIdUxiRNK+wnAxqX9cGB2Q/VFRIxYo+u7tI/tS4FLy/QdwA599Hka+GBHC4uIiJcZTiOLiIgYphIWERFRK2ERERG1EhYREVErYREREbUaPRsqYjiYPPucpktoq7uOfl/TJcRqICOLiIiolbCIiIhaCYuIiKiVsIiIiFoJi4iIqJWwiIiIWgmLiIiolbCIiIhaCYuIiKiVsIiIiFoJi4iIqJWwiIiIWh0PC0kTJV0i6WZJN0n6dGnfSNKFkm4vPzcs7ZL0HUkLJS2QtH2na46IGOmaGFk8DxxheytgJ+AwSVsBs4GLbE8FLirzAHsCU8trFnBc50uOiBjZOh4Wtu+3fW2ZXg7cAkwAZgAnlW4nAfuU6RnAXFeuAMZKel1nq46IGNkaPWYhaTLwVuBKYLzt+8uiB4DxZXoCcG/LaotKW+9tzZI0X9L8pUuXtq/oiIgRqLGwkLQB8FPgM7Yfb11m24BXZHu2j7c9zfa0cePGrcJKIyKikbCQtCZVUPzY9s9K84M9u5fKzyWlfTEwsWX1TUtbRER0SBNnQwk4AbjF9jdbFs0DZpbpmcDPW9oPLmdF7QQsa9ldFRERHdDEM7h3AQ4CbpB0XWn7PHA0cIakQ4G7gf3KsnOBvYCFwJPAIR2tNiIiOh8Wtn8FqJ/F0/vob+CwthYVEREDyhXcERFRK2ERERG1EhYREVErYREREbUSFhERUSthERERtRIWERFRK2ERERG1EhYREVGridt9RESsMpNnn9N0CW1z19Hva7qEP8rIIiIiaiUsIiKiVsIiIiJqJSwiIqJWwiIiImolLCIiolbCIiIianVNWEjaQ9JtkhZKmt10PRERI0lXhIWkUcD3gD2BrYADJG3VbFURESNHV4QFsAOw0PYdtp8FTgNmNFxTRMSI0S23+5gA3NsyvwjYsbWDpFnArDL7hKTbOlRbEzYBHurUm+lrnXqnESO/v+61uv/uXt/fgm4Ji1q2jweOb7qOTpA03/a0puuIocnvr3uN5N9dt+yGWgxMbJnftLRFREQHdEtYXA1MlTRF0lrA/sC8hmuKiBgxumI3lO3nJX0SOB8YBcyxfVPDZTVpROxuW43l99e9RuzvTrabriEiIoa5btkNFRERDUpYRERErYRFRETUSlhERAySpA0lbdN0HU3IAe4uIenrwFeAp4DzgG2Az9o+pdHCopakXwC9/6EtA+YD37f9dOerisGSdCnwfqqzR68BlgC/tn14k3V1WkYW3WN3248DewN3AZsD/9BoRTFYdwBPAD8or8eB5cAWZT6GtzHl395fAHNt7wi8p+GaOq4rrrMI4KXf1fuAM20vk9RkPTF4O9t+e8v8LyRdbfvtkkby9ULdYrSk1wH7AV9oupimZGTRPc6WdCvwNuAiSeOA7L7oDhtImtQzU6Y3KLPPNlNSrIAvUV0Q/HvbV0t6A3B7wzV1XI5ZdBFJGwHLbL8gaX3gVbYfaLquGJikvYD/AH4PCJgCfAK4FPiY7W81VlzEICUsuoSk9YDDgUm2Z0maCrzR9tkNlxaDIGltYMsye1sOancPSVsAxwHjbW9dzoZ6v+2vNFxaR2U3VPf4EdUui53L/GKqs6OiO7wNeDOwLbCfpIMbricG7wfAkcBzALYXUN3MdETJAe7usZntD0k6AMD2k8oR7q4g6WRgM+A64IXSbGBuUzXFClnP9lW9/rk931QxTUlYdI9nJa1LOV9f0mbAM82WFIM0DdjK2efbrR4q/956/u3tC9zfbEmdl7DoHkdRXYw3UdKPgV2AjzZaUQzWjcBrGYFfMKuJw6huTb6lpMXAncBHmi2p83KAu0uUM6EE7FR+XkF1NtSdjRYWtSRdAmwHXEXLaND2+5uqKVZcOQNxDdvLm66lCQmLLiHp18Ce5UpSJL2J6uK8rZutLOpI+rO+2m1f1ulaYvAkfcT2KZL6vK2H7W92uqYmZTdU9/hXqit/96I6BXMucGCzJcVgJBS61vrl56sarWKYSFh0CdvnSFoTuJDqf94P2P5dw2XFACT9yvY7JC3n5TcSFGDbr26otBgE29+XNAp43PYxTdfTtOyGGuYk/V9e/kUznepK4LsAbP9dA2VFjBiSrrK9Q9N1NC0ji+Fvfq/5axqpIlZK+Qt1PC3/5mzf01xFsQJ+Lem7wOnAH3oabV/bXEmdl5FFRJtJ+hTVqc8PAi+WZtsekQ/R6TblbLbebPvdHS+mQQmLYU7SGbb3k3QDr3yADvnCGf4kLQR2tP1w07VEDFV2Qw1/ny4/9260ilgZ91I9GS+6kKQxVCPDd5Wmy4Av2R5Rv9OMLLqQpE2Ah3P7iOGt5fz8NwNvBM7h5Rfljajz9LuVpJ9SXYV/Umk6CNjW9l80V1XnZWQxzEnaCTgaeAT4MnAysAmwhqSDbZ/XZH0xoJ7z8+8pr7XKC/rYpRjD1ma2/7Jl/ouSrmuqmKYkLIa/7wKfB8YAF1NdxX2FpC2BU6nuFxXDkO0vAkj6oO0zW5dJ+mAzVcUQPCXpHbZ/BSBpF+CphmvquOyGGuYkXWd7uzJ9i+03tSz7re23NlZcDIqka21vX9cWw5Ok7ah2QY2huqDyEeCjtq9vsq5Oy8hi+HuxZbr3XzNJ+mFM0p7AXsAESd9pWfRqRuDzELqV7euAbSW9usw/3mxFzciT8oa/bSU9Xm4ZsU2Z7pl/S9PFxYDuo7qo8mmqiyl7XvOA9zZYV6wASZ8uQbEc+KakayXt3nRdnZbdUBFtVu7pJWCL0nSb7ecaLClWgKTrbW8r6b3Ax4F/Ak4eabsRsxsqov12prpL8F1UoTFR0kzbv2y0qhisnuep7gXMtX3TSHykcUYWEW0m6Rrgw7ZvK/NbAKfafluzlcVgSPoRMAGYAmwLjAIuHWm/v4RFRJtJWtD7tix9tcXwJGkNqicd3mH7sfLUyk1tL2i2ss7KbqiI9psv6YfAKWX+QF55N+EYvv4UuM72HyR9BNge+HbDNXVcRhYRbSZpbeAw4B2l6XLgWNvP9L9WDBeSFlDtftoGOBH4IbCf7T4fl7u6SlhERAyg5wJKSf8bWGz7hJF4UWV2Q0W0SX+3le+RYxZdY7mkI6luIPjOcgxjzYZr6riMLCLaRNJUqqfj3dtr0UTgAdsLO19VrChJrwU+DFxt+3JJk4Bdbc9tuLSOyhXcEe1zDLDM9t2tL6pnWxzTcG0xSLYfAH4KrF2aHgLOaq6iZiQsItpnvO0bejeWtsmdLyeGQtLHgJ8A3y9NE4D/aqyghiQsItpn7ADL1u1UEbHSDgN2AR4HsH078JpGK2pAwiKifeaXv0pfRtJfU91QMLrDM7af7ZmRNJoReMfnnA0V0T6fAc6SdCAvhcM0qqflfaCpomKFXSbp88C6kv4c+ATwi4Zr6ricDRXRZpJ2A7YuszfZvrjJemLFlFNlDwV2p7qp4PnADz3CvjwTFhERA5C0PvC07RfK/ChgbdtPNltZZ+WYRUTEwC7i5SckrAv8T0O1NCZhERExsHVsP9EzU6bXa7CeRiQsIiIG9gdJf7wPlKS3AU81WE8jcjZURMTAPgOcKek+qgPcrwU+1GhFDcgB7oiIGuU56m8ssyPyGerZDRURMQBJhwHr277R9o3ABpI+0XRdnZaRRUTEACRdZ3u7Xm2/tf3WhkpqREYWEREDGyVJPTPlOou1GqynETnAHRExsPOA0yX13HX2b4D/brCeRmQ3VETEAMrtPmYB00vTAuC1tg9rrqrOy26oiIgB2H4RuBK4C9gBeDdwS5M1NSG7oSIi+iBpC+CA8noIOB3A9m5N1tWU7IaKiOiDpBeBy4FDe56XLukO229otrJmZDdURETf/gK4H7hE0g8kTae6gntEysgiImIA5RblM6h2R70bmAucZfuCRgvrsIRFRMQgSdoQ+CDwIdvT6/qvThIWERFRK8csIiKiVsIiIiJqJSwiIqJWwiJWC5JekHSdpBslnSlpyI+9lHSppGlDWG/sYG5dLWkLSedKul3StZLOkDR+gP6TJX14ReuJWJUSFrG6eMr2dra3Bp4FPt66UFIn7lYwFhgwLCStA5wDHGd7qu3tgWOBcQOsNhloe1iUu6lG9ClhEaujy4HNJe0q6XJJ84CbJa0j6UeSbpD0W0m7AUhaV9Jpkm6RdBawbs+GJD3RMr2vpBPL9HhJZ0m6vrx2Bo4GNisjnG/0U9uHgd/Y/kVPg+1Lbd9YRhCXl9HGtWWblO2+s2z3s5JGSfqGpKslLZD0N6WmNSQdK+lWSReW0cu+Zdn08plvkDRH0tql/S5JX5N0LTC7/Oz5vFNb52Nky72hYrVSRhB7Ut1WGmB7YGvbd0o6ArDtt0jaErig3P/nb4Enbb9J0jbAYL4gvwNcZvsD5S/yDYDZ5b22G2C9rYFr+lm2BPhz209LmgqcCkwr2/1723uXzzgLWGb77eVL/9eSLgDeRjUK2Qp4DdXN7uaU0cyJwHTbv5M0t3zmb5X3fbiMcJD0Hknb2b4OOAT40SD+W8QIkJFFrC7WlXQdMB+4BzihtF9l+84y/Q7gFADbtwJ3A1sA72ppX0B1C+o67waOK+u8YHvZKvgMawI/kHQDcCbVl35fdgcOLp/3SmBjYCrV5zvT9ou2HwAuKf3fCNxp+3dl/iSqz9zj9JbpHwKHlAD8EPCfK/2pYrWQkUWsLp7q49GXAH9Yye22XrW6zkpuC+Am4M/6WfZZ4EFgW6o/5J7up5+AT9k+/2WN0l5DrKn1v9FPgaOAi4FrbD88xG3GaiYjixhJLgcOhD/efnoScBvwS8oBZElbA9u0rPOgpDeVB+B8oKX9IqpdOZRjCGOA5cCramr4T2BnSe/raZD0rvK+Y4D7y/MTDgJ6Djj33u75wN9KWrPns5T7F/0a+Mty7GI8sGvpfxswWdLmZf4g4LK+irP9dNn+cWQXVLRIWMRIciywRtnNczrwUdvPUH0xbiDpFuBLvPyYwmzgbOD/Ud2BtMengd3Ktq4Btip/hf+6nL7b5wFu208BewOfKqfO3kx1BtXSUt9MSdcDW/LSX/wLgBfKgfTPUu0quhm4VtKNwPep9hL8FFhUlp1CdexlWQmAQ4AzS70vAv8xwH+nH5c+I+pGeTGw3BsqYjUiaQPbT0jaGLgK2KUcv1iRbfw9MMb2P7elyOhKOWYRsXo5W9JYYC3gy0MIirOAzagO4Ef8UUYWEW0g6S3Ayb2an7G9YxP1RKyshEVERNTKAe6IiKiVsIiIiFoJi4iIqJWwiIiIWgmLiIio9f8BOI4DgsX2C4wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(df\n",
    " .groupby(\"Product_Category\")[\"Cost\"]\n",
    " .mean()\n",
    " .sort_values(ascending=False)\n",
    " .plot(kind=\"bar\",xlabel=\"Product_Category\", ylabel=\"Cost\",title =\"Costs in States\")\n",
    ");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b941f865",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "sns.countplot(df[\"Year\"])\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Number of Transaction')\n",
    "plt.title('Yearly Transaction');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6a301c96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "sns.countplot(df[\"Month\"])\n",
    "plt.xlabel('Month')\n",
    "plt.ylabel('Number of Transaction')\n",
    "plt.title('Monthly Transaction');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4db0014e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zkIskSZK6TFmoJflwVb0+yVfpPNvzfqpq75FmJkmStIbrdURt4pq0989FIpIkSbq/KQu1qrqoLe5QVR/p3pbkUOC7o0xMkiRpTdfPZIL9J2l7xSznIUmSpFX0ukbtRcCLgW2TLOnatD6dh6ZLkiRphHpdo/Z/gOuATYAPdLXfBlw6yqQkSZLU+xq1a4BrkrwE+FVV/Q4gyXrAlsDVc5KhJEnSGqqfa9ROBu7pWr8b+NJo0pEkSdKEfgq1tavq9xMrbXnd0aUkSZIk6K9QW5nk3pvbJtkH+K/RpSRJkiTo76HsrwZOSPIxIMC1wMtHmpUkSZL6etbnz4Bdkjy0rd8+8qwkSZLU1xE1kuwFPBZ4UBIAqupdI8xLkiRpjTftNWpJPgX8DfBaOqc+XwA8csR5SZIkrfH6mUzwl1X1cuDmqnon8BfAo0ebliRJkvop1P67/fxtks2BO4HNpuuUZKsk5yS5MskV7UHuJHlHkuVJLmmvPbv6vCnJ0iQ/SfKMrvY9WtvSJIcP9hElSZJWT/1co3ZGkg2B9wEXAwV8po9+dwGHVdXFSdYHLkpyVtv2oap6f/fOSbYH9qNzLdzmwLeTTBy5+zjwdGAZcEGSJVV1ZR85SJIkrbb6mfV5ZFs8NckZwIOq6tY++l1H51mhVNVtSa4CtujRZR/gpKq6A/hFkqXATm3b0qr6OUCSk9q+FmqSJGlB62cywQvaETGANwL/nuQJg7xJkm2AJwDntaZDklya5NgkG7W2Lejco23CstY2VbskSdKC1s81am9rR8SeCDwNOAb4VL9v0O6/dirw+qr6NfBJ4FHADnSOuH1g0KSneJ+DklyY5MKVK1fORkhJkqR51U+hdnf7uRdwdFV9jT6f9ZlkHTpF2glV9WWAqrqhqu6uqnuAT3Pf6c3lwFZd3bdsbVO1309VHV1Vi6tq8aJFi/pJT5Ikaaz1U6gtT/JvdO6ldmaSB/bTL5074x4DXFVVH+xq754x+hzg8ra8BNgvyQOTbAtsB5wPXABsl2TbJOvSmXCwpI+8JUmSVmv9zPp8IbAH8P6quqUVWm/so99fAS8DLktySWt7M/CiJDvQmT16NfAqgKq6IsnJdCYJ3AUcXFV3AyQ5BPgmsBZwbFVd0denkyRJWo31M+vzt0lOBzZNsnVr/nEf/b5P50kGqzqzR5+jgKMmaT+zVz9JkqSFaNpCLclrgSOAG4B7WnMBfzrCvCRJktZ4/Zz6PBR4TFXdOOpkJEmSdJ9+JhNcC0x7g1tJkiTNrn6OqP0cODfJ14A7Jhq7Z3JKkiRp9vVTqP2yvdalz/unSZIkaeb6mfX5zrlIRJIkSffXz6zPRcA/Ao8FHjTRXlW7jjAvSZKkNV4/kwlOoHPftG2Bd9K5Se0FI8xJkiRJ9FeoPbyqjgHurKrvVtUrAY+mSZIkjVg/kwnubD+vS7IX8Ctg49GlJEmSJOivUHt3kg2Aw4B/BR4G/P1Is5IkSVLvQi3JWsB2VXUGnZvePnVOspIkSVLva9Sq6m7gRXOUiyRJkrr0c+rz/0/yMeCLwG8mGqvq4pFlJUmSpKkLtSTfqqrdgR1a07u6NhfO/JQkSRqpXkfUFgFUldelSZIkzYNehdoGSZ471caq+vII8pEkSVqtrPj4V4fu+4iDn91ze89CDXgWkEm2FWChJkmSNEK9CrVr2lMIJEmSNA963Z5jsiNpkiRJmiO9CrWXzVkWkiRJ+h+mLNSq6vK5TESSJEn31/PJBJIkSZo/UxZqSc5uP987TOAkWyU5J8mVSa5Icmhr3zjJWUl+2n5u1NqT5KNJlia5NMmOXbH2b/v/NMn+w+QjSZK0uul1RG2zJH8J7J3kCUl27H71Efsu4LCq2h7YBTg4yfbA4cDZVbUdcHZbB3gmsF17HQR8EjqFHXAEsDOwE3DERHEnSZK0kPW6PcfbgbcBWwIfXGXbtI+QqqrrgOva8m1JrgK2APYBntJ2Ow44F/in1n58VRXwgyQbJtms7XtWVd0EkOQsYA/gxL4+oSRJ0mpqykKtqk4BTknytqo6ciZvkmQb4AnAecCmrYgDuB7YtC1vAVzb1W1Za5uqXZIkaUHrdUQNgKo6MsnewJNa07lVdUa/b5DkocCpwOur6tfJfbdnq6pKUgPmPNX7HETnlClbb731bISUJEmaV9PO+kzyv4FDgSvb69Ak/9xP8CTr0CnSTuh6NugN7ZQm7eeK1r4c2Kqr+5atbar2+6mqo6tqcVUtXrRoUT/pSZIkjbV+bs+xF/D0qjq2qo6lc33Ys6brlM6hs2OAq6qq+xq3JcDEzM39gdO72l/eZn/uAtzaTpF+E9g9yUZtEsHurU2SJGlBm/bUZ7MhcFNb3qDPPn9F5+kGlyW5pLW9GXgPcHKSA4FrgBe2bWcCewJLgd8CBwBU1U1JjgQuaPu9a2JigSRJ0kLWT6H2v4EfJjmHzvM/n8R9t9SYUlV9n6mfF7rbJPsXcPAUsY4Fju0jV0mSpAWjn8kEJyY5F/jz1vRPVXX9SLOSJElSf6c+27ViS0aciyRJkrr4rE9JkqQxZaEmSZI0pnoWaknWSvLjuUpGkiRJ9+lZqFXV3cBPknirf0mSpDnWz2SCjYArkpwP/Gaisar2HllWkiRJ6qtQe9vIs5AkSdL/0M991L6b5JHAdlX17SQPBtYafWqSJElrtn4eyv7/AacA/9aatgBOG2FOkiRJor/bcxxM57mdvwaoqp8CjxhlUpIkSeqvULujqn4/sZJkbaBGl5IkSZKgv0Ltu0neDKyX5OnAl4CvjjYtSZIk9VOoHQ6sBC4DXgWcCbx1lElJkiSpv1mf9yQ5DjiPzinPn1SVpz4lSZJGbNpCLclewKeAnwEBtk3yqqr6+qiTkyRJWpP1c8PbDwBPraqlAEkeBXwNsFCTJEkaoX6uUbttokhrfg7cNqJ8JEmS1Ex5RC3Jc9vihUnOBE6mc43aC4AL5iA3SZKkNVqvU5/P7lq+AXhyW14JrDeyjCRJkgT0KNSq6oC5TESSJEn318+sz22B1wLbdO9fVXuPLi1JkiT1M+vzNOAYOk8juGek2UiSJOle/cz6/F1VfbSqzqmq7068puuU5NgkK5Jc3tX2jiTLk1zSXnt2bXtTkqVJfpLkGV3te7S2pUkOH/gTSpIkrab6OaL2kSRHAN8C7phorKqLp+n3WeBjwPGrtH+oqt7f3ZBke2A/4LHA5sC3kzy6bf448HRgGXBBkiVVdWUfeUuSJK3W+inU/hfwMmBX7jv1WW19SlX1vSTb9JnHPsBJVXUH8IskS4Gd2ralVfVzgCQntX0t1CRJ0oLXT6H2AuAPq+r3s/SehyR5OXAhcFhV3QxsAfyga59lrQ3g2lXad56lPCRJksZaP9eoXQ5sOEvv90ngUcAOwHV0Hk81K5IclOTCJBeuXLlytsJKkiTNm36OqG0I/DjJBdz/GrWBb89RVTdMLCf5NHBGW10ObNW165atjR7tq8Y+GjgaYPHixTVobpIkSeOmn0LtiNl6sySbVdV1bfU5dI7WASwBvpDkg3QmE2wHnA8E2K7dy205nQkHL56tfCRJksbZtIVaP7fimEySE4GnAJskWUan4HtKkh3oTEa4GnhVe48rkpxMZ5LAXcDBVXV3i3MI8E1gLeDYqrpimHwkSZJWN/08meA2OoUVwLrAOsBvquphvfpV1YsmaT6mx/5HAUdN0n4mcOZ0eUqSJC00/RxRW39iOUno3B5jl1EmJUmSpP5mfd6rOk4DnjHdvpIkSZqZfk59Prdr9QHAYuB3I8tIkiRJQH+zPp/dtXwXnUkA+4wkG0mSJN2rn2vUDpiLRCRJknR/UxZqSd7eo19V1ZEjyEeSJElNryNqv5mk7SHAgcDDAQs1SZKkEZqyUKuqe5/DmWR94FDgAOAkZvEZnZIkSZpcz2vUkmwMvAF4CXAcsGNV3TwXiUmSJK3pel2j9j7guXQedP6/qur2OctKkiRJPW94exidB6S/FfhVkl+3121Jfj036UmSJK25el2jNtBTCyRJkjS7LMYkSZLGlIWaJEnSmLJQkyRJGlMWapIkSWPKQk2SJGlMWahJkiSNKQs1SZKkMWWhJkmSNKYs1CRJksaUhZokSdKYslCTJEkaUyMr1JIcm2RFksu72jZOclaSn7afG7X2JPlokqVJLk2yY1ef/dv+P02y/6jylSRJGjejPKL2WWCPVdoOB86uqu2As9s6wDOB7drrIOCT0CnsgCOAnYGdgCMmijtJkqSFbmSFWlV9D7hpleZ9gOPa8nHAvl3tx1fHD4ANk2wGPAM4q6puqqqbgbP4n8WfJEnSgjTX16htWlXXteXrgU3b8hbAtV37LWttU7X/D0kOSnJhkgtXrlw5u1lLkiTNg3mbTFBVBdQsxju6qhZX1eJFixbNVlhJkqR5M9eF2g3tlCbt54rWvhzYqmu/LVvbVO2SJEkL3lwXakuAiZmb+wOnd7W/vM3+3AW4tZ0i/Sawe5KN2iSC3VubJEnSgrf2qAInORF4CrBJkmV0Zm++Bzg5yYHANcAL2+5nAnsCS4HfAgcAVNVNSY4ELmj7vauqVp2gIEmStCCNrFCrqhdNsWm3SfYt4OAp4hwLHDuLqUmSJK0WfDKBJEnSmLJQkyRJGlMWapIkSWPKQk2SJGlMWahJkiSNKQs1SZKkMWWhJkmSNKYs1CRJksaUhZokSdKYslCTJEkaUxZqkiRJY8pCTZIkaUxZqEmSJI0pCzVJkqQxZaEmSZI0pizUJEmSxpSFmiRJ0piyUJMkSRpTFmqSJEljykJNkiRpTFmoSZIkjSkLNUmSpDE1L4VakquTXJbkkiQXtraNk5yV5Kft50atPUk+mmRpkkuT7DgfOUuSJM21+Tyi9tSq2qGqFrf1w4Gzq2o74Oy2DvBMYLv2Ogj45JxnKkmSNA/G6dTnPsBxbfk4YN+u9uOr4wfAhkk2m4f8JEmS5tR8FWoFfCvJRUkOam2bVtV1bfl6YNO2vAVwbVffZa1NkiRpQVt7nt73iVW1PMkjgLOS/Lh7Y1VVkhokYCv4DgLYeuutZy9TSZKkeTIvR9Sqann7uQL4CrATcMPEKc32c0XbfTmwVVf3LVvbqjGPrqrFVbV40aJFo0xfkiRpTsx5oZbkIUnWn1gGdgcuB5YA+7fd9gdOb8tLgJe32Z+7ALd2nSKVJElasObj1OemwFeSTLz/F6rqG0kuAE5OciBwDfDCtv+ZwJ7AUuC3wAFzn7IkSdLcm/NCrap+Djx+kvYbgd0maS/g4DlITZIkaayM0+05JEmS1MVCTZIkaUxZqEmSJI0pCzVJkqQxZaEmSZI0pizUJEmSxpSFmiRJ0piyUJMkSRpTFmqSJEljykJNkiRpTFmoSZIkjSkLNUmSpDFloSZJkjSmLNQkSZLGlIWaJEnSmLJQkyRJGlMWapIkSWPKQk2SJGlMWahJkiSNKQs1SZKkMWWhJkmSNKYs1CRJksaUhZokSdKYWm0KtSR7JPlJkqVJDp/vfCRJkkZttSjUkqwFfBx4JrA98KIk289vVpIkSaO1WhRqwE7A0qr6eVX9HjgJ2Geec5IkSRqp1aVQ2wK4tmt9WWuTJElasFJV853DtJI8H9ijqv62rb8M2LmqDuna5yDgoLb6GOAn04TdBPivWUjPOMYxjnGMYxzjGGcmcR5ZVYsm27D2LCQwF5YDW3Wtb9na7lVVRwNH9xswyYVVtXimiRnHOMYxjnGMYxzjjCrO6nLq8wJguyTbJlkX2A9YMs85SZIkjdRqcUStqu5KcgjwTWAt4NiqumKe05IkSRqp1aJQA6iqM4EzZzFk36dJjWMc4xjHOMYxjnHmI85qMZlAkiRpTbS6XKMmSZK0xlnjCrUkxyZZkeTyGcbZKsk5Sa5MckWSQ4eM86Ak5yf5UYvzzhnmtVaSHyY5YwYxrk5yWZJLklw4gzgbJjklyY+TXJXkL4aI8ZiWx8Tr10leP2Q+f9/+jC9PcmKSBw0Z59AW44pBcpls7CXZOMlZSX7afm40ZJwXtHzuSdLX7KIp4ryv/X1dmuQrSTYcMs6RLcYlSb6VZPNh4nRtOyxJJdlkyHzekWR51zjac9h8kry2/RldkeRfhszni125XJ3kkiHj7JDkBxP/VpPsNGScxyf5v+3f/VeTPKyPOJP+Dhx0TPeIM9CY7hFnoDHdI85AY3qqOF3b+xrTPfIZaEz3ymeQMd0jn4HGdI84A43pHnEGGtOZ4rs4nUmM56Xz+MovpjOhcZg4h7QYff0eu5+qWqNewJOAHYHLZxhnM2DHtrw+8J/A9kPECfDQtrwOcB6wywzyegPwBeCMGcS4GthkFv6sjwP+ti2vC2w4w3hrAdfTud/MoH23AH4BrNfWTwZeMUScxwGXAw+mc43nt4E/GnbsAf8CHN6WDwfeO2ScP6Fz/8BzgcUzyGd3YO22/N4Z5POwruXXAZ8aJk5r34rORKJr+hmXU+TzDuAfBvy7nizOU9vf+QPb+iOG/Vxd2z8AvH3IfL4FPLMt7wmcO2ScC4Ant+VXAkf2EWfS34GDjukecQYa0z3iDDSme8QZaExPFWfQMd0jn4HGdI84A43pXp9rkDHdI5+BxnSPOAONaab4LqbzXbFfa/8U8Joh4zwB2IYhvl/XuCNqVfU94KZZiHNdVV3clm8DrmKIpyVUx+1tdZ32GurCwSRbAnsBnxmm/2xKsgGdL4RjAKrq91V1ywzD7gb8rKquGbL/2sB6SdamU2j9aogYfwKcV1W/raq7gO8Cz+2n4xRjbx86BS3t577DxKmqq6pqups89xPnW+1zAfyAzj0Lh4nz667Vh9DHmO7xb/NDwD/2E2OaOAOZIs5rgPdU1R1tnxUzySdJgBcCJw4Zp4CJIwUb0MeYniLOo4HvteWzgOf1EWeq34EDjemp4gw6pnvEGWhM94gz0Jie5jui7zE9i981U8UZaExPl0+/Y7pHnIHGdI84A43pHt/FuwKntPZ+xvOkcarqh1V1da++U1njCrVRSLINnWr5vCH7r9UOE68AzqqqoeIAH6bzj/+eIftPKOBbSS5K54kPw9gWWAn8ezqnYj+T5CEzzGs/+vhCm0xVLQfeD/wSuA64taq+NUSoy4G/TvLwJA+m8z++rabp08umVXVdW74e2HQGsWbbK4GvD9s5yVFJrgVeArx9yBj7AMur6kfD5tHlkHbq6tj0cYp5Co+m8/d/XpLvJvnzGeb018ANVfXTIfu/Hnhf+3N+P/CmIeNcwX3PT34BA47pVX4HDj2mZ/q7tI84A43pVeMMO6a748xkTE/yuYYa06vEGXpMT/HnPPCYXiXO6xlyTK8SZ+Axvep3MfAz4JauQr+vx1fO4nc6YKE2Y0keCpwKvH6V/3H1rarurqod6PxPb6ckjxsij2cBK6rqomFyWMUTq2pH4JnAwUmeNESMtemcXvlkVT0B+A2d0yBDadcF7A18acj+G9H5R7stsDnwkCQvHTROVV1F5/TJt4BvAJcAdw+T0ySxiyGPps62JG8B7gJOGDZGVb2lqrZqMQ6Zbv9Jcngw8GaGLPJW8UngUcAOdAr1DwwZZ21gYzqnMt4InNyOIAzrRQz5n4/mNcDftz/nv6cdwR7CK4G/S3IRndNHv++3Y6/fgYOM6dn4XdorzqBjerI4w4zp7jjt/Yca05PkM9SYniTOUGO6x9/XQGN6kjhDjelJ4gw8plf9Lgb+uN/P0SvOMN/p3SzUZiDJOnQGxglV9eWZxmunBs8B9hii+18Beye5GjgJ2DXJ54fMY3n7uQL4Cp0BO6hlwLKu/0mcQqdwG9YzgYur6oYh+z8N+EVVrayqO4EvA385TKCqOqaq/qyqngTcTOeaiGHdkGQzgPZz2lNpo5bkFcCzgJe0L9qZOoE+TqVN4lF0CusftXG9JXBxkj8YNFBV3dB+ed4DfJrhxjR0xvWX2+mN8+kcvR7swuCmnYJ/LvDFIXMB2J/OWIbOf2KG+lxV9eOq2r2q/ozOl+zP+uk3xe/Agcf0bP0unSrOoGO6j3z6GtOTxBlqTE+WzzBjeorPNfCY7vHnPNCYniLOwGN6ij+focZ063sLne/ivwA2bJ8LJnl8ZZ9xhvlOv5eF2pDa/ziOAa6qqg/OIM6itFlISdYDng78eNA4VfWmqtqyqrahc4rwO1U18BGjJA9Jsv7EMp0LcQeeIVtV1wPXJnlMa9oNuHLQOF1meuThl8AuSR7c/u52o3Mtw8CSPKL93JrOL6UvzCCvJXR+MdF+nj6DWDOWZA86p8/3rqrfziDOdl2r+zDcmL6sqh5RVdu0cb2MzkXD1w+Rz2Zdq89hiDHdnEbn4muSPJrOJJlhH9r8NODHVbVsyP7QuX7nyW15V2CoU6hdY/oBwFvpXDQ9XZ+pfgcONKZn8XfppHEGHdM94gw0pieLM8yY7pHPQGO6x5/zaQwwpqf5++p7TPeIM9CY7vHnM9CYnuK7+Co6hdbz2279jOdZ+U6/nxpg5sFCeNH5sr8OuJPOP5IDh4zzRDqH9C+lc/rrEmDPIeL8KfDDFudy+pj91UfMpzDkrE/gD4EftdcVwFtmkMcOwIXts50GbDRknIcANwIbzPDP5Z3tH8zlwOdos5yGiPMfdIrOHwG7zWTsAQ8Hzqbzy+jbwMZDxnlOW74DuAH45pBxlgLXdo3pfmZrThbn1PbnfCnwVToXYw8cZ5XtV9PfrM/J8vkccFnLZwmw2ZBx1gU+3z7bxcCuw34u4LPAq2c4fp4IXNTG4nnAnw0Z51A6R4b/E3gPdG6GPk2cSX8HDjqme8QZaEz3iDPQmO4RZ6AxPVWcQcd0j3wGGtM94gw0pnt9LgYY0z3yGWhM94gz0Jhmiu9iOt+J57dx9CWm+d7oEed1dMbzXXSK0c/0+2/fJxNIkiSNKU99SpIkjSkLNUmSpDFloSZJkjSmLNQkSZLGlIWaJEnSmFp7+l0kac2Q5G46tzxYh840+uOBD1XnpqKSNOcs1CTpPv9dnUe/TNww8wt0HhB9xHwmJWnN5alPSZpEdR6hdhCdB18nyTZJ/iPJxe31lwBJjk+y70S/JCek8+BtSZoxb3grSU2S26vqoau03QI8BrgNuKeqftceJ3RiVS1O8mQ6D5HeN8kGdO6Mvl1V3TXH6UtagDz1KUn9WQf4WJIdgLuBRwNU1XeTfCLJIjoP6j7VIk3SbLFQk6QpJPlDOkXZCjrXqd0APJ7OZSO/69r1eOClwH7AAXOcpqQFzEJNkibRjpB9CvhYVVU7rbmsqu5Jsj+wVtfun6Xz4Obrq+rKuc9W0kJloSZJ91kvySXcd3uOzwEfbNs+AZya5OXAN4DfTHSqqhuSXAWcNqfZSlrwnEwgSTOU5MF07r+2Y1XdOt/5SFo4vD2HJM1AkqcBVwH/apEmabZ5RE2SJGlMeURNkiRpTFmoSZIkjSkLNUmSpDFloSZJkjSmLNQkSZLGlIWaJEnSmPp/ORLZ3aGCuj4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "sns.countplot(df[\"Day\"])\n",
    "plt.xlabel('Day')\n",
    "plt.ylabel('Number of Transaction')\n",
    "plt.title('Daly Transaction');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7f6a5b7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "sns.countplot(df[\"Country\"])\n",
    "plt.xlabel('Country')\n",
    "plt.ylabel('Number of Transaction')\n",
    "plt.title('Country-Transaction');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c833b84b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "sns.countplot(df[\"Customer_Gender\"])\n",
    "plt.xlabel('Gender')\n",
    "plt.ylabel('Number of Transaction')\n",
    "plt.title('Gender-Transaction');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "965f064b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "sns.countplot(df[\"Age_Group\"])\n",
    "plt.xlabel('Age-Group')\n",
    "plt.ylabel('Number of Transaction')\n",
    "plt.title('Age-Transaction');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ffb0e3fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(['Country', 'Product_Category'])['Revenue'].sum()\n",
    "sns.countplot(data = df, x='Country', hue='Product_Category', palette=('winter'))\n",
    "plt.gcf().set_size_inches(12,6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b1b37348",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data = df, x='Country', hue='Customer_Gender',palette=('winter'))\n",
    "plt.xticks(rotation=60)\n",
    "sns.set_style(\"whitegrid\")\n",
    "plt.legend(['Male', 'Female'])\n",
    "plt.gcf().set_size_inches(11,6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a319a2bb",
   "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>Day</th>\n",
       "      <th>Year</th>\n",
       "      <th>Customer_Age</th>\n",
       "      <th>Order_Quantity</th>\n",
       "      <th>Unit_Cost</th>\n",
       "      <th>Unit_Price</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Cost</th>\n",
       "      <th>Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Day</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.007635</td>\n",
       "      <td>-0.014296</td>\n",
       "      <td>-0.002412</td>\n",
       "      <td>0.003133</td>\n",
       "      <td>0.003207</td>\n",
       "      <td>0.004623</td>\n",
       "      <td>0.003329</td>\n",
       "      <td>0.003853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <td>-0.007635</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.040994</td>\n",
       "      <td>0.123169</td>\n",
       "      <td>-0.217575</td>\n",
       "      <td>-0.213673</td>\n",
       "      <td>-0.181525</td>\n",
       "      <td>-0.215604</td>\n",
       "      <td>-0.208673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Customer_Age</th>\n",
       "      <td>-0.014296</td>\n",
       "      <td>0.040994</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.026887</td>\n",
       "      <td>-0.021374</td>\n",
       "      <td>-0.020262</td>\n",
       "      <td>0.004319</td>\n",
       "      <td>-0.016013</td>\n",
       "      <td>-0.009326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Order_Quantity</th>\n",
       "      <td>-0.002412</td>\n",
       "      <td>0.123169</td>\n",
       "      <td>0.026887</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.515835</td>\n",
       "      <td>-0.515925</td>\n",
       "      <td>-0.238863</td>\n",
       "      <td>-0.340382</td>\n",
       "      <td>-0.312895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unit_Cost</th>\n",
       "      <td>0.003133</td>\n",
       "      <td>-0.217575</td>\n",
       "      <td>-0.021374</td>\n",
       "      <td>-0.515835</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.997894</td>\n",
       "      <td>0.741020</td>\n",
       "      <td>0.829869</td>\n",
       "      <td>0.817865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unit_Price</th>\n",
       "      <td>0.003207</td>\n",
       "      <td>-0.213673</td>\n",
       "      <td>-0.020262</td>\n",
       "      <td>-0.515925</td>\n",
       "      <td>0.997894</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.749870</td>\n",
       "      <td>0.826301</td>\n",
       "      <td>0.818522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Profit</th>\n",
       "      <td>0.004623</td>\n",
       "      <td>-0.181525</td>\n",
       "      <td>0.004319</td>\n",
       "      <td>-0.238863</td>\n",
       "      <td>0.741020</td>\n",
       "      <td>0.749870</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.902233</td>\n",
       "      <td>0.956572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cost</th>\n",
       "      <td>0.003329</td>\n",
       "      <td>-0.215604</td>\n",
       "      <td>-0.016013</td>\n",
       "      <td>-0.340382</td>\n",
       "      <td>0.829869</td>\n",
       "      <td>0.826301</td>\n",
       "      <td>0.902233</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.988758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Revenue</th>\n",
       "      <td>0.003853</td>\n",
       "      <td>-0.208673</td>\n",
       "      <td>-0.009326</td>\n",
       "      <td>-0.312895</td>\n",
       "      <td>0.817865</td>\n",
       "      <td>0.818522</td>\n",
       "      <td>0.956572</td>\n",
       "      <td>0.988758</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Day      Year  Customer_Age  Order_Quantity  Unit_Cost  \\\n",
       "Day             1.000000 -0.007635     -0.014296       -0.002412   0.003133   \n",
       "Year           -0.007635  1.000000      0.040994        0.123169  -0.217575   \n",
       "Customer_Age   -0.014296  0.040994      1.000000        0.026887  -0.021374   \n",
       "Order_Quantity -0.002412  0.123169      0.026887        1.000000  -0.515835   \n",
       "Unit_Cost       0.003133 -0.217575     -0.021374       -0.515835   1.000000   \n",
       "Unit_Price      0.003207 -0.213673     -0.020262       -0.515925   0.997894   \n",
       "Profit          0.004623 -0.181525      0.004319       -0.238863   0.741020   \n",
       "Cost            0.003329 -0.215604     -0.016013       -0.340382   0.829869   \n",
       "Revenue         0.003853 -0.208673     -0.009326       -0.312895   0.817865   \n",
       "\n",
       "                Unit_Price    Profit      Cost   Revenue  \n",
       "Day               0.003207  0.004623  0.003329  0.003853  \n",
       "Year             -0.213673 -0.181525 -0.215604 -0.208673  \n",
       "Customer_Age     -0.020262  0.004319 -0.016013 -0.009326  \n",
       "Order_Quantity   -0.515925 -0.238863 -0.340382 -0.312895  \n",
       "Unit_Cost         0.997894  0.741020  0.829869  0.817865  \n",
       "Unit_Price        1.000000  0.749870  0.826301  0.818522  \n",
       "Profit            0.749870  1.000000  0.902233  0.956572  \n",
       "Cost              0.826301  0.902233  1.000000  0.988758  \n",
       "Revenue           0.818522  0.956572  0.988758  1.000000  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Corr=df.corr()\n",
    "Corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0d24faa7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x360 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(Corr,\n",
    "            cmap='winter', \n",
    "            annot=True)\n",
    "plt.figure(figsize=(10,5));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e27988d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_product=df['Product_Category'].value_counts()\n",
    "label= [df_product.index.tolist()]\n",
    "plt.pie(df_product,\n",
    "        labels=label[0],\n",
    "       autopct='%1.1f%%'\n",
    "       );\n",
    "plt.title(\"Product Category\")\n",
    "plt.gcf().set_size_inches(12,6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e94c0128",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Accessories = df[df['Product_Category'] == 'Accessories']\n",
    "df_Accessories = Accessories['Sub_Category'].value_counts()\n",
    "\n",
    "label = [df_Accessories.index.tolist()]\n",
    "plt.pie(df_Accessories, autopct='%1.1f%%',labels=label[0])\n",
    "plt.gcf().set_size_inches(12,6);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "04cd111f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Clothing = df[df['Product_Category'] == 'Clothing']\n",
    "df_Clothing = Clothing['Sub_Category'].value_counts()\n",
    "\n",
    "label = [df_Clothing.index.tolist()]\n",
    "plt.pie(df_Clothing, autopct='%1.1f%%',labels=label[0])\n",
    "plt.gcf().set_size_inches(12,6);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a0aef8a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_Country=df['Country'].value_counts()\n",
    "label= [df_Country.index.tolist()]\n",
    "plt.pie(df_Country,\n",
    "        labels=label[0],\n",
    "       autopct='%1.1f%%'\n",
    "       );\n",
    "plt.title(\"Country\")\n",
    "plt.gcf().set_size_inches(12,6)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99bff28e",
   "metadata": {},
   "source": [
    "#### Linear Regression:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "75c2d1e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import RidgeCV\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression, Lasso, LassoCV, Ridge, RidgeCV\n",
    "from sklearn.preprocessing import StandardScaler,PolynomialFeatures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "7e5c2969",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 113036 entries, 0 to 113035\n",
      "Data columns (total 18 columns):\n",
      " #   Column            Non-Null Count   Dtype \n",
      "---  ------            --------------   ----- \n",
      " 0   Date              113036 non-null  object\n",
      " 1   Day               113036 non-null  int64 \n",
      " 2   Month             113036 non-null  object\n",
      " 3   Year              113036 non-null  int64 \n",
      " 4   Customer_Age      113036 non-null  int64 \n",
      " 5   Age_Group         113036 non-null  object\n",
      " 6   Customer_Gender   113036 non-null  object\n",
      " 7   Country           113036 non-null  object\n",
      " 8   State             113036 non-null  object\n",
      " 9   Product_Category  113036 non-null  object\n",
      " 10  Sub_Category      113036 non-null  object\n",
      " 11  Product           113036 non-null  object\n",
      " 12  Order_Quantity    113036 non-null  int64 \n",
      " 13  Unit_Cost         113036 non-null  int64 \n",
      " 14  Unit_Price        113036 non-null  int64 \n",
      " 15  Profit            113036 non-null  int64 \n",
      " 16  Cost              113036 non-null  int64 \n",
      " 17  Revenue           113036 non-null  int64 \n",
      "dtypes: int64(9), object(9)\n",
      "memory usage: 15.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "64dbd8c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date                0\n",
       "Day                 0\n",
       "Month               0\n",
       "Year                0\n",
       "Customer_Age        0\n",
       "Age_Group           0\n",
       "Customer_Gender     0\n",
       "Country             0\n",
       "State               0\n",
       "Product_Category    0\n",
       "Sub_Category        0\n",
       "Product             0\n",
       "Order_Quantity      0\n",
       "Unit_Cost           0\n",
       "Unit_Price          0\n",
       "Profit              0\n",
       "Cost                0\n",
       "Revenue             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "8504ca8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date                1884\n",
       "Day                   31\n",
       "Month                 12\n",
       "Year                   6\n",
       "Customer_Age          70\n",
       "Age_Group              4\n",
       "Customer_Gender        2\n",
       "Country                6\n",
       "State                 53\n",
       "Product_Category       3\n",
       "Sub_Category          17\n",
       "Product              130\n",
       "Order_Quantity        32\n",
       "Unit_Cost             34\n",
       "Unit_Price            36\n",
       "Profit              1256\n",
       "Cost                 360\n",
       "Revenue             1876\n",
       "dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "590ce2e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "eed6de6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         False\n",
       "1         False\n",
       "2         False\n",
       "3         False\n",
       "4         False\n",
       "          ...  \n",
       "113031    False\n",
       "113032    False\n",
       "113033    False\n",
       "113034    False\n",
       "113035    False\n",
       "Length: 113036, dtype: bool"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdc619ae",
   "metadata": {},
   "source": [
    "##### Remove duplicated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "ab1691d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 112036 entries, 0 to 113035\n",
      "Data columns (total 18 columns):\n",
      " #   Column            Non-Null Count   Dtype \n",
      "---  ------            --------------   ----- \n",
      " 0   Date              112036 non-null  object\n",
      " 1   Day               112036 non-null  int64 \n",
      " 2   Month             112036 non-null  object\n",
      " 3   Year              112036 non-null  int64 \n",
      " 4   Customer_Age      112036 non-null  int64 \n",
      " 5   Age_Group         112036 non-null  object\n",
      " 6   Customer_Gender   112036 non-null  object\n",
      " 7   Country           112036 non-null  object\n",
      " 8   State             112036 non-null  object\n",
      " 9   Product_Category  112036 non-null  object\n",
      " 10  Sub_Category      112036 non-null  object\n",
      " 11  Product           112036 non-null  object\n",
      " 12  Order_Quantity    112036 non-null  int64 \n",
      " 13  Unit_Cost         112036 non-null  int64 \n",
      " 14  Unit_Price        112036 non-null  int64 \n",
      " 15  Profit            112036 non-null  int64 \n",
      " 16  Cost              112036 non-null  int64 \n",
      " 17  Revenue           112036 non-null  int64 \n",
      "dtypes: int64(9), object(9)\n",
      "memory usage: 16.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df=df.drop_duplicates()\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "d7a4b990",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "5a4a8fdc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date                object\n",
       "Day                  int64\n",
       "Month               object\n",
       "Year                 int64\n",
       "Customer_Age         int64\n",
       "Age_Group           object\n",
       "Customer_Gender     object\n",
       "Country             object\n",
       "State               object\n",
       "Product_Category    object\n",
       "Sub_Category        object\n",
       "Product             object\n",
       "Order_Quantity       int64\n",
       "Unit_Cost            int64\n",
       "Unit_Price           int64\n",
       "Profit               int64\n",
       "Cost                 int64\n",
       "Revenue              int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "11241fb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Date', 'Day', 'Month', 'Year', 'Customer_Age', 'Age_Group',\n",
       "       'Customer_Gender', 'Country', 'State', 'Product_Category',\n",
       "       'Sub_Category', 'Product', 'Order_Quantity', 'Unit_Cost', 'Unit_Price',\n",
       "       'Profit', 'Cost', 'Revenue'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "6cd9fa68",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('Order_Quantity',axis =1,inplace=True)\n",
    "df.drop('Unit_Cost',axis =1,inplace=True)\n",
    "df.drop('Unit_Price',axis =1,inplace=True)\n",
    "df.drop('Revenue',axis =1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "769132d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Day</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>Customer_Age</th>\n",
       "      <th>Age_Group</th>\n",
       "      <th>Customer_Gender</th>\n",
       "      <th>Country</th>\n",
       "      <th>State</th>\n",
       "      <th>Product_Category</th>\n",
       "      <th>Sub_Category</th>\n",
       "      <th>Product</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-11-26</td>\n",
       "      <td>26</td>\n",
       "      <td>November</td>\n",
       "      <td>2013</td>\n",
       "      <td>19</td>\n",
       "      <td>Youth (&lt;25)</td>\n",
       "      <td>M</td>\n",
       "      <td>Canada</td>\n",
       "      <td>British Columbia</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>Bike Racks</td>\n",
       "      <td>Hitch Rack - 4-Bike</td>\n",
       "      <td>590</td>\n",
       "      <td>360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-11-26</td>\n",
       "      <td>26</td>\n",
       "      <td>November</td>\n",
       "      <td>2015</td>\n",
       "      <td>19</td>\n",
       "      <td>Youth (&lt;25)</td>\n",
       "      <td>M</td>\n",
       "      <td>Canada</td>\n",
       "      <td>British Columbia</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>Bike Racks</td>\n",
       "      <td>Hitch Rack - 4-Bike</td>\n",
       "      <td>590</td>\n",
       "      <td>360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-03-23</td>\n",
       "      <td>23</td>\n",
       "      <td>March</td>\n",
       "      <td>2014</td>\n",
       "      <td>49</td>\n",
       "      <td>Adults (35-64)</td>\n",
       "      <td>M</td>\n",
       "      <td>Australia</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>Bike Racks</td>\n",
       "      <td>Hitch Rack - 4-Bike</td>\n",
       "      <td>1366</td>\n",
       "      <td>1035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-03-23</td>\n",
       "      <td>23</td>\n",
       "      <td>March</td>\n",
       "      <td>2016</td>\n",
       "      <td>49</td>\n",
       "      <td>Adults (35-64)</td>\n",
       "      <td>M</td>\n",
       "      <td>Australia</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>Bike Racks</td>\n",
       "      <td>Hitch Rack - 4-Bike</td>\n",
       "      <td>1188</td>\n",
       "      <td>900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-05-15</td>\n",
       "      <td>15</td>\n",
       "      <td>May</td>\n",
       "      <td>2014</td>\n",
       "      <td>47</td>\n",
       "      <td>Adults (35-64)</td>\n",
       "      <td>F</td>\n",
       "      <td>Australia</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>Bike Racks</td>\n",
       "      <td>Hitch Rack - 4-Bike</td>\n",
       "      <td>238</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  Day     Month  Year  Customer_Age       Age_Group  \\\n",
       "0  2013-11-26   26  November  2013            19     Youth (<25)   \n",
       "1  2015-11-26   26  November  2015            19     Youth (<25)   \n",
       "2  2014-03-23   23     March  2014            49  Adults (35-64)   \n",
       "3  2016-03-23   23     March  2016            49  Adults (35-64)   \n",
       "4  2014-05-15   15       May  2014            47  Adults (35-64)   \n",
       "\n",
       "  Customer_Gender    Country             State Product_Category Sub_Category  \\\n",
       "0               M     Canada  British Columbia      Accessories   Bike Racks   \n",
       "1               M     Canada  British Columbia      Accessories   Bike Racks   \n",
       "2               M  Australia   New South Wales      Accessories   Bike Racks   \n",
       "3               M  Australia   New South Wales      Accessories   Bike Racks   \n",
       "4               F  Australia   New South Wales      Accessories   Bike Racks   \n",
       "\n",
       "               Product  Profit  Cost  \n",
       "0  Hitch Rack - 4-Bike     590   360  \n",
       "1  Hitch Rack - 4-Bike     590   360  \n",
       "2  Hitch Rack - 4-Bike    1366  1035  \n",
       "3  Hitch Rack - 4-Bike    1188   900  \n",
       "4  Hitch Rack - 4-Bike     238   180  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "bb5f0ef6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date                object\n",
       "Day                  int64\n",
       "Month               object\n",
       "Year                 int64\n",
       "Customer_Age         int64\n",
       "Age_Group           object\n",
       "Customer_Gender     object\n",
       "Country             object\n",
       "State               object\n",
       "Product_Category    object\n",
       "Sub_Category        object\n",
       "Product             object\n",
       "Profit               int64\n",
       "Cost                 int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "23445149",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>26</td>\n",
       "      <td>2015</td>\n",
       "      <td>19</td>\n",
       "      <td>590</td>\n",
       "      <td>360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23</td>\n",
       "      <td>2014</td>\n",
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       "      <td>1366</td>\n",
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       "    <tr>\n",
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       "      <td>2014</td>\n",
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       "      <td>238</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Day  Year  Customer_Age  Profit  Cost\n",
       "0   26  2013            19     590   360\n",
       "1   26  2015            19     590   360\n",
       "2   23  2014            49    1366  1035\n",
       "3   23  2016            49    1188   900\n",
       "4   15  2014            47     238   180"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Bike=df.select_dtypes(exclude=['object']).copy()\n",
    "Bike.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "e7d41ec0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Accessories</td>\n",
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       "      <td>900</td>\n",
       "      <td>M</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Accessories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15</td>\n",
       "      <td>2014</td>\n",
       "      <td>47</td>\n",
       "      <td>238</td>\n",
       "      <td>180</td>\n",
       "      <td>F</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Accessories</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Day  Year  Customer_Age  Profit  Cost Customer_Gender    Country  \\\n",
       "0   26  2013            19     590   360               M     Canada   \n",
       "1   26  2015            19     590   360               M     Canada   \n",
       "2   23  2014            49    1366  1035               M  Australia   \n",
       "3   23  2016            49    1188   900               M  Australia   \n",
       "4   15  2014            47     238   180               F  Australia   \n",
       "\n",
       "  Product_Category  \n",
       "0      Accessories  \n",
       "1      Accessories  \n",
       "2      Accessories  \n",
       "3      Accessories  \n",
       "4      Accessories  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Adding new Columns:\n",
    "Bike['Customer_Age'] = df['Customer_Age']\n",
    "Bike['Customer_Gender'] = df['Customer_Gender']\n",
    "Bike['Country'] = df['Country']\n",
    "Bike['Product_Category'] = df['Product_Category']\n",
    "Bike.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "1889e84d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 112036 entries, 0 to 113035\n",
      "Data columns (total 8 columns):\n",
      " #   Column            Non-Null Count   Dtype \n",
      "---  ------            --------------   ----- \n",
      " 0   Day               112036 non-null  int64 \n",
      " 1   Year              112036 non-null  int64 \n",
      " 2   Customer_Age      112036 non-null  int64 \n",
      " 3   Profit            112036 non-null  int64 \n",
      " 4   Cost              112036 non-null  int64 \n",
      " 5   Customer_Gender   112036 non-null  object\n",
      " 6   Country           112036 non-null  object\n",
      " 7   Product_Category  112036 non-null  object\n",
      "dtypes: int64(5), object(3)\n",
      "memory usage: 7.7+ MB\n"
     ]
    }
   ],
   "source": [
    "Bike.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "036e89d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "   Day  Year  Customer_Age  Profit  Cost  Customer_Gender_F  \\\n",
       "0   26  2013            19     590   360                  0   \n",
       "1   26  2015            19     590   360                  0   \n",
       "2   23  2014            49    1366  1035                  0   \n",
       "3   23  2016            49    1188   900                  0   \n",
       "4   15  2014            47     238   180                  1   \n",
       "\n",
       "   Customer_Gender_M  Country_Australia  Country_Canada  Country_France  \\\n",
       "0                  1                  0               1               0   \n",
       "1                  1                  0               1               0   \n",
       "2                  1                  1               0               0   \n",
       "3                  1                  1               0               0   \n",
       "4                  0                  1               0               0   \n",
       "\n",
       "   Country_Germany  Country_United Kingdom  Country_United States  \\\n",
       "0                0                       0                      0   \n",
       "1                0                       0                      0   \n",
       "2                0                       0                      0   \n",
       "3                0                       0                      0   \n",
       "4                0                       0                      0   \n",
       "\n",
       "   Product_Category_Accessories  Product_Category_Bikes  \\\n",
       "0                             1                       0   \n",
       "1                             1                       0   \n",
       "2                             1                       0   \n",
       "3                             1                       0   \n",
       "4                             1                       0   \n",
       "\n",
       "   Product_Category_Clothing  \n",
       "0                          0  \n",
       "1                          0  \n",
       "2                          0  \n",
       "3                          0  \n",
       "4                          0  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Bike=pd.get_dummies(Bike)\n",
    "Bike.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "f34ecb8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(112036, 16)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Bike.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "18207193",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 112036 entries, 0 to 113035\n",
      "Data columns (total 16 columns):\n",
      " #   Column                        Non-Null Count   Dtype\n",
      "---  ------                        --------------   -----\n",
      " 0   Day                           112036 non-null  int64\n",
      " 1   Year                          112036 non-null  int64\n",
      " 2   Customer_Age                  112036 non-null  int64\n",
      " 3   Profit                        112036 non-null  int64\n",
      " 4   Cost                          112036 non-null  int64\n",
      " 5   Customer_Gender_F             112036 non-null  uint8\n",
      " 6   Customer_Gender_M             112036 non-null  uint8\n",
      " 7   Country_Australia             112036 non-null  uint8\n",
      " 8   Country_Canada                112036 non-null  uint8\n",
      " 9   Country_France                112036 non-null  uint8\n",
      " 10  Country_Germany               112036 non-null  uint8\n",
      " 11  Country_United Kingdom        112036 non-null  uint8\n",
      " 12  Country_United States         112036 non-null  uint8\n",
      " 13  Product_Category_Accessories  112036 non-null  uint8\n",
      " 14  Product_Category_Bikes        112036 non-null  uint8\n",
      " 15  Product_Category_Clothing     112036 non-null  uint8\n",
      "dtypes: int64(5), uint8(11)\n",
      "memory usage: 6.3 MB\n"
     ]
    }
   ],
   "source": [
    "Bike.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "34de10d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Day', 'Year', 'Customer_Age', 'Profit', 'Cost', 'Customer_Gender_F',\n",
       "       'Customer_Gender_M', 'Country_Australia', 'Country_Canada',\n",
       "       'Country_France', 'Country_Germany', 'Country_United Kingdom',\n",
       "       'Country_United States', 'Product_Category_Accessories',\n",
       "       'Product_Category_Bikes', 'Product_Category_Clothing'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Bike.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "10188620",
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = Bike.drop('Profit',axis=1), Bike['Profit']\n",
    "\n",
    "X_train_val, X_test, y_train_val, y_test = train_test_split(X, y, test_size=0.2, random_state=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "3345b6a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=.25, random_state=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "b7c5e54d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Day</th>\n",
       "      <th>Year</th>\n",
       "      <th>Customer_Age</th>\n",
       "      <th>Cost</th>\n",
       "      <th>Customer_Gender_F</th>\n",
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       "      <th>Country_Australia</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>34965</th>\n",
       "      <td>13</td>\n",
       "      <td>2016</td>\n",
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       "      <td>2016</td>\n",
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       "      <td>9</td>\n",
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       "      <td>41</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46176</th>\n",
       "      <td>7</td>\n",
       "      <td>2014</td>\n",
       "      <td>28</td>\n",
       "      <td>950</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Day  Year  Customer_Age  Cost  Customer_Gender_F  Customer_Gender_M  \\\n",
       "34965   13  2016            62    39                  0                  1   \n",
       "87667    3  2016            26    32                  1                  0   \n",
       "41461    9  2016            41    52                  0                  1   \n",
       "49369    4  2016            50  5736                  1                  0   \n",
       "46176    7  2014            28   950                  0                  1   \n",
       "\n",
       "       Country_Australia  Country_Canada  Country_France  Country_Germany  \\\n",
       "34965                  0               0               0                0   \n",
       "87667                  1               0               0                0   \n",
       "41461                  0               0               0                0   \n",
       "49369                  1               0               0                0   \n",
       "46176                  0               0               0                0   \n",
       "\n",
       "       Country_United Kingdom  Country_United States  \\\n",
       "34965                       0                      1   \n",
       "87667                       0                      0   \n",
       "41461                       0                      1   \n",
       "49369                       0                      0   \n",
       "46176                       1                      0   \n",
       "\n",
       "       Product_Category_Accessories  Product_Category_Bikes  \\\n",
       "34965                             1                       0   \n",
       "87667                             1                       0   \n",
       "41461                             1                       0   \n",
       "49369                             0                       1   \n",
       "46176                             0                       0   \n",
       "\n",
       "       Product_Category_Clothing  \n",
       "34965                          0  \n",
       "87667                          0  \n",
       "41461                          0  \n",
       "49369                          0  \n",
       "46176                          1  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_val.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "cdb70cb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Day</th>\n",
       "      <th>Year</th>\n",
       "      <th>Customer_Age</th>\n",
       "      <th>Cost</th>\n",
       "      <th>Customer_Gender_F</th>\n",
       "      <th>Customer_Gender_M</th>\n",
       "      <th>Country_Australia</th>\n",
       "      <th>Country_Canada</th>\n",
       "      <th>Country_France</th>\n",
       "      <th>Country_Germany</th>\n",
       "      <th>Country_United Kingdom</th>\n",
       "      <th>Country_United States</th>\n",
       "      <th>Product_Category_Accessories</th>\n",
       "      <th>Product_Category_Bikes</th>\n",
       "      <th>Product_Category_Clothing</th>\n",
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       "  <tbody>\n",
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       "      <th>15717</th>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>42346</th>\n",
       "      <td>23</td>\n",
       "      <td>2014</td>\n",
       "      <td>29</td>\n",
       "      <td>462</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "       Day  Year  Customer_Age  Cost  Customer_Gender_F  Customer_Gender_M  \\\n",
       "15717    9  2015            22    30                  1                  0   \n",
       "24462    3  2013            34    56                  0                  1   \n",
       "93532   20  2014            17   143                  0                  1   \n",
       "60870    4  2014            44   344                  0                  1   \n",
       "42346   23  2014            29   462                  1                  0   \n",
       "\n",
       "       Country_Australia  Country_Canada  Country_France  Country_Germany  \\\n",
       "15717                  1               0               0                0   \n",
       "24462                  0               0               0                0   \n",
       "93532                  0               0               0                0   \n",
       "60870                  0               0               0                0   \n",
       "42346                  0               0               0                0   \n",
       "\n",
       "       Country_United Kingdom  Country_United States  \\\n",
       "15717                       0                      0   \n",
       "24462                       0                      1   \n",
       "93532                       1                      0   \n",
       "60870                       0                      1   \n",
       "42346                       1                      0   \n",
       "\n",
       "       Product_Category_Accessories  Product_Category_Bikes  \\\n",
       "15717                             1                       0   \n",
       "24462                             1                       0   \n",
       "93532                             1                       0   \n",
       "60870                             0                       1   \n",
       "42346                             1                       0   \n",
       "\n",
       "       Product_Category_Clothing  \n",
       "15717                          0  \n",
       "24462                          0  \n",
       "93532                          0  \n",
       "60870                          0  \n",
       "42346                          0  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "1725fe1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34965      64\n",
       "87667      38\n",
       "41461      57\n",
       "49369    3138\n",
       "46176     250\n",
       "Name: Profit, dtype: int64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_val.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "bc922410",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15717     46\n",
       "24462     64\n",
       "93532    231\n",
       "60870    185\n",
       "42346    700\n",
       "Name: Profit, dtype: int64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "3decc905",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "c7a4b4dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8272720241402746\n"
     ]
    }
   ],
   "source": [
    "lm = LinearRegression().fit(X_train,y_train)\n",
    "print(lm.score(X_train,y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "3a233aed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8272720241402746"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "\n",
    "X_train_scaled =scaler.fit_transform(X_train.values)\n",
    "X_val_scaled = scaler.transform(X_val.values)\n",
    "X_test_scaled = scaler.transform(X_test.values)\n",
    "\n",
    "lm = LinearRegression().fit(X_train_scaled,y_train)\n",
    "lm.score(X_train_scaled,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "978947a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8942936864432963"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly = PolynomialFeatures(degree=3) \n",
    "\n",
    "X_train_poly = poly.fit_transform(X_train.values)\n",
    "X_val_poly = poly.transform(X_val.values)\n",
    "X_test_poly = poly.transform(X_test.values)\n",
    "\n",
    "lm_poly = LinearRegression().fit(X_train_poly,y_train)\n",
    "lm_poly.score (X_train_poly,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74ce8d95",
   "metadata": {},
   "source": [
    "##### Done"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "54603523",
   "metadata": {},
   "outputs": [],
   "source": [
    "for name, model in models:\n",
    "    model.fit(X_train, y_train)\n",
    "    preds = model.predict(X_test)\n",
    "    acc = accuracy_score(y_test, preds)\n",
    "    print(f'The accuracy of {name} is {acc:.3f}')"
   ]
  }
 ],
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   "language": "python",
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    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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