laptop-price-prediction-ml-model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "034f89d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8b2421c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('laptop_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ded624a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Company</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</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Apple</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>71378.6832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Apple</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>47895.5232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>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>30636.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Apple</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>135195.3360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Apple</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>96095.8080</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 Company   TypeName  Inches                    ScreenResolution  \\\n",
       "0           0   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "1           1   Apple  Ultrabook    13.3                            1440x900   \n",
       "2           2      HP   Notebook    15.6                   Full HD 1920x1080   \n",
       "3           3   Apple  Ultrabook    15.4  IPS Panel Retina Display 2880x1800   \n",
       "4           4   Apple  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  \n",
       "0  Intel Iris Plus Graphics 640  macOS  1.37kg   71378.6832  \n",
       "1        Intel HD Graphics 6000  macOS  1.34kg   47895.5232  \n",
       "2         Intel HD Graphics 620  No OS  1.86kg   30636.0000  \n",
       "3            AMD Radeon Pro 455  macOS  1.83kg  135195.3360  \n",
       "4  Intel Iris Plus Graphics 650  macOS  1.37kg   96095.8080  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9ca9d6ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1303, 12)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6e2c05c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.info of       Unnamed: 0 Company            TypeName  Inches  \\\n",
       "0              0   Apple           Ultrabook    13.3   \n",
       "1              1   Apple           Ultrabook    13.3   \n",
       "2              2      HP            Notebook    15.6   \n",
       "3              3   Apple           Ultrabook    15.4   \n",
       "4              4   Apple           Ultrabook    13.3   \n",
       "...          ...     ...                 ...     ...   \n",
       "1298        1298  Lenovo  2 in 1 Convertible    14.0   \n",
       "1299        1299  Lenovo  2 in 1 Convertible    13.3   \n",
       "1300        1300  Lenovo            Notebook    14.0   \n",
       "1301        1301      HP            Notebook    15.6   \n",
       "1302        1302    Asus            Notebook    15.6   \n",
       "\n",
       "                                ScreenResolution  \\\n",
       "0             IPS Panel Retina Display 2560x1600   \n",
       "1                                       1440x900   \n",
       "2                              Full HD 1920x1080   \n",
       "3             IPS Panel Retina Display 2880x1800   \n",
       "4             IPS Panel Retina Display 2560x1600   \n",
       "...                                          ...   \n",
       "1298   IPS Panel Full HD / Touchscreen 1920x1080   \n",
       "1299  IPS Panel Quad HD+ / Touchscreen 3200x1800   \n",
       "1300                                    1366x768   \n",
       "1301                                    1366x768   \n",
       "1302                                    1366x768   \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",
       "1298            Intel Core i7 6500U 2.5GHz   4GB            128GB SSD   \n",
       "1299            Intel Core i7 6500U 2.5GHz  16GB            512GB SSD   \n",
       "1300  Intel Celeron Dual Core N3050 1.6GHz   2GB   64GB Flash Storage   \n",
       "1301            Intel Core i7 6500U 2.5GHz   6GB              1TB HDD   \n",
       "1302  Intel Celeron Dual Core N3050 1.6GHz   4GB            500GB HDD   \n",
       "\n",
       "                               Gpu       OpSys  Weight        Price  \n",
       "0     Intel Iris Plus Graphics 640       macOS  1.37kg   71378.6832  \n",
       "1           Intel HD Graphics 6000       macOS  1.34kg   47895.5232  \n",
       "2            Intel HD Graphics 620       No OS  1.86kg   30636.0000  \n",
       "3               AMD Radeon Pro 455       macOS  1.83kg  135195.3360  \n",
       "4     Intel Iris Plus Graphics 650       macOS  1.37kg   96095.8080  \n",
       "...                            ...         ...     ...          ...  \n",
       "1298         Intel HD Graphics 520  Windows 10   1.8kg   33992.6400  \n",
       "1299         Intel HD Graphics 520  Windows 10   1.3kg   79866.7200  \n",
       "1300             Intel HD Graphics  Windows 10   1.5kg   12201.1200  \n",
       "1301            AMD Radeon R5 M330  Windows 10  2.19kg   40705.9200  \n",
       "1302             Intel HD Graphics  Windows 10   2.2kg   19660.3200  \n",
       "\n",
       "[1303 rows x 12 columns]>"
      ]
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     "execution_count": 5,
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   ],
   "source": [
    "df.info"
   ]
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  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0c0641f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a53795ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0          0\n",
       "Company             0\n",
       "TypeName            0\n",
       "Inches              0\n",
       "ScreenResolution    0\n",
       "Cpu                 0\n",
       "Ram                 0\n",
       "Memory              0\n",
       "Gpu                 0\n",
       "OpSys               0\n",
       "Weight              0\n",
       "Price               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
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   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4e3d1c72",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['Unnamed: 0'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5279431b",
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>Apple</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>71378.6832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</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>47895.5232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</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>30636.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</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>135195.3360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</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>96095.8080</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Inches                    ScreenResolution  \\\n",
       "0   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "1   Apple  Ultrabook    13.3                            1440x900   \n",
       "2      HP   Notebook    15.6                   Full HD 1920x1080   \n",
       "3   Apple  Ultrabook    15.4  IPS Panel Retina Display 2880x1800   \n",
       "4   Apple  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  \n",
       "0  Intel Iris Plus Graphics 640  macOS  1.37kg   71378.6832  \n",
       "1        Intel HD Graphics 6000  macOS  1.34kg   47895.5232  \n",
       "2         Intel HD Graphics 620  No OS  1.86kg   30636.0000  \n",
       "3            AMD Radeon Pro 455  macOS  1.83kg  135195.3360  \n",
       "4  Intel Iris Plus Graphics 650  macOS  1.37kg   96095.8080  "
      ]
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     "metadata": {},
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   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f2d540a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Ram'] = df['Ram'].str.replace('GB','')\n",
    "df['Weight'] = df['Weight'].str.replace('kg','')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5e4b48eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Company</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",
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       "      <th>Price</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</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>71378.6832</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</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>47895.5232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</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>30636.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</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>135195.3360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</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>96095.8080</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Inches                    ScreenResolution  \\\n",
       "0   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "1   Apple  Ultrabook    13.3                            1440x900   \n",
       "2      HP   Notebook    15.6                   Full HD 1920x1080   \n",
       "3   Apple  Ultrabook    15.4  IPS Panel Retina Display 2880x1800   \n",
       "4   Apple  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  \n",
       "0  Intel Iris Plus Graphics 640  macOS   1.37   71378.6832  \n",
       "1        Intel HD Graphics 6000  macOS   1.34   47895.5232  \n",
       "2         Intel HD Graphics 620  No OS   1.86   30636.0000  \n",
       "3            AMD Radeon Pro 455  macOS   1.83  135195.3360  \n",
       "4  Intel Iris Plus Graphics 650  macOS   1.37   96095.8080  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "99036b2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Ram'] = df['Ram'].astype('int32')\n",
    "df['Weight'] = df['Weight'].astype('float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "18c40d34",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1303 entries, 0 to 1302\n",
      "Data columns (total 11 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Company           1303 non-null   object \n",
      " 1   TypeName          1303 non-null   object \n",
      " 2   Inches            1303 non-null   float64\n",
      " 3   ScreenResolution  1303 non-null   object \n",
      " 4   Cpu               1303 non-null   object \n",
      " 5   Ram               1303 non-null   int32  \n",
      " 6   Memory            1303 non-null   object \n",
      " 7   Gpu               1303 non-null   object \n",
      " 8   OpSys             1303 non-null   object \n",
      " 9   Weight            1303 non-null   float32\n",
      " 10  Price             1303 non-null   float64\n",
      "dtypes: float32(1), float64(2), int32(1), object(7)\n",
      "memory usage: 101.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9fba1121",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6c29a054",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hp\\AppData\\Local\\Temp\\ipykernel_5656\\834922981.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",
      "  sns.distplot(df['Price'])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Price', ylabel='Density'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "dc63df61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Company'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f3c86831",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BkyoiIiIiCZhUEREREUnApIqIiIhIAiZVRERERBIwqSIiIiKSgEkVERERkQRMqoiIiIgkYFJFREREJAGTKiIiIiIJmFQRERERScCkioiIiEgCJlVEREREEjCpIiIiIpKASRURERGRBEyqiIiIiCRgUkVEREQkAZMqIiIiIgmYVBERERFJwKSKiIiISAImVUREREQSMKkiIiIikiBfk6oTJ06gU6dOsLGxgUqlQkBAgNr9wYMHQ6VSqT3atm2rVubx48fo378/zMzMYGFhAU9PTyQmJqqVuXz5Mj777DMYGBjA1tYWCxcuzNKWHTt2oEaNGjAwMICjoyMOHjyodl8IgenTp6Ns2bIwNDSEq6srbt26JecbQURERIVeviZVSUlJqFu3LlasWPHWMm3btsXDhw+Vx9atW9Xu9+/fHxEREQgMDMT+/ftx4sQJjBgxQrmfkJAANzc32NnZISwsDN9//z1mzpyJ1atXK2VCQkLQt29feHp64uLFi+jatSu6du2Kq1evKmUWLlyIpUuXYtWqVThz5gyMjY3h7u6OFy9eSPyOEBERUWFVLD+Dt2vXDu3atXtnGX19fVhbW2d77/r16zh06BDOnTuHRo0aAQCWLVuG9u3b44cffoCNjQ02b96MlJQUrFu3Dnp6eqhVqxbCw8OxaNEiJflasmQJ2rZtC19fXwDAnDlzEBgYiOXLl2PVqlUQQmDx4sWYNm0aunTpAgDYuHEjrKysEBAQgD59+sj6lhAREVEhVeDnVB0/fhxlypRB9erVMWrUKPz333/KvdDQUFhYWCgJFQC4urpCR0cHZ86cUco0a9YMenp6Shl3d3fcuHEDT548Ucq4urqqxXV3d0doaCgAICoqCjExMWplzM3N4ezsrJTJzsuXL5GQkKD2ICIioqKpQCdVbdu2xcaNGxEUFIQFCxYgODgY7dq1Q1paGgAgJiYGZcqUUXtPsWLFYGlpiZiYGKWMlZWVWpmM1zmVyXw/8/uyK5OdefPmwdzcXHnY2tp+0NdPREREhUe+Dv/lJPOwmqOjI+rUqYPKlSvj+PHjaN26dT627P1MnToVPj4+yuuEhAQmVkREREVUge6pepO9vT1KlSqF27dvAwCsra3x6NEjtTKpqal4/PixMg/L2toasbGxamUyXudUJvP9zO/Lrkx29PX1YWZmpvYgIiKioqlQJVUPHjzAf//9h7JlywIAXFxcEB8fj7CwMKXM0aNHkZ6eDmdnZ6XMiRMn8OrVK6VMYGAgqlevjhIlSihlgoKC1GIFBgbCxcUFAFCpUiVYW1urlUlISMCZM2eUMkRERPRxy9ekKjExEeHh4QgPDwfwekJ4eHg4oqOjkZiYCF9fX5w+fRr37t1DUFAQunTpgipVqsDd3R0A4ODggLZt22L48OE4e/YsTp06BW9vb/Tp0wc2NjYAgH79+kFPTw+enp6IiIiAv78/lixZojYsN27cOBw6dAg//vgjIiMjMXPmTJw/fx7e3t4AAJVKhfHjx+Pbb7/F3r17ceXKFQwcOBA2Njbo2rVrnn7PiIiIqGDK1zlV58+fR8uWLZXXGYnOoEGDsHLlSly+fBkbNmxAfHw8bGxs4Obmhjlz5kBfX195z+bNm+Ht7Y3WrVtDR0cHHh4eWLp0qXLf3NwcR44cgZeXFxo2bIhSpUph+vTpantZffLJJ9iyZQumTZuGr776ClWrVkVAQABq166tlJk0aRKSkpIwYsQIxMfH49NPP8WhQ4dgYGCgzW8RERERFRIqIYTI70Z8LBISEmBubo6nT59yfhURkSTf7Xn4zvshG8bjxbN/AQAGpqXwyaDFOdb5dbeyMppGRcT7fn4XqjlVRERERAUVkyoiIiIiCZhUEREREUnApIqIiIhIAiZVRERERBIwqSIiIiKSQKOkKiUlBTdu3EBqaqqs9hAREREVSrlKqpKTk+Hp6QkjIyPUqlUL0dHRAIAxY8Zg/vz5UhtIREREVBjkKqmaOnUqLl26hOPHj6vtKO7q6gp/f39pjSMiIiIqLHJ1TE1AQAD8/f3RpEkTqFQq5XqtWrVw584daY0jIiIiKixy1VMVFxeHMmXKZLmelJSklmQRERERfSxylVQ1atQIBw4cUF5nJFJr1qyBi4uLnJYRERERFSK5Gv6bO3cu2rVrh2vXriE1NRVLlizBtWvXEBISguDgYNltJCIiIirwctVT9emnnyI8PBypqalwdHTEkSNHUKZMGYSGhqJhw4ay20hERERU4OWqpwoAKleujF9++UVmW4iIiIgKrVz1VB08eBCHDx/Ocv3w4cP4/fffNW4UERERUWGTq6RqypQpSEtLy3JdCIEpU6Zo3CgiIiKiwiZXSdWtW7dQs2bNLNdr1KiB27dva9woIiIiosImV0mVubk57t69m+X67du3YWxsrHGjiIiIiAqbXCVVXbp0wfjx49V2T799+zYmTpyIzp07S2scERERUWGRq6Rq4cKFMDY2Ro0aNVCpUiVUqlQJDg4OKFmyJH744QfZbSQiIiIq8HK1pYK5uTlCQkIQGBiIS5cuwdDQEHXq1EGzZs1kt4+IiIioUMj1PlUqlQpubm5wc3OT2R4iIiKiQum9k6qlS5dixIgRMDAwwNKlS99ZduzYsRo3jIiIiKgwee+k6qeffkL//v1hYGCAn3766a3lVCoVkyoiIiL66Lx3UhUVFZXtcyIiIiLKxeq/V69eoXLlyrh+/bo22kNERERUKH1wUlW8eHG8ePFCG20hIiIiKrRytU+Vl5cXFixYgNTUVNntISIiIiqUcrWlwrlz5xAUFIQjR47A0dExy9E0u3fvltI4IiIiIlnGjRuHuLg4AEDp0qWxZMkSqfXnKqmysLCAh4eH1IYQERERaVNcXBxiY2O1Vv8HJVXp6en4/vvvcfPmTaSkpKBVq1aYOXMmDA0NtdU+IiIiokLhg+ZUfffdd/jqq69gYmKCcuXKYenSpfDy8tJW24iIiIgKjQ9KqjZu3Iiff/4Zhw8fRkBAAPbt24fNmzcjPT1dW+0jIiIiKhQ+KKmKjo5G+/btldeurq5QqVT4559/pDeMiIiIqDD5oKQqNTUVBgYGateKFy+OV69eSW0UERERUWHzQRPVhRAYPHgw9PX1lWsvXrzAyJEj1bZV4JYKRERE9LH5oKRq0KBBWa59/vnn0hpDREREBYe293Uqaj4oqVq/fr222kFEREQFjLb3dSpqcnVMDRERERGpY1JFREREJAGTKiIiIiIJmFQRERERSZCrA5WJiIgKC30Ty2yfE8nGpIqIiIq0hh7T87sJ9JHg8B8RERGRBOypIqIiixsXElFeYlJFREUWNy4k+ng8WhaYY5m0hOdqz3N6T5kxbT6oDRz+IyIiIpKASRURERGRBEyqiIiIiCRgUkVEREQkAZMqIiIiIgmYVBERERFJwKSKiIiISAImVUREREQSMKkiIiIikoBJFREREZEETKqIiIiIJMjXpOrEiRPo1KkTbGxsoFKpEBAQoHZfCIHp06ejbNmyMDQ0hKurK27duqVW5vHjx+jfvz/MzMxgYWEBT09PJCYmqpW5fPkyPvvsMxgYGMDW1hYLFy7M0pYdO3agRo0aMDAwgKOjIw4ePPjBbSEiIqKPV74mVUlJSahbty5WrFiR7f2FCxdi6dKlWLVqFc6cOQNjY2O4u7vjxYsXSpn+/fsjIiICgYGB2L9/P06cOIERI0Yo9xMSEuDm5gY7OzuEhYXh+++/x8yZM7F69WqlTEhICPr27QtPT09cvHgRXbt2RdeuXXH16tUPagsRERF9vIrlZ/B27dqhXbt22d4TQmDx4sWYNm0aunTpAgDYuHEjrKysEBAQgD59+uD69es4dOgQzp07h0aNGgEAli1bhvbt2+OHH36AjY0NNm/ejJSUFKxbtw56enqoVasWwsPDsWjRIiX5WrJkCdq2bQtfX18AwJw5cxAYGIjly5dj1apV79UWIiIi+rgV2DlVUVFRiImJgaurq3LN3Nwczs7OCA0NBQCEhobCwsJCSagAwNXVFTo6Ojhz5oxSplmzZtDT01PKuLu748aNG3jy5IlSJnOcjDIZcd6nLdl5+fIlEhIS1B5EBIwbNw79+vVDv379MG7cuPxuDhGRFAU2qYqJiQEAWFlZqV23srJS7sXExKBMmTJq94sVKwZLS0u1MtnVkTnG28pkvp9TW7Izb948mJubKw9bW9scvmqij0NcXBxiY2MRGxuLuLi4/G4OEX0kShqaodT/PUoamkmvP1+H/4q6qVOnwsfHR3mdkJDAxIqIiCifzGz2uVbrL7BJlbW1NQAgNjYWZcuWVa7HxsaiXr16SplHjx6pvS81NRWPHz9W3m9tbY3Y2Fi1MhmvcyqT+X5ObcmOvr4+9PX13+vrzc64ceOUv+JLly6NJUuW5LouIiIi0q4CO/xXqVIlWFtbIygoSLmWkJCAM2fOwMXFBQDg4uKC+Ph4hIWFKWWOHj2K9PR0ODs7K2VOnDiBV69eKWUCAwNRvXp1lChRQimTOU5GmYw479MWbeAQCRERUeGRr0lVYmIiwsPDER4eDuD1hPDw8HBER0dDpVJh/Pjx+Pbbb7F3715cuXIFAwcOhI2NDbp27QoAcHBwQNu2bTF8+HCcPXsWp06dgre3N/r06QMbGxsAQL9+/aCnpwdPT09ERETA398fS5YsURuWGzduHA4dOoQff/wRkZGRmDlzJs6fPw9vb28AeK+2EBER0cctX4f/zp8/j5YtWyqvMxKdQYMGwc/PD5MmTUJSUhJGjBiB+Ph4fPrppzh06BAMDAyU92zevBne3t5o3bo1dHR04OHhgaVLlyr3zc3NceTIEXh5eaFhw4YoVaoUpk+frraX1SeffIItW7Zg2rRp+Oqrr1C1alUEBASgdu3aSpn3aQsRERF9vPI1qWrRogWEEG+9r1KpMHv2bMyePfutZSwtLbFly5Z3xqlTpw7+/PPPd5bp2bMnevbsqVFbiIiI6ONVYOdUERERERUmTKqIiIiIJGBSRURERCQBkyoiIiIiCQrs5p9EVDi1CxiTY5mU5MfK89jkxzm+5/euyzRuF1FRws2hCyYmVURERIVMxubQVLBw+I+IiIhIAiZVRERERBJw+I+IiOgj9OCHmBzLpCWkqT3P6T3lv7TWuF2FGXuqiIiIiCRgUkVEREQkAZMqIiIiIgmYVBERERFJwInqRFQoddj9Y45lXiQnKM9jkxNyfM+B7hM1bhcRfbzYU0VEREQkAXuqiCjPqUyKQWR6TkRUFPC3GRHlueI9KuV3E4iIpGNSlY/iVv76zvtpz5LUnudUvvSoz6W0i4iIiD4c51QRERERScCkioiIiEgCDv8RERUS48aNQ1xcHACgdOnSWLJkST63iIgyY1JFRFRIxMXFITY2Nr+bQURvweE/IiIiIgmYVBERERFJwKSKiIiISAImVUREREQSMKkiIiIikoCr/4iINCRrq4OOO3a+8/6LpGTl+aOk5BzL7+/ZI1ftIKLcYVJFRKQhbnVAMp1d/yjHMi8T09Se5/QepyFlNG4X5YzDf0REREQSMKkiIiIikoBJFREREZEETKqIiIiIJGBSRURERCQBV/8RERUSKhPjbJ8TUcHApKoAK2lolO1zIvo46XftlidxZO27RfSxYVJVgM1q2SG/m0BEHyHuu0WUO0yqiHKJf80TEVFmTKqIcol/zRMRUWZMqoiI3qHjrnU5lnmRnKg8f5ScmON79nsM1bhdRHmhhIFlts8pe0yqqMjhsBwRkRzTmszI7yYUKkyqqMjhsBwREeUHbv5JREREJAGTKiIiIiIJOPxHVMBxjljuqUwMIDI9JyLSJiZVRG+xbb37O+8nJaZmeh6bY3kA6DPk8Ae3g3PEck+/u0t+N4GIPiIc/iMiIiKSgD1VxOElIiIiCZhUEYeXiIiIJGBSRUT0kem684933k9MfqE8f5T8IsfyABDQw1XjdhEVdkyqiIg0pDI2yvY5EX1cmFRRoRO0psM7779IfJHpeWyO5QGg9bADGreLPl763d3yuwlEVAAwqSLKpcwdEuycICIiJlVEudTZXc5/n2Wb372/VUJSaqbnsTmWB4Ax/T98PywiItIMk6qPQMzKWe+8n/YsXu15TuUBwHoUTy4nIiLKjJt/EhEREUnApIqIiIhIAg7/ERERFTLmRiWzfU75i0kVERFRITPSfWZ+N4GywaSKqIAzNMr+ORERFSwFek7VzJkzoVKp1B41atRQ7r948QJeXl4oWbIkTExM4OHhkeUMu+joaHTo0AFGRkYoU6YMfH19kZqaqlbm+PHjaNCgAfT19VGlShX4+fllacuKFStQsWJFGBgYwNnZGWfPntXK15wfLA31Ucro9cPSUD+/m6MxU0MVzI0Bc+PXzwu7Zh2Lwb3X60ezjvw7iIiooCrwv6Fr1aqFP/74/+dOFSv2/5s8YcIEHDhwADt27IC5uTm8vb3RvXt3nDp1CgCQlpaGDh06wNraGiEhIXj48CEGDhyI4sWLY+7cuQCAqKgodOjQASNHjsTmzZsRFBSEYcOGoWzZsnB3f70fkL+/P3x8fLBq1So4Oztj8eLFcHd3x40bN1CmTJk8/G5ox4yW9fK7CVINbFP4E0MiIip8CnRPFfA6ibK2tlYepUqVAgA8ffoUa9euxaJFi9CqVSs0bNgQ69evR0hICE6fPg0AOHLkCK5du4Zff/0V9erVQ7t27TBnzhysWLECKSkpAIBVq1ahUqVK+PHHH+Hg4ABvb2/06NEDP/30k9KGRYsWYfjw4RgyZAhq1qyJVatWwcjICOvWrcv7bwgREREVSAU+qbp16xZsbGxgb2+P/v37Izo6GgAQFhaGV69ewdX1/5+MXqNGDVSoUAGhoaEAgNDQUDg6OsLKykop4+7ujoSEBERERChlMteRUSajjpSUFISFhamV0dHRgaurq1LmbV6+fImEhAS1BxFRQadjbAqVqTlUpubQMTbN7+YQFRoFevjP2dkZfn5+qF69Oh4+fIhZs2bhs88+w9WrVxETEwM9PT1YWFiovcfKygoxMTEAgJiYGLWEKuN+xr13lUlISMDz58/x5MkTpKWlZVsmMjLyne2fN28eZs3KeXdyIqKCxKjbgPxuAlGhVKCTqnbt2inP69SpA2dnZ9jZ2WH79u0wNDTMx5a9n6lTp8LHx0d5nZCQAFtb23xsEREREWlLgR/+y8zCwgLVqlXD7du3YW1tjZSUFMTHx6uViY2NhbW1NQDA2to6y2rAjNc5lTEzM4OhoSFKlSoFXV3dbMtk1PE2+vr6MDMzU3sQERFR0VSokqrExETcuXMHZcuWRcOGDVG8eHEEBQUp92/cuIHo6Gi4uLgAAFxcXHDlyhU8evRIKRMYGAgzMzPUrFlTKZO5jowyGXXo6emhYcOGamXS09MRFBSklCEiIiIq0EnVl19+ieDgYNy7dw8hISHo1q0bdHV10bdvX5ibm8PT0xM+Pj44duwYwsLCMGTIELi4uKBJkyYAADc3N9SsWRMDBgzApUuXcPjwYUybNg1eXl7Q13+97H7kyJG4e/cuJk2ahMjISPz888/Yvn07JkyYoLTDx8cHv/zyCzZs2IDr169j1KhRSEpKwpAhQ/Ll+0JEREQFT4GeU/XgwQP07dsX//33H0qXLo1PP/0Up0+fRunSpQEAP/30E3R0dODh4YGXL1/C3d0dP//8s/J+XV1d7N+/H6NGjYKLiwuMjY0xaNAgzJ49WylTqVIlHDhwABMmTMCSJUtQvnx5rFmzRtmjCgB69+6NuLg4TJ8+HTExMahXrx4OHTqUZfI6ERERfbwKdFK1bdu2d943MDDAihUrsGLFireWsbOzw8GDB99ZT4sWLXDx4sV3lvH29oa3t/c7yxAREdHHq0AP/xEREREVFgW6p4qIiIiKvnHjxiEuLg4AULp0aSxZsiSfW5Q7TKqIiIgoX8XFxWXZuqgw4vAfERERkQRMqoiIiIgkYFJFREREJAGTKiIiIiIJmFQRERERScDVf0RERKQ1sT+F51gmLSFF7XlO77GaUE+zRmkJkyrKM0VlHxIiIqLsMKmiPFNU9iEhIiLKDudUEREREUnApIqIiIhIAiZVRERERBIwqSIiIiKSgBPViYiIKF9ZGphn+7ywYVJF0kT83Pmd9189S870/FGO5QGg1ui9GreLiIgKtplNx+R3E6Tg8B8RERGRBEyqiIiIiCRgUkVEREQkAZMqIiIiIgmYVBERERFJwNV/lGcsDFXZPiciIioKmFRRnhnfwjC/m0BERKQ1HP4jIiIikoBJFREREZEETKqIiIiIJOCcKiJSjBs3DnFxcQCA0qVLY8mSJfncIiKiwoNJFREp4uLiEBsbm9/NICIqlDj8R0RERCQBkyoiIiIiCZhUEREREUnApIqIiIhIAiZVRERERBIwqSIiIiKSgEkVERERkQTcp4qIiEgCbp5LTKqIiIgk4Oa5xOE/IiIiIgmYVBERERFJwOE/oo/Elzvb5ljmSfKrTM9jc3zPDz0OadwuIqKigkkVERHRewjY8e877ycnpas9z6k8AHTtWUrjdlHBweE/IiIiIgnYU0VERPmCWxBQUcOkioiI8gW3IKCihkkVERGRBCYmltk+p48HkyoiUhQ3VgEQmZ4T0fvq0XV2fjeB8hmTKiJSVOvGXwkkT89dl995Pz45RXkel5ySY3kA2OFRR+N2EWkLV/8RERERScA/S4mIKF/oGFtk+5yosGJSRURE+cKs+9j8bgKRVBz+IyIiIpKASRURERGRBEyqiIiIiCRgUkVEREQkAZMqIiIiIgmYVBERERFJwKSKiIiISAImVUREREQSMKn6QCtWrEDFihVhYGAAZ2dnnD17Nr+bRERERAUAk6oP4O/vDx8fH8yYMQMXLlxA3bp14e7ujkePHuV304iIiCifMan6AIsWLcLw4cMxZMgQ1KxZE6tWrYKRkRHWrVuX300jIiKifMaz/95TSkoKwsLCMHXqVOWajo4OXF1dERoamu17Xr58iZcvXyqvnz59CgBISEgAADx7/lxqG/X/r943PXv+QmocADDKJlbi81fS4yRkEycpj+IkP0/NkzjPk/Mmzss8ipOanJIncV4ly/+5zj6O3P+nb4vzOlZyHsVJkhrnbbFeJSfmSZwXyc+0EMc4y7VkrcTRy3It8bk24hhkufbshTbiGGUTR/7PgWE2PwfPnsv/uTb4vzgZP3dCiHe/QdB7+fvvvwUAERISonbd19dXODk5ZfueGTNmCAB88MEHH3zwwUcReNy/f/+duQJ7qrRo6tSp8PHxUV6np6fj8ePHKFmyJFQq1XvVkZCQAFtbW9y/fx9mZmbaairjME6ex2IcxmGcwhGLcQAhBJ49ewYbG5t3lmNS9Z5KlSoFXV1dxMbGql2PjY2FtbV1tu/R19eHvr6+2jULC4tcxTczM9P6f1DGYZz8iMU4jMM4hSPWxx7H3Nw8xzKcqP6e9PT00LBhQwQFBSnX0tPTERQUBBcXl3xsGRERERUE7Kn6AD4+Phg0aBAaNWoEJycnLF68GElJSRgyZEh+N42IiIjyGZOqD9C7d2/ExcVh+vTpiImJQb169XDo0CFYWVlpLaa+vj5mzJiRZRiRcRinsMdiHMZhnMIRi3Hen0qInNYHEhEREVFOOKeKiIiISAImVUREREQSMKkiIiIikoBJFREREZEETKpIa4QQiI6OxosX8s9oIyIiKmi4+o+0Jj09HQYGBoiIiEDVqlXzuzlE0j148AB79+5FdHQ0UlLUD5JetGhRPrWq8Mj4+HnfY7sob92+fRt37txBs2bNYGhoCCEE/61ywH2qCqBNmzZh1apViIqKQmhoKOzs7LB48WJUqlQJXbp0kRJDCIHbt28jJSUF1atXR7Fi8n8UdHR0ULVqVfz3339aT6oOHToEExMTfPrppwCAFStW4JdffkHNmjWxYsUKlChRQqvx88KDBw8AAOXLl9e4rsuXL6N27drQ0dHB5cuX31m2Tp06GscrioKCgtC5c2fY29sjMjIStWvXxr179yCEQIMGDfK7ee/Nx8cHc+bMgbGxsdpZpdmRlShu3LgR33//PW7dugUAqFatGnx9fTFgwAAp9Wd49eoVvvjiC3zzzTeoVKmS1LrzQ0JCQrbXVSoV9PX1oaenJyXOf//9h969e+Po0aNQqVS4desW7O3t4enpiRIlSuDHH3+UEqcoYlJVwKxcuRLTp0/H+PHj8d133yEtLQ3A6zMDFy9eLCWpioqKQufOnXHt2jUArz+kd+3ahUaNGmlc95vmz58PX19frFy5ErVr15ZefwZfX18sWLAAAHDlyhVMnDgRPj4+OHbsGHx8fLB+/XqNY3Tv3v29y+7evVvjeMDr3r5vv/0WP/74IxITEwEApqammDhxIr7++mvo6ORuBL9evXqIiYlBmTJlUK9ePahUKmTutM54rVKplJ9BTSxduvS9yo0dO1bjWACgq6uLhw8fokyZMmrX//vvP5QpU0bK1zR16lR8+eWXmDVrFkxNTbFr1y6UKVMG/fv3R9u2bTWuP68S34sXL+LVq1fK87eR1UOxaNEifPPNN/D29kbTpk0BACdPnsTIkSPx77//YsKECVLiAEDx4sWxa9cufPPNN9LqfJcXL17g8uXLePToEdLT09Xude7cWeP6LSws3vnvUL58eQwePBgzZszI9e8GAJgwYQKKFSuG6OhoODg4KNd79+4NHx+fQpdUJSQkKGf8HTx4EKmpqco9XV1ddOjQQVosDv8VMDVr1sTcuXPRtWtXmJqa4tKlS7C3t8fVq1fRokUL/PvvvxrH6NGjByIiIjB9+nQYGBjghx9+wIsXLxAWFibhK1BXokQJJCcnIzU1FXp6ejA0NFS7//jxYylxTExMcPXqVVSsWBEzZ87E1atXsXPnTly4cAHt27dHTEyMxjE+5DgiGUkc8PqDe+3atZg1a5baB9DMmTMxfPhwfPfdd7mq96+//kKFChWgUqnw119/vbOsnZ1drmJk9j69BCqVCnfv3tU4FvC6lzQjaczsn3/+QeXKlfH8+XONY5iamiI8PByVK1dGiRIlcPLkSdSqVQuXLl1Cly5dcO/ePY3qz/w16OjoaD3xzSuVKlXCrFmzMHDgQLXrGzZswMyZMxEVFSU13qBBg1CvXj2pyVp2Dh06hIEDB2b7O1rWv9HGjRvx9ddfY/DgwXBycgIAnD17Fhs2bMC0adMQFxeHH374Ab6+vvjqq69yHcfa2hqHDx9G3bp11T6H7t69izp16ih/4MmQ8bP9Npp+3/bv349vvvlG+YPB1NQUSUlJyn2VSgV/f3/06NFDozgZ2FNVwERFRaF+/fpZruvr66v9IGji5MmT2LlzpzJU1qRJE5QvXx5JSUkwNjaWEiPD4sWLpdb3Nnp6ekhOTgYA/PHHH8ovbEtLy7d2mX8oWYnSh9iwYQPWrFmj9ldunTp1UK5cOYwePTrXSVXmRElG0pQT2R+Ub5PRI6ZSqbBmzRqYmJgo99LS0nDixAnUqFFDSixjY2NlHlXZsmVx584d1KpVCwCk/PETFRWF0qVLK8+LiocPH+KTTz7Jcv2TTz7Bw4cPpcerWrUqZs+ejVOnTqFhw4ZZfsfJ6h0dM2YMevbsienTp2vt6LINGzbgxx9/RK9evZRrnTp1gqOjI/73v/8hKCgIFSpUwHfffadRUpWUlAQjI6Ms1x8/fiz9aJc9e/aovX716hUuXryIDRs2YNasWRrXv3r1aowZM0bt2u3bt2Fvbw8AWLhwIdatWyctqYKgAsXBwUEEBAQIIYQwMTERd+7cEUIIsXTpUlG/fn0pMVQqlYiJiVG7ZmxsLO7evSul/vzQqVMn4e7uLmbPni2KFy8uHjx4IIQQ4vDhw6Jq1ar53Lrc09fXFzdu3MhyPTIyUhgYGEiJYWtrKwYMGCDWrFkjbt++LaXO/FKxYkVRsWJFoVKphK2trfK6YsWKolq1asLNzU2cPn1aSqwuXbqI1atXCyGEmDhxoqhSpYr49ttvRYMGDUTr1q2lxMhrz58/FwsXLhTt2rUTDRs2FPXr11d7yFCrVi3x3XffZbk+Z84cUbt2bSkxMsv8M/Dmo1KlStLimJqaav3/j4GBgbh582aW6zdv3hSGhoZCCCHu3r2rPM+tdu3aiWnTpgkhXn8O3b17V6SlpYmePXsKDw8Pjep+X5s3bxadO3fWuJ6KFSuKyMhI5XXmz1UhhLh8+bIoXbq0xnEysKeqgPHx8YGXlxdevHgBIQTOnj2LrVu3Yt68eVizZo2UGCqVComJiWpDcTo6Onj27Jlar07GGLSm7ty5g/Xr1+POnTtYsmQJypQpg99//x0VKlRQ/rLX1PLlyzF69Gjs3LkTK1euRLly5QAAv//+u5T5LQBQv379955XcuHCBSkx69ati+XLl2eZk7R8+XLUrVtXSoy5c+fixIkTWLBgAYYPH45y5cqhefPmaN68OVq0aCFlkUFoaCj+++8/dOzYUbm2ceNGzJgxA0lJSejatSuWLVum0V/Be/fuxY0bN6Cnp4eWLVti9+7dWl2gsGjRImUYZNasWUhMTIS/vz+qVq2qlZV/ebGAxdPTE0eOHEGPHj3g5OSklZVes2bNQu/evXHixAllSPvUqVMICgrC9u3bpcfLq16+Hj164Pjx46hcubLWYtja2mLt2rWYP3++2vW1a9fC1tYWwOt5g5r+3C9cuBCtW7fG+fPnkZKSgkmTJiEiIgKPHz/GqVOnNKr7fTVp0gQjRozQuJ6HDx+q/V45duyY8r0CXk8defr0qcZxMnBOVQG0efNmzJw5E3fu3AEA2NjYYNasWfD09JRSf3Zj2CLTUlkhcZ5GcHAw2rVrh6ZNm+LEiRO4fv067O3tMX/+fJw/fx47d+7UOEZe+ZCu6BkzZkiJGRwcjA4dOqBChQpwcXEB8DpBuX//Pg4ePIjPPvtMSpwMDx8+RHBwMPbv3w9/f3+kp6dL+Tlo164dWrRogcmTJwN4vZigQYMGGDx4MBwcHPD999/jiy++wMyZM3MdQ1dXFzExMShduvRbJ6oXVm8uYLl69Srs7e3h5+eHDRs24NixY1LimJub4+DBg0qyoy0XLlzAokWLcP36dQCAg4MDJk6cmO3UB1lSUlIQFRWFypUra2W1c3JyMnr27InSpUvD0dERxYsXV7svY5hx79696NmzJ2rUqIHGjRsDAM6fP4/IyEjs3LkTHTt2xMqVK3Hr1i2NE/unT59i+fLluHTpEhITE9GgQQN4eXmhbNmyGn8dOXn+/DmmTp2K33//HTdu3NCoLhsbG2zcuBGurq7Z3j9y5AgGDRokbeiZSVUBlpycjMTEROkfDMHBwe9Vrnnz5hrHcnFxQc+ePeHj46M24fHs2bPo3r27sk2ApqKjo995v0KFClLi5Ie///4bP//8MyIjIwG8/gAaPXo0bGxspMVITk7GyZMncfz4cRw7dgwXL16Eg4MDWrRogZ9++knj+suWLYt9+/YpK0y//vprBAcH4+TJkwCAHTt2YMaMGcqK1NywtrbGL7/8gk6dOkFHRwexsbHKnCRtOn/+vJIc1KxZEw0bNpQeIy8WsGTE2bZtm9a20ciPLQ6Sk5MxZswYbNiwAQBw8+ZN2NvbY8yYMShXrhymTJkiJc7atWsxcuRIGBgYoGTJkmp/uMpchBEVFYX//e9/uHnzJgCgevXq+OKLL1CxYkUp9ee1EiVKqH2vhBB49uwZjIyM8Ouvv2q8arJPnz5ITk7G3r17s73fsWNHGBsbw9/fX6M4GZhUfYTed+K2jOE/ExMTXLlyBZUqVVL7MLh37x5q1Kghbbd1ba8gyU58fDx27tyJO3fuwNfXF5aWlrhw4QKsrKyU4cfC4JNPPlFLopo3b45mzZpJHTozMDDArVu3lG73Tz/9FO3atcPXX38NALh37x4cHR3x7NmzXMeYOXMmZs+e/V5DVjJ+Hh48eIC+ffvi1KlTsLCwAPD6Z+KTTz7Btm3bpOwnlsHQ0BCRkZGws7NT+39069Yt1KlTR8pqRuD1cPnSpUuxatUqrS1gMDc3R3h4eJ4lVePGjcOpU6ewePFitG3bFpcvX4a9vT1+++03zJw5853bSHwIa2trjB07FlOmTNFoO4P8ktO2HZnJTLr9/PzU/s/q6OigdOnScHZ2lvI76OLFi3BxcUGnTp0wadIkVKtWDQBw48YNLFiwAAcOHEBISIi0veU4p6oAyOu5OjntdZJBxgePhYUFHj58mOUX6MWLF6UmHm/+YsxYQbJo0aJcr5B7l8uXL8PV1RXm5ua4d+8ehg8fDktLS+zevRvR0dHYuHGjlDhVqlTB559/jv79+2ttA9XIyEgYGxujRo0aqFGjBhwcHKTPRbKyskJUVBRsbW2RkpKCCxcuqA2nPnv2LMtwyYeaOXMm+vTpg9u3b6Nz585Yv369kuxow7Bhw/Dq1Stcv34d1atXB/D6F/WQIUMwbNgwHDp0SFqsSpUqITw8PEuic+jQIbV9hDTVqFEjvHjxAvb29jAyMsrybyJjC5SuXbsiICBA61scZAgICIC/vz+aNGmi9nuvVq1ayhQLGVJSUtC7d2+tJ1Tx8fE4e/ZstnthvblNxYfIbr+67MjewmPw4MHS6spO/fr14e/vj2HDhmXZP7BEiRLYtm2b1M16mVQVAF27ds3TeJnnXwgh0L59e6xZs0YrvSt9+vTB5MmTsWPHDqhUKqSnp+PUqVP48ssvNfoF8KbsJm03atQINjY2+P777z9o48734ePjg8GDB2PhwoUwNTVVrrdv3x79+vWTFsfLywtbtmzB7Nmz0bBhQ3z++efo3bs3rK2tpcX477//cOXKFRw/fhyHDx/G119/DT09PTRv3hwtW7bE8OHDNY7Rvn17TJkyBQsWLEBAQACMjIzU5oNdvnxZygTfjMRwxowZ6NmzZ7bLwmUJDg5GSEiIklABr4dili1bJn2uW14sYAGAvn374u+//8bcuXNhZWWllYnqebXFQYa4uLhsp1AkJSVJ/foGDRoEf39/jbYyyMm+ffvQv39/JCYmwszMLMsQoya/U/Nr2468OA2jS5cuaNOmDQ4fPqzs4l+1alW4ubnhyZMnGDFiBFavXq1xHADcUoGyLjGV6eXLl2LYsGGiWLFiQqVSieLFiwsdHR3x+eefi9TUVK3EzOzWrVvCyMhIer1mZmbK8unM37979+4JfX196fFu3Lghpk+fLqpWrSqKFSsm2rRpIzZs2CA9Tnp6ujh37pwYNGiQKFasmNDR0ZFSb1xcnPjss8+ESqUSpqamYteuXWr3W7VqJb766ispsTJ79OiR+PPPP8Wff/4pHj16JLXuqlWrijNnzmS5fubMGVG5cmWpsYQQ4tdffxVVqlQRKpVKqFQqUa5cObFmzRqpMQwNDUV4eLjUOt+UV1scZPjss8/E0qVLhRD/f3sAIYTw9vYW7u7u0uKMGTNGmJubi2bNmglvb28xYcIEtYcMVatWFePGjRNJSUlS6isIateuLQ4cOCCEeL29gZ6enpg6dapo0qSJGDx4sNbjh4eHS/s9J4QQnFNVQOXF5NcMmedoaMv9+/dx5coVJCYmon79+tKHst6cJyaEwMOHDzFz5kxERkYiPDxcarwyZcrg8OHDqF+/vtr3LzAwEEOHDsX9+/elxsvs9OnTGDVqFC5fviylG/7ChQs4fvw4jh8/jpMnT+LZs2dwdHRU5lfJWq4PvF5RZGJiAl1dXbXrjx8/hqmpqcZDgBmSk5Ph7e2NTZs2Kd8jXV1dDBw4EMuWLZPSg/Xbb79h7ty5WLFihTIB//z58xgzZgwmT56stR5obS1gAYAGDRrg559/RpMmTaTXnV9OnjyJdu3a4fPPP4efnx+++OILXLt2DSEhIQgODpb2u7Vly5ZvvadSqXD06FGNYxgbG+PKlSta/V0N4K2TulUqFQwMDFClShVpc+Ly4jSMd7l06RIaNGggbUiTw38FTF5Ofs1Ltra2anuDyJbdPDEhBGxtbbFt2zbp8Tp37ozZs2cr++qoVCpER0dj8uTJ8PDwkB4PeH0cxZYtW+Dv74+EhAT07NlTSr1OTk6oX78+mjdvjuHDh6NZs2YwNzeXUneGoUOHvle5devWSYk3YcIEBAcHY+/evWrH+4wdOxYTJ07EypUrc1XvmyuVkpKS4OzsrCzRT01NRbFixTB06FCpSdW6devQsmVLVKpUCUZGRlob1pw/fz4mTpyI7777LtttAWTtXQdof4uDDJ9++inCw8Mxf/58ODo64siRI2jQoAFCQ0Ph6OgoLY6sbS3exd3dHefPn9d6UtW1a9ds51dlPhrp008/RUBAgMbDc3lxGkZeYk9VAdO2bVvEx8djw4YNWSa/mpmZSZ38msHU1BSXL1/WymocDw8PODk5KfsTZVi4cCHOnTuHHTt2SInz5jYRGStIqlSpopVf2E+fPkWPHj1w7tw5JCYmwsbGBjExMXBxccHBgwelHfdz8+ZNbN68GVu3bkVUVBRatWqF/v37o3v37mpHsGgi82Gj2qKjowM7OzvUr1//nRNh3zyyIrdKlSqFnTt3okWLFmrXjx07hl69eiEuLi5X9WYsy38fgwYNylWM7FStWhV3797NsjFrlSpVpMUAoEyyfts+djL+ms+rLQ7yU8ZWMbL/CF67di1mz56NIUOGZJv0yji0GQCCgoLw9ddf47vvvlM7Y/Cbb77BtGnTYG5uji+++ALOzs5Yu3atRrE6d+6MlJQUNG3aFHPmzEFUVBTKlSuHI0eOwNvbW9k6Qltk91QxqSpgDA0NERISkmUTvLCwMHz22WdKRq+JNydt79u3D61atcqSCLy5UiI3SpcujaNHj2b5i/DKlStwdXVFbGysxjHy06lTp9Q2x3vbBnO5paOjg8aNG6Nfv37o06eP1s4U0zYvLy9s3boVdnZ2GDJkCD7//HNYWlpqLZ6RkRHCwsKyrIyLiIiAk5OTtHM089Lff/+N48eP48SJEwgODsatW7dQtmxZtGjRAr/++quUGDntYSdj77q82uIgQ6tWrdC8efMsG/I+efIEHh4eUoblACA9PR3ffvstfvzxR2WnfVNTU0ycOBFff/21lFWB76pD5qq82rVrY/Xq1VnOaDx16hRGjBiBiIgI/PHHHxg6dGiOewTmJDo6GqNHj8b9+/cxduxYZZPrCRMmIC0tLctpEh8qp0VK8fHxCA4OZlJVVFWrVg2//vqr8tdBhrNnz6Jfv364ffu2xjGGDBnyXuVkHCBsaGiI8PBwtRVSwOtl/PXr15e2v86GDRtQqlQpdOjQAQAwadIkrF69GjVr1lQ+zGVJT0+Hn58fdu/ejXv37kGlUqFSpUro0aMHBgwYIHVF0a1bt7S2lUKGvNrj6+XLl9i9ezfWrVuHkJAQdOjQAZ6ennBzc5O+yqx169YoWbIkNm7cCAMDAwCvd2keNGgQHj9+jD/++CNX9Wbu1ctpaEJbvX/Jycn4888/sXXrVmzevBlCCKSmpmolljbY2dkpWxxkno94+/ZtNGjQQPqQj46ODkqWLImmTZti8+bNyh+PsbGxsLGxkfbzPXXqVKxduxazZs1SG3KeOXMmhg8frpWtXbTF0NAQ586dQ+3atdWuX7lyBU5OTnj+/Dn++usvODg4SPlDX5vy8vMOAFf/FTQBAQHCyclJnDt3Trl27tw50aRJE7Fnz578a1guNW7cWMyaNSvL9RkzZogGDRpIi1OtWjURFBQkhBAiJCREGBoaiv/973+iU6dOolu3btLipKeniw4dOgiVSiXq1asn+vTpI3r37i3q1KkjVCqV6NKli7RYmZ0/f15s2rRJbNq0SYSFhUmtOyAgQO2xY8cO8dVXX2lldVmGe/fuiZkzZwp7e3tRoUIF8ezZM6n1X7lyRdjY2IiSJUuKVq1aiVatWomSJUuKcuXKiatXr+a6Xh0dHREbGyuEeH0wuY6OTpZHxnWZDh8+LKZOnSpcXFyEgYGBqF+/vhg/frwICAgQjx8/lhrryZMn4ocffhCenp7C09NTLFq0SMTHx0ur39DQUFktm3nlbHh4uDAzM5MWJ4NKpRLh4eHC2dlZ1K5dW0RFRQkhhIiJiZH671S2bFnx22+/ZbkeEBAgbGxspMXJC02bNhVt27ZVWzH76NEj0bZtW/HZZ58JIYQIDAwU1apV0zhW5v9Tmf3777/S/x/lBU5UL2AGDx6M5OTkt05+zTzhV8ZGfNr2zTffoHv37rhz5w5atWoF4PV4/ZYtW6Se+3f//n1lfklAQAB69OiBESNGoGnTplnm1WjCz88PJ06cQFBQUJbVPkePHkXXrl2xceNGaXtwPXr0CL1790ZwcLDawoWWLVti27ZtUo5hyW51X48ePVCrVi34+/tLO3Mys4zeMSGEVna7r127Nm7duoXNmzcrx/v07dsX/fv3VztI/EMdPXpUGbbMi4nJGdq2bYvSpUtj4sSJOHjwoNY2NT1//jzc3d1haGio9JZnbKCbMcFbU40aNcKBAwcwZswYAP9//taaNWuU8y1lK1u2LIKDgzFkyBA0btwYO3bskLppKvD693GNGjWyXK9Ro4a039WzZ89+5/3p06dLibN27Vp06dIF5cuXVxYY3b9/XxmmBYDExERMmzZN41jiLYNlL1++hJ6ensb15zUO/xUw+TURVpsOHDiAuXPnIjw8HIaGhqhbty5mzJgBS0vLLN3LuZV5i4P69evDx8cHAwYMwJ07d1C3bl1ljoOm3Nzc0KpVq7dOpp07dy6Cg4Nx+PBhKfF69+6Nu3fvYuPGjcqHwLVr1zBo0CBUqVIFW7dulRInO3fv3kWdOnWkfe8yD/+dPHkSHTt2xJAhQ9C2bdtCeaxHXlq8eDFOnDiBEydOQF9fX5mo3qJFC+XYDRk+++wzVKlSBb/88ovaH3XDhg3D3bt3ceLEiVzXffXqVdSuXRunTp1C27Zt8fnnn2P9+vUYOXKkVrY4yPDm4drffvstvv32W0yePBnffvuttKTe2dkZzs7OWeYAjRkzBufOncPp06c1jvHmXNtXr14hKioKxYoVQ+XKlaWcuJEhPT0dR44cUTtjsE2bNtL+r2Z8nyZMmIA5c+aoLbxJS0vDiRMncO/ePelz7LQufzvK6GPz9OlTsWrVKtG4cWOpXbv9+vUTDRo0EJ6ensLIyEj8+++/QgghfvvtN1GzZk1pcaysrMTFixffev/ChQvCyspKWjwzMzNx9uzZLNfPnDkjzM3NpcV5U3Jyshg3bpyU7n0hhBg1apQoUaKEqFOnjli8eLGIi4uTUu/b+Pn5if379yuvfX19hbm5uXBxcRH37t2TFufJkyfi8OHDYtOmTWLDhg1qD225fPmyWLZsmejWrZsoXry4KFeunLS6DQwMxPXr17Ncj4iIEIaGhhrVrVKphJOTk1i9erW4dOmSGDZsmGjcuLFwcHAQ/fv3F5cvX9ao/nfFfXN4aefOncLY2Fjq76Djx48LY2Nj4eDgIIYOHSqGDh0qHBwchImJiThx4oS0OG96+vSp6Natm9i4caPWYmhDxoavKpVK2Nraqm0CW61aNeHm5iZOnz6d3838YBz+K4DS0tKwZ88etc0/u3TpotW9XLTtxIkTWLt2LXbt2gUbGxt0794dK1askFb/ihUrMG3aNNy/fx+7du1CyZIlAbxeNdm3b19pcR4/fvzOFXhWVlZ48uSJtHjp6enZbohZvHjxLOd+5VZOp8TLsGrVKlSoUAH29vYIDg5+6yozGStOgdc9hhl7UYWGhmL58uVYvHgx9u/fjwkTJkiJo80jQ7IjhMDFixdx/PhxHDt2DCdPnkR6erqUIeAMZmZmiI6OzjKMdf/+fbXjmHIjODgY69evx8SJE5Geng4PDw/88MMPaNasmUb15iQqKirL98jDwwPVq1dHWFiYtDjNmzfHjRs38PPPPytDzt27d8fo0aNhY2MjLc6bzMzMMGvWLHTq1AkDBgyQVm9wcDB++OEHtc8hX19faUcwZRyL07JlS+zevVv6eaP5hcN/BUxERAQ6d+6MmJgYZcXczZs3Ubp0aezbt0/acFleiImJgZ+fH9auXYuEhAT06tULq1atwqVLl1CzZk2txn727Bm2bt2KNWvWICwsTFoXv66uLmJiYt76QSZ7RVGXLl0QHx+PrVu3Kr+Y//77b/Tv3x8lSpSQsq/Tm0POsk+JB17PFXyfFX6yVuAYGRkhMjISFSpUwOTJk/Hw4UNs3LgRERERaNGiRa73qcqsWrVqaN++PebOnavVMwYBoFOnTjh16hQSEhJQt25dZbf7Zs2aSZ1fNXbsWOzZswc//PCDspz+1KlT8PX1hYeHBxYvXqxxjKSkJGzfvh1+fn74888/UaVKFXh6emLQoEFSz7T8mJw8eRKdOnWS9gfdr7/+iiFDhqB79+5qKxkDAgLg5+cn9XzTooZJVQHj4uKC0qVLY8OGDcoH2pMnTzB48GDExcUhJCQkn1v4fjp16oQTJ06gQ4cO6N+/P9q2bQtdXV0UL15cq0lVdj1iHh4eaNy4sZT6dXR00K5dO+jr62d7/+XLlzh06JC0pOr+/fvo3LkzIiIi1CaM1q5dG3v37i20O+xrW17MscurI0MAwNfXF82bN8dnn30mfbf7zFJSUuDr64tVq1Yp2zQUL14co0aNwvz589/6c59bt2/fxvr167Fp0ybExMSgbdu2bz0iRRPnz5/H9u3bER0djZSUFLV7snpH169fDxMTkywnHezYsQPJyclS5sC+OV9L/N9xXJs2bULz5s2xZcsWjWMAgIODA0aMGIEJEyaoXV+0aBF++eUXpfcqt3x8fDBnzhwYGxvDx8fnnWUXLVqkUay8xqSqgDE0NMT58+dRq1YttetXr15F48aNpe3rpG3FihXD2LFjMWrUKLV9lrSRVOVlj1ie73mC1784//jjD2VIwcHBQfomo/Hx8Th79iwePXqUZVhR9jBWXujfv7+yF9rWrVsRHR2NkiVLYu/evfjqq69w9epVjWN0794dffr0Qa9evSS0uGBJTk7GnTt3AACVK1fWak9cUlISNm/ejKlTpyI+Pl76atBt27Zh4MCBcHd3x5EjR+Dm5oabN28iNjYW3bp1k/Z/tVq1avjf//6XZVVwcHAwRowYgRs3bmgc481TLzJ6lVu1aoWpU6dqPESbQV9fHxEREVl27L99+zZq166NFy9eaFR/y5YtsWfPHlhYWOTJmYl5qfBO0imiqlWrhtjY2CxJ1aNHj6QfSaFNJ0+exNq1a9GwYUM4ODhgwIAB6NOnj/Q4mXvEMnZo1tXVxapVq6THAuQmS+9LpVKhTZs2aNOmjVbqz+u5QXlBW3PsMveidOjQAb6+vrh27ZpWjwzJkJSUhODg4Gx7W8aOHSs1lpGRkdRz8bJz4sQJrFu3Drt27YKOjg569eqlle075s6di59++gleXl4wNTXFkiVLUKlSJXzxxRcoW7astDjR0dHZHvVlZ2en8a7jGTLmIWmbra0tgoKCsnzm/PHHH1LOcM28HUlebk2SF9hTVcAcPHgQkyZNwsyZM5WT4k+fPo3Zs2dj/vz5+PTTT5Wy2j6vTYakpCT4+/tj3bp1OHv2LNLS0rBo0SIMHTpUyl9Vedkjll+CgoIQFBSUbS+SjAOI83JuUGH3vsvJZR4ZAgAXL15E+/btkZycjKSkJFhaWuLff/+FkZERypQpg7t370qLpc2hsn/++Qd+fn7w8/PD7du38cknn8DT0xO9evWSdl7mm4yNjREREYGKFSuiZMmSOH78OBwdHXH9+nW0atUKDx8+lBKnQoUKWL58eZZk+rfffoOXl5dyHqBMCQkJOHr0KKpXry51362VK1di/PjxGDp0qNrcOj8/PyxZsgRffPGFtFhFTv4sOqS3UalUyiPzDs1vvi6MO81GRkYKX19fYW1tLQwMDESnTp00rjM0NFQMGzZMmJqaCicnJ7Fs2TIRFxcnihUrJiIiIiS0On/NnDlT6OjoCCcnJ9GlSxfRtWtXtYcMRkZGyq7WRYm2dwbPS82bNxfDhw8XaWlpyi7k0dHRolmzZmLXrl3S4mzdulUUL15cdOzYUejp6YmOHTuKatWqCXNzczF48GCN6m7btq0oVqyYsLa2FpMmTRKRkZGSWv1u5cqVU7ZrcHR0FFu2bBFCvD55QeYO7pMmTRJ2dnbi6NGjIjU1VaSmpoqgoCBhZ2cnJk6cKCVGz549xbJly4QQr7c9qVq1qihevLgoVqyY2Llzp5QYGXbv3i2aNm0qLC0thaWlpWjatKkICAiQUne3bt3E06dPlefvegwYMEDMnz+/0PzfZU9VAfOuA00vX76MOnXqKK9lHG6aH9LS0rBv3z6sW7dO2qRUbfeI5ZeyZcti4cKFUpdKv6kozg3Kbmfwc+fO4fnz59J2Bs9OfHy8VnY7t7CwwJkzZ1C9enVYWFggNDQUDg4OOHPmDAYNGqTMt9NUnTp18MUXXyhDZZcuXVIbKps1a1au6+7cuTM8PT3RsWNH6OrqSmnv++jXrx8aNWqkTI5etmwZunTpgsDAQNSvX1/KClrg9ST/AQMGYMeOHcr2N+np6Rg4cCBWrVolZXdwa2trHD58GHXr1sWWLVswY8YMXLp0CRs2bMDq1asLzUaZQ4YMwdKlS2FqaprjPNWXL18iNDQUjo6OWlnEIBuTqgJOW1sDFGU3btzA2rVrsWnTJsTHx6NNmzaF4j9jdkqWLImzZ8+icuXKUuvN/P2Ii4vD7NmzMWTIkDyZG5QXtLkzeIYFCxagYsWK6N27NwCgZ8+e2LVrF8qWLYuDBw+ibt26GsfIULp0aYSEhKBq1aqoVq0ali1bBnd3d0RGRqJhw4ZISkqSEievhsry0uPHj/HixQvY2NggPT0dCxcuVL6XX375pdR5VcDrLXAuXboEQ0NDODo6Sj3M3dDQEDdv3oStrS0GDhwIGxsbzJ8/H9HR0ahZs6a00w8yhIWFKSv9atWqlWVH97xy7do1NG7cWNrPuVblb0cZvU1wcLAYOHCgMDY2FlWrVhWTJ0/OdmdtervU1FSxZ88eKcOM+WXSpEli9uzZ0uvNPMz8rkdhHGYWQrs7g2eoWLGiOHXqlBBCiCNHjggLCwtx+PBh4enpKdq0aSMlRoY2bdqIzZs3CyGEGDZsmHBychK//vqrcHd3F05OTtLi5NVQWX57/vy5+OGHH6SefpAXqlatKvz9/UViYqIoXbq0coh8eHi4KFmypLQ4sbGxomXLlkKlUokSJUqIEiVKCJVKJVq1aqV2yHJeSU1NFeHh4XkeNze4+q8AyW5rgJcvXyIgIKBITLjOa7q6uujatSu6du2a303JtRcvXmD16tX4448/UKdOnSy9SLndw0XWbuwFlTZ3Bs8QExOjrITav38/evXqBTc3N1SsWBHOzs5SYmSYO3cunj17BgD47rvvMHDgQGVxhozFChmaNWuGwMBAODo6omfPnhg3bhyOHj2KwMBAtG7dWlqcvPDy5UvMnDkTgYGB0NPTw6RJk9C1a1esX78e06ZNg66ubpZ9mDT14MED7N27N9tJ/jL2Wxo/fjz69+8PExMT2NnZKYfFnzhxQupqzTFjxuDZs2eIiIjIcubo2LFjpZ452rJly3duDHz06FHo6upK7fnVqvzO6ui1jh07CjMzM9G3b1+xf/9+kZqaKoQQRWbCNeVOixYt3vpo2bKlRnUHBQUJBwcHZcJoZvHx8aJmzZpaPbNMm8aMGSPKly8vtm3bJqKjo0V0dLTYunWrKF++vBg3bpyUGGXLllV6qqpVqya2b98uhHi9IMPU1FRKjLz233//ib///lsIIURaWpqYN2+e6NSpk/Dx8RGPHz/O59Z9mEmTJglzc3Ph4eEhypYtK4oVKyaGDx8uHB0dxdatW5XfsbL88ccfwsjISNSuXVsUK1ZM1KtXT1hYWAhzc3ON/69mdu7cObF7927x7Nkz5dr+/fvFyZMnpcXIyzNHx48fr/bw8vISTZs2Febm5mLs2LFSY+UFJlUFhK6urpgwYYK4efOm2nUmVaQtnTp1EosWLXrr/SVLlkhbYZjXXr58KcaOHSv09PSUVbP6+vpi/Pjx4sWLF1JieHl5CTs7O+Hq6ipKliypfMht3bpV1K9fX0oMyr1KlSqJ3377TQghxJUrV4RKpRJDhgwR6enpWonXuHFjMX36dCGEUFZoPnv2THTu3Fn8/PPPWompLSYmJtkeHH/hwoU8+4NhxowZ0lZN5iVOVC8gTp8+jbVr18Lf319ts8yyZcsWmf2WKPdu376NO3fuoFmzZjA0NIQQ4r3O0nsXOzs7HDp06K3720RGRsLNzU3axoV5JS0tDadOnYKjoyP09fW1tjP4q1evsGTJEty/fx+DBw9WJvH+9NNPMDU1xbBhw6TFqlSp0jv/vTXZpyohIUHZ8y4hIeGdZQvD3ngZ9PT0EBUVhXLlygF4Pcn77NmzWtvU1NTUFOHh4ahcuTJKlCiBkydPolatWrh06RK6dOmCe/fuaRxDCIGdO3fi2LFj2e5bJ+vInbw4czQnt2/fhpOTEx4/fqz1WDJxTlUB0aRJEzRp0gSLFy9Wtgbw8fFBeno6AgMDYWtrW6i3BqDc+e+//9CrVy8cO3YMKpUKt27dgr29PTw9PVGiRAn8+OOPua47NjY2yxytzIoVKybl4OG8pqurCzc3N1y/fh2VKlXS2odo8eLF8eWXX2a5LnueDvB6Lk1mr169wsWLF3Ho0CH4+vpqVHeJEiXw8OFDlClTBhYWFtkmbxlJfGFafZyWlqa2jUGxYsVgYmKitXjGxsbKPKqyZcvizp07yskY//77r5QY48ePV47CsbKy0vgPq7fJ2MS0YsWKyrzB6OhoODo64tdff9VKzDeFhobCwMAgT2LJxKSqgDE2NsbQoUMxdOhQZWuA+fPnY8qUKYV6awDKnQkTJqB48eKIjo5W61Hq3bs3fHx8NEqqypUrh6tXr771+KPLly9LX26eV2rXro27d+9me2yITHfu3MHixYuVZec1a9bE+PHjpR+yPG7cuGyvr1ixAufPn9eo7qNHj8LS0hLAu48MKWyDGkIIDB48WDkE+sWLFxg5cmSWndtl9e40adIEJ0+ehIODA9q3b4+JEyfiypUr2L17t3I6hqY2bdqE3bt3o3379lLqextbW1tcuHABQUFBys+2Ns4cBV7vk5eZ+L9Dos+fP49vvvlGejxt4/BfIaCNzTKpcMi82V/GZoz29va4e/cu6tSpo9G+NGPGjMHx48dx7ty5LH8RPn/+HE5OTmjZsiWWLl2q6ZeR5w4dOoSpU6dizpw5aNiwYZYPUhnDWIcPH0bnzp1Rr149NG3aFMDrozwuXbqEffv2ae2sxszu3r2LevXq5Thsl5NvvvkGM2bMUPb0elN0dDQ8PT0RGBioUZy8lNeHn9+9exeJiYmoU6cOkpKSMHHiRGU/rEWLFknZr6pSpUr4/fffs6xqleX58+cICgpCx44dAQBTp07Fy5cvlfvFihXD7NmzpfQg3b17FxUrVsxy3mPmQ6Ld3Nw0jpPn8msyFxHlzMTERFm8kDH5VYjXK4AsLS01qjsmJkbY2NgIW1tbsWDBAhEQECACAgLE/Pnzha2trbCxsRExMTEafw35IbvjnmQf8VSvXj0xefLkLNcnT56cZxPVFyxYIOzs7DSux9bWVtSrV09cuXIly71Vq1YJU1NT0bZtW43jkGb8/PxEnz59RHJyslbqX7lypejYsaPy2sTERDg7Oysrjq2trd+5uOVD6OjoiNjYWOV1r169Cu3vm8zYU0VUgLVv3x4NGzbEnDlzYGpqisuXL8POzg59+vRBWloadu3apVH9f/31F0aNGoXDhw8rwzsqlQru7u5YsWKF1ofPtOVdxz0Bco54MjAwwJUrV9QO8gZe76hdp04dvHjxQuMYGerXr682f0YIgZiYGMTFxeHnn3/GiBEjNKo/ISEB3t7e2L59O2bMmIHJkyfjwYMHGDp0KM6dO4fvv/9e4xikuefPn6Nbt244deoUKlasmGVO5IULFzSq/7PPPsOkSZPQqVMnAFDrHQeAX3/9FStWrEBoaKhGcYDXPVIxMTEoU6YMgNe9x+Hh4dKHzvMa51QRFWALFy5E69atcf78eaSkpGDSpEmIiIjA48ePcerUKY3rt7Ozw8GDB/HkyRPcvn0bQghUrVoVJUqUkND6/JMX52KWLl0a4eHhWZKq8PBw5YNCli5duqglVRlDJC1atJAyFGRmZoaNGzfCw8MDX3zxBfz9/REVFQUnJyclkaesLC0tcfPmTZQqVQolSpR458RxGavYBg0ahLCwMHz++edamah++/ZttYUdBgYG0NHRUV47OTnBy8tLaswMRaV/h0kVUQFWu3Zt3Lx5E8uXL4epqSkSExPRvXt3jBgxAt9++y1Wr14tJU6JEiXQuHFjKXUVBOvXr4eJiQl69uypdn3Hjh1ITk7GoEGDcl337Nmz8eWXX2L48OEYMWIE7t69i08++QTA6zlVCxYsgI+Pj0btz5AxV+pd9WXeEkFTTZo0gaOjI4KCgmBsbIxp06YxoXqHjO0zAGDx4sVaj3fgwAEcPnwYn376qVbqj4+PV5tD9ebq3/T0dLX7mlCpVFmSQm2tZsxT+Tn2SES5Ex4eXmjP5csLVatWFUePHs1y/fjx46JatWoa1Z0xFyQ9PV0sWrRIlCtXTpm/Va5cObF48WJpG0y+OSfszYfMOWJbtmwRlpaWolWrViIyMlL4+voKPT09MX78ePH8+XMpMUgz1atXF5cuXdJa/VWqVBE7d+58631/f39RuXJlKbFUKpVo37696Natm+jWrZsoVqyYcHNzU15nPAobzqkiKoQuXbqEBg0aFKp9g/KSgYEBIiMjUbFiRbXr9+7dg4ODA54/f57rut+cCwJAOZdP9l5ymeeGCSHQvn17rFmzRtnQMoOmw50eHh44fPgw5s2bhzFjxijXQ0JClFV0fn5+cHFx0ShOUfMhqy5l9CYeOHAAy5Ytw6pVq7L8bMswbtw4/PHHHwgLC8t2RXCjRo3g6uqKJUuWaBwrr1dn5hUmVUSFEJOqd6tQoYKygWFmv/32G7y8vPDgwYNc162jo4PY2FiULl1a02Z+sDcnDsvStGlT+Pn5ZZkfBrz+MJ0yZQpWrlyZ5ZDgj52Ojs57D1nJ+L9aokQJJCcnIzU1FUZGRlkmqms6bys2Nhb16tWDnp4evL29Ua1aNQDAjRs3sHz5cqSmpuLixYuwsrLSKE5RxjlVRFTk9O3bF2PHjoWpqSmaNWsG4HWvz7hx49CnTx+N669WrVqOH6aF6XiNP//8U21CcmaGhoZYsmQJPDw88rhVBV/mzVLv3buHKVOmYPDgwUqPXmhoKDZs2IB58+ZJiafteVtWVlYICQnBqFGjMGXKFLUVwW3atMHPP//MhCoH7KkiKoDe3GX4TfHx8QgODmZP1VukpKRgwIAB2LFjh7KhZXp6OgYOHIhVq1apHV/yoXR0dLB48WKYm5u/s5wmk+HfRls9VaS51q1bY9iwYejbt6/a9S1btmD16tU4fvx4/jQslx4/fozbt28DAKpUqaLsuk/vxqSKqAAqqvMN8trNmzdx6dIlGBoawtHRUcpKtuzmVOWVjL3KCuv+YUWZkZERLl26lO2+ZfXq1UNycrLUeC9evMgyHFuYDrwuqjj8R1QAMVmSo1q1asq8EFnyctn3mz2W2j6/jnLP1tYWv/zyCxYuXKh2fc2aNcqhxJpKSkrC5MmTsX37dvz3339Z7rPnOv8xqSKiIsHHxwdz5syBsbFxjvtELVq0KNdx8rJz/80hxs8//zzPYtOH+emnn+Dh4YHff/8dzs7OAICzZ8/i1q1bGp98kGHSpEk4duwYVq5ciQEDBmDFihX4+++/8b///Q/z58+XEoM0w+E/IioSWrZsiT179sDCwgItW7Z8Z9nME4yJZLl//z5WrlyJyMhIAICDgwNGjhwpraeqQoUK2LhxI1q0aAEzMzNcuHABVapUwaZNm7B161YcPHhQShzKPSZVREREhYCJiQmuXbuGChUqoHz58ti9ezecnJwQFRUFR0dHJCYm5ncTP3oc/iOiImPo0KE5llGpVFi7dm0etIY+Nn/++Sf+97//4e7du9ixYwfKlSuHTZs2oVKlSlKOlrG3t0dUVBQqVKiAGjVqYPv27XBycsK+fftgYWGh+RdAGst+YxIiokLIz88Px44dQ3x8PJ48eZLtozDtH0WFx65du+Du7g5DQ0NcuHBBOSPv6dOnmDt3rpQYQ4YMwaVLlwAAU6ZMwYoVK2BgYIAJEybA19dXSgzSDIf/iKjI8PLywtatW2FnZ4chQ4bg888/5/46lCfq16+PCRMmYODAgWr7iV28eBHt2rVDTEyM9Jh//fUXwsLCUKVKFdSpU0d6/fTh2FNFREXGihUr8PDhQ0yaNAn79u2Dra0tevXqhcOHD+fpqj36+Ny4cUPZvT8zc3NzxMfHa1R3aGgo9u/fr3YtY8L6yJEjsXz5cqVnjPIXkyoiKlL09fXRt29fBAYG4tq1a6hVqxZGjx6NihUrciIvaY21tbWyA3lmJ0+e1HgH/NmzZyMiIkJ5feXKFXh6esLV1RVTp07Fvn37pB2FQ5phUkVERVbGgbdCCG6MSFo1fPhwjBs3DmfOnIFKpcI///yDzZs348svv8SoUaM0qjs8PBytW7dWXm/btg3Ozs745ZdfMGHCBCxduhTbt2/X9EsgCbj6j4iKlJcvX2L37t1Yt24dTp48iY4dO2L58uVo27btWw8NJtLUlClTkJ6ejtatWyM5ORnNmjWDvr4+vvzyS4wZM0ajup88eaJ2kHFwcDDatWunvG7cuDHu37+vUQySgxPViajIGD16NLZt2wZbW1sMHToU/fv3R6lSpfK7WfQRSUlJwe3bt5GYmIiaNWvCxMRE4zrt7OywadMmNGvWDCkpKbCwsMC+ffuU3qsrV66gefPmXNlaADCpIqIiQ0dHBxUqVED9+vXfeUYfz8ojWd5nbzQAWLduXa5jjBo1CpcuXcKCBQsQEBCADRs24J9//oGenh4AYPPmzVi8eDHOnTuX6xgkB4f/iKjIGDhwYJ4eeEzk5+cHOzs71K9fX2srTOfMmYPu3bujefPmMDExwYYNG5SECnidsLm5uWklNn0Y9lQRERHlUl7ujfb06VOYmJhAV1dX7frjx49hYmKilmhR/mBSRUREpIHMiyNCQkLQoUMHeHp6ws3NjT2nHxkmVURERJL89ddf8PPzw8aNG5GamoqIiAgpk9WpcOD6YiIiIkm4N9rHjUkVERGRBl6+fImtW7eiTZs2qFatGq5cuYLly5cjOjqavVQfGa7+IyIiyqU390bbunUr90b7iHFOFRERUS5xbzTKjD1VREREucS90Sgz9lQRERERScCJ6kREREQSMKkiIiIikoBJFREREZEETKqIiIiIJGBSRURFTkxMDMaMGQN7e3vo6+vD1tYWnTp1QlBQUH43jYiKMG6pQERFyr1799C0aVNYWFjg+++/h6OjI169eoXDhw/Dy8sLkZGR+d1EIiqi2FNFREXK6NGjoVKpcPbsWXh4eKBatWqoVasWfHx8cPr0aQBAdHQ0unTpAhMTE5iZmaFXr16IjY1V6pg5cybq1auHdevWoUKFCjAxMcHo0aORlpaGhQsXwtraGmXKlMF3332nFlulUmHlypVo164dDA0NYW9vj507d6qVmTx5MqpVqwYjIyPY29vjm2++watXr7LE3rRpEypWrAhzc3P06dMHz549AwBs3LgRJUuWxMuXL9Xq7dq1KwYMGCD1e0lEH4ZJFREVGY8fP8ahQ4fg5eUFY2PjLPctLCyQnp6OLl264PHjxwgODkZgYCDu3r2L3r17q5W9c+cOfv/9dxw6dAhbt27F2rVr0aFDBzx48ADBwcFYsGABpk2bhjNnzqi975tvvoGHhwcuXbqE/v37o0+fPrh+/bpy39TUFH5+frh27RqWLFmCX375BT/99FOW2AEBAdi/fz/279+P4OBgzJ8/HwDQs2dPpKWlYe/evUr5R48e4cCBAxg6dKjG30Mi0oAgIioizpw5IwCI3bt3v7XMkSNHhK6uroiOjlauRURECADi7NmzQgghZsyYIYyMjERCQoJSxt3dXVSsWFGkpaUp16pXry7mzZunvAYgRo4cqRbP2dlZjBo16q3t+f7770XDhg2V19nF9vX1Fc7OzsrrUaNGiXbt2imvf/zxR2Fvby/S09PfGoeItI9zqoioyBDvcUDE9evXYWtrC1tbW+VazZo1YWFhgevXr6Nx48YAgIoVK8LU1FQpY2VlBV1dXejo6Khde/TokVr9Li4uWV6Hh4crr/39/bF06VLcuXMHiYmJSE1NhZmZmdp73oxdtmxZtTjDhw9H48aN8ffff6NcuXLw8/PD4MGDeVwKUT7j8B8RFRlVq1aFSqWSMhm9ePHiaq9VKlW219LT09+7ztDQUPTv3x/t27fH/v37cfHiRXz99ddISUnJMXbmOPXr10fdunWxceNGhIWFISIiAoMHD37vdhCRdjCpIqIiw9LSEu7u7lixYgWSkpKy3I+Pj4eDgwPu37+P+/fvK9evXbuG+Ph41KxZU+M2ZEyGz/zawcEBABASEgI7Ozt8/fXXaNSoEapWrYq//vorV3GGDRsGPz8/rF+/Hq6urmo9b0SUP5hUEVGRsmLFCqSlpcHJyQm7du3CrVu3cP36dSxduhQuLi5wdXWFo6Mj+vfvjwsXLuDs2bMYOHAgmjdvjkaNGmkcf8eOHVi3bh1u3ryJGTNm4OzZs/D29gbwuictOjoa27Ztw507d7B06VLs2bMnV3H69euHBw8e4JdffuEEdaICgkkVERUp9vb2uHDhAlq2bImJEyeidu3aaNOmDYKCgrBy5UqoVCr89ttvKFGiBJo1awZXV1fY29vD399fSvxZs2Zh27ZtqFOnDjZu3IitW7cqPWCdO3fGhAkT4O3tjXr16iEkJATffPNNruKYm5vDw8MDJiYm6Nq1q5S2E5FmVOJ9ZnYSEVGOVCoV9uzZk2dJTuvWrVGrVi0sXbo0T+IR0btx9R8RUSHz5MkTHD9+HMePH8fPP/+c380hov/DpIqIqJCpX78+njx5ggULFqB69er53Rwi+j8c/iMiIiKSgBPViYiIiCRgUkVEREQkAZMqIiIiIgmYVBERERFJwKSKiIiISAImVUREREQSMKkiIiIikoBJFREREZEETKqIiIiIJPh/TAB5cexBPEwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=df['Company'],y=df['Price'])\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e883225e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "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": 19,
   "id": "efe2bb51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=df['TypeName'],y=df['Price'])\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4c4f97ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hp\\AppData\\Local\\Temp\\ipykernel_5656\\1439577752.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",
      "  sns.distplot(df['Inches'])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Inches', ylabel='Density'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Inches'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "94aed230",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Inches', ylabel='Price'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=df['Inches'],y=df['Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "addb4c94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Full HD 1920x1080                                507\n",
       "1366x768                                         281\n",
       "IPS Panel Full HD 1920x1080                      230\n",
       "IPS Panel Full HD / Touchscreen 1920x1080         53\n",
       "Full HD / Touchscreen 1920x1080                   47\n",
       "1600x900                                          23\n",
       "Touchscreen 1366x768                              16\n",
       "Quad HD+ / Touchscreen 3200x1800                  15\n",
       "IPS Panel 4K Ultra HD 3840x2160                   12\n",
       "IPS Panel 4K Ultra HD / Touchscreen 3840x2160     11\n",
       "4K Ultra HD / Touchscreen 3840x2160               10\n",
       "4K Ultra HD 3840x2160                              7\n",
       "Touchscreen 2560x1440                              7\n",
       "IPS Panel 1366x768                                 7\n",
       "IPS Panel Quad HD+ / Touchscreen 3200x1800         6\n",
       "IPS Panel Retina Display 2560x1600                 6\n",
       "IPS Panel Retina Display 2304x1440                 6\n",
       "Touchscreen 2256x1504                              6\n",
       "IPS Panel Touchscreen 2560x1440                    5\n",
       "IPS Panel Retina Display 2880x1800                 4\n",
       "IPS Panel Touchscreen 1920x1200                    4\n",
       "1440x900                                           4\n",
       "IPS Panel 2560x1440                                4\n",
       "IPS Panel Quad HD+ 2560x1440                       3\n",
       "Quad HD+ 3200x1800                                 3\n",
       "1920x1080                                          3\n",
       "Touchscreen 2400x1600                              3\n",
       "2560x1440                                          3\n",
       "IPS Panel Touchscreen 1366x768                     3\n",
       "IPS Panel Touchscreen / 4K Ultra HD 3840x2160      2\n",
       "IPS Panel Full HD 2160x1440                        2\n",
       "IPS Panel Quad HD+ 3200x1800                       2\n",
       "IPS Panel Retina Display 2736x1824                 1\n",
       "IPS Panel Full HD 1920x1200                        1\n",
       "IPS Panel Full HD 2560x1440                        1\n",
       "IPS Panel Full HD 1366x768                         1\n",
       "Touchscreen / Full HD 1920x1080                    1\n",
       "Touchscreen / Quad HD+ 3200x1800                   1\n",
       "Touchscreen / 4K Ultra HD 3840x2160                1\n",
       "IPS Panel Touchscreen 2400x1600                    1\n",
       "Name: ScreenResolution, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ScreenResolution'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b039186d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Touchscreen'] = df['ScreenResolution'].apply(lambda x:1 if 'Touchscreen' in x else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "17ae9fbc",
   "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>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</th>\n",
       "      <th>Touchscreen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>947</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i7 7500U 2.7GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>1TB HDD</td>\n",
       "      <td>Nvidia GeForce 920MX</td>\n",
       "      <td>No OS</td>\n",
       "      <td>2.20</td>\n",
       "      <td>36496.80</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1059</th>\n",
       "      <td>MSI</td>\n",
       "      <td>Gaming</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i7 6700HQ 2.6GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>256GB SSD +  1TB HDD</td>\n",
       "      <td>Nvidia GeForce GTX 1060</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.94</td>\n",
       "      <td>96916.32</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>826</th>\n",
       "      <td>Asus</td>\n",
       "      <td>2 in 1 Convertible</td>\n",
       "      <td>13.3</td>\n",
       "      <td>Touchscreen / 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>Windows 10</td>\n",
       "      <td>1.10</td>\n",
       "      <td>72354.24</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1235</th>\n",
       "      <td>HP</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>4</td>\n",
       "      <td>1TB HDD</td>\n",
       "      <td>Intel HD Graphics 520</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.10</td>\n",
       "      <td>25467.84</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>17.3</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i5 8250U 1.6GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel UHD Graphics 620</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.50</td>\n",
       "      <td>49443.84</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company            TypeName  Inches                 ScreenResolution  \\\n",
       "947   Lenovo            Notebook    15.6                Full HD 1920x1080   \n",
       "1059     MSI              Gaming    15.6                Full HD 1920x1080   \n",
       "826     Asus  2 in 1 Convertible    13.3  Touchscreen / Full HD 1920x1080   \n",
       "1235      HP            Notebook    15.6                Full HD 1920x1080   \n",
       "129       HP            Notebook    17.3                Full HD 1920x1080   \n",
       "\n",
       "                              Cpu  Ram                Memory  \\\n",
       "947    Intel Core i7 7500U 2.7GHz    8               1TB HDD   \n",
       "1059  Intel Core i7 6700HQ 2.6GHz   16  256GB SSD +  1TB HDD   \n",
       "826    Intel Core i5 7200U 2.5GHz    8             256GB SSD   \n",
       "1235     Intel Core i3 6006U 2GHz    4               1TB HDD   \n",
       "129    Intel Core i5 8250U 1.6GHz    8             256GB SSD   \n",
       "\n",
       "                          Gpu       OpSys  Weight     Price  Touchscreen  \n",
       "947      Nvidia GeForce 920MX       No OS    2.20  36496.80            0  \n",
       "1059  Nvidia GeForce GTX 1060  Windows 10    2.94  96916.32            0  \n",
       "826     Intel HD Graphics 620  Windows 10    1.10  72354.24            1  \n",
       "1235    Intel HD Graphics 520  Windows 10    2.10  25467.84            0  \n",
       "129    Intel UHD Graphics 620  Windows 10    2.50  49443.84            0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "97d98054",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "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": 26,
   "id": "eeb7978d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Touchscreen', ylabel='Price'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=df['Touchscreen'],y=df['Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8fd3a24b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Ips'] = df['ScreenResolution'].apply(lambda x:1 if 'IPS' in x else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c4a92ec5",
   "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>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</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</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>71378.6832</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</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>47895.5232</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</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>30636.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</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>135195.3360</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</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>96095.8080</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Inches                    ScreenResolution  \\\n",
       "0   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "1   Apple  Ultrabook    13.3                            1440x900   \n",
       "2      HP   Notebook    15.6                   Full HD 1920x1080   \n",
       "3   Apple  Ultrabook    15.4  IPS Panel Retina Display 2880x1800   \n",
       "4   Apple  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  Touchscreen  Ips  \n",
       "0  Intel Iris Plus Graphics 640  macOS    1.37   71378.6832            0    1  \n",
       "1        Intel HD Graphics 6000  macOS    1.34   47895.5232            0    0  \n",
       "2         Intel HD Graphics 620  No OS    1.86   30636.0000            0    0  \n",
       "3            AMD Radeon Pro 455  macOS    1.83  135195.3360            0    1  \n",
       "4  Intel Iris Plus Graphics 650  macOS    1.37   96095.8080            0    1  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "cedafa70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Ips'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "23eec927",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Ips', ylabel='Price'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=df['Ips'],y=df['Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b5639225",
   "metadata": {},
   "outputs": [],
   "source": [
    "new = df['ScreenResolution'].str.split('x',n=1,expand=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6ae14685",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['X_res'] = new[0]\n",
    "df['Y_res'] = new[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f5e9a7d6",
   "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>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</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>X_res</th>\n",
       "      <th>Y_res</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>834</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>14.0</td>\n",
       "      <td>IPS Panel Full HD 1920x1080</td>\n",
       "      <td>Intel Core i5 7300U 2.6GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.32</td>\n",
       "      <td>87912.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>IPS Panel Full HD 1920</td>\n",
       "      <td>1080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>Asus</td>\n",
       "      <td>2 in 1 Convertible</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Touchscreen 1366x768</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>1TB HDD</td>\n",
       "      <td>Intel HD Graphics 520</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.20</td>\n",
       "      <td>39373.9200</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Touchscreen 1366</td>\n",
       "      <td>768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>Asus</td>\n",
       "      <td>2 in 1 Convertible</td>\n",
       "      <td>13.3</td>\n",
       "      <td>Full HD / Touchscreen 1920x1080</td>\n",
       "      <td>Intel Core i7 8550U 1.8GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>512GB SSD +  512GB SSD</td>\n",
       "      <td>Intel UHD Graphics 620</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.10</td>\n",
       "      <td>79866.7200</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Full HD / Touchscreen 1920</td>\n",
       "      <td>1080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>604</th>\n",
       "      <td>Dell</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>Intel Core i3 6006U 2GHz</td>\n",
       "      <td>4</td>\n",
       "      <td>1TB HDD</td>\n",
       "      <td>AMD Radeon R5 M420</td>\n",
       "      <td>Linux</td>\n",
       "      <td>2.18</td>\n",
       "      <td>24775.2000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1366</td>\n",
       "      <td>768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1016</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>Intel Core i3 7100U 2.4GHz</td>\n",
       "      <td>4</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.04</td>\n",
       "      <td>41025.0672</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1366</td>\n",
       "      <td>768</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company            TypeName  Inches                 ScreenResolution  \\\n",
       "834   Lenovo            Notebook    14.0      IPS Panel Full HD 1920x1080   \n",
       "590     Asus  2 in 1 Convertible    15.6             Touchscreen 1366x768   \n",
       "266     Asus  2 in 1 Convertible    13.3  Full HD / Touchscreen 1920x1080   \n",
       "604     Dell            Notebook    15.6                         1366x768   \n",
       "1016      HP            Notebook    15.6                         1366x768   \n",
       "\n",
       "                             Cpu  Ram                  Memory  \\\n",
       "834   Intel Core i5 7300U 2.6GHz    8               256GB SSD   \n",
       "590   Intel Core i5 7200U 2.5GHz    8                 1TB HDD   \n",
       "266   Intel Core i7 8550U 1.8GHz    8  512GB SSD +  512GB SSD   \n",
       "604     Intel Core i3 6006U 2GHz    4                 1TB HDD   \n",
       "1016  Intel Core i3 7100U 2.4GHz    4               256GB SSD   \n",
       "\n",
       "                         Gpu       OpSys  Weight       Price  Touchscreen  \\\n",
       "834    Intel HD Graphics 620  Windows 10    1.32  87912.0000            0   \n",
       "590    Intel HD Graphics 520  Windows 10    2.20  39373.9200            1   \n",
       "266   Intel UHD Graphics 620  Windows 10    1.10  79866.7200            1   \n",
       "604       AMD Radeon R5 M420       Linux    2.18  24775.2000            0   \n",
       "1016   Intel HD Graphics 620  Windows 10    2.04  41025.0672            0   \n",
       "\n",
       "      Ips                       X_res Y_res  \n",
       "834     1      IPS Panel Full HD 1920  1080  \n",
       "590     0            Touchscreen 1366   768  \n",
       "266     0  Full HD / Touchscreen 1920  1080  \n",
       "604     0                        1366   768  \n",
       "1016    0                        1366   768  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a67016b8",
   "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": 35,
   "id": "217653d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
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       "      <td>0</td>\n",
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       "      <td>2560</td>\n",
       "      <td>1600</td>\n",
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       "      <th>1</th>\n",
       "      <td>Apple</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",
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       "      <td>1.34</td>\n",
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       "      <td>0</td>\n",
       "      <td>1440</td>\n",
       "      <td>900</td>\n",
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       "      <th>2</th>\n",
       "      <td>HP</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>30636.0000</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",
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       "      <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>135195.3360</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>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>96095.8080</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   TypeName  Inches                    ScreenResolution  \\\n",
       "0   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "1   Apple  Ultrabook    13.3                            1440x900   \n",
       "2      HP   Notebook    15.6                   Full HD 1920x1080   \n",
       "3   Apple  Ultrabook    15.4  IPS Panel Retina Display 2880x1800   \n",
       "4   Apple  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  Touchscreen  Ips  \\\n",
       "0  Intel Iris Plus Graphics 640  macOS    1.37   71378.6832            0    1   \n",
       "1        Intel HD Graphics 6000  macOS    1.34   47895.5232            0    0   \n",
       "2         Intel HD Graphics 620  No OS    1.86   30636.0000            0    0   \n",
       "3            AMD Radeon Pro 455  macOS    1.83  135195.3360            0    1   \n",
       "4  Intel Iris Plus Graphics 650  macOS    1.37   96095.8080            0    1   \n",
       "\n",
       "  X_res Y_res  \n",
       "0  2560  1600  \n",
       "1  1440   900  \n",
       "2  1920  1080  \n",
       "3  2880  1800  \n",
       "4  2560  1600  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "1d526b74",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['X_res'] = df['X_res'].astype('int')\n",
    "df['Y_res'] = df['Y_res'].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "fac29fe5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1303 entries, 0 to 1302\n",
      "Data columns (total 15 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Company           1303 non-null   object \n",
      " 1   TypeName          1303 non-null   object \n",
      " 2   Inches            1303 non-null   float64\n",
      " 3   ScreenResolution  1303 non-null   object \n",
      " 4   Cpu               1303 non-null   object \n",
      " 5   Ram               1303 non-null   int32  \n",
      " 6   Memory            1303 non-null   object \n",
      " 7   Gpu               1303 non-null   object \n",
      " 8   OpSys             1303 non-null   object \n",
      " 9   Weight            1303 non-null   float32\n",
      " 10  Price             1303 non-null   float64\n",
      " 11  Touchscreen       1303 non-null   int64  \n",
      " 12  Ips               1303 non-null   int64  \n",
      " 13  X_res             1303 non-null   int32  \n",
      " 14  Y_res             1303 non-null   int32  \n",
      "dtypes: float32(1), float64(2), int32(3), int64(2), object(7)\n",
      "memory usage: 132.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "00a94443",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Inches         0.068197\n",
       "Ram            0.743007\n",
       "Weight         0.210370\n",
       "Price          1.000000\n",
       "Touchscreen    0.191226\n",
       "Ips            0.252208\n",
       "X_res          0.556529\n",
       "Y_res          0.552809\n",
       "Name: Price, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()['Price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a04a97db",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ppi'] = (((df['X_res']**2) + (df['Y_res']**2))**0.5/df['Inches']).astype('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f5b47d4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Inches         0.068197\n",
       "Ram            0.743007\n",
       "Weight         0.210370\n",
       "Price          1.000000\n",
       "Touchscreen    0.191226\n",
       "Ips            0.252208\n",
       "X_res          0.556529\n",
       "Y_res          0.552809\n",
       "ppi            0.473487\n",
       "Name: Price, dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()['Price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "624f0b2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['ScreenResolution'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "90f6d5b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</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",
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       "      <td>0</td>\n",
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       "      <td>15.4</td>\n",
       "      <td>Intel Core i7 2.7GHz</td>\n",
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       "      <td>1.83</td>\n",
       "      <td>135195.3360</td>\n",
       "      <td>0</td>\n",
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       "      <td>1800</td>\n",
       "      <td>220.534624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>Intel Core i5 3.1GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
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       "      <td>1600</td>\n",
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      ],
      "text/plain": [
       "  Company   TypeName  Inches                         Cpu  Ram  \\\n",
       "0   Apple  Ultrabook    13.3        Intel Core i5 2.3GHz    8   \n",
       "1   Apple  Ultrabook    13.3        Intel Core i5 1.8GHz    8   \n",
       "2      HP   Notebook    15.6  Intel Core i5 7200U 2.5GHz    8   \n",
       "3   Apple  Ultrabook    15.4        Intel Core i7 2.7GHz   16   \n",
       "4   Apple  Ultrabook    13.3        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  Touchscreen  Ips  X_res  Y_res         ppi  \n",
       "0   71378.6832            0    1   2560   1600  226.983005  \n",
       "1   47895.5232            0    0   1440    900  127.677940  \n",
       "2   30636.0000            0    0   1920   1080  141.211998  \n",
       "3  135195.3360            0    1   2880   1800  220.534624  \n",
       "4   96095.8080            0    1   2560   1600  226.983005  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "d3c28994",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['Inches','X_res','Y_res'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "8a09d7d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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>47895.5232</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>127.677940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</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>30636.0000</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>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>135195.3360</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>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>96095.8080</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   TypeName                         Cpu  Ram               Memory  \\\n",
       "0   Apple  Ultrabook        Intel Core i5 2.3GHz    8            128GB SSD   \n",
       "1   Apple  Ultrabook        Intel Core i5 1.8GHz    8  128GB Flash Storage   \n",
       "2      HP   Notebook  Intel Core i5 7200U 2.5GHz    8            256GB SSD   \n",
       "3   Apple  Ultrabook        Intel Core i7 2.7GHz   16            512GB SSD   \n",
       "4   Apple  Ultrabook        Intel Core i5 3.1GHz    8            256GB SSD   \n",
       "\n",
       "                            Gpu  OpSys  Weight        Price  Touchscreen  Ips  \\\n",
       "0  Intel Iris Plus Graphics 640  macOS    1.37   71378.6832            0    1   \n",
       "1        Intel HD Graphics 6000  macOS    1.34   47895.5232            0    0   \n",
       "2         Intel HD Graphics 620  No OS    1.86   30636.0000            0    0   \n",
       "3            AMD Radeon Pro 455  macOS    1.83  135195.3360            0    1   \n",
       "4  Intel Iris Plus Graphics 650  macOS    1.37   96095.8080            0    1   \n",
       "\n",
       "          ppi  \n",
       "0  226.983005  \n",
       "1  127.677940  \n",
       "2  141.211998  \n",
       "3  220.534624  \n",
       "4  226.983005  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "b825d055",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intel Core i5 7200U 2.5GHz       190\n",
       "Intel Core i7 7700HQ 2.8GHz      146\n",
       "Intel Core i7 7500U 2.7GHz       134\n",
       "Intel Core i7 8550U 1.8GHz        73\n",
       "Intel Core i5 8250U 1.6GHz        72\n",
       "                                ... \n",
       "Intel Core M M3-6Y30 0.9GHz        1\n",
       "AMD A9-Series 9420 2.9GHz          1\n",
       "Intel Core i3 6006U 2.2GHz         1\n",
       "AMD A6-Series 7310 2GHz            1\n",
       "Intel Xeon E3-1535M v6 3.1GHz      1\n",
       "Name: Cpu, Length: 118, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Cpu'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "0edc5c05",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Cpu Name'] = df['Cpu'].apply(lambda x:\" \".join(x.split()[0:3]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "900ee4c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Cpu</th>\n",
       "      <th>Ram</th>\n",
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       "      <th>Gpu</th>\n",
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       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>ppi</th>\n",
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       "      <th>0</th>\n",
       "      <td>Apple</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>71378.6832</td>\n",
       "      <td>0</td>\n",
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       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
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       "      <td>8</td>\n",
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       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>47895.5232</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>127.677940</td>\n",
       "      <td>Intel Core i5</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</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>30636.0000</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>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>135195.3360</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>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>96095.8080</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   TypeName                         Cpu  Ram               Memory  \\\n",
       "0   Apple  Ultrabook        Intel Core i5 2.3GHz    8            128GB SSD   \n",
       "1   Apple  Ultrabook        Intel Core i5 1.8GHz    8  128GB Flash Storage   \n",
       "2      HP   Notebook  Intel Core i5 7200U 2.5GHz    8            256GB SSD   \n",
       "3   Apple  Ultrabook        Intel Core i7 2.7GHz   16            512GB SSD   \n",
       "4   Apple  Ultrabook        Intel Core i5 3.1GHz    8            256GB SSD   \n",
       "\n",
       "                            Gpu  OpSys  Weight        Price  Touchscreen  Ips  \\\n",
       "0  Intel Iris Plus Graphics 640  macOS    1.37   71378.6832            0    1   \n",
       "1        Intel HD Graphics 6000  macOS    1.34   47895.5232            0    0   \n",
       "2         Intel HD Graphics 620  No OS    1.86   30636.0000            0    0   \n",
       "3            AMD Radeon Pro 455  macOS    1.83  135195.3360            0    1   \n",
       "4  Intel Iris Plus Graphics 650  macOS    1.37   96095.8080            0    1   \n",
       "\n",
       "          ppi       Cpu Name  \n",
       "0  226.983005  Intel Core i5  \n",
       "1  127.677940  Intel Core i5  \n",
       "2  141.211998  Intel Core i5  \n",
       "3  220.534624  Intel Core i7  \n",
       "4  226.983005  Intel Core i5  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "f80156be",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fetch_processor(text):\n",
    "    if text == 'Intel Core i7' or text == 'Intel Core i5' or text == 'Intel Core i3':\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": 49,
   "id": "97dd584a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Cpu brand'] = df['Cpu Name'].apply(fetch_processor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "a8cb339d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th>TypeName</th>\n",
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       "      <th>Gpu</th>\n",
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       "      <th>Price</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
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       "      <td>1.37</td>\n",
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       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</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>47895.5232</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>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>30636.0000</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>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>135195.3360</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>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>96095.8080</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   TypeName                         Cpu  Ram               Memory  \\\n",
       "0   Apple  Ultrabook        Intel Core i5 2.3GHz    8            128GB SSD   \n",
       "1   Apple  Ultrabook        Intel Core i5 1.8GHz    8  128GB Flash Storage   \n",
       "2      HP   Notebook  Intel Core i5 7200U 2.5GHz    8            256GB SSD   \n",
       "3   Apple  Ultrabook        Intel Core i7 2.7GHz   16            512GB SSD   \n",
       "4   Apple  Ultrabook        Intel Core i5 3.1GHz    8            256GB SSD   \n",
       "\n",
       "                            Gpu  OpSys  Weight        Price  Touchscreen  Ips  \\\n",
       "0  Intel Iris Plus Graphics 640  macOS    1.37   71378.6832            0    1   \n",
       "1        Intel HD Graphics 6000  macOS    1.34   47895.5232            0    0   \n",
       "2         Intel HD Graphics 620  No OS    1.86   30636.0000            0    0   \n",
       "3            AMD Radeon Pro 455  macOS    1.83  135195.3360            0    1   \n",
       "4  Intel Iris Plus Graphics 650  macOS    1.37   96095.8080            0    1   \n",
       "\n",
       "          ppi       Cpu Name      Cpu brand  \n",
       "0  226.983005  Intel Core i5  Intel Core i5  \n",
       "1  127.677940  Intel Core i5  Intel Core i5  \n",
       "2  141.211998  Intel Core i5  Intel Core i5  \n",
       "3  220.534624  Intel Core i7  Intel Core i7  \n",
       "4  226.983005  Intel Core i5  Intel Core i5  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "da7da8f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Cpu brand'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "6adf038f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=df['Cpu brand'],y=df['Price'])\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "b83b0272",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['Cpu','Cpu Name'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "c12052f8",
   "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>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</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>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>71378.6832</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>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>47895.5232</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>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>30636.0000</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>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>135195.3360</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>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>96095.8080</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   TypeName  Ram               Memory                           Gpu  \\\n",
       "0   Apple  Ultrabook    8            128GB SSD  Intel Iris Plus Graphics 640   \n",
       "1   Apple  Ultrabook    8  128GB Flash Storage        Intel HD Graphics 6000   \n",
       "2      HP   Notebook    8            256GB SSD         Intel HD Graphics 620   \n",
       "3   Apple  Ultrabook   16            512GB SSD            AMD Radeon Pro 455   \n",
       "4   Apple  Ultrabook    8            256GB SSD  Intel Iris Plus Graphics 650   \n",
       "\n",
       "   OpSys  Weight        Price  Touchscreen  Ips         ppi      Cpu brand  \n",
       "0  macOS    1.37   71378.6832            0    1  226.983005  Intel Core i5  \n",
       "1  macOS    1.34   47895.5232            0    0  127.677940  Intel Core i5  \n",
       "2  No OS    1.86   30636.0000            0    0  141.211998  Intel Core i5  \n",
       "3  macOS    1.83  135195.3360            0    1  220.534624  Intel Core i7  \n",
       "4  macOS    1.37   96095.8080            0    1  226.983005  Intel Core i5  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "1c629507",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Ram'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "153b8704",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=df['Ram'],y=df['Price'])\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "ab12fa56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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: Memory, dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Memory'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "c2aeabf0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hp\\AppData\\Local\\Temp\\ipykernel_5656\\4023190604.py:16: FutureWarning: The default value of regex will change from True to False in a future version.\n",
      "  df['first'] = df['first'].str.replace(r'\\D', '')\n",
      "C:\\Users\\Hp\\AppData\\Local\\Temp\\ipykernel_5656\\4023190604.py:25: FutureWarning: The default value of regex will change from True to False in a future version.\n",
      "  df['second'] = df['second'].str.replace(r'\\D', '')\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",
    "df[\"first\"]= new[0]\n",
    "df[\"first\"]=df[\"first\"].str.strip()\n",
    "\n",
    "df[\"second\"]= new[1]\n",
    "\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', '')\n",
    "\n",
    "df[\"second\"].fillna(\"0\", inplace = 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', '')\n",
    "\n",
    "df[\"first\"] = df[\"first\"].astype(int)\n",
    "df[\"second\"] = df[\"second\"].astype(int)\n",
    "\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",
    "df.drop(columns=['first', 'second', 'Layer1HDD', 'Layer1SSD', 'Layer1Hybrid',\n",
    "       'Layer1Flash_Storage', 'Layer2HDD', 'Layer2SSD', 'Layer2Hybrid',\n",
    "       'Layer2Flash_Storage'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "1243a465",
   "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>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>ppi</th>\n",
       "      <th>Cpu brand</th>\n",
       "      <th>HDD</th>\n",
       "      <th>SSD</th>\n",
       "      <th>Hybrid</th>\n",
       "      <th>Flash_Storage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>935</th>\n",
       "      <td>HP</td>\n",
       "      <td>Netbook</td>\n",
       "      <td>8</td>\n",
       "      <td>256 SSD</td>\n",
       "      <td>Intel HD Graphics 520</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.26</td>\n",
       "      <td>88924.3200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>176.232574</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>692</th>\n",
       "      <td>HP</td>\n",
       "      <td>Workstation</td>\n",
       "      <td>8</td>\n",
       "      <td>500 HDD</td>\n",
       "      <td>Nvidia Quadro M1200</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>3.14</td>\n",
       "      <td>101657.7072</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>127.335675</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603</th>\n",
       "      <td>MSI</td>\n",
       "      <td>Gaming</td>\n",
       "      <td>16</td>\n",
       "      <td>128 SSD +  1000 HDD</td>\n",
       "      <td>Nvidia GeForce GTX 970M</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>4.14</td>\n",
       "      <td>117162.7200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>127.335675</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>1000</td>\n",
       "      <td>128</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1233</th>\n",
       "      <td>MSI</td>\n",
       "      <td>Gaming</td>\n",
       "      <td>16</td>\n",
       "      <td>256 SSD +  1000 HDD</td>\n",
       "      <td>Nvidia GeForce GTX 1060</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.70</td>\n",
       "      <td>85141.4400</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>127.335675</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>1000</td>\n",
       "      <td>256</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>Asus</td>\n",
       "      <td>Gaming</td>\n",
       "      <td>16</td>\n",
       "      <td>256 SSD</td>\n",
       "      <td>Nvidia GeForce GTX 1080</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>3.60</td>\n",
       "      <td>159786.7200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>127.335675</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company     TypeName  Ram               Memory                      Gpu  \\\n",
       "935       HP      Netbook    8              256 SSD    Intel HD Graphics 520   \n",
       "692       HP  Workstation    8              500 HDD      Nvidia Quadro M1200   \n",
       "603      MSI       Gaming   16  128 SSD +  1000 HDD  Nvidia GeForce GTX 970M   \n",
       "1233     MSI       Gaming   16  256 SSD +  1000 HDD  Nvidia GeForce GTX 1060   \n",
       "247     Asus       Gaming   16              256 SSD  Nvidia GeForce GTX 1080   \n",
       "\n",
       "           OpSys  Weight        Price  Touchscreen  Ips         ppi  \\\n",
       "935   Windows 10    1.26   88924.3200            0    0  176.232574   \n",
       "692   Windows 10    3.14  101657.7072            0    0  127.335675   \n",
       "603   Windows 10    4.14  117162.7200            0    0  127.335675   \n",
       "1233  Windows 10    2.70   85141.4400            0    0  127.335675   \n",
       "247   Windows 10    3.60  159786.7200            0    0  127.335675   \n",
       "\n",
       "          Cpu brand   HDD  SSD  Hybrid  Flash_Storage  \n",
       "935   Intel Core i5     0  256       0              0  \n",
       "692   Intel Core i7   500    0       0              0  \n",
       "603   Intel Core i7  1000  128       0              0  \n",
       "1233  Intel Core i7  1000  256       0              0  \n",
       "247   Intel Core i7     0  256       0              0  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "4825c8b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['Memory'],inplace=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "c401a663",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
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       "      <th>Gpu</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
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       "      <td>8</td>\n",
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       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>71378.6832</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>47895.5232</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <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>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>30636.0000</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",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>16</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>135195.3360</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>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>96095.8080</td>\n",
       "      <td>0</td>\n",
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       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Ram                           Gpu  OpSys  Weight  \\\n",
       "0   Apple  Ultrabook    8  Intel Iris Plus Graphics 640  macOS    1.37   \n",
       "1   Apple  Ultrabook    8        Intel HD Graphics 6000  macOS    1.34   \n",
       "2      HP   Notebook    8         Intel HD Graphics 620  No OS    1.86   \n",
       "3   Apple  Ultrabook   16            AMD Radeon Pro 455  macOS    1.83   \n",
       "4   Apple  Ultrabook    8  Intel Iris Plus Graphics 650  macOS    1.37   \n",
       "\n",
       "         Price  Touchscreen  Ips         ppi      Cpu brand  HDD  SSD  Hybrid  \\\n",
       "0   71378.6832            0    1  226.983005  Intel Core i5    0  128       0   \n",
       "1   47895.5232            0    0  127.677940  Intel Core i5    0    0       0   \n",
       "2   30636.0000            0    0  141.211998  Intel Core i5    0  256       0   \n",
       "3  135195.3360            0    1  220.534624  Intel Core i7    0  512       0   \n",
       "4   96095.8080            0    1  226.983005  Intel Core i5    0  256       0   \n",
       "\n",
       "   Flash_Storage  \n",
       "0              0  \n",
       "1            128  \n",
       "2              0  \n",
       "3              0  \n",
       "4              0  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "379ea6e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ram              0.743007\n",
       "Weight           0.210370\n",
       "Price            1.000000\n",
       "Touchscreen      0.191226\n",
       "Ips              0.252208\n",
       "ppi              0.473487\n",
       "HDD             -0.096441\n",
       "SSD              0.670799\n",
       "Hybrid           0.007989\n",
       "Flash_Storage   -0.040511\n",
       "Name: Price, dtype: float64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()['Price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "9a2b3855",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['Hybrid','Flash_Storage'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "70763eca",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>30636.0000</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",
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       "      <th>3</th>\n",
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       "      <td>Ultrabook</td>\n",
       "      <td>16</td>\n",
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       "      <td>512</td>\n",
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       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
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       "      <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",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Ram                           Gpu  OpSys  Weight  \\\n",
       "0   Apple  Ultrabook    8  Intel Iris Plus Graphics 640  macOS    1.37   \n",
       "1   Apple  Ultrabook    8        Intel HD Graphics 6000  macOS    1.34   \n",
       "2      HP   Notebook    8         Intel HD Graphics 620  No OS    1.86   \n",
       "3   Apple  Ultrabook   16            AMD Radeon Pro 455  macOS    1.83   \n",
       "4   Apple  Ultrabook    8  Intel Iris Plus Graphics 650  macOS    1.37   \n",
       "\n",
       "         Price  Touchscreen  Ips         ppi      Cpu brand  HDD  SSD  \n",
       "0   71378.6832            0    1  226.983005  Intel Core i5    0  128  \n",
       "1   47895.5232            0    0  127.677940  Intel Core i5    0    0  \n",
       "2   30636.0000            0    0  141.211998  Intel Core i5    0  256  \n",
       "3  135195.3360            0    1  220.534624  Intel Core i7    0  512  \n",
       "4   96095.8080            0    1  226.983005  Intel Core i5    0  256  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "1ef359b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intel HD Graphics 620      281\n",
       "Intel HD Graphics 520      185\n",
       "Intel UHD Graphics 620      68\n",
       "Nvidia GeForce GTX 1050     66\n",
       "Nvidia GeForce GTX 1060     48\n",
       "                          ... \n",
       "AMD Radeon R5 520            1\n",
       "AMD Radeon R7                1\n",
       "Intel HD Graphics 540        1\n",
       "AMD Radeon 540               1\n",
       "ARM Mali T860 MP4            1\n",
       "Name: Gpu, Length: 110, dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gpu'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "e28521df",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Gpu brand'] = df['Gpu'].apply(lambda x:x.split()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "a3fa9f26",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>47895.5232</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>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>30636.0000</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>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>135195.3360</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>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>96095.8080</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                           Gpu  OpSys  Weight  \\\n",
       "0   Apple  Ultrabook    8  Intel Iris Plus Graphics 640  macOS    1.37   \n",
       "1   Apple  Ultrabook    8        Intel HD Graphics 6000  macOS    1.34   \n",
       "2      HP   Notebook    8         Intel HD Graphics 620  No OS    1.86   \n",
       "3   Apple  Ultrabook   16            AMD Radeon Pro 455  macOS    1.83   \n",
       "4   Apple  Ultrabook    8  Intel Iris Plus Graphics 650  macOS    1.37   \n",
       "\n",
       "         Price  Touchscreen  Ips         ppi      Cpu brand  HDD  SSD  \\\n",
       "0   71378.6832            0    1  226.983005  Intel Core i5    0  128   \n",
       "1   47895.5232            0    0  127.677940  Intel Core i5    0    0   \n",
       "2   30636.0000            0    0  141.211998  Intel Core i5    0  256   \n",
       "3  135195.3360            0    1  220.534624  Intel Core i7    0  512   \n",
       "4   96095.8080            0    1  226.983005  Intel Core i5    0  256   \n",
       "\n",
       "  Gpu brand  \n",
       "0     Intel  \n",
       "1     Intel  \n",
       "2     Intel  \n",
       "3       AMD  \n",
       "4     Intel  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "64b57bce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intel     722\n",
       "Nvidia    400\n",
       "AMD       180\n",
       "ARM         1\n",
       "Name: Gpu brand, dtype: int64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gpu brand'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "b65a63ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df['Gpu brand'] != 'ARM']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "9b9a9ac8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intel     722\n",
       "Nvidia    400\n",
       "AMD       180\n",
       "Name: Gpu brand, dtype: int64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gpu brand'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "95134c31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=df['Gpu brand'],y=df['Price'],estimator=np.median)\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "49fc2652",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hp\\AppData\\Local\\Temp\\ipykernel_5656\\1111925144.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df.drop(columns=['Gpu'],inplace=True)\n"
     ]
    }
   ],
   "source": [
    "df.drop(columns=['Gpu'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "09a01582",
   "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</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</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>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>71378.6832</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>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>47895.5232</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>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>30636.0000</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>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>135195.3360</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>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>96095.8080</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        Price  Touchscreen  Ips  \\\n",
       "0   Apple  Ultrabook    8  macOS    1.37   71378.6832            0    1   \n",
       "1   Apple  Ultrabook    8  macOS    1.34   47895.5232            0    0   \n",
       "2      HP   Notebook    8  No OS    1.86   30636.0000            0    0   \n",
       "3   Apple  Ultrabook   16  macOS    1.83  135195.3360            0    1   \n",
       "4   Apple  Ultrabook    8  macOS    1.37   96095.8080            0    1   \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  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "d5baad82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Windows 10      1072\n",
       "No OS             66\n",
       "Linux             62\n",
       "Windows 7         45\n",
       "Chrome OS         26\n",
       "macOS             13\n",
       "Mac OS X           8\n",
       "Windows 10 S       8\n",
       "Android            2\n",
       "Name: OpSys, dtype: int64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['OpSys'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "f7b5aca9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=df['OpSys'],y=df['Price'])\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "a862adba",
   "metadata": {},
   "outputs": [],
   "source": [
    "def cat_os(inp):\n",
    "    if inp == 'Windows 10' or inp == 'Windows 7' or inp == 'Windows 10 S':\n",
    "        return 'Windows'\n",
    "    elif inp == 'macOS' or inp == 'Mac OS X':\n",
    "        return 'Mac'\n",
    "    else:\n",
    "        return 'Others/No OS/Linux'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "27a05f17",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hp\\AppData\\Local\\Temp\\ipykernel_5656\\3648919379.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['os'] = df['OpSys'].apply(cat_os)\n"
     ]
    }
   ],
   "source": [
    "df['os'] = df['OpSys'].apply(cat_os)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "fcb3ae34",
   "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</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>ppi</th>\n",
       "      <th>Cpu brand</th>\n",
       "      <th>HDD</th>\n",
       "      <th>SSD</th>\n",
       "      <th>Gpu brand</th>\n",
       "      <th>os</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>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>71378.6832</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",
       "      <td>Mac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>47895.5232</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",
       "      <td>Mac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>30636.0000</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",
       "      <td>Others/No OS/Linux</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>16</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>135195.3360</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",
       "      <td>Mac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>96095.8080</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",
       "      <td>Mac</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Ram  OpSys  Weight        Price  Touchscreen  Ips  \\\n",
       "0   Apple  Ultrabook    8  macOS    1.37   71378.6832            0    1   \n",
       "1   Apple  Ultrabook    8  macOS    1.34   47895.5232            0    0   \n",
       "2      HP   Notebook    8  No OS    1.86   30636.0000            0    0   \n",
       "3   Apple  Ultrabook   16  macOS    1.83  135195.3360            0    1   \n",
       "4   Apple  Ultrabook    8  macOS    1.37   96095.8080            0    1   \n",
       "\n",
       "          ppi      Cpu brand  HDD  SSD Gpu brand                  os  \n",
       "0  226.983005  Intel Core i5    0  128     Intel                 Mac  \n",
       "1  127.677940  Intel Core i5    0    0     Intel                 Mac  \n",
       "2  141.211998  Intel Core i5    0  256     Intel  Others/No OS/Linux  \n",
       "3  220.534624  Intel Core i7    0  512       AMD                 Mac  \n",
       "4  226.983005  Intel Core i5    0  256     Intel                 Mac  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "ed3bc51f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hp\\AppData\\Local\\Temp\\ipykernel_5656\\3105339334.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df.drop(columns=['OpSys'],inplace=True)\n"
     ]
    }
   ],
   "source": [
    "df.drop(columns=['OpSys'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "e17e68dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=df['os'],y=df['Price'])\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "226a5c1b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hp\\AppData\\Local\\Temp\\ipykernel_5656\\1125578356.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",
      "  sns.distplot(df['Weight'])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Weight', ylabel='Density'>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Weight'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "864b88a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Weight', ylabel='Price'>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=df['Weight'],y=df['Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "f698866d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ram            0.742905\n",
       "Weight         0.209867\n",
       "Price          1.000000\n",
       "Touchscreen    0.192917\n",
       "Ips            0.253320\n",
       "ppi            0.475368\n",
       "HDD           -0.096891\n",
       "SSD            0.670660\n",
       "Name: Price, dtype: float64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()['Price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "44fedc78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "cdddbaa3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hp\\AppData\\Local\\Temp\\ipykernel_5656\\3556049916.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",
      "  sns.distplot(np.log(df['Price']))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Price', ylabel='Density'>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(np.log(df['Price']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "36b8e93f",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(columns=['Price'])\n",
    "y = np.log(df['Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "8f734e41",
   "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>Weight</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>ppi</th>\n",
       "      <th>Cpu brand</th>\n",
       "      <th>HDD</th>\n",
       "      <th>SSD</th>\n",
       "      <th>Gpu brand</th>\n",
       "      <th>os</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>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",
       "      <td>Mac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</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",
       "      <td>Mac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</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",
       "      <td>Others/No OS/Linux</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>16</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",
       "      <td>Mac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</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",
       "      <td>Mac</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>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",
       "      <td>Windows</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>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",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1300</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>2</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",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1301</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>6</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",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1302</th>\n",
       "      <td>Asus</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>4</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",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1302 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company            TypeName  Ram  Weight  Touchscreen  Ips         ppi  \\\n",
       "0      Apple           Ultrabook    8    1.37            0    1  226.983005   \n",
       "1      Apple           Ultrabook    8    1.34            0    0  127.677940   \n",
       "2         HP            Notebook    8    1.86            0    0  141.211998   \n",
       "3      Apple           Ultrabook   16    1.83            0    1  220.534624   \n",
       "4      Apple           Ultrabook    8    1.37            0    1  226.983005   \n",
       "...      ...                 ...  ...     ...          ...  ...         ...   \n",
       "1298  Lenovo  2 in 1 Convertible    4    1.80            1    1  157.350512   \n",
       "1299  Lenovo  2 in 1 Convertible   16    1.30            1    1  276.053530   \n",
       "1300  Lenovo            Notebook    2    1.50            0    0  111.935204   \n",
       "1301      HP            Notebook    6    2.19            0    0  100.454670   \n",
       "1302    Asus            Notebook    4    2.20            0    0  100.454670   \n",
       "\n",
       "                  Cpu brand   HDD  SSD Gpu brand                  os  \n",
       "0             Intel Core i5     0  128     Intel                 Mac  \n",
       "1             Intel Core i5     0    0     Intel                 Mac  \n",
       "2             Intel Core i5     0  256     Intel  Others/No OS/Linux  \n",
       "3             Intel Core i7     0  512       AMD                 Mac  \n",
       "4             Intel Core i5     0  256     Intel                 Mac  \n",
       "...                     ...   ...  ...       ...                 ...  \n",
       "1298          Intel Core i7     0  128     Intel             Windows  \n",
       "1299          Intel Core i7     0  512     Intel             Windows  \n",
       "1300  Other Intel Processor     0    0     Intel             Windows  \n",
       "1301          Intel Core i7  1000    0       AMD             Windows  \n",
       "1302  Other Intel Processor   500    0     Intel             Windows  \n",
       "\n",
       "[1302 rows x 12 columns]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "862d8fa9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       11.175755\n",
       "1       10.776777\n",
       "2       10.329931\n",
       "3       11.814476\n",
       "4       11.473101\n",
       "          ...    \n",
       "1298    10.433899\n",
       "1299    11.288115\n",
       "1300     9.409283\n",
       "1301    10.614129\n",
       "1302     9.886358\n",
       "Name: Price, Length: 1302, dtype: float64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "bc59f214",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.15,random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "6d35bebd",
   "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>Weight</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>ppi</th>\n",
       "      <th>Cpu brand</th>\n",
       "      <th>HDD</th>\n",
       "      <th>SSD</th>\n",
       "      <th>Gpu brand</th>\n",
       "      <th>os</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>Toshiba</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.454670</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1141</th>\n",
       "      <td>MSI</td>\n",
       "      <td>Gaming</td>\n",
       "      <td>8</td>\n",
       "      <td>2.40</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>128</td>\n",
       "      <td>Nvidia</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1049</th>\n",
       "      <td>Asus</td>\n",
       "      <td>Netbook</td>\n",
       "      <td>4</td>\n",
       "      <td>1.20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>135.094211</td>\n",
       "      <td>Other Intel Processor</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Others/No OS/Linux</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1020</th>\n",
       "      <td>Dell</td>\n",
       "      <td>2 in 1 Convertible</td>\n",
       "      <td>4</td>\n",
       "      <td>2.08</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i3</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>878</th>\n",
       "      <td>Dell</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>4</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>1000</td>\n",
       "      <td>128</td>\n",
       "      <td>Nvidia</td>\n",
       "      <td>Windows</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>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",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>Asus</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>16</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",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>Acer</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</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",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>527</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</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",
       "      <td>Others/No OS/Linux</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1193</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</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",
       "      <td>Mac</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1106 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Company            TypeName  Ram  Weight  Touchscreen  Ips         ppi  \\\n",
       "183   Toshiba            Notebook    8    2.00            0    0  100.454670   \n",
       "1141      MSI              Gaming    8    2.40            0    0  141.211998   \n",
       "1049     Asus             Netbook    4    1.20            0    0  135.094211   \n",
       "1020     Dell  2 in 1 Convertible    4    2.08            1    1  141.211998   \n",
       "878      Dell            Notebook    4    2.18            0    0  141.211998   \n",
       "...       ...                 ...  ...     ...          ...  ...         ...   \n",
       "466      Acer            Notebook    4    2.20            0    0  100.454670   \n",
       "299      Asus           Ultrabook   16    1.63            0    0  141.211998   \n",
       "493      Acer            Notebook    8    2.20            0    0  100.454670   \n",
       "527    Lenovo            Notebook    8    2.20            0    0  100.454670   \n",
       "1193    Apple           Ultrabook    8    0.92            0    1  226.415547   \n",
       "\n",
       "                  Cpu brand   HDD  SSD Gpu brand                  os  \n",
       "183           Intel Core i5     0  128     Intel             Windows  \n",
       "1141          Intel Core i7  1000  128    Nvidia             Windows  \n",
       "1049  Other Intel Processor     0    0     Intel  Others/No OS/Linux  \n",
       "1020          Intel Core i3  1000    0     Intel             Windows  \n",
       "878           Intel Core i5  1000  128    Nvidia             Windows  \n",
       "...                     ...   ...  ...       ...                 ...  \n",
       "466           Intel Core i3   500    0    Nvidia             Windows  \n",
       "299           Intel Core i7     0  512    Nvidia             Windows  \n",
       "493           AMD Processor  1000    0       AMD             Windows  \n",
       "527           Intel Core i3  2000    0    Nvidia  Others/No OS/Linux  \n",
       "1193  Other Intel Processor     0    0     Intel                 Mac  \n",
       "\n",
       "[1106 rows x 12 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "6fcdfc19",
   "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": 92,
   "id": "e16c6402",
   "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,AdaBoostRegressor,ExtraTreesRegressor\n",
    "from sklearn.svm import SVR\n",
    "from xgboost import XGBRegressor"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73c6eee7",
   "metadata": {},
   "source": [
    "***Linear Regression***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "3e3767af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.8073277448418629\n",
      "MAE 0.21017827976428752\n"
     ]
    }
   ],
   "source": [
    "step1 = ColumnTransformer(transformers=[\n",
    "        ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,7,10,11])\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": "markdown",
   "id": "ea070207",
   "metadata": {},
   "source": [
    "***Ridge Regression***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "3db60ce0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.812733103131181\n",
      "MAE 0.20926802242582948\n"
     ]
    }
   ],
   "source": [
    "step1 = ColumnTransformer(transformers=[\n",
    "        ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,7,10,11])\n",
    "],remainder='passthrough')\n",
    "\n",
    "step2 = Ridge(alpha=10)\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": "markdown",
   "id": "7ef21c20",
   "metadata": {},
   "source": [
    "***Lasso regression***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "8f4718d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.8071853945317105\n",
      "MAE 0.21114361613472565\n"
     ]
    }
   ],
   "source": [
    "step1 = ColumnTransformer(transformers=[\n",
    "        ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,7,10,11])\n",
    "],remainder='passthrough')\n",
    "\n",
    "step2 = Lasso(alpha=0.001)\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": "markdown",
   "id": "efd188d4",
   "metadata": {},
   "source": [
    "***KNN***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "197170d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.8027675291937718\n",
      "MAE 0.193456293739003\n"
     ]
    }
   ],
   "source": [
    "step1 = ColumnTransformer(transformers=[\n",
    "        ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,7,10,11])\n",
    "],remainder='passthrough')\n",
    "\n",
    "step2 = KNeighborsRegressor(n_neighbors=3)\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": "markdown",
   "id": "42ad6041",
   "metadata": {},
   "source": [
    "***Decision Trees***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "f03a7811",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.84025425972115\n",
      "MAE 0.18238342973890517\n"
     ]
    }
   ],
   "source": [
    "step1 = ColumnTransformer(transformers=[\n",
    "        ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,7,10,11])\n",
    "],remainder='passthrough')\n",
    "\n",
    "step2 = DecisionTreeRegressor(max_depth=8)\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": "markdown",
   "id": "9ca2d4df",
   "metadata": {},
   "source": [
    "***SVM***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "c76100a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.8083180902283282\n",
      "MAE 0.2023905942725167\n"
     ]
    }
   ],
   "source": [
    "step1 = ColumnTransformer(transformers=[\n",
    "        ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,7,10,11])\n",
    "],remainder='passthrough')\n",
    "\n",
    "step2 = SVR(kernel='rbf',C=10000,epsilon=0.1)\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": "markdown",
   "id": "0575badc",
   "metadata": {},
   "source": [
    "***Random forest***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "8f185433",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.8873402378382488\n",
      "MAE 0.15860130110457718\n"
     ]
    }
   ],
   "source": [
    "step1 = ColumnTransformer(transformers=[\n",
    "        ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,7,10,11])\n",
    "],remainder='passthrough')\n",
    "\n",
    "step2 = RandomForestRegressor(n_estimators=100,\n",
    "                             random_state=3,\n",
    "                             max_samples=0.5,\n",
    "                             max_features=0.75,\n",
    "                             max_depth=15)\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": "markdown",
   "id": "c34873c0",
   "metadata": {},
   "source": [
    "***Extra Trees***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "eb3416c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.8753793123440623\n",
      "MAE 0.15979519126758127\n"
     ]
    }
   ],
   "source": [
    "step1 = ColumnTransformer(transformers=[\n",
    "        ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,7,10,11])\n",
    "],remainder='passthrough')\n",
    "\n",
    "step2 = ExtraTreesRegressor(n_estimators=100,\n",
    "                           random_state=3,\n",
    "                           max_samples=None,\n",
    "                           max_features=0.75,\n",
    "                           max_depth=15)\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": "markdown",
   "id": "7ea648cc",
   "metadata": {},
   "source": [
    "***AdaBoost***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "804adb40",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.785954783476073\n",
      "MAE 0.2338315267677607\n"
     ]
    }
   ],
   "source": [
    "step1 = ColumnTransformer(transformers=[\n",
    "        ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,7,10,11])\n",
    "],remainder='passthrough')\n",
    "\n",
    "step2 = AdaBoostRegressor(n_estimators=15,learning_rate=1.0)\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": "markdown",
   "id": "363ff581",
   "metadata": {},
   "source": [
    "***Gradient Boost***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "1e3cfce1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.8837656473683979\n",
      "MAE 0.15879003642765274\n"
     ]
    }
   ],
   "source": [
    "step1 = ColumnTransformer(transformers=[\n",
    "        ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,7,10,11])\n",
    "],remainder='passthrough')\n",
    "\n",
    "step2 = GradientBoostingRegressor(n_estimators=500)\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": "markdown",
   "id": "8da5a1ac",
   "metadata": {},
   "source": [
    "***XGBoost***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "451a4438",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.8811773435850243\n",
      "MAE 0.16496203512600974\n"
     ]
    }
   ],
   "source": [
    "step1 = ColumnTransformer(transformers=[\n",
    "        ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,7,10,11])\n",
    "],remainder='passthrough')\n",
    "\n",
    "step2 = XGBRegressor(n_estimators=45,max_depth=5,learning_rate=0.5)\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": "markdown",
   "id": "c598572a",
   "metadata": {},
   "source": [
    "***Voting Regressor***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "f5fe10fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.8900425911406249\n",
      "MAE 0.1574830841864375\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import VotingRegressor,StackingRegressor\n",
    "\n",
    "step1 = ColumnTransformer(transformers=[\n",
    "        ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,7,10,11])\n",
    "],remainder='passthrough')\n",
    "\n",
    "rf = RandomForestRegressor(n_estimators=350,random_state=3,max_samples=None,max_features=0.75,max_depth=15)\n",
    "gbdt = GradientBoostingRegressor(n_estimators=100,max_features=0.5)\n",
    "xgb = XGBRegressor(n_estimators=25,learning_rate=0.3,max_depth=5)\n",
    "et = ExtraTreesRegressor(n_estimators=100,random_state=3,max_samples=None,max_features=0.75,max_depth=10)\n",
    "\n",
    "step2 = VotingRegressor([('rf',rf), ('gbdt',gbdt), ('xgb',xgb), ('et',et)],weights=[5,1,1,1])\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": "markdown",
   "id": "a511dea9",
   "metadata": {},
   "source": [
    "***Stacking***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "3c883bfd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.8815588427120795\n",
      "MAE 0.16618098376390192\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import VotingRegressor,StackingRegressor\n",
    "\n",
    "step1 = ColumnTransformer(transformers=[\n",
    "        ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,7,10,11])\n",
    "],remainder='passthrough')\n",
    "\n",
    "estimators = [\n",
    "    ('rf', RandomForestRegressor(n_estimators=350,random_state=3,max_samples=None,max_features=0.75,max_depth=15)),\n",
    "('gbdt', GradientBoostingRegressor(n_estimators=100,max_features=0.5)),\n",
    "('xgb', XGBRegressor(n_estimators=25,learning_rate=0.3,max_depth=5))\n",
    "             ]\n",
    "\n",
    "\n",
    "step2 = StackingRegressor(estimators=estimators, final_estimator=Ridge(alpha=100))\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": "markdown",
   "id": "4295a839",
   "metadata": {},
   "source": [
    "***Exporting Model***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "8eddfd49",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "pickle.dump(df,open('df.pkl','wb'))\n",
    "pickle.dump(pipe,open('pipe.pkl','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "864fa492",
   "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>Weight</th>\n",
       "      <th>Price</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>ppi</th>\n",
       "      <th>Cpu brand</th>\n",
       "      <th>HDD</th>\n",
       "      <th>SSD</th>\n",
       "      <th>Gpu brand</th>\n",
       "      <th>os</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>1.37</td>\n",
       "      <td>71378.6832</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",
       "      <td>Mac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>1.34</td>\n",
       "      <td>47895.5232</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",
       "      <td>Mac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>1.86</td>\n",
       "      <td>30636.0000</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",
       "      <td>Others/No OS/Linux</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>16</td>\n",
       "      <td>1.83</td>\n",
       "      <td>135195.3360</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",
       "      <td>Mac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>1.37</td>\n",
       "      <td>96095.8080</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",
       "      <td>Mac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1298</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>2 in 1 Convertible</td>\n",
       "      <td>4</td>\n",
       "      <td>1.80</td>\n",
       "      <td>33992.6400</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",
       "      <td>Windows</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>1.30</td>\n",
       "      <td>79866.7200</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",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1300</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>2</td>\n",
       "      <td>1.50</td>\n",
       "      <td>12201.1200</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",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1301</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>6</td>\n",
       "      <td>2.19</td>\n",
       "      <td>40705.9200</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",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1302</th>\n",
       "      <td>Asus</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>4</td>\n",
       "      <td>2.20</td>\n",
       "      <td>19660.3200</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",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1302 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company            TypeName  Ram  Weight        Price  Touchscreen  Ips  \\\n",
       "0      Apple           Ultrabook    8    1.37   71378.6832            0    1   \n",
       "1      Apple           Ultrabook    8    1.34   47895.5232            0    0   \n",
       "2         HP            Notebook    8    1.86   30636.0000            0    0   \n",
       "3      Apple           Ultrabook   16    1.83  135195.3360            0    1   \n",
       "4      Apple           Ultrabook    8    1.37   96095.8080            0    1   \n",
       "...      ...                 ...  ...     ...          ...          ...  ...   \n",
       "1298  Lenovo  2 in 1 Convertible    4    1.80   33992.6400            1    1   \n",
       "1299  Lenovo  2 in 1 Convertible   16    1.30   79866.7200            1    1   \n",
       "1300  Lenovo            Notebook    2    1.50   12201.1200            0    0   \n",
       "1301      HP            Notebook    6    2.19   40705.9200            0    0   \n",
       "1302    Asus            Notebook    4    2.20   19660.3200            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",
       "                      os  \n",
       "0                    Mac  \n",
       "1                    Mac  \n",
       "2     Others/No OS/Linux  \n",
       "3                    Mac  \n",
       "4                    Mac  \n",
       "...                  ...  \n",
       "1298             Windows  \n",
       "1299             Windows  \n",
       "1300             Windows  \n",
       "1301             Windows  \n",
       "1302             Windows  \n",
       "\n",
       "[1302 rows x 13 columns]"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
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   "execution_count": null,
   "id": "174208ae",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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