{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ed58a8f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: kaggle in /home/witek/.local/lib/python3.10/site-packages (1.5.13)\n",
      "Requirement already satisfied: python-slugify in /home/witek/.local/lib/python3.10/site-packages (from kaggle) (8.0.1)\n",
      "Requirement already satisfied: requests in /usr/lib/python3/dist-packages (from kaggle) (2.25.1)\n",
      "Requirement already satisfied: certifi in /usr/lib/python3/dist-packages (from kaggle) (2020.6.20)\n",
      "Requirement already satisfied: six>=1.10 in /usr/lib/python3/dist-packages (from kaggle) (1.16.0)\n",
      "Requirement already satisfied: python-dateutil in /home/witek/.local/lib/python3.10/site-packages (from kaggle) (2.8.2)\n",
      "Requirement already satisfied: urllib3 in /usr/lib/python3/dist-packages (from kaggle) (1.26.5)\n",
      "Requirement already satisfied: tqdm in /home/witek/.local/lib/python3.10/site-packages (from kaggle) (4.65.0)\n",
      "Requirement already satisfied: text-unidecode>=1.3 in /home/witek/.local/lib/python3.10/site-packages (from python-slugify->kaggle) (1.3)\n",
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: pandas in /home/witek/.local/lib/python3.10/site-packages (1.5.3)\n",
      "Requirement already satisfied: pytz>=2020.1 in /usr/lib/python3/dist-packages (from pandas) (2022.1)\n",
      "Requirement already satisfied: python-dateutil>=2.8.1 in /home/witek/.local/lib/python3.10/site-packages (from pandas) (2.8.2)\n",
      "Requirement already satisfied: numpy>=1.21.0 in /home/witek/.local/lib/python3.10/site-packages (from pandas) (1.24.2)\n",
      "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.8.1->pandas) (1.16.0)\n",
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: unzip in /home/witek/.local/lib/python3.10/site-packages (1.0.0)\n",
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: scikit-learn in /home/witek/.local/lib/python3.10/site-packages (1.2.2)\n",
      "Requirement already satisfied: joblib>=1.1.1 in /home/witek/.local/lib/python3.10/site-packages (from scikit-learn) (1.2.0)\n",
      "Requirement already satisfied: scipy>=1.3.2 in /home/witek/.local/lib/python3.10/site-packages (from scikit-learn) (1.10.1)\n",
      "Requirement already satisfied: numpy>=1.17.3 in /home/witek/.local/lib/python3.10/site-packages (from scikit-learn) (1.24.2)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /home/witek/.local/lib/python3.10/site-packages (from scikit-learn) (3.1.0)\n",
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: seaborn in /home/witek/.local/lib/python3.10/site-packages (0.12.2)\n",
      "Requirement already satisfied: pandas>=0.25 in /home/witek/.local/lib/python3.10/site-packages (from seaborn) (1.5.3)\n",
      "Requirement already satisfied: matplotlib!=3.6.1,>=3.1 in /home/witek/.local/lib/python3.10/site-packages (from seaborn) (3.7.1)\n",
      "Requirement already satisfied: numpy!=1.24.0,>=1.17 in /home/witek/.local/lib/python3.10/site-packages (from seaborn) (1.24.2)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /home/witek/.local/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (1.0.7)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /home/witek/.local/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (1.4.4)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /usr/lib/python3/dist-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (2.4.7)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/witek/.local/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (0.11.0)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /home/witek/.local/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (2.8.2)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /usr/lib/python3/dist-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (9.0.1)\n",
      "Requirement already satisfied: packaging>=20.0 in /home/witek/.local/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (23.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /home/witek/.local/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (4.39.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in /usr/lib/python3/dist-packages (from pandas>=0.25->seaborn) (2022.1)\n",
      "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.1->seaborn) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "#Pobieranie odpowiednich bibliotek\n",
    "!pip install kaggle\n",
    "!pip install pandas\n",
    "!pip install unzip\n",
    "!pip install scikit-learn\n",
    "!pip install seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "79101aff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: Your Kaggle API key is readable by other users on this system! To fix this, you can run 'chmod 600 /home/witek/.kaggle/kaggle.json'\n",
      "Downloading bike-sales-in-europe.zip to /home/witek/python-ws\n",
      "  0%|                                               | 0.00/1.15M [00:00<?, ?B/s]\n",
      "100%|██████████████████████████████████████| 1.15M/1.15M [00:00<00:00, 18.2MB/s]\n"
     ]
    }
   ],
   "source": [
    "!kaggle datasets download -d sadiqshah/bike-sales-in-europe\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "82f1ce24",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Archive:  bike-sales-in-europe.zip\n",
      "  inflating: Sales.csv               \n"
     ]
    }
   ],
   "source": [
    "!unzip -o bike-sales-in-europe.zip\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "f547e6a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e6a01b12",
   "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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113031</th>\n",
       "      <td>2016-04-12</td>\n",
       "      <td>12</td>\n",
       "      <td>April</td>\n",
       "      <td>2016</td>\n",
       "      <td>41</td>\n",
       "      <td>Adults (35-64)</td>\n",
       "      <td>M</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>England</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>Vests</td>\n",
       "      <td>Classic Vest, S</td>\n",
       "      <td>3</td>\n",
       "      <td>24</td>\n",
       "      <td>64</td>\n",
       "      <td>112</td>\n",
       "      <td>72</td>\n",
       "      <td>184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113032</th>\n",
       "      <td>2014-04-02</td>\n",
       "      <td>2</td>\n",
       "      <td>April</td>\n",
       "      <td>2014</td>\n",
       "      <td>18</td>\n",
       "      <td>Youth (&lt;25)</td>\n",
       "      <td>M</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Queensland</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>Vests</td>\n",
       "      <td>Classic Vest, M</td>\n",
       "      <td>22</td>\n",
       "      <td>24</td>\n",
       "      <td>64</td>\n",
       "      <td>655</td>\n",
       "      <td>528</td>\n",
       "      <td>1183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113033</th>\n",
       "      <td>2016-04-02</td>\n",
       "      <td>2</td>\n",
       "      <td>April</td>\n",
       "      <td>2016</td>\n",
       "      <td>18</td>\n",
       "      <td>Youth (&lt;25)</td>\n",
       "      <td>M</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Queensland</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>Vests</td>\n",
       "      <td>Classic Vest, M</td>\n",
       "      <td>22</td>\n",
       "      <td>24</td>\n",
       "      <td>64</td>\n",
       "      <td>655</td>\n",
       "      <td>528</td>\n",
       "      <td>1183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113034</th>\n",
       "      <td>2014-03-04</td>\n",
       "      <td>4</td>\n",
       "      <td>March</td>\n",
       "      <td>2014</td>\n",
       "      <td>37</td>\n",
       "      <td>Adults (35-64)</td>\n",
       "      <td>F</td>\n",
       "      <td>France</td>\n",
       "      <td>Seine (Paris)</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>Vests</td>\n",
       "      <td>Classic Vest, L</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>64</td>\n",
       "      <td>684</td>\n",
       "      <td>576</td>\n",
       "      <td>1260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113035</th>\n",
       "      <td>2016-03-04</td>\n",
       "      <td>4</td>\n",
       "      <td>March</td>\n",
       "      <td>2016</td>\n",
       "      <td>37</td>\n",
       "      <td>Adults (35-64)</td>\n",
       "      <td>F</td>\n",
       "      <td>France</td>\n",
       "      <td>Seine (Paris)</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>Vests</td>\n",
       "      <td>Classic Vest, L</td>\n",
       "      <td>23</td>\n",
       "      <td>24</td>\n",
       "      <td>64</td>\n",
       "      <td>655</td>\n",
       "      <td>552</td>\n",
       "      <td>1207</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>113036 rows × 18 columns</p>\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",
       "113031  2016-04-12   12     April  2016            41  Adults (35-64)   \n",
       "113032  2014-04-02    2     April  2014            18     Youth (<25)   \n",
       "113033  2016-04-02    2     April  2016            18     Youth (<25)   \n",
       "113034  2014-03-04    4     March  2014            37  Adults (35-64)   \n",
       "113035  2016-03-04    4     March  2016            37  Adults (35-64)   \n",
       "\n",
       "       Customer_Gender         Country             State Product_Category  \\\n",
       "0                    M          Canada  British Columbia      Accessories   \n",
       "1                    M          Canada  British Columbia      Accessories   \n",
       "2                    M       Australia   New South Wales      Accessories   \n",
       "3                    M       Australia   New South Wales      Accessories   \n",
       "4                    F       Australia   New South Wales      Accessories   \n",
       "...                ...             ...               ...              ...   \n",
       "113031               M  United Kingdom           England         Clothing   \n",
       "113032               M       Australia        Queensland         Clothing   \n",
       "113033               M       Australia        Queensland         Clothing   \n",
       "113034               F          France     Seine (Paris)         Clothing   \n",
       "113035               F          France     Seine (Paris)         Clothing   \n",
       "\n",
       "       Sub_Category              Product  Order_Quantity  Unit_Cost  \\\n",
       "0        Bike Racks  Hitch Rack - 4-Bike               8         45   \n",
       "1        Bike Racks  Hitch Rack - 4-Bike               8         45   \n",
       "2        Bike Racks  Hitch Rack - 4-Bike              23         45   \n",
       "3        Bike Racks  Hitch Rack - 4-Bike              20         45   \n",
       "4        Bike Racks  Hitch Rack - 4-Bike               4         45   \n",
       "...             ...                  ...             ...        ...   \n",
       "113031        Vests      Classic Vest, S               3         24   \n",
       "113032        Vests      Classic Vest, M              22         24   \n",
       "113033        Vests      Classic Vest, M              22         24   \n",
       "113034        Vests      Classic Vest, L              24         24   \n",
       "113035        Vests      Classic Vest, L              23         24   \n",
       "\n",
       "        Unit_Price  Profit  Cost  Revenue  \n",
       "0              120     590   360      950  \n",
       "1              120     590   360      950  \n",
       "2              120    1366  1035     2401  \n",
       "3              120    1188   900     2088  \n",
       "4              120     238   180      418  \n",
       "...            ...     ...   ...      ...  \n",
       "113031          64     112    72      184  \n",
       "113032          64     655   528     1183  \n",
       "113033          64     655   528     1183  \n",
       "113034          64     684   576     1260  \n",
       "113035          64     655   552     1207  \n",
       "\n",
       "[113036 rows x 18 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikes = pd.read_csv('Sales.csv')\n",
    "bikes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "07609710",
   "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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikes.isnull().sum()\n",
    "#Zbiór jest już wyczyszczony z artefaktów"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "8ce77456",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Normalizacja danych poprzez ustawienie małych liter w zbiorze\n",
    "bikes['Month'] = bikes['Month'].str.lower()\n",
    "bikes['Age_Group'] = bikes['Age_Group'].str.lower()\n",
    "bikes['Country'] = bikes['Country'].str.lower()\n",
    "bikes['State'] = bikes['State'].str.lower()\n",
    "bikes['Product_Category'] = bikes['Product_Category'].str.lower()\n",
    "bikes['Sub_Category'] = bikes['Sub_Category'].str.lower()\n",
    "bikes['Product'] = bikes['Product'].str.lower()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "5d3e05c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Podział na zbiory\n",
    "from sklearn.model_selection import train_test_split\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "0897b7b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "bikes_train, bikes_test = train_test_split(bikes, test_size=0.2, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "60d4cafa",
   "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>31242</th>\n",
       "      <td>2013-09-02</td>\n",
       "      <td>2</td>\n",
       "      <td>september</td>\n",
       "      <td>2013</td>\n",
       "      <td>25</td>\n",
       "      <td>young adults (25-34)</td>\n",
       "      <td>F</td>\n",
       "      <td>australia</td>\n",
       "      <td>queensland</td>\n",
       "      <td>accessories</td>\n",
       "      <td>helmets</td>\n",
       "      <td>sport-100 helmet, red</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>35</td>\n",
       "      <td>180</td>\n",
       "      <td>143</td>\n",
       "      <td>323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76421</th>\n",
       "      <td>2015-10-06</td>\n",
       "      <td>6</td>\n",
       "      <td>october</td>\n",
       "      <td>2015</td>\n",
       "      <td>29</td>\n",
       "      <td>young adults (25-34)</td>\n",
       "      <td>M</td>\n",
       "      <td>australia</td>\n",
       "      <td>queensland</td>\n",
       "      <td>accessories</td>\n",
       "      <td>tires and tubes</td>\n",
       "      <td>ll mountain tire</td>\n",
       "      <td>30</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>360</td>\n",
       "      <td>270</td>\n",
       "      <td>630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63417</th>\n",
       "      <td>2016-05-04</td>\n",
       "      <td>4</td>\n",
       "      <td>may</td>\n",
       "      <td>2016</td>\n",
       "      <td>44</td>\n",
       "      <td>adults (35-64)</td>\n",
       "      <td>F</td>\n",
       "      <td>united states</td>\n",
       "      <td>oregon</td>\n",
       "      <td>bikes</td>\n",
       "      <td>road bikes</td>\n",
       "      <td>road-750 black, 44</td>\n",
       "      <td>1</td>\n",
       "      <td>344</td>\n",
       "      <td>540</td>\n",
       "      <td>120</td>\n",
       "      <td>344</td>\n",
       "      <td>464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13214</th>\n",
       "      <td>2013-11-23</td>\n",
       "      <td>23</td>\n",
       "      <td>november</td>\n",
       "      <td>2013</td>\n",
       "      <td>42</td>\n",
       "      <td>adults (35-64)</td>\n",
       "      <td>F</td>\n",
       "      <td>united states</td>\n",
       "      <td>washington</td>\n",
       "      <td>accessories</td>\n",
       "      <td>bottles and cages</td>\n",
       "      <td>mountain bottle cage</td>\n",
       "      <td>29</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>110</td>\n",
       "      <td>116</td>\n",
       "      <td>226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17882</th>\n",
       "      <td>2013-12-25</td>\n",
       "      <td>25</td>\n",
       "      <td>december</td>\n",
       "      <td>2013</td>\n",
       "      <td>46</td>\n",
       "      <td>adults (35-64)</td>\n",
       "      <td>F</td>\n",
       "      <td>germany</td>\n",
       "      <td>nordrhein-westfalen</td>\n",
       "      <td>clothing</td>\n",
       "      <td>caps</td>\n",
       "      <td>awc logo cap</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>16</td>\n",
       "      <td>133</td>\n",
       "      <td>149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36385</th>\n",
       "      <td>2016-06-29</td>\n",
       "      <td>29</td>\n",
       "      <td>june</td>\n",
       "      <td>2016</td>\n",
       "      <td>40</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>helmets</td>\n",
       "      <td>sport-100 helmet, red</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>35</td>\n",
       "      <td>17</td>\n",
       "      <td>13</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11506</th>\n",
       "      <td>2014-03-04</td>\n",
       "      <td>4</td>\n",
       "      <td>march</td>\n",
       "      <td>2014</td>\n",
       "      <td>44</td>\n",
       "      <td>adults (35-64)</td>\n",
       "      <td>F</td>\n",
       "      <td>united states</td>\n",
       "      <td>california</td>\n",
       "      <td>accessories</td>\n",
       "      <td>bottles and cages</td>\n",
       "      <td>water bottle - 30 oz.</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>58</td>\n",
       "      <td>40</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52187</th>\n",
       "      <td>2015-12-18</td>\n",
       "      <td>18</td>\n",
       "      <td>december</td>\n",
       "      <td>2015</td>\n",
       "      <td>23</td>\n",
       "      <td>youth (&lt;25)</td>\n",
       "      <td>M</td>\n",
       "      <td>united kingdom</td>\n",
       "      <td>england</td>\n",
       "      <td>bikes</td>\n",
       "      <td>mountain bikes</td>\n",
       "      <td>mountain-400-w silver, 46</td>\n",
       "      <td>1</td>\n",
       "      <td>420</td>\n",
       "      <td>769</td>\n",
       "      <td>318</td>\n",
       "      <td>420</td>\n",
       "      <td>738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83391</th>\n",
       "      <td>2015-12-12</td>\n",
       "      <td>12</td>\n",
       "      <td>december</td>\n",
       "      <td>2015</td>\n",
       "      <td>26</td>\n",
       "      <td>young adults (25-34)</td>\n",
       "      <td>F</td>\n",
       "      <td>australia</td>\n",
       "      <td>victoria</td>\n",
       "      <td>accessories</td>\n",
       "      <td>tires and tubes</td>\n",
       "      <td>ml road tire</td>\n",
       "      <td>22</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>237</td>\n",
       "      <td>198</td>\n",
       "      <td>435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112433</th>\n",
       "      <td>2015-09-17</td>\n",
       "      <td>17</td>\n",
       "      <td>september</td>\n",
       "      <td>2015</td>\n",
       "      <td>32</td>\n",
       "      <td>young adults (25-34)</td>\n",
       "      <td>M</td>\n",
       "      <td>germany</td>\n",
       "      <td>hamburg</td>\n",
       "      <td>clothing</td>\n",
       "      <td>vests</td>\n",
       "      <td>classic vest, l</td>\n",
       "      <td>31</td>\n",
       "      <td>24</td>\n",
       "      <td>64</td>\n",
       "      <td>1101</td>\n",
       "      <td>744</td>\n",
       "      <td>1845</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22608 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Date  Day      Month  Year  Customer_Age             Age_Group  \\\n",
       "31242   2013-09-02    2  september  2013            25  young adults (25-34)   \n",
       "76421   2015-10-06    6    october  2015            29  young adults (25-34)   \n",
       "63417   2016-05-04    4        may  2016            44        adults (35-64)   \n",
       "13214   2013-11-23   23   november  2013            42        adults (35-64)   \n",
       "17882   2013-12-25   25   december  2013            46        adults (35-64)   \n",
       "...            ...  ...        ...   ...           ...                   ...   \n",
       "36385   2016-06-29   29       june  2016            40        adults (35-64)   \n",
       "11506   2014-03-04    4      march  2014            44        adults (35-64)   \n",
       "52187   2015-12-18   18   december  2015            23           youth (<25)   \n",
       "83391   2015-12-12   12   december  2015            26  young adults (25-34)   \n",
       "112433  2015-09-17   17  september  2015            32  young adults (25-34)   \n",
       "\n",
       "       Customer_Gender         Country                State Product_Category  \\\n",
       "31242                F       australia           queensland      accessories   \n",
       "76421                M       australia           queensland      accessories   \n",
       "63417                F   united states               oregon            bikes   \n",
       "13214                F   united states           washington      accessories   \n",
       "17882                F         germany  nordrhein-westfalen         clothing   \n",
       "...                ...             ...                  ...              ...   \n",
       "36385                F       australia      new south wales      accessories   \n",
       "11506                F   united states           california      accessories   \n",
       "52187                M  united kingdom              england            bikes   \n",
       "83391                F       australia             victoria      accessories   \n",
       "112433               M         germany              hamburg         clothing   \n",
       "\n",
       "             Sub_Category                    Product  Order_Quantity  \\\n",
       "31242             helmets      sport-100 helmet, red              11   \n",
       "76421     tires and tubes           ll mountain tire              30   \n",
       "63417          road bikes         road-750 black, 44               1   \n",
       "13214   bottles and cages       mountain bottle cage              29   \n",
       "17882                caps               awc logo cap              19   \n",
       "...                   ...                        ...             ...   \n",
       "36385             helmets      sport-100 helmet, red               1   \n",
       "11506   bottles and cages      water bottle - 30 oz.              20   \n",
       "52187      mountain bikes  mountain-400-w silver, 46               1   \n",
       "83391     tires and tubes               ml road tire              22   \n",
       "112433              vests            classic vest, l              31   \n",
       "\n",
       "        Unit_Cost  Unit_Price  Profit  Cost  Revenue  \n",
       "31242          13          35     180   143      323  \n",
       "76421           9          25     360   270      630  \n",
       "63417         344         540     120   344      464  \n",
       "13214           4          10     110   116      226  \n",
       "17882           7           9      16   133      149  \n",
       "...           ...         ...     ...   ...      ...  \n",
       "36385          13          35      17    13       30  \n",
       "11506           2           5      58    40       98  \n",
       "52187         420         769     318   420      738  \n",
       "83391           9          25     237   198      435  \n",
       "112433         24          64    1101   744     1845  \n",
       "\n",
       "[22608 rows x 18 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikes_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "8782204a",
   "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>47030</th>\n",
       "      <td>2014-01-10</td>\n",
       "      <td>10</td>\n",
       "      <td>january</td>\n",
       "      <td>2014</td>\n",
       "      <td>23</td>\n",
       "      <td>youth (&lt;25)</td>\n",
       "      <td>F</td>\n",
       "      <td>france</td>\n",
       "      <td>loiret</td>\n",
       "      <td>clothing</td>\n",
       "      <td>jerseys</td>\n",
       "      <td>short-sleeve classic jersey, l</td>\n",
       "      <td>2</td>\n",
       "      <td>42</td>\n",
       "      <td>54</td>\n",
       "      <td>12</td>\n",
       "      <td>84</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36579</th>\n",
       "      <td>2016-05-04</td>\n",
       "      <td>4</td>\n",
       "      <td>may</td>\n",
       "      <td>2016</td>\n",
       "      <td>34</td>\n",
       "      <td>young adults (25-34)</td>\n",
       "      <td>F</td>\n",
       "      <td>united states</td>\n",
       "      <td>california</td>\n",
       "      <td>accessories</td>\n",
       "      <td>helmets</td>\n",
       "      <td>sport-100 helmet, black</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>35</td>\n",
       "      <td>298</td>\n",
       "      <td>182</td>\n",
       "      <td>480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88485</th>\n",
       "      <td>2016-01-06</td>\n",
       "      <td>6</td>\n",
       "      <td>january</td>\n",
       "      <td>2016</td>\n",
       "      <td>34</td>\n",
       "      <td>young adults (25-34)</td>\n",
       "      <td>M</td>\n",
       "      <td>france</td>\n",
       "      <td>loiret</td>\n",
       "      <td>accessories</td>\n",
       "      <td>tires and tubes</td>\n",
       "      <td>touring tire tube</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>49</td>\n",
       "      <td>40</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12816</th>\n",
       "      <td>2014-07-15</td>\n",
       "      <td>15</td>\n",
       "      <td>july</td>\n",
       "      <td>2014</td>\n",
       "      <td>40</td>\n",
       "      <td>adults (35-64)</td>\n",
       "      <td>M</td>\n",
       "      <td>germany</td>\n",
       "      <td>bayern</td>\n",
       "      <td>accessories</td>\n",
       "      <td>bottles and cages</td>\n",
       "      <td>water bottle - 30 oz.</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>12</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109397</th>\n",
       "      <td>2015-11-29</td>\n",
       "      <td>29</td>\n",
       "      <td>november</td>\n",
       "      <td>2015</td>\n",
       "      <td>22</td>\n",
       "      <td>youth (&lt;25)</td>\n",
       "      <td>F</td>\n",
       "      <td>australia</td>\n",
       "      <td>queensland</td>\n",
       "      <td>bikes</td>\n",
       "      <td>touring bikes</td>\n",
       "      <td>touring-2000 blue, 46</td>\n",
       "      <td>1</td>\n",
       "      <td>755</td>\n",
       "      <td>1215</td>\n",
       "      <td>266</td>\n",
       "      <td>755</td>\n",
       "      <td>1021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50057</th>\n",
       "      <td>2015-11-16</td>\n",
       "      <td>16</td>\n",
       "      <td>november</td>\n",
       "      <td>2015</td>\n",
       "      <td>24</td>\n",
       "      <td>youth (&lt;25)</td>\n",
       "      <td>M</td>\n",
       "      <td>united states</td>\n",
       "      <td>washington</td>\n",
       "      <td>bikes</td>\n",
       "      <td>mountain bikes</td>\n",
       "      <td>mountain-200 silver, 38</td>\n",
       "      <td>3</td>\n",
       "      <td>1266</td>\n",
       "      <td>2320</td>\n",
       "      <td>1631</td>\n",
       "      <td>3798</td>\n",
       "      <td>5429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98047</th>\n",
       "      <td>2013-09-10</td>\n",
       "      <td>10</td>\n",
       "      <td>september</td>\n",
       "      <td>2013</td>\n",
       "      <td>28</td>\n",
       "      <td>young adults (25-34)</td>\n",
       "      <td>M</td>\n",
       "      <td>australia</td>\n",
       "      <td>new south wales</td>\n",
       "      <td>accessories</td>\n",
       "      <td>tires and tubes</td>\n",
       "      <td>ml road tire</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>153</td>\n",
       "      <td>108</td>\n",
       "      <td>261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5192</th>\n",
       "      <td>2016-05-26</td>\n",
       "      <td>26</td>\n",
       "      <td>may</td>\n",
       "      <td>2016</td>\n",
       "      <td>33</td>\n",
       "      <td>young adults (25-34)</td>\n",
       "      <td>M</td>\n",
       "      <td>australia</td>\n",
       "      <td>new south wales</td>\n",
       "      <td>accessories</td>\n",
       "      <td>bottles and cages</td>\n",
       "      <td>water bottle - 30 oz.</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "      <td>30</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77708</th>\n",
       "      <td>2013-11-11</td>\n",
       "      <td>11</td>\n",
       "      <td>november</td>\n",
       "      <td>2013</td>\n",
       "      <td>63</td>\n",
       "      <td>adults (35-64)</td>\n",
       "      <td>M</td>\n",
       "      <td>united states</td>\n",
       "      <td>california</td>\n",
       "      <td>accessories</td>\n",
       "      <td>tires and tubes</td>\n",
       "      <td>hl mountain tire</td>\n",
       "      <td>21</td>\n",
       "      <td>13</td>\n",
       "      <td>35</td>\n",
       "      <td>447</td>\n",
       "      <td>273</td>\n",
       "      <td>720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98539</th>\n",
       "      <td>2016-04-14</td>\n",
       "      <td>14</td>\n",
       "      <td>april</td>\n",
       "      <td>2016</td>\n",
       "      <td>46</td>\n",
       "      <td>adults (35-64)</td>\n",
       "      <td>M</td>\n",
       "      <td>united states</td>\n",
       "      <td>washington</td>\n",
       "      <td>accessories</td>\n",
       "      <td>tires and tubes</td>\n",
       "      <td>hl road tire</td>\n",
       "      <td>22</td>\n",
       "      <td>12</td>\n",
       "      <td>33</td>\n",
       "      <td>302</td>\n",
       "      <td>264</td>\n",
       "      <td>566</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>90428 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Date  Day      Month  Year  Customer_Age             Age_Group  \\\n",
       "47030   2014-01-10   10    january  2014            23           youth (<25)   \n",
       "36579   2016-05-04    4        may  2016            34  young adults (25-34)   \n",
       "88485   2016-01-06    6    january  2016            34  young adults (25-34)   \n",
       "12816   2014-07-15   15       july  2014            40        adults (35-64)   \n",
       "109397  2015-11-29   29   november  2015            22           youth (<25)   \n",
       "...            ...  ...        ...   ...           ...                   ...   \n",
       "50057   2015-11-16   16   november  2015            24           youth (<25)   \n",
       "98047   2013-09-10   10  september  2013            28  young adults (25-34)   \n",
       "5192    2016-05-26   26        may  2016            33  young adults (25-34)   \n",
       "77708   2013-11-11   11   november  2013            63        adults (35-64)   \n",
       "98539   2016-04-14   14      april  2016            46        adults (35-64)   \n",
       "\n",
       "       Customer_Gender        Country            State Product_Category  \\\n",
       "47030                F         france           loiret         clothing   \n",
       "36579                F  united states       california      accessories   \n",
       "88485                M         france           loiret      accessories   \n",
       "12816                M        germany           bayern      accessories   \n",
       "109397               F      australia       queensland            bikes   \n",
       "...                ...            ...              ...              ...   \n",
       "50057                M  united states       washington            bikes   \n",
       "98047                M      australia  new south wales      accessories   \n",
       "5192                 M      australia  new south wales      accessories   \n",
       "77708                M  united states       california      accessories   \n",
       "98539                M  united states       washington      accessories   \n",
       "\n",
       "             Sub_Category                         Product  Order_Quantity  \\\n",
       "47030             jerseys  short-sleeve classic jersey, l               2   \n",
       "36579             helmets         sport-100 helmet, black              14   \n",
       "88485     tires and tubes               touring tire tube              20   \n",
       "12816   bottles and cages           water bottle - 30 oz.               6   \n",
       "109397      touring bikes           touring-2000 blue, 46               1   \n",
       "...                   ...                             ...             ...   \n",
       "50057      mountain bikes         mountain-200 silver, 38               3   \n",
       "98047     tires and tubes                    ml road tire              12   \n",
       "5192    bottles and cages           water bottle - 30 oz.              15   \n",
       "77708     tires and tubes                hl mountain tire              21   \n",
       "98539     tires and tubes                    hl road tire              22   \n",
       "\n",
       "        Unit_Cost  Unit_Price  Profit  Cost  Revenue  \n",
       "47030          42          54      12    84       96  \n",
       "36579          13          35     298   182      480  \n",
       "88485           2           5      49    40       89  \n",
       "12816           2           5      18    12       30  \n",
       "109397        755        1215     266   755     1021  \n",
       "...           ...         ...     ...   ...      ...  \n",
       "50057        1266        2320    1631  3798     5429  \n",
       "98047           9          25     153   108      261  \n",
       "5192            2           5      35    30       65  \n",
       "77708          13          35     447   273      720  \n",
       "98539          12          33     302   264      566  \n",
       "\n",
       "[90428 rows x 18 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikes_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "4874398c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "adults (35-64)          55824\n",
       "young adults (25-34)    38654\n",
       "youth (<25)             17828\n",
       "seniors (64+)             730\n",
       "Name: Age_Group, dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikes[\"Age_Group\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "e1a03dd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bikes[\"Age_Group\"].value_counts().plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "c2ebf497",
   "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>Profit</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>1076.317146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>1102.724318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>243.800188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>199.472311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>308.004868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>239.334240</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Profit\n",
       "Year             \n",
       "2011  1076.317146\n",
       "2012  1102.724318\n",
       "2013   243.800188\n",
       "2014   199.472311\n",
       "2015   308.004868\n",
       "2016   239.334240"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " bikes[[\"Year\",\"Profit\"]].groupby(\"Year\").mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "3bbd4d07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Year'>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    " bikes[[\"Year\",\"Profit\"]].groupby(\"Year\").mean().plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "0786ffaf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f807fceafb0>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 593.625x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_theme()\n",
    "sns.relplot(data=bikes, x=\"Unit_Cost\", y=\"Unit_Price\", hue=\"Unit_Cost\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}