{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('laptop_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 2.3GHz</td>\n",
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       "      <td>Intel Iris Plus Graphics 640</td>\n",
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       "      <td>71378.6832</td>\n",
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       "      <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",
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       "      <td>1.34kg</td>\n",
       "      <td>47895.5232</td>\n",
       "    </tr>\n",
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       "      <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",
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       "      <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",
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       "      <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",
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       "      <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,
   "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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=[\"Unnamed: 0\"],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>No OS</td>\n",
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       "      <td>30636.0000</td>\n",
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       "      <td>Apple</td>\n",
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       "      <td>15.4</td>\n",
       "      <td>IPS Panel Retina Display 2880x1800</td>\n",
       "      <td>Intel Core i7 2.7GHz</td>\n",
       "      <td>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",
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       "    <tr>\n",
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       "      <td>Apple</td>\n",
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       "      <td>1.37kg</td>\n",
       "      <td>96095.8080</td>\n",
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      ],
      "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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     "execution_count": 6,
     "metadata": {},
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    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Ram']=df['Ram'].str.replace('GB','')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Weight']=df['Weight'].str.replace('kg','')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1.83</td>\n",
       "      <td>135195.3360</td>\n",
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       "      <td>1.37</td>\n",
       "      <td>96095.8080</td>\n",
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      ],
      "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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Ram']=df['Ram'].astype('int32')\n",
    "df['Weight']=df['Weight'].astype('float32')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\AppData\\Local\\Temp\\ipykernel_30876\\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": [
       "<Axes: xlabel='Price', ylabel='Density'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Company'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "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": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['TypeName'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "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": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\AppData\\Local\\Temp\\ipykernel_30876\\3033116036.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": [
       "<Axes: ylabel='Density'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot([df['Inches']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=df['Inches'],y=df['Price'])\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Inches', ylabel='Price'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=df['Inches'],y=df['Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ScreenResolution'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['TouchSrceen']=df['ScreenResolution'].apply(lambda x:1 if 'Touchscreen' in x else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>TouchSrceen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1257</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>500GB HDD</td>\n",
       "      <td>Intel HD Graphics 520</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.290</td>\n",
       "      <td>26107.20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Full HD 1920x1080</td>\n",
       "      <td>Intel Core i3 7100U 2.4GHz</td>\n",
       "      <td>4</td>\n",
       "      <td>128GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.500</td>\n",
       "      <td>29250.72</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>Microsoft</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.5</td>\n",
       "      <td>Touchscreen 2256x1504</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>4</td>\n",
       "      <td>128GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>Windows 10 S</td>\n",
       "      <td>1.252</td>\n",
       "      <td>58021.92</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>941</th>\n",
       "      <td>Asus</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>17.3</td>\n",
       "      <td>1600x900</td>\n",
       "      <td>Intel Pentium Quad Core N3710 1.6GHz</td>\n",
       "      <td>4</td>\n",
       "      <td>1TB HDD</td>\n",
       "      <td>Nvidia GeForce 920MX</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.800</td>\n",
       "      <td>28238.40</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2560x1440</td>\n",
       "      <td>Intel Core i7 7500U 2.7GHz</td>\n",
       "      <td>24</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.320</td>\n",
       "      <td>126912.96</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Company   TypeName  Inches             ScreenResolution  \\\n",
       "1257       Dell   Notebook    15.6                     1366x768   \n",
       "386      Lenovo   Notebook    13.3  IPS Panel Full HD 1920x1080   \n",
       "70    Microsoft  Ultrabook    13.5        Touchscreen 2256x1504   \n",
       "941        Asus   Notebook    17.3                     1600x900   \n",
       "438      Lenovo  Ultrabook    14.0                    2560x1440   \n",
       "\n",
       "                                       Cpu  Ram     Memory  \\\n",
       "1257              Intel Core i3 6006U 2GHz    4  500GB HDD   \n",
       "386             Intel Core i3 7100U 2.4GHz    4  128GB SSD   \n",
       "70              Intel Core i5 7200U 2.5GHz    4  128GB SSD   \n",
       "941   Intel Pentium Quad Core N3710 1.6GHz    4    1TB HDD   \n",
       "438             Intel Core i7 7500U 2.7GHz   24  512GB SSD   \n",
       "\n",
       "                        Gpu         OpSys  Weight      Price  TouchSrceen  \n",
       "1257  Intel HD Graphics 520    Windows 10   2.290   26107.20            0  \n",
       "386   Intel HD Graphics 620    Windows 10   1.500   29250.72            0  \n",
       "70    Intel HD Graphics 620  Windows 10 S   1.252   58021.92            1  \n",
       "941    Nvidia GeForce 920MX    Windows 10   2.800   28238.40            0  \n",
       "438   Intel HD Graphics 620    Windows 10   1.320  126912.96            0  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Ips']=df['ScreenResolution'].apply(lambda x:1 if 'IPS' in x else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "<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>TouchSrceen</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  TouchSrceen  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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "new = df['ScreenResolution'].str.split('x',n=1,expand=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['X_res']=new[0]\n",
    "df['Y_res']=new[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th>TypeName</th>\n",
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       "      <th>Y_res</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>IPS Panel Retina Display 2560</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
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       "      <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>1440</td>\n",
       "      <td>900</td>\n",
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       "    <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",
       "      <td>Full HD 1920</td>\n",
       "      <td>1080</td>\n",
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       "      <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",
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       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>135195.3360</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>IPS Panel Retina Display 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>IPS Panel Retina Display 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  TouchSrceen  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  IPS Panel Retina Display 2560  1600  \n",
       "1                           1440   900  \n",
       "2                   Full HD 1920  1080  \n",
       "3  IPS Panel Retina Display 2880  1800  \n",
       "4  IPS Panel Retina Display 2560  1600  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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",
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       "      <td>135195.3360</td>\n",
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       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
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       "      <td>8</td>\n",
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      ],
      "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  TouchSrceen  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": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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": 32,
   "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  TouchSrceen       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": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['PPI']= (((df['X_res']**2) + (df['Y_res']**2))**0.5/df['Inches']).astype('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\AppData\\Local\\Temp\\ipykernel_30876\\815546952.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  df.corr()['Price']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Inches         0.068197\n",
       "Ram            0.743007\n",
       "Weight         0.210370\n",
       "Price          1.000000\n",
       "TouchSrceen    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": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()['Price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['ScreenResolution'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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  TouchSrceen  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  "
      ]
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     "execution_count": 36,
     "metadata": {},
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    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['Inches'],inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
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      "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  TouchSrceen  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         PPI  \n",
       "0   2560   1600  226.983005  \n",
       "1   1440    900  127.677940  \n",
       "2   1920   1080  141.211998  \n",
       "3   2880   1800  220.534624  \n",
       "4   2560   1600  226.983005  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df[\"Cpu\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Cpu Name\"]=df['Cpu'].apply(lambda x:\" \".join(x.split()[0:3]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1440</td>\n",
       "      <td>900</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>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",
       "      <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>2880</td>\n",
       "      <td>1800</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>2560</td>\n",
       "      <td>1600</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  TouchSrceen  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         PPI       Cpu Name  \n",
       "0   2560   1600  226.983005  Intel Core i5  \n",
       "1   1440    900  127.677940  Intel Core i5  \n",
       "2   1920   1080  141.211998  Intel Core i5  \n",
       "3   2880   1800  220.534624  Intel Core i7  \n",
       "4   2560   1600  226.983005  Intel Core i5  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Cpu brand\"] = df[\"Cpu Name\"].apply(fetch_processor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>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>TouchSrceen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>X_res</th>\n",
       "      <th>Y_res</th>\n",
       "      <th>PPI</th>\n",
       "      <th>Cpu Name</th>\n",
       "      <th>Cpu brand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>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",
       "      <td>1</td>\n",
       "      <td>2560</td>\n",
       "      <td>1600</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>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>1440</td>\n",
       "      <td>900</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>1920</td>\n",
       "      <td>1080</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>2880</td>\n",
       "      <td>1800</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>2560</td>\n",
       "      <td>1600</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  TouchSrceen  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         PPI       Cpu Name      Cpu brand  \n",
       "0   2560   1600  226.983005  Intel Core i5  Intel Core i5  \n",
       "1   1440    900  127.677940  Intel Core i5  Intel Core i5  \n",
       "2   1920   1080  141.211998  Intel Core i5  Intel Core i5  \n",
       "3   2880   1800  220.534624  Intel Core i7  Intel Core i7  \n",
       "4   2560   1600  226.983005  Intel Core i5  Intel Core i5  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "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": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Ram'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "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": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\AppData\\Local\\Temp\\ipykernel_30876\\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\\DELL\\AppData\\Local\\Temp\\ipykernel_30876\\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": 49,
   "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>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>TouchSrceen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>X_res</th>\n",
       "      <th>Y_res</th>\n",
       "      <th>PPI</th>\n",
       "      <th>Cpu Name</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>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>Intel Core i5 2.3GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>128 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>2560</td>\n",
       "      <td>1600</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>Intel Core i5 1.8GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>128 Flash Storage</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>47895.5232</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1440</td>\n",
       "      <td>900</td>\n",
       "      <td>127.677940</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256 SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>30636.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1920</td>\n",
       "      <td>1080</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>Intel Core i7 2.7GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>512 SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>135195.3360</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2880</td>\n",
       "      <td>1800</td>\n",
       "      <td>220.534624</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>Intel Core i7</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
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       "      <td>Apple</td>\n",
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       "      <td>8</td>\n",
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       "      <td>0</td>\n",
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       "      <td>2560</td>\n",
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       "      <td>Intel Core i5</td>\n",
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      "text/plain": [
       "  Company   TypeName                         Cpu  Ram             Memory  \\\n",
       "0   Apple  Ultrabook        Intel Core i5 2.3GHz    8            128 SSD   \n",
       "1   Apple  Ultrabook        Intel Core i5 1.8GHz    8  128 Flash Storage   \n",
       "2      HP   Notebook  Intel Core i5 7200U 2.5GHz    8            256 SSD   \n",
       "3   Apple  Ultrabook        Intel Core i7 2.7GHz   16            512 SSD   \n",
       "4   Apple  Ultrabook        Intel Core i5 3.1GHz    8            256 SSD   \n",
       "\n",
       "                            Gpu  OpSys  Weight        Price  TouchSrceen  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         PPI       Cpu Name      Cpu brand  HDD  SSD  Hybrid  \\\n",
       "0   2560   1600  226.983005  Intel Core i5  Intel Core i5    0  128       0   \n",
       "1   1440    900  127.677940  Intel Core i5  Intel Core i5    0    0       0   \n",
       "2   1920   1080  141.211998  Intel Core i5  Intel Core i5    0  256       0   \n",
       "3   2880   1800  220.534624  Intel Core i7  Intel Core i7    0  512       0   \n",
       "4   2560   1600  226.983005  Intel Core i5  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": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['Memory'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
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       "      <td>16</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
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      "text/plain": [
       "  Company   TypeName                         Cpu  Ram  \\\n",
       "0   Apple  Ultrabook        Intel Core i5 2.3GHz    8   \n",
       "1   Apple  Ultrabook        Intel Core i5 1.8GHz    8   \n",
       "2      HP   Notebook  Intel Core i5 7200U 2.5GHz    8   \n",
       "3   Apple  Ultrabook        Intel Core i7 2.7GHz   16   \n",
       "4   Apple  Ultrabook        Intel Core i5 3.1GHz    8   \n",
       "\n",
       "                            Gpu  OpSys  Weight        Price  TouchSrceen  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         PPI       Cpu Name      Cpu brand  HDD  SSD  Hybrid  \\\n",
       "0   2560   1600  226.983005  Intel Core i5  Intel Core i5    0  128       0   \n",
       "1   1440    900  127.677940  Intel Core i5  Intel Core i5    0    0       0   \n",
       "2   1920   1080  141.211998  Intel Core i5  Intel Core i5    0  256       0   \n",
       "3   2880   1800  220.534624  Intel Core i7  Intel Core i7    0  512       0   \n",
       "4   2560   1600  226.983005  Intel Core i5  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  "
      ]
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     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\AppData\\Local\\Temp\\ipykernel_30876\\815546952.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  df.corr()['Price']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Ram              0.743007\n",
       "Weight           0.210370\n",
       "Price            1.000000\n",
       "TouchSrceen      0.191226\n",
       "Ips              0.252208\n",
       "X_res            0.556529\n",
       "Y_res            0.552809\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": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()['Price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['Hybrid','Flash_Storage'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>3</th>\n",
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       "      <td>Ultrabook</td>\n",
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       "      <td>16</td>\n",
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       "      <th>4</th>\n",
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       "      <td>Ultrabook</td>\n",
       "      <td>Intel Core i5 3.1GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName                         Cpu  Ram  \\\n",
       "0   Apple  Ultrabook        Intel Core i5 2.3GHz    8   \n",
       "1   Apple  Ultrabook        Intel Core i5 1.8GHz    8   \n",
       "2      HP   Notebook  Intel Core i5 7200U 2.5GHz    8   \n",
       "3   Apple  Ultrabook        Intel Core i7 2.7GHz   16   \n",
       "4   Apple  Ultrabook        Intel Core i5 3.1GHz    8   \n",
       "\n",
       "                            Gpu  OpSys  Weight        Price  TouchSrceen  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         PPI       Cpu Name      Cpu brand  HDD  SSD  \n",
       "0   2560   1600  226.983005  Intel Core i5  Intel Core i5    0  128  \n",
       "1   1440    900  127.677940  Intel Core i5  Intel Core i5    0    0  \n",
       "2   1920   1080  141.211998  Intel Core i5  Intel Core i5    0  256  \n",
       "3   2880   1800  220.534624  Intel Core i7  Intel Core i7    0  512  \n",
       "4   2560   1600  226.983005  Intel Core i5  Intel Core i5    0  256  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Gpu.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Gpu Brand']=df[\"Gpu\"].apply(lambda x:x.split()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>Cpu</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "      <th>TouchSrceen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>X_res</th>\n",
       "      <th>Y_res</th>\n",
       "      <th>PPI</th>\n",
       "      <th>Cpu Name</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>Intel Core i5 2.3GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>Intel Iris Plus Graphics 640</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>71378.6832</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2560</td>\n",
       "      <td>1600</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</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>Intel Core i5 1.8GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>47895.5232</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1440</td>\n",
       "      <td>900</td>\n",
       "      <td>127.677940</td>\n",
       "      <td>Intel Core i5</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>Intel Core i5 7200U 2.5GHz</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>1920</td>\n",
       "      <td>1080</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i5</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>Intel Core i7 2.7GHz</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>2880</td>\n",
       "      <td>1800</td>\n",
       "      <td>220.534624</td>\n",
       "      <td>Intel Core i7</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>Intel Core i5 3.1GHz</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>2560</td>\n",
       "      <td>1600</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</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                         Cpu  Ram  \\\n",
       "0   Apple  Ultrabook        Intel Core i5 2.3GHz    8   \n",
       "1   Apple  Ultrabook        Intel Core i5 1.8GHz    8   \n",
       "2      HP   Notebook  Intel Core i5 7200U 2.5GHz    8   \n",
       "3   Apple  Ultrabook        Intel Core i7 2.7GHz   16   \n",
       "4   Apple  Ultrabook        Intel Core i5 3.1GHz    8   \n",
       "\n",
       "                            Gpu  OpSys  Weight        Price  TouchSrceen  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         PPI       Cpu Name      Cpu brand  HDD  SSD Gpu Brand  \n",
       "0   2560   1600  226.983005  Intel Core i5  Intel Core i5    0  128     Intel  \n",
       "1   1440    900  127.677940  Intel Core i5  Intel Core i5    0    0     Intel  \n",
       "2   1920   1080  141.211998  Intel Core i5  Intel Core i5    0  256     Intel  \n",
       "3   2880   1800  220.534624  Intel Core i7  Intel Core i7    0  512       AMD  \n",
       "4   2560   1600  226.983005  Intel Core i5  Intel Core i5    0  256     Intel  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df['Gpu Brand']!= \"ARM\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intel     722\n",
       "Nvidia    400\n",
       "AMD       180\n",
       "Name: Gpu Brand, dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Gpu Brand\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=df['Gpu Brand'],y=df['Price'])\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=[\"Gpu\"],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>Cpu</th>\n",
       "      <th>Ram</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "      <th>TouchSrceen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>X_res</th>\n",
       "      <th>Y_res</th>\n",
       "      <th>PPI</th>\n",
       "      <th>Cpu Name</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>Intel Core i5 2.3GHz</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>2560</td>\n",
       "      <td>1600</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</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>Intel Core i5 1.8GHz</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>1440</td>\n",
       "      <td>900</td>\n",
       "      <td>127.677940</td>\n",
       "      <td>Intel Core i5</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>Intel Core i5 7200U 2.5GHz</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>1920</td>\n",
       "      <td>1080</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i5</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>Intel Core i7 2.7GHz</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>2880</td>\n",
       "      <td>1800</td>\n",
       "      <td>220.534624</td>\n",
       "      <td>Intel Core i7</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>Intel Core i5 3.1GHz</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>2560</td>\n",
       "      <td>1600</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</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                         Cpu  Ram  OpSys  Weight  \\\n",
       "0   Apple  Ultrabook        Intel Core i5 2.3GHz    8  macOS    1.37   \n",
       "1   Apple  Ultrabook        Intel Core i5 1.8GHz    8  macOS    1.34   \n",
       "2      HP   Notebook  Intel Core i5 7200U 2.5GHz    8  No OS    1.86   \n",
       "3   Apple  Ultrabook        Intel Core i7 2.7GHz   16  macOS    1.83   \n",
       "4   Apple  Ultrabook        Intel Core i5 3.1GHz    8  macOS    1.37   \n",
       "\n",
       "         Price  TouchSrceen  Ips  X_res  Y_res         PPI       Cpu Name  \\\n",
       "0   71378.6832            0    1   2560   1600  226.983005  Intel Core i5   \n",
       "1   47895.5232            0    0   1440    900  127.677940  Intel Core i5   \n",
       "2   30636.0000            0    0   1920   1080  141.211998  Intel Core i5   \n",
       "3  135195.3360            0    1   2880   1800  220.534624  Intel Core i7   \n",
       "4   96095.8080            0    1   2560   1600  226.983005  Intel Core i5   \n",
       "\n",
       "       Cpu brand  HDD  SSD Gpu Brand  \n",
       "0  Intel Core i5    0  128     Intel  \n",
       "1  Intel Core i5    0    0     Intel  \n",
       "2  Intel Core i5    0  256     Intel  \n",
       "3  Intel Core i7    0  512       AMD  \n",
       "4  Intel Core i5    0  256     Intel  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['OpSys'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "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": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cat_os(OS):\n",
    "    if OS== \"Windows 10\" or OS==\"Windows 7\" or OS==\"Windows 10 S\":\n",
    "        return 'Windows'\n",
    "    elif OS==\"macOS\" or OS==\"Mac OS X\":\n",
    "        return 'Mac'\n",
    "    else:\n",
    "        return 'No OS/Linux/Others'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"OS_Brand\"] = df[\"OpSys\"].apply(cat_os)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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>Cpu</th>\n",
       "      <th>Ram</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "      <th>TouchSrceen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>X_res</th>\n",
       "      <th>Y_res</th>\n",
       "      <th>PPI</th>\n",
       "      <th>Cpu Name</th>\n",
       "      <th>Cpu brand</th>\n",
       "      <th>HDD</th>\n",
       "      <th>SSD</th>\n",
       "      <th>Gpu Brand</th>\n",
       "      <th>OS_Brand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <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>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>71378.6832</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2560</td>\n",
       "      <td>1600</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</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>Intel Core i5 1.8GHz</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>1440</td>\n",
       "      <td>900</td>\n",
       "      <td>127.677940</td>\n",
       "      <td>Intel Core i5</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>Intel Core i5 7200U 2.5GHz</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>1920</td>\n",
       "      <td>1080</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>Intel</td>\n",
       "      <td>No OS/Linux/Others</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>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",
       "      <td>220.534624</td>\n",
       "      <td>Intel Core i7</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",
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       "      <td>Intel Core i5 3.1GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2560</td>\n",
       "      <td>1600</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</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                         Cpu  Ram  OpSys  Weight  \\\n",
       "0   Apple  Ultrabook        Intel Core i5 2.3GHz    8  macOS    1.37   \n",
       "1   Apple  Ultrabook        Intel Core i5 1.8GHz    8  macOS    1.34   \n",
       "2      HP   Notebook  Intel Core i5 7200U 2.5GHz    8  No OS    1.86   \n",
       "3   Apple  Ultrabook        Intel Core i7 2.7GHz   16  macOS    1.83   \n",
       "4   Apple  Ultrabook        Intel Core i5 3.1GHz    8  macOS    1.37   \n",
       "\n",
       "         Price  TouchSrceen  Ips  X_res  Y_res         PPI       Cpu Name  \\\n",
       "0   71378.6832            0    1   2560   1600  226.983005  Intel Core i5   \n",
       "1   47895.5232            0    0   1440    900  127.677940  Intel Core i5   \n",
       "2   30636.0000            0    0   1920   1080  141.211998  Intel Core i5   \n",
       "3  135195.3360            0    1   2880   1800  220.534624  Intel Core i7   \n",
       "4   96095.8080            0    1   2560   1600  226.983005  Intel Core i5   \n",
       "\n",
       "       Cpu brand  HDD  SSD Gpu Brand            OS_Brand  \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  No OS/Linux/Others  \n",
       "3  Intel Core i7    0  512       AMD                 Mac  \n",
       "4  Intel Core i5    0  256     Intel                 Mac  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=[\"OpSys\"],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  Company   TypeName                         Cpu  Ram  Weight        Price  \\\n",
       "0   Apple  Ultrabook        Intel Core i5 2.3GHz    8    1.37   71378.6832   \n",
       "1   Apple  Ultrabook        Intel Core i5 1.8GHz    8    1.34   47895.5232   \n",
       "2      HP   Notebook  Intel Core i5 7200U 2.5GHz    8    1.86   30636.0000   \n",
       "3   Apple  Ultrabook        Intel Core i7 2.7GHz   16    1.83  135195.3360   \n",
       "4   Apple  Ultrabook        Intel Core i5 3.1GHz    8    1.37   96095.8080   \n",
       "\n",
       "   TouchSrceen  Ips  X_res  Y_res         PPI       Cpu Name      Cpu brand  \\\n",
       "0            0    1   2560   1600  226.983005  Intel Core i5  Intel Core i5   \n",
       "1            0    0   1440    900  127.677940  Intel Core i5  Intel Core i5   \n",
       "2            0    0   1920   1080  141.211998  Intel Core i5  Intel Core i5   \n",
       "3            0    1   2880   1800  220.534624  Intel Core i7  Intel Core i7   \n",
       "4            0    1   2560   1600  226.983005  Intel Core i5  Intel Core i5   \n",
       "\n",
       "   HDD  SSD Gpu Brand            OS_Brand  \n",
       "0    0  128     Intel                 Mac  \n",
       "1    0    0     Intel                 Mac  \n",
       "2    0  256     Intel  No OS/Linux/Others  \n",
       "3    0  512       AMD                 Mac  \n",
       "4    0  256     Intel                 Mac  "
      ]
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     "execution_count": 69,
     "metadata": {},
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   "source": [
    "df.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['X_res','Y_res'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "  Company   TypeName                         Cpu  Ram  Weight        Price  \\\n",
       "0   Apple  Ultrabook        Intel Core i5 2.3GHz    8    1.37   71378.6832   \n",
       "1   Apple  Ultrabook        Intel Core i5 1.8GHz    8    1.34   47895.5232   \n",
       "2      HP   Notebook  Intel Core i5 7200U 2.5GHz    8    1.86   30636.0000   \n",
       "3   Apple  Ultrabook        Intel Core i7 2.7GHz   16    1.83  135195.3360   \n",
       "4   Apple  Ultrabook        Intel Core i5 3.1GHz    8    1.37   96095.8080   \n",
       "\n",
       "   TouchSrceen  Ips         PPI       Cpu Name      Cpu brand  HDD  SSD  \\\n",
       "0            0    1  226.983005  Intel Core i5  Intel Core i5    0  128   \n",
       "1            0    0  127.677940  Intel Core i5  Intel Core i5    0    0   \n",
       "2            0    0  141.211998  Intel Core i5  Intel Core i5    0  256   \n",
       "3            0    1  220.534624  Intel Core i7  Intel Core i7    0  512   \n",
       "4            0    1  226.983005  Intel Core i5  Intel Core i5    0  256   \n",
       "\n",
       "  Gpu Brand            OS_Brand  \n",
       "0     Intel                 Mac  \n",
       "1     Intel                 Mac  \n",
       "2     Intel  No OS/Linux/Others  \n",
       "3       AMD                 Mac  \n",
       "4     Intel                 Mac  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['Cpu'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['Cpu Name'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
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       "    </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>No OS/Linux/Others</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Ram  Weight        Price  TouchSrceen  Ips         PPI  \\\n",
       "0   Apple  Ultrabook    8    1.37   71378.6832            0    1  226.983005   \n",
       "1   Apple  Ultrabook    8    1.34   47895.5232            0    0  127.677940   \n",
       "2      HP   Notebook    8    1.86   30636.0000            0    0  141.211998   \n",
       "3   Apple  Ultrabook   16    1.83  135195.3360            0    1  220.534624   \n",
       "4   Apple  Ultrabook    8    1.37   96095.8080            0    1  226.983005   \n",
       "\n",
       "       Cpu brand  HDD  SSD Gpu Brand            OS_Brand  \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  No OS/Linux/Others  \n",
       "3  Intel Core i7    0  512       AMD                 Mac  \n",
       "4  Intel Core i5    0  256     Intel                 Mac  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=df['OS_Brand'],y=df['Price'])\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\AppData\\Local\\Temp\\ipykernel_30876\\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": [
       "<Axes: xlabel='Weight', ylabel='Density'>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Weight'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\AppData\\Local\\Temp\\ipykernel_30876\\58359773.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  sns.heatmap(df.corr())\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\AppData\\Local\\Temp\\ipykernel_30876\\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": [
       "<Axes: xlabel='Price', ylabel='Density'>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(np.log(df['Price']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "X= df.drop(columns=[\"Price\"])\n",
    "y=np.log(df['Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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>TouchSrceen</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_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>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>No OS/Linux/Others</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  TouchSrceen  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_Brand  \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  No OS/Linux/Others  \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": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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": 83,
   "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>TouchSrceen</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_Brand</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>No OS/Linux/Others</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>No OS/Linux/Others</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  TouchSrceen  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_Brand  \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  No OS/Linux/Others  \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  No OS/Linux/Others  \n",
       "1193  Other Intel Processor     0    0     Intel                 Mac  \n",
       "\n",
       "[1106 rows x 12 columns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2_Score 0.8073277448418643\n",
      "MAE 0.2101782797642889\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:828: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "step1=ColumnTransformer(transformers=[('col_tnf',OneHotEncoder(sparse=False,drop=\"first\"),[0,1,7,10,11])],remainder='passthrough')\n",
    "step2= LinearRegression()\n",
    "\n",
    "pipe = Pipeline([('step1',step1),('step2',step2)])\n",
    "pipe.fit(X_train,y_train)\n",
    "y_pred = pipe.predict(X_test)\n",
    "print('R2_Score',r2_score(y_test,y_pred))\n",
    "print('MAE',mean_absolute_error(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2_Score 0.8023665883791459\n",
      "MAE 0.19995757033698672\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:828: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "step1=ColumnTransformer(transformers=[('col_tnf',OneHotEncoder(sparse=False,drop=\"first\"),[0,1,7,10,11])],remainder='passthrough')\n",
    "step2= KNeighborsRegressor()\n",
    "\n",
    "pipe = Pipeline([('step1',step1),('step2',step2)])\n",
    "pipe.fit(X_train,y_train)\n",
    "y_pred = pipe.predict(X_test)\n",
    "print('R2_Score',r2_score(y_test,y_pred))\n",
    "print('MAE',mean_absolute_error(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2_Score 0.8506406854817128\n",
      "MAE 0.17736811667767963\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:828: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "step1=ColumnTransformer(transformers=[('col_tnf',OneHotEncoder(sparse=False,drop=\"first\"),[0,1,7,10,11])],remainder='passthrough')\n",
    "step2= DecisionTreeRegressor(max_depth=8)\n",
    "\n",
    "pipe = Pipeline([('step1',step1),('step2',step2)])\n",
    "pipe.fit(X_train,y_train)\n",
    "y_pred = pipe.predict(X_test)\n",
    "print('R2_Score',r2_score(y_test,y_pred))\n",
    "print('MAE',mean_absolute_error(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:828: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "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": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle.dump(df,open('df.pkl','wb'))\n",
    "pickle.dump(pipe,open('pipe.pkl','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>TouchSrceen</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_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>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>No OS/Linux/Others</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  TouchSrceen  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_Brand  \n",
       "0                    Mac  \n",
       "1                    Mac  \n",
       "2     No OS/Linux/Others  \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": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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  {
   "cell_type": "code",
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  {
   "cell_type": "code",
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  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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  {
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  {
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  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": []
  }
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