Laptop_Price_Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b8180475",
   "metadata": {},
   "source": [
    "<h1 style=\"text-align:center;\">LAPTOP PRICE PREDICTION</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "179645c2",
   "metadata": {},
   "source": [
    "<h2> Business Problem </h2>\n",
    "<p>Predict the Price of laptop on the basis of<p>\n",
    "    Context\n",
    "A dataset for 1300 laptop models.\n",
    "\n",
    "Content,\n",
    "Company Name,\n",
    "Product Name,\n",
    "Laptop Type,\n",
    "Screen Inches,\n",
    "Screen Resolution,\n",
    "CPU Model,\n",
    "RAM Characteristics,\n",
    "Memory,\n",
    "GPU Characteristics,\n",
    "Operating System,\n",
    "Laptop's Weight,\n",
    "Laptop's Price\n",
    "    \n",
    "Source-https://www.kaggle.com/ionaskel/laptop-prices\n",
    "    \n",
    "<h2>Machine Leaning Problem</h2>\n",
    "\n",
    "It is a Regresion problem, for a given columns we need to predict the price of laptop.\n",
    "\n",
    "<h2> Performance Metric</h2>\n",
    "\n",
    "1. R2 Score\n",
    "\n",
    "2. Mean Absolute Error\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9a18bc2",
   "metadata": {},
   "source": [
    "<h2>Reading data and basic stats</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4556d4dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "46c5dc94",
   "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>71378.6832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>1440x900</td>\n",
       "      <td>Intel Core i5 1.8GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>128GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34kg</td>\n",
       "      <td>47895.5232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86kg</td>\n",
       "      <td>30636.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>15.4</td>\n",
       "      <td>IPS Panel Retina Display 2880x1800</td>\n",
       "      <td>Intel Core i7 2.7GHz</td>\n",
       "      <td>16GB</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83kg</td>\n",
       "      <td>135195.3360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 3.1GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37kg</td>\n",
       "      <td>96095.8080</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 Company   TypeName  Inches                    ScreenResolution  \\\n",
       "0           0   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "1           1   Apple  Ultrabook    13.3                            1440x900   \n",
       "2           2      HP   Notebook    15.6                   Full HD 1920x1080   \n",
       "3           3   Apple  Ultrabook    15.4  IPS Panel Retina Display 2880x1800   \n",
       "4           4   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "\n",
       "                          Cpu   Ram               Memory  \\\n",
       "0        Intel Core i5 2.3GHz   8GB            128GB SSD   \n",
       "1        Intel Core i5 1.8GHz   8GB  128GB Flash Storage   \n",
       "2  Intel Core i5 7200U 2.5GHz   8GB            256GB SSD   \n",
       "3        Intel Core i7 2.7GHz  16GB            512GB SSD   \n",
       "4        Intel Core i5 3.1GHz   8GB            256GB SSD   \n",
       "\n",
       "                            Gpu  OpSys  Weight        Price  \n",
       "0  Intel Iris Plus Graphics 640  macOS  1.37kg   71378.6832  \n",
       "1        Intel HD Graphics 6000  macOS  1.34kg   47895.5232  \n",
       "2         Intel HD Graphics 620  No OS  1.86kg   30636.0000  \n",
       "3            AMD Radeon Pro 455  macOS  1.83kg  135195.3360  \n",
       "4  Intel Iris Plus Graphics 650  macOS  1.37kg   96095.8080  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"laptop_data.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b7ee7e3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'Company', 'TypeName', 'Inches', 'ScreenResolution',\n",
       "       'Cpu', 'Ram', 'Memory', 'Gpu', 'OpSys', 'Weight', 'Price'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a21047bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1303, 12)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05bda674",
   "metadata": {},
   "source": [
    "<h1>Data Cleaning</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3a67bd65",
   "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": "53934a21",
   "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d89591a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "28f0cb03",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=[\"Unnamed: 0\"],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2466dd72",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 2.3GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>128GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 640</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37kg</td>\n",
       "      <td>71378.6832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>1440x900</td>\n",
       "      <td>Intel Core i5 1.8GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>128GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34kg</td>\n",
       "      <td>47895.5232</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "  Company   TypeName  Inches                    ScreenResolution  \\\n",
       "0   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "1   Apple  Ultrabook    13.3                            1440x900   \n",
       "\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",
       "\n",
       "                            Gpu  OpSys  Weight       Price  \n",
       "0  Intel Iris Plus Graphics 640  macOS  1.37kg  71378.6832  \n",
       "1        Intel HD Graphics 6000  macOS  1.34kg  47895.5232  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7e9fc905",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Ram\"]=df[\"Ram\"].str.replace(\"GB\",\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5b7b5e16",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Weight\"]=df[\"Weight\"].str.replace(\"kg\",\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "dca1ed58",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Ram\"]=df[\"Ram\"].astype(\"int\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "50890dfa",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Weight\"]=df[\"Weight\"].astype(\"float\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "035dbaae",
   "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   float64\n",
      " 10  Price             1303 non-null   float64\n",
      "dtypes: float64(3), int32(1), object(7)\n",
      "memory usage: 107.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "204cb48a",
   "metadata": {},
   "source": [
    "<h2>Exploratory Data Analysis and Feature Engineering </h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "685153df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Price', ylabel='Density'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df[\"Price\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "56c4a3ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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umc7Ftrecat8yjn2d7a8v43MZJC8vB3on77Nsf6fFsZ+1/bZl/b4H+F0L2B14vO0DJW0APMb2BW3SGZakhwNr2/5l3d4ZeEh9+Pu2f9MgjSfbvlrShP9T2xe3zddsnfL3kyNO71PAi2xfAyDpicAxwNMbHPtR4Ln1g38G8AngeQPm4xG2/1AD4lG2D5C0cIB0zpP0ReCbwJ96O1t8AV46wGsuy37AVsDPbD9f0pMpgbqNrwFHAO+r27+gvLfWwR2YV2+9oPNSYCHwZknH2/5Ew3QulrSV7QsHyEPPd4C7gcuB+9oeLOmZwLOAOeOC88MpXYybeGj9+7C2rz9Bfj5GmTfq6LrrrZKeafu9DZP4r/p3VL/vL1M+1xcABwJ3At+ifB8bk3QnYyebBwGrAn+y/fCGSXwS+Anwy7r9MeB7lAD/LODNDdJ4B2XE/qcmeMyU99jKrAzuts8acZKr9gJ7Tf8XLapm7rF9dT3ufEnD/EhWkbQOsAtjgWwQm9e/B/bta/wFsH1D/7akR1FKY7+2fVHLvNxt+25JSFqtngSf1DKNtWwfJ+k9NX/3SLq3ZRo96wFb2v4jgKQDgFMo7+8iysm5iWcAu0u6gXICbX1VA6zX8vnjrQqsTvmd9n/v/gC8ukkCtr9S/35oiHz07Ahsbvs+AElHApcAjYJ733drFeAntv88ZH6eYXtLSZfU9G+vgyZbsX3/Z1uvBnaiXIE2tRWwd9/2nbb/uaZ3bsM8zK9/n9/idSc1K4P7KKsNqgWSvgp8vW7vDixoeOyjx5WalthueVl7IGVg17m2L5T0eMbO9o0N+wWQ9F1gf9tX1JPNxZTPYyNJh9j+bIvkbpK0BnAScLqk24EbJj1iaX+qJxjX/G1DqUoZxKOBv/Rt/5VyyfxnSX9ZxjET+fsBX7/f9yS9yPYPBjz+ANvbSXrKsMFZ0obAP7N0FVGrqhBgDeB39f4jBszOHsDBkn4HnAOcTflN3N4ynb/Wqq/e92YOA1wh9XOppz6pFgqarkOxipes33593/012ry+pL+hlOI3sD1f0sbAk2x/t006MEuDO6OtNgB4C7AP8Na6fQ7lkq6JQ1my1DR+uzHbxwPH921fC/xD23RGUEe9oe0r6v03AKfb3qNelZwHfLZpXmy/st79oKQzKT/405oeX72DMj3FRpLOA+bQsGQ6gaOB8yV9u26/DPiGpIfSYspp2zdIeg6wse0jauBYvWVefgacKGklykmmVzhperm/jqRnAU+VtEU9vj+PbephT6JUc32HwQPgvwOX1P+zKFdDrRfisb0ngKTHUv7PXwIeS/t49HngREqB66M1rX9rmx9Jr+rbXIlSrXd3iyTuk/QY27cC9H5bktal/Wd9BOUK81l1+2ZKzGgd3Gdlg2o/SY+j/MB+KOkhlLPknS3TeBVwiu02JbeRk3QEEzck/VPLdL5HraO2/TRJqwCXNG0AlHSp7c3r/TOAQ20fO/6xFvnZEngO5b2dN0jjT30PT6IEjWts/7VtGn1pbcXYj+M8202v0vrTOIDyI3+S7SfWQHS87ca9VCRdR7nEv9wD/NBUZlfdi/LZjn8Pbtlofb7tZ7TNQ9/xK1GC5zmM1Wlf0AtoLdN6HfBc4KmUWQ/PBc6x/dMB0noysB3le3OG7asGSOOIvs17KB0vDrV9W8PjX0dpe/oXSjUVwJaUuvgv2D6qRV4W2J4n6RLbW9R9l9l+WtM07k9rNgd3SW+iNDI80vZG9RLlP21v1zKdIyj10WdTGupOs31Pw2M/P9njtt862ePj0uovpT8YeCXwP23SqOlcaHurcV+AxkG59lb4AWWe/cMpJfnf15PnAttPaZGXDwA7AyfUXa+gBMGPNDj2VZM9bvuEyR6fJN2VgbVZsvrh1y3TuBTYAri47zNe2KZKUNLZwLa9OupBSXq/7Q8PmcZrgY0p//f7CzltTsQa3YyJvwV+BfwncKbt61se/8jJHrf9u8kenw6SdqC0PTyFUsi5EjjI9vdapvMTysnqvNqesBFwjAfoHTdbq2V69qG0zp8PYPuXkh7dNhHbb6gNqC8GdgO+JOl01/nmp9DfwPgh4IC2r9+Xj2/1b0s6hlJqaWvYOuq9KPX/LwReY/v3df82lCuCNnYHnmb77pqXgyhdIqcM7pQqk2UxYyeMxiT9M+V/9BtK11XVtNq20/yfbUvqfcYPneqACVwL/LheafUH1FbdD21/WEt2QfzxAHWwT6XUBb+AsaqCtr0wfijpnSzdS6tVMLW9lqSnUN7PR2uh7Rrbr5/i0J6LKHnvr6bqbRt4fJv8SPoE5fv6Z0qV4mbA221/fdID+1/cPo0JqiMlva1lG9YHazrrSzoaeDbwjy2OH3vtWV5yP9/2M3ol1HrpfvGgPRBqgN+BUs/8d7ZbDUzqLymPQu1VcortJ7Q8bkvgC8CmwBXUOmrbg3SrHEqtf31l7wRRG1dPaFNlMOL8LKL0ovjfIdN5J6Wkuz2la9s/UUpQk17JjUtjwoJA28ZRLd0FcTfgQjfvgtj7XDax/X9tXntcGtdNsNu22wbTh1OC1vMo1TNrUbrS7jlo3obRu+qV9EpKe987gLMHqQqZIO1f296g5TGPohS0RPlcBlr0Y7aX3M+S9F7gIZK2B/4fY/2XG5P0YuA1wLbAj4GvUrojtjXUmVBL9qcFuBX419aZsC+W9DwGrKOWNOnc+k16UEj6AuW93AFcKen0ur090GoQSf0yH8BYvf25wIEDBugbGbynzf1sf7J+5/5A+Zw/YPv0lml8CEDS6nX7jwNmZ6guiNUVlJ4bjeqRJ2J7w0GPHefcvtsXbd80SCKaeMDPHcANTatdq1636B0pVYp3SJrs+W20SkjS14GzKG0QVw/zwrM9uO9PqUK4nNKP9FRKYG5rD8ql5N4z2ajqvv60w5C0D3C06xz5KsP/d7PdtAfQMylB8BhKldcg3+ReA99FlB4LPT8eIK1jKe0hvTaJ3Sn/rxcOkFavKuQUhqgKkfR+4Gv9AV3SfJcVxJqmsSll4M4j6/ZvgT082NoGazBcF8Q1gKslXciSn0vjrpAaUTe9Qa+8J/BlSsPlQsp3+KmUk9gjJL3FzbugfkfS1ZRqmbeo9Ixq01tmMm0LhIdRrma+UOvbL6FcRXyu7QvP6moZuL/vKrYXD5nO2izZyt+0Jby/tP03wF29h2jXrQ2V+UAutf2n2sK+JfA5jxtU1CCdpRpP21QZ1QbH7SmX95tRBvkcM2DQGZqkK2xvOm7fQMP/R1gVchuwGNjX9pl1X6Mh/31p/ITSo6l3/LbAv9t+1mTHTZDObsBBwBJdEG1/s0UaE46qdosBg5K+STmZ72F70xrsf9K0Ib8vnTOZuNdYq6o8SScA7+8r5GxCaUt6N6VqsFG+JK1GGcl7h+17a/vK6m4wbUA9fvwV+f0PAQ+x3aoQXX+fWwHPp4xu/bPtJ7dJAwDbs+5WP5QPUrpJ/a7eFlMujQdJb2fKwJojgaOA6yh11Mv7ffVKGE+jnJH3oczP0Tady6kn5rq9MnDlgHlajdJg0wtkTY87ri8vC8ffWubh05SlGFeqt12AT87wd/ASYAPKlc27evtapnFZk30N01qHMi/RyynzpwySxtqUOuWXAo8e4PgF4z+HQd4PZdqP3u3Z9f//iQHSuWJZ+yiFqKbpXNxk3/K4AWdQxkd8BnjVIP+n3m22Vsu8nfJP38r2dQAqozkPlvR2259pmd6/1bRuq2nNAX4I/PcI89zEPbYtaSdKXeNhkvYaIJ3TgG9K+krd3psyl0VjtbSyI6X0PpexASFN7Vf/jmLA2ZuAtzE2gnglSo+gvWl/dTSSUmE95te1xHuwpOMZmwyqqWtr9U5vTpXXUaqNWum74ju5XvG9W1KrKz5JuwD/Qak2E+Wy/1222/wG/k+lu2yvB9FGLDkauBEvPcXFeZIGmezrSkkHU6r1oLSr/bx+t6dsg5L0GGBdSpte/yCxh1Ou0mfCQspJb1NK+8HvJf3Ug0zVMBNnpwZnr0so842M3z+HlqWnetzl47ZXGr9vOb2vs4D3UKYceEzNR6tSbl/+30wZuXY88H7gSy2OP4oy5cBHgE1n+v894s94VKXCQ8dt7wNc2zKNNSknzYsp1RmfBdYcIC/9V3wXM8AVH3AZfaXA+ltqVOqmjCB9DvCi+h1eTOm5cz2lH3/b9/PIvttalKkerhkgnYdQBg6dWG/vpATllSjVKlMdvyelquvO+rd3Oxl41Qx/jx9GmS7iBuAvg6QxK+vcJ6qDbfLYJOn9B6Vu+Zi66zWUoNq6p8owaknhtZQ6/3NV5j0/wvZGA6S1RU1rF0pp8Fu2v9jw2PsY66fc/wUYpB3hVcDHKXO6aJA0+tLp9ZY5x/ZJbY6fIu2hpkieab26fpUBYze7XPG1rf9fog1DZcTpZW7QriFpP0q12TrA6cCvKSeZ8z1AN73apbLXL/0eSjXpgbZbj/lQmSjsSTW9gUY2S/oHjxuDMlMk7UtpUH065eR5DuX38KO2ac3WapnJ+uK27qdr+10qo0N7Q8cPsd2mCmIkbN9aqw1eW7s8XUeLeVxUpirerd5+S+lRgltOJGZ7lIu0fAJ4mQcY9t0j6cvAExg7+b5Z0va29xkgrf7RiytRfiSte5fUniAfAzahjCaGctJqfCKu3UN39tgYgDWBY223nZTsTpUZM18PPLcG5rYLzpwm6fssWcBptIC9S0+Nz6lMBbJrve1OmbPnWNu/aJMRj6hLpSZYp0HSnm6wTkM9/nUuA5XmaoL57t2yh9WIPJhytXmR23XnXMpsLbnfS98IuP6HgAfbbr2S0kxaRlB+p+3HtUznPsqZfC/bi+q+a91yEMkoSTrPLeZbWUYaVwN/6/plrMHrStt/O0BaIykVqkzVegClYetllIFvK9n+QIs0LvG4HkxtejX1HdO74rvQ9jkqi1Js6xZzltR0+gs45wxTwKlXjocDm9luOrd8//HPYukZKtu+n4uA13rcOg22m6zTgKS9bX9lVD2sRkllJH6vUIFbTp8BszS4j9qoqg6GeP2RBGVJr6CUmp5NaVQ9FvjqqEpCLfPSmxfmeZT2g5NYsv9046kDVKYg3se1gbCWEL9oe7LpCaaVpItsP72/OqO3r00alNG7v67bjwNObFOd0pfWQF15R0llhPiLKd/B7SiNs8fY/vZkx02Qzn8BG1GmqejN22+3n2Npqbl+Jtr3QKKy8tunKbNk3gY8DrjKLeZ76pmt1TKjNnTVwZBeRflBnCmpF5RbDxyq9dAn1X64O1F6mDy69hg40YPPGz6I/sB7F6Wxrce0mxfmYcBVfT0mtgIuVB1J63YDbXamTAx3p6R/o4wl+Ijbz1T5l3oF8ctaD3oz7af8fR9wrqSzKP/v51ImwmtlFD1dVOYf+gLwt5TVhlam4WpDKiN1dwNeQhl9fCww3/ZEV9dNzKNMhTBsyXKYdRqQ9G7bn9DYaOt+pnTB/rrtXw2ZzzY+Qpl64IcuU648n9LLqrUVpeQ+dNXBiPLRC8q7USZsOoohg3Ktx92ZMgFYq9kyZwstOcCmFwR3pUw3gdsNtFloezOVudg/QgmKH3DL6W5Vpg2+ijKy88OUevuP2z6/ZTprMbaqz0DzhEi6DNje47ryusXcJ5IWUD7T4ynBdQ/gibbf0+DYHwHfoDTa3942/xOkdzzwVtu3DJnOapSeQ8+pu84BvuyGo9Alvcz2dyTtuYynPIqy1nLjz3lYGpvy9zJgC9v3qYtT/o6KpM8xZNXBNOTpAR+UATSCGfVqOr3ePztT6slPsP2FAfJzSS3xfIzS3fUbg9RzT5DuysCuto+e8slLHrcmZQKy/vrTRg1+fWkM3NOl75he0Li/2mIUn0sbGlsY+2GUpSIvYMCpEPrSHLq3zBTp7+26VOHyIOmHlGmzP0bpJnobZYxOq1HNsOIE9yMm2G23XCQjlqYhZtQbVUPzuDS/S6lC2Z5SJfNnSh11o5KPyoyF+1AGt5xM6fq3D6U/9ULbO7XIyxspg73Wo9QvbwP81O2H2Q/dlVdlbvkXUuZmuhW4BfjH5VwqfRNllOw54x56LnCLm68k1ktvW8b1lqGUtNuePEc28G1Y9er+bsr72Z1yxXi0B5hEb4UI7jF9VMcd1LrP/7Z9WtPLyOno/aMy38kOlFL7L1XWiH1q06ovleX5bgd+Smk07DXC72f70pZ5uZzSfvCzegJ8MmVumUkXKRmXhignh63oq35o29OlNubeRulC+XZK0Phy73NfHuqJ9z22Lx+3/6mUz6VVA/qwvWX60ul//oMpE9jdY/vdbdKZbVaIBtX6Tz+YslDyppI2A17uBqsFxZS+q8Fn1BtJQ3M/23fVktj6GpsStk099+P7esd8lVLC3cB1MZKW7rZ9tyQkrWb7apU5/BuzbUmn1jwNXI3osakK/kxZdGYmrD0+sAPYvlzS3AHSW7UX2Gs6v1BZs6EVj246hKFpyUnIHkQ5GTdq+B5vhQjulEWt3wV8BcD2QknfoNlqQTEJ2/vXevfejHp/ojQaNzn2JEbc+0fShykTof2KsR+Jab7i0P11tvX93DRgYAe4SWXxkpOA0yXdThlO3tbFkrayfWHbAyUdZ3uXehUx0Wjk5dltcI1JHms7bw8s3VvmdbToLdOjEQ18GwX3TQter9p2YqxBvpUVolpGQ645GstWS0pvYWwJuLMo69wO1LA1bEOzpGso1TADrTikJQfQiRJ07mLIsRG1R9AjKN00W+WtXhk9gXJi+BMtArOkbWz/rFbLjLeB7fH139NGZVnJH9k+dNz+N1J6A72mZXq93jL3D8yiVDW1/Xyv69scajqE6TBow/eKEty/B+xLWWVlS9VV5W2/eIaz9oBXS06rUhq2oAyRv9fN1qedjvx8C3iLZ2CQT18eHm77D1rGQs5uueboMgJzf1XLZMdeS1mI+lO276371gY+BTzZI1jwuqn6uidSphDpVYXMo1Q/vNL2rQ3T2QlYz/aX6vYFlInQDLzbDfv/S9rAA4z8nE5actH4lSifz/NsP7N1WitIcH88cAjwLEpj2XXA7k1+HDExSavYvmeixtNB++WOKF/zgG9TVuQZqpvdEHn4ru2XasmpEPqy0npk8jaU6RjurNsPp0zXMGWf+3oldBDlu78fZbWid1AG9h3sunTf8qQyMKc3+d+VbjkplqTzKN1Sb6zbl1Kq3VanTMTX6IpPfZOvSfqW7X+Y6pjpNq5n3z2UnkCHDlJYWSHq3G1fC7yw1u2u5DJ68W20mLQrlnIBpavhvZI2ch3FV0+k90565PQ6kjLVxOXAcg9cALZfWv+OalqIgymfdc8fJ9i3rLzcDuytMrPjD4H/AbbxgOuWjoLLylRnDpHEg3qBvTq3Xg39rv7Gm+o/6c7Y/Ez9bL9hVGmtEMG9x0sOl34HCe7D6P0w3knp7dJbhGIuZZKtmXKX7c/P4OujiRduvp/bT4Ug911iu4xabPTbrQ26HweeQeki+hLge5L2a1tinkXW7N+wvW/f5pwW6XgZ95c7TTwFwv3cct4dWMGC+zgjW958BTVHY9OkfoUyVwmUUvsWDFcyG8Y5KqNTT2bJapm2AXUYn6p/H0ypM72M8n3bjNKbo2396bWS3koprUOZlqHpik4XUxaS3sdlCtkfSNoc+LKkG2zv1jIvs8H5kt40QcPs3pQryqaeJukP1Ibzeh+GbDwfUH8vnw9RZiQdygpR5z4RSb+2vcFM5+OBStItlGAz4UnSMzRdau3jPp49M6MNTwAO6PXtlrQp8EHbr26ZzqMpKzq9gFK6OwN4W5N6WEnrLasKZqIA+UBQP4+TKCfv3kn76ZT1gF/hhgtbz1aD9o5ZKp0uB3eNeFXyGKOWKwGtiCRd6XFTtU60LwYj6QVA77Ns3TA7W43qt9Xp4NY/ICBGbtZWa0nakfKj75+s68AZyMpCLT0l7cKmB2vyKWkHqoftkhrMOxHQp0Ong3tMq1k5k6Wk/6Qskvx8yiRZr6ZdPewovYEywGu/un02Y/XmTfTWH2g96jIeWMbVMvzNKOr/O10tEysejc3n3vu7OvA928+dofw8hDIS9JopnxwxQim5R9f8uf69S9JjKavprDMTGZH0cspiIQ8CNqy9VA5sOqBKdSWqZVmeA7PigSfBPbrmu7Vv9ycYG+L+1RnKywHA1pTl8bB9qaQ2A5ueCdxImcf9fGZxO0fMPgnu0Qkqy+LdaPvDdXt1yijVq4HPzFC2/mr7jjK53/3a1IM+hrLoyG6UVapOocxXfuXoshhdtdJMZyBiRL5CmZAKSX9HmU/lK8AdlHmFZsKVkl4LrCxp49rr5SdND7Z9r+3TbO9JmfZ1EfBjlQW7IyaVBtXohP7JyiR9CVhs+4N1e0amd1ZZFep9wIsoVSrfBz7sFvPD12ltd6SU3udSRt4ebvvmkWc4OiXBPTpB0hXA5nWmyquB+a5raaouBTizOWxP0lGU2RNPBY61fcUMZykeQBLcoxMkvY8yKdZvgQ2ALW1b0hOAI20/e9IERpuXkfRyUVljtjfZ3USrKC3PuU/iASbBPTqjznu+DvCD3gygKuvnrr48Jw6TtJhJernYPmt55SVWXAnuESMmaWXGerlsRnq5xAxIb5mIEUsvl5gN0s89YhpM0Mvl85T1QyOWi1TLRIxYernEbJDgHjFi6eUSs0GCe0REB6VBNSKigxLcIyI6KME9IqKDEtwjIjoowT0iooP+P/tRg2Fa/fURAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"Company\"].value_counts().plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "bbd2ba19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": "0730eea3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"TypeName\"].value_counts().plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0a8295c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": "9e0d92c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(x=df[\"Inches\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "696edf8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Inches', ylabel='Price'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=\"Inches\",y=\"Price\",data=df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "861a5325",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Full HD 1920x1080                                507\n",
       "1366x768                                         281\n",
       "IPS Panel Full HD 1920x1080                      230\n",
       "IPS Panel Full HD / Touchscreen 1920x1080         53\n",
       "Full HD / Touchscreen 1920x1080                   47\n",
       "1600x900                                          23\n",
       "Touchscreen 1366x768                              16\n",
       "Quad HD+ / Touchscreen 3200x1800                  15\n",
       "IPS Panel 4K Ultra HD 3840x2160                   12\n",
       "IPS Panel 4K Ultra HD / Touchscreen 3840x2160     11\n",
       "4K Ultra HD / Touchscreen 3840x2160               10\n",
       "4K Ultra HD 3840x2160                              7\n",
       "Touchscreen 2560x1440                              7\n",
       "IPS Panel 1366x768                                 7\n",
       "IPS Panel Quad HD+ / Touchscreen 3200x1800         6\n",
       "IPS Panel Retina Display 2560x1600                 6\n",
       "IPS Panel Retina Display 2304x1440                 6\n",
       "Touchscreen 2256x1504                              6\n",
       "IPS Panel Touchscreen 2560x1440                    5\n",
       "IPS Panel Retina Display 2880x1800                 4\n",
       "IPS Panel Touchscreen 1920x1200                    4\n",
       "1440x900                                           4\n",
       "IPS Panel 2560x1440                                4\n",
       "IPS Panel Quad HD+ 2560x1440                       3\n",
       "Quad HD+ 3200x1800                                 3\n",
       "1920x1080                                          3\n",
       "Touchscreen 2400x1600                              3\n",
       "2560x1440                                          3\n",
       "IPS Panel Touchscreen 1366x768                     3\n",
       "IPS Panel Touchscreen / 4K Ultra HD 3840x2160      2\n",
       "IPS Panel Full HD 2160x1440                        2\n",
       "IPS Panel Quad HD+ 3200x1800                       2\n",
       "IPS Panel Retina Display 2736x1824                 1\n",
       "IPS Panel Full HD 1920x1200                        1\n",
       "IPS Panel Full HD 2560x1440                        1\n",
       "IPS Panel Full HD 1366x768                         1\n",
       "Touchscreen / Full HD 1920x1080                    1\n",
       "Touchscreen / Quad HD+ 3200x1800                   1\n",
       "Touchscreen / 4K Ultra HD 3840x2160                1\n",
       "IPS Panel Touchscreen 2400x1600                    1\n",
       "Name: ScreenResolution, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"ScreenResolution\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "25819f76",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Touchscreen\"]=df[\"ScreenResolution\"].apply(lambda x:1 if \"Touchscreen\" in x else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1e7dbfaf",
   "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>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 2.3GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 640</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>71378.6832</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>1440x900</td>\n",
       "      <td>Intel Core i5 1.8GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>47895.5232</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>30636.0000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>15.4</td>\n",
       "      <td>IPS Panel Retina Display 2880x1800</td>\n",
       "      <td>Intel Core i7 2.7GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>135195.3360</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 3.1GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>96095.8080</td>\n",
       "      <td>0</td>\n",
       "    </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  \n",
       "0  Intel Iris Plus Graphics 640  macOS    1.37   71378.6832            0  \n",
       "1        Intel HD Graphics 6000  macOS    1.34   47895.5232            0  \n",
       "2         Intel HD Graphics 620  No OS    1.86   30636.0000            0  \n",
       "3            AMD Radeon Pro 455  macOS    1.83  135195.3360            0  \n",
       "4  Intel Iris Plus Graphics 650  macOS    1.37   96095.8080            0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "950a3a0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"Touchscreen\"].value_counts().plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4b359554",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Touchscreen', ylabel='Price'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=df[\"Touchscreen\"],y=df[\"Price\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "cbe3acbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Ips\"]=df[\"ScreenResolution\"].apply(lambda x:1 if \"IPS\" in x else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c13d7946",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Inches</th>\n",
       "      <th>ScreenResolution</th>\n",
       "      <th>Cpu</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 2.3GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 640</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>71378.6832</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>1440x900</td>\n",
       "      <td>Intel Core i5 1.8GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>47895.5232</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>30636.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>15.4</td>\n",
       "      <td>IPS Panel Retina Display 2880x1800</td>\n",
       "      <td>Intel Core i7 2.7GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>135195.3360</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 3.1GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>96095.8080</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Inches                    ScreenResolution  \\\n",
       "0   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "1   Apple  Ultrabook    13.3                            1440x900   \n",
       "2      HP   Notebook    15.6                   Full HD 1920x1080   \n",
       "3   Apple  Ultrabook    15.4  IPS Panel Retina Display 2880x1800   \n",
       "4   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "\n",
       "                          Cpu  Ram               Memory  \\\n",
       "0        Intel Core i5 2.3GHz    8            128GB SSD   \n",
       "1        Intel Core i5 1.8GHz    8  128GB Flash Storage   \n",
       "2  Intel Core i5 7200U 2.5GHz    8            256GB SSD   \n",
       "3        Intel Core i7 2.7GHz   16            512GB SSD   \n",
       "4        Intel Core i5 3.1GHz    8            256GB SSD   \n",
       "\n",
       "                            Gpu  OpSys  Weight        Price  Touchscreen  Ips  \n",
       "0  Intel Iris Plus Graphics 640  macOS    1.37   71378.6832            0    1  \n",
       "1        Intel HD Graphics 6000  macOS    1.34   47895.5232            0    0  \n",
       "2         Intel HD Graphics 620  No OS    1.86   30636.0000            0    0  \n",
       "3            AMD Radeon Pro 455  macOS    1.83  135195.3360            0    1  \n",
       "4  Intel Iris Plus Graphics 650  macOS    1.37   96095.8080            0    1  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c7c2e65d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Ips', ylabel='Price'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=df[\"Ips\"],y=df[\"Price\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "72874045",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0               [IPS Panel Retina Display 2560, 1600]\n",
       "1                                         [1440, 900]\n",
       "2                                [Full HD 1920, 1080]\n",
       "3               [IPS Panel Retina Display 2880, 1800]\n",
       "4               [IPS Panel Retina Display 2560, 1600]\n",
       "                            ...                      \n",
       "1298     [IPS Panel Full HD / Touchscreen 1920, 1080]\n",
       "1299    [IPS Panel Quad HD+ / Touchscreen 3200, 1800]\n",
       "1300                                      [1366, 768]\n",
       "1301                                      [1366, 768]\n",
       "1302                                      [1366, 768]\n",
       "Name: ScreenResolution, Length: 1303, dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " df[\"ScreenResolution\"].str.split(\"x\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f115d4f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "temp= df[\"ScreenResolution\"].str.split(\"x\",n=1,expand=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "82f90f11",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"X_res\"]=temp[0]\n",
    "df[\"Y_res\"]=temp[1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a3389ee2",
   "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>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 2.3GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 640</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>71378.6832</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>IPS Panel Retina Display 2560</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>1440x900</td>\n",
       "      <td>Intel Core i5 1.8GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>47895.5232</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1440</td>\n",
       "      <td>900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>30636.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Full HD 1920</td>\n",
       "      <td>1080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>15.4</td>\n",
       "      <td>IPS Panel Retina Display 2880x1800</td>\n",
       "      <td>Intel Core i7 2.7GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>135195.3360</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>IPS Panel Retina Display 2880</td>\n",
       "      <td>1800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 3.1GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>96095.8080</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>IPS Panel Retina Display 2560</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Inches                    ScreenResolution  \\\n",
       "0   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "1   Apple  Ultrabook    13.3                            1440x900   \n",
       "2      HP   Notebook    15.6                   Full HD 1920x1080   \n",
       "3   Apple  Ultrabook    15.4  IPS Panel Retina Display 2880x1800   \n",
       "4   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "\n",
       "                          Cpu  Ram               Memory  \\\n",
       "0        Intel Core i5 2.3GHz    8            128GB SSD   \n",
       "1        Intel Core i5 1.8GHz    8  128GB Flash Storage   \n",
       "2  Intel Core i5 7200U 2.5GHz    8            256GB SSD   \n",
       "3        Intel Core i7 2.7GHz   16            512GB SSD   \n",
       "4        Intel Core i5 3.1GHz    8            256GB SSD   \n",
       "\n",
       "                            Gpu  OpSys  Weight        Price  Touchscreen  Ips  \\\n",
       "0  Intel Iris Plus Graphics 640  macOS    1.37   71378.6832            0    1   \n",
       "1        Intel HD Graphics 6000  macOS    1.34   47895.5232            0    0   \n",
       "2         Intel HD Graphics 620  No OS    1.86   30636.0000            0    0   \n",
       "3            AMD Radeon Pro 455  macOS    1.83  135195.3360            0    1   \n",
       "4  Intel Iris Plus Graphics 650  macOS    1.37   96095.8080            0    1   \n",
       "\n",
       "                           X_res Y_res  \n",
       "0  IPS Panel Retina Display 2560  1600  \n",
       "1                           1440   900  \n",
       "2                   Full HD 1920  1080  \n",
       "3  IPS Panel Retina Display 2880  1800  \n",
       "4  IPS Panel Retina Display 2560  1600  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a7779894",
   "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": "2efbc465",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "      <th>Y_res</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 2.3GHz</td>\n",
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       "      <td>macOS</td>\n",
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       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>1440x900</td>\n",
       "      <td>Intel Core i5 1.8GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>47895.5232</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1440</td>\n",
       "      <td>900</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>30636.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1920</td>\n",
       "      <td>1080</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>15.4</td>\n",
       "      <td>IPS Panel Retina Display 2880x1800</td>\n",
       "      <td>Intel Core i7 2.7GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>135195.3360</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2880</td>\n",
       "      <td>1800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 3.1GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>96095.8080</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2560</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Inches                    ScreenResolution  \\\n",
       "0   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "1   Apple  Ultrabook    13.3                            1440x900   \n",
       "2      HP   Notebook    15.6                   Full HD 1920x1080   \n",
       "3   Apple  Ultrabook    15.4  IPS Panel Retina Display 2880x1800   \n",
       "4   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "\n",
       "                          Cpu  Ram               Memory  \\\n",
       "0        Intel Core i5 2.3GHz    8            128GB SSD   \n",
       "1        Intel Core i5 1.8GHz    8  128GB Flash Storage   \n",
       "2  Intel Core i5 7200U 2.5GHz    8            256GB SSD   \n",
       "3        Intel Core i7 2.7GHz   16            512GB SSD   \n",
       "4        Intel Core i5 3.1GHz    8            256GB SSD   \n",
       "\n",
       "                            Gpu  OpSys  Weight        Price  Touchscreen  Ips  \\\n",
       "0  Intel Iris Plus Graphics 640  macOS    1.37   71378.6832            0    1   \n",
       "1        Intel HD Graphics 6000  macOS    1.34   47895.5232            0    0   \n",
       "2         Intel HD Graphics 620  No OS    1.86   30636.0000            0    0   \n",
       "3            AMD Radeon Pro 455  macOS    1.83  135195.3360            0    1   \n",
       "4  Intel Iris Plus Graphics 650  macOS    1.37   96095.8080            0    1   \n",
       "\n",
       "  X_res Y_res  \n",
       "0  2560  1600  \n",
       "1  1440   900  \n",
       "2  1920  1080  \n",
       "3  2880  1800  \n",
       "4  2560  1600  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "307eef27",
   "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": "b7ab9fb2",
   "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   float64\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: float64(3), int32(3), int64(2), object(7)\n",
      "memory usage: 137.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c80d7ada",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Inches         0.068197\n",
       "Ram            0.743007\n",
       "Weight         0.210370\n",
       "Price          1.000000\n",
       "Touchscreen    0.191226\n",
       "Ips            0.252208\n",
       "X_res          0.556529\n",
       "Y_res          0.552809\n",
       "Name: Price, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()['Price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d89c6f70",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ppi'] = (((df['X_res']**2) + (df['Y_res']**2))**0.5/df['Inches']).astype('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "971eae20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Inches         0.068197\n",
       "Ram            0.743007\n",
       "Weight         0.210370\n",
       "Price          1.000000\n",
       "Touchscreen    0.191226\n",
       "Ips            0.252208\n",
       "X_res          0.556529\n",
       "Y_res          0.552809\n",
       "ppi            0.473487\n",
       "Name: Price, dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()['Price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "224f50b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=[\"ScreenResolution\",\"X_res\",\"Y_res\",\"Inches\"],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "73dcc874",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>0</td>\n",
       "      <td>127.677940</td>\n",
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       "    <tr>\n",
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       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>30636.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>Intel Core i7 2.7GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
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       "      <td>135195.3360</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>Intel Core i5 3.1GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>96095.8080</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName                         Cpu  Ram               Memory  \\\n",
       "0   Apple  Ultrabook        Intel Core i5 2.3GHz    8            128GB SSD   \n",
       "1   Apple  Ultrabook        Intel Core i5 1.8GHz    8  128GB Flash Storage   \n",
       "2      HP   Notebook  Intel Core i5 7200U 2.5GHz    8            256GB SSD   \n",
       "3   Apple  Ultrabook        Intel Core i7 2.7GHz   16            512GB SSD   \n",
       "4   Apple  Ultrabook        Intel Core i5 3.1GHz    8            256GB SSD   \n",
       "\n",
       "                            Gpu  OpSys  Weight        Price  Touchscreen  Ips  \\\n",
       "0  Intel Iris Plus Graphics 640  macOS    1.37   71378.6832            0    1   \n",
       "1        Intel HD Graphics 6000  macOS    1.34   47895.5232            0    0   \n",
       "2         Intel HD Graphics 620  No OS    1.86   30636.0000            0    0   \n",
       "3            AMD Radeon Pro 455  macOS    1.83  135195.3360            0    1   \n",
       "4  Intel Iris Plus Graphics 650  macOS    1.37   96095.8080            0    1   \n",
       "\n",
       "          ppi  \n",
       "0  226.983005  \n",
       "1  127.677940  \n",
       "2  141.211998  \n",
       "3  220.534624  \n",
       "4  226.983005  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "891a408a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intel Core i5 7200U 2.5GHz       190\n",
       "Intel Core i7 7700HQ 2.8GHz      146\n",
       "Intel Core i7 7500U 2.7GHz       134\n",
       "Intel Core i7 8550U 1.8GHz        73\n",
       "Intel Core i5 8250U 1.6GHz        72\n",
       "                                ... \n",
       "Intel Core M M3-6Y30 0.9GHz        1\n",
       "AMD A9-Series 9420 2.9GHz          1\n",
       "Intel Core i3 6006U 2.2GHz         1\n",
       "AMD A6-Series 7310 2GHz            1\n",
       "Intel Xeon E3-1535M v6 3.1GHz      1\n",
       "Name: Cpu, Length: 118, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Cpu\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "cf2c5e67",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Cpu Name'] = df['Cpu'].apply(lambda x:\" \".join(x.split()[0:3]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "030a0159",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Cpu</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
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       "      <th>Price</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>ppi</th>\n",
       "      <th>Cpu Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <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>71378.6832</td>\n",
       "      <td>0</td>\n",
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       "      <td>226.983005</td>\n",
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       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>Intel Core i5 1.8GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>47895.5232</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>127.677940</td>\n",
       "      <td>Intel Core i5</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>30636.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>Intel Core i7 2.7GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>135195.3360</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>220.534624</td>\n",
       "      <td>Intel Core i7</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>Intel Core i5 3.1GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>96095.8080</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName                         Cpu  Ram               Memory  \\\n",
       "0   Apple  Ultrabook        Intel Core i5 2.3GHz    8            128GB SSD   \n",
       "1   Apple  Ultrabook        Intel Core i5 1.8GHz    8  128GB Flash Storage   \n",
       "2      HP   Notebook  Intel Core i5 7200U 2.5GHz    8            256GB SSD   \n",
       "3   Apple  Ultrabook        Intel Core i7 2.7GHz   16            512GB SSD   \n",
       "4   Apple  Ultrabook        Intel Core i5 3.1GHz    8            256GB SSD   \n",
       "\n",
       "                            Gpu  OpSys  Weight        Price  Touchscreen  Ips  \\\n",
       "0  Intel Iris Plus Graphics 640  macOS    1.37   71378.6832            0    1   \n",
       "1        Intel HD Graphics 6000  macOS    1.34   47895.5232            0    0   \n",
       "2         Intel HD Graphics 620  No OS    1.86   30636.0000            0    0   \n",
       "3            AMD Radeon Pro 455  macOS    1.83  135195.3360            0    1   \n",
       "4  Intel Iris Plus Graphics 650  macOS    1.37   96095.8080            0    1   \n",
       "\n",
       "          ppi       Cpu Name  \n",
       "0  226.983005  Intel Core i5  \n",
       "1  127.677940  Intel Core i5  \n",
       "2  141.211998  Intel Core i5  \n",
       "3  220.534624  Intel Core i7  \n",
       "4  226.983005  Intel Core i5  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "c13a033d",
   "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": 47,
   "id": "02976f00",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Cpu brand'] = df['Cpu Name'].apply(fetch_processor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "4c977713",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
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       "      <td>Intel Core i5</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>Intel Core i5 1.8GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>47895.5232</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>127.677940</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>Intel Core i5</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>30636.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>Intel Core i7 2.7GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>135195.3360</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>220.534624</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>Intel Core i7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>Intel Core i5 3.1GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>96095.8080</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName                         Cpu  Ram               Memory  \\\n",
       "0   Apple  Ultrabook        Intel Core i5 2.3GHz    8            128GB SSD   \n",
       "1   Apple  Ultrabook        Intel Core i5 1.8GHz    8  128GB Flash Storage   \n",
       "2      HP   Notebook  Intel Core i5 7200U 2.5GHz    8            256GB SSD   \n",
       "3   Apple  Ultrabook        Intel Core i7 2.7GHz   16            512GB SSD   \n",
       "4   Apple  Ultrabook        Intel Core i5 3.1GHz    8            256GB SSD   \n",
       "\n",
       "                            Gpu  OpSys  Weight        Price  Touchscreen  Ips  \\\n",
       "0  Intel Iris Plus Graphics 640  macOS    1.37   71378.6832            0    1   \n",
       "1        Intel HD Graphics 6000  macOS    1.34   47895.5232            0    0   \n",
       "2         Intel HD Graphics 620  No OS    1.86   30636.0000            0    0   \n",
       "3            AMD Radeon Pro 455  macOS    1.83  135195.3360            0    1   \n",
       "4  Intel Iris Plus Graphics 650  macOS    1.37   96095.8080            0    1   \n",
       "\n",
       "          ppi       Cpu Name      Cpu brand  \n",
       "0  226.983005  Intel Core i5  Intel Core i5  \n",
       "1  127.677940  Intel Core i5  Intel Core i5  \n",
       "2  141.211998  Intel Core i5  Intel Core i5  \n",
       "3  220.534624  Intel Core i7  Intel Core i7  \n",
       "4  226.983005  Intel Core i5  Intel Core i5  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "e1e51afa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Cpu brand'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "091af7dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": 51,
   "id": "00db0aab",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['Cpu','Cpu Name'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "caa7059d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>ppi</th>\n",
       "      <th>Cpu brand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 640</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>71378.6832</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>47895.5232</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>127.677940</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>30636.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>135195.3360</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>220.534624</td>\n",
       "      <td>Intel Core i7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>96095.8080</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Ram               Memory                           Gpu  \\\n",
       "0   Apple  Ultrabook    8            128GB SSD  Intel Iris Plus Graphics 640   \n",
       "1   Apple  Ultrabook    8  128GB Flash Storage        Intel HD Graphics 6000   \n",
       "2      HP   Notebook    8            256GB SSD         Intel HD Graphics 620   \n",
       "3   Apple  Ultrabook   16            512GB SSD            AMD Radeon Pro 455   \n",
       "4   Apple  Ultrabook    8            256GB SSD  Intel Iris Plus Graphics 650   \n",
       "\n",
       "   OpSys  Weight        Price  Touchscreen  Ips         ppi      Cpu brand  \n",
       "0  macOS    1.37   71378.6832            0    1  226.983005  Intel Core i5  \n",
       "1  macOS    1.34   47895.5232            0    0  127.677940  Intel Core i5  \n",
       "2  No OS    1.86   30636.0000            0    0  141.211998  Intel Core i5  \n",
       "3  macOS    1.83  135195.3360            0    1  220.534624  Intel Core i7  \n",
       "4  macOS    1.37   96095.8080            0    1  226.983005  Intel Core i5  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "8f83a8d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Ram'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "1b60cf82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": 55,
   "id": "72dbdafb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "256GB SSD                        412\n",
       "1TB HDD                          223\n",
       "500GB HDD                        132\n",
       "512GB SSD                        118\n",
       "128GB SSD +  1TB HDD              94\n",
       "128GB SSD                         76\n",
       "256GB SSD +  1TB HDD              73\n",
       "32GB Flash Storage                38\n",
       "2TB HDD                           16\n",
       "64GB Flash Storage                15\n",
       "512GB SSD +  1TB HDD              14\n",
       "1TB SSD                           14\n",
       "256GB SSD +  2TB HDD              10\n",
       "1.0TB Hybrid                       9\n",
       "256GB Flash Storage                8\n",
       "16GB Flash Storage                 7\n",
       "32GB SSD                           6\n",
       "180GB SSD                          5\n",
       "128GB Flash Storage                4\n",
       "512GB SSD +  2TB HDD               3\n",
       "16GB SSD                           3\n",
       "512GB Flash Storage                2\n",
       "1TB SSD +  1TB HDD                 2\n",
       "256GB SSD +  500GB HDD             2\n",
       "128GB SSD +  2TB HDD               2\n",
       "256GB SSD +  256GB SSD             2\n",
       "512GB SSD +  256GB SSD             1\n",
       "512GB SSD +  512GB SSD             1\n",
       "64GB Flash Storage +  1TB HDD      1\n",
       "1TB HDD +  1TB HDD                 1\n",
       "32GB HDD                           1\n",
       "64GB SSD                           1\n",
       "128GB HDD                          1\n",
       "240GB SSD                          1\n",
       "8GB SSD                            1\n",
       "508GB Hybrid                       1\n",
       "1.0TB HDD                          1\n",
       "512GB SSD +  1.0TB Hybrid          1\n",
       "256GB SSD +  1.0TB Hybrid          1\n",
       "Name: Memory, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Memory'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "068d5986",
   "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",
    "\n",
    "df['first'] = df['first'].str.replace(r'\\D', '')\n",
    "\n",
    "df[\"second\"].fillna(\"0\", inplace = True)\n",
    "\n",
    "df[\"Layer2HDD\"] = df[\"second\"].apply(lambda x: 1 if \"HDD\" in x else 0)\n",
    "df[\"Layer2SSD\"] = df[\"second\"].apply(lambda x: 1 if \"SSD\" in x else 0)\n",
    "df[\"Layer2Hybrid\"] = df[\"second\"].apply(lambda x: 1 if \"Hybrid\" in x else 0)\n",
    "df[\"Layer2Flash_Storage\"] = df[\"second\"].apply(lambda x: 1 if \"Flash Storage\" in x else 0)\n",
    "\n",
    "df['second'] = df['second'].str.replace(r'\\D', '')\n",
    "\n",
    "df[\"first\"] = df[\"first\"].astype(int)\n",
    "df[\"second\"] = df[\"second\"].astype(int)\n",
    "\n",
    "df[\"HDD\"]=(df[\"first\"]*df[\"Layer1HDD\"]+df[\"second\"]*df[\"Layer2HDD\"])\n",
    "df[\"SSD\"]=(df[\"first\"]*df[\"Layer1SSD\"]+df[\"second\"]*df[\"Layer2SSD\"])\n",
    "df[\"Hybrid\"]=(df[\"first\"]*df[\"Layer1Hybrid\"]+df[\"second\"]*df[\"Layer2Hybrid\"])\n",
    "df[\"Flash_Storage\"]=(df[\"first\"]*df[\"Layer1Flash_Storage\"]+df[\"second\"]*df[\"Layer2Flash_Storage\"])\n",
    "\n",
    "df.drop(columns=['first', 'second', 'Layer1HDD', 'Layer1SSD', 'Layer1Hybrid',\n",
    "       'Layer1Flash_Storage', 'Layer2HDD', 'Layer2SSD', 'Layer2Hybrid',\n",
    "       'Layer2Flash_Storage'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "fa9b8cd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>128 SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 640</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>71378.6832</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>128 Flash Storage</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>47895.5232</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>127.677940</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>256 SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>30636.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>16</td>\n",
       "      <td>512 SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>135195.3360</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>220.534624</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>512</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>256 SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>96095.8080</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Ram             Memory                           Gpu  \\\n",
       "0   Apple  Ultrabook    8            128 SSD  Intel Iris Plus Graphics 640   \n",
       "1   Apple  Ultrabook    8  128 Flash Storage        Intel HD Graphics 6000   \n",
       "2      HP   Notebook    8            256 SSD         Intel HD Graphics 620   \n",
       "3   Apple  Ultrabook   16            512 SSD            AMD Radeon Pro 455   \n",
       "4   Apple  Ultrabook    8            256 SSD  Intel Iris Plus Graphics 650   \n",
       "\n",
       "   OpSys  Weight        Price  Touchscreen  Ips         ppi      Cpu brand  \\\n",
       "0  macOS    1.37   71378.6832            0    1  226.983005  Intel Core i5   \n",
       "1  macOS    1.34   47895.5232            0    0  127.677940  Intel Core i5   \n",
       "2  No OS    1.86   30636.0000            0    0  141.211998  Intel Core i5   \n",
       "3  macOS    1.83  135195.3360            0    1  220.534624  Intel Core i7   \n",
       "4  macOS    1.37   96095.8080            0    1  226.983005  Intel Core i5   \n",
       "\n",
       "   HDD  SSD  Hybrid  Flash_Storage  \n",
       "0    0  128       0              0  \n",
       "1    0    0       0            128  \n",
       "2    0  256       0              0  \n",
       "3    0  512       0              0  \n",
       "4    0  256       0              0  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "0082e736",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['Memory'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "8c736278",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ram              0.743007\n",
       "Weight           0.210370\n",
       "Price            1.000000\n",
       "Touchscreen      0.191226\n",
       "Ips              0.252208\n",
       "ppi              0.473487\n",
       "HDD             -0.096441\n",
       "SSD              0.670799\n",
       "Hybrid           0.007989\n",
       "Flash_Storage   -0.040511\n",
       "Name: Price, dtype: float64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()['Price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "5a95f271",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['Hybrid','Flash_Storage'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "75b8e8f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>Apple</td>\n",
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       "      <td>macOS</td>\n",
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       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>47895.5232</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>127.677940</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <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>8</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>30636.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>16</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>135195.3360</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>220.534624</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>96095.8080</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Ram                           Gpu  OpSys  Weight  \\\n",
       "0   Apple  Ultrabook    8  Intel Iris Plus Graphics 640  macOS    1.37   \n",
       "1   Apple  Ultrabook    8        Intel HD Graphics 6000  macOS    1.34   \n",
       "2      HP   Notebook    8         Intel HD Graphics 620  No OS    1.86   \n",
       "3   Apple  Ultrabook   16            AMD Radeon Pro 455  macOS    1.83   \n",
       "4   Apple  Ultrabook    8  Intel Iris Plus Graphics 650  macOS    1.37   \n",
       "\n",
       "         Price  Touchscreen  Ips         ppi      Cpu brand  HDD  SSD  \n",
       "0   71378.6832            0    1  226.983005  Intel Core i5    0  128  \n",
       "1   47895.5232            0    0  127.677940  Intel Core i5    0    0  \n",
       "2   30636.0000            0    0  141.211998  Intel Core i5    0  256  \n",
       "3  135195.3360            0    1  220.534624  Intel Core i7    0  512  \n",
       "4   96095.8080            0    1  226.983005  Intel Core i5    0  256  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "5e2f746b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intel HD Graphics 620      281\n",
       "Intel HD Graphics 520      185\n",
       "Intel UHD Graphics 620      68\n",
       "Nvidia GeForce GTX 1050     66\n",
       "Nvidia GeForce GTX 1060     48\n",
       "                          ... \n",
       "AMD Radeon R5 520            1\n",
       "AMD Radeon R7                1\n",
       "Intel HD Graphics 540        1\n",
       "AMD Radeon 540               1\n",
       "ARM Mali T860 MP4            1\n",
       "Name: Gpu, Length: 110, dtype: int64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gpu'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "77ef1fb3",
   "metadata": {},
   "outputs": [],
   "source": [
    " df['Gpu brand'] = df['Gpu'].apply(lambda x:x.split()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "c0fc21db",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>30636.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>16</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>135195.3360</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>220.534624</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>512</td>\n",
       "      <td>AMD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>96095.8080</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Ram                           Gpu  OpSys  Weight  \\\n",
       "0   Apple  Ultrabook    8  Intel Iris Plus Graphics 640  macOS    1.37   \n",
       "1   Apple  Ultrabook    8        Intel HD Graphics 6000  macOS    1.34   \n",
       "2      HP   Notebook    8         Intel HD Graphics 620  No OS    1.86   \n",
       "3   Apple  Ultrabook   16            AMD Radeon Pro 455  macOS    1.83   \n",
       "4   Apple  Ultrabook    8  Intel Iris Plus Graphics 650  macOS    1.37   \n",
       "\n",
       "         Price  Touchscreen  Ips         ppi      Cpu brand  HDD  SSD  \\\n",
       "0   71378.6832            0    1  226.983005  Intel Core i5    0  128   \n",
       "1   47895.5232            0    0  127.677940  Intel Core i5    0    0   \n",
       "2   30636.0000            0    0  141.211998  Intel Core i5    0  256   \n",
       "3  135195.3360            0    1  220.534624  Intel Core i7    0  512   \n",
       "4   96095.8080            0    1  226.983005  Intel Core i5    0  256   \n",
       "\n",
       "  Gpu brand  \n",
       "0     Intel  \n",
       "1     Intel  \n",
       "2     Intel  \n",
       "3       AMD  \n",
       "4     Intel  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "fa680db9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intel     722\n",
       "Nvidia    400\n",
       "AMD       180\n",
       "ARM         1\n",
       "Name: Gpu brand, dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gpu brand'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "e1994e3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df['Gpu brand'] != 'ARM']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "43526db0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intel     722\n",
       "Nvidia    400\n",
       "AMD       180\n",
       "Name: Gpu brand, dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gpu brand'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "a59ccbdd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": 69,
   "id": "c3f21304",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['Gpu'],inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "696faab5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Ram</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>ppi</th>\n",
       "      <th>Cpu brand</th>\n",
       "      <th>HDD</th>\n",
       "      <th>SSD</th>\n",
       "      <th>Gpu brand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>71378.6832</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>47895.5232</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>127.677940</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>30636.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>16</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>135195.3360</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>220.534624</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>512</td>\n",
       "      <td>AMD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>96095.8080</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Ram  OpSys  Weight        Price  Touchscreen  Ips  \\\n",
       "0   Apple  Ultrabook    8  macOS    1.37   71378.6832            0    1   \n",
       "1   Apple  Ultrabook    8  macOS    1.34   47895.5232            0    0   \n",
       "2      HP   Notebook    8  No OS    1.86   30636.0000            0    0   \n",
       "3   Apple  Ultrabook   16  macOS    1.83  135195.3360            0    1   \n",
       "4   Apple  Ultrabook    8  macOS    1.37   96095.8080            0    1   \n",
       "\n",
       "          ppi      Cpu brand  HDD  SSD Gpu brand  \n",
       "0  226.983005  Intel Core i5    0  128     Intel  \n",
       "1  127.677940  Intel Core i5    0    0     Intel  \n",
       "2  141.211998  Intel Core i5    0  256     Intel  \n",
       "3  220.534624  Intel Core i7    0  512       AMD  \n",
       "4  226.983005  Intel Core i5    0  256     Intel  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "af6e9bb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Windows 10      1072\n",
       "No OS             66\n",
       "Linux             62\n",
       "Windows 7         45\n",
       "Chrome OS         26\n",
       "macOS             13\n",
       "Mac OS X           8\n",
       "Windows 10 S       8\n",
       "Android            2\n",
       "Name: OpSys, dtype: int64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['OpSys'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "20d0021b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": 73,
   "id": "3c36473d",
   "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": 74,
   "id": "0cf0ba1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['os'] = df['OpSys'].apply(cat_os)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "0aff08ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": 76,
   "id": "6eb7cefb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['OpSys'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "651824a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>ppi</th>\n",
       "      <th>Cpu brand</th>\n",
       "      <th>HDD</th>\n",
       "      <th>SSD</th>\n",
       "      <th>Gpu brand</th>\n",
       "      <th>os</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>1.37</td>\n",
       "      <td>71378.6832</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Mac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>1.34</td>\n",
       "      <td>47895.5232</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>127.677940</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Mac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>1.86</td>\n",
       "      <td>30636.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Others/No OS/Linux</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>16</td>\n",
       "      <td>1.83</td>\n",
       "      <td>135195.3360</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>220.534624</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>512</td>\n",
       "      <td>AMD</td>\n",
       "      <td>Mac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>1.37</td>\n",
       "      <td>96095.8080</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Mac</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Ram  Weight        Price  Touchscreen  Ips         ppi  \\\n",
       "0   Apple  Ultrabook    8    1.37   71378.6832            0    1  226.983005   \n",
       "1   Apple  Ultrabook    8    1.34   47895.5232            0    0  127.677940   \n",
       "2      HP   Notebook    8    1.86   30636.0000            0    0  141.211998   \n",
       "3   Apple  Ultrabook   16    1.83  135195.3360            0    1  220.534624   \n",
       "4   Apple  Ultrabook    8    1.37   96095.8080            0    1  226.983005   \n",
       "\n",
       "       Cpu brand  HDD  SSD Gpu brand                  os  \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  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "4a0adf0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Weight', ylabel='Density'>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Weight'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "162a6cd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Weight', ylabel='Price'>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=df['Weight'],y=df['Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "7c920946",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ram            0.742905\n",
       "Weight         0.209867\n",
       "Price          1.000000\n",
       "Touchscreen    0.192917\n",
       "Ips            0.253320\n",
       "ppi            0.475368\n",
       "HDD           -0.096891\n",
       "SSD            0.670660\n",
       "Name: Price, dtype: float64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()['Price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "c2526329",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1302 entries, 0 to 1302\n",
      "Data columns (total 13 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   Company      1302 non-null   object \n",
      " 1   TypeName     1302 non-null   object \n",
      " 2   Ram          1302 non-null   int32  \n",
      " 3   Weight       1302 non-null   float64\n",
      " 4   Price        1302 non-null   float64\n",
      " 5   Touchscreen  1302 non-null   int64  \n",
      " 6   Ips          1302 non-null   int64  \n",
      " 7   ppi          1302 non-null   float64\n",
      " 8   Cpu brand    1302 non-null   object \n",
      " 9   HDD          1302 non-null   int64  \n",
      " 10  SSD          1302 non-null   int64  \n",
      " 11  Gpu brand    1302 non-null   object \n",
      " 12  os           1302 non-null   object \n",
      "dtypes: float64(3), int32(1), int64(4), object(5)\n",
      "memory usage: 169.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "246983e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "c166e50e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Price', ylabel='Density'>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(np.log(df['Price']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e09c65a9",
   "metadata": {},
   "source": [
    "<h1>Model Selection</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "f2de2ed1",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(columns=['Price'])\n",
    "y = np.log(df['Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "01a869e5",
   "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": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "380ffd10",
   "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": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "9cbbbd58",
   "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": 88,
   "id": "b7df6110",
   "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": 89,
   "id": "e20cd89f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression,Ridge,Lasso\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor,GradientBoostingRegressor,AdaBoostRegressor,ExtraTreesRegressor\n",
    "from sklearn.svm import SVR\n",
    "from xgboost import XGBRegressor\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f58c9fef",
   "metadata": {},
   "source": [
    "<h1>Linear regression</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "edd72696",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.8071853947620582\n",
      "MAE 0.21114361575113452\n"
     ]
    }
   ],
   "source": [
    "step1 = ColumnTransformer(transformers=[\n",
    "    ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,7,10,11])\n",
    "],remainder='passthrough')\n",
    "\n",
    "step2 = Lasso(alpha=0.001)\n",
    "\n",
    "pipe = Pipeline([\n",
    "    ('step1',step1),\n",
    "    ('step2',step2)\n",
    "])\n",
    "\n",
    "pipe.fit(X_train,y_train)\n",
    "\n",
    "y_pred = pipe.predict(X_test)\n",
    "\n",
    "print('R2 score',r2_score(y_test,y_pred))\n",
    "print('MAE',mean_absolute_error(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2e3bf19",
   "metadata": {},
   "source": [
    "<h1> KNN</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "bd19bb82",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.80215040816626\n",
      "MAE 0.19323579887267534\n"
     ]
    }
   ],
   "source": [
    "step1 = ColumnTransformer(transformers=[\n",
    "    ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,7,10,11])\n",
    "],remainder='passthrough')\n",
    "\n",
    "step2 = KNeighborsRegressor(n_neighbors=3)\n",
    "\n",
    "pipe = Pipeline([\n",
    "    ('step1',step1),\n",
    "    ('step2',step2)\n",
    "])\n",
    "\n",
    "pipe.fit(X_train,y_train)\n",
    "\n",
    "y_pred = pipe.predict(X_test)\n",
    "\n",
    "print('R2 score',r2_score(y_test,y_pred))\n",
    "print('MAE',mean_absolute_error(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48928807",
   "metadata": {},
   "source": [
    "# Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "8e298f8d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.8435703898370801\n",
      "MAE 0.18011851056530426\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": "0cf1110e",
   "metadata": {},
   "source": [
    "# SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "e37fe3b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.8083168388456576\n",
      "MAE 0.20239400567756663\n"
     ]
    }
   ],
   "source": [
    "step1 = ColumnTransformer(transformers=[\n",
    "    ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,7,10,11])\n",
    "],remainder='passthrough')\n",
    "\n",
    "step2 = SVR(kernel='rbf',C=10000,epsilon=0.1)\n",
    "\n",
    "pipe = Pipeline([\n",
    "    ('step1',step1),\n",
    "    ('step2',step2)\n",
    "])\n",
    "\n",
    "pipe.fit(X_train,y_train)\n",
    "\n",
    "y_pred = pipe.predict(X_test)\n",
    "\n",
    "print('R2 score',r2_score(y_test,y_pred))\n",
    "print('MAE',mean_absolute_error(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "658a6820",
   "metadata": {},
   "source": [
    "# Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "13703246",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.8873402378382488\n",
      "MAE 0.15860130110457718\n"
     ]
    }
   ],
   "source": [
    "step1 = ColumnTransformer(transformers=[\n",
    "    ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,7,10,11])\n",
    "],remainder='passthrough')\n",
    "\n",
    "step2 = RandomForestRegressor(n_estimators=100,\n",
    "                              random_state=3,\n",
    "                              max_samples=0.5,\n",
    "                              max_features=0.75,\n",
    "                              max_depth=15)\n",
    "\n",
    "pipe = Pipeline([\n",
    "    ('step1',step1),\n",
    "    ('step2',step2)\n",
    "])\n",
    "\n",
    "pipe.fit(X_train,y_train)\n",
    "\n",
    "y_pred = pipe.predict(X_test)\n",
    "\n",
    "print('R2 score',r2_score(y_test,y_pred))\n",
    "print('MAE',mean_absolute_error(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29f34220",
   "metadata": {},
   "source": [
    "# ExtraTrees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "43422a33",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.8753793123440623\n",
      "MAE 0.15979519126758127\n"
     ]
    }
   ],
   "source": [
    "step1 = ColumnTransformer(transformers=[\n",
    "    ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,7,10,11])\n",
    "],remainder='passthrough')\n",
    "\n",
    "step2 = ExtraTreesRegressor(n_estimators=100,\n",
    "                              random_state=3,\n",
    "                              max_samples=0.5,\n",
    "                              max_features=0.75,\n",
    "                              max_depth=15)\n",
    "\n",
    "pipe = Pipeline([\n",
    "    ('step1',step1),\n",
    "    ('step2',step2)\n",
    "])\n",
    "\n",
    "pipe.fit(X_train,y_train)\n",
    "\n",
    "y_pred = pipe.predict(X_test)\n",
    "\n",
    "print('R2 score',r2_score(y_test,y_pred))\n",
    "print('MAE',mean_absolute_error(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee3fae16",
   "metadata": {},
   "source": [
    "# Stacking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "54db57bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.8800885694579196\n",
      "MAE 0.16703058111662536\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import VotingRegressor,StackingRegressor\n",
    "\n",
    "step1 = ColumnTransformer(transformers=[\n",
    "    ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,7,10,11])\n",
    "],remainder='passthrough')\n",
    "\n",
    "\n",
    "estimators = [\n",
    "    ('rf', RandomForestRegressor(n_estimators=350,random_state=3,max_samples=0.5,max_features=0.75,max_depth=15)),\n",
    "    ('gbdt',GradientBoostingRegressor(n_estimators=100,max_features=0.5)),\n",
    "    ('xgb', XGBRegressor(n_estimators=25,learning_rate=0.3,max_depth=5))\n",
    "]\n",
    "\n",
    "step2 = StackingRegressor(estimators=estimators, final_estimator=Ridge(alpha=100))\n",
    "\n",
    "pipe = Pipeline([\n",
    "    ('step1',step1),\n",
    "    ('step2',step2)\n",
    "])\n",
    "\n",
    "pipe.fit(X_train,y_train)\n",
    "\n",
    "y_pred = pipe.predict(X_test)\n",
    "\n",
    "print('R2 score',r2_score(y_test,y_pred))\n",
    "print('MAE',mean_absolute_error(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc330235",
   "metadata": {},
   "source": [
    "<h2>Observations</h2>\n",
    "\n",
    "<b> Random Forest Model gives the best R2 score</b><br>\n",
    "\n",
    "<p> R2 score 0.8873402378382488</p>\n",
    "<p> MAE 0.15860130110457718</p>\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bef5e6dc",
   "metadata": {},
   "source": [
    "# Exporting the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "52e9b925",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "pickle.dump(df,open('df.pkl','wb'))\n",
    "pickle.dump(pipe,open('pipe.pkl','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fdfab096",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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