laptop.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\admin\\AppData\\Local\\Temp\\ipykernel_1008\\2151744951.py:1: DeprecationWarning: \n",
      "Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),\n",
      "(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)\n",
      "but was not found to be installed on your system.\n",
      "If this would cause problems for you,\n",
      "please provide us feedback at https://github.com/pandas-dev/pandas/issues/54466\n",
      "        \n",
      "  import pandas as pd\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"./laptop_price.csv\", encoding='latin-1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "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>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>Memory</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>892</th>\n",
       "      <td>903</td>\n",
       "      <td>Samsung</td>\n",
       "      <td>Notebook 9</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>15.0</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>1.17kg</td>\n",
       "      <td>1699.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>444</th>\n",
       "      <td>451</td>\n",
       "      <td>HP</td>\n",
       "      <td>Omen 15-AX205na</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>128GB SSD +  1TB HDD</td>\n",
       "      <td>Nvidia GeForce GTX 1050</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.2kg</td>\n",
       "      <td>1099.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1037</th>\n",
       "      <td>1051</td>\n",
       "      <td>Toshiba</td>\n",
       "      <td>Tecra Z40-C-136</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>14.0</td>\n",
       "      <td>IPS Panel Full HD 1920x1080</td>\n",
       "      <td>Intel Core i7 6600U 2.6GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 520</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.47kg</td>\n",
       "      <td>1725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>415</td>\n",
       "      <td>Lenovo</td>\n",
       "      <td>IdeaPad 320-15ISK</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>500GB HDD</td>\n",
       "      <td>Intel HD Graphics 520</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.2kg</td>\n",
       "      <td>409.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>37</td>\n",
       "      <td>Acer</td>\n",
       "      <td>Aspire 3</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>Intel Core i3 7130U 2.7GHz</td>\n",
       "      <td>4GB</td>\n",
       "      <td>1TB HDD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>Linux</td>\n",
       "      <td>2.1kg</td>\n",
       "      <td>367.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      laptop_ID  Company            Product   TypeName  Inches  \\\n",
       "892         903  Samsung         Notebook 9  Ultrabook    15.0   \n",
       "444         451       HP    Omen 15-AX205na     Gaming    15.6   \n",
       "1037       1051  Toshiba    Tecra Z40-C-136  Ultrabook    14.0   \n",
       "408         415   Lenovo  IdeaPad 320-15ISK   Notebook    15.6   \n",
       "36           37     Acer           Aspire 3   Notebook    15.6   \n",
       "\n",
       "                 ScreenResolution                          Cpu  Ram  \\\n",
       "892             Full HD 1920x1080   Intel Core i7 7500U 2.7GHz  8GB   \n",
       "444   IPS Panel Full HD 1920x1080  Intel Core i7 7700HQ 2.8GHz  8GB   \n",
       "1037  IPS Panel Full HD 1920x1080   Intel Core i7 6600U 2.6GHz  8GB   \n",
       "408             Full HD 1920x1080     Intel Core i3 6006U 2GHz  4GB   \n",
       "36                       1366x768   Intel Core i3 7130U 2.7GHz  4GB   \n",
       "\n",
       "                    Memory                      Gpu       OpSys  Weight  \\\n",
       "892              256GB SSD    Intel HD Graphics 620  Windows 10  1.17kg   \n",
       "444   128GB SSD +  1TB HDD  Nvidia GeForce GTX 1050  Windows 10   2.2kg   \n",
       "1037             256GB SSD    Intel HD Graphics 520  Windows 10  1.47kg   \n",
       "408              500GB HDD    Intel HD Graphics 520  Windows 10   2.2kg   \n",
       "36                 1TB HDD    Intel HD Graphics 620       Linux   2.1kg   \n",
       "\n",
       "      Price_euros  \n",
       "892        1699.0  \n",
       "444        1099.0  \n",
       "1037       1725.0  \n",
       "408         409.0  \n",
       "36          367.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1303, 13)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1303 entries, 0 to 1302\n",
      "Data columns (total 13 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   Memory            1303 non-null   object \n",
      " 9   Gpu               1303 non-null   object \n",
      " 10  OpSys             1303 non-null   object \n",
      " 11  Weight            1303 non-null   object \n",
      " 12  Price_euros       1303 non-null   float64\n",
      "dtypes: float64(2), int64(1), object(10)\n",
      "memory usage: 132.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()\n",
    "#no null value is present in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>laptop_ID</th>\n",
       "      <th>Inches</th>\n",
       "      <th>Price_euros</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1303.000000</td>\n",
       "      <td>1303.000000</td>\n",
       "      <td>1303.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>660.155794</td>\n",
       "      <td>15.017191</td>\n",
       "      <td>1123.686992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>381.172104</td>\n",
       "      <td>1.426304</td>\n",
       "      <td>699.009043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.100000</td>\n",
       "      <td>174.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>331.500000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>599.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>659.000000</td>\n",
       "      <td>15.600000</td>\n",
       "      <td>977.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>990.500000</td>\n",
       "      <td>15.600000</td>\n",
       "      <td>1487.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1320.000000</td>\n",
       "      <td>18.400000</td>\n",
       "      <td>6099.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "         laptop_ID       Inches  Price_euros\n",
       "count  1303.000000  1303.000000  1303.000000\n",
       "mean    660.155794    15.017191  1123.686992\n",
       "std     381.172104     1.426304   699.009043\n",
       "min       1.000000    10.100000   174.000000\n",
       "25%     331.500000    14.000000   599.000000\n",
       "50%     659.000000    15.600000   977.000000\n",
       "75%     990.500000    15.600000  1487.880000\n",
       "max    1320.000000    18.400000  6099.000000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
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   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>Memory</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>128GB SSD</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>128GB Flash Storage</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",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>HP</td>\n",
       "      <td>250 G6</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</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>No OS</td>\n",
       "      <td>1.86kg</td>\n",
       "      <td>575.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Apple</td>\n",
       "      <td>MacBook Pro</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>15.4</td>\n",
       "      <td>IPS Panel Retina Display 2880x1800</td>\n",
       "      <td>Intel Core i7 2.7GHz</td>\n",
       "      <td>16GB</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83kg</td>\n",
       "      <td>2537.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</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 3.1GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37kg</td>\n",
       "      <td>1803.60</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",
       "2          3      HP       250 G6   Notebook    15.6   \n",
       "3          4   Apple  MacBook Pro  Ultrabook    15.4   \n",
       "4          5   Apple  MacBook Pro  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",
       "2                   Full HD 1920x1080  Intel Core i5 7200U 2.5GHz   8GB   \n",
       "3  IPS Panel Retina Display 2880x1800        Intel Core i7 2.7GHz  16GB   \n",
       "4  IPS Panel Retina Display 2560x1600        Intel Core i5 3.1GHz   8GB   \n",
       "\n",
       "                Memory                           Gpu  OpSys  Weight  \\\n",
       "0            128GB SSD  Intel Iris Plus Graphics 640  macOS  1.37kg   \n",
       "1  128GB Flash Storage        Intel HD Graphics 6000  macOS  1.34kg   \n",
       "2            256GB SSD         Intel HD Graphics 620  No OS  1.86kg   \n",
       "3            512GB SSD            AMD Radeon Pro 455  macOS  1.83kg   \n",
       "4            256GB SSD  Intel Iris Plus Graphics 650  macOS  1.37kg   \n",
       "\n",
       "   Price_euros  \n",
       "0      1339.69  \n",
       "1       898.94  \n",
       "2       575.00  \n",
       "3      2537.45  \n",
       "4      1803.60  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()\n",
    "#all values are unique "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. remove GB from Ram\n",
    "2. remove Kg from Weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#lets drop laptop ID\n",
    "df.drop(columns=['laptop_ID'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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>128GB SSD</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>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>128GB Flash Storage</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",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>250 G6</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</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>No OS</td>\n",
       "      <td>1.86kg</td>\n",
       "      <td>575.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>MacBook Pro</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>15.4</td>\n",
       "      <td>IPS Panel Retina Display 2880x1800</td>\n",
       "      <td>Intel Core i7 2.7GHz</td>\n",
       "      <td>16GB</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83kg</td>\n",
       "      <td>2537.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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 3.1GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37kg</td>\n",
       "      <td>1803.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company      Product   TypeName  Inches                    ScreenResolution  \\\n",
       "0   Apple  MacBook Pro  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "1   Apple  Macbook Air  Ultrabook    13.3                            1440x900   \n",
       "2      HP       250 G6   Notebook    15.6                   Full HD 1920x1080   \n",
       "3   Apple  MacBook Pro  Ultrabook    15.4  IPS Panel Retina Display 2880x1800   \n",
       "4   Apple  MacBook Pro  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "\n",
       "                          Cpu   Ram               Memory  \\\n",
       "0        Intel Core i5 2.3GHz   8GB            128GB SSD   \n",
       "1        Intel Core i5 1.8GHz   8GB  128GB Flash Storage   \n",
       "2  Intel Core i5 7200U 2.5GHz   8GB            256GB SSD   \n",
       "3        Intel Core i7 2.7GHz  16GB            512GB SSD   \n",
       "4        Intel Core i5 3.1GHz   8GB            256GB SSD   \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  \n",
       "2         Intel HD Graphics 620  No OS  1.86kg       575.00  \n",
       "3            AMD Radeon Pro 455  macOS  1.83kg      2537.45  \n",
       "4  Intel Iris Plus Graphics 650  macOS  1.37kg      1803.60  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Ram'] = df['Ram'].str.replace('GB','')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Weight'] = df['Weight'].str.replace('kg','')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>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>Memory</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>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>128GB SSD</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>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>128GB Flash Storage</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",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>250 G6</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>575.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>MacBook Pro</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>15.4</td>\n",
       "      <td>IPS Panel Retina Display 2880x1800</td>\n",
       "      <td>Intel Core i7 2.7GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>2537.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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 3.1GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>1803.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company      Product   TypeName  Inches                    ScreenResolution  \\\n",
       "0   Apple  MacBook Pro  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "1   Apple  Macbook Air  Ultrabook    13.3                            1440x900   \n",
       "2      HP       250 G6   Notebook    15.6                   Full HD 1920x1080   \n",
       "3   Apple  MacBook Pro  Ultrabook    15.4  IPS Panel Retina Display 2880x1800   \n",
       "4   Apple  MacBook Pro  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "\n",
       "                          Cpu Ram               Memory  \\\n",
       "0        Intel Core i5 2.3GHz   8            128GB SSD   \n",
       "1        Intel Core i5 1.8GHz   8  128GB Flash Storage   \n",
       "2  Intel Core i5 7200U 2.5GHz   8            256GB SSD   \n",
       "3        Intel Core i7 2.7GHz  16            512GB SSD   \n",
       "4        Intel Core i5 3.1GHz   8            256GB SSD   \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  \n",
       "2         Intel HD Graphics 620  No OS   1.86       575.00  \n",
       "3            AMD Radeon Pro 455  macOS   1.83      2537.45  \n",
       "4  Intel Iris Plus Graphics 650  macOS   1.37      1803.60  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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   Company           1303 non-null   object \n",
      " 1   Product           1303 non-null   object \n",
      " 2   TypeName          1303 non-null   object \n",
      " 3   Inches            1303 non-null   float64\n",
      " 4   ScreenResolution  1303 non-null   object \n",
      " 5   Cpu               1303 non-null   object \n",
      " 6   Ram               1303 non-null   object \n",
      " 7   Memory            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), object(10)\n",
      "memory usage: 122.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Ram']=df['Ram'].astype('int32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Weight'] = df['Weight'].astype('float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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   Company           1303 non-null   object \n",
      " 1   Product           1303 non-null   object \n",
      " 2   TypeName          1303 non-null   object \n",
      " 3   Inches            1303 non-null   float64\n",
      " 4   ScreenResolution  1303 non-null   object \n",
      " 5   Cpu               1303 non-null   object \n",
      " 6   Ram               1303 non-null   int32  \n",
      " 7   Memory            1303 non-null   object \n",
      " 8   Gpu               1303 non-null   object \n",
      " 9   OpSys             1303 non-null   object \n",
      " 10  Weight            1303 non-null   float32\n",
      " 11  Price_euros       1303 non-null   float64\n",
      "dtypes: float32(1), float64(2), int32(1), object(8)\n",
      "memory usage: 112.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\admin\\AppData\\Local\\Temp\\ipykernel_1008\\3412786447.py:1: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sb.distplot(df['Price_euros'])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Price_euros', ylabel='Density'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.distplot(df['Price_euros'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Company'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#from this graph i can  say that data is left skewed so there are chances of facing difficulty in convergence\n",
    "df['Company'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.barplot(x=df['Company'],y=df['Price_euros'])\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='TypeName'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['TypeName'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.barplot(x=df['TypeName'],y=df['Price_euros'])\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Inches\n",
       "15.6    665\n",
       "14.0    197\n",
       "13.3    164\n",
       "17.3    164\n",
       "12.5     39\n",
       "11.6     33\n",
       "12.0      6\n",
       "13.5      6\n",
       "13.9      6\n",
       "12.3      5\n",
       "10.1      4\n",
       "15.4      4\n",
       "15.0      4\n",
       "13.0      2\n",
       "18.4      1\n",
       "17.0      1\n",
       "14.1      1\n",
       "11.3      1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Inches'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x16605d0e9f0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.displot(df['Inches'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Inches', ylabel='Price_euros'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.scatterplot(x=df['Inches'],y=df['Price_euros'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ScreenResolution\n",
       "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: count, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ScreenResolution'].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create a new columnb\n",
    "df['TouchScreen']=df['ScreenResolution'].apply(lambda x:1 if 'Touchscreen' in x else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>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>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price_euros</th>\n",
       "      <th>TouchScreen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>529</th>\n",
       "      <td>Dell</td>\n",
       "      <td>Latitude 5580</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i5 7440HQ 2.8GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.90</td>\n",
       "      <td>1377.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>576</th>\n",
       "      <td>Toshiba</td>\n",
       "      <td>Tecra X40-D-10H</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Full HD / Touchscreen 1920x1080</td>\n",
       "      <td>Intel Core i7 7500U 2.7GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.24</td>\n",
       "      <td>1865.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>Dell</td>\n",
       "      <td>Latitude 7480</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i7 7600U 2.8GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>Intel HD Graphics</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.36</td>\n",
       "      <td>1680.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>727</th>\n",
       "      <td>Dell</td>\n",
       "      <td>Inspiron 5378</td>\n",
       "      <td>2 in 1 Convertible</td>\n",
       "      <td>13.3</td>\n",
       "      <td>Full HD / Touchscreen 1920x1080</td>\n",
       "      <td>Intel Core i3 7100U 2.4GHz</td>\n",
       "      <td>4</td>\n",
       "      <td>1TB HDD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.71</td>\n",
       "      <td>649.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>918</th>\n",
       "      <td>Dell</td>\n",
       "      <td>Inspiron 7567</td>\n",
       "      <td>Gaming</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i7 7700HQ 2.8GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>128GB SSD +  1TB HDD</td>\n",
       "      <td>Nvidia GeForce GTX 1050 Ti</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.60</td>\n",
       "      <td>1349.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company          Product            TypeName  Inches  \\\n",
       "529     Dell    Latitude 5580            Notebook    15.6   \n",
       "576  Toshiba  Tecra X40-D-10H           Ultrabook    14.0   \n",
       "407     Dell    Latitude 7480           Ultrabook    14.0   \n",
       "727     Dell    Inspiron 5378  2 in 1 Convertible    13.3   \n",
       "918     Dell    Inspiron 7567              Gaming    15.6   \n",
       "\n",
       "                    ScreenResolution                          Cpu  Ram  \\\n",
       "529                Full HD 1920x1080  Intel Core i5 7440HQ 2.8GHz    8   \n",
       "576  Full HD / Touchscreen 1920x1080   Intel Core i7 7500U 2.7GHz   16   \n",
       "407                Full HD 1920x1080   Intel Core i7 7600U 2.8GHz    8   \n",
       "727  Full HD / Touchscreen 1920x1080   Intel Core i3 7100U 2.4GHz    4   \n",
       "918                Full HD 1920x1080  Intel Core i7 7700HQ 2.8GHz   16   \n",
       "\n",
       "                   Memory                         Gpu       OpSys  Weight  \\\n",
       "529             256GB SSD       Intel HD Graphics 620  Windows 10    1.90   \n",
       "576             512GB SSD       Intel HD Graphics 620  Windows 10    1.24   \n",
       "407             512GB SSD           Intel HD Graphics  Windows 10    1.36   \n",
       "727               1TB HDD       Intel HD Graphics 620  Windows 10    1.71   \n",
       "918  128GB SSD +  1TB HDD  Nvidia GeForce GTX 1050 Ti  Windows 10    2.60   \n",
       "\n",
       "     Price_euros  TouchScreen  \n",
       "529       1377.0            0  \n",
       "576       1865.0            1  \n",
       "407       1680.0            0  \n",
       "727        649.0            1  \n",
       "918       1349.0            0  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='TouchScreen'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['TouchScreen'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['IpsPanel'] = df['ScreenResolution'].apply(lambda x: 1 if 'IPS' in x else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>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>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price_euros</th>\n",
       "      <th>TouchScreen</th>\n",
       "      <th>IpsPanel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>128GB SSD</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>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>128GB Flash Storage</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",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>250 G6</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>575.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>MacBook Pro</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>15.4</td>\n",
       "      <td>IPS Panel Retina Display 2880x1800</td>\n",
       "      <td>Intel Core i7 2.7GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>2537.45</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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 3.1GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>1803.60</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company      Product   TypeName  Inches                    ScreenResolution  \\\n",
       "0   Apple  MacBook Pro  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "1   Apple  Macbook Air  Ultrabook    13.3                            1440x900   \n",
       "2      HP       250 G6   Notebook    15.6                   Full HD 1920x1080   \n",
       "3   Apple  MacBook Pro  Ultrabook    15.4  IPS Panel Retina Display 2880x1800   \n",
       "4   Apple  MacBook Pro  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "\n",
       "                          Cpu  Ram               Memory  \\\n",
       "0        Intel Core i5 2.3GHz    8            128GB SSD   \n",
       "1        Intel Core i5 1.8GHz    8  128GB Flash Storage   \n",
       "2  Intel Core i5 7200U 2.5GHz    8            256GB SSD   \n",
       "3        Intel Core i7 2.7GHz   16            512GB SSD   \n",
       "4        Intel Core i5 3.1GHz    8            256GB SSD   \n",
       "\n",
       "                            Gpu  OpSys  Weight  Price_euros  TouchScreen  \\\n",
       "0  Intel Iris Plus Graphics 640  macOS    1.37      1339.69            0   \n",
       "1        Intel HD Graphics 6000  macOS    1.34       898.94            0   \n",
       "2         Intel HD Graphics 620  No OS    1.86       575.00            0   \n",
       "3            AMD Radeon Pro 455  macOS    1.83      2537.45            0   \n",
       "4  Intel Iris Plus Graphics 650  macOS    1.37      1803.60            0   \n",
       "\n",
       "   IpsPanel  \n",
       "0         1  \n",
       "1         0  \n",
       "2         0  \n",
       "3         1  \n",
       "4         1  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='IpsPanel'>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['IpsPanel'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='IpsPanel', ylabel='Price_euros'>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.barplot(x=df['IpsPanel'],y=df['Price_euros'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "new = df['ScreenResolution'].str.split('x' ,n=1,expand=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['X_res'] = new[0]\n",
    "df['Y_res'] = new[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0               IPS Panel Retina Display 2560\n",
       "1                                        1440\n",
       "2                                Full HD 1920\n",
       "3               IPS Panel Retina Display 2880\n",
       "4               IPS Panel Retina Display 2560\n",
       "                        ...                  \n",
       "1298     IPS Panel Full HD / Touchscreen 1920\n",
       "1299    IPS Panel Quad HD+ / Touchscreen 3200\n",
       "1300                                     1366\n",
       "1301                                     1366\n",
       "1302                                     1366\n",
       "Name: X_res, Length: 1303, dtype: object"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['X_res']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1600\n",
       "1        900\n",
       "2       1080\n",
       "3       1800\n",
       "4       1600\n",
       "        ... \n",
       "1298    1080\n",
       "1299    1800\n",
       "1300     768\n",
       "1301     768\n",
       "1302     768\n",
       "Name: Y_res, Length: 1303, dtype: object"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Y_res']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['X_res']=df['X_res'].str.replace(',','').str.findall(r'(\\d+\\.?\\d+)').apply(lambda x:x[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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",
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       "      <th>IpsPanel</th>\n",
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       "      <th>Y_res</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>MacBook Pro</td>\n",
       "      <td>Ultrabook</td>\n",
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       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
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       "      <td>1600</td>\n",
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       "      <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>128GB Flash Storage</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",
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       "      <td>900</td>\n",
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       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>250 G6</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>575.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1920</td>\n",
       "      <td>1080</td>\n",
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       "      <td>Apple</td>\n",
       "      <td>MacBook Pro</td>\n",
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       "      <td>15.4</td>\n",
       "      <td>IPS Panel Retina Display 2880x1800</td>\n",
       "      <td>Intel Core i7 2.7GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>2537.45</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2880</td>\n",
       "      <td>1800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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 3.1GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>1803.60</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2560</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company      Product   TypeName  Inches                    ScreenResolution  \\\n",
       "0   Apple  MacBook Pro  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "1   Apple  Macbook Air  Ultrabook    13.3                            1440x900   \n",
       "2      HP       250 G6   Notebook    15.6                   Full HD 1920x1080   \n",
       "3   Apple  MacBook Pro  Ultrabook    15.4  IPS Panel Retina Display 2880x1800   \n",
       "4   Apple  MacBook Pro  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "\n",
       "                          Cpu  Ram               Memory  \\\n",
       "0        Intel Core i5 2.3GHz    8            128GB SSD   \n",
       "1        Intel Core i5 1.8GHz    8  128GB Flash Storage   \n",
       "2  Intel Core i5 7200U 2.5GHz    8            256GB SSD   \n",
       "3        Intel Core i7 2.7GHz   16            512GB SSD   \n",
       "4        Intel Core i5 3.1GHz    8            256GB SSD   \n",
       "\n",
       "                            Gpu  OpSys  Weight  Price_euros  TouchScreen  \\\n",
       "0  Intel Iris Plus Graphics 640  macOS    1.37      1339.69            0   \n",
       "1        Intel HD Graphics 6000  macOS    1.34       898.94            0   \n",
       "2         Intel HD Graphics 620  No OS    1.86       575.00            0   \n",
       "3            AMD Radeon Pro 455  macOS    1.83      2537.45            0   \n",
       "4  Intel Iris Plus Graphics 650  macOS    1.37      1803.60            0   \n",
       "\n",
       "   IpsPanel X_res Y_res  \n",
       "0         1  2560  1600  \n",
       "1         0  1440   900  \n",
       "2         0  1920  1080  \n",
       "3         1  2880  1800  \n",
       "4         1  2560  1600  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['X_res']=df['X_res'].astype('int32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Y_res']=df['Y_res'].astype('int32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1303 entries, 0 to 1302\n",
      "Data columns (total 16 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Company           1303 non-null   object \n",
      " 1   Product           1303 non-null   object \n",
      " 2   TypeName          1303 non-null   object \n",
      " 3   Inches            1303 non-null   float64\n",
      " 4   ScreenResolution  1303 non-null   object \n",
      " 5   Cpu               1303 non-null   object \n",
      " 6   Ram               1303 non-null   int32  \n",
      " 7   Memory            1303 non-null   object \n",
      " 8   Gpu               1303 non-null   object \n",
      " 9   OpSys             1303 non-null   object \n",
      " 10  Weight            1303 non-null   float32\n",
      " 11  Price_euros       1303 non-null   float64\n",
      " 12  TouchScreen       1303 non-null   int64  \n",
      " 13  IpsPanel          1303 non-null   int64  \n",
      " 14  X_res             1303 non-null   int32  \n",
      " 15  Y_res             1303 non-null   int32  \n",
      "dtypes: float32(1), float64(2), int32(3), int64(2), object(8)\n",
      "memory usage: 142.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['PPI'] = ((df['X_res']**2) + (df['Y_res']**2))**0.5/df['Inches'].astype('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>Macbook Air</td>\n",
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       "      <td>13.3</td>\n",
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       "      <td>8</td>\n",
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       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>898.94</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1440</td>\n",
       "      <td>900</td>\n",
       "      <td>127.677940</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>250 G6</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>575.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1920</td>\n",
       "      <td>1080</td>\n",
       "      <td>141.211998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>MacBook Pro</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>15.4</td>\n",
       "      <td>IPS Panel Retina Display 2880x1800</td>\n",
       "      <td>Intel Core i7 2.7GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>2537.45</td>\n",
       "      <td>0</td>\n",
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       "      <td>2880</td>\n",
       "      <td>1800</td>\n",
       "      <td>220.534624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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 3.1GHz</td>\n",
       "      <td>8</td>\n",
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       "      <td>Intel Iris Plus Graphics 650</td>\n",
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       "      <td>1803.60</td>\n",
       "      <td>0</td>\n",
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       "      <td>2560</td>\n",
       "      <td>1600</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company      Product   TypeName  Inches                    ScreenResolution  \\\n",
       "0   Apple  MacBook Pro  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "1   Apple  Macbook Air  Ultrabook    13.3                            1440x900   \n",
       "2      HP       250 G6   Notebook    15.6                   Full HD 1920x1080   \n",
       "3   Apple  MacBook Pro  Ultrabook    15.4  IPS Panel Retina Display 2880x1800   \n",
       "4   Apple  MacBook Pro  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "\n",
       "                          Cpu  Ram               Memory  \\\n",
       "0        Intel Core i5 2.3GHz    8            128GB SSD   \n",
       "1        Intel Core i5 1.8GHz    8  128GB Flash Storage   \n",
       "2  Intel Core i5 7200U 2.5GHz    8            256GB SSD   \n",
       "3        Intel Core i7 2.7GHz   16            512GB SSD   \n",
       "4        Intel Core i5 3.1GHz    8            256GB SSD   \n",
       "\n",
       "                            Gpu  OpSys  Weight  Price_euros  TouchScreen  \\\n",
       "0  Intel Iris Plus Graphics 640  macOS    1.37      1339.69            0   \n",
       "1        Intel HD Graphics 6000  macOS    1.34       898.94            0   \n",
       "2         Intel HD Graphics 620  No OS    1.86       575.00            0   \n",
       "3            AMD Radeon Pro 455  macOS    1.83      2537.45            0   \n",
       "4  Intel Iris Plus Graphics 650  macOS    1.37      1803.60            0   \n",
       "\n",
       "   IpsPanel  X_res  Y_res         PPI  \n",
       "0         1   2560   1600  226.983005  \n",
       "1         0   1440    900  127.677940  \n",
       "2         0   1920   1080  141.211998  \n",
       "3         1   2880   1800  220.534624  \n",
       "4         1   2560   1600  226.983005  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['ScreenResolution'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['X_res','Y_res','Inches'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Notebook</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>575.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>MacBook Pro</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>Intel Core i7 2.7GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>2537.45</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>220.534624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>MacBook Pro</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>Intel Core i5 3.1GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>1803.60</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company      Product   TypeName                         Cpu  Ram  \\\n",
       "0   Apple  MacBook Pro  Ultrabook        Intel Core i5 2.3GHz    8   \n",
       "1   Apple  Macbook Air  Ultrabook        Intel Core i5 1.8GHz    8   \n",
       "2      HP       250 G6   Notebook  Intel Core i5 7200U 2.5GHz    8   \n",
       "3   Apple  MacBook Pro  Ultrabook        Intel Core i7 2.7GHz   16   \n",
       "4   Apple  MacBook Pro  Ultrabook        Intel Core i5 3.1GHz    8   \n",
       "\n",
       "                Memory                           Gpu  OpSys  Weight  \\\n",
       "0            128GB SSD  Intel Iris Plus Graphics 640  macOS    1.37   \n",
       "1  128GB Flash Storage        Intel HD Graphics 6000  macOS    1.34   \n",
       "2            256GB SSD         Intel HD Graphics 620  No OS    1.86   \n",
       "3            512GB SSD            AMD Radeon Pro 455  macOS    1.83   \n",
       "4            256GB SSD  Intel Iris Plus Graphics 650  macOS    1.37   \n",
       "\n",
       "   Price_euros  TouchScreen  IpsPanel         PPI  \n",
       "0      1339.69            0         1  226.983005  \n",
       "1       898.94            0         0  127.677940  \n",
       "2       575.00            0         0  141.211998  \n",
       "3      2537.45            0         1  220.534624  \n",
       "4      1803.60            0         1  226.983005  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cpu\n",
       "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: count, Length: 118, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Cpu'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                         [Intel, Core, i5, 2.3GHz]\n",
       "1                         [Intel, Core, i5, 1.8GHz]\n",
       "2                  [Intel, Core, i5, 7200U, 2.5GHz]\n",
       "3                         [Intel, Core, i7, 2.7GHz]\n",
       "4                         [Intel, Core, i5, 3.1GHz]\n",
       "                           ...                     \n",
       "1298               [Intel, Core, i7, 6500U, 2.5GHz]\n",
       "1299               [Intel, Core, i7, 6500U, 2.5GHz]\n",
       "1300    [Intel, Celeron, Dual, Core, N3050, 1.6GHz]\n",
       "1301               [Intel, Core, i7, 6500U, 2.5GHz]\n",
       "1302    [Intel, Celeron, Dual, Core, N3050, 1.6GHz]\n",
       "Name: Cpu, Length: 1303, dtype: object"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Cpu'].apply(lambda x:x.split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Cpu_name']= df['Cpu'].apply(lambda x:\" \".join(x.split()[0:3]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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",
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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>Product</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Cpu</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price_euros</th>\n",
       "      <th>TouchScreen</th>\n",
       "      <th>IpsPanel</th>\n",
       "      <th>PPI</th>\n",
       "      <th>Cpu_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>MacBook Pro</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>Intel Core i5 2.3GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB SSD</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>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Macbook Air</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>Intel Core i5 1.8GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB Flash Storage</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>127.677940</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>250 G6</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>575.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>MacBook Pro</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>Intel Core i7 2.7GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>2537.45</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>220.534624</td>\n",
       "      <td>Intel Core i7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>MacBook Pro</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>Intel Core i5 3.1GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>1803.60</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company      Product   TypeName                         Cpu  Ram  \\\n",
       "0   Apple  MacBook Pro  Ultrabook        Intel Core i5 2.3GHz    8   \n",
       "1   Apple  Macbook Air  Ultrabook        Intel Core i5 1.8GHz    8   \n",
       "2      HP       250 G6   Notebook  Intel Core i5 7200U 2.5GHz    8   \n",
       "3   Apple  MacBook Pro  Ultrabook        Intel Core i7 2.7GHz   16   \n",
       "4   Apple  MacBook Pro  Ultrabook        Intel Core i5 3.1GHz    8   \n",
       "\n",
       "                Memory                           Gpu  OpSys  Weight  \\\n",
       "0            128GB SSD  Intel Iris Plus Graphics 640  macOS    1.37   \n",
       "1  128GB Flash Storage        Intel HD Graphics 6000  macOS    1.34   \n",
       "2            256GB SSD         Intel HD Graphics 620  No OS    1.86   \n",
       "3            512GB SSD            AMD Radeon Pro 455  macOS    1.83   \n",
       "4            256GB SSD  Intel Iris Plus Graphics 650  macOS    1.37   \n",
       "\n",
       "   Price_euros  TouchScreen  IpsPanel         PPI       Cpu_name  \n",
       "0      1339.69            0         1  226.983005  Intel Core i5  \n",
       "1       898.94            0         0  127.677940  Intel Core i5  \n",
       "2       575.00            0         0  141.211998  Intel Core i5  \n",
       "3      2537.45            0         1  220.534624  Intel Core i7  \n",
       "4      1803.60            0         1  226.983005  Intel Core i5  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fetch_processor(text):\n",
    "    if text == 'Intel Core i3' or text == 'Intel Core i5' or text == 'Intel Core i7':\n",
    "        return text\n",
    "    else:\n",
    "        if text.split()[0] == 'Intel':\n",
    "            return 'Other Intel Processor'\n",
    "        else:\n",
    "            return 'AMD Processor'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>Company</th>\n",
       "      <th>Product</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Cpu</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price_euros</th>\n",
       "      <th>TouchScreen</th>\n",
       "      <th>IpsPanel</th>\n",
       "      <th>PPI</th>\n",
       "      <th>Cpu_name</th>\n",
       "      <th>Cpu_brand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>MacBook Pro</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>Intel Core i5 2.3GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB SSD</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>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Macbook Air</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>Intel Core i5 1.8GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB Flash Storage</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>127.677940</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>250 G6</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>575.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>MacBook Pro</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>Intel Core i7 2.7GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>2537.45</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>220.534624</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>Intel Core i7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>MacBook Pro</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>Intel Core i5 3.1GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>1803.60</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company      Product   TypeName                         Cpu  Ram  \\\n",
       "0   Apple  MacBook Pro  Ultrabook        Intel Core i5 2.3GHz    8   \n",
       "1   Apple  Macbook Air  Ultrabook        Intel Core i5 1.8GHz    8   \n",
       "2      HP       250 G6   Notebook  Intel Core i5 7200U 2.5GHz    8   \n",
       "3   Apple  MacBook Pro  Ultrabook        Intel Core i7 2.7GHz   16   \n",
       "4   Apple  MacBook Pro  Ultrabook        Intel Core i5 3.1GHz    8   \n",
       "\n",
       "                Memory                           Gpu  OpSys  Weight  \\\n",
       "0            128GB SSD  Intel Iris Plus Graphics 640  macOS    1.37   \n",
       "1  128GB Flash Storage        Intel HD Graphics 6000  macOS    1.34   \n",
       "2            256GB SSD         Intel HD Graphics 620  No OS    1.86   \n",
       "3            512GB SSD            AMD Radeon Pro 455  macOS    1.83   \n",
       "4            256GB SSD  Intel Iris Plus Graphics 650  macOS    1.37   \n",
       "\n",
       "   Price_euros  TouchScreen  IpsPanel         PPI       Cpu_name  \\\n",
       "0      1339.69            0         1  226.983005  Intel Core i5   \n",
       "1       898.94            0         0  127.677940  Intel Core i5   \n",
       "2       575.00            0         0  141.211998  Intel Core i5   \n",
       "3      2537.45            0         1  220.534624  Intel Core i7   \n",
       "4      1803.60            0         1  226.983005  Intel Core i5   \n",
       "\n",
       "       Cpu_brand  \n",
       "0  Intel Core i5  \n",
       "1  Intel Core i5  \n",
       "2  Intel Core i5  \n",
       "3  Intel Core i7  \n",
       "4  Intel Core i5  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Cpu_brand'] =df['Cpu_name'].apply(fetch_processor)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cpu_brand\n",
       "Intel Core i7            527\n",
       "Intel Core i5            423\n",
       "Other Intel Processor    154\n",
       "Intel Core i3            136\n",
       "AMD Processor             63\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Cpu_brand'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Cpu_brand', ylabel='Price_euros'>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.barplot(x=df['Cpu_brand'],y=df['Price_euros'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['Cpu_name'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>Company</th>\n",
       "      <th>Product</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Cpu</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price_euros</th>\n",
       "      <th>TouchScreen</th>\n",
       "      <th>IpsPanel</th>\n",
       "      <th>PPI</th>\n",
       "      <th>Cpu_brand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>MacBook Pro</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>Intel Core i5 2.3GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB SSD</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>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Macbook Air</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>Intel Core i5 1.8GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB Flash Storage</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>127.677940</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>250 G6</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>575.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>MacBook Pro</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>Intel Core i7 2.7GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>2537.45</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>220.534624</td>\n",
       "      <td>Intel Core i7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>MacBook Pro</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>Intel Core i5 3.1GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>1803.60</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company      Product   TypeName                         Cpu  Ram  \\\n",
       "0   Apple  MacBook Pro  Ultrabook        Intel Core i5 2.3GHz    8   \n",
       "1   Apple  Macbook Air  Ultrabook        Intel Core i5 1.8GHz    8   \n",
       "2      HP       250 G6   Notebook  Intel Core i5 7200U 2.5GHz    8   \n",
       "3   Apple  MacBook Pro  Ultrabook        Intel Core i7 2.7GHz   16   \n",
       "4   Apple  MacBook Pro  Ultrabook        Intel Core i5 3.1GHz    8   \n",
       "\n",
       "                Memory                           Gpu  OpSys  Weight  \\\n",
       "0            128GB SSD  Intel Iris Plus Graphics 640  macOS    1.37   \n",
       "1  128GB Flash Storage        Intel HD Graphics 6000  macOS    1.34   \n",
       "2            256GB SSD         Intel HD Graphics 620  No OS    1.86   \n",
       "3            512GB SSD            AMD Radeon Pro 455  macOS    1.83   \n",
       "4            256GB SSD  Intel Iris Plus Graphics 650  macOS    1.37   \n",
       "\n",
       "   Price_euros  TouchScreen  IpsPanel         PPI      Cpu_brand  \n",
       "0      1339.69            0         1  226.983005  Intel Core i5  \n",
       "1       898.94            0         0  127.677940  Intel Core i5  \n",
       "2       575.00            0         0  141.211998  Intel Core i5  \n",
       "3      2537.45            0         1  220.534624  Intel Core i7  \n",
       "4      1803.60            0         1  226.983005  Intel Core i5  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['Cpu'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>Product</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
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       "      <th>Weight</th>\n",
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       "      <th>IpsPanel</th>\n",
       "      <th>PPI</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>Apple</td>\n",
       "      <td>MacBook Pro</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB SSD</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>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Macbook Air</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB Flash Storage</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>127.677940</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>250 G6</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>575.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>MacBook Pro</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>2537.45</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>220.534624</td>\n",
       "      <td>Intel Core i7</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>MacBook Pro</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>1803.60</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company      Product   TypeName  Ram               Memory  \\\n",
       "0   Apple  MacBook Pro  Ultrabook    8            128GB SSD   \n",
       "1   Apple  Macbook Air  Ultrabook    8  128GB Flash Storage   \n",
       "2      HP       250 G6   Notebook    8            256GB SSD   \n",
       "3   Apple  MacBook Pro  Ultrabook   16            512GB SSD   \n",
       "4   Apple  MacBook Pro  Ultrabook    8            256GB SSD   \n",
       "\n",
       "                            Gpu  OpSys  Weight  Price_euros  TouchScreen  \\\n",
       "0  Intel Iris Plus Graphics 640  macOS    1.37      1339.69            0   \n",
       "1        Intel HD Graphics 6000  macOS    1.34       898.94            0   \n",
       "2         Intel HD Graphics 620  No OS    1.86       575.00            0   \n",
       "3            AMD Radeon Pro 455  macOS    1.83      2537.45            0   \n",
       "4  Intel Iris Plus Graphics 650  macOS    1.37      1803.60            0   \n",
       "\n",
       "   IpsPanel         PPI      Cpu_brand  \n",
       "0         1  226.983005  Intel Core i5  \n",
       "1         0  127.677940  Intel Core i5  \n",
       "2         0  141.211998  Intel Core i5  \n",
       "3         1  220.534624  Intel Core i7  \n",
       "4         1  226.983005  Intel Core i5  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Memory\n",
       "256GB SSD                        412\n",
       "1TB HDD                          223\n",
       "500GB HDD                        132\n",
       "512GB SSD                        118\n",
       "128GB SSD +  1TB HDD              94\n",
       "128GB SSD                         76\n",
       "256GB SSD +  1TB HDD              73\n",
       "32GB Flash Storage                38\n",
       "2TB HDD                           16\n",
       "64GB Flash Storage                15\n",
       "512GB SSD +  1TB HDD              14\n",
       "1TB SSD                           14\n",
       "256GB SSD +  2TB HDD              10\n",
       "1.0TB Hybrid                       9\n",
       "256GB Flash Storage                8\n",
       "16GB Flash Storage                 7\n",
       "32GB SSD                           6\n",
       "180GB SSD                          5\n",
       "128GB Flash Storage                4\n",
       "512GB SSD +  2TB HDD               3\n",
       "16GB SSD                           3\n",
       "512GB Flash Storage                2\n",
       "1TB SSD +  1TB HDD                 2\n",
       "256GB SSD +  500GB HDD             2\n",
       "128GB SSD +  2TB HDD               2\n",
       "256GB SSD +  256GB SSD             2\n",
       "512GB SSD +  256GB SSD             1\n",
       "512GB SSD +  512GB SSD             1\n",
       "64GB Flash Storage +  1TB HDD      1\n",
       "1TB HDD +  1TB HDD                 1\n",
       "32GB HDD                           1\n",
       "64GB SSD                           1\n",
       "128GB HDD                          1\n",
       "240GB SSD                          1\n",
       "8GB SSD                            1\n",
       "508GB Hybrid                       1\n",
       "1.0TB HDD                          1\n",
       "512GB SSD +  1.0TB Hybrid          1\n",
       "256GB SSD +  1.0TB Hybrid          1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Memory'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<>:1: SyntaxWarning: invalid escape sequence '\\.'\n",
      "<>:1: SyntaxWarning: invalid escape sequence '\\.'\n",
      "C:\\Users\\admin\\AppData\\Local\\Temp\\ipykernel_1008\\1263156207.py:1: SyntaxWarning: invalid escape sequence '\\.'\n",
      "  df['Memory'] = df['Memory'].astype(str).replace('\\.0', '', regex=True)\n"
     ]
    }
   ],
   "source": [
    "df['Memory'] = df['Memory'].astype(str).replace('\\.0', '', regex=True)\n",
    "df['Memory'] = df['Memory'].str.replace('GB', '')\n",
    "df['Memory'] = df['Memory'].str.replace('TB', '000')\n",
    "new = df['Memory'].str.split(\"+\", n=1, expand=True)\n",
    "\n",
    "# Create 'first' and 'second' columns\n",
    "df['first'] = new[0].str.strip()\n",
    "df['second'] = new[1].fillna(\"0\")\n",
    "\n",
    "# Apply lambda functions to identify storage types\n",
    "df['Layer1HDD'] = df['first'].apply(lambda x: 1 if \"HDD\" in x else 0)\n",
    "df['Layer1SSD'] = df['first'].apply(lambda x: 1 if \"SSD\" in x else 0)\n",
    "df['Layer1Hybrid'] = df['first'].apply(lambda x: 1 if \"Hybrid\" in x else 0)\n",
    "df['Layer1Flash_Storage'] = df['first'].apply(lambda x: 1 if \"Flash Storage\" in x else 0)\n",
    "\n",
    "df['first'] = df['first'].str.replace(r'\\D', '', regex=True)\n",
    "\n",
    "df['Layer2HDD'] = df['second'].apply(lambda x: 1 if \"HDD\" in x else 0)\n",
    "df['Layer2SSD'] = df['second'].apply(lambda x: 1 if \"SSD\" in x else 0)\n",
    "df['Layer2Hybrid'] = df['second'].apply(lambda x: 1 if \"Hybrid\" in x else 0)\n",
    "df['Layer2Flash_Storage'] = df['second'].apply(lambda x: 1 if \"Flash Storage\" in x else 0)\n",
    "\n",
    "df['second'] = df['second'].str.replace(r'\\D', '', regex=True)\n",
    "\n",
    "# Handle empty strings that result from the replacement\n",
    "df['first'] = df['first'].replace('', '0').astype(int)\n",
    "df['second'] = df['second'].replace('', '0').astype(int)\n",
    "\n",
    "# Calculate the storage capacities\n",
    "df['HDD'] = (df['first'] * df['Layer1HDD'] + df['second'] * df['Layer2HDD'])\n",
    "df['SSD'] = (df['first'] * df['Layer1SSD'] + df['second'] * df['Layer2SSD'])\n",
    "df['Hybrid'] = (df['first'] * df['Layer1Hybrid'] + df['second'] * df['Layer2Hybrid'])\n",
    "df['Flash_Storage'] = (df['first'] * df['Layer1Flash_Storage'] + df['second'] * df['Layer2Flash_Storage'])\n",
    "\n",
    "# Drop unnecessary columns\n",
    "df.drop(columns=['first', 'second', 'Layer1HDD', 'Layer1SSD', 'Layer1Hybrid', \n",
    "                 'Layer1Flash_Storage', 'Layer2HDD', 'Layer2SSD', 'Layer2Hybrid', \n",
    "                 'Layer2Flash_Storage'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Macbook Air</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>128 Flash Storage</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>127.677940</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>250 G6</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>256 SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>575.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>MacBook Pro</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>16</td>\n",
       "      <td>512 SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>2537.45</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>220.534624</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>512</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>MacBook Pro</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>256 SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>1803.60</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company      Product   TypeName  Ram             Memory  \\\n",
       "0   Apple  MacBook Pro  Ultrabook    8            128 SSD   \n",
       "1   Apple  Macbook Air  Ultrabook    8  128 Flash Storage   \n",
       "2      HP       250 G6   Notebook    8            256 SSD   \n",
       "3   Apple  MacBook Pro  Ultrabook   16            512 SSD   \n",
       "4   Apple  MacBook Pro  Ultrabook    8            256 SSD   \n",
       "\n",
       "                            Gpu  OpSys  Weight  Price_euros  TouchScreen  \\\n",
       "0  Intel Iris Plus Graphics 640  macOS    1.37      1339.69            0   \n",
       "1        Intel HD Graphics 6000  macOS    1.34       898.94            0   \n",
       "2         Intel HD Graphics 620  No OS    1.86       575.00            0   \n",
       "3            AMD Radeon Pro 455  macOS    1.83      2537.45            0   \n",
       "4  Intel Iris Plus Graphics 650  macOS    1.37      1803.60            0   \n",
       "\n",
       "   IpsPanel         PPI      Cpu_brand  HDD  SSD  Hybrid  Flash_Storage  \n",
       "0         1  226.983005  Intel Core i5    0  128       0              0  \n",
       "1         0  127.677940  Intel Core i5    0    0       0            128  \n",
       "2         0  141.211998  Intel Core i5    0  256       0              0  \n",
       "3         1  220.534624  Intel Core i7    0  512       0              0  \n",
       "4         1  226.983005  Intel Core i5    0  256       0              0  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
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   "source": [
    "df.head()"
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  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['Memory'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
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       "  Company      Product   TypeName  Ram                           Gpu  OpSys  \\\n",
       "0   Apple  MacBook Pro  Ultrabook    8  Intel Iris Plus Graphics 640  macOS   \n",
       "1   Apple  Macbook Air  Ultrabook    8        Intel HD Graphics 6000  macOS   \n",
       "2      HP       250 G6   Notebook    8         Intel HD Graphics 620  No OS   \n",
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       "4   Apple  MacBook Pro  Ultrabook    8  Intel Iris Plus Graphics 650  macOS   \n",
       "\n",
       "   Weight  Price_euros  TouchScreen  IpsPanel         PPI      Cpu_brand  HDD  \\\n",
       "0    1.37      1339.69            0         1  226.983005  Intel Core i5    0   \n",
       "1    1.34       898.94            0         0  127.677940  Intel Core i5    0   \n",
       "2    1.86       575.00            0         0  141.211998  Intel Core i5    0   \n",
       "3    1.83      2537.45            0         1  220.534624  Intel Core i7    0   \n",
       "4    1.37      1803.60            0         1  226.983005  Intel Core i5    0   \n",
       "\n",
       "   SSD  Hybrid  Flash_Storage  \n",
       "0  128       0              0  \n",
       "1    0       0            128  \n",
       "2  256       0              0  \n",
       "3  512       0              0  \n",
       "4  256       0              0  "
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   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['Hybrid','Flash_Storage'],inplace=True)"
   ]
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  {
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   "execution_count": 68,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "  Company      Product   TypeName  Ram                           Gpu  OpSys  \\\n",
       "0   Apple  MacBook Pro  Ultrabook    8  Intel Iris Plus Graphics 640  macOS   \n",
       "1   Apple  Macbook Air  Ultrabook    8        Intel HD Graphics 6000  macOS   \n",
       "2      HP       250 G6   Notebook    8         Intel HD Graphics 620  No OS   \n",
       "3   Apple  MacBook Pro  Ultrabook   16            AMD Radeon Pro 455  macOS   \n",
       "4   Apple  MacBook Pro  Ultrabook    8  Intel Iris Plus Graphics 650  macOS   \n",
       "\n",
       "   Weight  Price_euros  TouchScreen  IpsPanel         PPI      Cpu_brand  HDD  \\\n",
       "0    1.37      1339.69            0         1  226.983005  Intel Core i5    0   \n",
       "1    1.34       898.94            0         0  127.677940  Intel Core i5    0   \n",
       "2    1.86       575.00            0         0  141.211998  Intel Core i5    0   \n",
       "3    1.83      2537.45            0         1  220.534624  Intel Core i7    0   \n",
       "4    1.37      1803.60            0         1  226.983005  Intel Core i5    0   \n",
       "\n",
       "   SSD  \n",
       "0  128  \n",
       "1    0  \n",
       "2  256  \n",
       "3  512  \n",
       "4  256  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gpu\n",
       "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: count, Length: 110, dtype: int64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
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   "source": [
    "df['Gpu'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       Intel\n",
       "1       Intel\n",
       "2       Intel\n",
       "3         AMD\n",
       "4       Intel\n",
       "        ...  \n",
       "1298    Intel\n",
       "1299    Intel\n",
       "1300    Intel\n",
       "1301      AMD\n",
       "1302    Intel\n",
       "Name: Gpu, Length: 1303, dtype: object"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df['Gpu'].apply(lambda x:x.split()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Gpu_brand']=df['Gpu'].apply(lambda x:x.split()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "  Company      Product   TypeName  Ram                           Gpu  OpSys  \\\n",
       "0   Apple  MacBook Pro  Ultrabook    8  Intel Iris Plus Graphics 640  macOS   \n",
       "1   Apple  Macbook Air  Ultrabook    8        Intel HD Graphics 6000  macOS   \n",
       "2      HP       250 G6   Notebook    8         Intel HD Graphics 620  No OS   \n",
       "3   Apple  MacBook Pro  Ultrabook   16            AMD Radeon Pro 455  macOS   \n",
       "4   Apple  MacBook Pro  Ultrabook    8  Intel Iris Plus Graphics 650  macOS   \n",
       "\n",
       "   Weight  Price_euros  TouchScreen  IpsPanel         PPI      Cpu_brand  HDD  \\\n",
       "0    1.37      1339.69            0         1  226.983005  Intel Core i5    0   \n",
       "1    1.34       898.94            0         0  127.677940  Intel Core i5    0   \n",
       "2    1.86       575.00            0         0  141.211998  Intel Core i5    0   \n",
       "3    1.83      2537.45            0         1  220.534624  Intel Core i7    0   \n",
       "4    1.37      1803.60            0         1  226.983005  Intel Core i5    0   \n",
       "\n",
       "   SSD Gpu_brand  \n",
       "0  128     Intel  \n",
       "1    0     Intel  \n",
       "2  256     Intel  \n",
       "3  512       AMD  \n",
       "4  256     Intel  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gpu_brand\n",
       "Intel     722\n",
       "Nvidia    400\n",
       "AMD       180\n",
       "ARM         1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gpu_brand'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "df =df[df['Gpu_brand'] !='ARM']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gpu_brand\n",
       "Intel     722\n",
       "Nvidia    400\n",
       "AMD       180\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gpu_brand'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Gpu_brand', ylabel='Price_euros'>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.barplot(x=df['Gpu_brand'],y=df['Price_euros'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['Gpu'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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>Company</th>\n",
       "      <th>Product</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>IpsPanel</th>\n",
       "      <th>PPI</th>\n",
       "      <th>Cpu_brand</th>\n",
       "      <th>HDD</th>\n",
       "      <th>SSD</th>\n",
       "      <th>Gpu_brand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>MacBook Pro</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</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>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Macbook Air</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</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>127.677940</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>250 G6</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>575.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>MacBook Pro</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>16</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>2537.45</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>220.534624</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>512</td>\n",
       "      <td>AMD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>MacBook Pro</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>1803.60</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company      Product   TypeName  Ram  OpSys  Weight  Price_euros  \\\n",
       "0   Apple  MacBook Pro  Ultrabook    8  macOS    1.37      1339.69   \n",
       "1   Apple  Macbook Air  Ultrabook    8  macOS    1.34       898.94   \n",
       "2      HP       250 G6   Notebook    8  No OS    1.86       575.00   \n",
       "3   Apple  MacBook Pro  Ultrabook   16  macOS    1.83      2537.45   \n",
       "4   Apple  MacBook Pro  Ultrabook    8  macOS    1.37      1803.60   \n",
       "\n",
       "   TouchScreen  IpsPanel         PPI      Cpu_brand  HDD  SSD Gpu_brand  \n",
       "0            0         1  226.983005  Intel Core i5    0  128     Intel  \n",
       "1            0         0  127.677940  Intel Core i5    0    0     Intel  \n",
       "2            0         0  141.211998  Intel Core i5    0  256     Intel  \n",
       "3            0         1  220.534624  Intel Core i7    0  512       AMD  \n",
       "4            0         1  226.983005  Intel Core i5    0  256     Intel  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OpSys\n",
       "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: count, dtype: int64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['OpSys'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cat_os(text):\n",
    "    if text == 'Windows 10' or text == 'Windows 7' or text == 'Windows 10 S':\n",
    "        return 'Windows'\n",
    "    if text == 'Mac OS X' or text == 'macOS':\n",
    "        return 'Mac'\n",
    "    else:\n",
    "        return 'Others'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['OpSys'] = df['OpSys'].apply(cat_os)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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>Company</th>\n",
       "      <th>Product</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>IpsPanel</th>\n",
       "      <th>PPI</th>\n",
       "      <th>Cpu_brand</th>\n",
       "      <th>HDD</th>\n",
       "      <th>SSD</th>\n",
       "      <th>Gpu_brand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>MacBook Pro</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>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Macbook Air</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>127.677940</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>250 G6</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>Others</td>\n",
       "      <td>1.86</td>\n",
       "      <td>575.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>MacBook Pro</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>220.534624</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>512</td>\n",
       "      <td>AMD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>MacBook Pro</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>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company      Product   TypeName  Ram   OpSys  Weight  Price_euros  \\\n",
       "0   Apple  MacBook Pro  Ultrabook    8     Mac    1.37      1339.69   \n",
       "1   Apple  Macbook Air  Ultrabook    8     Mac    1.34       898.94   \n",
       "2      HP       250 G6   Notebook    8  Others    1.86       575.00   \n",
       "3   Apple  MacBook Pro  Ultrabook   16     Mac    1.83      2537.45   \n",
       "4   Apple  MacBook Pro  Ultrabook    8     Mac    1.37      1803.60   \n",
       "\n",
       "   TouchScreen  IpsPanel         PPI      Cpu_brand  HDD  SSD Gpu_brand  \n",
       "0            0         1  226.983005  Intel Core i5    0  128     Intel  \n",
       "1            0         0  127.677940  Intel Core i5    0    0     Intel  \n",
       "2            0         0  141.211998  Intel Core i5    0  256     Intel  \n",
       "3            0         1  220.534624  Intel Core i7    0  512       AMD  \n",
       "4            0         1  226.983005  Intel Core i5    0  256     Intel  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Product\n",
       "XPS 13                                  30\n",
       "Inspiron 3567                           29\n",
       "250 G6                                  21\n",
       "Legion Y520-15IKBN                      19\n",
       "Vostro 3568                             19\n",
       "                                        ..\n",
       "SmartBook 130                            1\n",
       "A541NA-GO342 (N3350/4GB/500GB/Linux)     1\n",
       "17-X047na (i3-6006U/8GB/1TB/W10)         1\n",
       "V310-15ISK (i5-6200U/4GB/1TB/FHD/No      1\n",
       "15-cd005nv (A9-9420/6GB/256GB/Radeon     1\n",
       "Name: count, Length: 617, dtype: int64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Product'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Company', 'Product', 'TypeName', 'Ram', 'OpSys', 'Weight',\n",
       "       'Price_euros', 'TouchScreen', 'IpsPanel', 'PPI', 'Cpu_brand', 'HDD',\n",
       "       'SSD', 'Gpu_brand'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sb.barplot(x=df['Product'],y=df['Price_euros'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['Product'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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>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>IpsPanel</th>\n",
       "      <th>PPI</th>\n",
       "      <th>Cpu_brand</th>\n",
       "      <th>HDD</th>\n",
       "      <th>SSD</th>\n",
       "      <th>Gpu_brand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>816</th>\n",
       "      <td>Razer</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>16</td>\n",
       "      <td>Windows</td>\n",
       "      <td>1.29</td>\n",
       "      <td>1799.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>352.465147</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>512</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Company   TypeName  Ram    OpSys  Weight  Price_euros  TouchScreen  \\\n",
       "816   Razer  Ultrabook   16  Windows    1.29       1799.0            1   \n",
       "\n",
       "     IpsPanel         PPI      Cpu_brand  HDD  SSD Gpu_brand  \n",
       "816         1  352.465147  Intel Core i7    0  512     Intel  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(columns=['Price_euros'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['Price_euros']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>TouchScreen</th>\n",
       "      <th>IpsPanel</th>\n",
       "      <th>PPI</th>\n",
       "      <th>Cpu_brand</th>\n",
       "      <th>HDD</th>\n",
       "      <th>SSD</th>\n",
       "      <th>Gpu_brand</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>128</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>127.677940</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>Others</td>\n",
       "      <td>1.86</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>220.534624</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>512</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1298</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>2 in 1 Convertible</td>\n",
       "      <td>4</td>\n",
       "      <td>Windows</td>\n",
       "      <td>1.80</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>157.350512</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1299</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>2 in 1 Convertible</td>\n",
       "      <td>16</td>\n",
       "      <td>Windows</td>\n",
       "      <td>1.30</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>276.053530</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>512</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1300</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>2</td>\n",
       "      <td>Windows</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111.935204</td>\n",
       "      <td>Other Intel Processor</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1301</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>6</td>\n",
       "      <td>Windows</td>\n",
       "      <td>2.19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.454670</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>AMD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1302</th>\n",
       "      <td>Asus</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>4</td>\n",
       "      <td>Windows</td>\n",
       "      <td>2.20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.454670</td>\n",
       "      <td>Other Intel Processor</td>\n",
       "      <td>500</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1302 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company            TypeName  Ram    OpSys  Weight  TouchScreen  IpsPanel  \\\n",
       "0      Apple           Ultrabook    8      Mac    1.37            0         1   \n",
       "1      Apple           Ultrabook    8      Mac    1.34            0         0   \n",
       "2         HP            Notebook    8   Others    1.86            0         0   \n",
       "3      Apple           Ultrabook   16      Mac    1.83            0         1   \n",
       "4      Apple           Ultrabook    8      Mac    1.37            0         1   \n",
       "...      ...                 ...  ...      ...     ...          ...       ...   \n",
       "1298  Lenovo  2 in 1 Convertible    4  Windows    1.80            1         1   \n",
       "1299  Lenovo  2 in 1 Convertible   16  Windows    1.30            1         1   \n",
       "1300  Lenovo            Notebook    2  Windows    1.50            0         0   \n",
       "1301      HP            Notebook    6  Windows    2.19            0         0   \n",
       "1302    Asus            Notebook    4  Windows    2.20            0         0   \n",
       "\n",
       "             PPI              Cpu_brand   HDD  SSD Gpu_brand  \n",
       "0     226.983005          Intel Core i5     0  128     Intel  \n",
       "1     127.677940          Intel Core i5     0    0     Intel  \n",
       "2     141.211998          Intel Core i5     0  256     Intel  \n",
       "3     220.534624          Intel Core i7     0  512       AMD  \n",
       "4     226.983005          Intel Core i5     0  256     Intel  \n",
       "...          ...                    ...   ...  ...       ...  \n",
       "1298  157.350512          Intel Core i7     0  128     Intel  \n",
       "1299  276.053530          Intel Core i7     0  512     Intel  \n",
       "1300  111.935204  Other Intel Processor     0    0     Intel  \n",
       "1301  100.454670          Intel Core i7  1000    0       AMD  \n",
       "1302  100.454670  Other Intel Processor   500    0     Intel  \n",
       "\n",
       "[1302 rows x 12 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = np.log(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       7.200194\n",
       "1       6.801216\n",
       "2       6.354370\n",
       "3       7.838915\n",
       "4       7.497540\n",
       "          ...   \n",
       "1298    6.458338\n",
       "1299    7.312553\n",
       "1300    5.433722\n",
       "1301    6.638568\n",
       "1302    5.910797\n",
       "Name: Price_euros, Length: 1302, dtype: float64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train , X_test , y_train , y_test = train_test_split(X,y,test_size=0.2, random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "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>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Ram</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>TouchScreen</th>\n",
       "      <th>IpsPanel</th>\n",
       "      <th>PPI</th>\n",
       "      <th>Cpu_brand</th>\n",
       "      <th>HDD</th>\n",
       "      <th>SSD</th>\n",
       "      <th>Gpu_brand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Acer</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>Windows</td>\n",
       "      <td>2.20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>Nvidia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>863</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Gaming</td>\n",
       "      <td>8</td>\n",
       "      <td>Others</td>\n",
       "      <td>2.50</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>Nvidia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552</th>\n",
       "      <td>Dell</td>\n",
       "      <td>Gaming</td>\n",
       "      <td>16</td>\n",
       "      <td>Windows</td>\n",
       "      <td>4.42</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>127.335675</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>1000</td>\n",
       "      <td>256</td>\n",
       "      <td>Nvidia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>967</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>4</td>\n",
       "      <td>Windows</td>\n",
       "      <td>2.04</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i3</td>\n",
       "      <td>500</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>Windows</td>\n",
       "      <td>2.04</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.454670</td>\n",
       "      <td>Other Intel Processor</td>\n",
       "      <td>2000</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466</th>\n",
       "      <td>Acer</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>4</td>\n",
       "      <td>Windows</td>\n",
       "      <td>2.20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.454670</td>\n",
       "      <td>Intel Core i3</td>\n",
       "      <td>500</td>\n",
       "      <td>0</td>\n",
       "      <td>Nvidia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>Asus</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>16</td>\n",
       "      <td>Windows</td>\n",
       "      <td>1.63</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>512</td>\n",
       "      <td>Nvidia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>Acer</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>Windows</td>\n",
       "      <td>2.20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.454670</td>\n",
       "      <td>AMD Processor</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>AMD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>527</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>Others</td>\n",
       "      <td>2.20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.454670</td>\n",
       "      <td>Intel Core i3</td>\n",
       "      <td>2000</td>\n",
       "      <td>0</td>\n",
       "      <td>Nvidia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1193</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>Mac</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.415547</td>\n",
       "      <td>Other Intel Processor</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1041 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company   TypeName  Ram    OpSys  Weight  TouchScreen  IpsPanel  \\\n",
       "91      Acer   Notebook    8  Windows    2.20            0         0   \n",
       "863   Lenovo     Gaming    8   Others    2.50            0         1   \n",
       "552     Dell     Gaming   16  Windows    4.42            0         1   \n",
       "967       HP   Notebook    4  Windows    2.04            0         0   \n",
       "365       HP   Notebook    8  Windows    2.04            0         0   \n",
       "...      ...        ...  ...      ...     ...          ...       ...   \n",
       "466     Acer   Notebook    4  Windows    2.20            0         0   \n",
       "299     Asus  Ultrabook   16  Windows    1.63            0         0   \n",
       "493     Acer   Notebook    8  Windows    2.20            0         0   \n",
       "527   Lenovo   Notebook    8   Others    2.20            0         0   \n",
       "1193   Apple  Ultrabook    8      Mac    0.92            0         1   \n",
       "\n",
       "             PPI              Cpu_brand   HDD  SSD Gpu_brand  \n",
       "91    141.211998          Intel Core i7  1000    0    Nvidia  \n",
       "863   141.211998          Intel Core i5  1000    0    Nvidia  \n",
       "552   127.335675          Intel Core i7  1000  256    Nvidia  \n",
       "967   141.211998          Intel Core i3   500    0     Intel  \n",
       "365   100.454670  Other Intel Processor  2000    0     Intel  \n",
       "...          ...                    ...   ...  ...       ...  \n",
       "466   100.454670          Intel Core i3   500    0    Nvidia  \n",
       "299   141.211998          Intel Core i7     0  512    Nvidia  \n",
       "493   100.454670          AMD Processor  1000    0       AMD  \n",
       "527   100.454670          Intel Core i3  2000    0    Nvidia  \n",
       "1193  226.415547  Other Intel Processor     0    0     Intel  \n",
       "\n",
       "[1041 rows x 12 columns]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.metrics import r2_score,mean_absolute_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression, Ridge, Lasso\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor,GradientBoostingRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2_score 0.8002405966122771\n",
      "MAE 0.2045399581562227\n"
     ]
    }
   ],
   "source": [
    "step1 = ColumnTransformer(transformers=[\n",
    "    ('col_tnf',OneHotEncoder(sparse_output=False,drop='first'),[0,1,3,8,11])\n",
    "],remainder='passthrough')\n",
    "\n",
    "step2 = LinearRegression()\n",
    "\n",
    "pipe = Pipeline([\n",
    "    ('step1',step1),\n",
    "    ('step2',step2)\n",
    "])\n",
    "\n",
    "pipe.fit(X_train,y_train)\n",
    "\n",
    "y_pred = pipe.predict(X_test)\n",
    "\n",
    "print('R2_score',r2_score(y_test,y_pred))\n",
    "print(\"MAE\",mean_absolute_error(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "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>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>IpsPanel</th>\n",
       "      <th>PPI</th>\n",
       "      <th>Cpu_brand</th>\n",
       "      <th>HDD</th>\n",
       "      <th>SSD</th>\n",
       "      <th>Gpu_brand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>539</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>Windows</td>\n",
       "      <td>1.87</td>\n",
       "      <td>859.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>157.350512</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>Nvidia</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Company  TypeName  Ram    OpSys  Weight  Price_euros  TouchScreen  \\\n",
       "539  Lenovo  Notebook    8  Windows    1.87        859.0            0   \n",
       "\n",
       "     IpsPanel         PPI      Cpu_brand  HDD  SSD Gpu_brand  \n",
       "539         1  157.350512  Intel Core i7    0  256    Nvidia  "
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Ram</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>TouchScreen</th>\n",
       "      <th>IpsPanel</th>\n",
       "      <th>PPI</th>\n",
       "      <th>Cpu_brand</th>\n",
       "      <th>HDD</th>\n",
       "      <th>SSD</th>\n",
       "      <th>Gpu_brand</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>128</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>127.677940</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>Others</td>\n",
       "      <td>1.86</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>220.534624</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>512</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Ram   OpSys  Weight  TouchScreen  IpsPanel         PPI  \\\n",
       "0   Apple  Ultrabook    8     Mac    1.37            0         1  226.983005   \n",
       "1   Apple  Ultrabook    8     Mac    1.34            0         0  127.677940   \n",
       "2      HP   Notebook    8  Others    1.86            0         0  141.211998   \n",
       "3   Apple  Ultrabook   16     Mac    1.83            0         1  220.534624   \n",
       "4   Apple  Ultrabook    8     Mac    1.37            0         1  226.983005   \n",
       "\n",
       "       Cpu_brand  HDD  SSD Gpu_brand  \n",
       "0  Intel Core i5    0  128     Intel  \n",
       "1  Intel Core i5    0    0     Intel  \n",
       "2  Intel Core i5    0  256     Intel  \n",
       "3  Intel Core i7    0  512       AMD  \n",
       "4  Intel Core i5    0  256     Intel  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2_score 0.8025355126649508\n",
      "mae 0.20379724333815175\n"
     ]
    }
   ],
   "source": [
    "step1 = ColumnTransformer(transformers=[\n",
    "    (\"col_tnf\",OneHotEncoder(sparse_output=False,drop='first'),[0,1,3,8,11])\n",
    "],remainder='passthrough')\n",
    "\n",
    "step2 = Ridge(alpha=10)\n",
    "\n",
    "pipe = Pipeline(\n",
    "    [\n",
    "        ('step1',step1),\n",
    "        ('step2',step2)\n",
    "    ]\n",
    ")\n",
    "pipe.fit(X_train,y_train)\n",
    "y_pred = pipe.predict(X_test)\n",
    "\n",
    "print(\"r2_score\",r2_score(y_test,y_pred))\n",
    "print(\"mae\",mean_absolute_error(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7903461515013456\n"
     ]
    }
   ],
   "source": [
    "step1 = ColumnTransformer(transformers=[\n",
    "    ('col_tnf',OneHotEncoder(sparse_output=False,drop='first'),[0,1,3,8,11])\n",
    "],remainder='passthrough')\n",
    "\n",
    "step2 = KNeighborsRegressor()\n",
    "\n",
    "pipe = Pipeline(\n",
    "    [\n",
    "        ('step1',step1),\n",
    "        ('step2',step2)\n",
    "    ]\n",
    ")\n",
    "\n",
    "pipe.fit(X_train,y_train)\n",
    "y_pred=pipe.predict(X_test)\n",
    "\n",
    "print(r2_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7740417269627947\n"
     ]
    }
   ],
   "source": [
    "step1 = ColumnTransformer(transformers=[\n",
    "    ('col_tnf',OneHotEncoder(sparse_output=False,drop='first'),[0,1,3,8,11])\n",
    "],remainder='passthrough')\n",
    "\n",
    "step2 = DecisionTreeRegressor(max_depth=10)\n",
    "\n",
    "pipe = Pipeline(\n",
    "    [\n",
    "        ('step1',step1),\n",
    "        ('step2',step2)\n",
    "    ]\n",
    ")\n",
    "pipe.fit(X_train,y_train)\n",
    "y_pred = pipe.predict(X_test)\n",
    "print(r2_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8570628355712434\n"
     ]
    }
   ],
   "source": [
    "step1 = ColumnTransformer(transformers=[\n",
    "    ('col_tnf',OneHotEncoder(sparse_output=False,drop='first'),[0,1,3,8,11])\n",
    "],remainder='passthrough')\n",
    "\n",
    "step2 = RandomForestRegressor()\n",
    "\n",
    "pipe = Pipeline(\n",
    "    [\n",
    "        ('step1',step1),\n",
    "        ('step2',step2)\n",
    "    ]\n",
    ")\n",
    "pipe.fit(X_train,y_train)\n",
    "y_pred = pipe.predict(X_test)\n",
    "print(r2_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8634482676073856\n"
     ]
    }
   ],
   "source": [
    "step1 = ColumnTransformer(transformers=[\n",
    "    ('col_tnf',OneHotEncoder(sparse_output=False,drop='first'),[0,1,3,8,11])\n",
    "],remainder='passthrough')\n",
    "\n",
    "step2 = GradientBoostingRegressor()\n",
    "\n",
    "pipe = Pipeline(\n",
    "    [\n",
    "        ('step1',step1),\n",
    "        ('step2',step2)\n",
    "    ]\n",
    ")\n",
    "pipe.fit(X_train,y_train)\n",
    "y_pred = pipe.predict(X_test)\n",
    "print(r2_score(y_test,y_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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