price_predictor.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d00e9c4c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: numpy in c:\\users\\deep blade\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (1.21.4)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: You are using pip version 20.2.3; however, version 22.0.3 is available.\n",
      "You should consider upgrading via the 'c:\\users\\deep blade\\appdata\\local\\programs\\python\\python39\\python.exe -m pip install --upgrade pip' command.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pandas in c:\\users\\deep blade\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (1.4.0)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\deep blade\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from pandas) (2021.3)\n",
      "Requirement already satisfied: numpy>=1.18.5; platform_machine != \"aarch64\" and platform_machine != \"arm64\" and python_version < \"3.10\" in c:\\users\\deep blade\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from pandas) (1.21.4)\n",
      "Requirement already satisfied: python-dateutil>=2.8.1 in c:\\users\\deep blade\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from pandas) (2.8.2)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\deep blade\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from python-dateutil>=2.8.1->pandas) (1.16.0)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: You are using pip version 20.2.3; however, version 22.0.3 is available.\n",
      "You should consider upgrading via the 'c:\\users\\deep blade\\appdata\\local\\programs\\python\\python39\\python.exe -m pip install --upgrade pip' command.\n"
     ]
    }
   ],
   "source": [
    "!pip install numpy\n",
    "!pip install pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b2c6d086",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "55b0f803",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('laptop_price.csv', encoding='latin-1')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bfe9ede",
   "metadata": {},
   "source": [
    "# Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7c0f4cf8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>laptop_ID</th>\n",
       "      <th>Company</th>\n",
       "      <th>Product</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Inches</th>\n",
       "      <th>ScreenResolution</th>\n",
       "      <th>Cpu</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price_euros</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Apple</td>\n",
       "      <td>MacBook Pro</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 2.3GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>Intel Iris Plus Graphics 640</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37kg</td>\n",
       "      <td>1339.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Apple</td>\n",
       "      <td>Macbook Air</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>1440x900</td>\n",
       "      <td>Intel Core i5 1.8GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34kg</td>\n",
       "      <td>898.94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   laptop_ID Company      Product   TypeName  Inches  \\\n",
       "0          1   Apple  MacBook Pro  Ultrabook    13.3   \n",
       "1          2   Apple  Macbook Air  Ultrabook    13.3   \n",
       "\n",
       "                     ScreenResolution                   Cpu  Ram  \\\n",
       "0  IPS Panel Retina Display 2560x1600  Intel Core i5 2.3GHz  8GB   \n",
       "1                            1440x900  Intel Core i5 1.8GHz  8GB   \n",
       "\n",
       "                            Gpu  OpSys  Weight  Price_euros  \n",
       "0  Intel Iris Plus Graphics 640  macOS  1.37kg      1339.69  \n",
       "1        Intel HD Graphics 6000  macOS  1.34kg       898.94  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b2673dd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1303, 12)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "59bef782",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "laptop_ID           0\n",
       "Company             0\n",
       "Product             0\n",
       "TypeName            0\n",
       "Inches              0\n",
       "ScreenResolution    0\n",
       "Cpu                 0\n",
       "Ram                 0\n",
       "Gpu                 0\n",
       "OpSys               0\n",
       "Weight              0\n",
       "Price_euros         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "733560b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1303 entries, 0 to 1302\n",
      "Data columns (total 12 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   laptop_ID         1303 non-null   int64  \n",
      " 1   Company           1303 non-null   object \n",
      " 2   Product           1303 non-null   object \n",
      " 3   TypeName          1303 non-null   object \n",
      " 4   Inches            1303 non-null   float64\n",
      " 5   ScreenResolution  1303 non-null   object \n",
      " 6   Cpu               1303 non-null   object \n",
      " 7   Ram               1303 non-null   object \n",
      " 8   Gpu               1303 non-null   object \n",
      " 9   OpSys             1303 non-null   object \n",
      " 10  Weight            1303 non-null   object \n",
      " 11  Price_euros       1303 non-null   float64\n",
      "dtypes: float64(2), int64(1), object(9)\n",
      "memory usage: 122.3+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7873ec40",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Ram'] = data['Ram'].str.replace('GB','').astype('int32')\n",
    "data['Weight'] = data['Weight'].str.replace('kg','').astype('float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "39e2a341",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>laptop_ID</th>\n",
       "      <th>Company</th>\n",
       "      <th>Product</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Inches</th>\n",
       "      <th>ScreenResolution</th>\n",
       "      <th>Cpu</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price_euros</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Apple</td>\n",
       "      <td>MacBook Pro</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 2.3GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>Intel Iris Plus Graphics 640</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>1339.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Apple</td>\n",
       "      <td>Macbook Air</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>1440x900</td>\n",
       "      <td>Intel Core i5 1.8GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>898.94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   laptop_ID Company      Product   TypeName  Inches  \\\n",
       "0          1   Apple  MacBook Pro  Ultrabook    13.3   \n",
       "1          2   Apple  Macbook Air  Ultrabook    13.3   \n",
       "\n",
       "                     ScreenResolution                   Cpu  Ram  \\\n",
       "0  IPS Panel Retina Display 2560x1600  Intel Core i5 2.3GHz    8   \n",
       "1                            1440x900  Intel Core i5 1.8GHz    8   \n",
       "\n",
       "                            Gpu  OpSys  Weight  Price_euros  \n",
       "0  Intel Iris Plus Graphics 640  macOS    1.37      1339.69  \n",
       "1        Intel HD Graphics 6000  macOS    1.34       898.94  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "211da773",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "laptop_ID      0.067830\n",
       "Inches         0.068197\n",
       "Ram            0.743007\n",
       "Weight         0.210370\n",
       "Price_euros    1.000000\n",
       "Name: Price_euros, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.corr()['Price_euros']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ee8007d",
   "metadata": {},
   "source": [
    "### Company Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e70362eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dell         297\n",
       "Lenovo       297\n",
       "HP           274\n",
       "Asus         158\n",
       "Acer         103\n",
       "MSI           54\n",
       "Toshiba       48\n",
       "Apple         21\n",
       "Samsung        9\n",
       "Razer          7\n",
       "Mediacom       7\n",
       "Microsoft      6\n",
       "Xiaomi         4\n",
       "Vero           4\n",
       "Chuwi          3\n",
       "Google         3\n",
       "Fujitsu        3\n",
       "LG             3\n",
       "Huawei         2\n",
       "Name: Company, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Company'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "af7fb897",
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_company(inpt):\n",
    "    if inpt == 'Samsung' or inpt == 'Razer' or inpt == 'Mediacom' or inpt == 'Microsoft'or inpt == 'Xiaomi'or inpt == 'Vero'or inpt == 'Chuwi'or inpt == 'Google'or inpt == 'Fujitsu'or inpt == 'LG'or inpt == 'Huawei':\n",
    "        return 'Other'\n",
    "    else:\n",
    "        return inpt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e98b54a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Company'] = data['Company'].apply(add_company)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b95226e2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dell       297\n",
       "Lenovo     297\n",
       "HP         274\n",
       "Asus       158\n",
       "Acer       103\n",
       "MSI         54\n",
       "Other       51\n",
       "Toshiba     48\n",
       "Apple       21\n",
       "Name: Company, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Company'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "591ed25f",
   "metadata": {},
   "source": [
    "### Product Column "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5d1fa28a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "618"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data['Product'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "084a6741",
   "metadata": {},
   "source": [
    "### TypeName Column "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fedaf27c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Notebook              727\n",
       "Gaming                205\n",
       "Ultrabook             196\n",
       "2 in 1 Convertible    121\n",
       "Workstation            29\n",
       "Netbook                25\n",
       "Name: TypeName, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['TypeName'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "810807f2",
   "metadata": {},
   "source": [
    "### ScreenResolution Column "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "746e2fee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Full HD 1920x1080                                507\n",
       "1366x768                                         281\n",
       "IPS Panel Full HD 1920x1080                      230\n",
       "IPS Panel Full HD / Touchscreen 1920x1080         53\n",
       "Full HD / Touchscreen 1920x1080                   47\n",
       "1600x900                                          23\n",
       "Touchscreen 1366x768                              16\n",
       "Quad HD+ / Touchscreen 3200x1800                  15\n",
       "IPS Panel 4K Ultra HD 3840x2160                   12\n",
       "IPS Panel 4K Ultra HD / Touchscreen 3840x2160     11\n",
       "4K Ultra HD / Touchscreen 3840x2160               10\n",
       "4K Ultra HD 3840x2160                              7\n",
       "Touchscreen 2560x1440                              7\n",
       "IPS Panel 1366x768                                 7\n",
       "IPS Panel Quad HD+ / Touchscreen 3200x1800         6\n",
       "IPS Panel Retina Display 2560x1600                 6\n",
       "IPS Panel Retina Display 2304x1440                 6\n",
       "Touchscreen 2256x1504                              6\n",
       "IPS Panel Touchscreen 2560x1440                    5\n",
       "IPS Panel Retina Display 2880x1800                 4\n",
       "IPS Panel Touchscreen 1920x1200                    4\n",
       "1440x900                                           4\n",
       "IPS Panel 2560x1440                                4\n",
       "IPS Panel Quad HD+ 2560x1440                       3\n",
       "Quad HD+ 3200x1800                                 3\n",
       "1920x1080                                          3\n",
       "Touchscreen 2400x1600                              3\n",
       "2560x1440                                          3\n",
       "IPS Panel Touchscreen 1366x768                     3\n",
       "IPS Panel Touchscreen / 4K Ultra HD 3840x2160      2\n",
       "IPS Panel Full HD 2160x1440                        2\n",
       "IPS Panel Quad HD+ 3200x1800                       2\n",
       "IPS Panel Retina Display 2736x1824                 1\n",
       "IPS Panel Full HD 1920x1200                        1\n",
       "IPS Panel Full HD 2560x1440                        1\n",
       "IPS Panel Full HD 1366x768                         1\n",
       "Touchscreen / Full HD 1920x1080                    1\n",
       "Touchscreen / Quad HD+ 3200x1800                   1\n",
       "Touchscreen / 4K Ultra HD 3840x2160                1\n",
       "IPS Panel Touchscreen 2400x1600                    1\n",
       "Name: ScreenResolution, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['ScreenResolution'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a3ce5a33",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Touchscreen'] = data['ScreenResolution'].apply(lambda x:1 if 'Touchscreen' in x else 0)\n",
    "data['Ips'] = data['ScreenResolution'].apply(lambda x:1 if 'IPS' in x else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "523cde81",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>laptop_ID</th>\n",
       "      <th>Company</th>\n",
       "      <th>Product</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Inches</th>\n",
       "      <th>ScreenResolution</th>\n",
       "      <th>Cpu</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price_euros</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Apple</td>\n",
       "      <td>MacBook Pro</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 2.3GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>Intel Iris Plus Graphics 640</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>1339.69</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Apple</td>\n",
       "      <td>Macbook Air</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>1440x900</td>\n",
       "      <td>Intel Core i5 1.8GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>898.94</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   laptop_ID Company      Product   TypeName  Inches  \\\n",
       "0          1   Apple  MacBook Pro  Ultrabook    13.3   \n",
       "1          2   Apple  Macbook Air  Ultrabook    13.3   \n",
       "\n",
       "                     ScreenResolution                   Cpu  Ram  \\\n",
       "0  IPS Panel Retina Display 2560x1600  Intel Core i5 2.3GHz    8   \n",
       "1                            1440x900  Intel Core i5 1.8GHz    8   \n",
       "\n",
       "                            Gpu  OpSys  Weight  Price_euros  Touchscreen  Ips  \n",
       "0  Intel Iris Plus Graphics 640  macOS    1.37      1339.69            0    1  \n",
       "1        Intel HD Graphics 6000  macOS    1.34       898.94            0    0  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1867cb80",
   "metadata": {},
   "source": [
    "### Cpu Column "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "55a7df9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intel Core i5 7200U 2.5GHz       190\n",
       "Intel Core i7 7700HQ 2.8GHz      146\n",
       "Intel Core i7 7500U 2.7GHz       134\n",
       "Intel Core i7 8550U 1.8GHz        73\n",
       "Intel Core i5 8250U 1.6GHz        72\n",
       "                                ... \n",
       "Intel Core M M3-6Y30 0.9GHz        1\n",
       "AMD A9-Series 9420 2.9GHz          1\n",
       "Intel Core i3 6006U 2.2GHz         1\n",
       "AMD A6-Series 7310 2GHz            1\n",
       "Intel Xeon E3-1535M v6 3.1GHz      1\n",
       "Name: Cpu, Length: 118, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Cpu'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1e710218",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['cpu_name'] = data['Cpu'].apply(lambda x:\" \".join(x.split()[0:3]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0c432fbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intel Core i7               527\n",
       "Intel Core i5               423\n",
       "Intel Core i3               136\n",
       "Intel Celeron Dual           80\n",
       "Intel Pentium Quad           27\n",
       "Intel Core M                 19\n",
       "AMD A9-Series 9420           12\n",
       "Intel Celeron Quad            8\n",
       "AMD A6-Series 9220            8\n",
       "AMD A12-Series 9720P          7\n",
       "Intel Atom x5-Z8350           5\n",
       "AMD A8-Series 7410            4\n",
       "Intel Atom x5-Z8550           4\n",
       "Intel Pentium Dual            3\n",
       "AMD A9-Series 9410            3\n",
       "AMD Ryzen 1700                3\n",
       "AMD A9-Series A9-9420         2\n",
       "AMD A10-Series 9620P          2\n",
       "Intel Atom X5-Z8350           2\n",
       "AMD E-Series E2-9000e         2\n",
       "Intel Xeon E3-1535M           2\n",
       "Intel Xeon E3-1505M           2\n",
       "AMD E-Series 7110             2\n",
       "AMD A10-Series 9600P          2\n",
       "AMD A6-Series A6-9220         2\n",
       "AMD A10-Series A10-9620P      2\n",
       "AMD Ryzen 1600                1\n",
       "Intel Atom x5-Z8300           1\n",
       "AMD E-Series E2-6110          1\n",
       "AMD FX 9830P                  1\n",
       "AMD E-Series E2-9000          1\n",
       "AMD A6-Series 7310            1\n",
       "Intel Atom Z8350              1\n",
       "AMD A12-Series 9700P          1\n",
       "AMD A4-Series 7210            1\n",
       "AMD FX 8800P                  1\n",
       "AMD E-Series 9000e            1\n",
       "Samsung Cortex A72&A53        1\n",
       "AMD E-Series 9000             1\n",
       "AMD E-Series 6110             1\n",
       "Name: cpu_name, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['cpu_name'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "55b79115",
   "metadata": {},
   "outputs": [],
   "source": [
    "def set_processor(name):\n",
    "    if name == 'Intel Core i7' or name == 'Intel Core i5' or name == 'Intel Core i3':\n",
    "        return name\n",
    "    else:\n",
    "        if name.split()[0] == 'AMD':\n",
    "            return 'AMD'\n",
    "        else:\n",
    "            return 'Other'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0348b55f",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['cpu_name'] = data['cpu_name'].apply(set_processor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "02a0b648",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intel Core i7    527\n",
       "Intel Core i5    423\n",
       "Other            155\n",
       "Intel Core i3    136\n",
       "AMD               62\n",
       "Name: cpu_name, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['cpu_name'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a27a2c1a",
   "metadata": {},
   "source": [
    "### Ram Column "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b45a5072",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8     619\n",
       "4     375\n",
       "16    200\n",
       "6      41\n",
       "12     25\n",
       "2      22\n",
       "32     17\n",
       "24      3\n",
       "64      1\n",
       "Name: Ram, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Ram'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f68c2262",
   "metadata": {},
   "source": [
    "### Gpu Column "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2a0ad804",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intel HD Graphics 620      281\n",
       "Intel HD Graphics 520      185\n",
       "Intel UHD Graphics 620      68\n",
       "Nvidia GeForce GTX 1050     66\n",
       "Nvidia GeForce GTX 1060     48\n",
       "                          ... \n",
       "AMD Radeon R5 520            1\n",
       "AMD Radeon R7                1\n",
       "Intel HD Graphics 540        1\n",
       "AMD Radeon 540               1\n",
       "ARM Mali T860 MP4            1\n",
       "Name: Gpu, Length: 110, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Gpu'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "23e9b1fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['gpu_name'] = data['Gpu'].apply(lambda x:\" \".join(x.split()[0:1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "449b0463",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intel     722\n",
       "Nvidia    400\n",
       "AMD       180\n",
       "ARM         1\n",
       "Name: gpu_name, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['gpu_name'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b3c20799",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1303, 16)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "2a617275",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data[data['gpu_name'] != 'ARM']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b7d71b6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1302, 16)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "1e765412",
   "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_ID</th>\n",
       "      <th>Company</th>\n",
       "      <th>Product</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Inches</th>\n",
       "      <th>ScreenResolution</th>\n",
       "      <th>Cpu</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price_euros</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>cpu_name</th>\n",
       "      <th>gpu_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Apple</td>\n",
       "      <td>MacBook Pro</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 2.3GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>Intel Iris Plus Graphics 640</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>1339.69</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Apple</td>\n",
       "      <td>Macbook Air</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>1440x900</td>\n",
       "      <td>Intel Core i5 1.8GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>898.94</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   laptop_ID Company      Product   TypeName  Inches  \\\n",
       "0          1   Apple  MacBook Pro  Ultrabook    13.3   \n",
       "1          2   Apple  Macbook Air  Ultrabook    13.3   \n",
       "\n",
       "                     ScreenResolution                   Cpu  Ram  \\\n",
       "0  IPS Panel Retina Display 2560x1600  Intel Core i5 2.3GHz    8   \n",
       "1                            1440x900  Intel Core i5 1.8GHz    8   \n",
       "\n",
       "                            Gpu  OpSys  Weight  Price_euros  Touchscreen  Ips  \\\n",
       "0  Intel Iris Plus Graphics 640  macOS    1.37      1339.69            0    1   \n",
       "1        Intel HD Graphics 6000  macOS    1.34       898.94            0    0   \n",
       "\n",
       "        cpu_name gpu_name  \n",
       "0  Intel Core i5    Intel  \n",
       "1  Intel Core i5    Intel  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96e3bc46",
   "metadata": {},
   "source": [
    "### OpSys Column "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e765d214",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Windows 10      1072\n",
       "No OS             66\n",
       "Linux             62\n",
       "Windows 7         45\n",
       "Chrome OS         26\n",
       "macOS             13\n",
       "Mac OS X           8\n",
       "Windows 10 S       8\n",
       "Android            2\n",
       "Name: OpSys, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['OpSys'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f44b8c87",
   "metadata": {},
   "outputs": [],
   "source": [
    "def set_os(inpt):\n",
    "    if inpt == 'Windows 10' or inpt == 'Windows 7' or inpt == 'Windows 10 S':\n",
    "        return 'Windows'\n",
    "    elif inpt == 'macOS' or inpt == 'Mac OS X':\n",
    "        return 'Mac'\n",
    "    elif inpt == 'Linux':\n",
    "        return inpt\n",
    "    else:\n",
    "        return 'Other'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e55c6d05",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['OpSys'] = data['OpSys'].apply(set_os)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "243c2c60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Windows    1125\n",
       "Other        94\n",
       "Linux        62\n",
       "Mac          21\n",
       "Name: OpSys, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['OpSys'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "70d8e1de",
   "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_ID</th>\n",
       "      <th>Company</th>\n",
       "      <th>Product</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Inches</th>\n",
       "      <th>ScreenResolution</th>\n",
       "      <th>Cpu</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price_euros</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>cpu_name</th>\n",
       "      <th>gpu_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Apple</td>\n",
       "      <td>MacBook Pro</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 2.3GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>Intel Iris Plus Graphics 640</td>\n",
       "      <td>Mac</td>\n",
       "      <td>1.37</td>\n",
       "      <td>1339.69</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Apple</td>\n",
       "      <td>Macbook Air</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>1440x900</td>\n",
       "      <td>Intel Core i5 1.8GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>Mac</td>\n",
       "      <td>1.34</td>\n",
       "      <td>898.94</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   laptop_ID Company      Product   TypeName  Inches  \\\n",
       "0          1   Apple  MacBook Pro  Ultrabook    13.3   \n",
       "1          2   Apple  Macbook Air  Ultrabook    13.3   \n",
       "\n",
       "                     ScreenResolution                   Cpu  Ram  \\\n",
       "0  IPS Panel Retina Display 2560x1600  Intel Core i5 2.3GHz    8   \n",
       "1                            1440x900  Intel Core i5 1.8GHz    8   \n",
       "\n",
       "                            Gpu OpSys  Weight  Price_euros  Touchscreen  Ips  \\\n",
       "0  Intel Iris Plus Graphics 640   Mac    1.37      1339.69            0    1   \n",
       "1        Intel HD Graphics 6000   Mac    1.34       898.94            0    0   \n",
       "\n",
       "        cpu_name gpu_name  \n",
       "0  Intel Core i5    Intel  \n",
       "1  Intel Core i5    Intel  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b8d854e",
   "metadata": {},
   "source": [
    "### Drop unnecessary columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "59537119",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.drop(columns=['laptop_ID', 'Inches', 'Product', 'ScreenResolution', 'Cpu', 'Gpu'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "a0a4e3cd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Ram</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price_euros</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>cpu_name</th>\n",
       "      <th>gpu_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>Mac</td>\n",
       "      <td>1.37</td>\n",
       "      <td>1339.69</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>Mac</td>\n",
       "      <td>1.34</td>\n",
       "      <td>898.94</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>Other</td>\n",
       "      <td>1.86</td>\n",
       "      <td>575.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>16</td>\n",
       "      <td>Mac</td>\n",
       "      <td>1.83</td>\n",
       "      <td>2537.45</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>AMD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>Mac</td>\n",
       "      <td>1.37</td>\n",
       "      <td>1803.60</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Ram  OpSys  Weight  Price_euros  Touchscreen  Ips  \\\n",
       "0   Apple  Ultrabook    8    Mac    1.37      1339.69            0    1   \n",
       "1   Apple  Ultrabook    8    Mac    1.34       898.94            0    0   \n",
       "2      HP   Notebook    8  Other    1.86       575.00            0    0   \n",
       "3   Apple  Ultrabook   16    Mac    1.83      2537.45            0    1   \n",
       "4   Apple  Ultrabook    8    Mac    1.37      1803.60            0    1   \n",
       "\n",
       "        cpu_name gpu_name  \n",
       "0  Intel Core i5    Intel  \n",
       "1  Intel Core i5    Intel  \n",
       "2  Intel Core i5    Intel  \n",
       "3  Intel Core i7      AMD  \n",
       "4  Intel Core i5    Intel  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8385bf63",
   "metadata": {},
   "source": [
    "### One-Hot Encoding "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b67f4a4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.get_dummies(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "66f09269",
   "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>Ram</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price_euros</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>Company_Acer</th>\n",
       "      <th>Company_Apple</th>\n",
       "      <th>Company_Asus</th>\n",
       "      <th>Company_Dell</th>\n",
       "      <th>Company_HP</th>\n",
       "      <th>...</th>\n",
       "      <th>OpSys_Other</th>\n",
       "      <th>OpSys_Windows</th>\n",
       "      <th>cpu_name_AMD</th>\n",
       "      <th>cpu_name_Intel Core i3</th>\n",
       "      <th>cpu_name_Intel Core i5</th>\n",
       "      <th>cpu_name_Intel Core i7</th>\n",
       "      <th>cpu_name_Other</th>\n",
       "      <th>gpu_name_AMD</th>\n",
       "      <th>gpu_name_Intel</th>\n",
       "      <th>gpu_name_Nvidia</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8</td>\n",
       "      <td>1.37</td>\n",
       "      <td>1339.69</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>1.34</td>\n",
       "      <td>898.94</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Ram  Weight  Price_euros  Touchscreen  Ips  Company_Acer  Company_Apple  \\\n",
       "0    8    1.37      1339.69            0    1             0              1   \n",
       "1    8    1.34       898.94            0    0             0              1   \n",
       "\n",
       "   Company_Asus  Company_Dell  Company_HP  ...  OpSys_Other  OpSys_Windows  \\\n",
       "0             0             0           0  ...            0              0   \n",
       "1             0             0           0  ...            0              0   \n",
       "\n",
       "   cpu_name_AMD  cpu_name_Intel Core i3  cpu_name_Intel Core i5  \\\n",
       "0             0                       0                       1   \n",
       "1             0                       0                       1   \n",
       "\n",
       "   cpu_name_Intel Core i7  cpu_name_Other  gpu_name_AMD  gpu_name_Intel  \\\n",
       "0                       0               0             0               1   \n",
       "1                       0               0             0               1   \n",
       "\n",
       "   gpu_name_Nvidia  \n",
       "0                0  \n",
       "1                0  \n",
       "\n",
       "[2 rows x 32 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "013bbf52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1302, 32)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5becd450",
   "metadata": {},
   "source": [
    "# Model Building and Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "d3cdaad8",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.drop('Price_euros', axis=1)\n",
    "y = data['Price_euros']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "c1c56c80",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing c:\\users\\deep blade\\appdata\\local\\pip\\cache\\wheels\\e4\\7b\\98\\b6466d71b8d738a0c547008b9eb39bf8676d1ff6ca4b22af1c\\sklearn-0.0-py2.py3-none-any.whl\n",
      "Collecting scikit-learn\n",
      "  Using cached scikit_learn-1.0.2-cp39-cp39-win_amd64.whl (7.2 MB)\n",
      "Requirement already satisfied: scipy>=1.1.0 in c:\\users\\deep blade\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from scikit-learn->sklearn) (1.7.3)\n",
      "Collecting joblib>=0.11\n",
      "  Using cached joblib-1.1.0-py2.py3-none-any.whl (306 kB)\n",
      "Collecting threadpoolctl>=2.0.0\n",
      "  Using cached threadpoolctl-3.1.0-py3-none-any.whl (14 kB)\n",
      "Requirement already satisfied: numpy>=1.14.6 in c:\\users\\deep blade\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from scikit-learn->sklearn) (1.21.4)\n",
      "Installing collected packages: joblib, threadpoolctl, scikit-learn, sklearn\n",
      "Successfully installed joblib-1.1.0 scikit-learn-1.0.2 sklearn-0.0 threadpoolctl-3.1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: You are using pip version 20.2.3; however, version 22.0.3 is available.\n",
      "You should consider upgrading via the 'c:\\users\\deep blade\\appdata\\local\\programs\\python\\python39\\python.exe -m pip install --upgrade pip' command.\n"
     ]
    }
   ],
   "source": [
    "!pip install sklearn"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5d2d5c8",
   "metadata": {},
   "source": [
    "### Divide dataset into training and testing set "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "c27f0bc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "110097a0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((976, 31), (326, 31))"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66cc17ab",
   "metadata": {},
   "source": [
    "### model training "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "d5870149",
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_acc(model):\n",
    "    model.fit(X_train, y_train)\n",
    "    acc = model.score(X_test, y_test)\n",
    "    print(str(model)+ ' --> ' +str(acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "16d3b53e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearRegression() --> 0.6660017698459928\n",
      "Lasso() --> 0.6680214949765905\n",
      "DecisionTreeRegressor() --> 0.6204726372396996\n",
      "RandomForestRegressor() --> 0.723248390357115\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lr = LinearRegression()\n",
    "model_acc(lr)\n",
    "\n",
    "from sklearn.linear_model import Lasso\n",
    "lasso = Lasso()\n",
    "model_acc(lasso)\n",
    "\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "dt = DecisionTreeRegressor()\n",
    "model_acc(dt)\n",
    "\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "rf = RandomForestRegressor()\n",
    "model_acc(rf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8121555d",
   "metadata": {},
   "source": [
    "### Hyperparameter tunning "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "73ec02ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7360688743299033"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "parameters = {'n_estimators':[10, 50, 100],\n",
    "              'criterion':['squared_error','absolute_error','poisson']}\n",
    "\n",
    "grid_obj = GridSearchCV(estimator=rf, param_grid=parameters)\n",
    "\n",
    "grid_fit = grid_obj.fit(X_train, y_train)\n",
    "\n",
    "best_model = grid_fit.best_estimator_\n",
    "\n",
    "best_model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "6be3fb13",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Ram', 'Weight', 'Touchscreen', 'Ips', 'Company_Acer', 'Company_Apple',\n",
       "       'Company_Asus', 'Company_Dell', 'Company_HP', 'Company_Lenovo',\n",
       "       'Company_MSI', 'Company_Other', 'Company_Toshiba',\n",
       "       'TypeName_2 in 1 Convertible', 'TypeName_Gaming', 'TypeName_Netbook',\n",
       "       'TypeName_Notebook', 'TypeName_Ultrabook', 'TypeName_Workstation',\n",
       "       'OpSys_Linux', 'OpSys_Mac', 'OpSys_Other', 'OpSys_Windows',\n",
       "       'cpu_name_AMD', 'cpu_name_Intel Core i3', 'cpu_name_Intel Core i5',\n",
       "       'cpu_name_Intel Core i7', 'cpu_name_Other', 'gpu_name_AMD',\n",
       "       'gpu_name_Intel', 'gpu_name_Nvidia'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ed81d27",
   "metadata": {},
   "source": [
    "### Save model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "89bd6b26",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open('predictor.pickle', 'wb') as file:\n",
    "    pickle.dump(best_model, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "3c35b622",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\deep blade\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([1850.2652])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_value = best_model.predict([[8, 1.3, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0]])\n",
    "pred_value"
   ]
  }
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