{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "23983c15",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "75d122bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>city</th>\n",
       "      <th>store_name</th>\n",
       "      <th>manufacturer</th>\n",
       "      <th>brand</th>\n",
       "      <th>class</th>\n",
       "      <th>size</th>\n",
       "      <th>sku</th>\n",
       "      <th>price_bracket</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>value_sales</th>\n",
       "      <th>volume_sales</th>\n",
       "      <th>average_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AL BAHA</td>\n",
       "      <td>HM No  57296  GS-CENTER-AL BAHA MAIN RD  AL BAHA</td>\n",
       "      <td>NOVA FOODS</td>\n",
       "      <td>LARA</td>\n",
       "      <td>COCONUT</td>\n",
       "      <td>0.75L</td>\n",
       "      <td>LARA COCONUT 0.75L TWIN PACK</td>\n",
       "      <td>21-30</td>\n",
       "      <td>2024</td>\n",
       "      <td>12</td>\n",
       "      <td>830.86</td>\n",
       "      <td>30.1</td>\n",
       "      <td>27.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AL KHARJ</td>\n",
       "      <td>HM No  55697  GS-CENTER-AL KHARJ MAIN RD  AL K...</td>\n",
       "      <td>PALM &amp; GRAIN GROUP</td>\n",
       "      <td>NAJMA</td>\n",
       "      <td>CANOLA</td>\n",
       "      <td>0.5L</td>\n",
       "      <td>NAJMA CANOLA 0.5L TWIN PACK</td>\n",
       "      <td>41-50</td>\n",
       "      <td>2024</td>\n",
       "      <td>10</td>\n",
       "      <td>373.10</td>\n",
       "      <td>9.1</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RIYADH</td>\n",
       "      <td>HM No  86781  GS-CENTER-RIYADH MAIN RD  RIYADH</td>\n",
       "      <td>AL HILAL INDUSTRIES</td>\n",
       "      <td>BAYTNA</td>\n",
       "      <td>SUNFLOWER</td>\n",
       "      <td>0.75L</td>\n",
       "      <td>BAYTNA SUNFLOWER 0.75L ECO</td>\n",
       "      <td>101+</td>\n",
       "      <td>2023</td>\n",
       "      <td>1</td>\n",
       "      <td>171.70</td>\n",
       "      <td>1.7</td>\n",
       "      <td>101.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DAMMAM</td>\n",
       "      <td>HM No  95753  GS-CENTER-DAMMAM MAIN RD  DAMMAM</td>\n",
       "      <td>PALM &amp; GRAIN GROUP</td>\n",
       "      <td>NOUR</td>\n",
       "      <td>CORN</td>\n",
       "      <td>0.6L</td>\n",
       "      <td>NOUR CORN 0.6L TWIN PACK</td>\n",
       "      <td>61-70</td>\n",
       "      <td>2022</td>\n",
       "      <td>2</td>\n",
       "      <td>1226.10</td>\n",
       "      <td>20.1</td>\n",
       "      <td>61.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>JAZAN</td>\n",
       "      <td>HM No  56338  GS-CENTER-JAZAN MAIN RD  JAZAN</td>\n",
       "      <td>DESERT SUN CO</td>\n",
       "      <td>NOUR</td>\n",
       "      <td>VEGETABLE</td>\n",
       "      <td>1L</td>\n",
       "      <td>NOUR VEGETABLE 1L</td>\n",
       "      <td>81-90</td>\n",
       "      <td>2024</td>\n",
       "      <td>2</td>\n",
       "      <td>996.30</td>\n",
       "      <td>12.3</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city                                         store_name  \\\n",
       "0   AL BAHA   HM No  57296  GS-CENTER-AL BAHA MAIN RD  AL BAHA   \n",
       "1  AL KHARJ  HM No  55697  GS-CENTER-AL KHARJ MAIN RD  AL K...   \n",
       "2    RIYADH     HM No  86781  GS-CENTER-RIYADH MAIN RD  RIYADH   \n",
       "3    DAMMAM     HM No  95753  GS-CENTER-DAMMAM MAIN RD  DAMMAM   \n",
       "4     JAZAN       HM No  56338  GS-CENTER-JAZAN MAIN RD  JAZAN   \n",
       "\n",
       "          manufacturer   brand      class   size  \\\n",
       "0           NOVA FOODS    LARA    COCONUT  0.75L   \n",
       "1   PALM & GRAIN GROUP   NAJMA     CANOLA   0.5L   \n",
       "2  AL HILAL INDUSTRIES  BAYTNA  SUNFLOWER  0.75L   \n",
       "3   PALM & GRAIN GROUP    NOUR       CORN   0.6L   \n",
       "4        DESERT SUN CO    NOUR  VEGETABLE     1L   \n",
       "\n",
       "                            sku price_bracket  year  month  value_sales  \\\n",
       "0  LARA COCONUT 0.75L TWIN PACK         21-30  2024     12       830.86   \n",
       "1   NAJMA CANOLA 0.5L TWIN PACK         41-50  2024     10       373.10   \n",
       "2    BAYTNA SUNFLOWER 0.75L ECO          101+  2023      1       171.70   \n",
       "3      NOUR CORN 0.6L TWIN PACK         61-70  2022      2      1226.10   \n",
       "4             NOUR VEGETABLE 1L         81-90  2024      2       996.30   \n",
       "\n",
       "   volume_sales  average_price  \n",
       "0          30.1           27.6  \n",
       "1           9.1           41.0  \n",
       "2           1.7          101.0  \n",
       "3          20.1           61.0  \n",
       "4          12.3           81.0  "
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"oil_sales_assignment_dataset.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "74315548",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2000, 13)"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "8a0b77a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['city', 'store_name', 'manufacturer', 'brand', 'class', 'size', 'sku',\n",
       "       'price_bracket', 'year', 'month', 'value_sales', 'volume_sales',\n",
       "       'average_price'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "b7058a92",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2000 entries, 0 to 1999\n",
      "Data columns (total 13 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   city           2000 non-null   object \n",
      " 1   store_name     2000 non-null   object \n",
      " 2   manufacturer   2000 non-null   object \n",
      " 3   brand          2000 non-null   object \n",
      " 4   class          2000 non-null   object \n",
      " 5   size           2000 non-null   object \n",
      " 6   sku            2000 non-null   object \n",
      " 7   price_bracket  2000 non-null   object \n",
      " 8   year           2000 non-null   int64  \n",
      " 9   month          2000 non-null   int64  \n",
      " 10  value_sales    2000 non-null   float64\n",
      " 11  volume_sales   2000 non-null   float64\n",
      " 12  average_price  2000 non-null   float64\n",
      "dtypes: float64(3), int64(2), object(8)\n",
      "memory usage: 203.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "e4b3b178",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>value_sales</th>\n",
       "      <th>volume_sales</th>\n",
       "      <th>average_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2022.990500</td>\n",
       "      <td>6.639500</td>\n",
       "      <td>614.839355</td>\n",
       "      <td>9.972100</td>\n",
       "      <td>60.990230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.820211</td>\n",
       "      <td>3.468942</td>\n",
       "      <td>750.794991</td>\n",
       "      <td>9.862369</td>\n",
       "      <td>29.457029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2022.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.960000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2022.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>37.337500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2023.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>368.320000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>61.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2024.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>794.100000</td>\n",
       "      <td>13.800000</td>\n",
       "      <td>81.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2024.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>6253.200000</td>\n",
       "      <td>81.700000</td>\n",
       "      <td>140.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              year        month  value_sales  volume_sales  average_price\n",
       "count  2000.000000  2000.000000  2000.000000   2000.000000    2000.000000\n",
       "mean   2022.990500     6.639500   614.839355      9.972100      60.990230\n",
       "std       0.820211     3.468942   750.794991      9.862369      29.457029\n",
       "min    2022.000000     1.000000     6.960000      0.500000      11.000000\n",
       "25%    2022.000000     4.000000   132.000000      2.900000      37.337500\n",
       "50%    2023.000000     7.000000   368.320000      7.000000      61.000000\n",
       "75%    2024.000000    10.000000   794.100000     13.800000      81.000000\n",
       "max    2024.000000    12.000000  6253.200000     81.700000     140.000000"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "5fe1eb31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city             0\n",
       "store_name       0\n",
       "manufacturer     0\n",
       "brand            0\n",
       "class            0\n",
       "size             0\n",
       "sku              0\n",
       "price_bracket    0\n",
       "year             0\n",
       "month            0\n",
       "value_sales      0\n",
       "volume_sales     0\n",
       "average_price    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "425959b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.int64(0)"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "5ecd7730",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>store_name</th>\n",
       "      <th>manufacturer</th>\n",
       "      <th>brand</th>\n",
       "      <th>class</th>\n",
       "      <th>size</th>\n",
       "      <th>sku</th>\n",
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       "      <th>year</th>\n",
       "      <th>month</th>\n",
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       "      <th>average_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AL BAHA</td>\n",
       "      <td>HM No  57296  GS-CENTER-AL BAHA MAIN RD  AL BAHA</td>\n",
       "      <td>NOVA FOODS</td>\n",
       "      <td>LARA</td>\n",
       "      <td>COCONUT</td>\n",
       "      <td>0.75</td>\n",
       "      <td>LARA COCONUT 0.75L TWIN PACK</td>\n",
       "      <td>21-30</td>\n",
       "      <td>2024</td>\n",
       "      <td>12</td>\n",
       "      <td>830.86</td>\n",
       "      <td>30.1</td>\n",
       "      <td>27.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AL KHARJ</td>\n",
       "      <td>HM No  55697  GS-CENTER-AL KHARJ MAIN RD  AL K...</td>\n",
       "      <td>PALM &amp; GRAIN GROUP</td>\n",
       "      <td>NAJMA</td>\n",
       "      <td>CANOLA</td>\n",
       "      <td>0.50</td>\n",
       "      <td>NAJMA CANOLA 0.5L TWIN PACK</td>\n",
       "      <td>41-50</td>\n",
       "      <td>2024</td>\n",
       "      <td>10</td>\n",
       "      <td>373.10</td>\n",
       "      <td>9.1</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RIYADH</td>\n",
       "      <td>HM No  86781  GS-CENTER-RIYADH MAIN RD  RIYADH</td>\n",
       "      <td>AL HILAL INDUSTRIES</td>\n",
       "      <td>BAYTNA</td>\n",
       "      <td>SUNFLOWER</td>\n",
       "      <td>0.75</td>\n",
       "      <td>BAYTNA SUNFLOWER 0.75L ECO</td>\n",
       "      <td>101+</td>\n",
       "      <td>2023</td>\n",
       "      <td>1</td>\n",
       "      <td>171.70</td>\n",
       "      <td>1.7</td>\n",
       "      <td>101.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DAMMAM</td>\n",
       "      <td>HM No  95753  GS-CENTER-DAMMAM MAIN RD  DAMMAM</td>\n",
       "      <td>PALM &amp; GRAIN GROUP</td>\n",
       "      <td>NOUR</td>\n",
       "      <td>CORN</td>\n",
       "      <td>0.60</td>\n",
       "      <td>NOUR CORN 0.6L TWIN PACK</td>\n",
       "      <td>61-70</td>\n",
       "      <td>2022</td>\n",
       "      <td>2</td>\n",
       "      <td>1226.10</td>\n",
       "      <td>20.1</td>\n",
       "      <td>61.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>JAZAN</td>\n",
       "      <td>HM No  56338  GS-CENTER-JAZAN MAIN RD  JAZAN</td>\n",
       "      <td>DESERT SUN CO</td>\n",
       "      <td>NOUR</td>\n",
       "      <td>VEGETABLE</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NOUR VEGETABLE 1L</td>\n",
       "      <td>81-90</td>\n",
       "      <td>2024</td>\n",
       "      <td>2</td>\n",
       "      <td>996.30</td>\n",
       "      <td>12.3</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city                                         store_name  \\\n",
       "0   AL BAHA   HM No  57296  GS-CENTER-AL BAHA MAIN RD  AL BAHA   \n",
       "1  AL KHARJ  HM No  55697  GS-CENTER-AL KHARJ MAIN RD  AL K...   \n",
       "2    RIYADH     HM No  86781  GS-CENTER-RIYADH MAIN RD  RIYADH   \n",
       "3    DAMMAM     HM No  95753  GS-CENTER-DAMMAM MAIN RD  DAMMAM   \n",
       "4     JAZAN       HM No  56338  GS-CENTER-JAZAN MAIN RD  JAZAN   \n",
       "\n",
       "          manufacturer   brand      class  size                           sku  \\\n",
       "0           NOVA FOODS    LARA    COCONUT  0.75  LARA COCONUT 0.75L TWIN PACK   \n",
       "1   PALM & GRAIN GROUP   NAJMA     CANOLA  0.50   NAJMA CANOLA 0.5L TWIN PACK   \n",
       "2  AL HILAL INDUSTRIES  BAYTNA  SUNFLOWER  0.75    BAYTNA SUNFLOWER 0.75L ECO   \n",
       "3   PALM & GRAIN GROUP    NOUR       CORN  0.60      NOUR CORN 0.6L TWIN PACK   \n",
       "4        DESERT SUN CO    NOUR  VEGETABLE  1.00             NOUR VEGETABLE 1L   \n",
       "\n",
       "  price_bracket  year  month  value_sales  volume_sales  average_price  \n",
       "0         21-30  2024     12       830.86          30.1           27.6  \n",
       "1         41-50  2024     10       373.10           9.1           41.0  \n",
       "2          101+  2023      1       171.70           1.7          101.0  \n",
       "3         61-70  2022      2      1226.10          20.1           61.0  \n",
       "4         81-90  2024      2       996.30          12.3           81.0  "
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def remove_L(L):\n",
    "    new_L = L.replace(\"L\",\"\")\n",
    "    return float(new_L)\n",
    "\n",
    "df[\"size\"] = df[\"size\"].apply(remove_L)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "b97d694b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['LARA COCONUT 0.75L TWIN PACK', 'NAJMA CANOLA 0.5L TWIN PACK',\n",
       "       'BAYTNA SUNFLOWER 0.75L ECO', ..., 'LARA COCONUT 0.5L PREMIUM',\n",
       "       'LARA SUNFLOWER 3L', 'NAJMA CORN 0.75L ECO'],\n",
       "      shape=(1571,), dtype=object)"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"sku\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "db916b5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['AL BAHA', 'AL KHARJ', 'RIYADH', 'DAMMAM', 'JAZAN', 'TAIF', 'HAIL',\n",
       "       'MAKKAH', 'AL AHSA', 'TABUK', 'YANBU', 'JEDDAH'], dtype=object)"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"city\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "87b9a550",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['AL BAHA', 'AL KHARJ', 'RIYADH', 'DAMMAM', 'Other', 'TAIF', 'HAIL',\n",
       "       'MAKKAH', 'TABUK', 'YANBU', 'JEDDAH'], dtype=object)"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_city = df[\"city\"].value_counts().index[:10].tolist()\n",
    "\n",
    "def group_city(city):\n",
    "    if city in top_city:\n",
    "        return city\n",
    "    return \"Other\"\n",
    "\n",
    "df[\"city\"] = df[\"city\"].apply(group_city)\n",
    "df[\"city\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "51c3fdfd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        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>city</th>\n",
       "      <th>store_name</th>\n",
       "      <th>manufacturer</th>\n",
       "      <th>brand</th>\n",
       "      <th>class</th>\n",
       "      <th>size</th>\n",
       "      <th>sku</th>\n",
       "      <th>price_bracket</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>value_sales</th>\n",
       "      <th>volume_sales</th>\n",
       "      <th>average_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AL BAHA</td>\n",
       "      <td>HM No  57296  GS-CENTER-AL BAHA MAIN RD  AL BAHA</td>\n",
       "      <td>NOVA FOODS</td>\n",
       "      <td>LARA</td>\n",
       "      <td>COCONUT</td>\n",
       "      <td>0.75</td>\n",
       "      <td>LARA COCONUT 0.75L TWIN PACK</td>\n",
       "      <td>21-30</td>\n",
       "      <td>2024</td>\n",
       "      <td>12</td>\n",
       "      <td>830.86</td>\n",
       "      <td>30.1</td>\n",
       "      <td>27.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AL KHARJ</td>\n",
       "      <td>HM No  55697  GS-CENTER-AL KHARJ MAIN RD  AL K...</td>\n",
       "      <td>PALM &amp; GRAIN GROUP</td>\n",
       "      <td>NAJMA</td>\n",
       "      <td>CANOLA</td>\n",
       "      <td>0.50</td>\n",
       "      <td>NAJMA CANOLA 0.5L TWIN PACK</td>\n",
       "      <td>41-50</td>\n",
       "      <td>2024</td>\n",
       "      <td>10</td>\n",
       "      <td>373.10</td>\n",
       "      <td>9.1</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RIYADH</td>\n",
       "      <td>HM No  86781  GS-CENTER-RIYADH MAIN RD  RIYADH</td>\n",
       "      <td>AL HILAL INDUSTRIES</td>\n",
       "      <td>BAYTNA</td>\n",
       "      <td>SUNFLOWER</td>\n",
       "      <td>0.75</td>\n",
       "      <td>BAYTNA SUNFLOWER 0.75L ECO</td>\n",
       "      <td>101+</td>\n",
       "      <td>2023</td>\n",
       "      <td>1</td>\n",
       "      <td>171.70</td>\n",
       "      <td>1.7</td>\n",
       "      <td>101.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DAMMAM</td>\n",
       "      <td>HM No  95753  GS-CENTER-DAMMAM MAIN RD  DAMMAM</td>\n",
       "      <td>PALM &amp; GRAIN GROUP</td>\n",
       "      <td>NOUR</td>\n",
       "      <td>CORN</td>\n",
       "      <td>0.60</td>\n",
       "      <td>NOUR CORN 0.6L TWIN PACK</td>\n",
       "      <td>61-70</td>\n",
       "      <td>2022</td>\n",
       "      <td>2</td>\n",
       "      <td>1226.10</td>\n",
       "      <td>20.1</td>\n",
       "      <td>61.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Other</td>\n",
       "      <td>HM No  56338  GS-CENTER-JAZAN MAIN RD  JAZAN</td>\n",
       "      <td>DESERT SUN CO</td>\n",
       "      <td>NOUR</td>\n",
       "      <td>VEGETABLE</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NOUR VEGETABLE 1L</td>\n",
       "      <td>81-90</td>\n",
       "      <td>2024</td>\n",
       "      <td>2</td>\n",
       "      <td>996.30</td>\n",
       "      <td>12.3</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city                                         store_name  \\\n",
       "0   AL BAHA   HM No  57296  GS-CENTER-AL BAHA MAIN RD  AL BAHA   \n",
       "1  AL KHARJ  HM No  55697  GS-CENTER-AL KHARJ MAIN RD  AL K...   \n",
       "2    RIYADH     HM No  86781  GS-CENTER-RIYADH MAIN RD  RIYADH   \n",
       "3    DAMMAM     HM No  95753  GS-CENTER-DAMMAM MAIN RD  DAMMAM   \n",
       "4     Other       HM No  56338  GS-CENTER-JAZAN MAIN RD  JAZAN   \n",
       "\n",
       "          manufacturer   brand      class  size                           sku  \\\n",
       "0           NOVA FOODS    LARA    COCONUT  0.75  LARA COCONUT 0.75L TWIN PACK   \n",
       "1   PALM & GRAIN GROUP   NAJMA     CANOLA  0.50   NAJMA CANOLA 0.5L TWIN PACK   \n",
       "2  AL HILAL INDUSTRIES  BAYTNA  SUNFLOWER  0.75    BAYTNA SUNFLOWER 0.75L ECO   \n",
       "3   PALM & GRAIN GROUP    NOUR       CORN  0.60      NOUR CORN 0.6L TWIN PACK   \n",
       "4        DESERT SUN CO    NOUR  VEGETABLE  1.00             NOUR VEGETABLE 1L   \n",
       "\n",
       "  price_bracket  year  month  value_sales  volume_sales  average_price  \n",
       "0         21-30  2024     12       830.86          30.1           27.6  \n",
       "1         41-50  2024     10       373.10           9.1           41.0  \n",
       "2          101+  2023      1       171.70           1.7          101.0  \n",
       "3         61-70  2022      2      1226.10          20.1           61.0  \n",
       "4         81-90  2024      2       996.30          12.3           81.0  "
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "03dcc497",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "        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>city</th>\n",
       "      <th>store_name</th>\n",
       "      <th>manufacturer</th>\n",
       "      <th>brand</th>\n",
       "      <th>class</th>\n",
       "      <th>size</th>\n",
       "      <th>sku</th>\n",
       "      <th>price_bracket</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>value_sales</th>\n",
       "      <th>volume_sales</th>\n",
       "      <th>average_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AL BAHA</td>\n",
       "      <td>HM No  57296  GS-CENTER-AL BAHA MAIN RD  AL BAHA</td>\n",
       "      <td>NOVA FOODS</td>\n",
       "      <td>LARA</td>\n",
       "      <td>COCONUT</td>\n",
       "      <td>0.75</td>\n",
       "      <td>LARA COCONUT 0.75L TWIN PACK</td>\n",
       "      <td>21-30</td>\n",
       "      <td>2024</td>\n",
       "      <td>12</td>\n",
       "      <td>830.86</td>\n",
       "      <td>30.1</td>\n",
       "      <td>27.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AL KHARJ</td>\n",
       "      <td>HM No  55697  GS-CENTER-AL KHARJ MAIN RD  AL K...</td>\n",
       "      <td>PALM &amp; GRAIN GROUP</td>\n",
       "      <td>NAJMA</td>\n",
       "      <td>CANOLA</td>\n",
       "      <td>0.50</td>\n",
       "      <td>NAJMA CANOLA 0.5L TWIN PACK</td>\n",
       "      <td>41-50</td>\n",
       "      <td>2024</td>\n",
       "      <td>10</td>\n",
       "      <td>373.10</td>\n",
       "      <td>9.1</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RIYADH</td>\n",
       "      <td>HM No  86781  GS-CENTER-RIYADH MAIN RD  RIYADH</td>\n",
       "      <td>AL HILAL INDUSTRIES</td>\n",
       "      <td>BAYTNA</td>\n",
       "      <td>SUNFLOWER</td>\n",
       "      <td>0.75</td>\n",
       "      <td>BAYTNA SUNFLOWER 0.75L ECO</td>\n",
       "      <td>101-151</td>\n",
       "      <td>2023</td>\n",
       "      <td>1</td>\n",
       "      <td>171.70</td>\n",
       "      <td>1.7</td>\n",
       "      <td>101.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DAMMAM</td>\n",
       "      <td>HM No  95753  GS-CENTER-DAMMAM MAIN RD  DAMMAM</td>\n",
       "      <td>PALM &amp; GRAIN GROUP</td>\n",
       "      <td>NOUR</td>\n",
       "      <td>CORN</td>\n",
       "      <td>0.60</td>\n",
       "      <td>NOUR CORN 0.6L TWIN PACK</td>\n",
       "      <td>61-70</td>\n",
       "      <td>2022</td>\n",
       "      <td>2</td>\n",
       "      <td>1226.10</td>\n",
       "      <td>20.1</td>\n",
       "      <td>61.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Other</td>\n",
       "      <td>HM No  56338  GS-CENTER-JAZAN MAIN RD  JAZAN</td>\n",
       "      <td>DESERT SUN CO</td>\n",
       "      <td>NOUR</td>\n",
       "      <td>VEGETABLE</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NOUR VEGETABLE 1L</td>\n",
       "      <td>81-90</td>\n",
       "      <td>2024</td>\n",
       "      <td>2</td>\n",
       "      <td>996.30</td>\n",
       "      <td>12.3</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city                                         store_name  \\\n",
       "0   AL BAHA   HM No  57296  GS-CENTER-AL BAHA MAIN RD  AL BAHA   \n",
       "1  AL KHARJ  HM No  55697  GS-CENTER-AL KHARJ MAIN RD  AL K...   \n",
       "2    RIYADH     HM No  86781  GS-CENTER-RIYADH MAIN RD  RIYADH   \n",
       "3    DAMMAM     HM No  95753  GS-CENTER-DAMMAM MAIN RD  DAMMAM   \n",
       "4     Other       HM No  56338  GS-CENTER-JAZAN MAIN RD  JAZAN   \n",
       "\n",
       "          manufacturer   brand      class  size                           sku  \\\n",
       "0           NOVA FOODS    LARA    COCONUT  0.75  LARA COCONUT 0.75L TWIN PACK   \n",
       "1   PALM & GRAIN GROUP   NAJMA     CANOLA  0.50   NAJMA CANOLA 0.5L TWIN PACK   \n",
       "2  AL HILAL INDUSTRIES  BAYTNA  SUNFLOWER  0.75    BAYTNA SUNFLOWER 0.75L ECO   \n",
       "3   PALM & GRAIN GROUP    NOUR       CORN  0.60      NOUR CORN 0.6L TWIN PACK   \n",
       "4        DESERT SUN CO    NOUR  VEGETABLE  1.00             NOUR VEGETABLE 1L   \n",
       "\n",
       "  price_bracket  year  month  value_sales  volume_sales  average_price  \n",
       "0         21-30  2024     12       830.86          30.1           27.6  \n",
       "1         41-50  2024     10       373.10           9.1           41.0  \n",
       "2       101-151  2023      1       171.70           1.7          101.0  \n",
       "3         61-70  2022      2      1226.10          20.1           61.0  \n",
       "4         81-90  2024      2       996.30          12.3           81.0  "
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def price_(limit_num):\n",
    "    limit_num = limit_num.replace(\"$\",\"\")\n",
    "    for i in limit_num:\n",
    "        if i == \"-\":\n",
    "            return limit_num\n",
    "        elif i == \"+\":\n",
    "            num = limit_num.replace(\"+\",\"\")\n",
    "            num_min = int(num)\n",
    "            num_max = num_min+50\n",
    "            return f\"{num_min}-{num_max}\"\n",
    "\n",
    "df[\"price_bracket\"] = df[\"price_bracket\"].apply(price_)\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "a917a321",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>manufacturer</th>\n",
       "      <th>brand</th>\n",
       "      <th>class</th>\n",
       "      <th>size</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>value_sales</th>\n",
       "      <th>volume_sales</th>\n",
       "      <th>average_price</th>\n",
       "      <th>price_min</th>\n",
       "      <th>price_max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AL BAHA</td>\n",
       "      <td>NOVA FOODS</td>\n",
       "      <td>LARA</td>\n",
       "      <td>COCONUT</td>\n",
       "      <td>0.75</td>\n",
       "      <td>2024</td>\n",
       "      <td>12</td>\n",
       "      <td>830.86</td>\n",
       "      <td>30.1</td>\n",
       "      <td>27.6</td>\n",
       "      <td>21</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AL KHARJ</td>\n",
       "      <td>PALM &amp; GRAIN GROUP</td>\n",
       "      <td>NAJMA</td>\n",
       "      <td>CANOLA</td>\n",
       "      <td>0.50</td>\n",
       "      <td>2024</td>\n",
       "      <td>10</td>\n",
       "      <td>373.10</td>\n",
       "      <td>9.1</td>\n",
       "      <td>41.0</td>\n",
       "      <td>41</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RIYADH</td>\n",
       "      <td>AL HILAL INDUSTRIES</td>\n",
       "      <td>BAYTNA</td>\n",
       "      <td>SUNFLOWER</td>\n",
       "      <td>0.75</td>\n",
       "      <td>2023</td>\n",
       "      <td>1</td>\n",
       "      <td>171.70</td>\n",
       "      <td>1.7</td>\n",
       "      <td>101.0</td>\n",
       "      <td>101</td>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DAMMAM</td>\n",
       "      <td>PALM &amp; GRAIN GROUP</td>\n",
       "      <td>NOUR</td>\n",
       "      <td>CORN</td>\n",
       "      <td>0.60</td>\n",
       "      <td>2022</td>\n",
       "      <td>2</td>\n",
       "      <td>1226.10</td>\n",
       "      <td>20.1</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Other</td>\n",
       "      <td>DESERT SUN CO</td>\n",
       "      <td>NOUR</td>\n",
       "      <td>VEGETABLE</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2024</td>\n",
       "      <td>2</td>\n",
       "      <td>996.30</td>\n",
       "      <td>12.3</td>\n",
       "      <td>81.0</td>\n",
       "      <td>81</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city         manufacturer   brand      class  size  year  month  \\\n",
       "0   AL BAHA           NOVA FOODS    LARA    COCONUT  0.75  2024     12   \n",
       "1  AL KHARJ   PALM & GRAIN GROUP   NAJMA     CANOLA  0.50  2024     10   \n",
       "2    RIYADH  AL HILAL INDUSTRIES  BAYTNA  SUNFLOWER  0.75  2023      1   \n",
       "3    DAMMAM   PALM & GRAIN GROUP    NOUR       CORN  0.60  2022      2   \n",
       "4     Other        DESERT SUN CO    NOUR  VEGETABLE  1.00  2024      2   \n",
       "\n",
       "   value_sales  volume_sales  average_price  price_min  price_max  \n",
       "0       830.86          30.1           27.6         21         30  \n",
       "1       373.10           9.1           41.0         41         50  \n",
       "2       171.70           1.7          101.0        101        151  \n",
       "3      1226.10          20.1           61.0         61         70  \n",
       "4       996.30          12.3           81.0         81         90  "
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def price_min(price_limit):\n",
    "    min = price_limit.split('-')[0]\n",
    "    return int(min)\n",
    "def price_max(price_limit):\n",
    "    max = price_limit.split('-')[1]\n",
    "    return int(max)\n",
    "\n",
    "df[\"price_min\"] = df[\"price_bracket\"].apply(price_min)\n",
    "df[\"price_max\"] = df[\"price_bracket\"].apply(price_max)\n",
    "df.drop([\"store_name\",\"sku\",\"price_bracket\"],axis=1,inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "f3cb5341",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['NOVA FOODS', 'PALM & GRAIN GROUP', 'AL HILAL INDUSTRIES',\n",
       "       'DESERT SUN CO', 'NAJDI CONSUMER', 'ARABIAN HARVEST CO',\n",
       "       'BLUE OASIS CO', 'SAHARA EDIBLES'], dtype=object)"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"manufacturer\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "417dd994",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['LARA', 'NAJMA', 'BAYTNA', 'NOUR', 'RIMAL', 'HILAL', 'GULF GOLD',\n",
       "       'SABAYA', 'RAWABI', 'ZAHRA'], dtype=object)"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"brand\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "2668696f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['COCONUT', 'CANOLA', 'SUNFLOWER', 'CORN', 'VEGETABLE'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"class\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "0a6e6bd8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2000 entries, 0 to 1999\n",
      "Data columns (total 12 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   city           2000 non-null   object \n",
      " 1   manufacturer   2000 non-null   object \n",
      " 2   brand          2000 non-null   object \n",
      " 3   class          2000 non-null   object \n",
      " 4   size           2000 non-null   float64\n",
      " 5   year           2000 non-null   int64  \n",
      " 6   month          2000 non-null   int64  \n",
      " 7   value_sales    2000 non-null   float64\n",
      " 8   volume_sales   2000 non-null   float64\n",
      " 9   average_price  2000 non-null   float64\n",
      " 10  price_min      2000 non-null   int64  \n",
      " 11  price_max      2000 non-null   int64  \n",
      "dtypes: float64(4), int64(4), object(4)\n",
      "memory usage: 187.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "0fc5ab86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "size             1.339165\n",
       "year             0.017541\n",
       "month           -0.051363\n",
       "value_sales      2.784480\n",
       "volume_sales     1.992426\n",
       "average_price    0.313002\n",
       "price_min        0.003073\n",
       "price_max        0.774463\n",
       "dtype: float64"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.select_dtypes(\"number\").skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "5c81060d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.select_dtypes(\"number\").corr(),annot=True,fmt=\".2f\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "b43ec45e",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_cols = [\"month\",\"price_min\",\"price_max\"]\n",
    "one_cols = [\"city\",\"manufacturer\",\"brand\",\"class\"]\n",
    "ske_cols = [\"size\",\"volume_sales\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "f255e81a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X = df.drop([\"value_sales\",'average_price'],axis=1)\n",
    "y = df[\"value_sales\"]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "0749aa10",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler,FunctionTransformer,OneHotEncoder\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "num_pipe = Pipeline(steps=[\n",
    "    (\"scaler\",StandardScaler())\n",
    "])\n",
    "one_pipe = Pipeline(steps=[\n",
    "    (\"encoder\",OneHotEncoder(handle_unknown=\"ignore\"))\n",
    "])\n",
    "ske_pipe = Pipeline(steps=[\n",
    "    (\"ske\",FunctionTransformer(np.log1p)),\n",
    "    (\"scaler\",StandardScaler())\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "3c746dc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-7 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "}\n",
       "\n",
       "#sk-container-id-7.light {\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: black;\n",
       "  --sklearn-color-background: white;\n",
       "  --sklearn-color-border-box: black;\n",
       "  --sklearn-color-icon: #696969;\n",
       "}\n",
       "\n",
       "#sk-container-id-7.dark {\n",
       "  --sklearn-color-text-on-default-background: white;\n",
       "  --sklearn-color-background: #111;\n",
       "  --sklearn-color-border-box: white;\n",
       "  --sklearn-color-icon: #878787;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-7 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-7 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: center;\n",
       "  justify-content: center;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-7 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-7 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-7 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-7 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-7 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-7 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-7 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".estimator-table {\n",
       "    font-family: monospace;\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table summary::marker {\n",
       "    font-size: 0.7rem;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "    margin-top: 0;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       "/*\n",
       "    `table td`is set in notebook with right text-align.\n",
       "    We need to overwrite it.\n",
       "*/\n",
       ".estimator-table table td.param {\n",
       "    text-align: left;\n",
       "    position: relative;\n",
       "    padding: 0;\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td.value {\n",
       "    color:rgb(255, 94, 0);\n",
       "    background-color: transparent;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       "/*\n",
       "    Styles for parameter documentation links\n",
       "    We need styling for visited so jupyter doesn't overwrite it\n",
       "*/\n",
       "a.param-doc-link,\n",
       "a.param-doc-link:link,\n",
       "a.param-doc-link:visited {\n",
       "    text-decoration: underline dashed;\n",
       "    text-underline-offset: .3em;\n",
       "    color: inherit;\n",
       "    display: block;\n",
       "    padding: .5em;\n",
       "}\n",
       "\n",
       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
       "a.param-doc-link::before {\n",
       "    position: absolute;\n",
       "    content: \"\";\n",
       "    inset: 0;\n",
       "}\n",
       "\n",
       ".param-doc-description {\n",
       "    display: none;\n",
       "    position: absolute;\n",
       "    z-index: 9999;\n",
       "    left: 0;\n",
       "    padding: .5ex;\n",
       "    margin-left: 1.5em;\n",
       "    color: var(--sklearn-color-text);\n",
       "    box-shadow: .3em .3em .4em #999;\n",
       "    width: max-content;\n",
       "    text-align: left;\n",
       "    max-height: 10em;\n",
       "    overflow-y: auto;\n",
       "\n",
       "    /* unfitted */\n",
       "    background: var(--sklearn-color-unfitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       "/* Fitted state for parameter tooltips */\n",
       ".fitted .param-doc-description {\n",
       "    /* fitted */\n",
       "    background: var(--sklearn-color-fitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".param-doc-link:hover .param-doc-description {\n",
       "    display: block;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ColumnTransformer(transformers=[(&#x27;one_pip_col&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;encoder&#x27;,\n",
       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                 [&#x27;city&#x27;, &#x27;manufacturer&#x27;, &#x27;brand&#x27;, &#x27;class&#x27;]),\n",
       "                                (&#x27;num_pip_col&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler())]),\n",
       "                                 [&#x27;month&#x27;, &#x27;price_min&#x27;, &#x27;price_max&#x27;]),\n",
       "                                (&#x27;ske_pip_col&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;ske&#x27;,\n",
       "                                                  FunctionTransformer(func=&lt;ufunc &#x27;log1p&#x27;&gt;)),\n",
       "                                                 (&#x27;scaler&#x27;, StandardScaler())]),\n",
       "                                 [&#x27;size&#x27;, &#x27;volume_sales&#x27;])])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-49\" type=\"checkbox\" ><label for=\"sk-estimator-id-49\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>ColumnTransformer</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for ColumnTransformer</span></a><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></div></label><div class=\"sk-toggleable__content \" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('transformers',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=transformers,-list%20of%20tuples\">\n",
       "            transformers\n",
       "            <span class=\"param-doc-description\">transformers: list of tuples<br><br>List of (name, transformer, columns) tuples specifying the<br>transformer objects to be applied to subsets of the data.<br><br>name : str<br>    Like in Pipeline and FeatureUnion, this allows the transformer and<br>    its parameters to be set using ``set_params`` and searched in grid<br>    search.<br>transformer : {'drop', 'passthrough'} or estimator<br>    Estimator must support :term:`fit` and :term:`transform`.<br>    Special-cased strings 'drop' and 'passthrough' are accepted as<br>    well, to indicate to drop the columns or to pass them through<br>    untransformed, respectively.<br>columns :  str, array-like of str, int, array-like of int,                 array-like of bool, slice or callable<br>    Indexes the data on its second axis. Integers are interpreted as<br>    positional columns, while strings can reference DataFrame columns<br>    by name.  A scalar string or int should be used where<br>    ``transformer`` expects X to be a 1d array-like (vector),<br>    otherwise a 2d array will be passed to the transformer.<br>    A callable is passed the input data `X` and can return any of the<br>    above. To select multiple columns by name or dtype, you can use<br>    :obj:`make_column_selector`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">[(&#x27;one_pip_col&#x27;, ...), (&#x27;num_pip_col&#x27;, ...), ...]</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('remainder',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=remainder,-%7B%27drop%27%2C%20%27passthrough%27%7D%20or%20estimator%2C%20default%3D%27drop%27\">\n",
       "            remainder\n",
       "            <span class=\"param-doc-description\">remainder: {'drop', 'passthrough'} or estimator, default='drop'<br><br>By default, only the specified columns in `transformers` are<br>transformed and combined in the output, and the non-specified<br>columns are dropped. (default of ``'drop'``).<br>By specifying ``remainder='passthrough'``, all remaining columns that<br>were not specified in `transformers`, but present in the data passed<br>to `fit` will be automatically passed through. This subset of columns<br>is concatenated with the output of the transformers. For dataframes,<br>extra columns not seen during `fit` will be excluded from the output<br>of `transform`.<br>By setting ``remainder`` to be an estimator, the remaining<br>non-specified columns will use the ``remainder`` estimator. The<br>estimator must support :term:`fit` and :term:`transform`.<br>Note that using this feature requires that the DataFrame columns<br>input at :term:`fit` and :term:`transform` have identical order.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;drop&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('sparse_threshold',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=sparse_threshold,-float%2C%20default%3D0.3\">\n",
       "            sparse_threshold\n",
       "            <span class=\"param-doc-description\">sparse_threshold: float, default=0.3<br><br>If the output of the different transformers contains sparse matrices,<br>these will be stacked as a sparse matrix if the overall density is<br>lower than this value. Use ``sparse_threshold=0`` to always return<br>dense.  When the transformed output consists of all dense data, the<br>stacked result will be dense, and this keyword will be ignored.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.3</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
       "            n_jobs\n",
       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>Number of jobs to run in parallel.<br>``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.<br>``-1`` means using all processors. See :term:`Glossary <n_jobs>`<br>for more details.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('transformer_weights',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=transformer_weights,-dict%2C%20default%3DNone\">\n",
       "            transformer_weights\n",
       "            <span class=\"param-doc-description\">transformer_weights: dict, default=None<br><br>Multiplicative weights for features per transformer. The output of the<br>transformer is multiplied by these weights. Keys are transformer names,<br>values the weights.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=verbose,-bool%2C%20default%3DFalse\">\n",
       "            verbose\n",
       "            <span class=\"param-doc-description\">verbose: bool, default=False<br><br>If True, the time elapsed while fitting each transformer will be<br>printed as it is completed.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose_feature_names_out',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=verbose_feature_names_out,-bool%2C%20str%20or%20Callable%5B%5Bstr%2C%20str%5D%2C%20str%5D%2C%20default%3DTrue\">\n",
       "            verbose_feature_names_out\n",
       "            <span class=\"param-doc-description\">verbose_feature_names_out: bool, str or Callable[[str, str], str], default=True<br><br>- If True, :meth:`ColumnTransformer.get_feature_names_out` will prefix<br>  all feature names with the name of the transformer that generated that<br>  feature. It is equivalent to setting<br>  `verbose_feature_names_out=\"{transformer_name}__{feature_name}\"`.<br>- If False, :meth:`ColumnTransformer.get_feature_names_out` will not<br>  prefix any feature names and will error if feature names are not<br>  unique.<br>- If ``Callable[[str, str], str]``,<br>  :meth:`ColumnTransformer.get_feature_names_out` will rename all the features<br>  using the name of the transformer. The first argument of the callable is the<br>  transformer name and the second argument is the feature name. The returned<br>  string will be the new feature name.<br>- If ``str``, it must be a string ready for formatting. The given string will<br>  be formatted using two field names: ``transformer_name`` and ``feature_name``.<br>  e.g. ``\"{feature_name}__{transformer_name}\"``. See :meth:`str.format` method<br>  from the standard library for more info.<br><br>.. versionadded:: 1.0<br><br>.. versionchanged:: 1.6<br>    `verbose_feature_names_out` can be a callable or a string to be formatted.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('force_int_remainder_cols',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=force_int_remainder_cols,-bool%2C%20default%3DFalse\">\n",
       "            force_int_remainder_cols\n",
       "            <span class=\"param-doc-description\">force_int_remainder_cols: bool, default=False<br><br>This parameter has no effect.<br><br>.. note::<br>    If you do not access the list of columns for the remainder columns<br>    in the `transformers_` fitted attribute, you do not need to set<br>    this parameter.<br><br>.. versionadded:: 1.5<br><br>.. versionchanged:: 1.7<br>   The default value for `force_int_remainder_cols` will change from<br>   `True` to `False` in version 1.7.<br><br>.. deprecated:: 1.7<br>   `force_int_remainder_cols` is deprecated and will be removed in 1.9.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-50\" type=\"checkbox\" ><label for=\"sk-estimator-id-50\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>one_pip_col</div></div></label><div class=\"sk-toggleable__content \" data-param-prefix=\"one_pip_col__\"><pre>[&#x27;city&#x27;, &#x27;manufacturer&#x27;, &#x27;brand&#x27;, &#x27;class&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-51\" type=\"checkbox\" ><label for=\"sk-estimator-id-51\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>OneHotEncoder</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></div></label><div class=\"sk-toggleable__content \" data-param-prefix=\"one_pip_col__encoder__\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('categories',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=categories,-%27auto%27%20or%20a%20list%20of%20array-like%2C%20default%3D%27auto%27\">\n",
       "            categories\n",
       "            <span class=\"param-doc-description\">categories: 'auto' or a list of array-like, default='auto'<br><br>Categories (unique values) per feature:<br><br>- 'auto' : Determine categories automatically from the training data.<br>- list : ``categories[i]`` holds the categories expected in the ith<br>  column. The passed categories should not mix strings and numeric<br>  values within a single feature, and should be sorted in case of<br>  numeric values.<br><br>The used categories can be found in the ``categories_`` attribute.<br><br>.. versionadded:: 0.20</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('drop',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=drop,-%7B%27first%27%2C%20%27if_binary%27%7D%20or%20an%20array-like%20of%20shape%20%28n_features%2C%29%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3DNone\">\n",
       "            drop\n",
       "            <span class=\"param-doc-description\">drop: {'first', 'if_binary'} or an array-like of shape (n_features,),             default=None<br><br>Specifies a methodology to use to drop one of the categories per<br>feature. This is useful in situations where perfectly collinear<br>features cause problems, such as when feeding the resulting data<br>into an unregularized linear regression model.<br><br>However, dropping one category breaks the symmetry of the original<br>representation and can therefore induce a bias in downstream models,<br>for instance for penalized linear classification or regression models.<br><br>- None : retain all features (the default).<br>- 'first' : drop the first category in each feature. If only one<br>  category is present, the feature will be dropped entirely.<br>- 'if_binary' : drop the first category in each feature with two<br>  categories. Features with 1 or more than 2 categories are<br>  left intact.<br>- array : ``drop[i]`` is the category in feature ``X[:, i]`` that<br>  should be dropped.<br><br>When `max_categories` or `min_frequency` is configured to group<br>infrequent categories, the dropping behavior is handled after the<br>grouping.<br><br>.. versionadded:: 0.21<br>   The parameter `drop` was added in 0.21.<br><br>.. versionchanged:: 0.23<br>   The option `drop='if_binary'` was added in 0.23.<br><br>.. versionchanged:: 1.1<br>    Support for dropping infrequent categories.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('sparse_output',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=sparse_output,-bool%2C%20default%3DTrue\">\n",
       "            sparse_output\n",
       "            <span class=\"param-doc-description\">sparse_output: bool, default=True<br><br>When ``True``, it returns a :class:`scipy.sparse.csr_matrix`,<br>i.e. a sparse matrix in \"Compressed Sparse Row\" (CSR) format.<br><br>.. versionadded:: 1.2<br>   `sparse` was renamed to `sparse_output`</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('dtype',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=dtype,-number%20type%2C%20default%3Dnp.float64\">\n",
       "            dtype\n",
       "            <span class=\"param-doc-description\">dtype: number type, default=np.float64<br><br>Desired dtype of output.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&lt;class &#x27;numpy.float64&#x27;&gt;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('handle_unknown',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=handle_unknown,-%7B%27error%27%2C%20%27ignore%27%2C%20%27infrequent_if_exist%27%2C%20%27warn%27%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20default%3D%27error%27\">\n",
       "            handle_unknown\n",
       "            <span class=\"param-doc-description\">handle_unknown: {'error', 'ignore', 'infrequent_if_exist', 'warn'},                      default='error'<br><br>Specifies the way unknown categories are handled during :meth:`transform`.<br><br>- 'error' : Raise an error if an unknown category is present during transform.<br>- 'ignore' : When an unknown category is encountered during<br>  transform, the resulting one-hot encoded columns for this feature<br>  will be all zeros. In the inverse transform, an unknown category<br>  will be denoted as None.<br>- 'infrequent_if_exist' : When an unknown category is encountered<br>  during transform, the resulting one-hot encoded columns for this<br>  feature will map to the infrequent category if it exists. The<br>  infrequent category will be mapped to the last position in the<br>  encoding. During inverse transform, an unknown category will be<br>  mapped to the category denoted `'infrequent'` if it exists. If the<br>  `'infrequent'` category does not exist, then :meth:`transform` and<br>  :meth:`inverse_transform` will handle an unknown category as with<br>  `handle_unknown='ignore'`. Infrequent categories exist based on<br>  `min_frequency` and `max_categories`. Read more in the<br>  :ref:`User Guide <encoder_infrequent_categories>`.<br>- 'warn' : When an unknown category is encountered during transform<br>  a warning is issued, and the encoding then proceeds as described for<br>  `handle_unknown=\"infrequent_if_exist\"`.<br><br>.. versionchanged:: 1.1<br>    `'infrequent_if_exist'` was added to automatically handle unknown<br>    categories and infrequent categories.<br><br>.. versionadded:: 1.6<br>   The option `\"warn\"` was added in 1.6.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;ignore&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('min_frequency',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=min_frequency,-int%20or%20float%2C%20default%3DNone\">\n",
       "            min_frequency\n",
       "            <span class=\"param-doc-description\">min_frequency: int or float, default=None<br><br>Specifies the minimum frequency below which a category will be<br>considered infrequent.<br><br>- If `int`, categories with a smaller cardinality will be considered<br>  infrequent.<br><br>- If `float`, categories with a smaller cardinality than<br>  `min_frequency * n_samples`  will be considered infrequent.<br><br>.. versionadded:: 1.1<br>    Read more in the :ref:`User Guide <encoder_infrequent_categories>`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_categories',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=max_categories,-int%2C%20default%3DNone\">\n",
       "            max_categories\n",
       "            <span class=\"param-doc-description\">max_categories: int, default=None<br><br>Specifies an upper limit to the number of output features for each input<br>feature when considering infrequent categories. If there are infrequent<br>categories, `max_categories` includes the category representing the<br>infrequent categories along with the frequent categories. If `None`,<br>there is no limit to the number of output features.<br><br>.. versionadded:: 1.1<br>    Read more in the :ref:`User Guide <encoder_infrequent_categories>`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('feature_name_combiner',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=feature_name_combiner,-%22concat%22%20or%20callable%2C%20default%3D%22concat%22\">\n",
       "            feature_name_combiner\n",
       "            <span class=\"param-doc-description\">feature_name_combiner: \"concat\" or callable, default=\"concat\"<br><br>Callable with signature `def callable(input_feature, category)` that returns a<br>string. This is used to create feature names to be returned by<br>:meth:`get_feature_names_out`.<br><br>`\"concat\"` concatenates encoded feature name and category with<br>`feature + \"_\" + str(category)`.E.g. feature X with values 1, 6, 7 create<br>feature names `X_1, X_6, X_7`.<br><br>.. versionadded:: 1.3</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;concat&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-52\" type=\"checkbox\" ><label for=\"sk-estimator-id-52\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>num_pip_col</div></div></label><div class=\"sk-toggleable__content \" data-param-prefix=\"num_pip_col__\"><pre>[&#x27;month&#x27;, &#x27;price_min&#x27;, &#x27;price_max&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-53\" type=\"checkbox\" ><label for=\"sk-estimator-id-53\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content \" data-param-prefix=\"num_pip_col__scaler__\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('copy',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=copy,-bool%2C%20default%3DTrue\">\n",
       "            copy\n",
       "            <span class=\"param-doc-description\">copy: bool, default=True<br><br>If False, try to avoid a copy and do inplace scaling instead.<br>This is not guaranteed to always work inplace; e.g. if the data is<br>not a NumPy array or scipy.sparse CSR matrix, a copy may still be<br>returned.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('with_mean',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=with_mean,-bool%2C%20default%3DTrue\">\n",
       "            with_mean\n",
       "            <span class=\"param-doc-description\">with_mean: bool, default=True<br><br>If True, center the data before scaling.<br>This does not work (and will raise an exception) when attempted on<br>sparse matrices, because centering them entails building a dense<br>matrix which in common use cases is likely to be too large to fit in<br>memory.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('with_std',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=with_std,-bool%2C%20default%3DTrue\">\n",
       "            with_std\n",
       "            <span class=\"param-doc-description\">with_std: bool, default=True<br><br>If True, scale the data to unit variance (or equivalently,<br>unit standard deviation).</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-54\" type=\"checkbox\" ><label for=\"sk-estimator-id-54\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>ske_pip_col</div></div></label><div class=\"sk-toggleable__content \" data-param-prefix=\"ske_pip_col__\"><pre>[&#x27;size&#x27;, &#x27;volume_sales&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-55\" type=\"checkbox\" ><label for=\"sk-estimator-id-55\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>log1p</div><div class=\"caption\">FunctionTransformer</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html\">?<span>Documentation for FunctionTransformer</span></a></div></label><div class=\"sk-toggleable__content \" data-param-prefix=\"ske_pip_col__ske__\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('func',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=func,-callable%2C%20default%3DNone\">\n",
       "            func\n",
       "            <span class=\"param-doc-description\">func: callable, default=None<br><br>The callable to use for the transformation. This will be passed<br>the same arguments as transform, with args and kwargs forwarded.<br>If func is None, then func will be the identity function.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&lt;ufunc &#x27;log1p&#x27;&gt;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('inverse_func',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=inverse_func,-callable%2C%20default%3DNone\">\n",
       "            inverse_func\n",
       "            <span class=\"param-doc-description\">inverse_func: callable, default=None<br><br>The callable to use for the inverse transformation. This will be<br>passed the same arguments as inverse transform, with args and<br>kwargs forwarded. If inverse_func is None, then inverse_func<br>will be the identity function.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('validate',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=validate,-bool%2C%20default%3DFalse\">\n",
       "            validate\n",
       "            <span class=\"param-doc-description\">validate: bool, default=False<br><br>Indicate that the input X array should be checked before calling<br>``func``. The possibilities are:<br><br>- If False, there is no input validation.<br>- If True, then X will be converted to a 2-dimensional NumPy array or<br>  sparse matrix. If the conversion is not possible an exception is<br>  raised.<br><br>.. versionchanged:: 0.22<br>   The default of ``validate`` changed from True to False.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('accept_sparse',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=accept_sparse,-bool%2C%20default%3DFalse\">\n",
       "            accept_sparse\n",
       "            <span class=\"param-doc-description\">accept_sparse: bool, default=False<br><br>Indicate that func accepts a sparse matrix as input. If validate is<br>False, this has no effect. Otherwise, if accept_sparse is false,<br>sparse matrix inputs will cause an exception to be raised.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('check_inverse',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=check_inverse,-bool%2C%20default%3DTrue\">\n",
       "            check_inverse\n",
       "            <span class=\"param-doc-description\">check_inverse: bool, default=True<br><br>Whether to check that or ``func`` followed by ``inverse_func`` leads to<br>the original inputs. It can be used for a sanity check, raising a<br>warning when the condition is not fulfilled.<br><br>.. versionadded:: 0.20</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('feature_names_out',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=feature_names_out,-callable%2C%20%27one-to-one%27%20or%20None%2C%20default%3DNone\">\n",
       "            feature_names_out\n",
       "            <span class=\"param-doc-description\">feature_names_out: callable, 'one-to-one' or None, default=None<br><br>Determines the list of feature names that will be returned by the<br>`get_feature_names_out` method. If it is 'one-to-one', then the output<br>feature names will be equal to the input feature names. If it is a<br>callable, then it must take two positional arguments: this<br>`FunctionTransformer` (`self`) and an array-like of input feature names<br>(`input_features`). It must return an array-like of output feature<br>names. The `get_feature_names_out` method is only defined if<br>`feature_names_out` is not None.<br><br>See ``get_feature_names_out`` for more details.<br><br>.. versionadded:: 1.1</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('kw_args',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=kw_args,-dict%2C%20default%3DNone\">\n",
       "            kw_args\n",
       "            <span class=\"param-doc-description\">kw_args: dict, default=None<br><br>Dictionary of additional keyword arguments to pass to func.<br><br>.. versionadded:: 0.18</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('inv_kw_args',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=inv_kw_args,-dict%2C%20default%3DNone\">\n",
       "            inv_kw_args\n",
       "            <span class=\"param-doc-description\">inv_kw_args: dict, default=None<br><br>Dictionary of additional keyword arguments to pass to inverse_func.<br><br>.. versionadded:: 0.18</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-56\" type=\"checkbox\" ><label for=\"sk-estimator-id-56\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content \" data-param-prefix=\"ske_pip_col__scaler__\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('copy',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=copy,-bool%2C%20default%3DTrue\">\n",
       "            copy\n",
       "            <span class=\"param-doc-description\">copy: bool, default=True<br><br>If False, try to avoid a copy and do inplace scaling instead.<br>This is not guaranteed to always work inplace; e.g. if the data is<br>not a NumPy array or scipy.sparse CSR matrix, a copy may still be<br>returned.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('with_mean',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=with_mean,-bool%2C%20default%3DTrue\">\n",
       "            with_mean\n",
       "            <span class=\"param-doc-description\">with_mean: bool, default=True<br><br>If True, center the data before scaling.<br>This does not work (and will raise an exception) when attempted on<br>sparse matrices, because centering them entails building a dense<br>matrix which in common use cases is likely to be too large to fit in<br>memory.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('with_std',\n",
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       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=with_std,-bool%2C%20default%3DTrue\">\n",
       "            with_std\n",
       "            <span class=\"param-doc-description\">with_std: bool, default=True<br><br>If True, scale the data to unit variance (or equivalently,<br>unit standard deviation).</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
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      ],
      "text/plain": [
       "ColumnTransformer(transformers=[('one_pip_col',\n",
       "                                 Pipeline(steps=[('encoder',\n",
       "                                                  OneHotEncoder(handle_unknown='ignore'))]),\n",
       "                                 ['city', 'manufacturer', 'brand', 'class']),\n",
       "                                ('num_pip_col',\n",
       "                                 Pipeline(steps=[('scaler', StandardScaler())]),\n",
       "                                 ['month', 'price_min', 'price_max']),\n",
       "                                ('ske_pip_col',\n",
       "                                 Pipeline(steps=[('ske',\n",
       "                                                  FunctionTransformer(func=<ufunc 'log1p'>)),\n",
       "                                                 ('scaler', StandardScaler())]),\n",
       "                                 ['size', 'volume_sales'])])"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.compose import ColumnTransformer\n",
    "\n",
    "preprocess = ColumnTransformer(transformers=[\n",
    "    (\"one_pip_col\",one_pipe,one_cols),\n",
    "    (\"num_pip_col\",num_pipe,num_cols),\n",
    "    (\"ske_pip_col\",ske_pipe,ske_cols)\n",
    "])\n",
    "preprocess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "889fc3b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor, ExtraTreesRegressor ,AdaBoostRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from xgboost import XGBRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "5d88387d",
   "metadata": {},
   "outputs": [],
   "source": [
    "results = []\n",
    "models = {\n",
    "    'Decision Tree': DecisionTreeRegressor(random_state=42),\n",
    "    'Random Forest': RandomForestRegressor(random_state=42, n_jobs=-1),\n",
    "    'Extra Trees': ExtraTreesRegressor(random_state=42, n_jobs=-1),\n",
    "    'Gradient Boosting': GradientBoostingRegressor(random_state=42),\n",
    "    'K-Neighbors': KNeighborsRegressor(),\n",
    "    \"XGBoost\": XGBRegressor(random_state=42),\n",
    "    \"AdaBoost\": AdaBoostRegressor(random_state=42),\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "26b02daf",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = preprocess.fit_transform(X_train)\n",
    "X_test = preprocess.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "c91553b3",
   "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>Name</th>\n",
       "      <th>accuracy_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.964259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.987792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Extra Trees</td>\n",
       "      <td>0.987004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Gradient Boosting</td>\n",
       "      <td>0.988802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>K-Neighbors</td>\n",
       "      <td>0.758445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.988465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>0.918885</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Name  accuracy_score\n",
       "0      Decision Tree        0.964259\n",
       "1      Random Forest        0.987792\n",
       "2        Extra Trees        0.987004\n",
       "3  Gradient Boosting        0.988802\n",
       "4        K-Neighbors        0.758445\n",
       "5            XGBoost        0.988465\n",
       "6           AdaBoost        0.918885"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for name,model in models.items():\n",
    "    model.fit(X_train,y_train)\n",
    "    y_pred = model.predict(X_test)\n",
    "    acc = r2_score(y_test,y_pred)\n",
    "    results.append({\n",
    "        \"Name\":name,\n",
    "        \"accuracy_score\":acc,\n",
    "    })\n",
    "\n",
    "results_df = pd.DataFrame(results)\n",
    "results_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "bec4c885",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9888022339486906"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_model = models[\"Gradient Boosting\"]\n",
    "y_pred = final_model.predict(X_test)\n",
    "r2_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "c9db8de1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['columns.pkl']"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from joblib import dump\n",
    "\n",
    "dump(final_model,\"model.pkl\")\n",
    "dump(preprocess,\"preprocess.pkl\")\n",
    "dump(X.columns.tolist(),\"columns.pkl\")"
   ]
  }
 ],
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