LaptopPricePredictor.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "82edeb04",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "203583d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\AppData\\Local\\Temp\\ipykernel_11356\\4141408244.py:2: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n",
      "  df = df.applymap(lambda x: x.encode('utf-8').decode('utf-8') if isinstance(x, str) else x)\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('laptop_data.csv')\n",
    "df = df.applymap(lambda x: x.encode('utf-8').decode('utf-8') if isinstance(x, str) else x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5106314a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Inches</th>\n",
       "      <th>ScreenResolution</th>\n",
       "      <th>Cpu</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 2.3GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>128GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 640</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37kg</td>\n",
       "      <td>71378.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>1440x900</td>\n",
       "      <td>Intel Core i5 1.8GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>128GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34kg</td>\n",
       "      <td>47895.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86kg</td>\n",
       "      <td>30636.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>15.4</td>\n",
       "      <td>IPS Panel Retina Display 2880x1800</td>\n",
       "      <td>Intel Core i7 2.7GHz</td>\n",
       "      <td>16GB</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83kg</td>\n",
       "      <td>135195.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 3.1GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37kg</td>\n",
       "      <td>96095.81</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   71378.68  \n",
       "1        Intel HD Graphics 6000  macOS  1.34kg   47895.52  \n",
       "2         Intel HD Graphics 620  No OS  1.86kg   30636.00  \n",
       "3            AMD Radeon Pro 455  macOS  1.83kg  135195.34  \n",
       "4  Intel Iris Plus Graphics 650  macOS  1.37kg   96095.81  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5df0e810",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1303, 12)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2721be49",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1303 entries, 0 to 1302\n",
      "Data columns (total 12 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Unnamed: 0        1303 non-null   int64  \n",
      " 1   Company           1303 non-null   object \n",
      " 2   TypeName          1303 non-null   object \n",
      " 3   Inches            1303 non-null   float64\n",
      " 4   ScreenResolution  1303 non-null   object \n",
      " 5   Cpu               1303 non-null   object \n",
      " 6   Ram               1303 non-null   object \n",
      " 7   Memory            1303 non-null   object \n",
      " 8   Gpu               1303 non-null   object \n",
      " 9   OpSys             1303 non-null   object \n",
      " 10  Weight            1303 non-null   object \n",
      " 11  Price             1303 non-null   float64\n",
      "dtypes: float64(2), int64(1), object(9)\n",
      "memory usage: 122.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d623a6c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a3e5e8f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0          0\n",
       "Company             0\n",
       "TypeName            0\n",
       "Inches              0\n",
       "ScreenResolution    0\n",
       "Cpu                 0\n",
       "Ram                 0\n",
       "Memory              0\n",
       "Gpu                 0\n",
       "OpSys               0\n",
       "Weight              0\n",
       "Price               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7da78c39",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['Unnamed: 0'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "533fe856",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Inches</th>\n",
       "      <th>ScreenResolution</th>\n",
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       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 2.3GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>128GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 640</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37kg</td>\n",
       "      <td>71378.68</td>\n",
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       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</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",
       "      <td>macOS</td>\n",
       "      <td>1.34kg</td>\n",
       "      <td>47895.52</td>\n",
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       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>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>30636.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>15.4</td>\n",
       "      <td>IPS Panel Retina Display 2880x1800</td>\n",
       "      <td>Intel Core i7 2.7GHz</td>\n",
       "      <td>16GB</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83kg</td>\n",
       "      <td>135195.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 3.1GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37kg</td>\n",
       "      <td>96095.81</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Inches                    ScreenResolution  \\\n",
       "0   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "1   Apple  Ultrabook    13.3                            1440x900   \n",
       "2      HP   Notebook    15.6                   Full HD 1920x1080   \n",
       "3   Apple  Ultrabook    15.4  IPS Panel Retina Display 2880x1800   \n",
       "4   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "\n",
       "                          Cpu   Ram               Memory  \\\n",
       "0        Intel Core i5 2.3GHz   8GB            128GB SSD   \n",
       "1        Intel Core i5 1.8GHz   8GB  128GB Flash Storage   \n",
       "2  Intel Core i5 7200U 2.5GHz   8GB            256GB SSD   \n",
       "3        Intel Core i7 2.7GHz  16GB            512GB SSD   \n",
       "4        Intel Core i5 3.1GHz   8GB            256GB SSD   \n",
       "\n",
       "                            Gpu  OpSys  Weight      Price  \n",
       "0  Intel Iris Plus Graphics 640  macOS  1.37kg   71378.68  \n",
       "1        Intel HD Graphics 6000  macOS  1.34kg   47895.52  \n",
       "2         Intel HD Graphics 620  No OS  1.86kg   30636.00  \n",
       "3            AMD Radeon Pro 455  macOS  1.83kg  135195.34  \n",
       "4  Intel Iris Plus Graphics 650  macOS  1.37kg   96095.81  "
      ]
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     "metadata": {},
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   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b0c543e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Ram'] = df['Ram'].str.replace('GB','')\n",
    "df['Weight'] = df['Weight'].str.replace('kg','')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "291e2927",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Inches</th>\n",
       "      <th>ScreenResolution</th>\n",
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       "      <th>Memory</th>\n",
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       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
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       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 2.3GHz</td>\n",
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       "      <td>Apple</td>\n",
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       "      <td>1440x900</td>\n",
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       "      <td>128GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
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       "      <td>47895.52</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>30636.00</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>135195.34</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>96095.81</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Inches                    ScreenResolution  \\\n",
       "0   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "1   Apple  Ultrabook    13.3                            1440x900   \n",
       "2      HP   Notebook    15.6                   Full HD 1920x1080   \n",
       "3   Apple  Ultrabook    15.4  IPS Panel Retina Display 2880x1800   \n",
       "4   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "\n",
       "                          Cpu Ram               Memory  \\\n",
       "0        Intel Core i5 2.3GHz   8            128GB SSD   \n",
       "1        Intel Core i5 1.8GHz   8  128GB Flash Storage   \n",
       "2  Intel Core i5 7200U 2.5GHz   8            256GB SSD   \n",
       "3        Intel Core i7 2.7GHz  16            512GB SSD   \n",
       "4        Intel Core i5 3.1GHz   8            256GB SSD   \n",
       "\n",
       "                            Gpu  OpSys Weight      Price  \n",
       "0  Intel Iris Plus Graphics 640  macOS   1.37   71378.68  \n",
       "1        Intel HD Graphics 6000  macOS   1.34   47895.52  \n",
       "2         Intel HD Graphics 620  No OS   1.86   30636.00  \n",
       "3            AMD Radeon Pro 455  macOS   1.83  135195.34  \n",
       "4  Intel Iris Plus Graphics 650  macOS   1.37   96095.81  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9d4c27a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Ram'] = df['Ram'].astype('int32')\n",
    "df['Weight'] = df['Weight'].astype('float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "356bdba8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1303 entries, 0 to 1302\n",
      "Data columns (total 11 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Company           1303 non-null   object \n",
      " 1   TypeName          1303 non-null   object \n",
      " 2   Inches            1303 non-null   float64\n",
      " 3   ScreenResolution  1303 non-null   object \n",
      " 4   Cpu               1303 non-null   object \n",
      " 5   Ram               1303 non-null   int32  \n",
      " 6   Memory            1303 non-null   object \n",
      " 7   Gpu               1303 non-null   object \n",
      " 8   OpSys             1303 non-null   object \n",
      " 9   Weight            1303 non-null   float32\n",
      " 10  Price             1303 non-null   float64\n",
      "dtypes: float32(1), float64(2), int32(1), object(7)\n",
      "memory usage: 101.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "51e9c4c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "37e41400",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\AppData\\Local\\Temp\\ipykernel_11356\\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",
      "C:\\Users\\HP\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Price', ylabel='Density'>"
      ]
     },
     "execution_count": 15,
     "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": 16,
   "id": "505d0590",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Company'>"
      ]
     },
     "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['Company'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "894b0057",
   "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": 18,
   "id": "b2f90ba1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='TypeName'>"
      ]
     },
     "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": [
    "df['TypeName'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "110dbb85",
   "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": 20,
   "id": "8ba1598a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\AppData\\Local\\Temp\\ipykernel_11356\\1439577752.py:1: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(df['Inches'])\n",
      "C:\\Users\\HP\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Inches', ylabel='Density'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Inches'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "114dabbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Inches', ylabel='Price'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=df['Inches'],y=df['Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "46a2daff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ScreenResolution\n",
       "Full HD 1920x1080                                507\n",
       "1366x768                                         281\n",
       "IPS Panel Full HD 1920x1080                      230\n",
       "IPS Panel Full HD / Touchscreen 1920x1080         53\n",
       "Full HD / Touchscreen 1920x1080                   47\n",
       "1600x900                                          23\n",
       "Touchscreen 1366x768                              16\n",
       "Quad HD+ / Touchscreen 3200x1800                  15\n",
       "IPS Panel 4K Ultra HD 3840x2160                   12\n",
       "IPS Panel 4K Ultra HD / Touchscreen 3840x2160     11\n",
       "4K Ultra HD / Touchscreen 3840x2160               10\n",
       "4K Ultra HD 3840x2160                              7\n",
       "Touchscreen 2560x1440                              7\n",
       "IPS Panel 1366x768                                 7\n",
       "IPS Panel Quad HD+ / Touchscreen 3200x1800         6\n",
       "IPS Panel Retina Display 2560x1600                 6\n",
       "IPS Panel Retina Display 2304x1440                 6\n",
       "Touchscreen 2256x1504                              6\n",
       "IPS Panel Touchscreen 2560x1440                    5\n",
       "IPS Panel Retina Display 2880x1800                 4\n",
       "IPS Panel Touchscreen 1920x1200                    4\n",
       "1440x900                                           4\n",
       "IPS Panel 2560x1440                                4\n",
       "IPS Panel Quad HD+ 2560x1440                       3\n",
       "Quad HD+ 3200x1800                                 3\n",
       "1920x1080                                          3\n",
       "Touchscreen 2400x1600                              3\n",
       "2560x1440                                          3\n",
       "IPS Panel Touchscreen 1366x768                     3\n",
       "IPS Panel Touchscreen / 4K Ultra HD 3840x2160      2\n",
       "IPS Panel Full HD 2160x1440                        2\n",
       "IPS Panel Quad HD+ 3200x1800                       2\n",
       "IPS Panel Retina Display 2736x1824                 1\n",
       "IPS Panel Full HD 1920x1200                        1\n",
       "IPS Panel Full HD 2560x1440                        1\n",
       "IPS Panel Full HD 1366x768                         1\n",
       "Touchscreen / Full HD 1920x1080                    1\n",
       "Touchscreen / Quad HD+ 3200x1800                   1\n",
       "Touchscreen / 4K Ultra HD 3840x2160                1\n",
       "IPS Panel Touchscreen 2400x1600                    1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ScreenResolution'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4e4f26f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Touchscreen'] = df['ScreenResolution'].apply(lambda x:1 if 'Touchscreen' in x else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "07992d2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Inches</th>\n",
       "      <th>ScreenResolution</th>\n",
       "      <th>Cpu</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "      <th>Touchscreen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Chuwi</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Atom x5-Z8300 1.44GHz</td>\n",
       "      <td>4</td>\n",
       "      <td>64GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.89</td>\n",
       "      <td>13053.07</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1258</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i7 6500U 2.5GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>1.0TB Hybrid</td>\n",
       "      <td>AMD Radeon R5 M330</td>\n",
       "      <td>Windows 7</td>\n",
       "      <td>2.32</td>\n",
       "      <td>47685.60</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Dell</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>Touchscreen / Quad HD+ 3200x1800</td>\n",
       "      <td>Intel Core i7 8550U 1.8GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>Intel UHD Graphics 620</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.20</td>\n",
       "      <td>99580.32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1030</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>4</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.64</td>\n",
       "      <td>51841.44</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>863</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Gaming</td>\n",
       "      <td>15.6</td>\n",
       "      <td>IPS Panel Full HD 1920x1080</td>\n",
       "      <td>Intel Core i5 7300HQ 2.5GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>1TB HDD</td>\n",
       "      <td>Nvidia GeForce GTX 1050</td>\n",
       "      <td>No OS</td>\n",
       "      <td>2.50</td>\n",
       "      <td>54185.76</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company   TypeName  Inches                  ScreenResolution  \\\n",
       "30     Chuwi   Notebook    15.6                 Full HD 1920x1080   \n",
       "1258  Lenovo   Notebook    15.6                 Full HD 1920x1080   \n",
       "33      Dell  Ultrabook    13.3  Touchscreen / Quad HD+ 3200x1800   \n",
       "1030      HP   Notebook    14.0                          1366x768   \n",
       "863   Lenovo     Gaming    15.6       IPS Panel Full HD 1920x1080   \n",
       "\n",
       "                              Cpu  Ram              Memory  \\\n",
       "30    Intel Atom x5-Z8300 1.44GHz    4  64GB Flash Storage   \n",
       "1258   Intel Core i7 6500U 2.5GHz    8        1.0TB Hybrid   \n",
       "33     Intel Core i7 8550U 1.8GHz   16           512GB SSD   \n",
       "1030   Intel Core i5 7200U 2.5GHz    4           256GB SSD   \n",
       "863   Intel Core i5 7300HQ 2.5GHz    8             1TB HDD   \n",
       "\n",
       "                          Gpu       OpSys  Weight     Price  Touchscreen  \n",
       "30          Intel HD Graphics  Windows 10    1.89  13053.07            0  \n",
       "1258       AMD Radeon R5 M330   Windows 7    2.32  47685.60            0  \n",
       "33     Intel UHD Graphics 620  Windows 10    1.20  99580.32            1  \n",
       "1030    Intel HD Graphics 620  Windows 10    1.64  51841.44            0  \n",
       "863   Nvidia GeForce GTX 1050       No OS    2.50  54185.76            0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2fa50fd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Touchscreen'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Touchscreen'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d1b428b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Touchscreen', ylabel='Price'>"
      ]
     },
     "execution_count": 26,
     "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.barplot(x=df['Touchscreen'],y=df['Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8580c689",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Ips'] = df['ScreenResolution'].apply(lambda x:1 if 'IPS' in x else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4fb51139",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Inches</th>\n",
       "      <th>ScreenResolution</th>\n",
       "      <th>Cpu</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 2.3GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 640</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>71378.68</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>47895.52</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>30636.00</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>135195.34</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>96095.81</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Inches                    ScreenResolution  \\\n",
       "0   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "1   Apple  Ultrabook    13.3                            1440x900   \n",
       "2      HP   Notebook    15.6                   Full HD 1920x1080   \n",
       "3   Apple  Ultrabook    15.4  IPS Panel Retina Display 2880x1800   \n",
       "4   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "\n",
       "                          Cpu  Ram               Memory  \\\n",
       "0        Intel Core i5 2.3GHz    8            128GB SSD   \n",
       "1        Intel Core i5 1.8GHz    8  128GB Flash Storage   \n",
       "2  Intel Core i5 7200U 2.5GHz    8            256GB SSD   \n",
       "3        Intel Core i7 2.7GHz   16            512GB SSD   \n",
       "4        Intel Core i5 3.1GHz    8            256GB SSD   \n",
       "\n",
       "                            Gpu  OpSys  Weight      Price  Touchscreen  Ips  \n",
       "0  Intel Iris Plus Graphics 640  macOS    1.37   71378.68            0    1  \n",
       "1        Intel HD Graphics 6000  macOS    1.34   47895.52            0    0  \n",
       "2         Intel HD Graphics 620  No OS    1.86   30636.00            0    0  \n",
       "3            AMD Radeon Pro 455  macOS    1.83  135195.34            0    1  \n",
       "4  Intel Iris Plus Graphics 650  macOS    1.37   96095.81            0    1  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "44ef7296",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Ips'>"
      ]
     },
     "execution_count": 29,
     "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['Ips'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b8fd50ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Ips', ylabel='Price'>"
      ]
     },
     "execution_count": 30,
     "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.barplot(x=df['Ips'],y=df['Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b0cee465",
   "metadata": {},
   "outputs": [],
   "source": [
    "new = df['ScreenResolution'].str.split('x',n=1,expand=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a84439f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['X_res'] = new[0]\n",
    "df['Y_res'] = new[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "8a36d2c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Inches</th>\n",
       "      <th>ScreenResolution</th>\n",
       "      <th>Cpu</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>X_res</th>\n",
       "      <th>Y_res</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>908</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>IPS Panel Full HD 1920x1080</td>\n",
       "      <td>Intel Core i5 6300HQ 2.3GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB SSD +  1TB HDD</td>\n",
       "      <td>Nvidia GeForce GTX 960M</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.60</td>\n",
       "      <td>42037.92</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>IPS Panel Full HD 1920</td>\n",
       "      <td>1080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>17.3</td>\n",
       "      <td>1600x900</td>\n",
       "      <td>Intel Core i7 7500U 2.7GHz</td>\n",
       "      <td>6</td>\n",
       "      <td>128GB SSD +  1TB HDD</td>\n",
       "      <td>Nvidia GeForce 940MX</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.80</td>\n",
       "      <td>50562.72</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1600</td>\n",
       "      <td>900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1202</th>\n",
       "      <td>Dell</td>\n",
       "      <td>2 in 1 Convertible</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD / Touchscreen 1920x1080</td>\n",
       "      <td>Intel Core i7 8550U 1.8GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>Intel UHD Graphics 620</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.00</td>\n",
       "      <td>68464.80</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Full HD / Touchscreen 1920</td>\n",
       "      <td>1080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>733</th>\n",
       "      <td>Acer</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>4</td>\n",
       "      <td>500GB HDD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.40</td>\n",
       "      <td>29783.52</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1366</td>\n",
       "      <td>768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426</th>\n",
       "      <td>Dell</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i7 8550U 1.8GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD +  2TB HDD</td>\n",
       "      <td>AMD Radeon 530</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.02</td>\n",
       "      <td>60867.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Full HD 1920</td>\n",
       "      <td>1080</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company            TypeName  Inches                 ScreenResolution  \\\n",
       "908   Lenovo            Notebook    15.6      IPS Panel Full HD 1920x1080   \n",
       "557   Lenovo            Notebook    17.3                         1600x900   \n",
       "1202    Dell  2 in 1 Convertible    15.6  Full HD / Touchscreen 1920x1080   \n",
       "733     Acer            Notebook    15.6                         1366x768   \n",
       "426     Dell            Notebook    15.6                Full HD 1920x1080   \n",
       "\n",
       "                              Cpu  Ram                Memory  \\\n",
       "908   Intel Core i5 6300HQ 2.3GHz    8  128GB SSD +  1TB HDD   \n",
       "557    Intel Core i7 7500U 2.7GHz    6  128GB SSD +  1TB HDD   \n",
       "1202   Intel Core i7 8550U 1.8GHz   16             512GB SSD   \n",
       "733    Intel Core i5 7200U 2.5GHz    4             500GB HDD   \n",
       "426    Intel Core i7 8550U 1.8GHz    8  256GB SSD +  2TB HDD   \n",
       "\n",
       "                          Gpu       OpSys  Weight     Price  Touchscreen  Ips  \\\n",
       "908   Nvidia GeForce GTX 960M  Windows 10    2.60  42037.92            0    1   \n",
       "557      Nvidia GeForce 940MX  Windows 10    2.80  50562.72            0    0   \n",
       "1202   Intel UHD Graphics 620  Windows 10    2.00  68464.80            1    0   \n",
       "733     Intel HD Graphics 620  Windows 10    2.40  29783.52            0    0   \n",
       "426            AMD Radeon 530  Windows 10    2.02  60867.07            0    0   \n",
       "\n",
       "                           X_res Y_res  \n",
       "908       IPS Panel Full HD 1920  1080  \n",
       "557                         1600   900  \n",
       "1202  Full HD / Touchscreen 1920  1080  \n",
       "733                         1366   768  \n",
       "426                 Full HD 1920  1080  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7e34b4df",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['X_res'] = df['X_res'].str.replace(',','').str.findall(r'(\\d+\\.?\\d+)').apply(lambda x:x[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ea8467a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <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",
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       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
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       "      <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",
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       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
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       "      <td>0</td>\n",
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       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
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       "      <td>IPS Panel Retina Display 2880x1800</td>\n",
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       "      <td>512GB SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>135195.34</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2880</td>\n",
       "      <td>1800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
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       "      <td>Intel Core i5 3.1GHz</td>\n",
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       "      <td>1600</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Inches                    ScreenResolution  \\\n",
       "0   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "1   Apple  Ultrabook    13.3                            1440x900   \n",
       "2      HP   Notebook    15.6                   Full HD 1920x1080   \n",
       "3   Apple  Ultrabook    15.4  IPS Panel Retina Display 2880x1800   \n",
       "4   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "\n",
       "                          Cpu  Ram               Memory  \\\n",
       "0        Intel Core i5 2.3GHz    8            128GB SSD   \n",
       "1        Intel Core i5 1.8GHz    8  128GB Flash Storage   \n",
       "2  Intel Core i5 7200U 2.5GHz    8            256GB SSD   \n",
       "3        Intel Core i7 2.7GHz   16            512GB SSD   \n",
       "4        Intel Core i5 3.1GHz    8            256GB SSD   \n",
       "\n",
       "                            Gpu  OpSys  Weight      Price  Touchscreen  Ips  \\\n",
       "0  Intel Iris Plus Graphics 640  macOS    1.37   71378.68            0    1   \n",
       "1        Intel HD Graphics 6000  macOS    1.34   47895.52            0    0   \n",
       "2         Intel HD Graphics 620  No OS    1.86   30636.00            0    0   \n",
       "3            AMD Radeon Pro 455  macOS    1.83  135195.34            0    1   \n",
       "4  Intel Iris Plus Graphics 650  macOS    1.37   96095.81            0    1   \n",
       "\n",
       "  X_res Y_res  \n",
       "0  2560  1600  \n",
       "1  1440   900  \n",
       "2  1920  1080  \n",
       "3  2880  1800  \n",
       "4  2560  1600  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "dcb9da28",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['X_res'] = df['X_res'].astype('int')\n",
    "df['Y_res'] = df['Y_res'].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e9020c73",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1303 entries, 0 to 1302\n",
      "Data columns (total 15 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Company           1303 non-null   object \n",
      " 1   TypeName          1303 non-null   object \n",
      " 2   Inches            1303 non-null   float64\n",
      " 3   ScreenResolution  1303 non-null   object \n",
      " 4   Cpu               1303 non-null   object \n",
      " 5   Ram               1303 non-null   int32  \n",
      " 6   Memory            1303 non-null   object \n",
      " 7   Gpu               1303 non-null   object \n",
      " 8   OpSys             1303 non-null   object \n",
      " 9   Weight            1303 non-null   float32\n",
      " 10  Price             1303 non-null   float64\n",
      " 11  Touchscreen       1303 non-null   int64  \n",
      " 12  Ips               1303 non-null   int64  \n",
      " 13  X_res             1303 non-null   int32  \n",
      " 14  Y_res             1303 non-null   int32  \n",
      "dtypes: float32(1), float64(2), int32(3), int64(2), object(7)\n",
      "memory usage: 132.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "f7e12f23",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows with non-numeric values in 'Price' column:\n",
      "Empty DataFrame\n",
      "Columns: [Company, TypeName, Inches, ScreenResolution, Cpu, Ram, Memory, Gpu, OpSys, Weight, Price, Touchscreen, Ips, X_res, Y_res]\n",
      "Index: []\n"
     ]
    }
   ],
   "source": [
    "df['Price'] = pd.to_numeric(df['Price'], errors='coerce')\n",
    "\n",
    "# Identify rows with non-numeric values in the 'Price' column\n",
    "non_numeric_rows = df[df['Price'].isna()]\n",
    "\n",
    "# Print the rows with non-numeric values\n",
    "print(\"Rows with non-numeric values in 'Price' column:\")\n",
    "print(non_numeric_rows)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "dbd95a94",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ppi'] = (((df['X_res']**2) + (df['Y_res']**2))**0.5/df['Inches']).astype('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "98975041",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "43a9d35d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['ScreenResolution'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "5a9b5380",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2</th>\n",
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       "      <td>15.6</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
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       "      <td>15.4</td>\n",
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       "      <td>1.83</td>\n",
       "      <td>135195.34</td>\n",
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       "      <td>1800</td>\n",
       "      <td>220.534624</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>Intel Core i5 3.1GHz</td>\n",
       "      <td>8</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Inches                         Cpu  Ram  \\\n",
       "0   Apple  Ultrabook    13.3        Intel Core i5 2.3GHz    8   \n",
       "1   Apple  Ultrabook    13.3        Intel Core i5 1.8GHz    8   \n",
       "2      HP   Notebook    15.6  Intel Core i5 7200U 2.5GHz    8   \n",
       "3   Apple  Ultrabook    15.4        Intel Core i7 2.7GHz   16   \n",
       "4   Apple  Ultrabook    13.3        Intel Core i5 3.1GHz    8   \n",
       "\n",
       "                Memory                           Gpu  OpSys  Weight  \\\n",
       "0            128GB SSD  Intel Iris Plus Graphics 640  macOS    1.37   \n",
       "1  128GB Flash Storage        Intel HD Graphics 6000  macOS    1.34   \n",
       "2            256GB SSD         Intel HD Graphics 620  No OS    1.86   \n",
       "3            512GB SSD            AMD Radeon Pro 455  macOS    1.83   \n",
       "4            256GB SSD  Intel Iris Plus Graphics 650  macOS    1.37   \n",
       "\n",
       "       Price  Touchscreen  Ips  X_res  Y_res         ppi  \n",
       "0   71378.68            0    1   2560   1600  226.983005  \n",
       "1   47895.52            0    0   1440    900  127.677940  \n",
       "2   30636.00            0    0   1920   1080  141.211998  \n",
       "3  135195.34            0    1   2880   1800  220.534624  \n",
       "4   96095.81            0    1   2560   1600  226.983005  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "ef361f90",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['Inches','X_res','Y_res'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "2d327aa0",
   "metadata": {},
   "outputs": [
    {
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       "      <td>1.34</td>\n",
       "      <td>47895.52</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>127.677940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>30636.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>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>135195.34</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>220.534624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>Intel Core i5 3.1GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>96095.81</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName                         Cpu  Ram               Memory  \\\n",
       "0   Apple  Ultrabook        Intel Core i5 2.3GHz    8            128GB SSD   \n",
       "1   Apple  Ultrabook        Intel Core i5 1.8GHz    8  128GB Flash Storage   \n",
       "2      HP   Notebook  Intel Core i5 7200U 2.5GHz    8            256GB SSD   \n",
       "3   Apple  Ultrabook        Intel Core i7 2.7GHz   16            512GB SSD   \n",
       "4   Apple  Ultrabook        Intel Core i5 3.1GHz    8            256GB SSD   \n",
       "\n",
       "                            Gpu  OpSys  Weight      Price  Touchscreen  Ips  \\\n",
       "0  Intel Iris Plus Graphics 640  macOS    1.37   71378.68            0    1   \n",
       "1        Intel HD Graphics 6000  macOS    1.34   47895.52            0    0   \n",
       "2         Intel HD Graphics 620  No OS    1.86   30636.00            0    0   \n",
       "3            AMD Radeon Pro 455  macOS    1.83  135195.34            0    1   \n",
       "4  Intel Iris Plus Graphics 650  macOS    1.37   96095.81            0    1   \n",
       "\n",
       "          ppi  \n",
       "0  226.983005  \n",
       "1  127.677940  \n",
       "2  141.211998  \n",
       "3  220.534624  \n",
       "4  226.983005  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "81f9ec40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cpu\n",
       "Intel Core i5 7200U 2.5GHz       190\n",
       "Intel Core i7 7700HQ 2.8GHz      146\n",
       "Intel Core i7 7500U 2.7GHz       134\n",
       "Intel Core i7 8550U 1.8GHz        73\n",
       "Intel Core i5 8250U 1.6GHz        72\n",
       "                                ... \n",
       "Intel Core M M3-6Y30 0.9GHz        1\n",
       "AMD A9-Series 9420 2.9GHz          1\n",
       "Intel Core i3 6006U 2.2GHz         1\n",
       "AMD A6-Series 7310 2GHz            1\n",
       "Intel Xeon E3-1535M v6 3.1GHz      1\n",
       "Name: count, Length: 118, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Cpu'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "34ce8f2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Cpu Name'] = df['Cpu'].apply(lambda x:\" \".join(x.split()[0:3]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "8928f81c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Cpu</th>\n",
       "      <th>Ram</th>\n",
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       "      <th>Gpu</th>\n",
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       "      <th>Touchscreen</th>\n",
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       "      <th>ppi</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>Intel Core i5 2.3GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 640</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>71378.68</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
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       "      <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>47895.52</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>127.677940</td>\n",
       "      <td>Intel Core i5</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>30636.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>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>135195.34</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>220.534624</td>\n",
       "      <td>Intel Core i7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>Intel Core i5 3.1GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>96095.81</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName                         Cpu  Ram               Memory  \\\n",
       "0   Apple  Ultrabook        Intel Core i5 2.3GHz    8            128GB SSD   \n",
       "1   Apple  Ultrabook        Intel Core i5 1.8GHz    8  128GB Flash Storage   \n",
       "2      HP   Notebook  Intel Core i5 7200U 2.5GHz    8            256GB SSD   \n",
       "3   Apple  Ultrabook        Intel Core i7 2.7GHz   16            512GB SSD   \n",
       "4   Apple  Ultrabook        Intel Core i5 3.1GHz    8            256GB SSD   \n",
       "\n",
       "                            Gpu  OpSys  Weight      Price  Touchscreen  Ips  \\\n",
       "0  Intel Iris Plus Graphics 640  macOS    1.37   71378.68            0    1   \n",
       "1        Intel HD Graphics 6000  macOS    1.34   47895.52            0    0   \n",
       "2         Intel HD Graphics 620  No OS    1.86   30636.00            0    0   \n",
       "3            AMD Radeon Pro 455  macOS    1.83  135195.34            0    1   \n",
       "4  Intel Iris Plus Graphics 650  macOS    1.37   96095.81            0    1   \n",
       "\n",
       "          ppi       Cpu Name  \n",
       "0  226.983005  Intel Core i5  \n",
       "1  127.677940  Intel Core i5  \n",
       "2  141.211998  Intel Core i5  \n",
       "3  220.534624  Intel Core i7  \n",
       "4  226.983005  Intel Core i5  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "5a23ed7d",
   "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": 48,
   "id": "224c21db",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Cpu brand'] = df['Cpu Name'].apply(fetch_processor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "c3c72072",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>TypeName</th>\n",
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       "      <th>Ram</th>\n",
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       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>ppi</th>\n",
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       "      <td>Apple</td>\n",
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       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
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       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
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       "      <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>47895.52</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>127.677940</td>\n",
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       "      <td>Intel Core i5</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>30636.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>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>135195.34</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>220.534624</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>Intel Core i7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>Intel Core i5 3.1GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>96095.81</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName                         Cpu  Ram               Memory  \\\n",
       "0   Apple  Ultrabook        Intel Core i5 2.3GHz    8            128GB SSD   \n",
       "1   Apple  Ultrabook        Intel Core i5 1.8GHz    8  128GB Flash Storage   \n",
       "2      HP   Notebook  Intel Core i5 7200U 2.5GHz    8            256GB SSD   \n",
       "3   Apple  Ultrabook        Intel Core i7 2.7GHz   16            512GB SSD   \n",
       "4   Apple  Ultrabook        Intel Core i5 3.1GHz    8            256GB SSD   \n",
       "\n",
       "                            Gpu  OpSys  Weight      Price  Touchscreen  Ips  \\\n",
       "0  Intel Iris Plus Graphics 640  macOS    1.37   71378.68            0    1   \n",
       "1        Intel HD Graphics 6000  macOS    1.34   47895.52            0    0   \n",
       "2         Intel HD Graphics 620  No OS    1.86   30636.00            0    0   \n",
       "3            AMD Radeon Pro 455  macOS    1.83  135195.34            0    1   \n",
       "4  Intel Iris Plus Graphics 650  macOS    1.37   96095.81            0    1   \n",
       "\n",
       "          ppi       Cpu Name      Cpu brand  \n",
       "0  226.983005  Intel Core i5  Intel Core i5  \n",
       "1  127.677940  Intel Core i5  Intel Core i5  \n",
       "2  141.211998  Intel Core i5  Intel Core i5  \n",
       "3  220.534624  Intel Core i7  Intel Core i7  \n",
       "4  226.983005  Intel Core i5  Intel Core i5  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "bdc8ec9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Cpu brand'>"
      ]
     },
     "execution_count": 50,
     "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['Cpu brand'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "1a8350f0",
   "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": 52,
   "id": "fc54795b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['Cpu','Cpu Name'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "367e9992",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>ppi</th>\n",
       "      <th>Cpu brand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 640</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>71378.68</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>47895.52</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>127.677940</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>30636.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>135195.34</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>220.534624</td>\n",
       "      <td>Intel Core i7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>96095.81</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Ram               Memory                           Gpu  \\\n",
       "0   Apple  Ultrabook    8            128GB SSD  Intel Iris Plus Graphics 640   \n",
       "1   Apple  Ultrabook    8  128GB Flash Storage        Intel HD Graphics 6000   \n",
       "2      HP   Notebook    8            256GB SSD         Intel HD Graphics 620   \n",
       "3   Apple  Ultrabook   16            512GB SSD            AMD Radeon Pro 455   \n",
       "4   Apple  Ultrabook    8            256GB SSD  Intel Iris Plus Graphics 650   \n",
       "\n",
       "   OpSys  Weight      Price  Touchscreen  Ips         ppi      Cpu brand  \n",
       "0  macOS    1.37   71378.68            0    1  226.983005  Intel Core i5  \n",
       "1  macOS    1.34   47895.52            0    0  127.677940  Intel Core i5  \n",
       "2  No OS    1.86   30636.00            0    0  141.211998  Intel Core i5  \n",
       "3  macOS    1.83  135195.34            0    1  220.534624  Intel Core i7  \n",
       "4  macOS    1.37   96095.81            0    1  226.983005  Intel Core i5  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "b6cd52b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Ram'>"
      ]
     },
     "execution_count": 54,
     "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": 55,
   "id": "ca0e2e0b",
   "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": 56,
   "id": "c4a1224b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Memory\n",
       "256GB SSD                        412\n",
       "1TB HDD                          223\n",
       "500GB HDD                        132\n",
       "512GB SSD                        118\n",
       "128GB SSD +  1TB HDD              94\n",
       "128GB SSD                         76\n",
       "256GB SSD +  1TB HDD              73\n",
       "32GB Flash Storage                38\n",
       "2TB HDD                           16\n",
       "64GB Flash Storage                15\n",
       "512GB SSD +  1TB HDD              14\n",
       "1TB SSD                           14\n",
       "256GB SSD +  2TB HDD              10\n",
       "1.0TB Hybrid                       9\n",
       "256GB Flash Storage                8\n",
       "16GB Flash Storage                 7\n",
       "32GB SSD                           6\n",
       "180GB SSD                          5\n",
       "128GB Flash Storage                4\n",
       "512GB SSD +  2TB HDD               3\n",
       "16GB SSD                           3\n",
       "512GB Flash Storage                2\n",
       "1TB SSD +  1TB HDD                 2\n",
       "256GB SSD +  500GB HDD             2\n",
       "128GB SSD +  2TB HDD               2\n",
       "256GB SSD +  256GB SSD             2\n",
       "512GB SSD +  256GB SSD             1\n",
       "512GB SSD +  512GB SSD             1\n",
       "64GB Flash Storage +  1TB HDD      1\n",
       "1TB HDD +  1TB HDD                 1\n",
       "32GB HDD                           1\n",
       "64GB SSD                           1\n",
       "128GB HDD                          1\n",
       "240GB SSD                          1\n",
       "8GB SSD                            1\n",
       "508GB Hybrid                       1\n",
       "1.0TB HDD                          1\n",
       "512GB SSD +  1.0TB Hybrid          1\n",
       "256GB SSD +  1.0TB Hybrid          1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Memory'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "2391ad9b",
   "metadata": {},
   "outputs": [],
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "93f6bdec",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Assuming df is your DataFrame\n",
    "\n",
    "# Extract only numeric parts from 'first' column\n",
    "df['first'] = df['first'].str.extract('(\\d+)', expand=False)\n",
    "\n",
    "# Replace missing values in 'second' column with \"0\"\n",
    "df[\"second\"].fillna(\"0\", inplace=True)\n",
    "\n",
    "# Convert 'second' column to string\n",
    "df['second'] = df['second'].astype(str)\n",
    "\n",
    "# Extract only numeric parts from 'second' column\n",
    "df['second'] = df['second'].str.extract('(\\d+)', expand=False)\n",
    "\n",
    "# Convert 'first' and 'second' columns to numeric\n",
    "df[\"first\"] = pd.to_numeric(df[\"first\"], errors='coerce')\n",
    "df[\"second\"] = pd.to_numeric(df[\"second\"], errors='coerce')\n",
    "\n",
    "# Create columns indicating the presence of specific terms in 'second' column\n",
    "df[\"Layer2HDD\"] = df[\"second\"].apply(lambda x: 1 if \"HDD\" in str(x) else 0)\n",
    "df[\"Layer2SSD\"] = df[\"second\"].apply(lambda x: 1 if \"SSD\" in str(x) else 0)\n",
    "df[\"Layer2Hybrid\"] = df[\"second\"].apply(lambda x: 1 if \"Hybrid\" in str(x) else 0)\n",
    "df[\"Layer2Flash_Storage\"] = df[\"second\"].apply(lambda x: 1 if \"Flash Storage\" in str(x) else 0)\n",
    "\n",
    "# Perform calculations based on cleaned 'first' and 'second' columns\n",
    "df[\"HDD\"] = df[\"first\"] * df[\"Layer1HDD\"] + df[\"second\"] * df[\"Layer2HDD\"]\n",
    "df[\"SSD\"] = df[\"first\"] * df[\"Layer1SSD\"] + df[\"second\"] * df[\"Layer2SSD\"]\n",
    "df[\"Hybrid\"] = df[\"first\"] * df[\"Layer1Hybrid\"] + df[\"second\"] * df[\"Layer2Hybrid\"]\n",
    "df[\"Flash_Storage\"] = df[\"first\"] * df[\"Layer1Flash_Storage\"] + df[\"second\"] * df[\"Layer2Flash_Storage\"]\n",
    "\n",
    "# Drop unnecessary columns\n",
    "df.drop(columns=['first', 'second', 'Layer1HDD', 'Layer1SSD', 'Layer1Hybrid', 'Layer1Flash_Storage', \n",
    "                 'Layer2HDD', 'Layer2SSD', 'Layer2Hybrid', 'Layer2Flash_Storage'], inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "919f2130",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>ppi</th>\n",
       "      <th>Cpu brand</th>\n",
       "      <th>HDD</th>\n",
       "      <th>SSD</th>\n",
       "      <th>Hybrid</th>\n",
       "      <th>Flash_Storage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>4</td>\n",
       "      <td>500 HDD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.10</td>\n",
       "      <td>38468.16</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>821</th>\n",
       "      <td>Toshiba</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>4</td>\n",
       "      <td>128 SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.25</td>\n",
       "      <td>58075.20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111.935204</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>314</th>\n",
       "      <td>Asus</td>\n",
       "      <td>2 in 1 Convertible</td>\n",
       "      <td>2</td>\n",
       "      <td>32 Flash Storage</td>\n",
       "      <td>Intel HD Graphics 500</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.10</td>\n",
       "      <td>14652.00</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1208</th>\n",
       "      <td>Acer</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>1000 HDD</td>\n",
       "      <td>Nvidia GeForce 940MX</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>3.30</td>\n",
       "      <td>33513.12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>106.113062</td>\n",
       "      <td>Intel Core i3</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>724</th>\n",
       "      <td>MSI</td>\n",
       "      <td>Gaming</td>\n",
       "      <td>8</td>\n",
       "      <td>128 SSD +  1000 HDD</td>\n",
       "      <td>Nvidia GeForce GTX 1050 Ti</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.70</td>\n",
       "      <td>71847.01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>127.335675</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Company            TypeName  Ram               Memory  \\\n",
       "262        HP            Notebook    4              500 HDD   \n",
       "821   Toshiba           Ultrabook    4              128 SSD   \n",
       "314      Asus  2 in 1 Convertible    2     32 Flash Storage   \n",
       "1208     Acer            Notebook    8             1000 HDD   \n",
       "724       MSI              Gaming    8  128 SSD +  1000 HDD   \n",
       "\n",
       "                             Gpu       OpSys  Weight     Price  Touchscreen  \\\n",
       "262        Intel HD Graphics 620  Windows 10    2.10  38468.16            0   \n",
       "821        Intel HD Graphics 620  Windows 10    1.25  58075.20            0   \n",
       "314        Intel HD Graphics 500  Windows 10    1.10  14652.00            1   \n",
       "1208        Nvidia GeForce 940MX  Windows 10    3.30  33513.12            0   \n",
       "724   Nvidia GeForce GTX 1050 Ti  Windows 10    2.70  71847.01            0   \n",
       "\n",
       "      Ips         ppi              Cpu brand   HDD  SSD  Hybrid  Flash_Storage  \n",
       "262     1  141.211998          Intel Core i5   500    0       0              0  \n",
       "821     0  111.935204          Intel Core i5     0  128       0              0  \n",
       "314     0  135.094211  Other Intel Processor     0    0       0             32  \n",
       "1208    0  106.113062          Intel Core i3  1000    0       0              0  \n",
       "724     0  127.335675          Intel Core i7     0  128       0              0  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "79b1c60f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['Memory'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "cf1a2da5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>128</td>\n",
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       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>30636.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i5</td>\n",
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       "      <td>0</td>\n",
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       "      <td>Apple</td>\n",
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       "      <td>16</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
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       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>512</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>96095.81</td>\n",
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       "      <td>256</td>\n",
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      "text/plain": [
       "  Company   TypeName  Ram                           Gpu  OpSys  Weight  \\\n",
       "0   Apple  Ultrabook    8  Intel Iris Plus Graphics 640  macOS    1.37   \n",
       "1   Apple  Ultrabook    8        Intel HD Graphics 6000  macOS    1.34   \n",
       "2      HP   Notebook    8         Intel HD Graphics 620  No OS    1.86   \n",
       "3   Apple  Ultrabook   16            AMD Radeon Pro 455  macOS    1.83   \n",
       "4   Apple  Ultrabook    8  Intel Iris Plus Graphics 650  macOS    1.37   \n",
       "\n",
       "       Price  Touchscreen  Ips         ppi      Cpu brand  HDD  SSD  Hybrid  \\\n",
       "0   71378.68            0    1  226.983005  Intel Core i5    0  128       0   \n",
       "1   47895.52            0    0  127.677940  Intel Core i5    0    0       0   \n",
       "2   30636.00            0    0  141.211998  Intel Core i5    0  256       0   \n",
       "3  135195.34            0    1  220.534624  Intel Core i7    0  512       0   \n",
       "4   96095.81            0    1  226.983005  Intel Core i5    0  256       0   \n",
       "\n",
       "   Flash_Storage  \n",
       "0              0  \n",
       "1            128  \n",
       "2              0  \n",
       "3              0  \n",
       "4              0  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0797800",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "845e8723",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['Hybrid','Flash_Storage'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "5e069ba6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "  Company   TypeName  Ram                           Gpu  OpSys  Weight  \\\n",
       "0   Apple  Ultrabook    8  Intel Iris Plus Graphics 640  macOS    1.37   \n",
       "1   Apple  Ultrabook    8        Intel HD Graphics 6000  macOS    1.34   \n",
       "2      HP   Notebook    8         Intel HD Graphics 620  No OS    1.86   \n",
       "3   Apple  Ultrabook   16            AMD Radeon Pro 455  macOS    1.83   \n",
       "4   Apple  Ultrabook    8  Intel Iris Plus Graphics 650  macOS    1.37   \n",
       "\n",
       "       Price  Touchscreen  Ips         ppi      Cpu brand  HDD  SSD  \n",
       "0   71378.68            0    1  226.983005  Intel Core i5    0  128  \n",
       "1   47895.52            0    0  127.677940  Intel Core i5    0    0  \n",
       "2   30636.00            0    0  141.211998  Intel Core i5    0  256  \n",
       "3  135195.34            0    1  220.534624  Intel Core i7    0  512  \n",
       "4   96095.81            0    1  226.983005  Intel Core i5    0  256  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
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    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "65722933",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gpu\n",
       "Intel HD Graphics 620      281\n",
       "Intel HD Graphics 520      185\n",
       "Intel UHD Graphics 620      68\n",
       "Nvidia GeForce GTX 1050     66\n",
       "Nvidia GeForce GTX 1060     48\n",
       "                          ... \n",
       "AMD Radeon R5 520            1\n",
       "AMD Radeon R7                1\n",
       "Intel HD Graphics 540        1\n",
       "AMD Radeon 540               1\n",
       "ARM Mali T860 MP4            1\n",
       "Name: count, Length: 110, dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df['Gpu'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "75b44cba",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Gpu brand'] = df['Gpu'].apply(lambda x:x.split()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "4ca7c61a",
   "metadata": {},
   "outputs": [
    {
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       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>47895.52</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>127.677940</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>30636.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>16</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>135195.34</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>220.534624</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>512</td>\n",
       "      <td>AMD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>96095.81</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Ram                           Gpu  OpSys  Weight  \\\n",
       "0   Apple  Ultrabook    8  Intel Iris Plus Graphics 640  macOS    1.37   \n",
       "1   Apple  Ultrabook    8        Intel HD Graphics 6000  macOS    1.34   \n",
       "2      HP   Notebook    8         Intel HD Graphics 620  No OS    1.86   \n",
       "3   Apple  Ultrabook   16            AMD Radeon Pro 455  macOS    1.83   \n",
       "4   Apple  Ultrabook    8  Intel Iris Plus Graphics 650  macOS    1.37   \n",
       "\n",
       "       Price  Touchscreen  Ips         ppi      Cpu brand  HDD  SSD Gpu brand  \n",
       "0   71378.68            0    1  226.983005  Intel Core i5    0  128     Intel  \n",
       "1   47895.52            0    0  127.677940  Intel Core i5    0    0     Intel  \n",
       "2   30636.00            0    0  141.211998  Intel Core i5    0  256     Intel  \n",
       "3  135195.34            0    1  220.534624  Intel Core i7    0  512       AMD  \n",
       "4   96095.81            0    1  226.983005  Intel Core i5    0  256     Intel  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "558506dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gpu brand\n",
       "Intel     722\n",
       "Nvidia    400\n",
       "AMD       180\n",
       "ARM         1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gpu brand'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "39d984fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df['Gpu brand'] != 'ARM']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "7bd66b54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gpu brand\n",
       "Intel     722\n",
       "Nvidia    400\n",
       "AMD       180\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gpu brand'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "a102cfaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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16tQpJSYmGrUdO3ZU69atlZ+fL0nKz89XbGysIiIijJqkpCS5XC7t3bvXqDl9G7U1tduoqqpSYWGhV42fn58SExONmjOprKyUy+XyWgAAwNXL0tCUkJCg7Oxs5ebmasGCBTp06JB+9atf6dixY3I6nQoMDFSTJk283hMRESGn0ylJcjqdXoGpdrx27Gw1LpdLJ06c0DfffKOampoz1tRu40ymTZumsLAwY4mOjj6vYwAAAK4Mln6NSp8+fYyfu3TpooSEBLVp00arV69WSEiIhZ39tIkTJyozM9N47XK5CE4AAFzFLL88d7omTZro5z//uT799FNFRkaqqqpKZWVlXjUlJSWKjIyUJEVGRtZ5mq729U/V2O12hYSEqHnz5vL39z9jTe02ziQoKEh2u91rAQAAVy+fCk3Hjx/XP/7xD7Vs2VJxcXFq0KCB8vLyjPGioiIVFxfL4XBIkhwOh/bs2eP1lNumTZtkt9sVExNj1Jy+jdqa2m0EBgYqLi7Oq8btdisvL8+oAQAAsDQ0PfHEE9q6das+//xzbd++Xb/5zW/k7++v+++/X2FhYUpPT1dmZqbefvttFRYWavDgwXI4HOrRo4ckqXfv3oqJidGgQYP04YcfauPGjZo0aZIyMjIUFBQkSRo2bJg+++wzjRs3TgcOHND8+fO1evVqjRkzxugjMzNTL774opYvX679+/dr+PDhqqio0ODBgy05LgAAwPdYek/Tl19+qfvvv1///Oc/1aJFC918881677331KJFC0nSzJkz5efnp5SUFFVWViopKUnz58833u/v768NGzZo+PDhcjgcCg0NVVpamqZOnWrUtGvXTjk5ORozZoxmz56tVq1aafHixUpKSjJqBgwYoNLSUmVlZcnpdKpbt27Kzc2tc3M4AACov2wej8djdRNXA5fLpbCwMJWXl3N/0wV44IEHjPvLIiIitGLFCos7AuBrer7Q0+oW4GO2jdx23u89l7/fPnVPEwAAgK8iNAEAAJhAaAIAADDB0hvBUVfc2JesbsFS9u+OG0n+6++O1/vjUfjcQ1a3AAD4N840AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMCLC6AeB07gahZ/wZAACrEZrgU45f38fqFgAAOCMuzwEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmOAzoenPf/6zbDabRo8ebaw7efKkMjIy1KxZMzVq1EgpKSkqKSnxel9xcbGSk5PVsGFDhYeHa+zYsaqurvaq2bJli7p3766goCB16NBB2dnZdfY/b948tW3bVsHBwUpISFBBQcGl+JgAAOAK5ROhaefOnfqf//kfdenSxWv9mDFjtH79eq1Zs0Zbt27VkSNH1L9/f2O8pqZGycnJqqqq0vbt27V8+XJlZ2crKyvLqDl06JCSk5N16623avfu3Ro9erSGDh2qjRs3GjWrVq1SZmamJk+erPfff19du3ZVUlKSjh49euk/PAAAuCJYHpqOHz+u1NRUvfjii7rmmmuM9eXl5VqyZIlmzJih2267TXFxcVq2bJm2b9+u9957T5L01ltvad++fXr55ZfVrVs39enTR0899ZTmzZunqqoqSdLChQvVrl07Pf/88+rUqZNGjBih++67TzNnzjT2NWPGDD3yyCMaPHiwYmJitHDhQjVs2FBLly69vAcDAAD4LMtDU0ZGhpKTk5WYmOi1vrCwUKdOnfJa37FjR7Vu3Vr5+fmSpPz8fMXGxioiIsKoSUpKksvl0t69e42aH247KSnJ2EZVVZUKCwu9avz8/JSYmGjUnEllZaVcLpfXAuDqM2rUKD3wwAN64IEHNGrUKKvbAWChACt3/pe//EXvv/++du7cWWfM6XQqMDBQTZo08VofEREhp9Np1JwemGrHa8fOVuNyuXTixAl99913qqmpOWPNgQMHfrT3adOm6cknnzT3QQFcsUpLS+vcSwmgfrqgM01VVVUqKiqqc+O1GYcPH9aoUaP0yiuvKDg4+ELasMTEiRNVXl5uLIcPH7a6JQAAcAmdV2j617/+pfT0dDVs2FC/+MUvVFxcLEkaOXKk/vznP5vaRmFhoY4eParu3bsrICBAAQEB2rp1q+bMmaOAgABFRESoqqpKZWVlXu8rKSlRZGSkJCkyMrLO/wBrX/9Ujd1uV0hIiJo3by5/f/8z1tRu40yCgoJkt9u9FgAAcPU6r9A0ceJEffjhh9qyZYvXWaLExEStWrXK1DZuv/127dmzR7t37zaW+Ph4paamGj83aNBAeXl5xnuKiopUXFwsh8MhSXI4HNqzZ4/XU26bNm2S3W5XTEyMUXP6NmprarcRGBiouLg4rxq32628vDyjBgAA4LzuaVq3bp1WrVqlHj16yGazGet/8Ytf6B//+IepbTRu3FidO3f2WhcaGqpmzZoZ69PT05WZmammTZvKbrdr5MiRcjgc6tGjhySpd+/eiomJ0aBBgzR9+nQ5nU5NmjRJGRkZCgoKkiQNGzZMc+fO1bhx4zRkyBBt3rxZq1evVk5OjrHfzMxMpaWlKT4+XjfddJNmzZqliooKDR48+HwODwAAuAqdV2gqLS1VeHh4nfUVFRVeIepCzZw5U35+fkpJSVFlZaWSkpI0f/58Y9zf318bNmzQ8OHD5XA4FBoaqrS0NE2dOtWoadeunXJycjRmzBjNnj1brVq10uLFi5WUlGTUDBgwQKWlpcrKypLT6VS3bt2Um5tb5+ZwAABQf51XaIqPj1dOTo5GjhwpSUZQWrx48QVd0tqyZYvX6+DgYM2bN0/z5s370fe0adNGb7zxxlm3e8stt+iDDz44a82IESM0YsQI070CAID65bxC0zPPPKM+ffpo3759qq6u1uzZs7Vv3z5t375dW7duvdg9AgAAWO68bgS/+eabtXv3blVXVys2NlZvvfWWwsPDlZ+fr7i4uIvdIwAAgOXOe3LLn/3sZ3rxxRcvZi8AAAA+67zONL3xxhteX3hba+PGjXrzzTcvuCkAAABfc16hacKECaqpqamz3uPxaMKECRfcFAAAgK85r9B08OBBY/LI03Xs2FGffvrpBTcFAADga84rNIWFhemzzz6rs/7TTz9VaGjoBTcFAADga84rNPXt21ejR4/2mv37008/1eOPP6577733ojUHAADgK84rNE2fPl2hoaHq2LGj2rVrp3bt2qlTp05q1qyZ/vu///ti9wgAAGC585pyICwsTNu3b9emTZv04YcfKiQkRF26dFGvXr0udn8AAAA+4bznabLZbOrdu7d69+59MfsBAADwSaZD05w5c/Too48qODhYc+bMOWvtY489dsGNAQAA+BLToWnmzJlKTU1VcHCwZs6c+aN1NpuN0ARcRYqnxlrdgqWqy5pJ8v/3z0fq/fFonbXH6hYAy5gOTYcOHTrjzwAAAPXBOT89d+rUKf3sZz/T/v37L0U/AAAAPumcQ1ODBg108uTJS9ELAACAzzqveZoyMjL07LPPqrq6+mL3AwAA4JPOa8qBnTt3Ki8vT2+99ZZiY2PrfHXKa6+9dlGaAwAA8BXnFZqaNGmilJSUi90LAACAzzqn0OR2u/Xcc8/pk08+UVVVlW677TZNmTJFISEhl6o/AAAAn3BO9zQ9/fTT+v3vf69GjRrp2muv1Zw5c5SRkXGpegMAAPAZ5xSaXnrpJc2fP18bN27UunXrtH79er3yyityu92Xqj8AAACfcE6hqbi4WHfddZfxOjExUTabTUeOHLnojQEAAPiScwpN1dXVCg4O9lrXoEEDnTp16qI2BQAA4GvO6UZwj8ejhx9+WEFBQca6kydPatiwYV7TDjDlAAAAuNqcU2hKS0urs+7BBx+8aM0AAAD4qnMKTcuWLbtUfQAAAPi085rcEgDqi6ZBNWf8GUD9Q2gCgLP4/Q1lVrcAwEec1xf2AgAA1DeEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATLA1NCxYsUJcuXWS322W32+VwOPTmm28a4ydPnlRGRoaaNWumRo0aKSUlRSUlJV7bKC4uVnJysho2bKjw8HCNHTtW1dXVXjVbtmxR9+7dFRQUpA4dOig7O7tOL/PmzVPbtm0VHByshIQEFRQUXJLPDAAArkyWhqZWrVrpz3/+swoLC7Vr1y7ddttt6tu3r/bu3StJGjNmjNavX681a9Zo69atOnLkiPr372+8v6amRsnJyaqqqtL27du1fPlyZWdnKysry6g5dOiQkpOTdeutt2r37t0aPXq0hg4dqo0bNxo1q1atUmZmpiZPnqz3339fXbt2VVJSko4ePXr5DgYAAPBpNo/H47G6idM1bdpUzz33nO677z61aNFCK1as0H333SdJOnDggDp16qT8/Hz16NFDb775pu6++24dOXJEERERkqSFCxdq/PjxKi0tVWBgoMaPH6+cnBx9/PHHxj4GDhyosrIy5ebmSpISEhJ04403au7cuZIkt9ut6OhojRw5UhMmTDhjn5WVlaqsrDReu1wuRUdHq7y8XHa7/bw/f9zYl877vbj6FD73kNUtqHhqrNUtwIe0ztpjdQvq+UJPq1uAj9k2ctt5v9flciksLMzU32+fuaeppqZGf/nLX1RRUSGHw6HCwkKdOnVKiYmJRk3Hjh3VunVr5efnS5Ly8/MVGxtrBCZJSkpKksvlMs5W5efne22jtqZ2G1VVVSosLPSq8fPzU2JiolFzJtOmTVNYWJixREdHX/hBAAAAPsvy0LRnzx41atRIQUFBGjZsmNauXauYmBg5nU4FBgaqSZMmXvURERFyOp2SJKfT6RWYasdrx85W43K5dOLECX3zzTeqqak5Y03tNs5k4sSJKi8vN5bDhw+f1+cHAABXhgCrG7j++uu1e/dulZeX69VXX1VaWpq2bt1qdVs/KSgoSEFBQVa3AQAALhPLQ1NgYKA6dOggSYqLi9POnTs1e/ZsDRgwQFVVVSorK/M621RSUqLIyEhJUmRkZJ2n3Gqfrju95odP3JWUlMhutyskJET+/v7y9/c/Y03tNgAAACy/PPdDbrdblZWViouLU4MGDZSXl2eMFRUVqbi4WA6HQ5LkcDi0Z88er6fcNm3aJLvdrpiYGKPm9G3U1tRuIzAwUHFxcV41brdbeXl5Rg0AAIClZ5omTpyoPn36qHXr1jp27JhWrFihLVu2aOPGjQoLC1N6eroyMzPVtGlT2e12jRw5Ug6HQz169JAk9e7dWzExMRo0aJCmT58up9OpSZMmKSMjw7h0NmzYMM2dO1fjxo3TkCFDtHnzZq1evVo5OTlGH5mZmUpLS1N8fLxuuukmzZo1SxUVFRo8eLAlxwUAAPgeS0PT0aNH9dBDD+nrr79WWFiYunTpoo0bN+qOO+6QJM2cOVN+fn5KSUlRZWWlkpKSNH/+fOP9/v7+2rBhg4YPHy6Hw6HQ0FClpaVp6tSpRk27du2Uk5OjMWPGaPbs2WrVqpUWL16spKQko2bAgAEqLS1VVlaWnE6nunXrptzc3Do3hwMAgPrL5+ZpulKdyzwPZ8M8TTgd8zTB1zBPE3xRvZunCQAAwJcRmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACZaGpmnTpunGG29U48aNFR4ern79+qmoqMir5uTJk8rIyFCzZs3UqFEjpaSkqKSkxKumuLhYycnJatiwocLDwzV27FhVV1d71WzZskXdu3dXUFCQOnTooOzs7Dr9zJs3T23btlVwcLASEhJUUFBw0T8zAAC4MlkamrZu3aqMjAy999572rRpk06dOqXevXuroqLCqBkzZozWr1+vNWvWaOvWrTpy5Ij69+9vjNfU1Cg5OVlVVVXavn27li9fruzsbGVlZRk1hw4dUnJysm699Vbt3r1bo0eP1tChQ7Vx40ajZtWqVcrMzNTkyZP1/vvvq2vXrkpKStLRo0cvz8EAAAA+zebxeDxWN1GrtLRU4eHh2rp1q3r16qXy8nK1aNFCK1as0H333SdJOnDggDp16qT8/Hz16NFDb775pu6++24dOXJEERERkqSFCxdq/PjxKi0tVWBgoMaPH6+cnBx9/PHHxr4GDhyosrIy5ebmSpISEhJ04403au7cuZIkt9ut6OhojRw5UhMmTKjTa2VlpSorK43XLpdL0dHRKi8vl91uP+9jEDf2pfN+L64+hc89ZHULKp4aa3UL8CGts/ZY3YJ6vtDT6hbgY7aN3Hbe73W5XAoLCzP199un7mkqLy+XJDVt2lSSVFhYqFOnTikxMdGo6dixo1q3bq38/HxJUn5+vmJjY43AJElJSUlyuVzau3evUXP6NmprardRVVWlwsJCrxo/Pz8lJiYaNT80bdo0hYWFGUt0dPSFfnwAAODDfCY0ud1ujR49Wj179lTnzp0lSU6nU4GBgWrSpIlXbUREhJxOp1FzemCqHa8dO1uNy+XSiRMn9M0336impuaMNbXb+KGJEyeqvLzcWA4fPnx+HxwAAFwRAqxuoFZGRoY+/vhjvfvuu1a3YkpQUJCCgoKsbgMAAFwmPnGmacSIEdqwYYPefvtttWrVylgfGRmpqqoqlZWVedWXlJQoMjLSqPnh03S1r3+qxm63KyQkRM2bN5e/v/8Za2q3AQAA6jdLQ5PH49GIESO0du1abd68We3atfMaj4uLU4MGDZSXl2esKyoqUnFxsRwOhyTJ4XBoz549Xk+5bdq0SXa7XTExMUbN6duorandRmBgoOLi4rxq3G638vLyjBoAAFC/WXp5LiMjQytWrND//d//qXHjxsb9Q2FhYQoJCVFYWJjS09OVmZmppk2bym63a+TIkXI4HOrRo4ckqXfv3oqJidGgQYM0ffp0OZ1OTZo0SRkZGcbls2HDhmnu3LkaN26chgwZos2bN2v16tXKyckxesnMzFRaWpri4+N10003adasWaqoqNDgwYMv/4EBAAA+x9LQtGDBAknSLbfc4rV+2bJlevjhhyVJM2fOlJ+fn1JSUlRZWamkpCTNnz/fqPX399eGDRs0fPhwORwOhYaGKi0tTVOnTjVq2rVrp5ycHI0ZM0azZ89Wq1attHjxYiUlJRk1AwYMUGlpqbKysuR0OtWtWzfl5ubWuTkcAADUTz41T9OV7FzmeTgb5mnC6ZinCb6GeZrgi+rlPE0AAAC+itAEAABgAqEJAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACZYGpreeecd3XPPPYqKipLNZtO6deu8xj0ej7KystSyZUuFhIQoMTFRBw8e9Kr59ttvlZqaKrvdriZNmig9PV3Hjx/3qvnoo4/0q1/9SsHBwYqOjtb06dPr9LJmzRp17NhRwcHBio2N1RtvvHHRPy8AALhyWRqaKioq1LVrV82bN++M49OnT9ecOXO0cOFC7dixQ6GhoUpKStLJkyeNmtTUVO3du1ebNm3Shg0b9M477+jRRx81xl0ul3r37q02bdqosLBQzz33nKZMmaJFixYZNdu3b9f999+v9PR0ffDBB+rXr5/69eunjz/++NJ9eAAAcEWxeTwej9VNSJLNZtPatWvVr18/Sd+fZYqKitLjjz+uJ554QpJUXl6uiIgIZWdna+DAgdq/f79iYmK0c+dOxcfHS5Jyc3N111136csvv1RUVJQWLFigP/zhD3I6nQoMDJQkTZgwQevWrdOBAwckSQMGDFBFRYU2bNhg9NOjRw9169ZNCxcuNNW/y+VSWFiYysvLZbfbz/s4xI196bzfi6tP4XMPWd2CiqfGWt0CfEjrrD1Wt6CeL/S0ugX4mG0jt533e8/l77fP3tN06NAhOZ1OJSYmGuvCwsKUkJCg/Px8SVJ+fr6aNGliBCZJSkxMlJ+fn3bs2GHU9OrVywhMkpSUlKSioiJ99913Rs3p+6mtqd3PmVRWVsrlcnktAADg6uWzocnpdEqSIiIivNZHREQYY06nU+Hh4V7jAQEBatq0qVfNmbZx+j5+rKZ2/EymTZumsLAwY4mOjj7XjwgAAK4gPhuafN3EiRNVXl5uLIcPH7a6JQAAcAn5bGiKjIyUJJWUlHitLykpMcYiIyN19OhRr/Hq6mp9++23XjVn2sbp+/ixmtrxMwkKCpLdbvdaAADA1ctnQ1O7du0UGRmpvLw8Y53L5dKOHTvkcDgkSQ6HQ2VlZSosLDRqNm/eLLfbrYSEBKPmnXfe0alTp4yaTZs26frrr9c111xj1Jy+n9qa2v0AAABYGpqOHz+u3bt3a/fu3ZK+v/l79+7dKi4uls1m0+jRo/WnP/1Jr7/+uvbs2aOHHnpIUVFRxhN2nTp10p133qlHHnlEBQUF2rZtm0aMGKGBAwcqKipKkvTAAw8oMDBQ6enp2rt3r1atWqXZs2crMzPT6GPUqFHKzc3V888/rwMHDmjKlCnatWuXRowYcbkPCQAA8FEBVu58165duvXWW43XtUEmLS1N2dnZGjdunCoqKvToo4+qrKxMN998s3JzcxUcHGy855VXXtGIESN0++23y8/PTykpKZozZ44xHhYWprfeeksZGRmKi4tT8+bNlZWV5TWX0y9/+UutWLFCkyZN0u9//3tdd911WrdunTp37nwZjgIAALgS+Mw8TVc65mnCpcA8TfA1zNMEX1Tv52kCAADwJYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoekH5s2bp7Zt2yo4OFgJCQkqKCiwuiUAAOADCE2nWbVqlTIzMzV58mS9//776tq1q5KSknT06FGrWwMAABYjNJ1mxowZeuSRRzR48GDFxMRo4cKFatiwoZYuXWp1awAAwGIBVjfgK6qqqlRYWKiJEyca6/z8/JSYmKj8/Pw69ZWVlaqsrDRel5eXS5JcLtcF9VFTeeKC3o+ry4X+Pl0Mx07WWN0CfIgv/E5Wn6i2ugX4mAv5vax9r8fj+claQtO/ffPNN6qpqVFERITX+oiICB04cKBO/bRp0/Tkk0/WWR8dHX3JekT9E/bCMKtbALxNC7O6A6COsPEX/nt57NgxhYWdfTuEpvM0ceJEZWZmGq/dbre+/fZbNWvWTDabzcLOrnwul0vR0dE6fPiw7Ha71e0A/E7C5/A7efF4PB4dO3ZMUVFRP1lLaPq35s2by9/fXyUlJV7rS0pKFBkZWac+KChIQUFBXuuaNGlyKVusd+x2O/8YwKfwOwlfw+/kxfFTZ5hqcSP4vwUGBiouLk55eXnGOrfbrby8PDkcDgs7AwAAvoAzTafJzMxUWlqa4uPjddNNN2nWrFmqqKjQ4MGDrW4NAABYjNB0mgEDBqi0tFRZWVlyOp3q1q2bcnNz69wcjksrKChIkydPrnP5E7AKv5PwNfxOWsPmMfOMHQAAQD3HPU0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABOZpAoAf8Hg8Kiws1Oeffy6bzaZ27drphhtu4HslgXqO0ATLvP7666Zr77333kvYCfAfb7/9ttLT0/XFF1+odhq72uC0dOlS9erVy+IOUd99+eWXev3111VcXKyqqiqvsRkzZljUVf3A5JawjJ+fuavDNptNNTU1l7gbQPr000/VtWtXJSQkaNSoUerYsaM8Ho/27dunOXPmaNeuXfroo4/Uvn17q1tFPZWXl6d7771X7du314EDB9S5c2d9/vnn8ng86t69uzZv3mx1i1c1QhMA/NuIESO0f/9+ry/uruXxeJSYmKiYmBi98MILFnQHSDfddJP69OmjJ598Uo0bN9aHH36o8PBwpaam6s4779Tw4cOtbvGqxo3g8DknT560ugXUU1u2bNHo0aPPOGaz2TR69Gi9/fbbl7cp4DT79+/XQw89JEkKCAjQiRMn1KhRI02dOlXPPvusxd1d/QhN8Ak1NTV66qmndO2116pRo0b67LPPJEl//OMftWTJEou7Q31RXFys2NjYHx3v3Lmzvvjii8vYEeAtNDTUuI+pZcuW+sc//mGMffPNN1a1VW8QmuATnn76aWVnZ2v69OkKDAw01nfu3FmLFy+2sDPUJ8ePH1fDhg1/dLxhw4b617/+dRk7Arz16NFD7777riTprrvu0uOPP66nn35aQ4YMUY8ePSzu7urH03PwCS+99JIWLVqk22+/XcOGDTPWd+3aVQcOHLCwM9Q3+/btk9PpPOMY/5OH1WbMmKHjx49Lkp588kkdP35cq1at0nXXXceTc5cBoQk+4auvvlKHDh3qrHe73Tp16pQFHaG+uv3223Wm52NsNps8Hg9zNcFSpz+5GRoaqoULF1rYTf1DaIJPiImJ0d///ne1adPGa/2rr76qG264waKuUN8cOnTI6hYA+DBCE3xCVlaW0tLS9NVXX8ntduu1115TUVGRXnrpJW3YsMHq9lBP/DC0A76gadOm+uSTT9S8eXNdc801Zz3b+e23317GzuofQhN8Qt++fbV+/XpNnTpVoaGhysrKUvfu3bV+/XrdcccdVreHeqK4uNhUXevWrS9xJ8B/zJw5U40bN5YkzZo1y9pm6jkmtwSAf/P39zd+Pv0rVE5fxwz1QP3FmSb4hPbt22vnzp1q1qyZ1/qysjJ1797dmLcJuJRsNptatWqlhx9+WPfcc48CAvgnEtZzuVyma+12+yXsBJxpgk/w8/OT0+lUeHi41/qSkhK1bt1alZWVFnWG+sTpdGr58uVatmyZysrK9OCDDyo9PV2dOnWyujXUY35+fqaf2uQs6KXFf6Ngqddff934eePGjQoLCzNe19TUKC8vT23btrWgM9RHkZGRGj9+vMaPH693331Xy5YtU0JCgmJiYpSenq709HTTXzQNXCynf3XP559/rgkTJujhhx+Ww+GQJOXn52v58uWaNm2aVS3WG5xpgqVq/wDVzoFzugYNGqht27Z6/vnndffdd1vRHqCSkhLdf//92rp1q0pLS9W0aVOrW0I9dvvtt2vo0KG6//77vdavWLFCixYt0pYtW6xprJ7gv0ywlNvtltvtVuvWrXX06FHjtdvtVmVlpYqKighMsMT27ds1dOhQ/fznP9fx48c1b948NWnSxOq2UM/l5+crPj6+zvr4+HgVFBRY0FH9QmiCTzh06JCaN29udRuo577++ms9++yz6tixo37zm9/Ibrdr27ZtKigo0LBhw7g0B8tFR0frxRdfrLN+8eLFio6OtqCj+oXLc/AZeXl5ysvLM844nW7p0qUWdYX6pEGDBrr22muVlpame++9Vw0aNDhjXZcuXS5zZ8D33njjDaWkpKhDhw5KSEiQJBUUFOjgwYP661//qrvuusviDq9uhCb4hCeffFJTp05VfHy8WrZsWedJkbVr11rUGeqT088k1f4O/vCfSOZpgtUOHz6sBQsWGF9m3qlTJw0bNowzTZcBoQk+oWXLlpo+fboGDRpkdSuox7744oufrDl27Jg6d+58GboB4GuYcgA+oaqqSr/85S+tbgP13I9999yxY8e0cuVKLVmyRLt27eJMEy6rjz76SJ07d5afn58++uijs9Zy6fjS4kwTfML48ePVqFEj/fGPf7S6FcDwzjvvaMmSJfrrX/+qqKgo9e/fXykpKbrxxhutbg31yOmT/9ZOdHmmP91cOr70ONMEn3Dy5EktWrRIf/vb39SlS5c6N+DOmDHDos5Q3zidTmVnZ2vJkiVyuVz63e9+p8rKSq1bt04xMTFWt4d66NChQ2rRooXxM6zDmSb4hFtvvfWs46fPiAtcKvfcc4/eeecdJScnKzU1VXfeeaf8/f3VoEEDffjhh4QmWO7kyZMKDg62uo16i9AEAP8WEBCgxx57TMOHD9d1111nrCc0wVfY7Xb95je/0YMPPqjbb7+ducMuMy7PwVL9+/f/yRqbzaa//vWvl6Eb1HfvvvuulixZori4OHXq1EmDBg3SwIEDrW4LMCxfvlwrVqxQ3759FRYWpgEDBujBBx884yzhuPg40wRLDR482FTdsmXLLnEnwH9UVFRo1apVWrp0qQoKClRTU6MZM2ZoyJAhaty4sdXtATp27JheffVVrVy5Ups3b1b79u314IMPKisry+rWrmqEJgA4i6KiIi1ZskT/+7//q7KyMt1xxx16/fXXrW4LMOzbt0+pqan66KOPeHruEuNiKACcxfXXX6/p06fryy+/1MqVK61uB5D0/Q3hq1evVr9+/dS9e3d9++23Gjt2rNVtXfU40wQAwBVi48aNWrFihdatW6eAgADdd999Sk1NVa9evaxurV4gNAEAcIVo2LCh7r77bqWmpuquu+760S+VxqVBaAIA4Apx7NgxHkawEFMOAADg42q/PuVsbDabqqurL1NH9ROhCQAAH7d27dofHcvPz9ecOXPkdrsvY0f1E5fnAAC4AhUVFWnChAlav369UlNTNXXqVLVp08bqtq5qTDkAAMAV5MiRI3rkkUcUGxur6upq7d69W8uXLycwXQaEJgAArgDl5eUaP368OnTooL179yovL0/r169X586drW6t3uCeJgAAfNz06dP17LPPKjIyUitXrlTfvn2tbqle4p4mAAB8nJ+fn0JCQpSYmCh/f/8frXvttdcuY1f1D2eaAADwcQ899NBPTjmAS48zTQAAACZwIzgAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBwI+w2Wxat26d1W148cWegPqC0ATAZzmdTo0aNUodOnRQcHCwIiIi1LNnTy1YsED/+te/rG4PQD3DPE0AfNJnn32mnj17qkmTJnrmmWcUGxuroKAg7dmzR4sWLdK1116re++91+o26zh16pQaNGhgdRsALgHONAHwSf/v//0/BQQEaNeuXfrd736nTp06qX379urbt69ycnJ0zz33GLU2m00LFixQnz59FBISovbt2+vVV181xrds2SKbzaaysjJj3e7du2Wz2fT555+ftY+vv/76R7f7+eefy2azadWqVfr1r3+t4OBgvfLKK/rnP/+p+++/X9dee60aNmyo2NhYrVy50mu7t9xyix577DGNGzdOTZs2VWRkpKZMmeJVc/DgQfXq1UvBwcGKiYnRpk2bzv1AArhoCE0AfM4///lPvfXWW8rIyFBoaOgZa344O/If//hHpaSk6MMPP1RqaqoGDhyo/fv3X3AvZrY7YcIEjRo1Svv371dSUpJOnjypuLg45eTk6OOPP9ajjz6qQYMGqaCgwOt9y5cvV2hoqHbs2KHp06dr6tSpRjByu93q37+/AgMDtWPHDi1cuFDjx4+/4M8D4AJ4AMDHvPfeex5Jntdee81rfbNmzTyhoaGe0NBQz7hx44z1kjzDhg3zqk1ISPAMHz7c4/F4PG+//bZHkue7774zxj/44AOPJM+hQ4d+tI+f2u6hQ4c8kjyzZs36yc+UnJzsefzxx43Xv/71rz0333yzV82NN97oGT9+vMfj8Xg2btzoCQgI8Hz11VfG+JtvvumR5Fm7du1P7g/Axcc9TQCuGAUFBXK73UpNTVVlZaXXmMPhqPN69+7dF7xPM9uNj4/3el1TU6NnnnlGq1ev1ldffaWqqipVVlaqYcOGXnVdunTxet2yZUsdPXpUkrR//35FR0crKirqR3sBcHkRmgD4nA4dOshms6moqMhrffv27SVJISEh57Q9P7/v70TwnPZVm6dOnbrALv/jh5cQn3vuOc2ePVuzZs1SbGysQkNDNXr0aFVVVXnV/fCGcZvNJrfbfdH6AnBxcU8TAJ/TrFkz3XHHHZo7d64qKipMvee9996r87pTp06SpBYtWkj6/qbuWmbPQp1tuz9m27Zt6tu3rx588EF17dpV7du31yeffGJqf7U6deqkw4cPe/X8w14AXF6EJgA+af78+aqurlZ8fLxWrVql/fv3q6ioSC+//LIOHDggf39/r/o1a9Zo6dKl+uSTTzR58mQVFBRoxIgRkr4/cxUdHa0pU6bo4MGDysnJ0fPPP2+qj7Nt98dcd9112rRpk7Zv3679+/frv/7rv1RSUnJOnz8xMVE///nPlZaWpg8//FB///vf9Yc//OGctgHg4iI0AfBJP/vZz/TBBx8oMTFREydOVNeuXRUfH68XXnhBTzzxhJ566imv+ieffFJ/+ctf1KVLF7300ktauXKlYmJiJH1/GWzlypU6cOCAunTpomeffVZ/+tOfTPVxtu3+mEmTJql79+5KSkrSLbfcosjISPXr1++cPr+fn5/Wrl2rEydO6KabbtLQoUP19NNPn9M2AFxcNs/pF/kB4Apks9m0du3acw4mAHAuONMEAABgAqEJAADABKYcAHDF4y4DAJcDZ5oAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACYQmAAAAEwhNAAAAJvx/n3eZ9dggDF8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=df['Gpu brand'],y=df['Price'],estimator=np.median)\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "233a8672",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['Gpu'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "995b12b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Ram</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>ppi</th>\n",
       "      <th>Cpu brand</th>\n",
       "      <th>HDD</th>\n",
       "      <th>SSD</th>\n",
       "      <th>Gpu brand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>71378.68</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>47895.52</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>127.677940</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>30636.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>16</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>135195.34</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>220.534624</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>512</td>\n",
       "      <td>AMD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>96095.81</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Ram  OpSys  Weight      Price  Touchscreen  Ips  \\\n",
       "0   Apple  Ultrabook    8  macOS    1.37   71378.68            0    1   \n",
       "1   Apple  Ultrabook    8  macOS    1.34   47895.52            0    0   \n",
       "2      HP   Notebook    8  No OS    1.86   30636.00            0    0   \n",
       "3   Apple  Ultrabook   16  macOS    1.83  135195.34            0    1   \n",
       "4   Apple  Ultrabook    8  macOS    1.37   96095.81            0    1   \n",
       "\n",
       "          ppi      Cpu brand  HDD  SSD Gpu brand  \n",
       "0  226.983005  Intel Core i5    0  128     Intel  \n",
       "1  127.677940  Intel Core i5    0    0     Intel  \n",
       "2  141.211998  Intel Core i5    0  256     Intel  \n",
       "3  220.534624  Intel Core i7    0  512       AMD  \n",
       "4  226.983005  Intel Core i5    0  256     Intel  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "87ea542b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OpSys\n",
       "Windows 10      1072\n",
       "No OS             66\n",
       "Linux             62\n",
       "Windows 7         45\n",
       "Chrome OS         26\n",
       "macOS             13\n",
       "Mac OS X           8\n",
       "Windows 10 S       8\n",
       "Android            2\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['OpSys'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "14cbb277",
   "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": 75,
   "id": "14d789f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def cat_os(inp):\n",
    "    if inp == 'Windows 10' or inp == 'Windows 7' or inp == 'Windows 10 S':\n",
    "        return 'Windows'\n",
    "    elif inp == 'macOS' or inp == 'Mac OS X':\n",
    "        return 'Mac'\n",
    "    else:\n",
    "        return 'Others/No OS/Linux'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "fc8e7750",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['os'] = df['OpSys'].apply(cat_os)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "11c5a038",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Ram</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>ppi</th>\n",
       "      <th>Cpu brand</th>\n",
       "      <th>HDD</th>\n",
       "      <th>SSD</th>\n",
       "      <th>Gpu brand</th>\n",
       "      <th>os</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>71378.68</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Mac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>47895.52</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>127.677940</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Mac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>30636.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Others/No OS/Linux</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>16</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>135195.34</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>220.534624</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>512</td>\n",
       "      <td>AMD</td>\n",
       "      <td>Mac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>96095.81</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Mac</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Ram  OpSys  Weight      Price  Touchscreen  Ips  \\\n",
       "0   Apple  Ultrabook    8  macOS    1.37   71378.68            0    1   \n",
       "1   Apple  Ultrabook    8  macOS    1.34   47895.52            0    0   \n",
       "2      HP   Notebook    8  No OS    1.86   30636.00            0    0   \n",
       "3   Apple  Ultrabook   16  macOS    1.83  135195.34            0    1   \n",
       "4   Apple  Ultrabook    8  macOS    1.37   96095.81            0    1   \n",
       "\n",
       "          ppi      Cpu brand  HDD  SSD Gpu brand                  os  \n",
       "0  226.983005  Intel Core i5    0  128     Intel                 Mac  \n",
       "1  127.677940  Intel Core i5    0    0     Intel                 Mac  \n",
       "2  141.211998  Intel Core i5    0  256     Intel  Others/No OS/Linux  \n",
       "3  220.534624  Intel Core i7    0  512       AMD                 Mac  \n",
       "4  226.983005  Intel Core i5    0  256     Intel                 Mac  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "a283996d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['OpSys'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "9b43fbf7",
   "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'],y=df['Price'])\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "1d2865dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\AppData\\Local\\Temp\\ipykernel_11356\\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",
      "C:\\Users\\HP\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Weight', ylabel='Density'>"
      ]
     },
     "execution_count": 80,
     "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": 81,
   "id": "5cb512c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Weight', ylabel='Price'>"
      ]
     },
     "execution_count": 81,
     "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['Weight'],y=df['Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "3aaf8bc8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\AppData\\Local\\Temp\\ipykernel_11356\\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",
      "C:\\Users\\HP\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Price', ylabel='Density'>"
      ]
     },
     "execution_count": 82,
     "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": 83,
   "id": "8f1293c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(columns=['Price'])\n",
    "y = np.log(df['Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "8f7fb4b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>ppi</th>\n",
       "      <th>Cpu brand</th>\n",
       "      <th>HDD</th>\n",
       "      <th>SSD</th>\n",
       "      <th>Gpu brand</th>\n",
       "      <th>os</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>1.37</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Mac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>1.34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>127.677940</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Mac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>1.86</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Others/No OS/Linux</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>16</td>\n",
       "      <td>1.83</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>220.534624</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>512</td>\n",
       "      <td>AMD</td>\n",
       "      <td>Mac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>1.37</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Mac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1298</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>2 in 1 Convertible</td>\n",
       "      <td>4</td>\n",
       "      <td>1.80</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>157.350512</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1299</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>2 in 1 Convertible</td>\n",
       "      <td>16</td>\n",
       "      <td>1.30</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>276.053530</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>512</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1300</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>2</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111.935204</td>\n",
       "      <td>Other Intel Processor</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1301</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>6</td>\n",
       "      <td>2.19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.454670</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>AMD</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1302</th>\n",
       "      <td>Asus</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>4</td>\n",
       "      <td>2.20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.454670</td>\n",
       "      <td>Other Intel Processor</td>\n",
       "      <td>500</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1302 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company            TypeName  Ram  Weight  Touchscreen  Ips         ppi  \\\n",
       "0      Apple           Ultrabook    8    1.37            0    1  226.983005   \n",
       "1      Apple           Ultrabook    8    1.34            0    0  127.677940   \n",
       "2         HP            Notebook    8    1.86            0    0  141.211998   \n",
       "3      Apple           Ultrabook   16    1.83            0    1  220.534624   \n",
       "4      Apple           Ultrabook    8    1.37            0    1  226.983005   \n",
       "...      ...                 ...  ...     ...          ...  ...         ...   \n",
       "1298  Lenovo  2 in 1 Convertible    4    1.80            1    1  157.350512   \n",
       "1299  Lenovo  2 in 1 Convertible   16    1.30            1    1  276.053530   \n",
       "1300  Lenovo            Notebook    2    1.50            0    0  111.935204   \n",
       "1301      HP            Notebook    6    2.19            0    0  100.454670   \n",
       "1302    Asus            Notebook    4    2.20            0    0  100.454670   \n",
       "\n",
       "                  Cpu brand   HDD  SSD Gpu brand                  os  \n",
       "0             Intel Core i5     0  128     Intel                 Mac  \n",
       "1             Intel Core i5     0    0     Intel                 Mac  \n",
       "2             Intel Core i5     0  256     Intel  Others/No OS/Linux  \n",
       "3             Intel Core i7     0  512       AMD                 Mac  \n",
       "4             Intel Core i5     0  256     Intel                 Mac  \n",
       "...                     ...   ...  ...       ...                 ...  \n",
       "1298          Intel Core i7     0  128     Intel             Windows  \n",
       "1299          Intel Core i7     0  512     Intel             Windows  \n",
       "1300  Other Intel Processor     0    0     Intel             Windows  \n",
       "1301          Intel Core i7  1000    0       AMD             Windows  \n",
       "1302  Other Intel Processor   500    0     Intel             Windows  \n",
       "\n",
       "[1302 rows x 12 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "9f66cd48",
   "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": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "60a0aaf0",
   "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": 87,
   "id": "3e665d44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>ppi</th>\n",
       "      <th>Cpu brand</th>\n",
       "      <th>HDD</th>\n",
       "      <th>SSD</th>\n",
       "      <th>Gpu brand</th>\n",
       "      <th>os</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>Toshiba</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.454670</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1141</th>\n",
       "      <td>MSI</td>\n",
       "      <td>Gaming</td>\n",
       "      <td>8</td>\n",
       "      <td>2.40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>Nvidia</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1049</th>\n",
       "      <td>Asus</td>\n",
       "      <td>Netbook</td>\n",
       "      <td>4</td>\n",
       "      <td>1.20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>135.094211</td>\n",
       "      <td>Other Intel Processor</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Others/No OS/Linux</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1020</th>\n",
       "      <td>Dell</td>\n",
       "      <td>2 in 1 Convertible</td>\n",
       "      <td>4</td>\n",
       "      <td>2.08</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i3</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>878</th>\n",
       "      <td>Dell</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>4</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>Nvidia</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466</th>\n",
       "      <td>Acer</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>4</td>\n",
       "      <td>2.20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.454670</td>\n",
       "      <td>Intel Core i3</td>\n",
       "      <td>500</td>\n",
       "      <td>0</td>\n",
       "      <td>Nvidia</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>Asus</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>16</td>\n",
       "      <td>1.63</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>512</td>\n",
       "      <td>Nvidia</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>Acer</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>2.20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.454670</td>\n",
       "      <td>AMD Processor</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>AMD</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>527</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>2.20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.454670</td>\n",
       "      <td>Intel Core i3</td>\n",
       "      <td>2000</td>\n",
       "      <td>0</td>\n",
       "      <td>Nvidia</td>\n",
       "      <td>Others/No OS/Linux</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1193</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.415547</td>\n",
       "      <td>Other Intel Processor</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Mac</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1106 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Company            TypeName  Ram  Weight  Touchscreen  Ips         ppi  \\\n",
       "183   Toshiba            Notebook    8    2.00            0    0  100.454670   \n",
       "1141      MSI              Gaming    8    2.40            0    0  141.211998   \n",
       "1049     Asus             Netbook    4    1.20            0    0  135.094211   \n",
       "1020     Dell  2 in 1 Convertible    4    2.08            1    1  141.211998   \n",
       "878      Dell            Notebook    4    2.18            0    0  141.211998   \n",
       "...       ...                 ...  ...     ...          ...  ...         ...   \n",
       "466      Acer            Notebook    4    2.20            0    0  100.454670   \n",
       "299      Asus           Ultrabook   16    1.63            0    0  141.211998   \n",
       "493      Acer            Notebook    8    2.20            0    0  100.454670   \n",
       "527    Lenovo            Notebook    8    2.20            0    0  100.454670   \n",
       "1193    Apple           Ultrabook    8    0.92            0    1  226.415547   \n",
       "\n",
       "                  Cpu brand   HDD  SSD Gpu brand                  os  \n",
       "183           Intel Core i5     0  128     Intel             Windows  \n",
       "1141          Intel Core i7     0  128    Nvidia             Windows  \n",
       "1049  Other Intel Processor     0    0     Intel  Others/No OS/Linux  \n",
       "1020          Intel Core i3  1000    0     Intel             Windows  \n",
       "878           Intel Core i5     0  128    Nvidia             Windows  \n",
       "...                     ...   ...  ...       ...                 ...  \n",
       "466           Intel Core i3   500    0    Nvidia             Windows  \n",
       "299           Intel Core i7     0  512    Nvidia             Windows  \n",
       "493           AMD Processor  1000    0       AMD             Windows  \n",
       "527           Intel Core i3  2000    0    Nvidia  Others/No OS/Linux  \n",
       "1193  Other Intel Processor     0    0     Intel                 Mac  \n",
       "\n",
       "[1106 rows x 12 columns]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "87288c45",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.metrics import r2_score,mean_absolute_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "e83816b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting xgboost\n",
      "  Downloading xgboost-2.1.0-py3-none-win_amd64.whl.metadata (2.1 kB)\n",
      "Requirement already satisfied: numpy in c:\\users\\hp\\appdata\\roaming\\python\\python311\\site-packages (from xgboost) (1.26.4)\n",
      "Requirement already satisfied: scipy in c:\\users\\hp\\appdata\\roaming\\python\\python311\\site-packages (from xgboost) (1.13.0)\n",
      "Downloading xgboost-2.1.0-py3-none-win_amd64.whl (124.9 MB)\n",
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      "   ---------------------------------------- 124.9/124.9 MB 1.1 MB/s eta 0:00:00\n",
      "Installing collected packages: xgboost\n",
      "Successfully installed xgboost-2.1.0\n"
     ]
    }
   ],
   "source": [
    "!pip install xgboost\n",
    "from sklearn.linear_model import LinearRegression,Ridge,Lasso\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor,GradientBoostingRegressor,AdaBoostRegressor,ExtraTreesRegressor\n",
    "from sklearn.svm import SVR\n",
    "from xgboost import XGBRegressor"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9b9b064",
   "metadata": {},
   "source": [
    "### Linear regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "62d1c597",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\anaconda3\\Lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:868: 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 0.8102205190267955\n",
      "MAE 0.2085356342378598\n"
     ]
    }
   ],
   "source": [
    "step1 = ColumnTransformer(transformers=[\n",
    "    ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,7,10,11])\n",
    "],remainder='passthrough')\n",
    "\n",
    "step2 = LinearRegression()\n",
    "\n",
    "pipe = Pipeline([\n",
    "    ('step1',step1),\n",
    "    ('step2',step2)\n",
    "])\n",
    "\n",
    "pipe.fit(X_train,y_train)\n",
    "\n",
    "y_pred = pipe.predict(X_test)\n",
    "\n",
    "print('R2 score',r2_score(y_test,y_pred))\n",
    "print('MAE',mean_absolute_error(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5401e577",
   "metadata": {},
   "source": [
    "### Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "767f57d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\anaconda3\\Lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:868: 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 0.8335647276786514\n",
      "MAE 0.18740244396930714\n"
     ]
    }
   ],
   "source": [
    "step1 = ColumnTransformer(transformers=[\n",
    "    ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,7,10,11])\n",
    "],remainder='passthrough')\n",
    "\n",
    "step2 = DecisionTreeRegressor(max_depth=8)\n",
    "\n",
    "pipe = Pipeline([\n",
    "    ('step1',step1),\n",
    "    ('step2',step2)\n",
    "])\n",
    "\n",
    "pipe.fit(X_train,y_train)\n",
    "\n",
    "y_pred = pipe.predict(X_test)\n",
    "\n",
    "print('R2 score',r2_score(y_test,y_pred))\n",
    "print('MAE',mean_absolute_error(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4e6d7be",
   "metadata": {},
   "source": [
    "### Exporting the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "d35eb7b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "# Save the dataframe\n",
    "with open(r'C:\\\\laptop_price\\\\df.pkl', 'wb') as df_file:\n",
    "    pickle.dump(df, df_file)\n",
    "\n",
    "# Save the pipeline\n",
    "with open(r'C:\\\\laptop_price\\\\pipe.pkl', 'wb') as pipe_file:\n",
    "    pickle.dump(pipe, pipe_file)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "64618e65",
   "metadata": {},
   "outputs": [
    {
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       "      <th>ppi</th>\n",
       "      <th>Cpu brand</th>\n",
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       "      <th>SSD</th>\n",
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       "      Company            TypeName  Ram  Weight  Touchscreen  Ips         ppi  \\\n",
       "183   Toshiba            Notebook    8    2.00            0    0  100.454670   \n",
       "1141      MSI              Gaming    8    2.40            0    0  141.211998   \n",
       "1049     Asus             Netbook    4    1.20            0    0  135.094211   \n",
       "1020     Dell  2 in 1 Convertible    4    2.08            1    1  141.211998   \n",
       "878      Dell            Notebook    4    2.18            0    0  141.211998   \n",
       "...       ...                 ...  ...     ...          ...  ...         ...   \n",
       "466      Acer            Notebook    4    2.20            0    0  100.454670   \n",
       "299      Asus           Ultrabook   16    1.63            0    0  141.211998   \n",
       "493      Acer            Notebook    8    2.20            0    0  100.454670   \n",
       "527    Lenovo            Notebook    8    2.20            0    0  100.454670   \n",
       "1193    Apple           Ultrabook    8    0.92            0    1  226.415547   \n",
       "\n",
       "                  Cpu brand   HDD  SSD Gpu brand                  os  \n",
       "183           Intel Core i5     0  128     Intel             Windows  \n",
       "1141          Intel Core i7     0  128    Nvidia             Windows  \n",
       "1049  Other Intel Processor     0    0     Intel  Others/No OS/Linux  \n",
       "1020          Intel Core i3  1000    0     Intel             Windows  \n",
       "878           Intel Core i5     0  128    Nvidia             Windows  \n",
       "...                     ...   ...  ...       ...                 ...  \n",
       "466           Intel Core i3   500    0    Nvidia             Windows  \n",
       "299           Intel Core i7     0  512    Nvidia             Windows  \n",
       "493           AMD Processor  1000    0       AMD             Windows  \n",
       "527           Intel Core i3  2000    0    Nvidia  Others/No OS/Linux  \n",
       "1193  Other Intel Processor     0    0     Intel                 Mac  \n",
       "\n",
       "[1106 rows x 12 columns]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a90ec14-d48b-47c2-8980-ad177ada2deb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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