laptop_cost_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "048bc10a",
   "metadata": {},
   "source": [
    "### About Dataset\n",
    "Dataset consists of different laptops from a low budget, low end laptop to high budget, high end laptops and there spesifications listed their. \n",
    "\n",
    "Specification consists of following components:-\n",
    "- CPU\n",
    "- GPU\n",
    "- RAM\n",
    "- Storage\n",
    "- Touch Screen\n",
    "- Screen Dimentions\n",
    "- Laptop Brand\n",
    "- Total Price of Laptop"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "648126ab",
   "metadata": {},
   "source": [
    "This dataset will help people get an idea of there budget and the specs of laptop they are buying."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d5a1bdc",
   "metadata": {},
   "source": [
    "Dataset : https://www.kaggle.com/datasets/juanmerinobermejo/laptops-price-dataset/data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad0fff57",
   "metadata": {},
   "source": [
    "## Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "94cceb0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "\n",
    "\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import OneHotEncoder, FunctionTransformer\n",
    "from sklearn.pipeline import Pipeline, make_pipeline\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.metrics import r2_score, mean_absolute_error\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "from xgboost import XGBRegressor as xgb\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fe30703",
   "metadata": {},
   "source": [
    "## Reading the CSV file and specifying the encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4414af65",
   "metadata": {
    "scrolled": true
   },
   "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>Laptop</th>\n",
       "      <th>Status</th>\n",
       "      <th>Brand</th>\n",
       "      <th>Model</th>\n",
       "      <th>CPU</th>\n",
       "      <th>RAM</th>\n",
       "      <th>Storage</th>\n",
       "      <th>Storage type</th>\n",
       "      <th>GPU</th>\n",
       "      <th>Screen</th>\n",
       "      <th>Touch</th>\n",
       "      <th>Final Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ASUS ExpertBook B1 B1502CBA-EJ0436X Intel Core...</td>\n",
       "      <td>New</td>\n",
       "      <td>Asus</td>\n",
       "      <td>ExpertBook</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>8</td>\n",
       "      <td>512</td>\n",
       "      <td>SSD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.6</td>\n",
       "      <td>No</td>\n",
       "      <td>1009.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alurin Go Start Intel Celeron N4020/8GB/256GB ...</td>\n",
       "      <td>New</td>\n",
       "      <td>Alurin</td>\n",
       "      <td>Go</td>\n",
       "      <td>Intel Celeron</td>\n",
       "      <td>8</td>\n",
       "      <td>256</td>\n",
       "      <td>SSD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.6</td>\n",
       "      <td>No</td>\n",
       "      <td>299.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ASUS ExpertBook B1 B1502CBA-EJ0424X Intel Core...</td>\n",
       "      <td>New</td>\n",
       "      <td>Asus</td>\n",
       "      <td>ExpertBook</td>\n",
       "      <td>Intel Core i3</td>\n",
       "      <td>8</td>\n",
       "      <td>256</td>\n",
       "      <td>SSD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.6</td>\n",
       "      <td>No</td>\n",
       "      <td>789.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MSI Katana GF66 12UC-082XES Intel Core i7-1270...</td>\n",
       "      <td>New</td>\n",
       "      <td>MSI</td>\n",
       "      <td>Katana</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>16</td>\n",
       "      <td>1000</td>\n",
       "      <td>SSD</td>\n",
       "      <td>RTX 3050</td>\n",
       "      <td>15.6</td>\n",
       "      <td>No</td>\n",
       "      <td>1199.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HP 15S-FQ5085NS Intel Core i5-1235U/16GB/512GB...</td>\n",
       "      <td>New</td>\n",
       "      <td>HP</td>\n",
       "      <td>15S</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>16</td>\n",
       "      <td>512</td>\n",
       "      <td>SSD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.6</td>\n",
       "      <td>No</td>\n",
       "      <td>669.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2155</th>\n",
       "      <td>Razer Blade 17 FHD 360Hz Intel Core i7-11800H/...</td>\n",
       "      <td>Refurbished</td>\n",
       "      <td>Razer</td>\n",
       "      <td>Blade</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>16</td>\n",
       "      <td>1000</td>\n",
       "      <td>SSD</td>\n",
       "      <td>RTX 3060</td>\n",
       "      <td>17.3</td>\n",
       "      <td>No</td>\n",
       "      <td>2699.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2156</th>\n",
       "      <td>Razer Blade 17 FHD 360Hz Intel Core i7-11800H/...</td>\n",
       "      <td>Refurbished</td>\n",
       "      <td>Razer</td>\n",
       "      <td>Blade</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>16</td>\n",
       "      <td>1000</td>\n",
       "      <td>SSD</td>\n",
       "      <td>RTX 3070</td>\n",
       "      <td>17.3</td>\n",
       "      <td>No</td>\n",
       "      <td>2899.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2157</th>\n",
       "      <td>Razer Blade 17 FHD 360Hz Intel Core i7-11800H/...</td>\n",
       "      <td>Refurbished</td>\n",
       "      <td>Razer</td>\n",
       "      <td>Blade</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>32</td>\n",
       "      <td>1000</td>\n",
       "      <td>SSD</td>\n",
       "      <td>RTX 3080</td>\n",
       "      <td>17.3</td>\n",
       "      <td>No</td>\n",
       "      <td>3399.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2158</th>\n",
       "      <td>Razer Book 13 Intel Evo Core i7-1165G7/16GB/1T...</td>\n",
       "      <td>Refurbished</td>\n",
       "      <td>Razer</td>\n",
       "      <td>Book</td>\n",
       "      <td>Intel Evo Core i7</td>\n",
       "      <td>16</td>\n",
       "      <td>1000</td>\n",
       "      <td>SSD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.4</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1899.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2159</th>\n",
       "      <td>Razer Book FHD+ Intel Evo Core i7-1165G7/16GB/...</td>\n",
       "      <td>Refurbished</td>\n",
       "      <td>Razer</td>\n",
       "      <td>Book</td>\n",
       "      <td>Intel Evo Core i7</td>\n",
       "      <td>16</td>\n",
       "      <td>256</td>\n",
       "      <td>SSD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.4</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1699.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2160 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 Laptop       Status   Brand  \\\n",
       "0     ASUS ExpertBook B1 B1502CBA-EJ0436X Intel Core...          New    Asus   \n",
       "1     Alurin Go Start Intel Celeron N4020/8GB/256GB ...          New  Alurin   \n",
       "2     ASUS ExpertBook B1 B1502CBA-EJ0424X Intel Core...          New    Asus   \n",
       "3     MSI Katana GF66 12UC-082XES Intel Core i7-1270...          New     MSI   \n",
       "4     HP 15S-FQ5085NS Intel Core i5-1235U/16GB/512GB...          New      HP   \n",
       "...                                                 ...          ...     ...   \n",
       "2155  Razer Blade 17 FHD 360Hz Intel Core i7-11800H/...  Refurbished   Razer   \n",
       "2156  Razer Blade 17 FHD 360Hz Intel Core i7-11800H/...  Refurbished   Razer   \n",
       "2157  Razer Blade 17 FHD 360Hz Intel Core i7-11800H/...  Refurbished   Razer   \n",
       "2158  Razer Book 13 Intel Evo Core i7-1165G7/16GB/1T...  Refurbished   Razer   \n",
       "2159  Razer Book FHD+ Intel Evo Core i7-1165G7/16GB/...  Refurbished   Razer   \n",
       "\n",
       "           Model                CPU  RAM  Storage Storage type       GPU  \\\n",
       "0     ExpertBook      Intel Core i5    8      512          SSD       NaN   \n",
       "1             Go      Intel Celeron    8      256          SSD       NaN   \n",
       "2     ExpertBook      Intel Core i3    8      256          SSD       NaN   \n",
       "3         Katana      Intel Core i7   16     1000          SSD  RTX 3050   \n",
       "4            15S      Intel Core i5   16      512          SSD       NaN   \n",
       "...          ...                ...  ...      ...          ...       ...   \n",
       "2155       Blade      Intel Core i7   16     1000          SSD  RTX 3060   \n",
       "2156       Blade      Intel Core i7   16     1000          SSD  RTX 3070   \n",
       "2157       Blade      Intel Core i7   32     1000          SSD  RTX 3080   \n",
       "2158        Book  Intel Evo Core i7   16     1000          SSD       NaN   \n",
       "2159        Book  Intel Evo Core i7   16      256          SSD       NaN   \n",
       "\n",
       "      Screen Touch  Final Price  \n",
       "0       15.6    No      1009.00  \n",
       "1       15.6    No       299.00  \n",
       "2       15.6    No       789.00  \n",
       "3       15.6    No      1199.00  \n",
       "4       15.6    No       669.01  \n",
       "...      ...   ...          ...  \n",
       "2155    17.3    No      2699.99  \n",
       "2156    17.3    No      2899.99  \n",
       "2157    17.3    No      3399.99  \n",
       "2158    13.4   Yes      1899.99  \n",
       "2159    13.4   Yes      1699.99  \n",
       "\n",
       "[2160 rows x 12 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('data/laptops.csv', encoding='latin1')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "831c8314",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2160 entries, 0 to 2159\n",
      "Data columns (total 12 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   Laptop        2160 non-null   object \n",
      " 1   Status        2160 non-null   object \n",
      " 2   Brand         2160 non-null   object \n",
      " 3   Model         2160 non-null   object \n",
      " 4   CPU           2160 non-null   object \n",
      " 5   RAM           2160 non-null   int64  \n",
      " 6   Storage       2160 non-null   int64  \n",
      " 7   Storage type  2118 non-null   object \n",
      " 8   GPU           789 non-null    object \n",
      " 9   Screen        2156 non-null   float64\n",
      " 10  Touch         2160 non-null   object \n",
      " 11  Final Price   2160 non-null   float64\n",
      "dtypes: float64(2), int64(2), object(8)\n",
      "memory usage: 202.6+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c812482a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RAM</th>\n",
       "      <th>Storage</th>\n",
       "      <th>Screen</th>\n",
       "      <th>Final Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2160.000000</td>\n",
       "      <td>2160.000000</td>\n",
       "      <td>2156.000000</td>\n",
       "      <td>2160.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>15.413889</td>\n",
       "      <td>596.294444</td>\n",
       "      <td>15.168112</td>\n",
       "      <td>1312.638509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.867815</td>\n",
       "      <td>361.220506</td>\n",
       "      <td>1.203329</td>\n",
       "      <td>911.475417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.100000</td>\n",
       "      <td>201.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>256.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>661.082500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>512.000000</td>\n",
       "      <td>15.600000</td>\n",
       "      <td>1031.945000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>15.600000</td>\n",
       "      <td>1708.970000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>128.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>7150.470000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               RAM      Storage       Screen  Final Price\n",
       "count  2160.000000  2160.000000  2156.000000  2160.000000\n",
       "mean     15.413889   596.294444    15.168112  1312.638509\n",
       "std       9.867815   361.220506     1.203329   911.475417\n",
       "min       4.000000     0.000000    10.100000   201.050000\n",
       "25%       8.000000   256.000000    14.000000   661.082500\n",
       "50%      16.000000   512.000000    15.600000  1031.945000\n",
       "75%      16.000000  1000.000000    15.600000  1708.970000\n",
       "max     128.000000  4000.000000    18.000000  7150.470000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "77c3eb21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Laptop          2160\n",
       "Status             2\n",
       "Brand             27\n",
       "Model            121\n",
       "CPU               28\n",
       "RAM                9\n",
       "Storage           12\n",
       "Storage type       2\n",
       "GPU               44\n",
       "Screen            29\n",
       "Touch              2\n",
       "Final Price     1440\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0712c12",
   "metadata": {},
   "source": [
    "#### Dropping unwanted columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "481b0281",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(columns=['Model'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a9154693",
   "metadata": {},
   "outputs": [],
   "source": [
    "laptops = data['Laptop']\n",
    "data.drop(columns=['Laptop'], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40e0f9c5",
   "metadata": {},
   "source": [
    "#### specifying CPU Brand\n",
    "separating starting word of CPUs which stands for producer's company"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e3401fbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "mask = data['CPU'].str.startswith(('Intel', 'AMD', 'Apple'))\n",
    "data = data[mask]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9a3158c",
   "metadata": {},
   "source": [
    "## Fill missing values\n",
    "replacing `NaN` values with `Mode` of the feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f93299ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Screen'].fillna(data['Screen'].mode()[0], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "134c6cca",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['CPU_brand'] = data['CPU'].str.split().str[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fa0e6f76",
   "metadata": {},
   "outputs": [],
   "source": [
    "# laptops = data['Laptop']\n",
    "# data.drop(columns=['Laptop'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3353a996",
   "metadata": {},
   "outputs": [],
   "source": [
    "counts = data['Brand'].value_counts()\n",
    "data = data[data['Brand'].isin(counts[counts >= 25].index)]\n",
    "\n",
    "data = data[data['Final Price'] < 5000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "22111f23",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['GPU'].fillna('NO_GPU', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "868a8964",
   "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>Status</th>\n",
       "      <th>Brand</th>\n",
       "      <th>CPU</th>\n",
       "      <th>RAM</th>\n",
       "      <th>Storage</th>\n",
       "      <th>Storage type</th>\n",
       "      <th>GPU</th>\n",
       "      <th>Screen</th>\n",
       "      <th>Touch</th>\n",
       "      <th>Final Price</th>\n",
       "      <th>CPU_brand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>New</td>\n",
       "      <td>Asus</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>8</td>\n",
       "      <td>512</td>\n",
       "      <td>SSD</td>\n",
       "      <td>NO_GPU</td>\n",
       "      <td>15.6</td>\n",
       "      <td>No</td>\n",
       "      <td>1009.00</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>New</td>\n",
       "      <td>Alurin</td>\n",
       "      <td>Intel Celeron</td>\n",
       "      <td>8</td>\n",
       "      <td>256</td>\n",
       "      <td>SSD</td>\n",
       "      <td>NO_GPU</td>\n",
       "      <td>15.6</td>\n",
       "      <td>No</td>\n",
       "      <td>299.00</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>New</td>\n",
       "      <td>Asus</td>\n",
       "      <td>Intel Core i3</td>\n",
       "      <td>8</td>\n",
       "      <td>256</td>\n",
       "      <td>SSD</td>\n",
       "      <td>NO_GPU</td>\n",
       "      <td>15.6</td>\n",
       "      <td>No</td>\n",
       "      <td>789.00</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>New</td>\n",
       "      <td>MSI</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>16</td>\n",
       "      <td>1000</td>\n",
       "      <td>SSD</td>\n",
       "      <td>RTX 3050</td>\n",
       "      <td>15.6</td>\n",
       "      <td>No</td>\n",
       "      <td>1199.00</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>New</td>\n",
       "      <td>HP</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>16</td>\n",
       "      <td>512</td>\n",
       "      <td>SSD</td>\n",
       "      <td>NO_GPU</td>\n",
       "      <td>15.6</td>\n",
       "      <td>No</td>\n",
       "      <td>669.01</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2155</th>\n",
       "      <td>Refurbished</td>\n",
       "      <td>Razer</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>16</td>\n",
       "      <td>1000</td>\n",
       "      <td>SSD</td>\n",
       "      <td>RTX 3060</td>\n",
       "      <td>17.3</td>\n",
       "      <td>No</td>\n",
       "      <td>2699.99</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2156</th>\n",
       "      <td>Refurbished</td>\n",
       "      <td>Razer</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>16</td>\n",
       "      <td>1000</td>\n",
       "      <td>SSD</td>\n",
       "      <td>RTX 3070</td>\n",
       "      <td>17.3</td>\n",
       "      <td>No</td>\n",
       "      <td>2899.99</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2157</th>\n",
       "      <td>Refurbished</td>\n",
       "      <td>Razer</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>32</td>\n",
       "      <td>1000</td>\n",
       "      <td>SSD</td>\n",
       "      <td>RTX 3080</td>\n",
       "      <td>17.3</td>\n",
       "      <td>No</td>\n",
       "      <td>3399.99</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2158</th>\n",
       "      <td>Refurbished</td>\n",
       "      <td>Razer</td>\n",
       "      <td>Intel Evo Core i7</td>\n",
       "      <td>16</td>\n",
       "      <td>1000</td>\n",
       "      <td>SSD</td>\n",
       "      <td>NO_GPU</td>\n",
       "      <td>13.4</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1899.99</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2159</th>\n",
       "      <td>Refurbished</td>\n",
       "      <td>Razer</td>\n",
       "      <td>Intel Evo Core i7</td>\n",
       "      <td>16</td>\n",
       "      <td>256</td>\n",
       "      <td>SSD</td>\n",
       "      <td>NO_GPU</td>\n",
       "      <td>13.4</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1699.99</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2033 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Status   Brand                CPU  RAM  Storage Storage type  \\\n",
       "0             New    Asus      Intel Core i5    8      512          SSD   \n",
       "1             New  Alurin      Intel Celeron    8      256          SSD   \n",
       "2             New    Asus      Intel Core i3    8      256          SSD   \n",
       "3             New     MSI      Intel Core i7   16     1000          SSD   \n",
       "4             New      HP      Intel Core i5   16      512          SSD   \n",
       "...           ...     ...                ...  ...      ...          ...   \n",
       "2155  Refurbished   Razer      Intel Core i7   16     1000          SSD   \n",
       "2156  Refurbished   Razer      Intel Core i7   16     1000          SSD   \n",
       "2157  Refurbished   Razer      Intel Core i7   32     1000          SSD   \n",
       "2158  Refurbished   Razer  Intel Evo Core i7   16     1000          SSD   \n",
       "2159  Refurbished   Razer  Intel Evo Core i7   16      256          SSD   \n",
       "\n",
       "           GPU  Screen Touch  Final Price CPU_brand  \n",
       "0       NO_GPU    15.6    No      1009.00     Intel  \n",
       "1       NO_GPU    15.6    No       299.00     Intel  \n",
       "2       NO_GPU    15.6    No       789.00     Intel  \n",
       "3     RTX 3050    15.6    No      1199.00     Intel  \n",
       "4       NO_GPU    15.6    No       669.01     Intel  \n",
       "...        ...     ...   ...          ...       ...  \n",
       "2155  RTX 3060    17.3    No      2699.99     Intel  \n",
       "2156  RTX 3070    17.3    No      2899.99     Intel  \n",
       "2157  RTX 3080    17.3    No      3399.99     Intel  \n",
       "2158    NO_GPU    13.4   Yes      1899.99     Intel  \n",
       "2159    NO_GPU    13.4   Yes      1699.99     Intel  \n",
       "\n",
       "[2033 rows x 11 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1 = data.copy()\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b68e0612",
   "metadata": {},
   "source": [
    "## Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7e6b1710",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='count', ylabel='Brand'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data['Brand'], order=data['Brand'].value_counts().index, palette='magma')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cde1e49f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Final Price', ylabel='CPU'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "sns.barplot(x=data['Final Price'], y=data['CPU'], palette='magma')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "847cbaad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Final Price', ylabel='GPU'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 12))\n",
    "sns.barplot(x=data['Final Price'], y=data['GPU'], palette='magma')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0e02d959",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='RAM', ylabel='Final Price'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=data['RAM'], y=data['Final Price'], palette='crest')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "655cb00e",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data.corr(numeric_only=True), cmap=\"YlGnBu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "cdaab3b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Status', ylabel='count'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cAEBubi4cHBzEkACAgIAAyOVy5OXl3fW4dXV1MBgMRgsRUU/VrjOKw4cPo76+vlV7bW0tjhw50uFBtXj55ZdhMBgwaNAgWFhYoKmpCa+99hpCQ0MBADqdDgDg4uJitJ2Li4vYp9Pp4OzsbNRvaWkJR0dHseZOCQkJWL16dafNg4ioO7unoDh37pz454sXLxq90TY1NeHAgQN46KGHOm1w//u//4u0tDTs3r0bQ4YMQUFBARYvXgyNRoOwsLBOO86d4uLiEBMTI64bDAZotdr7djwiInN2T0ExbNgwyGQyyGSyu15isrW1NbrR3FFLly7Fyy+/jJCQEACAl5cXvv/+eyQkJCAsLAxqtRoAUF5eDldXV3G78vJyDBs2DACgVqtRUVFhtN/GxkZUVlaK299JoVBAoVB02jyIiLqzewqKkpISCIKARx55BCdPnkS/fv3EPmtrazg7O8PCwqLTBnfr1i3I5ca3USwsLNDc3AwA8PDwgFqtRlZWlhgMBoMBeXl5mD9/PgDA398fVVVVyM/Ph4+PDwDg0KFDaG5uhp+fX6eNlYjoQXVPQeHu7g4A4hv1/TZ9+nS89tprcHNzw5AhQ/D1119jw4YNmDdvHgBAJpNh8eLFePXVVzFgwAB4eHhgxYoV0Gg0mDlzJgDA09MTQUFBiIiIQEpKChoaGhAdHY2QkBB+4omIqA3a/fHY4uJifPXVV6ioqGgVHPHx8R0eGHD7Y6wrVqzAggULUFFRAY1Gg7/+9a9G+1+2bBlqamoQGRmJqqoqPPXUUzhw4ABsbGzEmrS0NERHR2PSpEmQy+UIDg5GUlJSp4yRiOhBJxN++TXnNnr33Xcxf/589O3bF2q1GjKZ7P92KJPhzJkznTpIUzMYDFCpVNDr9VAqle3ah8/SXZ08KjJn+evmmHoIRJLu5X2tXWcUr776Kl577TXExsa2a4BERNR9tOsLdzdv3sSsWbM6eyxERGSG2hUUs2bNwsGDBzt7LEREZIbadenpsccew4oVK3DixAl4eXnBysrKqH/hwoWdMjgiIjK9dgXFtm3b0Lt3b2RnZyM7O9uoTyaTMSiIiB4g7QqKkpKSzh4HERGZKbN+HgUREZleu84oWr4Z/Wu2b9/ersEQEZH5aVdQ3Lx502i9oaEBFy5cQFVVVac+j4KIiEyvXUGRnp7eqq25uRnz58/Ho48+2uFBERGR+ei0exRyuRwxMTHYuHFjZ+2SiIjMQKfezL569SoaGxs7c5dERGRi7br09MunvwGAIAi4fv06Pvvss/v65DkiIup67QqKr7/+2mhdLpejX79+WL9+/W9+IoqIiLqXdgXFV1991dnjICIiM9XuBxcBwI0bN1BUVAQAGDhwoNGjUYmI6MHQrpvZNTU1mDdvHlxdXTF27FiMHTsWGo0G4eHhuHXrVmePkYiITKhdQRETE4Ps7Gx8+umnqKqqQlVVFT755BNkZ2fjxRdf7OwxEhGRCbXr0tNHH32EDz/8EOPHjxfbpk6dCltbWzzzzDPYunVrZ42PiIhMrF1nFLdu3YKLi0urdmdnZ156IiJ6wLQrKPz9/bFy5UrU1taKbT///DNWr14Nf3//ThscERGZXrsuPW3atAlBQUHo378/vL29AQBnz56FQqHgI1KJiB4w7QoKLy8vFBcXIy0tDZcvXwYAPPvsswgNDYWtrW2nDpCIiEyrXUGRkJAAFxcXREREGLVv374dN27cQGxsbKcMjoiITK9d9yjeeecdDBo0qFX7kCFDkJKS0uFBERGR+WjXGYVOp4Orq2ur9n79+uH69esdHhQRtV/pGi9TD4G6kFv8+ft+jHadUWi1Whw7dqxV+7Fjx6DRaDo8KCIiMh/tOqOIiIjA4sWL0dDQID76NCsrC8uWLeM3s4mIHjDtCoqlS5fixx9/xIIFC1BfXw8AsLGxQWxsLOLi4jp1gEREZFrtCgqZTIa1a9dixYoVuHTpEmxtbTFgwAAoFIrOHh8REZlYh35mvHfv3hg5cmRnjYWIiMxQpz4z+3744Ycf8Pzzz8PJyQm2trbw8vLC6dOnxX5BEBAfHw9XV1fY2toiICAAxcXFRvuorKxEaGgolEolHBwcEB4ejurq6q6eChFRt2TWQXHz5k2MHj0aVlZW+OKLL3Dx4kWsX78effr0EWsSExORlJSElJQU5OXlwc7ODoGBgUa/QxUaGorCwkJkZmZi//79yMnJQWRkpCmmRETU7XTo0tP9tnbtWmi1WuzYsUNs8/DwEP8sCAI2bdqE5cuXY8aMGQCAXbt2wcXFBRkZGQgJCcGlS5dw4MABnDp1Cr6+vgCALVu2YOrUqXjzzTf5cV4iot9g1mcU+/btg6+vL2bNmgVnZ2cMHz4c7777rthfUlICnU6HgIAAsU2lUsHPzw+5ubkAgNzcXDg4OIghAQABAQGQy+XIy8u763Hr6upgMBiMFiKinsqsg+Lbb7/F1q1bMWDAAPzrX//C/PnzsXDhQuzcuRPA7W+IA2j1bAwXFxexT6fTwdnZ2ajf0tISjo6OYs2dEhISoFKpxEWr1Xb21IiIug2zDorm5maMGDECr7/+OoYPH47IyEhERETc99+TiouLg16vF5eysrL7ejwiInNm1kHh6uqKwYMHG7V5enqitLQUAKBWqwEA5eXlRjXl5eVin1qtRkVFhVF/Y2MjKisrxZo7KRQKKJVKo4WIqKcy66AYPXo0ioqKjNq++eYbuLu7A7h9Y1utViMrK0vsNxgMyMvLE5+05+/vj6qqKuTn54s1hw4dQnNzM/z8/LpgFkRE3ZtZf+ppyZIlePLJJ/H666/jmWeewcmTJ7Ft2zZs27YNwO1viC9evBivvvoqBgwYAA8PD6xYsQIajQYzZ84EcPsMJCgoSLxk1dDQgOjoaISEhPATT0REbWDWQTFy5Eikp6cjLi4Oa9asgYeHBzZt2oTQ0FCxZtmyZaipqUFkZCSqqqrw1FNP4cCBA7CxsRFr0tLSEB0djUmTJkEulyM4OBhJSUmmmBIRUbcjEwRBMPUgzJ3BYIBKpYJer2/3/Qqfpbs6eVRkzvLXzTHZsfk8ip6lvc+juJf3NbO+R0FERKbHoCAiIkkMCiIiksSgICIiSQwKIiKSxKAgIiJJDAoiIpLEoCAiIkkMCiIiksSgICIiSQwKIiKSxKAgIiJJDAoiIpLEoCAiIkkMCiIiksSgICIiSQwKIiKSxKAgIiJJDAoiIpLEoCAiIkkMCiIiksSgICIiSQwKIiKSxKAgIiJJDAoiIpLEoCAiIkkMCiIiksSgICIiSQwKIiKSxKAgIiJJDAoiIpLEoCAiIkndKijeeOMNyGQyLF68WGyrra1FVFQUnJyc0Lt3bwQHB6O8vNxou9LSUkybNg29evWCs7Mzli5disbGxi4ePRFR99RtguLUqVN45513MHToUKP2JUuW4NNPP8XevXuRnZ2Na9eu4emnnxb7m5qaMG3aNNTX1+P48ePYuXMnUlNTER8f39VTICLqlrpFUFRXVyM0NBTvvvsu+vTpI7br9Xq899572LBhAyZOnAgfHx/s2LEDx48fx4kTJwAABw8exMWLF/H+++9j2LBhmDJlCl555RUkJyejvr7eVFMiIuo2ukVQREVFYdq0aQgICDBqz8/PR0NDg1H7oEGD4ObmhtzcXABAbm4uvLy84OLiItYEBgbCYDCgsLDwrserq6uDwWAwWoiIeipLUw/gt/zzn//EmTNncOrUqVZ9Op0O1tbWcHBwMGp3cXGBTqcTa34ZEi39LX13k5CQgNWrV3fC6ImIuj+zPqMoKyvDokWLkJaWBhsbmy47blxcHPR6vbiUlZV12bGJiMyNWQdFfn4+KioqMGLECFhaWsLS0hLZ2dlISkqCpaUlXFxcUF9fj6qqKqPtysvLoVarAQBqtbrVp6Ba1ltq7qRQKKBUKo0WIqKeyqyDYtKkSTh//jwKCgrExdfXF6GhoeKfrayskJWVJW5TVFSE0tJS+Pv7AwD8/f1x/vx5VFRUiDWZmZlQKpUYPHhwl8+JiKi7Met7FPb29nj88ceN2uzs7ODk5CS2h4eHIyYmBo6OjlAqlfjb3/4Gf39/jBo1CgAwefJkDB48GLNnz0ZiYiJ0Oh2WL1+OqKgoKBSKLp8TEVF3Y9ZB0RYbN26EXC5HcHAw6urqEBgYiLffflvst7CwwP79+zF//nz4+/vDzs4OYWFhWLNmjQlHTUTUfXS7oDh8+LDRuo2NDZKTk5GcnPyr27i7u+Pzzz+/zyMjInowmfU9CiIiMj0GBRERSWJQEBGRJAYFERFJYlAQEZEkBgUREUliUBARkSQGBRERSWJQEBGRJAYFERFJYlAQEZEkBgUREUliUBARkSQGBRERSWJQEBGRJAYFERFJYlAQEZEkBgUREUliUBARkSQGBRERSWJQEBGRJAYFERFJYlAQEZEkBgUREUliUBARkSQGBRERSWJQEBGRJAYFERFJYlAQEZEkBgUREUliUBARkSSzDoqEhASMHDkS9vb2cHZ2xsyZM1FUVGRUU1tbi6ioKDg5OaF3794IDg5GeXm5UU1paSmmTZuGXr16wdnZGUuXLkVjY2NXToWIqNsy66DIzs5GVFQUTpw4gczMTDQ0NGDy5MmoqakRa5YsWYJPP/0Ue/fuRXZ2Nq5du4ann35a7G9qasK0adNQX1+P48ePY+fOnUhNTUV8fLwppkRE1O3IBEEQTD2Itrpx4wacnZ2RnZ2NsWPHQq/Xo1+/fti9ezf+/Oc/AwAuX74MT09P5ObmYtSoUfjiiy/whz/8AdeuXYOLiwsAICUlBbGxsbhx4wasra1/87gGgwEqlQp6vR5KpbJdY/dZuqtd21H3lL9ujsmOXbrGy2THpq7nFn++Xdvdy/uaWZ9R3Emv1wMAHB0dAQD5+floaGhAQECAWDNo0CC4ubkhNzcXAJCbmwsvLy8xJAAgMDAQBoMBhYWFdz1OXV0dDAaD0UJE1FN1m6Bobm7G4sWLMXr0aDz++OMAAJ1OB2trazg4OBjVuri4QKfTiTW/DImW/pa+u0lISIBKpRIXrVbbybMhIuo+uk1QREVF4cKFC/jnP/95348VFxcHvV4vLmVlZff9mERE5srS1ANoi+joaOzfvx85OTno37+/2K5Wq1FfX4+qqiqjs4ry8nKo1Wqx5uTJk0b7a/lUVEvNnRQKBRQKRSfPgoioezLrMwpBEBAdHY309HQcOnQIHh4eRv0+Pj6wsrJCVlaW2FZUVITS0lL4+/sDAPz9/XH+/HlUVFSINZmZmVAqlRg8eHDXTISIqBsz6zOKqKgo7N69G5988gns7e3FewoqlQq2trZQqVQIDw9HTEwMHB0doVQq8be//Q3+/v4YNWoUAGDy5MkYPHgwZs+ejcTEROh0OixfvhxRUVE8ayAiagOzDoqtW7cCAMaPH2/UvmPHDsydOxcAsHHjRsjlcgQHB6Ourg6BgYF4++23xVoLCwvs378f8+fPh7+/P+zs7BAWFoY1a9Z01TSIiLo1sw6KtnzFw8bGBsnJyUhOTv7VGnd3d3z++eedOTQioh7DrO9REBGR6TEoiIhIEoOCiIgkMSiIiEgSg4KIiCQxKIiISBKDgoiIJDEoiIhIEoOCiIgkMSiIiEgSg4KIiCQxKIiISBKDgoiIJDEoiIhIEoOCiIgkMSiIiEgSg4KIiCQxKIiISBKDgoiIJDEoiIhIEoOCiIgkMSiIiEgSg4KIiCQxKIiISBKDgoiIJDEoiIhIEoOCiIgkMSiIiEgSg4KIiCQxKIiISBKDgoiIJDEoiIhIUo8KiuTkZDz88MOwsbGBn58fTp48aeohERGZvR4TFHv27EFMTAxWrlyJM2fOwNvbG4GBgaioqDD10IiIzFqPCYoNGzYgIiICL7zwAgYPHoyUlBT06tUL27dvN/XQiIjMmqWpB9AV6uvrkZ+fj7i4OLFNLpcjICAAubm5rerr6upQV1cnruv1egCAwWBo9xia6n5u97bU/XTktdJRP9U2mezY1PXa+1pr2U4QhN+s7RFB8Z///AdNTU1wcXExandxccHly5db1SckJGD16tWt2rVa7X0bIz1YVFv+y9RDoJ4iQdWhzX/66SeoVNL76BFBca/i4uIQExMjrjc3N6OyshJOTk6QyWQmHFn3YjAYoNVqUVZWBqVSaerh0AOMr7V7JwgCfvrpJ2g0mt+s7RFB0bdvX1hYWKC8vNyovby8HGq1ulW9QqGAQqEwanNwcLifQ3ygKZVK/uOlLsHX2r35rTOJFj3iZra1tTV8fHyQlZUltjU3NyMrKwv+/v4mHBkRkfnrEWcUABATE4OwsDD4+vriiSeewKZNm1BTU4MXXnjB1EMjIjJrPSYo/vKXv+DGjRuIj4+HTqfDsGHDcODAgVY3uKnzKBQKrFy5stVlPKLOxtfa/SUT2vLZKCIi6rF6xD0KIiJqPwYFERFJYlAQEZEkBgURdZljx47By8sLVlZWmDlzZof2NX78eCxevFiy5uGHH8amTZvatL97qb0Xc+fO7fBcTY1BQW0yd+5cyGQyvPHGG0btGRkZ/LZ6D9HyGpDJZLCysoKHhweWLVuG2traNu8jJiYGw4YNQ0lJCVJTU+/fYP+/U6dOITIy8r4f50HHoKA2s7Gxwdq1a3Hz5k1TD4VMJCgoCNevX8e3336LjRs34p133sHKlSvbvP3Vq1cxceJE9O/fv92/dlBfX9/m2n79+qFXr17tOg79HwYFtVlAQADUajUSEhJ+tebo0aMYM2YMbG1todVqsXDhQtTU1AAA3nrrLTz++ONibcvZSEpKitExli9ffv8mQR2iUCigVquh1Woxc+ZMBAQEIDMzE8DtXztISEiAh4cHbG1t4e3tjQ8//BAA8N1330Emk+HHH3/EvHnzIJPJkJqaitTU1FaBcedZ6qpVqzBs2DD84x//gIeHB2xsbMS+xsZGREdHQ6VSoW/fvlixYoXRr6H+8nKSIAhYtWoV3NzcoFAooNFosHDhQqNj37p1C/PmzYO9vT3c3Nywbds2o/6ysjI888wzcHBwgKOjI2bMmIHvvvtO7G9qakJMTAwcHBzg5OSEZcuWtenXWc0dg4LazMLCAq+//jq2bNmCf//73636r169iqCgIAQHB+PcuXPYs2cPjh49iujoaADAuHHjcPHiRdy4cQMAkJ2djb59++Lw4cMAgIaGBuTm5mL8+PFdNSXqgAsXLuD48eOwtrYGcPtXl3ft2oWUlBQUFhZiyZIleP7555GdnQ2tVovr169DqVRi06ZNuH79Ov7yl7+0+VhXrlzBRx99hI8//hgFBQVi+86dO2FpaYmTJ09i8+bN2LBhA/7xj3/cdR8fffSReBZUXFyMjIwMeHl5GdWsX78evr6++Prrr7FgwQLMnz8fRUVFAG6/PgMDA2Fvb48jR47g2LFj6N27N4KCgsSznPXr1yM1NRXbt2/H0aNHUVlZifT09Hv5azVPAlEbhIWFCTNmzBAEQRBGjRolzJs3TxAEQUhPTxdaXkbh4eFCZGSk0XZHjhwR5HK58PPPPwvNzc2Ck5OTsHfvXkEQBGHYsGFCQkKCoFarBUEQhKNHjwpWVlZCTU1NF82K7kVYWJhgYWEh2NnZCQqFQgAgyOVy4cMPPxRqa2uFXr16CcePHzfaJjw8XHj22WfFdZVKJezYsUNc37Fjh6BSqYy2+eVrShAEYeXKlYKVlZVQUVFhVDdu3DjB09NTaG5uFttiY2MFT09Pcd3d3V3YuHGjIAiCsH79euF3v/udUF9ff9f5ubu7C88//7y43tzcLDg7Owtbt24VBEEQ/ud//kcYOHCg0fHq6uoEW1tb4V//+pcgCILg6uoqJCYmiv0NDQ1C//79xX873RXPKOierV27Fjt37sSlS5eM2s+ePYvU1FT07t1bXAIDA9Hc3IySkhLIZDKMHTsWhw8fRlVVFS5evIgFCxagrq4Oly9fRnZ2NkaOHMlrymZswoQJKCgoQF5eHsLCwvDCCy8gODgYV65cwa1bt/D73//e6P//rl27cPXq1Q4f193dHf369WvVPmrUKKPLVP7+/iguLkZTU+uHN82aNQs///wzHnnkEURERCA9PR2NjY1GNUOHDhX/LJPJoFarxcclnz17FleuXIG9vb04P0dHR9TW1uLq1avQ6/W4fv06/Pz8xH1YWlrC19e3w/M3tR7zW0/UecaOHYvAwEDExcVh7ty5Ynt1dTX++te/trruCwBubm4Abn+kcdu2bThy5AiGDx8OpVIphkd2djbGjRvXVdOgdrCzs8Njjz0GANi+fTu8vb3x3nvvifeePvvsMzz00ENG20j9/pJcLm91Db+hoeGux+0orVaLoqIifPnll8jMzMSCBQuwbt06ZGdnw8rKCgDE/7aQyWRobm4GcPv17ePjg7S0tFb7vluIPUgYFNQub7zxBoYNG4aBAweKbSNGjMDFixfFN5K7GTduHBYvXoy9e/eK9yLGjx+PL7/8EseOHcOLL754v4dOnUQul+Pvf/87YmJi8M0330ChUKC0tPSewr5fv3746aefUFNTI4bBL+9B/Ja8vDyj9RMnTmDAgAGwsLC4a72trS2mT5+O6dOnIyoqCoMGDcL58+cxYsSI3zzWiBEjsGfPHjg7O//qMy9cXV2Rl5eHsWPHArh9sz0/P79N+zdnvPRE7eLl5YXQ0FAkJSWJbbGxsTh+/Diio6NRUFCA4uJifPLJJ+LNbOD2qX2fPn2we/duo6DIyMhAXV0dRo8e3dVToQ6YNWsWLCws8M477+Cll17CkiVLsHPnTly9ehVnzpzBli1bsHPnzl/d3s/PD7169cLf//53XL16Fbt3776n71eUlpYiJiYGRUVF+OCDD7BlyxYsWrTorrWpqal47733cOHCBXz77bd4//33YWtrC3d39zYdKzQ0FH379sWMGTNw5MgRlJSU4PDhw1i4cKH44Y5FixbhjTfeQEZGBi5fvowFCxagqqqqzfMxVwwKarc1a9aIp+XA7RDIzs7GN998gzFjxmD48OGIj483etSiTCbDmDFjIJPJ8NRTT4nbKZVK+Pr6dsolBuo6lpaWiI6ORmJiIuLi4rBixQokJCTA09MTQUFB+Oyzz+Dh4fGr2zs6OuL999/H559/Di8vL3zwwQdYtWpVm48/Z84c/Pzzz3jiiScQFRWFRYsW/eoX7BwcHPDuu+9i9OjRGDp0KL788kt8+umncHJyatOxevXqhZycHLi5ueHpp5+Gp6cnwsPDUVtbK55hvPjii5g9ezbCwsLg7+8Pe3t7/OlPf2rzfMwVf2aciIgk8YyCiIgkMSiIiEgSg4KIiCQxKIiISBKDgoiIJDEoiIhIEoOCiIgkMSiIiEgSg4Kog27cuIH58+eLD8RRq9UIDAzEsWPHANz+NnpGRsY97/d+PcOZ6F7xRwGJOig4OBj19fXYuXMnHnnkEZSXlyMrKws//vijqYdG1DlM/DwMom7t5s2bAgDh8OHDd+13d3cXAIiLu7u7IAiCcOXKFeGPf/yj4OzsLNjZ2Qm+vr5CZmamuN24ceOMtmv5p7py5UrB29vb6BgbN24U9ysIgvDVV18JI0eOFHr16iWoVCrhySefFL777rtOnTf1LLz0RNQBLQ+wafn12zudOnUKALBjxw5cv35dXK+ursbUqVORlZWFr7/+GkFBQZg+fTpKS0sBAB9//DH69++PNWvW4Pr167h+/XqbxtPY2IiZM2di3LhxOHfuHHJzcxEZGWn0cB+ie8VLT0QdYGlpidTUVERERCAlJQUjRozAuHHjEBISgqFDh4oPtHFwcIBarRa38/b2hre3t7j+yiuvID09Hfv27UN0dDQcHR1hYWEBe3t7o+1+i8FggF6vxx/+8Ac8+uijAABPT89Omi31VDyjIOqg4OBgXLt2Dfv27UNQUBAOHz6MESNGSD5Xobq6Gi+99BI8PT3h4OCA3r1749KlS+IZRXs5Ojpi7ty5CAwMxPTp07F58+Y2n40Q/RoGBVEnsLGxwe9//3usWLECx48fx9y5c7Fy5cpfrX/ppZeQnp6O119/HUeOHEFBQQG8vLxQX18veZy2PDp0x44dyM3NxZNPPok9e/bgd7/7HU6cONH+yVGPx6Agug8GDx6MmpoaALefw9zU1GTUf+zYMcydOxd/+tOf4OXlBbVaje+++86oxtrautV2/fr1g06nMwqLuz06dPjw4YiLi8Px48fx+OOPY/fu3Z0zMeqRGBREHfDjjz9i4sSJeP/993Hu3DmUlJRg7969SExMxIwZMwDc/j5EVlYWdDodbt68CQAYMGAAPv74YxQUFODs2bN47rnnjJ4W2LJdTk4OfvjhB/znP/8BcPuxsTdu3EBiYiKuXr2K5ORkfPHFF+I2JSUliIuLQ25uLr7//nscPHgQxcXFvE9BHWPqj10RdWe1tbXCyy+/LIwYMUJQqVRCr169hIEDBwrLly8Xbt26JQiCIOzbt0947LHHBEtLS/FjrCUlJcKECRMEW1tbQavVCm+99ZYwbtw4YdGiReK+c3NzhaFDhwoKhUL45T/VrVu3ClqtVrCzsxPmzJkjvPbaa+J+dTqdMHPmTMHV1VWwtrYW3N3dhfj4eKGpqamr/kroAcRHoRIRkSReeiIiIkkMCiIiksSgICIiSQwKIiKSxKAgIiJJDAoiIpLEoCAiIkkMCiIiksSgICIiSQwKIiKSxKAgIiJJDAoiIpL0/wC89cEcPOyiEAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4, 4))\n",
    "sns.countplot(x=data['Status'], palette='tab10')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4ca60b72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Touch', ylabel='Final Price'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4, 4))\n",
    "sns.barplot(x=data['Touch'], y=data['Final Price'], palette='tab10')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "63adc56a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Final Price', ylabel='Density'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data['Final Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ce40b305",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Final Price'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,3))\n",
    "plt.subplot(2,2,1)\n",
    "sns.boxplot(data['RAM'] , orient='h', palette='mako', fill=False)\n",
    "\n",
    "plt.subplot(2,2,2)\n",
    "sns.boxplot(data['Final Price'] , orient='h', palette='mako', fill=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "722b030f",
   "metadata": {},
   "source": [
    "## Feature Engineering\n",
    "Extracting features from CPU and GPU features. \n",
    "Specifying company, model, version, etc., extracted from these features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1db13541",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Intel Core i5', 'Intel Celeron', 'Intel Core i3', 'Intel Core i7',\n",
       "       'AMD Ryzen 5', 'AMD Ryzen 7', 'AMD Ryzen 3', 'Apple M1',\n",
       "       'AMD Athlon', 'Apple M2', 'AMD Ryzen 9', 'Intel Core i9',\n",
       "       'AMD 3020e', 'Intel Evo Core i7', 'Intel Evo Core i5',\n",
       "       'Intel Pentium', 'Apple M2 Pro', 'AMD Radeon 5', 'AMD 3015e',\n",
       "       'Apple M1 Pro', 'Intel Core M3', 'AMD Radeon 9', 'AMD 3015Ce'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.CPU.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "2bdfdc47",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def preprocess_cpu(data):\n",
    "    data['Evo_model'] = False\n",
    "    data['Pro_model'] = False\n",
    "    data['Radeon_model'] = False\n",
    "    data['Ryzen_model'] = False\n",
    "    data['CPU_model'] = \"\"\n",
    "\n",
    "    for index, cpu in data.iterrows():\n",
    "        cpu_name = cpu['CPU']\n",
    "        cpu_split = cpu_name.split()\n",
    "\n",
    "        if cpu_split[0] == 'Intel':\n",
    "            if cpu_split[1] == 'Evo':\n",
    "                data.at[index, 'CPU_model'] = cpu_split[-1]\n",
    "                data.at[index, 'Evo_model'] = True\n",
    "            elif cpu_split[1] == 'Core':\n",
    "                data.at[index, 'CPU_model'] = cpu_split[-1]\n",
    "            elif cpu_split[-1] in ['Celeron', 'M3', 'Pentium']:\n",
    "                data.at[index, 'CPU_model'] = cpu_split[-1]\n",
    "\n",
    "        elif cpu_split[0] == 'AMD':\n",
    "            if cpu_split[1] == 'Radeon':\n",
    "                data.at[index, 'CPU_model'] = cpu_split[-1]\n",
    "                data.at[index, 'Radeon_model'] = True\n",
    "            elif cpu_split[1] == 'Ryzen':\n",
    "                data.at[index, 'CPU_model'] = cpu_split[-1]\n",
    "                data.at[index, 'Ryzen_model'] = True\n",
    "            elif cpu_split[-1] in ['3015Ce', '3015e', '3020e', 'Athlon']:\n",
    "                data.at[index, 'CPU_model'] = cpu_split[-1]\n",
    "\n",
    "        elif cpu_split[0] == 'Apple':\n",
    "            if cpu_split[-1] == 'Pro':\n",
    "                data.at[index, 'Pro_model'] = True\n",
    "            data.at[index, 'CPU_model'] = cpu_split[1]\n",
    "\n",
    "    data.drop(columns=['CPU'], inplace=True)\n",
    "    return data\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "dbbf5890",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['NO_GPU', 'RTX 3050', 'RTX 4060', 'RTX 4050', 'RTX 3060',\n",
       "       'RTX 4070', 'RTX 2050', 'GTX 1650', 'RTX 3070', '610 M',\n",
       "       'RTX 4080', 'RX 6500M', 'MX 550', 'RTX 3080', 'RTX 4090',\n",
       "       'RX 7600S', 'A 370M', 'GTX 1660', 'RTX A1000', 'RTX 3000',\n",
       "       'T 1200', 'Radeon Pro 5300M', 'A 730M', 'Radeon Pro RX 560X',\n",
       "       'RTX A5500', 'Radeon Pro 5500M', 'T 500', 'T 550', 'RTX A3000',\n",
       "       'T 2000', 'T 600', 'T 1000', 'Radeon RX 6600M', 'MX 330',\n",
       "       'RTX A2000', 'MX 450', 'RTX 2070', 'RX 6800S', 'RTX 2080',\n",
       "       'RTX 2060', 'GTX 1050', 'MX 130', 'P 500', 'RX 6700M', 'GTX 1070'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.GPU.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "8a318d08",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_gpu(data):\n",
    "    data['RTX_model'] = False\n",
    "    data['GTX_model'] = False\n",
    "    data['MX_model'] = False\n",
    "    data['RX_model'] = False\n",
    "    data['T_model'] = False\n",
    "    data['A_model'] = False\n",
    "    data['Radeon_model'] = False\n",
    "    data['GPU_model'] = \"\"\n",
    "\n",
    "    for index, gpu in data.iterrows():\n",
    "        gpu_name = gpu['GPU']\n",
    "        gpu_split = gpu_name.split()\n",
    "\n",
    "        if gpu_split[0] == 'RTX':\n",
    "            data.at[index, 'GPU_model'] = gpu_split[-1]\n",
    "            data.at[index, 'RTX_model'] = True\n",
    "\n",
    "        elif gpu_split[0] == 'GTX':\n",
    "            data.at[index, 'GPU_model'] = gpu_split[-1]\n",
    "            data.at[index, 'GTX_model'] = True\n",
    "\n",
    "        elif gpu_split[0] == 'MX':\n",
    "            data.at[index, 'GPU_model'] = gpu_split[-1]\n",
    "            data.at[index, 'MX_model'] = True\n",
    "\n",
    "        elif gpu_split[0] == 'RX':\n",
    "            data.at[index, 'GPU_model'] = gpu_split[-1]\n",
    "            data.at[index, 'RX_model'] = True\n",
    "\n",
    "        elif gpu_split[0] == 'T':\n",
    "            data.at[index, 'GPU_model'] = gpu_split[-1]\n",
    "            data.at[index, 'T_model'] = True\n",
    "\n",
    "        elif gpu_split[0] == 'A':\n",
    "            data.at[index, 'GPU_model'] = gpu_split[-1]\n",
    "            data.at[index, 'A_model'] = True\n",
    "\n",
    "        elif gpu_split[0] == 'Radeon':\n",
    "            data.at[index, 'GPU_model'] = gpu_split[-1]\n",
    "            data.at[index, 'Radeon_model'] = True\n",
    "\n",
    "    data.drop(columns=['GPU'], inplace=True)\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f60dc72c",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = preprocess_cpu(data)\n",
    "data = preprocess_gpu(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8b4919cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['New' 'Refurbished']\n",
      "['SSD' 'eMMC' 'nan']\n",
      "['No' 'Yes']\n",
      "['AMD' 'Apple' 'Intel']\n",
      "['3' '3015Ce' '3015e' '3020e' '5' '7' '9' 'Athlon' 'Celeron' 'M1' 'M2'\n",
      " 'M3' 'Pentium' 'i3' 'i5' 'i7' 'i9']\n",
      "['Acer' 'Alurin' 'Apple' 'Asus' 'Dell' 'Gigabyte' 'HP' 'LG' 'Lenovo' 'MSI'\n",
      " 'Medion' 'Microsoft' 'Razer']\n",
      "['' '1000' '1050' '1070' '1200' '130' '1650' '1660' '2000' '2050' '2060'\n",
      " '2070' '2080' '3000' '3050' '3060' '3070' '3080' '330' '370M' '4050'\n",
      " '4060' '4070' '4080' '4090' '450' '500' '5300M' '550' '5500M' '560X'\n",
      " '600' '6500M' '6600M' '6700M' '6800S' '730M' '7600S' 'A1000' 'A2000'\n",
      " 'A3000' 'A5500']\n"
     ]
    }
   ],
   "source": [
    "print(np.unique(data['Status']))\n",
    "\n",
    "data['Storage type'] = data['Storage type'].astype(str)\n",
    "print(np.unique(data['Storage type']))\n",
    "\n",
    "print(np.unique(data['Touch']))\n",
    "\n",
    "print(np.unique(data['CPU_brand']))\n",
    "print(np.unique(data['CPU_model']))\n",
    "\n",
    "print(np.unique(data['Brand']))\n",
    "\n",
    "print(np.unique(data['GPU_model']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1dba369b",
   "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>Status</th>\n",
       "      <th>Brand</th>\n",
       "      <th>RAM</th>\n",
       "      <th>Storage</th>\n",
       "      <th>Storage type</th>\n",
       "      <th>Screen</th>\n",
       "      <th>Touch</th>\n",
       "      <th>Final Price</th>\n",
       "      <th>CPU_brand</th>\n",
       "      <th>Evo_model</th>\n",
       "      <th>...</th>\n",
       "      <th>Radeon_model</th>\n",
       "      <th>Ryzen_model</th>\n",
       "      <th>CPU_model</th>\n",
       "      <th>RTX_model</th>\n",
       "      <th>GTX_model</th>\n",
       "      <th>MX_model</th>\n",
       "      <th>RX_model</th>\n",
       "      <th>T_model</th>\n",
       "      <th>A_model</th>\n",
       "      <th>GPU_model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>New</td>\n",
       "      <td>Asus</td>\n",
       "      <td>8</td>\n",
       "      <td>512</td>\n",
       "      <td>SSD</td>\n",
       "      <td>15.6</td>\n",
       "      <td>No</td>\n",
       "      <td>1009.00</td>\n",
       "      <td>Intel</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>i5</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>New</td>\n",
       "      <td>Alurin</td>\n",
       "      <td>8</td>\n",
       "      <td>256</td>\n",
       "      <td>SSD</td>\n",
       "      <td>15.6</td>\n",
       "      <td>No</td>\n",
       "      <td>299.00</td>\n",
       "      <td>Intel</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Celeron</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>New</td>\n",
       "      <td>Asus</td>\n",
       "      <td>8</td>\n",
       "      <td>256</td>\n",
       "      <td>SSD</td>\n",
       "      <td>15.6</td>\n",
       "      <td>No</td>\n",
       "      <td>789.00</td>\n",
       "      <td>Intel</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>i3</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>New</td>\n",
       "      <td>MSI</td>\n",
       "      <td>16</td>\n",
       "      <td>1000</td>\n",
       "      <td>SSD</td>\n",
       "      <td>15.6</td>\n",
       "      <td>No</td>\n",
       "      <td>1199.00</td>\n",
       "      <td>Intel</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>i7</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>3050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>New</td>\n",
       "      <td>HP</td>\n",
       "      <td>16</td>\n",
       "      <td>512</td>\n",
       "      <td>SSD</td>\n",
       "      <td>15.6</td>\n",
       "      <td>No</td>\n",
       "      <td>669.01</td>\n",
       "      <td>Intel</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>i5</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2155</th>\n",
       "      <td>Refurbished</td>\n",
       "      <td>Razer</td>\n",
       "      <td>16</td>\n",
       "      <td>1000</td>\n",
       "      <td>SSD</td>\n",
       "      <td>17.3</td>\n",
       "      <td>No</td>\n",
       "      <td>2699.99</td>\n",
       "      <td>Intel</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>i7</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>3060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2156</th>\n",
       "      <td>Refurbished</td>\n",
       "      <td>Razer</td>\n",
       "      <td>16</td>\n",
       "      <td>1000</td>\n",
       "      <td>SSD</td>\n",
       "      <td>17.3</td>\n",
       "      <td>No</td>\n",
       "      <td>2899.99</td>\n",
       "      <td>Intel</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>i7</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>3070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2157</th>\n",
       "      <td>Refurbished</td>\n",
       "      <td>Razer</td>\n",
       "      <td>32</td>\n",
       "      <td>1000</td>\n",
       "      <td>SSD</td>\n",
       "      <td>17.3</td>\n",
       "      <td>No</td>\n",
       "      <td>3399.99</td>\n",
       "      <td>Intel</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>i7</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>3080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2158</th>\n",
       "      <td>Refurbished</td>\n",
       "      <td>Razer</td>\n",
       "      <td>16</td>\n",
       "      <td>1000</td>\n",
       "      <td>SSD</td>\n",
       "      <td>13.4</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1899.99</td>\n",
       "      <td>Intel</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>i7</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2159</th>\n",
       "      <td>Refurbished</td>\n",
       "      <td>Razer</td>\n",
       "      <td>16</td>\n",
       "      <td>256</td>\n",
       "      <td>SSD</td>\n",
       "      <td>13.4</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1699.99</td>\n",
       "      <td>Intel</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>i7</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2033 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Status   Brand  RAM  Storage Storage type  Screen Touch  \\\n",
       "0             New    Asus    8      512          SSD    15.6    No   \n",
       "1             New  Alurin    8      256          SSD    15.6    No   \n",
       "2             New    Asus    8      256          SSD    15.6    No   \n",
       "3             New     MSI   16     1000          SSD    15.6    No   \n",
       "4             New      HP   16      512          SSD    15.6    No   \n",
       "...           ...     ...  ...      ...          ...     ...   ...   \n",
       "2155  Refurbished   Razer   16     1000          SSD    17.3    No   \n",
       "2156  Refurbished   Razer   16     1000          SSD    17.3    No   \n",
       "2157  Refurbished   Razer   32     1000          SSD    17.3    No   \n",
       "2158  Refurbished   Razer   16     1000          SSD    13.4   Yes   \n",
       "2159  Refurbished   Razer   16      256          SSD    13.4   Yes   \n",
       "\n",
       "      Final Price CPU_brand  Evo_model  ...  Radeon_model  Ryzen_model  \\\n",
       "0         1009.00     Intel      False  ...         False        False   \n",
       "1          299.00     Intel      False  ...         False        False   \n",
       "2          789.00     Intel      False  ...         False        False   \n",
       "3         1199.00     Intel      False  ...         False        False   \n",
       "4          669.01     Intel      False  ...         False        False   \n",
       "...           ...       ...        ...  ...           ...          ...   \n",
       "2155      2699.99     Intel      False  ...         False        False   \n",
       "2156      2899.99     Intel      False  ...         False        False   \n",
       "2157      3399.99     Intel      False  ...         False        False   \n",
       "2158      1899.99     Intel       True  ...         False        False   \n",
       "2159      1699.99     Intel       True  ...         False        False   \n",
       "\n",
       "      CPU_model RTX_model  GTX_model  MX_model  RX_model  T_model  A_model  \\\n",
       "0            i5     False      False     False     False    False    False   \n",
       "1       Celeron     False      False     False     False    False    False   \n",
       "2            i3     False      False     False     False    False    False   \n",
       "3            i7      True      False     False     False    False    False   \n",
       "4            i5     False      False     False     False    False    False   \n",
       "...         ...       ...        ...       ...       ...      ...      ...   \n",
       "2155         i7      True      False     False     False    False    False   \n",
       "2156         i7      True      False     False     False    False    False   \n",
       "2157         i7      True      False     False     False    False    False   \n",
       "2158         i7     False      False     False     False    False    False   \n",
       "2159         i7     False      False     False     False    False    False   \n",
       "\n",
       "      GPU_model  \n",
       "0                \n",
       "1                \n",
       "2                \n",
       "3          3050  \n",
       "4                \n",
       "...         ...  \n",
       "2155       3060  \n",
       "2156       3070  \n",
       "2157       3080  \n",
       "2158             \n",
       "2159             \n",
       "\n",
       "[2033 rows x 21 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a77d280e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# encoder = OneHotEncoder(sparse=False)\n",
    "col_to_encode = []\n",
    "for col in data.columns:\n",
    "    if data[col].dtype == 'object':\n",
    "        col_to_encode.append(col)\n",
    "\n",
    "\n",
    "data = pd.get_dummies(data, columns=col_to_encode)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c0cca5f",
   "metadata": {},
   "source": [
    "# Model Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "add36ab0",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.drop(columns=['Final Price'])\n",
    "y = np.log(data['Final Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "3d343097",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.15,random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5660caa",
   "metadata": {},
   "source": [
    "### Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "63b50f33",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.8093255088772433\n",
      "MAE 0.20838780029143134\n"
     ]
    }
   ],
   "source": [
    "lr = LinearRegression()\n",
    "\n",
    "lr.fit(X_train, y_train)\n",
    "\n",
    "y_pred_lr = lr.predict(X_test)\n",
    "\n",
    "print('R2 score',r2_score(y_test,y_pred_lr))\n",
    "print('MAE',mean_absolute_error(y_test,y_pred_lr))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ea03e76",
   "metadata": {},
   "source": [
    "### Decision Tree Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "abd634f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.758281148966192\n",
      "MAE 0.2201856312343934\n"
     ]
    }
   ],
   "source": [
    "dt = DecisionTreeRegressor(criterion='squared_error', max_depth=25, max_features=16)\n",
    "\n",
    "dt.fit(X_train, y_train)\n",
    "\n",
    "y_pred_dt = dt.predict(X_test)\n",
    "\n",
    "print('R2 score',r2_score(y_test,y_pred_dt))\n",
    "print('MAE',mean_absolute_error(y_test,y_pred_dt))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "641c46eb",
   "metadata": {},
   "source": [
    "### AdaBoost Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d95b74ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.7073866913709199\n",
      "MAE 0.27660629962618033\n"
     ]
    }
   ],
   "source": [
    "ad = AdaBoostRegressor()\n",
    "\n",
    "ad.fit(X_train, y_train)\n",
    "\n",
    "y_pred_ad = ad.predict(X_test)\n",
    "\n",
    "print('R2 score',r2_score(y_test,y_pred_ad))\n",
    "print('MAE',mean_absolute_error(y_test,y_pred_ad))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59d6b9a8",
   "metadata": {},
   "source": [
    "### Gradient Boosting Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "bcbff172",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.850047785386959\n",
      "MAE 0.17957644725598929\n"
     ]
    }
   ],
   "source": [
    "gb = GradientBoostingRegressor(n_estimators=150, max_depth=7)\n",
    "\n",
    "gb.fit(X_train, y_train)\n",
    "\n",
    "y_pred_gb = gb.predict(X_test)\n",
    "\n",
    "print('R2 score',r2_score(y_test,y_pred_gb))\n",
    "print('MAE',mean_absolute_error(y_test,y_pred_gb))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ae9fcec",
   "metadata": {},
   "source": [
    "### ExtraTrees Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a3c9d1fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.8152251707805155\n",
      "MAE 0.19703958625214038\n"
     ]
    }
   ],
   "source": [
    "et = ExtraTreesRegressor(n_estimators=200, max_depth=17)\n",
    "\n",
    "et.fit(X_train, y_train)\n",
    "\n",
    "y_pred_et = et.predict(X_test)\n",
    "\n",
    "print('R2 score',r2_score(y_test,y_pred_et))\n",
    "print('MAE',mean_absolute_error(y_test,y_pred_et))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53180efc",
   "metadata": {},
   "source": [
    "### XGBRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "2dc38732",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.8445652587170106\n",
      "MAE 0.18054872569010774\n"
     ]
    }
   ],
   "source": [
    "from xgboost import XGBRegressor\n",
    "\n",
    "step2 = XGBRegressor()\n",
    "\n",
    "pipe = Pipeline([\n",
    "    ('step2',step2)\n",
    "])\n",
    "\n",
    "pipe.fit(X_train,y_train)\n",
    "\n",
    "y_pred = pipe.predict(X_test)\n",
    "\n",
    "print('R2 score',r2_score(y_test,y_pred))\n",
    "print('MAE',mean_absolute_error(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10d87059",
   "metadata": {},
   "source": [
    "### XGBRegressor\n",
    "applying *GridSearchCV* on XGBRegressor for `Hyperparameter tuning`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3b9c5dbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# param_grid = {\n",
    "#     'n_estimators': [100, 200, 300],\n",
    "#     'max_depth': [3, 5, 7],\n",
    "#     'subsample': [0.6, 0.8, 1.0],\n",
    "# }\n",
    "\n",
    "# xgb_regressor = xgb.XGBRegressor(random_state=7)\n",
    "\n",
    "# grid_search = GridSearchCV(estimator=xgb_regressor, param_grid=param_grid, cv=5, verbose=2, n_jobs=-1)\n",
    "# grid_search.fit(X_train, y_train)\n",
    "\n",
    "\n",
    "# print(\"Best parameters found: \", grid_search.best_params_)\n",
    "# print(\"Best score: \", grid_search.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "02db7153",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.8471479961630588\n",
      "MAE 0.18050793581294697\n"
     ]
    }
   ],
   "source": [
    "xg = XGBRegressor(n_estimators=200, subsample=1, max_depth=3)\n",
    "\n",
    "\n",
    "xg.fit(X_train,y_train)\n",
    "\n",
    "y_pred_xg = xg.predict(X_test)\n",
    "\n",
    "print('R2 score',r2_score(y_test,y_pred_xg))\n",
    "print('MAE',mean_absolute_error(y_test,y_pred_xg))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c590a6cc",
   "metadata": {},
   "source": [
    "### Random Forest Regressor\n",
    "applying *GridSearchCV* on RandomForestRegressor for `Hyperparameter tuning`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a86aeb7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# param_grid = {\n",
    "#     'n_estimators': [100, 150, 200, 250],\n",
    "#     'max_depth': [10, 15, 18, 20, 25],\n",
    "#     'max_samples': [0.3, 0.4, 0.8],\n",
    "#     'max_features': [0.05, 0.08, 0.1]\n",
    "# }\n",
    "\n",
    "# rf_regressor = RandomForestRegressor(random_state=7)\n",
    "\n",
    "# grid_search = GridSearchCV(estimator=rf_regressor,\n",
    "#                            param_grid=param_grid,\n",
    "#                            cv=5,\n",
    "#                            verbose=2,\n",
    "#                            n_jobs=-1)\n",
    "\n",
    "# grid_search.fit(X_train, y_train)\n",
    "\n",
    "# print(\"Best parameters found: \", grid_search.best_params_)\n",
    "# print(\"Best score: \", grid_search.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "621d26ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.8500343140515466\n",
      "MAE 0.18406006370622613\n"
     ]
    }
   ],
   "source": [
    "rf = RandomForestRegressor(n_estimators=300,\n",
    "                              random_state=6,\n",
    "                              max_samples=.6,\n",
    "                              max_features=0.08,\n",
    "                              max_depth=21)\n",
    "\n",
    "rf.fit(X_train,y_train)\n",
    "\n",
    "y_pred_rf = rf.predict(X_test)\n",
    "\n",
    "print('R2 score',r2_score(y_test,y_pred_rf))\n",
    "print('MAE',mean_absolute_error(y_test,y_pred_rf))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca9f5c26",
   "metadata": {},
   "source": [
    "So far, the best accuracy is obtained by `RandomForestRegression` followed by `GradientBoostRegression`. Hence, we use RandomForestRegression for model preperation."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3e2f14f",
   "metadata": {},
   "source": []
  }
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