AutoPredictor.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e7dec365",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "from xgboost import XGBRegressor\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3fd20a99",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('Dataset.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7e5b2ccf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>year</th>\n",
       "      <th>price</th>\n",
       "      <th>transmission</th>\n",
       "      <th>mileage</th>\n",
       "      <th>fuelType</th>\n",
       "      <th>tax</th>\n",
       "      <th>mpg</th>\n",
       "      <th>engineSize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Fiesta</td>\n",
       "      <td>2017</td>\n",
       "      <td>12000</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>15944</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>150</td>\n",
       "      <td>57.7</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Focus</td>\n",
       "      <td>2018</td>\n",
       "      <td>14000</td>\n",
       "      <td>Manual</td>\n",
       "      <td>9083</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>150</td>\n",
       "      <td>57.7</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Focus</td>\n",
       "      <td>2017</td>\n",
       "      <td>13000</td>\n",
       "      <td>Manual</td>\n",
       "      <td>12456</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>150</td>\n",
       "      <td>57.7</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fiesta</td>\n",
       "      <td>2019</td>\n",
       "      <td>17500</td>\n",
       "      <td>Manual</td>\n",
       "      <td>10460</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>145</td>\n",
       "      <td>40.3</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Fiesta</td>\n",
       "      <td>2019</td>\n",
       "      <td>16500</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>1482</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>145</td>\n",
       "      <td>48.7</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74995</th>\n",
       "      <td>BMW</td>\n",
       "      <td>2016</td>\n",
       "      <td>12998</td>\n",
       "      <td>Manual</td>\n",
       "      <td>20565</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>0</td>\n",
       "      <td>74.3</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74996</th>\n",
       "      <td>toyota</td>\n",
       "      <td>2017</td>\n",
       "      <td>6995</td>\n",
       "      <td>Manual</td>\n",
       "      <td>20564</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>150</td>\n",
       "      <td>68.9</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74997</th>\n",
       "      <td>Ford</td>\n",
       "      <td>2018</td>\n",
       "      <td>10990</td>\n",
       "      <td>Manual</td>\n",
       "      <td>20563</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>145</td>\n",
       "      <td>65.7</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74998</th>\n",
       "      <td>Ford</td>\n",
       "      <td>2017</td>\n",
       "      <td>7999</td>\n",
       "      <td>Manual</td>\n",
       "      <td>28907</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>125</td>\n",
       "      <td>54.3</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74999</th>\n",
       "      <td>Ford</td>\n",
       "      <td>2015</td>\n",
       "      <td>13583</td>\n",
       "      <td>Manual</td>\n",
       "      <td>21718</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>20</td>\n",
       "      <td>67.3</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>75000 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         model  year  price transmission  mileage fuelType  tax   mpg  \\\n",
       "0       Fiesta  2017  12000    Automatic    15944   Petrol  150  57.7   \n",
       "1        Focus  2018  14000       Manual     9083   Petrol  150  57.7   \n",
       "2        Focus  2017  13000       Manual    12456   Petrol  150  57.7   \n",
       "3       Fiesta  2019  17500       Manual    10460   Petrol  145  40.3   \n",
       "4       Fiesta  2019  16500    Automatic     1482   Petrol  145  48.7   \n",
       "...        ...   ...    ...          ...      ...      ...  ...   ...   \n",
       "74995      BMW  2016  12998       Manual    20565   Diesel    0  74.3   \n",
       "74996   toyota  2017   6995       Manual    20564   Petrol  150  68.9   \n",
       "74997     Ford  2018  10990       Manual    20563   Petrol  145  65.7   \n",
       "74998     Ford  2017   7999       Manual    28907   Petrol  125  54.3   \n",
       "74999     Ford  2015  13583       Manual    21718   Diesel   20  67.3   \n",
       "\n",
       "       engineSize  \n",
       "0             1.0  \n",
       "1             1.0  \n",
       "2             1.0  \n",
       "3             1.5  \n",
       "4             1.0  \n",
       "...           ...  \n",
       "74995         1.5  \n",
       "74996         1.0  \n",
       "74997         1.0  \n",
       "74998         1.2  \n",
       "74999         2.0  \n",
       "\n",
       "[75000 rows x 9 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9e71451d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "model           0\n",
       "year            0\n",
       "price           0\n",
       "transmission    0\n",
       "mileage         0\n",
       "fuelType        0\n",
       "tax             0\n",
       "mpg             0\n",
       "engineSize      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "41b7b8f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Automatic' 'Manual' 'Semi-Auto' 'Other']\n"
     ]
    }
   ],
   "source": [
    "print(dataset['transmission'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "43ed56ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Petrol' 'Diesel' 'Hybrid' 'Electric' 'Other']\n"
     ]
    }
   ],
   "source": [
    "print(dataset['fuelType'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "671128e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.replace({'transmission' : {'Automatic' : 0, 'Manual' : 1, 'Semi-Auto' : 2, 'Other' : 3}}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "01169923",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.replace({'fuelType' : {'Petrol' : 0, 'Diesel' : 1, 'Hybrid' : 2, 'Electric' : 3, 'Other' : 4}}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "796070fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>year</th>\n",
       "      <th>price</th>\n",
       "      <th>transmission</th>\n",
       "      <th>mileage</th>\n",
       "      <th>fuelType</th>\n",
       "      <th>tax</th>\n",
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       "      <td>Focus</td>\n",
       "      <td>2018</td>\n",
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       "      <td>Focus</td>\n",
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       "      <td>1</td>\n",
       "      <td>12456</td>\n",
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       "      <td>150</td>\n",
       "      <td>57.7</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fiesta</td>\n",
       "      <td>2019</td>\n",
       "      <td>17500</td>\n",
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       "      <td>40.3</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Fiesta</td>\n",
       "      <td>2019</td>\n",
       "      <td>16500</td>\n",
       "      <td>0</td>\n",
       "      <td>1482</td>\n",
       "      <td>0</td>\n",
       "      <td>145</td>\n",
       "      <td>48.7</td>\n",
       "      <td>1.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>74995</th>\n",
       "      <td>BMW</td>\n",
       "      <td>2016</td>\n",
       "      <td>12998</td>\n",
       "      <td>1</td>\n",
       "      <td>20565</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>74.3</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74996</th>\n",
       "      <td>toyota</td>\n",
       "      <td>2017</td>\n",
       "      <td>6995</td>\n",
       "      <td>1</td>\n",
       "      <td>20564</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>68.9</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74997</th>\n",
       "      <td>Ford</td>\n",
       "      <td>2018</td>\n",
       "      <td>10990</td>\n",
       "      <td>1</td>\n",
       "      <td>20563</td>\n",
       "      <td>0</td>\n",
       "      <td>145</td>\n",
       "      <td>65.7</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74998</th>\n",
       "      <td>Ford</td>\n",
       "      <td>2017</td>\n",
       "      <td>7999</td>\n",
       "      <td>1</td>\n",
       "      <td>28907</td>\n",
       "      <td>0</td>\n",
       "      <td>125</td>\n",
       "      <td>54.3</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74999</th>\n",
       "      <td>Ford</td>\n",
       "      <td>2015</td>\n",
       "      <td>13583</td>\n",
       "      <td>1</td>\n",
       "      <td>21718</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>67.3</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>75000 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         model  year  price  transmission  mileage  fuelType  tax   mpg  \\\n",
       "0       Fiesta  2017  12000             0    15944         0  150  57.7   \n",
       "1        Focus  2018  14000             1     9083         0  150  57.7   \n",
       "2        Focus  2017  13000             1    12456         0  150  57.7   \n",
       "3       Fiesta  2019  17500             1    10460         0  145  40.3   \n",
       "4       Fiesta  2019  16500             0     1482         0  145  48.7   \n",
       "...        ...   ...    ...           ...      ...       ...  ...   ...   \n",
       "74995      BMW  2016  12998             1    20565         1    0  74.3   \n",
       "74996   toyota  2017   6995             1    20564         0  150  68.9   \n",
       "74997     Ford  2018  10990             1    20563         0  145  65.7   \n",
       "74998     Ford  2017   7999             1    28907         0  125  54.3   \n",
       "74999     Ford  2015  13583             1    21718         1   20  67.3   \n",
       "\n",
       "       engineSize  \n",
       "0             1.0  \n",
       "1             1.0  \n",
       "2             1.0  \n",
       "3             1.5  \n",
       "4             1.0  \n",
       "...           ...  \n",
       "74995         1.5  \n",
       "74996         1.0  \n",
       "74997         1.0  \n",
       "74998         1.2  \n",
       "74999         2.0  \n",
       "\n",
       "[75000 rows x 9 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "98661019",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = dataset.drop(['model', 'price'], axis=1).values\n",
    "y = dataset['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "946f9ec3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2.0170e+03, 0.0000e+00, 1.5944e+04, ..., 1.5000e+02, 5.7700e+01,\n",
       "        1.0000e+00],\n",
       "       [2.0180e+03, 1.0000e+00, 9.0830e+03, ..., 1.5000e+02, 5.7700e+01,\n",
       "        1.0000e+00],\n",
       "       [2.0170e+03, 1.0000e+00, 1.2456e+04, ..., 1.5000e+02, 5.7700e+01,\n",
       "        1.0000e+00],\n",
       "       ...,\n",
       "       [2.0180e+03, 1.0000e+00, 2.0563e+04, ..., 1.4500e+02, 6.5700e+01,\n",
       "        1.0000e+00],\n",
       "       [2.0170e+03, 1.0000e+00, 2.8907e+04, ..., 1.2500e+02, 5.4300e+01,\n",
       "        1.2000e+00],\n",
       "       [2.0150e+03, 1.0000e+00, 2.1718e+04, ..., 2.0000e+01, 6.7300e+01,\n",
       "        2.0000e+00]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a4a01839",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        12000\n",
       "1        14000\n",
       "2        13000\n",
       "3        17500\n",
       "4        16500\n",
       "         ...  \n",
       "74995    12998\n",
       "74996     6995\n",
       "74997    10990\n",
       "74998     7999\n",
       "74999    13583\n",
       "Name: price, Length: 75000, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "11bc183d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>StandardScaler()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "StandardScaler()"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "scaler.fit(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "21a19ea2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.03009016, -1.62040435, -0.3236475 , ...,  0.54647325,\n",
       "         0.08894775, -1.05036414],\n",
       "       [ 0.42117195,  0.14970396, -0.63186376, ...,  0.54647325,\n",
       "         0.08894775, -1.05036414],\n",
       "       [-0.03009016,  0.14970396, -0.4803387 , ...,  0.54647325,\n",
       "         0.08894775, -1.05036414],\n",
       "       ...,\n",
       "       [ 0.42117195,  0.14970396, -0.11614846, ...,  0.46797767,\n",
       "         0.5690561 , -1.05036414],\n",
       "       [-0.03009016,  0.14970396,  0.25868851, ...,  0.15399535,\n",
       "        -0.11509829, -0.68069377],\n",
       "       [-0.93261437,  0.14970396, -0.06426247, ..., -1.49441184,\n",
       "         0.66507777,  0.79798771]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "standardized_x = scaler.transform(x)\n",
    "standardized_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "59e03d61",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = standardized_x\n",
    "y = dataset['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a35dc60b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(75000, 7) (67500, 7) (7500, 7)\n",
      "(75000,) (67500,) (7500,)\n"
     ]
    }
   ],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=.1, random_state=42)\n",
    "\n",
    "print(x.shape, x_train.shape, x_test.shape)\n",
    "print(y.shape, y_train.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "027206a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_model = XGBRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "485b0a34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "             predictor=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "             predictor=None, random_state=None, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "             predictor=None, random_state=None, ...)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f003d510",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score on training data :  0.9433386692076354\n",
      "Mean absolute error on training data :  1488.0344786512587\n"
     ]
    }
   ],
   "source": [
    "training_data_pred = xgb_model.predict(x_train)\n",
    "\n",
    "score_train = metrics.r2_score(y_train, training_data_pred)\n",
    "mean_abs_error = metrics.mean_absolute_error(y_train, training_data_pred)\n",
    "\n",
    "print('Score on training data : ', score_train)\n",
    "print('Mean absolute error on training data : ', mean_abs_error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9e48b363",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score on test data :  0.9360363090230093\n",
      "Mean absolute error on test data :  1605.0523907552083\n"
     ]
    }
   ],
   "source": [
    "test_data_pred = xgb_model.predict(x_test)\n",
    "\n",
    "score_test = metrics.r2_score(y_test, test_data_pred)\n",
    "mean_abs_error = metrics.mean_absolute_error(y_test, test_data_pred)\n",
    "\n",
    "print('Score on test data : ', score_test)\n",
    "print('Mean absolute error on test data : ', mean_abs_error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "951bc98d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[17605.312]\n",
      "This car price estimation is :  [17605.312]\n"
     ]
    }
   ],
   "source": [
    "input_data = (2019, 1, 10460, 0, 145, 40.3, 1.5)\n",
    "\n",
    "input_changed = np.array(input_data).reshape(1, -1)\n",
    "\n",
    "std_input = scaler.transform(input_changed)\n",
    "prediction = xgb_model.predict(std_input)\n",
    "\n",
    "print(prediction)\n",
    "\n",
    "print('This car price estimation is : ', prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1ab433c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8249213b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['scaler.pkl']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(xgb_model, 'xgb_model.pkl')\n",
    "joblib.dump(scaler, 'scaler.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3d561f12",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f652b3a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 Score: 0.9360363090230093\n"
     ]
    }
   ],
   "source": [
    "r2 = r2_score(y_test, test_data_pred)\n",
    "print(\"R2 Score:\", r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "32965685",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test, test_data_pred, alpha=0.5)\n",
    "plt.xlabel(\"Real Price\")\n",
    "plt.ylabel(\"Predicted Price\")\n",
    "plt.title(\"Real vs Predicted Prices\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5ad74917",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score on test data :  0.9360363090230093\n",
      "Mean absolute error on test data :  1605.0523907552083\n"
     ]
    }
   ],
   "source": [
    "test_data_pred = xgb_model.predict(x_test)\n",
    "\n",
    "score_test = metrics.r2_score(y_test, test_data_pred)\n",
    "mean_abs_error = metrics.mean_absolute_error(y_test, test_data_pred)\n",
    "\n",
    "print('Score on test data : ', score_test)\n",
    "print('Mean absolute error on test data : ', mean_abs_error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2f40fcd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}