05_Laptop_price_predict.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e3be153c",
   "metadata": {},
   "source": [
    "# Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "96ddc57e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "import lightgbm as lgb\n",
    "from sklearn.model_selection import cross_validate, cross_val_score, cross_val_predict\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import koreanize_matplotlib\n",
    "\n",
    "# 그래프에 retina display 적용\n",
    "%config InlineBackend.figure_format = 'retina'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d065140",
   "metadata": {},
   "source": [
    "# Data Load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "94b86574",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Fabricante</th>\n",
       "      <th>Tipo</th>\n",
       "      <th>Pulgadas</th>\n",
       "      <th>ScreenResolution</th>\n",
       "      <th>CPU</th>\n",
       "      <th>RAM</th>\n",
       "      <th>Disco</th>\n",
       "      <th>GPU</th>\n",
       "      <th>OS</th>\n",
       "      <th>Peso</th>\n",
       "      <th>Precio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i7 7500U 2.7GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.84kg</td>\n",
       "      <td>747.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Acer</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i7 8550U 1.8GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>1TB HDD</td>\n",
       "      <td>Nvidia GeForce MX150</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.2kg</td>\n",
       "      <td>422.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>IPS Panel Full HD 1920x1080</td>\n",
       "      <td>Intel Core i7 7500U 2.7GHz</td>\n",
       "      <td>4GB</td>\n",
       "      <td>500GB HDD</td>\n",
       "      <td>AMD Radeon 530</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.1kg</td>\n",
       "      <td>422.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id Fabricante      Tipo  Pulgadas             ScreenResolution  \\\n",
       "0   0         HP  Notebook      15.6            Full HD 1920x1080   \n",
       "1   1       Acer  Notebook      15.6            Full HD 1920x1080   \n",
       "2   2         HP  Notebook      15.6  IPS Panel Full HD 1920x1080   \n",
       "\n",
       "                          CPU  RAM      Disco                    GPU  \\\n",
       "0  Intel Core i7 7500U 2.7GHz  8GB  256GB SSD  Intel HD Graphics 620   \n",
       "1  Intel Core i7 8550U 1.8GHz  8GB    1TB HDD   Nvidia GeForce MX150   \n",
       "2  Intel Core i7 7500U 2.7GHz  4GB  500GB HDD         AMD Radeon 530   \n",
       "\n",
       "           OS    Peso  Precio  \n",
       "0  Windows 10  1.84kg   747.0  \n",
       "1  Windows 10   2.2kg   422.0  \n",
       "2  Windows 10   2.1kg   422.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(950, 12)\n",
      "--------------------------------------------------------------------------------\n",
      "test\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Fabricante</th>\n",
       "      <th>Tipo</th>\n",
       "      <th>Pulgadas</th>\n",
       "      <th>ScreenResolution</th>\n",
       "      <th>CPU</th>\n",
       "      <th>RAM</th>\n",
       "      <th>Disco</th>\n",
       "      <th>GPU</th>\n",
       "      <th>OS</th>\n",
       "      <th>Peso</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>950</td>\n",
       "      <td>Asus</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i3 6006U 2GHz</td>\n",
       "      <td>4GB</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 520</td>\n",
       "      <td>Linux</td>\n",
       "      <td>2kg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>951</td>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>AMD E-Series 7110 1.8GHz</td>\n",
       "      <td>4GB</td>\n",
       "      <td>500GB HDD</td>\n",
       "      <td>AMD Radeon R2 Graphics</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.86kg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>952</td>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Gaming</td>\n",
       "      <td>15.6</td>\n",
       "      <td>IPS Panel Full HD 1920x1080</td>\n",
       "      <td>Intel Core i7 7700HQ 2.8GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>256GB SSD +  1TB HDD</td>\n",
       "      <td>Nvidia GeForce GTX 1060</td>\n",
       "      <td>No OS</td>\n",
       "      <td>2.4kg</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Id Fabricante      Tipo  Pulgadas             ScreenResolution  \\\n",
       "0  950       Asus  Notebook      15.6            Full HD 1920x1080   \n",
       "1  951         HP  Notebook      15.6                     1366x768   \n",
       "2  952     Lenovo    Gaming      15.6  IPS Panel Full HD 1920x1080   \n",
       "\n",
       "                           CPU  RAM                 Disco  \\\n",
       "0     Intel Core i3 6006U 2GHz  4GB             256GB SSD   \n",
       "1     AMD E-Series 7110 1.8GHz  4GB             500GB HDD   \n",
       "2  Intel Core i7 7700HQ 2.8GHz  8GB  256GB SSD +  1TB HDD   \n",
       "\n",
       "                       GPU          OS    Peso  \n",
       "0    Intel HD Graphics 520       Linux     2kg  \n",
       "1   AMD Radeon R2 Graphics  Windows 10  1.86kg  \n",
       "2  Nvidia GeForce GTX 1060       No OS   2.4kg  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(350, 11)\n",
      "--------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Precio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>950</th>\n",
       "      <td>0</td>\n",
       "      <td>689.108462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>951</th>\n",
       "      <td>1</td>\n",
       "      <td>689.108462</td>\n",
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       "    <tr>\n",
       "      <th>952</th>\n",
       "      <td>2</td>\n",
       "      <td>689.108462</td>\n",
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       "  </tbody>\n",
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      "text/plain": [
       "     Unnamed: 0      Precio\n",
       "Id                         \n",
       "950           0  689.108462\n",
       "951           1  689.108462\n",
       "952           2  689.108462"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train = pd.read_csv(\"train.csv\")\n",
    "test = pd.read_csv(\"test.csv\")\n",
    "sub = pd.read_csv(\"sample.csv\", index_col='Id')\n",
    "\n",
    "print(\"train\")\n",
    "display(train.head(3))\n",
    "print(train.shape)\n",
    "\n",
    "print('-'*80)\n",
    "\n",
    "print(\"test\")\n",
    "display(test.head(3))\n",
    "print(test.shape)\n",
    "\n",
    "print('-'*80)\n",
    "\n",
    "display(sub.head(3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1966f9ce",
   "metadata": {},
   "source": [
    "# EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ec34153",
   "metadata": {},
   "source": [
    "- train/test info & describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "463e66df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 950 entries, 0 to 949\n",
      "Data columns (total 12 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Id                950 non-null    int64  \n",
      " 1   Fabricante        950 non-null    object \n",
      " 2   Tipo              950 non-null    object \n",
      " 3   Pulgadas          950 non-null    float64\n",
      " 4   ScreenResolution  950 non-null    object \n",
      " 5   CPU               950 non-null    object \n",
      " 6   RAM               950 non-null    object \n",
      " 7   Disco             950 non-null    object \n",
      " 8   GPU               950 non-null    object \n",
      " 9   OS                950 non-null    object \n",
      " 10  Peso              950 non-null    object \n",
      " 11  Precio            950 non-null    float64\n",
      "dtypes: float64(2), int64(1), object(9)\n",
      "memory usage: 89.2+ KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 350 entries, 0 to 349\n",
      "Data columns (total 11 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Id                350 non-null    int64  \n",
      " 1   Fabricante        350 non-null    object \n",
      " 2   Tipo              350 non-null    object \n",
      " 3   Pulgadas          350 non-null    float64\n",
      " 4   ScreenResolution  350 non-null    object \n",
      " 5   CPU               350 non-null    object \n",
      " 6   RAM               350 non-null    object \n",
      " 7   Disco             350 non-null    object \n",
      " 8   GPU               350 non-null    object \n",
      " 9   OS                350 non-null    object \n",
      " 10  Peso              350 non-null    object \n",
      "dtypes: float64(1), int64(1), object(9)\n",
      "memory usage: 30.2+ KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(train.info())\n",
    "display(test.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1ba90be3",
   "metadata": {},
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
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       "      <th>Precio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>950.000000</td>\n",
       "      <td>950.000000</td>\n",
       "      <td>950.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>474.500000</td>\n",
       "      <td>14.974421</td>\n",
       "      <td>684.927368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>274.385677</td>\n",
       "      <td>1.451727</td>\n",
       "      <td>408.872239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.100000</td>\n",
       "      <td>107.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>237.250000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>379.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>474.500000</td>\n",
       "      <td>15.600000</td>\n",
       "      <td>603.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>711.750000</td>\n",
       "      <td>15.600000</td>\n",
       "      <td>912.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>949.000000</td>\n",
       "      <td>18.400000</td>\n",
       "      <td>3368.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Id    Pulgadas       Precio\n",
       "count  950.000000  950.000000   950.000000\n",
       "mean   474.500000   14.974421   684.927368\n",
       "std    274.385677    1.451727   408.872239\n",
       "min      0.000000   10.100000   107.000000\n",
       "25%    237.250000   14.000000   379.000000\n",
       "50%    474.500000   15.600000   603.000000\n",
       "75%    711.750000   15.600000   912.000000\n",
       "max    949.000000   18.400000  3368.000000"
      ]
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       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Pulgadas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>350.000000</td>\n",
       "      <td>350.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1124.500000</td>\n",
       "      <td>15.132857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>101.180532</td>\n",
       "      <td>1.354767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>950.000000</td>\n",
       "      <td>11.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1037.250000</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1124.500000</td>\n",
       "      <td>15.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1211.750000</td>\n",
       "      <td>15.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1299.000000</td>\n",
       "      <td>17.300000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Id    Pulgadas\n",
       "count   350.000000  350.000000\n",
       "mean   1124.500000   15.132857\n",
       "std     101.180532    1.354767\n",
       "min     950.000000   11.300000\n",
       "25%    1037.250000   14.000000\n",
       "50%    1124.500000   15.600000\n",
       "75%    1211.750000   15.600000\n",
       "max    1299.000000   17.300000"
      ]
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fabricante</th>\n",
       "      <th>Tipo</th>\n",
       "      <th>ScreenResolution</th>\n",
       "      <th>CPU</th>\n",
       "      <th>RAM</th>\n",
       "      <th>Disco</th>\n",
       "      <th>GPU</th>\n",
       "      <th>OS</th>\n",
       "      <th>Peso</th>\n",
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       "  <tbody>\n",
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       "      <th>count</th>\n",
       "      <td>950</td>\n",
       "      <td>950</td>\n",
       "      <td>950</td>\n",
       "      <td>950</td>\n",
       "      <td>950</td>\n",
       "      <td>950</td>\n",
       "      <td>950</td>\n",
       "      <td>950</td>\n",
       "      <td>950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>37</td>\n",
       "      <td>102</td>\n",
       "      <td>9</td>\n",
       "      <td>34</td>\n",
       "      <td>95</td>\n",
       "      <td>9</td>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Dell</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.2kg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>221</td>\n",
       "      <td>523</td>\n",
       "      <td>378</td>\n",
       "      <td>151</td>\n",
       "      <td>463</td>\n",
       "      <td>304</td>\n",
       "      <td>215</td>\n",
       "      <td>788</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Fabricante      Tipo   ScreenResolution                         CPU  \\\n",
       "count         950       950                950                         950   \n",
       "unique         19         6                 37                         102   \n",
       "top          Dell  Notebook  Full HD 1920x1080  Intel Core i5 7200U 2.5GHz   \n",
       "freq          221       523                378                         151   \n",
       "\n",
       "        RAM      Disco                    GPU          OS   Peso  \n",
       "count   950        950                    950         950    950  \n",
       "unique    9         34                     95           9    165  \n",
       "top     8GB  256GB SSD  Intel HD Graphics 620  Windows 10  2.2kg  \n",
       "freq    463        304                    215         788     86  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Fabricante</th>\n",
       "      <th>Tipo</th>\n",
       "      <th>ScreenResolution</th>\n",
       "      <th>CPU</th>\n",
       "      <th>RAM</th>\n",
       "      <th>Disco</th>\n",
       "      <th>GPU</th>\n",
       "      <th>OS</th>\n",
       "      <th>Peso</th>\n",
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       "  <tbody>\n",
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       "      <th>count</th>\n",
       "      <td>350</td>\n",
       "      <td>350</td>\n",
       "      <td>350</td>\n",
       "      <td>350</td>\n",
       "      <td>350</td>\n",
       "      <td>350</td>\n",
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       "      <td>350</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
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       "      <td>8</td>\n",
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       "      <td>112</td>\n",
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       "      <th>top</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i7 7700HQ 2.8GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.2kg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>80</td>\n",
       "      <td>201</td>\n",
       "      <td>129</td>\n",
       "      <td>43</td>\n",
       "      <td>156</td>\n",
       "      <td>108</td>\n",
       "      <td>66</td>\n",
       "      <td>281</td>\n",
       "      <td>34</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Fabricante      Tipo   ScreenResolution                          CPU  \\\n",
       "count         350       350                350                          350   \n",
       "unique         16         6                 29                           60   \n",
       "top            HP  Notebook  Full HD 1920x1080  Intel Core i7 7700HQ 2.8GHz   \n",
       "freq           80       201                129                           43   \n",
       "\n",
       "        RAM      Disco                    GPU          OS   Peso  \n",
       "count   350        350                    350         350    350  \n",
       "unique    8         27                     70           8    112  \n",
       "top     8GB  256GB SSD  Intel HD Graphics 620  Windows 10  2.2kg  \n",
       "freq    156        108                     66         281     34  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(train.describe())\n",
    "display(test.describe())\n",
    "display(train.describe(include='object'))\n",
    "display(test.describe(include='object'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b870da33",
   "metadata": {},
   "source": [
    "- feature unique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a754e0ba",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fabricante : \n",
      " ['HP' 'Acer' 'Google' 'MSI' 'Dell' 'Asus' 'Lenovo' 'Toshiba' 'Razer'\n",
      " 'Fujitsu' 'LG' 'Apple' 'Microsoft' 'Xiaomi' 'Mediacom' 'Samsung' 'Vero'\n",
      " 'Chuwi' 'Huawei'] \n",
      "\n",
      "Tipo : \n",
      " ['Notebook' 'Ultrabook' 'Gaming' 'Netbook' '2 in 1 Convertible'\n",
      " 'Workstation'] \n",
      "\n",
      "ScreenResolution : \n",
      " ['Full HD 1920x1080' 'IPS Panel Full HD 1920x1080' '1366x768'\n",
      " 'Touchscreen 2400x1600' 'Quad HD+ / Touchscreen 3200x1800'\n",
      " 'IPS Panel 4K Ultra HD / Touchscreen 3840x2160' 'Touchscreen 1366x768'\n",
      " 'IPS Panel Full HD / Touchscreen 1920x1080'\n",
      " 'Full HD / Touchscreen 1920x1080' 'IPS Panel 1366x768'\n",
      " 'IPS Panel Touchscreen 1920x1200' '4K Ultra HD / Touchscreen 3840x2160'\n",
      " '1600x900' 'IPS Panel Retina Display 2880x1800'\n",
      " 'IPS Panel Retina Display 2560x1600' '4K Ultra HD 3840x2160' '1440x900'\n",
      " 'IPS Panel Touchscreen 2560x1440' 'IPS Panel Touchscreen 1366x768'\n",
      " 'IPS Panel 4K Ultra HD 3840x2160' 'Touchscreen 2256x1504'\n",
      " 'IPS Panel Quad HD+ / Touchscreen 3200x1800' 'IPS Panel 2560x1440'\n",
      " 'IPS Panel Full HD 1920x1200' 'Touchscreen 2560x1440'\n",
      " 'IPS Panel Retina Display 2304x1440' 'IPS Panel Quad HD+ 2560x1440'\n",
      " 'IPS Panel Quad HD+ 3200x1800' '1920x1080' '2560x1440'\n",
      " 'IPS Panel Retina Display 2736x1824' 'IPS Panel Touchscreen 2400x1600'\n",
      " 'Touchscreen / 4K Ultra HD 3840x2160' 'IPS Panel Full HD 2160x1440'\n",
      " 'Touchscreen / Full HD 1920x1080' 'Quad HD+ 3200x1800'\n",
      " 'IPS Panel Touchscreen / 4K Ultra HD 3840x2160'] \n",
      "\n",
      "CPU : \n",
      " ['Intel Core i7 7500U 2.7GHz' 'Intel Core i7 8550U 1.8GHz'\n",
      " 'AMD A8-Series 7410 2.2GHz' 'AMD A9-Series 9410 2.9GHz'\n",
      " 'Intel Core i5 7Y57 1.2GHz' 'Intel Core i5 7300HQ 2.5GHz'\n",
      " 'Intel Core i5 7200U 2.5GHz' 'Intel Celeron Dual Core N3060 1.6GHz'\n",
      " 'Intel Pentium Quad Core N3710 1.6GHz' 'Intel Core i7 7700HQ 2.8GHz'\n",
      " 'Intel Core M 6Y54 1.1GHz' 'Intel Core i3 6006U 2.0GHz'\n",
      " 'Intel Celeron Dual Core N3350 1.1GHz' 'Intel Core i5 6300HQ 2.3GHz'\n",
      " 'Intel Celeron Dual Core 3855U 1.6GHz' 'Intel Core i5 7440HQ 2.8GHz'\n",
      " 'Intel Core M 6Y75 1.2GHz' 'Intel Core i3 7100U 2.4GHz'\n",
      " 'AMD A6-Series 9220 2.5GHz' 'Intel Core i5 8250U 1.6GHz'\n",
      " 'Intel Core i7 6500U 2.5GHz' 'Intel Core i3 6006U 2GHz'\n",
      " 'Intel Core i5 6200U 2.3GHz' 'Intel Core i5 7300U 2.6GHz'\n",
      " 'Intel Core i7 6820HK 2.7GHz' 'Intel Core i7 6700HQ 2.6GHz'\n",
      " 'AMD A10-Series 9600P 2.4GHz' 'AMD E-Series 6110 1.5GHz'\n",
      " 'AMD E-Series E2-6110 1.5GHz' 'Intel Core i3 6100U 2.3GHz'\n",
      " 'Intel Atom x5-Z8550 1.44GHz' 'Intel Pentium Quad Core N4200 1.1GHz'\n",
      " 'AMD A6-Series A6-9220 2.5GHz' 'Intel Core i7 6600U 2.6GHz'\n",
      " 'AMD A12-Series 9720P 3.6GHz' 'Intel Celeron Dual Core N3050 1.6GHz'\n",
      " 'AMD A4-Series 7210 2.2GHz' 'Intel Pentium Quad Core N3700 1.6GHz'\n",
      " 'Intel Core i7 2.7GHz' 'Intel Core i5 2.3GHz'\n",
      " 'Intel Celeron Quad Core N3160 1.6GHz' 'Intel Core i5 1.6GHz'\n",
      " 'Intel Core i7 7820HQ 2.9GHz' 'Intel Core i3 6100U 2.1GHz'\n",
      " 'Intel Celeron Quad Core N3450 1.1GHz' 'AMD Ryzen 1700 3GHz'\n",
      " 'Intel Core i5 2.9GHz' 'Intel Core M 7Y30 1.0GHz'\n",
      " 'Intel Core i7 7660U 2.5GHz' 'Intel Core i7 6560U 2.2GHz'\n",
      " 'Intel Xeon E3-1505M V6 3GHz' 'AMD A9-Series 9420 3GHz'\n",
      " 'Intel Atom Z8350 1.92GHz' 'Intel Celeron Dual Core 3205U 1.5GHz'\n",
      " 'Intel Core i5 1.8GHz' 'Intel Core i5 6300U 2.4GHz'\n",
      " 'Intel Core i7 7560U 2.4GHz' 'Intel Core i7 2.8GHz' 'Intel Core M 1.2GHz'\n",
      " 'Intel Core i7 7600U 2.8GHz' 'Intel Core i7 7820HK 2.9GHz'\n",
      " 'Intel Celeron Quad Core N3710 1.6GHz' 'AMD E-Series E2-9000e 1.5GHz'\n",
      " 'Intel Core i5 7Y54 1.2GHz' 'Intel Core i7 6500U 2.50GHz'\n",
      " 'Intel Celeron Dual Core N3060 1.60GHz' 'Intel Core i7 8650U 1.9GHz'\n",
      " 'Intel Xeon E3-1535M v6 3.1GHz' 'Intel Atom x5-Z8350 1.44GHz'\n",
      " 'Intel Core i7 6820HQ 2.7GHz' 'Intel Core i5 3.1GHz'\n",
      " 'AMD E-Series 9000 2.2GHz' 'AMD A9-Series A9-9420 3GHz'\n",
      " 'Intel Core i3 7130U 2.7GHz' 'AMD A10-Series A10-9620P 2.5GHz'\n",
      " 'AMD E-Series 9000e 1.5GHz' 'Intel Core M 1.1GHz'\n",
      " 'Intel Core i5 7200U 2.70GHz' 'Intel Celeron Dual Core N3350 2GHz'\n",
      " 'Intel Core i5 7200U 2.7GHz' 'AMD A12-Series 9720P 2.7GHz'\n",
      " 'Intel Core i5 6260U 1.8GHz' 'Intel Core i7 6920HQ 2.9GHz'\n",
      " 'Intel Core M M3-6Y30 0.9GHz' 'Intel Core i5 7200U 2.50GHz'\n",
      " 'Intel Core i3 6006U 2.2GHz' 'Intel Core i7 7Y75 1.3GHz'\n",
      " 'Samsung Cortex A72&A53 2.0GHz' 'Intel Core i7 2.9GHz'\n",
      " 'AMD FX 8800P 2.1GHz' 'Intel Atom X5-Z8350 1.44GHz'\n",
      " 'AMD E-Series E2-9000 2.2GHz' 'AMD FX 9830P 3GHz'\n",
      " 'Intel Core M 6Y30 0.9GHz' 'Intel Core M M7-6Y75 1.2GHz'\n",
      " 'AMD A10-Series 9620P 2.5GHz' 'Intel Core i7 7700HQ 2.7GHz'\n",
      " 'Intel Core i5 6440HQ 2.6GHz' 'Intel Pentium Dual Core 4405U 2.1GHz'\n",
      " 'Intel Pentium Dual Core N4200 1.1GHz'\n",
      " 'Intel Celeron Dual Core N3350 2.0GHz' 'Intel Core i5 7500U 2.7GHz'] \n",
      "\n",
      "RAM : \n",
      " ['8GB' '4GB' '6GB' '12GB' '16GB' '32GB' '2GB' '24GB' '64GB'] \n",
      "\n",
      "Disco : \n",
      " ['256GB SSD' '1TB HDD' '500GB HDD' '1.0TB Hybrid' '128GB SSD'\n",
      " '128GB SSD +  1TB HDD' '16GB Flash Storage' '256GB SSD +  1TB HDD'\n",
      " '32GB Flash Storage' '512GB SSD' '128GB Flash Storage'\n",
      " '256GB SSD +  256GB SSD' '2TB HDD' '64GB SSD' '64GB Flash Storage'\n",
      " '512GB SSD +  1TB HDD' '256GB SSD +  2TB HDD' '256GB Flash Storage'\n",
      " '512GB SSD +  2TB HDD' '1TB SSD' '128GB HDD'\n",
      " '64GB Flash Storage +  1TB HDD' '32GB HDD' '32GB SSD'\n",
      " '256GB SSD +  500GB HDD' '180GB SSD' '512GB Flash Storage'\n",
      " '1TB HDD +  1TB HDD' '512GB SSD +  512GB SSD' '128GB SSD +  2TB HDD'\n",
      " '16GB SSD' '512GB SSD +  256GB SSD' '508GB Hybrid' '1TB SSD +  1TB HDD'] \n",
      "\n",
      "GPU : \n",
      " ['Intel HD Graphics 620' 'Nvidia GeForce MX150' 'AMD Radeon 530'\n",
      " 'AMD Radeon R5' 'AMD Radeon R7 M440' 'Intel HD Graphics 615'\n",
      " 'Nvidia GeForce GTX 1050' 'Intel HD Graphics 400' 'Intel HD Graphics 405'\n",
      " 'Nvidia GeForce GTX 1050 Ti' 'Intel HD Graphics 515'\n",
      " 'Intel HD Graphics 520' 'Intel HD Graphics 500' 'Nvidia GeForce GTX 965M'\n",
      " 'Intel HD Graphics 510' 'Nvidia GeForce 930MX' 'Nvidia GeForce MX130'\n",
      " 'Nvidia Quadro M1200' 'Nvidia GeForce GTX 1060' 'Nvidia GeForce 940MX'\n",
      " 'Intel UHD Graphics 620' 'AMD Radeon R5 M430' 'Intel HD Graphics'\n",
      " 'Nvidia GeForce GTX 980M' 'AMD Radeon R7 M445' 'Nvidia GeForce GTX 970M'\n",
      " 'Nvidia GeForce GTX 1070' 'Nvidia Quadro M520M' 'AMD Radeon R5 M420X'\n",
      " 'AMD Radeon R2' 'Nvidia GeForce GTX 960M' 'AMD Radeon RX 550'\n",
      " 'Intel HD Graphics 505' 'AMD Radeon R7 M460' 'AMD Radeon 520'\n",
      " 'Nvidia GeForce GTX 950M' 'AMD Radeon R3' 'AMD FirePro W4190M '\n",
      " 'Nvidia GeForce 150MX' 'AMD Radeon Pro 455'\n",
      " 'Intel Iris Plus Graphics 640' 'Nvidia GeForce 930M'\n",
      " 'Intel HD Graphics 6000' 'Nvidia Quadro M2200M'\n",
      " 'Nvidia GeForce GTX 930MX' 'Nvidia GeForce 920MX' 'AMD Radeon R5 M420'\n",
      " 'AMD Radeon RX 580' 'Nvidia Quadro M2200' 'Intel Iris Graphics 550'\n",
      " 'Intel Iris Graphics 540' 'Nvidia GeForce 930MX ' 'AMD Radeon R5 M330'\n",
      " 'AMD FirePro W6150M' 'Nvidia GeForce GTX 960' 'Nvidia Quadro M620'\n",
      " 'Nvidia GeForce 920M' 'AMD Radeon Pro 555' 'AMD Radeon R4'\n",
      " 'Nvidia GeForce GTX 1080' 'Nvidia GeForce GTX 940MX'\n",
      " 'Nvidia GeForce GTX 1050M' 'Nvidia GeForce GTX1050 Ti'\n",
      " 'AMD Radeon R4 Graphics' 'Nvidia GeForce 920'\n",
      " 'Intel Iris Plus Graphics 650' 'AMD Radeon R2 Graphics'\n",
      " 'Nvidia GeForce GTX 960<U+039C>' 'Nvidia Quadro M1000M' 'AMD Radeon R7'\n",
      " 'Nvidia Quadro M500M' 'Intel HD Graphics 5300' 'AMD FirePro W4190M'\n",
      " 'AMD R17M-M1-70' 'AMD Radeon RX 540' 'Intel HD Graphics 540'\n",
      " 'Nvidia GTX 980 SLI' 'Nvidia GeForce 920MX ' 'Intel Graphics 620'\n",
      " 'ARM Mali T860 MP4' 'Nvidia GeForce GTX 980 ' 'Nvidia GeForce GTX1060'\n",
      " 'Nvidia GeForce GT 940MX' 'Intel HD Graphics 620 ' 'AMD Radeon R5 M315'\n",
      " 'AMD Radeon Pro 560' 'AMD Radeon R9 M385' 'Intel HD Graphics 630'\n",
      " 'Nvidia GeForce GTX 1050Ti' 'Nvidia GeForce 960M' 'Intel HD Graphics 530'\n",
      " 'AMD Radeon RX 560' 'AMD FirePro W5130M' 'Nvidia GeForce GTX 1070M'\n",
      " 'Nvidia GeForce GTX 940M'] \n",
      "\n",
      "OS : \n",
      " ['Windows 10' 'Chrome OS' 'No OS' 'Linux' 'Windows 7' 'Windows 10 S'\n",
      " 'macOS' 'Android' 'Mac OS X'] \n",
      "\n",
      "Peso : \n",
      " ['1.84kg' '2.2kg' '2.1kg' '2.4kg' '2.04kg' '1.1kg' '2.7kg' '2.25kg'\n",
      " '1.26kg' '1.86kg' '3kg' '2.62kg' '1.16kg' '1.9kg' '2.75kg' '2.23kg'\n",
      " '1.45kg' '1.64kg' '0.97kg' '2.0kg' '2.6kg' '2.73kg' '1.63kg' '1.37kg'\n",
      " '1.68kg' '2.71kg' '2.16kg' '1.49kg' '1.65kg' '1.75kg' '1.43kg' '1.55kg'\n",
      " '2kg' '1.4kg' '1.95kg' '3.58kg' '2.36kg' '4.42kg' '1.10kg' '4.0kg'\n",
      " '1.29kg' '1.5kg' '2.65kg' '2.94kg' '2.18kg' '1.36kg' '1.85kg' '1.56kg'\n",
      " '1.25kg' '1.6kg' '2.56kg' '1.79kg' '0.69kg' '2.8kg' '3.78kg' '1.87kg'\n",
      " '2.08kg' '1.91kg' '1.7kg' '1.32kg' '2.06kg' '2.69kg' '2.83kg' '2.5kg'\n",
      " '2.3kg' '1.09kg' '2.02kg' '1.17kg' '1.83kg' '3.2kg' '3.21kg' '1.58kg'\n",
      " '2.45kg' '1.14kg' '2.33kg' '1.35kg' '2.67kg' '1.59kg' '2.20kg' '1.2kg'\n",
      " '1.8kg' '1.21kg' '1.44kg' '1.74kg' '2.43kg' '2.63kg' '2.9kg' '1.39kg'\n",
      " '1.3kg' '1.54kg' '2.591kg' '3.0kg' '3.31kg' '4.14kg' '1.42kg' '2.32kg'\n",
      " '1.13kg' '1.05kg' '2.31kg' '1.34kg' '4.3kg' '1.23kg' '1.96kg' '2.29kg'\n",
      " '1.93kg' '1.08kg' '1.28kg' '0.920kg' '1.48kg' '2.07kg' '4.7kg' '0.81kg'\n",
      " '3.42kg' '1.12kg' '4.6kg' '4.36kg' '3.14kg' '1.88kg' '1.18kg' '1.38kg'\n",
      " '0.98kg' '1.99kg' '2.17kg' '2.99kg' '1.62kg' '2.59kg' '1.41kg' '2.09kg'\n",
      " '3.35kg' '1.22kg' '1.47kg' '2.13kg' '2.05kg' '2.19kg' '1.90kg' '3.8kg'\n",
      " '1.76kg' '3.74kg' '1.31kg' '4.4kg' '2.15kg' '4.2kg' '1.15kg' '2.37kg'\n",
      " '1.27kg' '2.77kg' '1.24kg' '3.52kg' '3.25kg' '1.11kg' '0.91kg' '2.24kg'\n",
      " '4kg' '2.26kg' '2.38kg' '1.19kg' '3.49kg' '2.03kg' '0.99kg' '2.79kg'\n",
      " '1.70kg' '2.191kg' '1.252kg' '2.34kg' '2.14kg'] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "feature = ['Fabricante', 'Tipo', 'ScreenResolution', 'CPU', 'RAM', 'Disco', 'GPU', 'OS', 'Peso']\n",
    "\n",
    "for f in feature:\n",
    "    print(f'{f} : \\n {train[f].unique()} \\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34afdd1b",
   "metadata": {},
   "source": [
    "- Check the null value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "cd611c1a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train :\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Id                  0.0\n",
       "Fabricante          0.0\n",
       "Tipo                0.0\n",
       "Pulgadas            0.0\n",
       "ScreenResolution    0.0\n",
       "CPU                 0.0\n",
       "RAM                 0.0\n",
       "Disco               0.0\n",
       "GPU                 0.0\n",
       "OS                  0.0\n",
       "Peso                0.0\n",
       "Precio              0.0\n",
       "dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------------------------\n",
      "test :\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Id                  0.0\n",
       "Fabricante          0.0\n",
       "Tipo                0.0\n",
       "Pulgadas            0.0\n",
       "ScreenResolution    0.0\n",
       "CPU                 0.0\n",
       "RAM                 0.0\n",
       "Disco               0.0\n",
       "GPU                 0.0\n",
       "OS                  0.0\n",
       "Peso                0.0\n",
       "dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('train :')\n",
    "display(train.isnull().mean())\n",
    "\n",
    "print('-'*80)\n",
    "\n",
    "print('test :')\n",
    "display(test.isnull().mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed8d89bd",
   "metadata": {},
   "source": [
    "- visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "655634c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1152x1296 with 5 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 1022,
       "width": 945
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(5, 1, figsize=(16,18))\n",
    "\n",
    "_=sns.countplot(data=train, x='Fabricante', ax=axes[0])\n",
    "_=sns.countplot(data=train, x='Tipo', ax=axes[1])\n",
    "_=sns.countplot(data=train, x='RAM', ax=axes[2])\n",
    "_=sns.countplot(data=train, x='OS', ax=axes[3])\n",
    "_=sns.countplot(data=train, x='Pulgadas', ax=axes[4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "8d3e16d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 371,
       "width": 939
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(16,6))\n",
    "\n",
    "_=sns.histplot(data=train, x='Id', y='Pulgadas', ax=axes[0], bins=50)\n",
    "_=sns.histplot(data=train, x='Id', y='Precio', ax=axes[1], bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "6f62cf19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 469,
       "width": 643
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "_=sns.heatmap(data=train.corr())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31d29b4e",
   "metadata": {},
   "source": [
    "# Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1deb57b9",
   "metadata": {},
   "source": [
    "- Peso feature : Except kg."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "0db069d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1.84\n",
       "1       2.2\n",
       "2       2.1\n",
       "3       2.4\n",
       "4      2.04\n",
       "       ... \n",
       "945    2.14\n",
       "946    1.48\n",
       "947     1.2\n",
       "948     2.3\n",
       "949    2.04\n",
       "Name: Peso, Length: 950, dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0         2\n",
       "1      1.86\n",
       "2       2.4\n",
       "3       2.7\n",
       "4      1.95\n",
       "       ... \n",
       "345    2.19\n",
       "346    1.05\n",
       "347    1.91\n",
       "348     2.1\n",
       "349       2\n",
       "Name: Peso, Length: 350, dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# train\n",
    "Peso_kg1 = []\n",
    "for p1 in train['Peso']:\n",
    "    Peso_kg1.append(p1[:-2])\n",
    "    \n",
    "train['Peso'] = Peso_kg1\n",
    "\n",
    "# test\n",
    "Peso_kg2 = []\n",
    "for p2 in test['Peso']:\n",
    "    Peso_kg2.append(p2[:-2])\n",
    "    \n",
    "test['Peso'] = Peso_kg2\n",
    "\n",
    "display(train['Peso'])\n",
    "display(test['Peso'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86bb5f19",
   "metadata": {},
   "source": [
    "- RAM feature : Except GB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "928a621a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      8\n",
       "1      8\n",
       "2      4\n",
       "3      4\n",
       "4      6\n",
       "      ..\n",
       "945    8\n",
       "946    4\n",
       "947    8\n",
       "948    8\n",
       "949    8\n",
       "Name: RAM, Length: 950, dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0      4\n",
       "1      4\n",
       "2      8\n",
       "3      8\n",
       "4      4\n",
       "      ..\n",
       "345    6\n",
       "346    8\n",
       "347    8\n",
       "348    4\n",
       "349    8\n",
       "Name: RAM, Length: 350, dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# train\n",
    "RAM_GB1 = []\n",
    "for r1 in train['RAM']:\n",
    "    RAM_GB1.append(r1[:-2])\n",
    "    \n",
    "train['RAM'] = RAM_GB1\n",
    "\n",
    "# test\n",
    "RAM_GB2 = []\n",
    "for r2 in test['RAM']:\n",
    "    RAM_GB2.append(r2[:-2])\n",
    "    \n",
    "test['RAM'] = RAM_GB2\n",
    "\n",
    "display(train['RAM'])\n",
    "display(test['RAM'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3ae6a9f",
   "metadata": {},
   "source": [
    "- LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "5fea36df",
   "metadata": {},
   "outputs": [],
   "source": [
    "le = LabelEncoder()\n",
    "feature_encoder = ['Fabricante', 'Tipo', 'ScreenResolution', 'CPU', 'Disco', 'GPU', 'OS']\n",
    "# train\n",
    "for e  in feature_encoder:\n",
    "    train[e] = le.fit_transform(train[e]) \n",
    "    test[e] = le.fit_transform(test[e])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "bee3f9fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_astype = ['RAM', 'Peso']\n",
    "\n",
    "for a in feature_astype:\n",
    "    train[a] = train[a].astype(float)\n",
    "    test[a] = test[a].astype(float)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c63bb083",
   "metadata": {},
   "source": [
    "- Drop id column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "febcc4e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------train----------\n"
     ]
    },
    {
     "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>Fabricante</th>\n",
       "      <th>Tipo</th>\n",
       "      <th>Pulgadas</th>\n",
       "      <th>ScreenResolution</th>\n",
       "      <th>CPU</th>\n",
       "      <th>RAM</th>\n",
       "      <th>Disco</th>\n",
       "      <th>GPU</th>\n",
       "      <th>OS</th>\n",
       "      <th>Peso</th>\n",
       "      <th>Precio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>15.6</td>\n",
       "      <td>8</td>\n",
       "      <td>83</td>\n",
       "      <td>8.0</td>\n",
       "      <td>14</td>\n",
       "      <td>45</td>\n",
       "      <td>5</td>\n",
       "      <td>1.84</td>\n",
       "      <td>747.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>15.6</td>\n",
       "      <td>8</td>\n",
       "      <td>92</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9</td>\n",
       "      <td>87</td>\n",
       "      <td>5</td>\n",
       "      <td>2.20</td>\n",
       "      <td>422.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>15.6</td>\n",
       "      <td>14</td>\n",
       "      <td>83</td>\n",
       "      <td>4.0</td>\n",
       "      <td>23</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>2.10</td>\n",
       "      <td>422.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Fabricante  Tipo  Pulgadas  ScreenResolution  CPU  RAM  Disco  GPU  OS  \\\n",
       "0           7     3      15.6                 8   83  8.0     14   45   5   \n",
       "1           0     3      15.6                 8   92  8.0      9   87   5   \n",
       "2           7     3      15.6                14   83  4.0     23    6   5   \n",
       "\n",
       "   Peso  Precio  \n",
       "0  1.84   747.0  \n",
       "1  2.20   422.0  \n",
       "2  2.10   422.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------test----------\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>Fabricante</th>\n",
       "      <th>Tipo</th>\n",
       "      <th>Pulgadas</th>\n",
       "      <th>ScreenResolution</th>\n",
       "      <th>CPU</th>\n",
       "      <th>RAM</th>\n",
       "      <th>Disco</th>\n",
       "      <th>GPU</th>\n",
       "      <th>OS</th>\n",
       "      <th>Peso</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>15.6</td>\n",
       "      <td>7</td>\n",
       "      <td>26</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>15.6</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>15.6</td>\n",
       "      <td>13</td>\n",
       "      <td>51</td>\n",
       "      <td>8.0</td>\n",
       "      <td>15</td>\n",
       "      <td>49</td>\n",
       "      <td>3</td>\n",
       "      <td>2.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Fabricante  Tipo  Pulgadas  ScreenResolution  CPU  RAM  Disco  GPU  OS  \\\n",
       "0           2     3      15.6                 7   26  4.0     13   27   1   \n",
       "1           6     3      15.6                 0   12  4.0     20    5   4   \n",
       "2           8     1      15.6                13   51  8.0     15   49   3   \n",
       "\n",
       "   Peso  \n",
       "0  2.00  \n",
       "1  1.86  \n",
       "2  2.40  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train = train.drop('Id', axis=1)\n",
    "test = test.drop('Id', axis=1)\n",
    "\n",
    "print('----------train----------')\n",
    "display(train.head(3))\n",
    "print('----------test----------')\n",
    "display(test.head(3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d63fafb5",
   "metadata": {},
   "source": [
    "- Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "1141f852",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 12 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 589,
       "width": 932
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_=train.hist(figsize=(16,10), bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4b6cc96",
   "metadata": {},
   "source": [
    "# Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb4483c7",
   "metadata": {},
   "source": [
    "- train/test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b6ed560d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------X_train----------\n"
     ]
    },
    {
     "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>Fabricante</th>\n",
       "      <th>Tipo</th>\n",
       "      <th>Pulgadas</th>\n",
       "      <th>ScreenResolution</th>\n",
       "      <th>CPU</th>\n",
       "      <th>RAM</th>\n",
       "      <th>Disco</th>\n",
       "      <th>GPU</th>\n",
       "      <th>OS</th>\n",
       "      <th>Peso</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>15.6</td>\n",
       "      <td>8</td>\n",
       "      <td>83</td>\n",
       "      <td>8.0</td>\n",
       "      <td>14</td>\n",
       "      <td>45</td>\n",
       "      <td>5</td>\n",
       "      <td>1.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>15.6</td>\n",
       "      <td>8</td>\n",
       "      <td>92</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9</td>\n",
       "      <td>87</td>\n",
       "      <td>5</td>\n",
       "      <td>2.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>15.6</td>\n",
       "      <td>14</td>\n",
       "      <td>83</td>\n",
       "      <td>4.0</td>\n",
       "      <td>23</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>2.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>15.6</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>23</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>2.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>15.6</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>5</td>\n",
       "      <td>2.04</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>945</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>15.6</td>\n",
       "      <td>0</td>\n",
       "      <td>62</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9</td>\n",
       "      <td>45</td>\n",
       "      <td>5</td>\n",
       "      <td>2.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>946</th>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8</td>\n",
       "      <td>68</td>\n",
       "      <td>4.0</td>\n",
       "      <td>14</td>\n",
       "      <td>45</td>\n",
       "      <td>5</td>\n",
       "      <td>1.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947</th>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>13.3</td>\n",
       "      <td>8</td>\n",
       "      <td>56</td>\n",
       "      <td>8.0</td>\n",
       "      <td>14</td>\n",
       "      <td>39</td>\n",
       "      <td>5</td>\n",
       "      <td>1.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>948</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>15.6</td>\n",
       "      <td>0</td>\n",
       "      <td>83</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>2.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>949</th>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>15.6</td>\n",
       "      <td>0</td>\n",
       "      <td>83</td>\n",
       "      <td>8.0</td>\n",
       "      <td>19</td>\n",
       "      <td>45</td>\n",
       "      <td>5</td>\n",
       "      <td>2.04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>950 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Fabricante  Tipo  Pulgadas  ScreenResolution  CPU  RAM  Disco  GPU  OS  \\\n",
       "0             7     3      15.6                 8   83  8.0     14   45   5   \n",
       "1             0     3      15.6                 8   92  8.0      9   87   5   \n",
       "2             7     3      15.6                14   83  4.0     23    6   5   \n",
       "3             0     3      15.6                 0    8  4.0     23   15   5   \n",
       "4             7     3      15.6                 8    9  6.0      0   22   5   \n",
       "..          ...   ...       ...               ...  ...  ...    ...  ...  ..   \n",
       "945           4     3      15.6                 0   62  8.0      9   45   5   \n",
       "946           7     4      14.0                 8   68  4.0     14   45   5   \n",
       "947          16     3      13.3                 8   56  8.0     14   39   5   \n",
       "948           4     3      15.6                 0   83  8.0      9   20   2   \n",
       "949           7     3      15.6                 0   83  8.0     19   45   5   \n",
       "\n",
       "     Peso  \n",
       "0    1.84  \n",
       "1    2.20  \n",
       "2    2.10  \n",
       "3    2.40  \n",
       "4    2.04  \n",
       "..    ...  \n",
       "945  2.14  \n",
       "946  1.48  \n",
       "947  1.20  \n",
       "948  2.30  \n",
       "949  2.04  \n",
       "\n",
       "[950 rows x 10 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------y_train----------\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0      747.0\n",
       "1      422.0\n",
       "2      422.0\n",
       "3      237.0\n",
       "4      337.0\n",
       "       ...  \n",
       "945    331.0\n",
       "946    663.0\n",
       "947    743.0\n",
       "948    494.0\n",
       "949    385.0\n",
       "Name: Precio, Length: 950, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------X_test----------\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fabricante</th>\n",
       "      <th>Tipo</th>\n",
       "      <th>Pulgadas</th>\n",
       "      <th>ScreenResolution</th>\n",
       "      <th>CPU</th>\n",
       "      <th>RAM</th>\n",
       "      <th>Disco</th>\n",
       "      <th>GPU</th>\n",
       "      <th>OS</th>\n",
       "      <th>Peso</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>15.6</td>\n",
       "      <td>7</td>\n",
       "      <td>26</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>2.00</td>\n",
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       "      <td>12</td>\n",
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       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1.86</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>15.6</td>\n",
       "      <td>13</td>\n",
       "      <td>51</td>\n",
       "      <td>8.0</td>\n",
       "      <td>15</td>\n",
       "      <td>49</td>\n",
       "      <td>3</td>\n",
       "      <td>2.40</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>17.3</td>\n",
       "      <td>7</td>\n",
       "      <td>51</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13</td>\n",
       "      <td>48</td>\n",
       "      <td>4</td>\n",
       "      <td>2.70</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20</td>\n",
       "      <td>27</td>\n",
       "      <td>4</td>\n",
       "      <td>1.95</td>\n",
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       "      <th>...</th>\n",
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       "      <th>345</th>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>15.6</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>2.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>13.0</td>\n",
       "      <td>14</td>\n",
       "      <td>48</td>\n",
       "      <td>8.0</td>\n",
       "      <td>21</td>\n",
       "      <td>30</td>\n",
       "      <td>4</td>\n",
       "      <td>1.05</td>\n",
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       "      <td>13</td>\n",
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       "      <td>4</td>\n",
       "      <td>1.91</td>\n",
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       "      <td>8</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>2.10</td>\n",
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       "    <tr>\n",
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       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>15.6</td>\n",
       "      <td>7</td>\n",
       "      <td>48</td>\n",
       "      <td>8.0</td>\n",
       "      <td>15</td>\n",
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       "      <td>4</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>350 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Fabricante  Tipo  Pulgadas  ScreenResolution  CPU  RAM  Disco  GPU  OS  \\\n",
       "0             2     3      15.6                 7   26  4.0     13   27   1   \n",
       "1             6     3      15.6                 0   12  4.0     20    5   4   \n",
       "2             8     1      15.6                13   51  8.0     15   49   3   \n",
       "3             9     1      17.3                 7   51  8.0     13   48   4   \n",
       "4             6     3      14.0                 0   34  4.0     20   27   4   \n",
       "..          ...   ...       ...               ...  ...  ...    ...  ...  ..   \n",
       "345           6     3      15.6                 0   42  6.0      8   11   4   \n",
       "346           7     4      13.0                14   48  8.0     21   30   4   \n",
       "347           6     3      15.6                 0   36  8.0     13   30   4   \n",
       "348           0     3      15.6                 0   58  4.0      8   25   1   \n",
       "349           2     3      15.6                 7   48  8.0     15   30   4   \n",
       "\n",
       "     Peso  \n",
       "0    2.00  \n",
       "1    1.86  \n",
       "2    2.40  \n",
       "3    2.70  \n",
       "4    1.95  \n",
       "..    ...  \n",
       "345  2.19  \n",
       "346  1.05  \n",
       "347  1.91  \n",
       "348  2.10  \n",
       "349  2.00  \n",
       "\n",
       "[350 rows x 10 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "label_name = 'Precio'\n",
    "\n",
    "X_train = train.iloc[:, :-1]\n",
    "y_train = train[label_name]\n",
    "X_test = test\n",
    "\n",
    "print('----------X_train----------')\n",
    "display(X_train)\n",
    "\n",
    "print('----------y_train----------')\n",
    "display(y_train)\n",
    "\n",
    "print('----------X_test----------')\n",
    "display(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c98dbe12",
   "metadata": {},
   "source": [
    "- model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "1aa46cab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(random_state=42)"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = RandomForestClassifier(random_state=42)\n",
    "model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a38e2b6d",
   "metadata": {},
   "source": [
    "- GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "a2f33068",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rfc 파라미터 :  {'criterion': 'gini', 'max_depth': 5, 'max_features': 'auto', 'n_estimators': 200}\n",
      "rfc 예측 정확도 : 0.0463\n"
     ]
    }
   ],
   "source": [
    "param_rfc = { \n",
    "    'n_estimators': [100, 200, 300],\n",
    "    'max_features': ['auto', 'sqrt', 'log2'],\n",
    "    'max_depth' : [4,5,6,7,8],\n",
    "    'criterion' :['gini', 'entropy']\n",
    "}\n",
    "\n",
    "gscv = GridSearchCV(estimator=model, param_grid=param_rfc, scoring='accuracy')\n",
    "gscv.fit(X_train, y_train)\n",
    "print('rfc 파라미터 : ', gscv.best_params_)\n",
    "print('rfc 예측 정확도 : {:.4f}'.format(gscv.best_score_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "2ab5b6d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(max_depth=5, n_estimators=200)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_model = RandomForestClassifier(criterion='gini', max_depth=5, max_features='auto', n_estimators=200)\n",
    "best_model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9619f2cd",
   "metadata": {},
   "source": [
    "- KFold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "bfdbcf51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KFold(n_splits=5, random_state=42, shuffle=True)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kf = KFold(n_splits=5, shuffle=True, random_state=42)\n",
    "kf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d98c4871",
   "metadata": {},
   "source": [
    "- cross validation predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "dedb5aad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([551., 515., 391., 226., 337.])"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_valid_predict = cross_val_predict(best_model, X_train, y_train, cv=kf, n_jobs=-1)\n",
    "y_valid_predict[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0819887",
   "metadata": {},
   "source": [
    "- Metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "1925a7ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "240.30962921767585"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse = np.square(y_train - y_valid_predict).mean()\n",
    "rmse = np.sqrt(mse)\n",
    "rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa68ac5d",
   "metadata": {},
   "source": [
    "- Compare y_train & y_valid_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "9bb73efa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 479,
       "width": 721
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "_=sns.regplot(x=y_train, y=y_valid_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "5173027c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 479,
       "width": 737
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "_=sns.kdeplot(y_train)\n",
    "_=sns.kdeplot(y_valid_predict)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "091c6ed6",
   "metadata": {},
   "source": [
    "- best model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "4d5111a1",
   "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>fit_time</th>\n",
       "      <th>score_time</th>\n",
       "      <th>test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.460765</td>\n",
       "      <td>0.065824</td>\n",
       "      <td>0.031579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.454812</td>\n",
       "      <td>0.066792</td>\n",
       "      <td>0.042105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.449372</td>\n",
       "      <td>0.061867</td>\n",
       "      <td>0.042105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.447827</td>\n",
       "      <td>0.063802</td>\n",
       "      <td>0.052632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.536074</td>\n",
       "      <td>0.062858</td>\n",
       "      <td>0.042105</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fit_time  score_time  test_score\n",
       "0  0.460765    0.065824    0.031579\n",
       "1  0.454812    0.066792    0.042105\n",
       "2  0.449372    0.061867    0.042105\n",
       "3  0.447827    0.063802    0.052632\n",
       "4  0.536074    0.062858    0.042105"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(cross_validate(best_model, X_train, y_train, cv=5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "87a93b82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(max_depth=5, n_estimators=200)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "592c4cc2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([226., 269., 673., 612., 226.])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred = best_model.predict(X_test)\n",
    "pred[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49c01692",
   "metadata": {},
   "source": [
    "- feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "31e2eb5b",
   "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>Feature_Importances</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CPU</th>\n",
       "      <td>0.165976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Peso</th>\n",
       "      <td>0.144336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Disco</th>\n",
       "      <td>0.136136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GPU</th>\n",
       "      <td>0.123382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAM</th>\n",
       "      <td>0.112027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ScreenResolution</th>\n",
       "      <td>0.089356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fabricante</th>\n",
       "      <td>0.078142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OS</th>\n",
       "      <td>0.058388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pulgadas</th>\n",
       "      <td>0.050126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tipo</th>\n",
       "      <td>0.042130</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Feature_Importances\n",
       "CPU                          0.165976\n",
       "Peso                         0.144336\n",
       "Disco                        0.136136\n",
       "GPU                          0.123382\n",
       "RAM                          0.112027\n",
       "ScreenResolution             0.089356\n",
       "Fabricante                   0.078142\n",
       "OS                           0.058388\n",
       "Pulgadas                     0.050126\n",
       "Tipo                         0.042130"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fi = pd.DataFrame(best_model.feature_importances_)\n",
    "fi.index = X_train.columns\n",
    "fi.columns = ['Feature_Importances']\n",
    "fi = fi.sort_values(by='Feature_Importances', ascending=False)\n",
    "fi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "92fa8be2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 479,
       "width": 770
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "_=sns.barplot(x=fi['Feature_Importances'], y=fi.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "160dbe0f",
   "metadata": {},
   "source": [
    "# Submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "0e0a249b",
   "metadata": {},
   "outputs": [],
   "source": [
    "sub = pd.read_csv(\"sample.csv\", index_col='Id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "7a65e29b",
   "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>Precio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>950</th>\n",
       "      <td>226.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>951</th>\n",
       "      <td>269.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>952</th>\n",
       "      <td>673.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>953</th>\n",
       "      <td>673.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>954</th>\n",
       "      <td>226.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1295</th>\n",
       "      <td>287.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1296</th>\n",
       "      <td>446.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1297</th>\n",
       "      <td>287.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1298</th>\n",
       "      <td>306.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1299</th>\n",
       "      <td>673.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>350 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Precio\n",
       "Id          \n",
       "950    226.0\n",
       "951    269.0\n",
       "952    673.0\n",
       "953    673.0\n",
       "954    226.0\n",
       "...      ...\n",
       "1295   287.0\n",
       "1296   446.0\n",
       "1297   287.0\n",
       "1298   306.0\n",
       "1299   673.0\n",
       "\n",
       "[350 rows x 1 columns]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub['Precio'] = pred\n",
    "sub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "89650fb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "sub.to_csv(\"rfc_gscv.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "1f4411a6",
   "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>Id</th>\n",
       "      <th>Precio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>950</td>\n",
       "      <td>226.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>951</td>\n",
       "      <td>269.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>952</td>\n",
       "      <td>673.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>953</td>\n",
       "      <td>673.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>954</td>\n",
       "      <td>226.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>1295</td>\n",
       "      <td>287.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>1296</td>\n",
       "      <td>446.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>1297</td>\n",
       "      <td>287.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>1298</td>\n",
       "      <td>306.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>1299</td>\n",
       "      <td>673.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>350 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Id  Precio\n",
       "0     950   226.0\n",
       "1     951   269.0\n",
       "2     952   673.0\n",
       "3     953   673.0\n",
       "4     954   226.0\n",
       "..    ...     ...\n",
       "345  1295   287.0\n",
       "346  1296   446.0\n",
       "347  1297   287.0\n",
       "348  1298   306.0\n",
       "349  1299   673.0\n",
       "\n",
       "[350 rows x 2 columns]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv(\"rfc_gscv.csv\")"
   ]
  }
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