Laptop Price Predictor.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "dd9066f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import all libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "110b9fd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"laptop_price.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f5c81f3b",
   "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "50f46fb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1303, 12)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b93bf52d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(columns = {'Unnamed: 0':'id'}, inplace = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "143b0fb3",
   "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   id                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": 7,
   "id": "eb6796e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a7e1a094",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                  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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b6ac1996",
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop the id column\n",
    "df = df.drop(\"id\",axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0cd93d1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Inches</th>\n",
       "      <th>ScreenResolution</th>\n",
       "      <th>Cpu</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <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>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 2.3GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>128GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 640</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37kg</td>\n",
       "      <td>71378.6832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>1440x900</td>\n",
       "      <td>Intel Core i5 1.8GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>128GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34kg</td>\n",
       "      <td>47895.5232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86kg</td>\n",
       "      <td>30636.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>15.4</td>\n",
       "      <td>IPS Panel Retina Display 2880x1800</td>\n",
       "      <td>Intel Core i7 2.7GHz</td>\n",
       "      <td>16GB</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83kg</td>\n",
       "      <td>135195.3360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 3.1GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37kg</td>\n",
       "      <td>96095.8080</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Inches                    ScreenResolution  \\\n",
       "0   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "1   Apple  Ultrabook    13.3                            1440x900   \n",
       "2      HP   Notebook    15.6                   Full HD 1920x1080   \n",
       "3   Apple  Ultrabook    15.4  IPS Panel Retina Display 2880x1800   \n",
       "4   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "\n",
       "                          Cpu   Ram               Memory  \\\n",
       "0        Intel Core i5 2.3GHz   8GB            128GB SSD   \n",
       "1        Intel Core i5 1.8GHz   8GB  128GB Flash Storage   \n",
       "2  Intel Core i5 7200U 2.5GHz   8GB            256GB SSD   \n",
       "3        Intel Core i7 2.7GHz  16GB            512GB SSD   \n",
       "4        Intel Core i5 3.1GHz   8GB            256GB SSD   \n",
       "\n",
       "                            Gpu  OpSys  Weight        Price  \n",
       "0  Intel Iris Plus Graphics 640  macOS  1.37kg   71378.6832  \n",
       "1        Intel HD Graphics 6000  macOS  1.34kg   47895.5232  \n",
       "2         Intel HD Graphics 620  No OS  1.86kg   30636.0000  \n",
       "3            AMD Radeon Pro 455  macOS  1.83kg  135195.3360  \n",
       "4  Intel Iris Plus Graphics 650  macOS  1.37kg   96095.8080  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "75000f13",
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove the GB part\n",
    "df['Ram']=df['Ram'].str.replace(\"GB\",\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9c6a9760",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Weight'] = df['Weight'].str.replace(\"kg\",\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "04d201a9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df['Ram']=df['Ram'].astype('int32')\n",
    "df['Weight'] = df['Weight'].astype(\"float32\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7758c3c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# univariate analysis on price column\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e8b9745d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\Umang\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Price', ylabel='Density'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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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": "6bfe7fc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# the data is left skewd which is obvisous in real world scenario"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "12954054",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 17,
     "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": [
    "# now Company column\n",
    "df['Company'].value_counts().plot(kind='bar')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7240c4be",
   "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": 19,
   "id": "6d844e8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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q23PaeO3JkPc33Ep+fyW/Nxis99evbpm1wCEd2zOAdX16rYiIGKVf4f5jYJakwyTtCswDVvTptSIiYpS+dMvY3iTpDcC/AdOAz9q+rh+vtYNa6Q6aRHl/w63k91fye4MBen99OaEaERHtygzViIgCJdwjIgqUcI8YAJIeJelJbdcR5Zgy4S5p3y5th7VRS2w/Sbt1advqbzqMJP0xcDXwnXr7aEkZXTYkBjVbpswJVUmXAi+0fW+9PRtYbvuoditrhqRjujTfA/zS9qbJrqdpki4ATrP9+3r7IOBbtp/RbmUTJ+lK4ETgEttPr9uutf3UditrhqTpwGuBmXSM0LP9mrZqatKgZstUuhLTB4BvSjqVarmD/wu8ot2SGnUOcAxwLdUM4aPq+/tJer3tC9ssrgHfAL4i6aVUE+RWAH/dakXN2WT7HqnbxO4inA/8O/BdYHPLtfTDQGbLlAl32xdI2gW4ENiL6ijwppbLatKtwJkj8wnqo4d3AO8Hvk71voeW7U/VE+K+QXUE+Drbl7VaVHN+JunPgWmSZgFvAkp5bwCPtv3Otovol0HNluK7ZST9M1uua3MicDNVGGL7TS2U1ThJV9s+ultbt8eGhaS3dW4CrwR+CvwEwPaH2qirSZIeDbwbeAHVe/w34P22f9tqYQ2R9A/AZbb/te1amjTo2TIVjtxXjdq+spUq+m+1pI9TLa8M8GfAz+sTkb9vr6wJ22vU9nljtA8t2w9Shfu7266lT94MvEvS73jk36Jt791iTU0Y6Gwp/si9U/21/on15uqRk3MlkPQo4K+AP6I6+vshVT/8b6m+Ft/fYnmNkbQXVTAM/fuR9E1GrZbayfZLJrGcmIBBzJYpE+6STgCWUn1lEtVJufm2f9BeVdErSUcB/wKMDDv7NfCqAVuzaLtI+u/betz29yerln6T9BLgOfXmJba/1WY9TRrUbJlK4X4l8Oe2V9fbTwS+VMJQOgBJxwPvBR7PlsPNDm+rpiZJugx4t+2L6+0TgA/YPq7NuppSH/kdQXUkv7q+glkRJC0Gngl8oW56OXCl7SKurTyo2TIV+txH7DLyHx/A9s/rM9yl+AzwVqp+vxKHm+0xEuwAti+RtEebBTWlHkL3CeAXVEd+h0l6ne1vt1tZY14EHG37IQBJS6lOiBcR7gxotkylcF8l6TNUX+2hGoc6UCdAJuiegsKgm5sl/S2P/P3OAG5psZ4mfRB4ru01AJKeAFwAlPT3fCzwm/r+Y1qsox8GMlumUrfMbsACHjnh+APgHNsbWy2sIfVX32lUY9offk+2r2qtqAZJ2gd4H1v+/d5r+79aLawBkn5g+zkd2wK+39k2zCS9HFgMXEz1t3sOcLbtZdt84pAY1GyZMuEOD/drPolH+jVbP6PdFEkXd2m27RMnvZg+krQ38FAho2X+tL77fKpzJcup/m2eTvXv8+1t1da0ermIZ1KF3+W2f9VySY0axGyZMuE+qGe0ozeSnkI1rbtztMx82z9rr6qJkXTuNh72sK+9IukI2zeOse5RSd8qT2AAs2UqhftAntGeKEln2P78qJmcDythBieUP1qmRJKW2D6r9G+Vg5otU+mE6kCe0W7AyIiRYmZsjqG40TKS/sb2P3aZxg60P319omyfVd994eilFCTt3kJJ/TKQ2TKVwn0gz2hPlO1P1rfva7uWPitxtMwN9e3oaeyluYxqxdLx2obVQGbLVAr3v6Q6o/0mOs5ot1pRg+qLA7yRrdfMLmUK+2uoRst8nUf+fq9utaIJsv3N+u6Dtr/S+Zik01soqVGS/gA4GHiUpKdT/d0A9gYe3VphzRvIbJkyfe7w8BntJwMPUd4swGuoJjL9lOr9AWVNYQeQ9Biq0TL3tV1LUyRdZfuY8dqGjaT5wP8E5rDlt5P7gM/Z/nobdfXDIGbLlAn3brMAqdYEL2KiiKTLbf9h23X0i6RnAp/lkXML9wCvsd36198dJemFVLM3/wfw5Y6H9gZm2z62lcIaJumltr/Wdh39MqjZMpXC/UbgxaNnAdo+ot3KmlFf7GEW1QUDSpzEdC2wwPa/19t/RDVRZGgvRSfpacDRwN8Df9fx0H3AxSVM0BpRB+CRwMMnUm3/fXsVNWdQs2Uq9bmvH/mPX7sZWN9WMX3wFKoLWZzII90yrrdLcN9IsAPY/qGkoe6asX2NpJ8BL7C9tO16+kXSJ6j62J8LfBp4GXBFq0U1ayCzpfhw75gFeJ2kf2XLWYA/bq2w5v0JcPgg9PU1qWMCzBWSPgl8ierv92fAJW3V1RTbmyXtJ2nX0v52HY6z/VRVF/1+n6QPUp0YH2qDni3Fhzvwxx337wRG1tDeAOwz+eX0zTVUizO1fsTQsA+O2n5Px/1S+hR/CVwqaQXwwEhjKRPQgP9X3z4o6XHAXVT90sNuoLOl+HC3PdTD5bbDgcCNkn7Mln3uQz0U0vZz265hEqyrf3aizMlo35L0WOCfgKuoPpQ/3WpFDRjJFknH276087H6+gqtmkonVJ8IfBw40PZRkp4KvMT2P7RcWiPGuqpPSUMhSz4pByBpD9sPjL/ncJG028gKifUKirsDv2171cSmDOpQ1uKP3Dt8CngHMDKj81pJXwSKCPeSQrybkk/KSXo21RyFPYFD61E0r7P9V+1W1pgfUc9GrQN9o6SrGPIZqvXf7Thg+qi1nfamWn67VTu1XcAkerTt0WGwqZVK+kDSsyT9WNL9kn4nabOke9uuq0HH2X4V8F/1UgvPplp9rwQfBk6m6ovG9jU8cr3RoSXpDyQ9g3qGqqRj6p8TKGOG6q5UH8g7U3WnjfzcS3Xw0aqpdOT+63r8qQEkvQy4o92SGvVRYB7wFaoZga+iGvdeipGFp0o7KQeA7dura3Q8rIRLJZ5MNUN1BtWJ8ZE3eB/wrpZqakz9bfn7kj5n+5eD1q02lcJ9AbAEOELSf1ItOvWKdktqlu01kqbZ3gycWy+TW4pvdjkp96lWK2rO7ZKOA1xPY38TjywqNrTqsftLS5+hCjxO0rcZsG61qRTutv28epnYnWzfVy+2VYoH62C4RtI/Un0rGeolcUdI2glYaftu4GuSvgXsbvueditrzOuBj1AtsrWWapbxglYratYMVVfQuo/qA/kYYKHtC9stqzEfpvqWsgIenpzWerfaVOpz/xqA7Qc6Fp36aov1NO2VVH/PBVRjpWcAL221oobYfoiO8e62NxYU7FCNWnuF7QNtH2D7DNt3tV1Ug15j+17gBcABVKt5Lm63pGbZvn1UU+vdasUfuUs6gmr43GM6ZpRBdUZ76C8YIGkuMMP2x+rt71P9D2SqUQprtvH0YXKhpJcCX3d543cvk3QL1eJhX6u/oZRkpK/9RcC59ZGttvWEITOQ3WrFj3Ovw+804CXUX5tq9wHLbA91v7SkS4F5I0cOkq6mWk9mT6r/kU5qsbzG1OvI7EE1wum3VIFh23u3WlhDJB1LdUL8NOB6qn+bn2+1qIaoulbswVQnwJ9GNUzwkrYvQ9cUSftTdas9j+rf5YXAm9v+9lV8uI+Q9GzbP2q7jqZJ+rHtZ3Zsf9T2G+r7/2H7We1VF9urDooPAa+w3fpY6SbU50yOBm62fbek/YCDbV/bbmVlK75bpsPtks4Djqfqsvgh1afr2nbLmrAt1rAYCfba9EmupW8krRz9LaRb2zCqTzb+CdWR+xOA84Ai1nKvvdd255LGdwP/hyEfrSbp77bxsG2/f9KK6WIqnVA9l6pb5nFUXxG/WbcNu8slvXZ0o6TXUcAMTkm7S9oX2F/SPpL2rX9mUv0tS3AN9brutp9o+53DfBGSLg6VdDY8vPzAecBN7ZbUiAe6/ACcCbyzraJGTKVumWtsP21U29W2j26ppEZIOgD4BtViYSMX5ngGsBtwmu07WyqtEZLeDLyFKsjXdTx0L/Ap2x9to64mSVKBJ4kfVp88/QLVJSCfC3zb9v9ut6pmSdoLeDNVsC8HPmi71RVap1K4fxf4HNV64AAvB15dwtd6AEknUo0KArjO9vfarKdpkt5o+5/brqMf6kXt/pqtL24+1Bda6ViLH2AXqnWdLqVaR6eIq4TV3yrfRtXFtBT4yKBcQWsqhfuhVFP0n03V534ZVZ/7L1stLLZp1PDVrbiAiyyrurj5J4Ar6RgfPexdM5Iu3sbDLuDD65+AP6Wa+f4x2/e3XNIWpky4x3Cqh9GN/CMdPTbatl8zySU1TtKVpQwLHK0eKXO67S+Pu/OQkfQQVXfoJra8cMxADNMtPtwH/Yx2bJukt49qMtWVbn5o+5YWSmqcpPdSXUHrPLa80Mpv2qqpSZJ+YLv16fhTzVQI99HhANVkmDOB/WzvOcklxXaQ9J4uzftSreXxXtvLJrmkxtWzU0ez7cMnvZg+kPS3VJfa+zJbXkawiA+vQVV8uHcaxDPasWPqE1nfbftqNzG+0j+8BtWUmMTU5Yz2MYNyRjt2jO3flLI+iaRdgL/kkQt0XAJ80vbvWyuqQbZLWn11aBQf7qPOaD9l0M5ox46ph36W8gH9caqhgufU26+s2/6itYoaVPqH16Aqvltm0M9ox7ZJ+ilb/t2g6nNfB7zK9o2TX1Wzxphgt1XbsJL0aaoPr6V10yuBzbaL+PAaVMUfudueSksslOjFo7YN3DVIlzNrwGZJT7D9CwBJhzMA64E36JmjPqi+V4/tjz4qPtxjuE2RSWbvAC6WdDPVN8rHU13QohSlf3gNpOK7ZSKGQb2g1pOowv1G2xvHecrAk/QWquUG9qG6vN7IqJmZVFdnKmqJjEGTcI9oiaQzqP4f/JdR7a8FHrD9xXYqa4ak/wUcBzwZ+Dnwn1RLLJxre922nhsTl3CPaImknwDP6bim70j73sDFpSxJUF96bg5V0D+7/rnb9uxWCytc+twj2jNtdLAD2L63Hj5YikdRXbP4MfXPOqrlf6OPEu4R7dlF0h6jR/7UM6l3bammxkhaQrUM9X3A5VQrsX4oEwgnR4YJRrTnM8BX66tKAVDfX1Y/NuwOpbpozK+o+tvXUl1iLyZB+twjWiTp9cDZwJ5UY/gfABbb/nirhTWkXiLiSKr+9uOAo4DfAD+y3W1RuGhIwj1iAEjak+r/x6364EsgaQbVxemPo5qYtp/tx7ZaVOES7hHRF5LeRBXmxwO/pxrz/qP69qe2H2qxvOLlhGpE9MtM4KvAW23f0XItU06O3CMiCpTRMhEDSNLz264hhluO3CMGkKTbbB/adh0xvNLnHtESSSvGegjYbzJrifIk3CPa89+AM4DRVwcTcOzklxMlSbhHtOc/gAdtf3/0A5JWt1BPFCR97hERBcpomYiIAiXcIyIKlHCPiChQwj0iokAZLRPRMknHA+8FHk/1/6QA2z68zbpiuGW0TETLJN0IvJXq4tGbR9pt39VaUTH0cuQe0b57bH+77SKiLDlyj2iZpMXANODrwMaRdttXtVZUDL2Ee0TLJF3cpdm2T5z0YqIYCfeIiAKlzz2iJZLOsP15SW/r9rjtD012TVGOhHtEe/aob/dqtYooUrplIiIKlBmqEREFSrhHRBQo4R4RUaCEe0SLJB0h6SRJe45qP6WtmqIMCfeIlkh6E3A+8EbgZ5Lmdjz8gXaqilJkKGREe14LPMP2/ZJmAl+VNNP2R6hWhozYYQn3iPZMs30/gO1bJZ1AFfCPJ+EeE5RumYj2/ErS0SMbddC/GNgfeEpbRUUZMokpoiWSZgCbbP+qy2PH2760hbKiEAn3iIgCpVsmIqJACfeIiAIl3CMiCpRwj4goUMI9IqJA/x9EMnmeIOK/wgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# now new column type_name\n",
    "df['TypeName'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f26a76c9",
   "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": 21,
   "id": "d3e4cd89",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\Umang\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Inches', ylabel='Density'>"
      ]
     },
     "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": [
    "# next column size\n",
    "sns.distplot(df['Inches'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "fc64dad8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Inches', ylabel='Price'>"
      ]
     },
     "execution_count": 22,
     "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['Inches'],y=df['Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b580f6d7",
   "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>Inches</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Inches</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.237993</td>\n",
       "      <td>0.827631</td>\n",
       "      <td>0.068197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ram</th>\n",
       "      <td>0.237993</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.383874</td>\n",
       "      <td>0.743007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weight</th>\n",
       "      <td>0.827631</td>\n",
       "      <td>0.383874</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.210370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Price</th>\n",
       "      <td>0.068197</td>\n",
       "      <td>0.743007</td>\n",
       "      <td>0.210370</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Inches       Ram    Weight     Price\n",
       "Inches  1.000000  0.237993  0.827631  0.068197\n",
       "Ram     0.237993  1.000000  0.383874  0.743007\n",
       "Weight  0.827631  0.383874  1.000000  0.210370\n",
       "Price   0.068197  0.743007  0.210370  1.000000"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "cf9f287b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now screen Resolution which is difficault"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a4322fe5",
   "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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ScreenResolution'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7be40f3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Touchscreen']=df['ScreenResolution'].apply(lambda x : 1 if 'Touchscreen' in x else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "85b3f59c",
   "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>314</th>\n",
       "      <td>Asus</td>\n",
       "      <td>2 in 1 Convertible</td>\n",
       "      <td>11.6</td>\n",
       "      <td>Touchscreen 1366x768</td>\n",
       "      <td>Intel Celeron Dual Core N3350 1.1GHz</td>\n",
       "      <td>2</td>\n",
       "      <td>32GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics 500</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.10</td>\n",
       "      <td>14652.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>Dell</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i5 8250U 1.6GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>1TB HDD</td>\n",
       "      <td>AMD Radeon 530</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.90</td>\n",
       "      <td>35324.64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>HP</td>\n",
       "      <td>Gaming</td>\n",
       "      <td>15.6</td>\n",
       "      <td>IPS Panel Full HD 1920x1080</td>\n",
       "      <td>Intel Core i7 7700HQ 2.8GHz</td>\n",
       "      <td>12</td>\n",
       "      <td>128GB SSD +  1TB HDD</td>\n",
       "      <td>Nvidia GeForce GTX 1050</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.62</td>\n",
       "      <td>66546.72</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>962</th>\n",
       "      <td>Acer</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>12</td>\n",
       "      <td>128GB SSD +  1TB HDD</td>\n",
       "      <td>Nvidia GeForce GTX 950M</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.40</td>\n",
       "      <td>53759.52</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Company            TypeName  Inches             ScreenResolution  \\\n",
       "314    Asus  2 in 1 Convertible    11.6         Touchscreen 1366x768   \n",
       "349    Dell           Ultrabook    15.6            Full HD 1920x1080   \n",
       "99       HP              Gaming    15.6  IPS Panel Full HD 1920x1080   \n",
       "962    Acer            Notebook    15.6            Full HD 1920x1080   \n",
       "\n",
       "                                      Cpu  Ram                Memory  \\\n",
       "314  Intel Celeron Dual Core N3350 1.1GHz    2    32GB Flash Storage   \n",
       "349            Intel Core i5 8250U 1.6GHz    8               1TB HDD   \n",
       "99            Intel Core i7 7700HQ 2.8GHz   12  128GB SSD +  1TB HDD   \n",
       "962            Intel Core i5 7200U 2.5GHz   12  128GB SSD +  1TB HDD   \n",
       "\n",
       "                         Gpu       OpSys  Weight     Price  Touchscreen  \n",
       "314    Intel HD Graphics 500  Windows 10    1.10  14652.00            1  \n",
       "349           AMD Radeon 530  Windows 10    1.90  35324.64            0  \n",
       "99   Nvidia GeForce GTX 1050  Windows 10    2.62  66546.72            0  \n",
       "962  Nvidia GeForce GTX 950M  Windows 10    2.40  53759.52            0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "63085258",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 28,
     "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": 29,
   "id": "fd5fc66a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Touchscreen', ylabel='Price'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6MUgkSZ0YJJKkTgwSSVInBokkqRODRJLUiUEiSerEIJEkdWKQSJI6MUgkSZ0MLUiSnJLkviTfSLI9yUda/fQkm5M80r5n97W5Nslokh1JLu2rL0qyrW27oU25S5uW945W35pkwbCOR5I02DDPSJ4H/kFVvQ24GFieZClwDbClqhYCW9o6SS6gN1XuhcBy4MYkYzM03gSspTeP+8K2HWAN8ExVnQ9cD1w3xOORJA0wtCCpnh+21Ve2TwErgA2tvgG4rC2vAG6vquer6lFgFFiSZC5walXdW1UF3DquzVhfdwLLxs5WJElTY6j3SJLMSPIAsBfYXFVbgbOrajdA+z6r7T4PeKyv+a5Wm9eWx9cPaVNVB4BngTMGjGNtkpEkI/v27TtGRydJgiEHSVUdrKqLgfn0zi4ummD3QWcSNUF9ojbjx7G+qhZX1eI5c+YcYdSSpJdiSp7aqqrvA1+id2/jyXa5iva9t+22Czinr9l84IlWnz+gfkibJDOB04Cnh3EMkqTBhvnU1pwkb2jLs4BLgG8Bm4DVbbfVwF1teROwsj2JdR69m+r3tctf+5Msbfc/Vo1rM9bX5cA97T6KJGmKzBxi33OBDe3Jq1cAG6vq80nuBTYmWQPsBK4AqKrtSTYCDwEHgKuq6mDr60rgFmAWcHf7ANwM3JZklN6ZyMohHo8kaYChBUlVfRN4+4D694Blh2mzDlg3oD4CvOj+SlU9RwsiSdL08JftkqRODBJJUicGiSSpE4NEktSJQSJJ6sQgkSR1YpBIkjoxSCRJnRgkkqRODBJJUicGiSSpE4NEktSJQSJJ6sQgkSR1YpBIkjoxSCRJnQxzqt1zkvyfJA8n2Z7kt1r99CSbkzzSvmf3tbk2yWiSHUku7asvSrKtbbuhTblLm5b3jlbfmmTBsI5HkjTYMM9IDgD/uqr+LrAUuCrJBcA1wJaqWghsaeu0bSuBC4HlwI1tml6Am4C19OZxX9i2A6wBnqmq84HrgeuGeDySpAGGFiRVtbuqvtaW9wMPA/OAFcCGttsG4LK2vAK4vaqer6pHgVFgSZK5wKlVdW9VFXDruDZjfd0JLBs7W5EkTY0puUfSLjm9HdgKnF1Vu6EXNsBZbbd5wGN9zXa12ry2PL5+SJuqOgA8C5wx4O+vTTKSZGTfvn3H6KgkSTAFQZLkdcD/Aj5YVT+YaNcBtZqgPlGbQwtV66tqcVUtnjNnzpGGLEl6CYYaJEleSS9EPlVVn23lJ9vlKtr33lbfBZzT13w+8ESrzx9QP6RNkpnAacDTx/5IJEmHM8yntgLcDDxcVf+lb9MmYHVbXg3c1Vdf2Z7EOo/eTfX72uWv/UmWtj5XjWsz1tflwD3tPookaYrMHGLf7wR+HdiW5IFW+/fAR4GNSdYAO4ErAKpqe5KNwEP0nvi6qqoOtnZXArcAs4C72wd6QXVbklF6ZyIrh3g8kqQBhhYkVfUVBt/DAFh2mDbrgHUD6iPARQPqz9GCSJI0PfxluySpE4NEktSJQSJJ6sQgkSR1YpBIkjoxSCRJnRgkkqRODBJJUicGiSSpE4NEktSJQSJJ6sQgkSR1YpBIkjoxSCRJnRgkkqRODBJJUifDnGr3E0n2Jnmwr3Z6ks1JHmnfs/u2XZtkNMmOJJf21Rcl2da23dCm26VNyXtHq29NsmBYxyJJOrxhnpHcAiwfV7sG2FJVC4EtbZ0kF9CbJvfC1ubGJDNam5uAtfTmcF/Y1+ca4JmqOh+4HrhuaEciSTqsoQVJVX2Z3jzq/VYAG9ryBuCyvvrtVfV8VT0KjAJLkswFTq2qe6uqgFvHtRnr605g2djZiiRp6kz1PZKzq2o3QPs+q9XnAY/17ber1ea15fH1Q9pU1QHgWeCMQX80ydokI0lG9u3bd4wORZIEx8/N9kFnEjVBfaI2Ly5Wra+qxVW1eM6cOUc5REnSIFMdJE+2y1W0772tvgs4p2+/+cATrT5/QP2QNklmAqfx4ktpkqQhm+og2QSsbsurgbv66ivbk1jn0bupfl+7/LU/ydJ2/2PVuDZjfV0O3NPuo0iSptDMYXWc5NPALwJnJtkFfBj4KLAxyRpgJ3AFQFVtT7IReAg4AFxVVQdbV1fSewJsFnB3+wDcDNyWZJTemcjKYR2LJOnwhhYkVfW+w2xadpj91wHrBtRHgIsG1J+jBZEkafocLzfbJUknKINEktSJQSJJ6sQgkSR1YpBIkjoxSCRJnRgkkqRODBJJUicGiSSpE4NEktSJQSJJ6sQgkSR1YpBIkjoxSCRJnRgkkqRODBJJUicnfJAkWZ5kR5LRJNdM93gk6WRzQgdJkhnAfwfeA1wAvC/JBdM7Kkk6uZzQQQIsAUar6ttV9WPgdmDFNI9Jkk4qQ5uzfYrMAx7rW98F/Pz4nZKsBda21R8m2TEFYztZnAk8Nd2DOB7k46unewg6lP9tjvlwjkUvf/twG070IBn0r1MvKlStB9YPfzgnnyQjVbV4uschjed/m1PnRL+0tQs4p299PvDENI1Fkk5KJ3qQ/CWwMMl5SV4FrAQ2TfOYJOmkckJf2qqqA0n+BfAFYAbwiaraPs3DOtl4yVDHK//bnCKpetEtBUmSJu1Ev7QlSZpmBokkqRODREfFV9PoeJXkE0n2JnlwusdysjBI9JL5ahod524Blk/3IE4mBomOhq+m0XGrqr4MPD3d4ziZGCQ6GoNeTTNvmsYiaZoZJDoak3o1jaSTg0Gio+GraST9DYNER8NX00j6GwaJXrKqOgCMvZrmYWCjr6bR8SLJp4F7gTcl2ZVkzXSP6eXOV6RIkjrxjESS1IlBIknqxCCRJHVikEiSOjFIJEmdnNAzJErDkOQMYEtbfSNwENjX1pe094sdbd8/rKrXdRyidFzx8V9pAkl+F/hhVX38GPV3TIMkyYyqOnis+pOOhpe2pElIsizJ15Nsa/NdvLrVv5PkzLa8OMmX2vLrknyy7f/NJL/W19e6JN9I8tUkZ7faFUkebPUvt9qMJB/v6+M3+/7mh5J8BbgiybuT3Jvka0k+k+R1bb9FSf48yf1JvpBkbqt/Kcl1Se5L8ldJ/t7U/Uvq5cggkY7sFHpzXPyjqnoLvUvCVx6hzX8Anq2qt1TVW4F7Wv21wFer6m3Al4F/1uofAi5t9fe22lrgPODtrY9P9fX/XFW9C/jfwO8Al1TVzwEjwG8neSXwX4HLq2oR8AlgXV/7mVW1BPgg8OFJ/0tIA3iPRDqyGcCjVfVXbX0DcBXwhxO0uYTeO8gAqKpn2uKPgc+35fuBX27LfwHckmQj8Nm+Pv64vZKGquqfY+OO9r2U3uRif5EE4FW014MAFwGbW30GsLuv/djfuB9YMMFxSEdkkEhH9qMJth3gp2f2p/TVw+BX679QP70xeZD2v8Gq+kCSnwd+FXggycUT9NE/pgCbq+p9/RuTvAXYXlXvOEz758ePQTpaXtqSjuwUYEGS89v6rwN/3pa/Ayxqy7/W1+aL9F5sCUCS2RP9gSQ/U1Vbq+pDwFP0XtP/ReADSWa2fU4f0PSrwDvHxpbkNUl+FtgBzEnyjlZ/ZZILJ3m80ktikEhH9hzwG8BnkmwDfgL8cdv2EeCPkvxfev/vfszvAbPHbqADv3SEv/H77ab6g/TunXwD+BNgJ/DN1sc/Ht+oqvYB/wT4dJJv0guWN7dHlC8HrmttHwB+4SUfuTQJPv4rSerEMxJJUicGiSSpE4NEktSJQSJJ6sQgkSR1YpBIkjoxSCRJnfx/nrF2BDuOGxMAAAAASUVORK5CYII=\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'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7e8876d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Ips']=df['ScreenResolution'].apply(lambda x : 1 if 'IPS' in x else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "62427573",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Ips', ylabel='Price'>"
      ]
     },
     "execution_count": 31,
     "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'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b27bb085",
   "metadata": {},
   "outputs": [],
   "source": [
    " new = df['ScreenResolution'].str.split('x',n=1,expand=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "cb781d11",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['X_res'] = new[0]\n",
    "df['Y_res'] = new[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "729298d8",
   "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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <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>14.0</td>\n",
       "      <td>IPS Panel Full HD / Touchscreen 1920x1080</td>\n",
       "      <td>Intel Core i7 6500U 2.5GHz</td>\n",
       "      <td>4</td>\n",
       "      <td>128GB SSD</td>\n",
       "      <td>Intel HD Graphics 520</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.80</td>\n",
       "      <td>33992.6400</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>IPS Panel Full HD / Touchscreen 1920</td>\n",
       "      <td>1080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1299</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>2 in 1 Convertible</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Quad HD+ / Touchscreen 3200x1800</td>\n",
       "      <td>Intel Core i7 6500U 2.5GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>Intel HD Graphics 520</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.30</td>\n",
       "      <td>79866.7200</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>IPS Panel Quad HD+ / Touchscreen 3200</td>\n",
       "      <td>1800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1300</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>Intel Celeron Dual Core N3050 1.6GHz</td>\n",
       "      <td>2</td>\n",
       "      <td>64GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.50</td>\n",
       "      <td>12201.1200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1366</td>\n",
       "      <td>768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1301</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>Intel Core i7 6500U 2.5GHz</td>\n",
       "      <td>6</td>\n",
       "      <td>1TB HDD</td>\n",
       "      <td>AMD Radeon R5 M330</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.19</td>\n",
       "      <td>40705.9200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1366</td>\n",
       "      <td>768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1302</th>\n",
       "      <td>Asus</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>Intel Celeron Dual Core N3050 1.6GHz</td>\n",
       "      <td>4</td>\n",
       "      <td>500GB HDD</td>\n",
       "      <td>Intel HD Graphics</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.20</td>\n",
       "      <td>19660.3200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1366</td>\n",
       "      <td>768</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1303 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company            TypeName  Inches  \\\n",
       "0      Apple           Ultrabook    13.3   \n",
       "1      Apple           Ultrabook    13.3   \n",
       "2         HP            Notebook    15.6   \n",
       "3      Apple           Ultrabook    15.4   \n",
       "4      Apple           Ultrabook    13.3   \n",
       "...      ...                 ...     ...   \n",
       "1298  Lenovo  2 in 1 Convertible    14.0   \n",
       "1299  Lenovo  2 in 1 Convertible    13.3   \n",
       "1300  Lenovo            Notebook    14.0   \n",
       "1301      HP            Notebook    15.6   \n",
       "1302    Asus            Notebook    15.6   \n",
       "\n",
       "                                ScreenResolution  \\\n",
       "0             IPS Panel Retina Display 2560x1600   \n",
       "1                                       1440x900   \n",
       "2                              Full HD 1920x1080   \n",
       "3             IPS Panel Retina Display 2880x1800   \n",
       "4             IPS Panel Retina Display 2560x1600   \n",
       "...                                          ...   \n",
       "1298   IPS Panel Full HD / Touchscreen 1920x1080   \n",
       "1299  IPS Panel Quad HD+ / Touchscreen 3200x1800   \n",
       "1300                                    1366x768   \n",
       "1301                                    1366x768   \n",
       "1302                                    1366x768   \n",
       "\n",
       "                                       Cpu  Ram               Memory  \\\n",
       "0                     Intel Core i5 2.3GHz    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",
       "1298            Intel Core i7 6500U 2.5GHz    4            128GB SSD   \n",
       "1299            Intel Core i7 6500U 2.5GHz   16            512GB SSD   \n",
       "1300  Intel Celeron Dual Core N3050 1.6GHz    2   64GB Flash Storage   \n",
       "1301            Intel Core i7 6500U 2.5GHz    6              1TB HDD   \n",
       "1302  Intel Celeron Dual Core N3050 1.6GHz    4            500GB HDD   \n",
       "\n",
       "                               Gpu       OpSys  Weight        Price  \\\n",
       "0     Intel Iris Plus Graphics 640       macOS    1.37   71378.6832   \n",
       "1           Intel HD Graphics 6000       macOS    1.34   47895.5232   \n",
       "2            Intel HD Graphics 620       No OS    1.86   30636.0000   \n",
       "3               AMD Radeon Pro 455       macOS    1.83  135195.3360   \n",
       "4     Intel Iris Plus Graphics 650       macOS    1.37   96095.8080   \n",
       "...                            ...         ...     ...          ...   \n",
       "1298         Intel HD Graphics 520  Windows 10    1.80   33992.6400   \n",
       "1299         Intel HD Graphics 520  Windows 10    1.30   79866.7200   \n",
       "1300             Intel HD Graphics  Windows 10    1.50   12201.1200   \n",
       "1301            AMD Radeon R5 M330  Windows 10    2.19   40705.9200   \n",
       "1302             Intel HD Graphics  Windows 10    2.20   19660.3200   \n",
       "\n",
       "      Touchscreen  Ips                                  X_res Y_res  \n",
       "0               0    1          IPS Panel Retina Display 2560  1600  \n",
       "1               0    0                                   1440   900  \n",
       "2               0    0                           Full HD 1920  1080  \n",
       "3               0    1          IPS Panel Retina Display 2880  1800  \n",
       "4               0    1          IPS Panel Retina Display 2560  1600  \n",
       "...           ...  ...                                    ...   ...  \n",
       "1298            1    1   IPS Panel Full HD / Touchscreen 1920  1080  \n",
       "1299            1    1  IPS Panel Quad HD+ / Touchscreen 3200  1800  \n",
       "1300            0    0                                   1366   768  \n",
       "1301            0    0                                   1366   768  \n",
       "1302            0    0                                   1366   768  \n",
       "\n",
       "[1303 rows x 15 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3a92379b",
   "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": 36,
   "id": "a8a468ac",
   "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>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>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>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": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "15f1857a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# the datatype is object so we have to change\n",
    "df['X_res'] = df['X_res'].astype('int')\n",
    "df['Y_res'] = df['Y_res'].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "1319ca36",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1303 entries, 0 to 1302\n",
      "Data columns (total 15 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Company           1303 non-null   object \n",
      " 1   TypeName          1303 non-null   object \n",
      " 2   Inches            1303 non-null   float64\n",
      " 3   ScreenResolution  1303 non-null   object \n",
      " 4   Cpu               1303 non-null   object \n",
      " 5   Ram               1303 non-null   int32  \n",
      " 6   Memory            1303 non-null   object \n",
      " 7   Gpu               1303 non-null   object \n",
      " 8   OpSys             1303 non-null   object \n",
      " 9   Weight            1303 non-null   float32\n",
      " 10  Price             1303 non-null   float64\n",
      " 11  Touchscreen       1303 non-null   int64  \n",
      " 12  Ips               1303 non-null   int64  \n",
      " 13  X_res             1303 non-null   int32  \n",
      " 14  Y_res             1303 non-null   int32  \n",
      "dtypes: float32(1), float64(2), int32(3), int64(2), object(7)\n",
      "memory usage: 132.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "aadfdabc",
   "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": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()['Price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d3a3ed6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create new column from the X_res ,Y_res and Inches which is calles ppi\n",
    "df['ppi']=(((df['X_res']**2) + (df['Y_res']**2))**0.5/df['Inches']).astype('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "7dbc2f42",
   "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": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()['Price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "6a413bf1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['ScreenResolution'],inplace =True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "be0022b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['Inches','X_res','Y_res'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "89d050c7",
   "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": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Cpu'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ff8e0798",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define new five columns form the Cpu columns \n",
    "# 1) core i5\n",
    "# 2) core i3 and core i7 and one for amd processors \n",
    "df['Cpu Name']=df['Cpu'].apply(lambda x:\" \".join(x.split()[0:3]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "9ff04a94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0            Intel Core i5\n",
       "1            Intel Core i5\n",
       "2            Intel Core i5\n",
       "3            Intel Core i7\n",
       "4            Intel Core i5\n",
       "               ...        \n",
       "1298         Intel Core i7\n",
       "1299         Intel Core i7\n",
       "1300    Intel Celeron Dual\n",
       "1301         Intel Core i7\n",
       "1302    Intel Celeron Dual\n",
       "Name: Cpu Name, Length: 1303, dtype: object"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Cpu Name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "e3330d53",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fetch_processor(text):\n",
    "    if text =='Intel Core i5' or text==\"Intel Core i7\" or text==\"Intel Core i3\":\n",
    "        return text\n",
    "    else:\n",
    "        \n",
    "        if(text.split()[0]== 'Intel'):\n",
    "            return 'Other Intel Processor'\n",
    "        else:\n",
    "            return 'AMD Processor'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "ac39371f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Cpu Brand'] = df['Cpu Name'].apply(fetch_processor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "6af49a34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Cpu</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>ppi</th>\n",
       "      <th>Cpu Name</th>\n",
       "      <th>Cpu Brand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1207</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>AMD E-Series 9000 2.2GHz</td>\n",
       "      <td>4</td>\n",
       "      <td>500GB HDD</td>\n",
       "      <td>AMD Radeon R2 Graphics</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.20</td>\n",
       "      <td>15930.7200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.454670</td>\n",
       "      <td>AMD E-Series 9000</td>\n",
       "      <td>AMD Processor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1267</th>\n",
       "      <td>Dell</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>Intel Core i7 7500U 2.7GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>1TB HDD</td>\n",
       "      <td>AMD Radeon R5 M430</td>\n",
       "      <td>Linux</td>\n",
       "      <td>2.30</td>\n",
       "      <td>42943.1472</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.454670</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>Intel Core i7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>Acer</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>AMD A8-Series 7410 2.2GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>AMD Radeon R5</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.23</td>\n",
       "      <td>24988.3200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>AMD A8-Series 7410</td>\n",
       "      <td>AMD Processor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>988</th>\n",
       "      <td>Dell</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>Intel Core i7 7500U 2.7GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>1TB HDD</td>\n",
       "      <td>AMD Radeon R7 M445</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.36</td>\n",
       "      <td>39906.7200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.454670</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>Intel Core i7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>524</th>\n",
       "      <td>Dell</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>Intel Core i5 8250U 1.6GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB SSD +  1TB HDD</td>\n",
       "      <td>AMD Radeon 530</td>\n",
       "      <td>Linux</td>\n",
       "      <td>2.80</td>\n",
       "      <td>47365.9200</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>127.335675</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",
       "1207  Lenovo  Notebook    AMD E-Series 9000 2.2GHz    4             500GB HDD   \n",
       "1267    Dell  Notebook  Intel Core i7 7500U 2.7GHz    8               1TB HDD   \n",
       "313     Acer  Notebook   AMD A8-Series 7410 2.2GHz    8             256GB SSD   \n",
       "988     Dell  Notebook  Intel Core i7 7500U 2.7GHz    8               1TB HDD   \n",
       "524     Dell  Notebook  Intel Core i5 8250U 1.6GHz    8  128GB SSD +  1TB HDD   \n",
       "\n",
       "                         Gpu       OpSys  Weight       Price  Touchscreen  \\\n",
       "1207  AMD Radeon R2 Graphics  Windows 10    2.20  15930.7200            0   \n",
       "1267      AMD Radeon R5 M430       Linux    2.30  42943.1472            0   \n",
       "313            AMD Radeon R5  Windows 10    2.23  24988.3200            0   \n",
       "988       AMD Radeon R7 M445  Windows 10    2.36  39906.7200            0   \n",
       "524           AMD Radeon 530       Linux    2.80  47365.9200            0   \n",
       "\n",
       "      Ips         ppi            Cpu Name      Cpu Brand  \n",
       "1207    0  100.454670   AMD E-Series 9000  AMD Processor  \n",
       "1267    0  100.454670       Intel Core i7  Intel Core i7  \n",
       "313     0  141.211998  AMD A8-Series 7410  AMD Processor  \n",
       "988     0  100.454670       Intel Core i7  Intel Core i7  \n",
       "524     1  127.335675       Intel Core i5  Intel Core i5  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "bbba6677",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAFXCAYAAABZbA7IAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAbnUlEQVR4nO3df7RndV3v8efL4YfEj4AYCAEdtNGCSrQRMcpVUEGZYhnesfBylRa3e0lJb7cLlYXVJMuC5ZUbFpXeKX/QmLokaRmsCStbXXH44Q9+XSZAGEEYUQO5BM7wvn/sfTjfOed75pz5cc7+zt7Px1pnfb/fz3d/z3nPZ33mdfb57M/eO1WFJKlfntF1AZKk3c9wl6QeMtwlqYcMd0nqIcNdknrIcJekHtqr6wIADjvssFqxYkXXZUjSHuWGG274alUtH/feRIT7ihUr2LBhQ9dlSNIeJcmX5nrPaRlJ6iHDXZJ6yHCXpB4y3CWphwx3Seohw12Seshwl6QeMtwlqYcm4iSm3WHFBVd3XQIA91z8iq5LkCT33CWpjwx3Seohw12Seshwl6QeMtwlqYcMd0nqoQWFe5J7knwhyc1JNrRthya5Nsmd7eMhI9tfmGRjkjuSnLZYxUuSxtuRPfcfraoTqmpV+/oCYH1VrQTWt69JchywGjgeOB24PMmy3VizJGkeuzItcwawtn2+Fnj1SPuVVfVEVd0NbARO3IWfI0naQQsN9wKuSXJDknPbtiOq6gGA9vHwtv0o4L6Rz25q2yRJS2Shlx84uaruT3I4cG2S27ezbca01ayNml8S5wI8+9nPXmAZkqSFWNCee1Xd3z4+BHyMZprlwSRHArSPD7WbbwKOGfn40cD9Y77nFVW1qqpWLV8+9ubdkqSdNG+4J9k/yYFTz4GfAL4IXAWc3W52NvDx9vlVwOok+yY5FlgJXL+7C5ckzW0h0zJHAB9LMrX9B6vqk0k+C6xLcg5wL3AmQFXdkmQdcCuwBTivqrYuSvWSpLHmDfequgt44Zj2h4FT5/jMGmDNLlcnSdopnqEqST1kuEtSDxnuktRDhrsk9ZDhLkk9ZLhLUg8Z7pLUQ4a7JPWQ4S5JPWS4S1IPGe6S1EOGuyT1kOEuST1kuEtSDxnuktRDhrsk9ZDhLkk9ZLhLUg8Z7pLUQ4a7JPWQ4S5JPWS4S1IPGe6S1EOGuyT1kOEuST20V9cFaPdbccHVXZcAwD0Xv6LrEqTBcs9dknrIcJekHjLcJamHDHdJ6qEFh3uSZUluSvKJ9vWhSa5Ncmf7eMjIthcm2ZjkjiSnLUbhkqS57cie+/nAbSOvLwDWV9VKYH37miTHAauB44HTgcuTLNs95UqSFmJB4Z7kaOAVwJ+NNJ8BrG2frwVePdJ+ZVU9UVV3AxuBE3dLtZKkBVnonvu7gF8DnhppO6KqHgBoHw9v248C7hvZblPbJklaIvOGe5KfBh6qqhsW+D0zpq3GfN9zk2xIsmHz5s0L/NaSpIVYyJ77ycCrktwDXAmckuT9wINJjgRoHx9qt98EHDPy+aOB+2d+06q6oqpWVdWq5cuX78I/QZI007zhXlUXVtXRVbWC5kDp31fVWcBVwNntZmcDH2+fXwWsTrJvkmOBlcD1u71ySdKcduXaMhcD65KcA9wLnAlQVbckWQfcCmwBzquqrbtcqSRpwXYo3KvqU8Cn2ucPA6fOsd0aYM0u1iZJ2kmeoSpJPWS4S1IPGe6S1EOGuyT1kOEuST1kuEtSDxnuktRDhrsk9ZDhLkk9ZLhLUg8Z7pLUQ4a7JPWQ4S5JPWS4S1IPGe6S1EOGuyT1kOEuST1kuEtSDxnuktRDhrsk9ZDhLkk9ZLhLUg8Z7pLUQ4a7JPWQ4S5JPWS4S1IPGe6S1EOGuyT1kOEuST1kuEtSDxnuktRD84Z7kmcmuT7J55LckuTtbfuhSa5Ncmf7eMjIZy5MsjHJHUlOW8x/gCRptoXsuT8BnFJVLwROAE5PchJwAbC+qlYC69vXJDkOWA0cD5wOXJ5k2SLULkmaw7zhXo1vti/3br8KOANY27avBV7dPj8DuLKqnqiqu4GNwIm7s2hJ0vYtaM49ybIkNwMPAddW1WeAI6rqAYD28fB286OA+0Y+vqltm/k9z02yIcmGzZs378I/QZI004LCvaq2VtUJwNHAiUm+dzubZ9y3GPM9r6iqVVW1avny5QsqVpK0MDu0WqaqvgF8imYu/cEkRwK0jw+1m20Cjhn52NHA/btaqCRp4RayWmZ5koPb5/sBPwbcDlwFnN1udjbw8fb5VcDqJPsmORZYCVy/m+uWJG3HXgvY5khgbbvi5RnAuqr6RJJ/AdYlOQe4FzgToKpuSbIOuBXYApxXVVsXp3xJ0jjzhntVfR540Zj2h4FT5/jMGmDNLlcnSdopnqEqST1kuEtSDxnuktRDhrsk9ZDhLkk9ZLhLUg8Z7pLUQ4a7JPWQ4S5JPWS4S1IPGe6S1EOGuyT1kOEuST1kuEtSDxnuktRDhrsk9ZDhLkk9ZLhLUg8Z7pLUQ4a7JPWQ4S5JPWS4S1IPGe6S1EOGuyT1kOEuST1kuEtSDxnuktRDhrsk9ZDhLkk9ZLhLUg/NG+5JjklyXZLbktyS5Py2/dAk1ya5s308ZOQzFybZmOSOJKct5j9AkjTbQvbctwD/raq+BzgJOC/JccAFwPqqWgmsb1/TvrcaOB44Hbg8ybLFKF6SNN684V5VD1TVje3zR4HbgKOAM4C17WZrgVe3z88ArqyqJ6rqbmAjcOJurluStB07NOeeZAXwIuAzwBFV9QA0vwCAw9vNjgLuG/nYprZNkrREFhzuSQ4APgL8SlU9sr1Nx7TVmO93bpINSTZs3rx5oWVIkhZgQeGeZG+aYP9AVX20bX4wyZHt+0cCD7Xtm4BjRj5+NHD/zO9ZVVdU1aqqWrV8+fKdrV+SNMZCVssE+HPgtqq6dOStq4Cz2+dnAx8faV+dZN8kxwIrget3X8mSpPnstYBtTgZeD3whyc1t268DFwPrkpwD3AucCVBVtyRZB9xKs9LmvKraursLlyTNbd5wr6pPM34eHeDUOT6zBlizC3VJknaBZ6hKUg8Z7pLUQ4a7JPWQ4S5JPWS4S1IPGe6S1EOGuyT1kOEuST1kuEtSDxnuktRDhrsk9ZDhLkk9ZLhLUg8t5JK/0h5rxQVXd10C91z8iq5L0AC55y5JPWS4S1IPGe6S1EOGuyT1kOEuST1kuEtSD7kUUhoIl4UOi3vuktRDhrsk9ZDhLkk9ZLhLUg8Z7pLUQ4a7JPWQ4S5JPWS4S1IPGe6S1EPzhnuS9yZ5KMkXR9oOTXJtkjvbx0NG3rswycYkdyQ5bbEKlyTNbSF77v8bOH1G2wXA+qpaCaxvX5PkOGA1cHz7mcuTLNtt1UqSFmTecK+qfwS+NqP5DGBt+3wt8OqR9iur6omquhvYCJy4e0qVJC3Uzs65H1FVDwC0j4e37UcB941st6ltkyQtod19QDVj2mrshsm5STYk2bB58+bdXIYkDdvOhvuDSY4EaB8fats3AceMbHc0cP+4b1BVV1TVqqpatXz58p0sQ5I0zs6G+1XA2e3zs4GPj7SvTrJvkmOBlcD1u1aiJGlHzXuzjiQfAn4EOCzJJuC3gYuBdUnOAe4FzgSoqluSrANuBbYA51XV1kWqXZI0h3nDvapeN8dbp86x/Rpgza4UJUnaNZ6hKkk9ZLhLUg8Z7pLUQ4a7JPWQ4S5JPWS4S1IPGe6S1EOGuyT1kOEuST007xmqktQ3Ky64uusSuOfiVyzq93fPXZJ6yHCXpB4y3CWphwx3Seohw12Seshwl6QeMtwlqYcMd0nqIcNdknrIcJekHjLcJamHDHdJ6iHDXZJ6yHCXpB4y3CWphwx3Seohw12Seshwl6QeMtwlqYcMd0nqIcNdknpo0cI9yelJ7kiyMckFi/VzJEmzLUq4J1kG/BHwk8BxwOuSHLcYP0uSNNti7bmfCGysqruq6kngSuCMRfpZkqQZUlW7/5smPwecXlW/2L5+PfDSqvrlkW3OBc5tX74AuGO3F7LjDgO+2nURE8K+mGZfTLMvpk1CXzynqpaPe2OvRfqBGdO2zW+RqroCuGKRfv5OSbKhqlZ1XccksC+m2RfT7Itpk94XizUtswk4ZuT10cD9i/SzJEkzLFa4fxZYmeTYJPsAq4GrFulnSZJmWJRpmarakuSXgb8DlgHvrapbFuNn7WYTNU3UMftimn0xzb6YNtF9sSgHVCVJ3fIMVUnqIcNdknrIcJdGJFmW5A+6rkOTJckzkry26zp2xCDDPcmlSU7uuo5JkOSwGa/PSvLuJOcmGXe+Qq9V1VbgB4b4bx+VxmuTnNk+P7UdF/81yeByo6qeAn553g0nyCAPqCbZDHwJWA78FfChqrqp26q6keTGqnpx+/w3gR8GPgj8NLCpqt7SZX1dSHIJsBL4MPDYVHtVfbSzopZYksuBw4F9gEeAfYG/AX4KeLCqzu+wvE4keRvwOE1mjI6Lr3VW1HYMNdxvqqoXJVlJswZ/Nc2SzQ/RBP3/7bTAJTTVF+3zG4EfrqrHkuwN3FhV39dthUsvyfvGNFdVvXHJi+lIki9U1fe14+ArwJFV9WSSvYCbBjou7h7TXFX13CUvZgEW6/IDk64AqupO4HeB303y/cDrgL8FvqvD2pbafkleRDNFt6yqHgOoqm8l2dptad2oqjd0XcME2AJPj4PPthcAnDqHZajj4tiua9gRQw33WfOpVfV54PPAhUtfTqe+AlzaPv9akiOr6oEk30H7H3xokhwNXAacTLMj8Gng/Kra1GlhS+srSQ6oqm9W1elTjUm+E3iyw7o60/4V81+Al7dNnwL+pKq+1VlR2zHUaZkDquqbXdcxydpr8u9bVf+v61qWWpJraY47/GXbdBbwC1X1491VNRmS7A/sX1UPdV3LUkvyZ8DewNq26fXA1qmr306aoYb7d1fV7UlePO79qrpxqWvqin0xW5Kbq+qE+dr6zHExW5LPVdUL52ubFEOdlnkrzbXkLxnzXgGnLG05nbIvZvtqkrNoDrBDcyzm4Q7r6YLjYratSZ5XVf8KkOS5wMQefxjknru0PUmeDfwv4GVt0z/TzLl/qbuq1LUkpwLvA+6iOW73HOANVXVdp4XNwXCXpAVKsi/NneMC3F5VT3Rc0pwGd6aZNJ8k70xyUJK9k6xPMjVNowFLciawT7uy7pXAh+Y6JjEJDHdptp+oqkdoz9IFng/8925L0gR4W1U9muSHgNNoVs28p+Oa5jTocG+vmXFWkt9qXz87yYld19UF+2Ibe7ePP0VzxvJEnl6+FBwX25g6ePoK4D1V9XGayzNMpEGHO3A5zUGz17WvHwX+qLtyOmVfTPubJLcDq4D1SZYD/95xTV1xXEz7cpI/AV4L/G07/z6xGTqxhS2Rl1bVebT/cavq60zwb+JFZl+0quoCmkBb1Z59+BhwRrdVdcZxMe21NLcOPb2qvgEcygRP1w093L/VnolZAO0e2lPdltQZ+6LVHjjbUlVb2ytlvh94VsdldcVxMe1I4OqqujPJjwBnAtd3WtF2DD3c3w18DDg8yRqaa4j8frcldca+mLZHHThbZI6LaR+hOZHpu4A/B46luUzFRBrsOvf2hgMnAV8DTqVZt7q+qm7rtLAO2BfbGrkk9DuAL1TVB0cvjTwUjottTd37IMmvAY9X1WWTPC4GG+4ASf6lql42/5b9Z19MS/IJ4MvAjwE/QHODhusn9Roii8lxMS3JZ4B3Ab8BvLKq7k7yxar63m4rG2/o0zLXJHlNMuxbqrXsi2l71IGzRea4mPYGmgPta9pgP5bmeMxEGvqe+6PA/jTrV6eWulVVHdRdVd2wL7bVzrevrKr3tQcRD6iqcXfi6TXHxbaS7Ac8u6ru6LqW+Qw63KVxkvw2zRr3F1TV85M8C/hwVXlT9QFL8krgD2kuQXBskhOA36mqV3Vb2XhDveTv05K8ipE7q1TVJ7qsp0v2xdN+BngRcCNAVd2f5MBuS+qO4+JpFwEn0tyBiaq6uZ2amUiDnnNPcjFwPnBr+3V+2zY49sU2nqzmT9qptd37d1xPZxwX29hSVf82o21ipz4GPS2T5PPACVX1VPt6Gc2d3b+/28qWnn0xLcmvAiuBHwfeAbwR+GBVXdZpYR1wXExL8ufAeuAC4DXAm4G9q+qXOi1sDoPec28dPPL827sqYkIcPPJ8sH1RVX8I/DXNSSsvAH5riME+4uCR54MdF8CbgOOBJ2hOXvo34Fe6LGh7hj7n/g7gpiTX0Zyg8XLgwm5L6ox90WrnUf+pqq5tX++XZEVV3dNtZZ1wXLTam8X/Rvs18QY9LQOQ5EjgJTQD9zNV9ZWOS+qMfdFIsgH4wap6sn29D/DPVfWSbivrhuOikeRa4Mz23AeSHAJcWVWndVrYHAYZ7klOAw6sqr+e0f4LwENTe2xDYF/MluTmqjphRtvE3uV+MTguZht3qYFJvvzAUOfc3w78w5j29cDvLHEtXbMvZtvcLv8DIMkZwFc7rKcLjovZnkpz83QAkjyHCV4tM9Q592+rqs0zG6vqKwNc9mZfzPZLwAeSTN2U4j7g9R3W0wXHxWy/AXw6ydQvvZcD53ZYz3YNNdyfmWSvqtoy2phkb2C/jmrqin0xQ1X9K3BSkgNopi4f7bqmDjguZqiqT6a5IfZJbdNbqmpi/6Ib6rTMR4E/Hd0DaZ//cfvekNgXMyT59iSX0pyJeF2SS5IMbQmg42K8HwR+pP06abtbdmyo4f6bwIPAl5LckOQG4B5gc/vekNgXs72X5l6hr22/HgHe12lFS89xMcMcZ+u+o9uq5jbI1TJT2iu8fVf7cmNVPd5lPV2yL6bNsVpmVtsQOC6m7Wln6w51zh2AdqB+oes6JoF9sY3Hk/xQVX0aIMnJNDfsGBzHxSwH09yZCib8bN1Bh7s0h18C/mJknv3rwNkd1qPJ8PvsQWfrGu7SiPZP7bOq6oVJDgKoqkc6Lksda+8n+xTNQdSps3X/xySfrTvIOfd2OdOcqurGpaqla/bFbEn+vqpO6bqOLjkuZkvyj1X18vm3nAxDDffrtvN2Dek/tn0xW5JLaC75+2Hgsan2qhrMEkDHxWxJ3kZz7OWv2HZcfG3OD3VokOEubU+Sccseq6reuOTFaGIkGXcP3aqq5y55MQsw6HBP8m3AW2lueHtukpU0980c3G3E7ItGezPs59As+/tGx+V0znGx5xrqSUxT3gc8SXPWGcAm4Pe6K6dTg++LJL8I3AJcBtw+evGwAXNcJC9N8rkk30zyL0m+p+uaFmLo4f68qnon8C14ek1vui2pM/ZFc1ed46vqZTRhNrHL3JaQ4wL+CPhV4DuAS4F3dVrNAg093J9sz8CbuhHy82huoTVE9kVzY+zNAFV1F7Bvx/VMAscFPKOqrq2qJ6rqw8DyrgtaiKGvc78I+CRwTJIPACcDb+i0ou5chH1xdJJ3z/W6qt7cQU1duwjHxcFJfnau15O6imrQB1QBknwHzYkJAf7PJF/Cc7ENvS+SbPcs1Kpau1S1TBLHxdjVU1MmdhXVoMM9yfqqOnW+tiGwLzSO42LPNchpmSTPBL4NOKy9ye3UAaKDgGd1VlgH7AuN47jY8w0y3IH/TLMy4lnADUwP3EdojowPiX2hcRwXe7ihT8u8qaou67qOSWBfaBzHxZ5r0OEOkOQHgRWM/BVTVX/RWUEdGnpfJLmM7dzNfqCrZQY/LuDpg8o/D3x323Qb8KGqeri7qrZvqNMyACT5S+B5wM3A1ra5gEENXLAvWhu6LmDSOC6gPSP174G/A26imaJ6CfDrSU6pqtu7rG8ug95zT3IbcFwNuRNa9sVsSfavqsfm37K/HBeQ5K+BdVW1bkb7a4Cfr6rXdFPZ9g39DNUvAt/ZdRETwr5oJXlZkltp/vQmyQuTXN5xWV1xXMD3zQx2gKr6CPC9HdSzIIOelgEOA25Ncj0jp1RX1RAvGGVfTHsXcBpwFUBVfS7JHnOTht3McTFy7fYdfK9TQw/3i7ouYIJc1HUBk6Sq7ku2uT7W1rm27bmLui5gAhye5K1j2sMEX2dm0OFeVf/QdQ2Twr7Yxn3tCpFKsg/wZtopmqFxXADwp8CBc7z3Z0tZyI4Y5AHVJI8yfslbaK4VcdASl9QZ+2K2JIcB/xP4MZp+uAZ486TeTm0xOC72fIMMd2l7kpxcVf88X5uGYcaVQmeZ1PMfDHdphiQ3VtWL52vTMCR5kmbV0DrgfmbcrGRSrxY66Dl3aVSSqTswLZ9xAO0gYFk3VWkCHAmcCfwHYAvwV8BHqurrnVY1j6Gvc5dG7QMcQLPTc+DI1yPAz3VYlzpUVQ9X1R9X1Y8C/wk4GLglyes7LWweTstIMyR5TlV9qes6NFmSvBh4HfDjNFfKvKSqbu22qrkZ7tIMSZ5Pc0PkFWx7saxTuqpJ3UnyduCnaZbDXgl8sqq2dFvV/Ax3aYYknwP+mGbv7OmTl6rqhs6KUmeSPAXcBTzeNk2F5tSy0O/vpLB5eEBVmm1LVb2n6yI0MY7tuoCd4Z67NEOSi4CHgI+x7fVUBnMSk+aX5GSaq0Ke13Ut4xju0gxJ7h7TXFX13CUvRhMlyQk0N+14LXA38NFJvVOV0zLSDFW1R/4ZrsXRHmBfTbNS5mGade5pl0ZOLPfcpVaSn93e+1X10aWqRZOjPaD6T8A5VbWxbbtr0v+Sc89dmvbK7bxXgOE+TK+h2XO/LsknaZZDZvsf6Z577pK0AEn2B15NMz1zCrAW+FhVXdNlXXMx3CVpByU5lPZ6M5N6cpvhLkk95IXDpBFJntHehUnaoxnu0oiqegq4pOs6pF1luEuzXZPkNZlxh2xpT+KcuzRDe//Q/WkuGvY43jdUeyDDXZJ6yGkZaYY0zkrytvb1MUlO7LouaUe45y7NkOQ9wFPAKVX1PUkOAa6pqpd0XJq0YF5+QJrtpVX14iQ3AVTV15Ps03VR0o5wWkaa7VtJltHecSfJcpo9eWmPYbhLs72b5kYdhydZA3wa+P1uS5J2jHPu0hhJvhs4lWYZ5Pqquq3jkqQdYrhLY7TTMkcwclyqqu7triJpx3hAVZohyZuA3wYepDmRKTTz7xN5l3tpHPfcpRmSbKRZMfNw17VIO8sDqtJs9wH/1nUR0q5wz11qJXlr+/R44AXA1cATU+9X1aVd1CXtDOfcpWkHto/3tl/7tF/QrnmX9hTuuUszJDmzqj48X5s0yQx3aYYkN1bVi+drkyaZ0zJSK8lPAj8FHJXk3SNvHQRs6aYqaecY7tK0+4EbgFe1j1MeBd7SSUXSTnJaRpohyQHACpqDqP9aVf/ebUXSjnOdu9RKsleSdwJ3A2uB9wP3JXlnkr27rU7aMYa7NO0PgEOB51bVD1TVi4DnAQcDf9hlYdKOclpGaiW5E3h+zfhP0V5E7PaqWtlNZdKOc89dmlYzg71t3IonMWkPY7hL025N8h9nNiY5C7i9g3qknea0jNRKchTwUeBxmqWQBbwE2A/4mar6coflSTvEcJdmSHIKzcXDAtxSVes7LknaYYa7JPWQc+6S1EOGuyT1kOEuST1kuEtSDxnuktRD/x8FYoMxU9+anQAAAABJRU5ErkJggg==\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": 51,
   "id": "3d4b46a6",
   "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": 52,
   "id": "53ca891f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['Cpu','Cpu Name'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "ebdd3e1e",
   "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": [
    "# now lets talk about ram\n",
    "df['Ram'].value_counts().plot(kind='bar')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "fa7efc5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Ram', ylabel='Price'>"
      ]
     },
     "execution_count": 54,
     "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": [
    "# it has linear relation with price column which is very important\n",
    "sns.barplot(x=df['Ram'], y =df['Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "b2299436",
   "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": [
    "# now lets take look at memory column\n",
    "df['Memory'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "cc63ed13",
   "metadata": {},
   "outputs": [],
   "source": [
    "# we are going to make the 4 different columns out of this\n",
    "# 1) HDD\n",
    "# 2) SSD\n",
    "# 3) Flash drive\n",
    "# 4) Hybridabs\n",
    "\n",
    "df['Memory'] = df['Memory'].astype(str).replace('\\.0','')\n",
    "df['Memory'] = df['Memory'].str.replace('GB','')\n",
    "df['Memory'] = df['Memory'].str.replace('TB','000')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "5114093c",
   "metadata": {},
   "outputs": [],
   "source": [
    "new= df['Memory'].str.split(\"+\",n=1,expand =True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "6d70ced3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                 128 SSD\n",
       "1       128 Flash Storage\n",
       "2                 256 SSD\n",
       "3                 512 SSD\n",
       "4                 256 SSD\n",
       "              ...        \n",
       "1298              128 SSD\n",
       "1299              512 SSD\n",
       "1300     64 Flash Storage\n",
       "1301             1000 HDD\n",
       "1302              500 HDD\n",
       "Name: 0, Length: 1303, dtype: object"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "c4991beb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['first'] = new[0]\n",
    "df['first']=df['first'].str.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "bc5620df",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['second'] = new[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "27871951",
   "metadata": {},
   "outputs": [],
   "source": [
    "# make columns with if contains HDD,SSD makes 1 and 0\n",
    "df['HDD1'] = df['first'].apply(lambda x : 1 if \"HDD\" in x else 0)\n",
    "df['SSD1'] = df['first'].apply(lambda x : 1 if \"SSD\" in x else 0)\n",
    "df['HYBRID1'] = df['first'].apply(lambda x : 1 if \"Hybrid\" in x else 0)\n",
    "df['FLASH1'] = df['first'].apply(lambda x : 1 if \"Flash Storage\" in x else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "91af11f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                 128 SSD\n",
       "1       128 Flash Storage\n",
       "2                 256 SSD\n",
       "3                 512 SSD\n",
       "4                 256 SSD\n",
       "              ...        \n",
       "1298              128 SSD\n",
       "1299              512 SSD\n",
       "1300     64 Flash Storage\n",
       "1301             1000 HDD\n",
       "1302              500 HDD\n",
       "Name: first, Length: 1303, dtype: object"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['first'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "5f61fc39",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\AppData\\Local\\Temp\\ipykernel_1320\\1997310448.py:1: FutureWarning: The default value of regex will change from True to False in a future version.\n",
      "  df['first'] = df['first'].str.replace(r'\\D','')\n"
     ]
    }
   ],
   "source": [
    "df['first'] = df['first'].str.replace(r'\\D','')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "d85f3766",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        128\n",
       "1        128\n",
       "2        256\n",
       "3        512\n",
       "4        256\n",
       "        ... \n",
       "1298     128\n",
       "1299     512\n",
       "1300      64\n",
       "1301    1000\n",
       "1302     500\n",
       "Name: first, Length: 1303, dtype: object"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['first']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "012cc21f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['second'] = new[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "0c5f1ab7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       None\n",
       "1       None\n",
       "2       None\n",
       "3       None\n",
       "4       None\n",
       "        ... \n",
       "1298    None\n",
       "1299    None\n",
       "1300    None\n",
       "1301    None\n",
       "1302    None\n",
       "Name: second, Length: 1303, dtype: object"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['second']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "663936d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# same goes  for the second column in new ehich contains both ssd and hdd\n",
    "# first we have to add - to all none values\n",
    "\n",
    "df['second'].fillna(\"0\",inplace=True)\n",
    "\n",
    "df['HDD2'] = df['second'].apply(lambda x : 1 if \"HDD\" in x else 0)\n",
    "df['SSD2'] = df['second'].apply(lambda x : 1 if \"SSD\" in x else 0)\n",
    "df['HYBRID2'] = df['second'].apply(lambda x : 1 if \"Hybrid\" in x else 0)\n",
    "df['FLASH2'] = df['second'].apply(lambda x : 1 if \"Flash Storage\" in x else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "d46c24a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\AppData\\Local\\Temp\\ipykernel_1320\\3014104400.py:1: FutureWarning: The default value of regex will change from True to False in a future version.\n",
      "  df['second'] = df['second'].str.replace(r'\\D', '')\n"
     ]
    }
   ],
   "source": [
    "df['second'] = df['second'].str.replace(r'\\D', '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "b596cfb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert both in int\n",
    "df['first'] = df['first'].astype(int)\n",
    "df['second'] = df['second'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "682edbbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['HDD'] = (df['first']*df['HDD1'] + df[\"second\"]*df['HDD2'])\n",
    "df['SSD'] = (df['first']*df['SSD1'] + df[\"second\"]*df['SSD2'])\n",
    "df['Hybrid'] = (df['first']*df['HYBRID1'] + df[\"second\"]*df['HYBRID2'])\n",
    "df['Flash Storage'] = (df['first']*df['FLASH1'] + df[\"second\"]*df['FLASH2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "8bb98e74",
   "metadata": {},
   "outputs": [],
   "source": [
    "# NOW DROP THE ALL OTHER COLUMNS FROM THE DATA\n",
    "df =df.drop(columns=['first','second','HDD1','HDD2','SSD1','SSD2','FLASH1','FLASH2','HYBRID1','HYBRID2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "0a19da8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>ppi</th>\n",
       "      <th>Cpu Brand</th>\n",
       "      <th>HDD</th>\n",
       "      <th>SSD</th>\n",
       "      <th>Hybrid</th>\n",
       "      <th>Flash Storage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>587</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>12</td>\n",
       "      <td>256 SSD</td>\n",
       "      <td>Intel HD Graphics 520</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.40</td>\n",
       "      <td>89137.44</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>157.350512</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1174</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>1000 HDD</td>\n",
       "      <td>Nvidia GeForce 940MX</td>\n",
       "      <td>No OS</td>\n",
       "      <td>2.20</td>\n",
       "      <td>37775.52</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>256 SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.20</td>\n",
       "      <td>31808.16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.454670</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>761</th>\n",
       "      <td>Dell</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>16</td>\n",
       "      <td>256 SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.18</td>\n",
       "      <td>99047.52</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>176.232574</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103</th>\n",
       "      <td>HP</td>\n",
       "      <td>Workstation</td>\n",
       "      <td>8</td>\n",
       "      <td>1000 HDD</td>\n",
       "      <td>AMD FirePro W6150M</td>\n",
       "      <td>Windows 7</td>\n",
       "      <td>3.00</td>\n",
       "      <td>154458.72</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>127.335675</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company     TypeName  Ram    Memory                    Gpu       OpSys  \\\n",
       "587   Lenovo    Ultrabook   12   256 SSD  Intel HD Graphics 520  Windows 10   \n",
       "1174  Lenovo     Notebook    8  1000 HDD   Nvidia GeForce 940MX       No OS   \n",
       "209   Lenovo     Notebook    8   256 SSD  Intel HD Graphics 620  Windows 10   \n",
       "761     Dell    Ultrabook   16   256 SSD  Intel HD Graphics 620  Windows 10   \n",
       "1103      HP  Workstation    8  1000 HDD     AMD FirePro W6150M   Windows 7   \n",
       "\n",
       "      Weight      Price  Touchscreen  Ips         ppi      Cpu Brand   HDD  \\\n",
       "587     1.40   89137.44            0    1  157.350512  Intel Core i7     0   \n",
       "1174    2.20   37775.52            0    0  141.211998  Intel Core i7  1000   \n",
       "209     2.20   31808.16            0    0  100.454670  Intel Core i5     0   \n",
       "761     1.18   99047.52            0    0  176.232574  Intel Core i7     0   \n",
       "1103    3.00  154458.72            0    1  127.335675  Intel Core i7  1000   \n",
       "\n",
       "      SSD  Hybrid  Flash Storage  \n",
       "587   256       0              0  \n",
       "1174    0       0              0  \n",
       "209   256       0              0  \n",
       "761   256       0              0  \n",
       "1103    0       0              0  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "8f7ad92e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['Memory'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "e9a8d882",
   "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             -0.093701\n",
       "SSD              0.670799\n",
       "Hybrid           0.008747\n",
       "Flash Storage   -0.040511\n",
       "Name: Price, dtype: float64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()['Price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "80317066",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['Hybrid','Flash Storage'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "af8d000b",
   "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": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now for GPU column\n",
    "df['Gpu'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "4237299e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# extract brans name\n",
    "df['Gpu Brand']=df['Gpu'].apply(lambda x: x.split()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "3e28c54c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "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": [
    "df['Gpu Brand'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "7890154d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Gpu Brand', ylabel='Price'>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZIAAAEGCAYAAABPdROvAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAc8ElEQVR4nO3de7QeVZ3m8e9DIjcV5BJCOqENSpQJqGCy6Ix4j0rsURN7QhtmaOJMeqUXC9vLaEfo6aW2PekBZmy6UWEm3SgJXkhMq2TpgGIQbRw68YBoDJchEgkJCTlcjRciCc/8UfvImzfvuYQ6dd4c8nzWOquqflW73l0vJ/zOrl21t2wTERHxbB3U7QpERMTolkQSERG1JJFEREQtSSQREVFLEklERNQyttsVGGnHHnusJ0+e3O1qRESMKrfddtvDtsd12nfAJZLJkyfT09PT7WpERIwqku7vb19ubUVERC1JJBERUUsSSURE1JJEEhERtSSRRERELUkkERFRSxJJRETUkkQSERG1HHAvJEbEs7do0SK2bdvG8ccfz6WXXtrt6sR+IokkIoZs27ZtbNmypdvViP1Mbm1FREQtSSQREVFLEklERNSSRBIREbUkkURERC2NJhJJH5K0XtJPJX1Z0qGSjpZ0o6R7y/KoluMvkrRB0j2SzmqJT5O0ruy7XJJK/BBJy0t8jaTJTV5PRETsrbFEImki8H5guu1TgTHAPOBCYLXtKcDqso2kqWX/KcAs4ApJY8rprgQWAlPKz6wSXwA8Zvsk4DLgkqauJyIiOmv61tZY4DBJY4HDgQeB2cDSsn8pMKeszwautb3T9kZgA3CGpAnAEbZvtW1gWVuZvnOtBGb2tVYiImJkNJZIbG8B/iewCdgKPGH728B421vLMVuB40qRicADLafYXGITy3p7fI8ytncBTwDHtNdF0kJJPZJ6ent7h+cCIyICaPDN9tL3MRs4EXgc+Iqkcwcq0iHmAeIDldkzYC8BlgBMnz59r/0Ro8WZnz6zq59/8OMHcxAH8cDjD3S9Lj/48x909fPjGU3e2noLsNF2r+2ngK8CrwEeKrerKMvt5fjNwAkt5SdR3QrbXNbb43uUKbfPjgQebeRqIiKioyYTySZghqTDS7/FTOAuYBUwvxwzH7iurK8C5pUnsU6k6lRfW25/7ZA0o5znvLYyfeeaC9xU+lEiImKENHZry/YaSSuB24FdwI+obi+9AFghaQFVsjm7HL9e0grgznL8BbZ3l9OdD1wNHAZcX34ArgKukbSBqiUyr6nriYiIzhod/df2x4GPt4V3UrVOOh2/GFjcId4DnNoh/iQlEUVERHfkzfaIiKgliSQiImpJIomIiFqSSCIiopYkkoiIqCWJJCIiakkiiYiIWhp9jyQinlt8uHmap/HhGUAinpFEEhFD9tSZT3W7CrEfyq2tiIioJYkkIiJqSSKJiIhakkgiIqKWJJKIiKgliSQiImppLJFIermkO1p+fiHpg5KOlnSjpHvL8qiWMhdJ2iDpHklntcSnSVpX9l1eZkqkzKa4vMTXSJrc1PVERERnjSUS2/fYPs32acA04NfA14ALgdW2pwCryzaSplLNcHgKMAu4QtKYcrorgYVU0+9OKfsBFgCP2T4JuAy4pKnriYiIzkbq1tZM4Ge27wdmA0tLfCkwp6zPBq61vdP2RmADcIakCcARtm8t87EvayvTd66VwMy+1kpERIyMkUok84Avl/XxtrcClOVxJT4ReKClzOYSm1jW2+N7lLG9C3gCOKaB+kdERD8aTySSDgbeBXxlsEM7xDxAfKAy7XVYKKlHUk9vb+8g1YiIiH0xEi2StwO3236obD9UbldRlttLfDNwQku5ScCDJT6pQ3yPMpLGAkcCj7ZXwPYS29NtTx83btywXFRERFRGIpGcwzO3tQBWAfPL+nzgupb4vPIk1olUnepry+2vHZJmlP6P89rK9J1rLnBT6UeJiIgR0ujov5IOB94K/FlL+GJghaQFwCbgbADb6yWtAO4EdgEX2N5dypwPXA0cBlxffgCuAq6RtIGqJTKvyeuJiIi9NZpIbP+ats5v249QPcXV6fjFwOIO8R7g1A7xJymJKCIiuiNvtkdERC1JJBERUUsSSURE1JJEEhERtSSRRERELUkkERFRSxJJRETUkkQSERG1JJFEREQtSSQREVFLEklERNSSRBIREbUkkURERC1JJBERUUsSSURE1JJEEhERtTSaSCS9SNJKSXdLukvSv5V0tKQbJd1blke1HH+RpA2S7pF0Vkt8mqR1Zd/lZcpdyrS8y0t8jaTJTV5PRETsrekWyT8AN9g+GXgVcBdwIbDa9hRgddlG0lSqqXJPAWYBV0gaU85zJbCQah73KWU/wALgMdsnAZcBlzR8PRER0aaxRCLpCOD1VPOqY/u3th8HZgNLy2FLgTllfTZwre2dtjcCG4AzJE0AjrB9q20Dy9rK9J1rJTCzr7USEREjo8kWyUuAXuDzkn4k6Z8kPR8Yb3srQFkeV46fCDzQUn5ziU0s6+3xPcrY3gU8Qdsc8QCSFkrqkdTT29s7XNcXERE0m0jGAq8GrrR9OvArym2sfnRqSXiA+EBl9gzYS2xPtz193LhxA9c6IiL2SZOJZDOw2faasr2SKrE8VG5XUZbbW44/oaX8JODBEp/UIb5HGUljgSOBR4f9SiIiol+NJRLb24AHJL28hGYCdwKrgPklNh+4rqyvAuaVJ7FOpOpUX1tuf+2QNKP0f5zXVqbvXHOBm0o/SkREjJCxDZ//z4EvSjoYuA/4T1TJa4WkBcAm4GwA2+slraBKNruAC2zvLuc5H7gaOAy4vvxA1ZF/jaQNVC2ReQ1fT0REtGk0kdi+A5jeYdfMfo5fDCzuEO8BTu0Qf5KSiCIiojvyZntERNSSRBIREbUkkURERC1Nd7bHs7Bo0SK2bdvG8ccfz6WXXtrt6kREDCiJZD+0bds2tmzZ0u1qREQMSW5tRURELUkkERFRSxJJRETUkkQSERG1JJFEREQteWqrzbS/WNbtKvDCh3cwBtj08I6u1ue2/3Fe1z47IkaPtEgiIqKWJJKIiKgliSQiImpJIomIiFqSSCIiopZGE4mkn0taJ+kOST0ldrSkGyXdW5ZHtRx/kaQNku6RdFZLfFo5zwZJl5cpdynT8i4v8TWSJjd5PRERsbeRaJG8yfZptvtmSrwQWG17CrC6bCNpKtVUuacAs4ArJI0pZa4EFlLN4z6l7AdYADxm+yTgMuCSEbiexj198PPZfcgRPH3w87tdlYiIQXXjPZLZwBvL+lLgZuCjJX6t7Z3AxjIP+xmSfg4cYftWAEnLgDlU87bPBj5RzrUS+Iwk2fZIXEhTfjXlbd2uQkTEkDXdIjHwbUm3SVpYYuNtbwUoy+NKfCLwQEvZzSU2say3x/coY3sX8ARwTHslJC2U1COpp7e3d1guLCIiKk23SM60/aCk44AbJd09wLHqEPMA8YHK7BmwlwBLAKZPnz6qWysREfubRlskth8sy+3A14AzgIckTQAoy+3l8M3ACS3FJwEPlvikDvE9ykgaCxwJPNrEtURERGeNJRJJz5f0wr514G3AT4FVwPxy2HzgurK+CphXnsQ6kapTfW25/bVD0ozytNZ5bWX6zjUXuGm0949ERIw2Td7aGg98rTypOxb4ku0bJP0QWCFpAbAJOBvA9npJK4A7gV3ABbZ3l3OdD1wNHEbVyX59iV8FXFM65h+leuorIiJGUGOJxPZ9wKs6xB8BZvZTZjGwuEO8Bzi1Q/xJSiKKiIjuGNKtLUkvk7Ra0k/L9isl/VWzVYuIiNFgqH0k/whcBDwFYPsn5DZSREQw9ERyuO21bbFdw12ZiIgYfYaaSB6W9FLKOxqS5gJbG6tVRESMGkPtbL+A6oW+kyVtATYC5zZWq4iIGDWGlEjKE1hvKe+DHGR7R7PVioiI0WKoT239raQX2f6V7R2SjpL035quXMRwWLRoEeeddx6LFi3qdlUinpOG2kfydtuP923Yfgz4w0ZqFDHMtm3bxpYtW9i2bVu3qxLxnDTURDJG0iF9G5IOAw4Z4PiIiDhADLWz/QvAakmfp3py6z9TzSUSEREHuKF2tl8qaR3V0CYC/sb2txqtWUREjApDHmvLdutgiREREcAgiUTSLbZfK2kHe04YJcC2j2i0dhERsd8bMJHYfm1ZvnBkqhMREaPNoE9tSTqob9TfiIiIdoMmEttPAz+W9PsjUJ+IiBhlhtrZPgFYL2kt8Ku+oO13DVZQ0higB9hi+x2SjgaWA5OBnwN/XF5wRNJFwAJgN/D+vifDJE3jmRkS/w/wAdsu77YsA6YBjwDvsf3zIV5TjIBNn3xFt6vArkePBsay69H7u1qf3//Yuq59dkSThppI/rrGZ3wAuAvo65i/EFht+2JJF5btj0qaSjXHySnA7wHfkfSyMt3ulcBC4F+pEsksqifIFgCP2T5J0jzgEuA9NeoaERH7aMBbW5IOlfRBqulsTwZ+YPt7fT+DnVzSJODfAf/UEp7NMy8zLgXmtMSvtb3T9kZgA3CGpAnAEbZvtW2qFsicDudaCcxUmSQ+IiJGxmB9JEuB6cA64O3Ap/bx/H8PLAKebomNt70VoCyPK/GJwAMtx20usYllvT2+Rxnbu4AngGPaKyFpoaQeST29vb37eAkRETGQwRLJVNvn2v7fwFzgdUM9saR3ANtt3zbUIh1iHiA+UJk9A/YS29NtTx83btwQqxMREUMxWB/JU30rtnft412jM4F3SfpD4FDgCElfAB6SNMH21nLbans5fjNwQkv5ScCDJT6pQ7y1zGZJY4EjgUf3pZIREVHPYC2SV0n6RfnZAbyyb13SLwYqaPsi25NsT6bqRL/J9rnAKmB+OWw+cF1ZXwXMk3SIpBOBKcDacvtrh6QZpf/jvLYyfeeaWz5jrxZJREQ0Z7A328c08JkXAyskLQA2UXXkY3u9pBXAncAu4ILyxBbA+Tzz+G/rmF9XAddI2kDVEpnXQH0jImIAQx60sQ7bNwM3l/VHqEYR7nTcYmBxh3gPcGqH+JOURBTRn2MPfRrYVZYRMdxGJJFEdNNHXvl4t6sQ8Zw21BkSIyIiOkoiiYiIWpJIIiKiliSSiIioJYkkIiJqyVNbEXHA+t7r39DtKuw33vD9Qcfh7VdaJBERUUsSSURE1JJEEhERtSSRRERELUkkERFRSxJJRETUkkQSERG1JJFEREQtjSUSSYdKWivpx5LWS/rrEj9a0o2S7i3Lo1rKXCRpg6R7JJ3VEp8maV3Zd3mZKZEym+LyEl8jaXJT1xMREZ012SLZCbzZ9quA04BZkmYAFwKrbU8BVpdtJE2lmuHwFGAWcIWkvhkarwQWUk2/O6XsB1gAPGb7JOAy4JIGryciIjpoLJG48suy+bzyY2A2sLTElwJzyvps4FrbO21vBDYAZ0iaABxh+9YyH/uytjJ951oJzOxrrURExMhotI9E0hhJdwDbgRttrwHG294KUJbHlcMnAg+0FN9cYhPLent8jzK2dwFPAMc0cjEREdFRo4nE9m7bpwGTqFoXe8273qJTS8IDxAcqs+eJpYWSeiT19Pb2DlLriIjYFyPy1Jbtx4Gbqfo2Hiq3qyjL7eWwzcAJLcUmAQ+W+KQO8T3KSBoLHAk82uHzl9iebnv6uHHjhueiIiICaPaprXGSXlTWDwPeAtwNrALml8PmA9eV9VXAvPIk1olUnepry+2vHZJmlP6P89rK9J1rLnBT6UeJiIgR0uR8JBOApeXJq4OAFba/IelWYIWkBcAm4GwA2+slrQDuBHYBF9jeXc51PnA1cBhwffkBuAq4RtIGqpbIvAavJyIiOmgskdj+CXB6h/gjwMx+yiwGFneI9wB79a/YfpKSiCIiojvyZntERNSSRBIREbUkkURERC1JJBERUUsSSURE1JJEEhERtSSRRERELUkkERFRSxJJRETUkkQSERG1JJFEREQtSSQREVFLEklERNSSRBIREbUkkURERC1JJBERUUuTU+2eIOm7ku6StF7SB0r8aEk3Srq3LI9qKXORpA2S7pF0Vkt8mqR1Zd/lZcpdyrS8y0t8jaTJTV1PRER01mSLZBfwYdv/BpgBXCBpKnAhsNr2FGB12absmwecAswCrijT9AJcCSykmsd9StkPsAB4zPZJwGXAJQ1eT0REdNBYIrG91fbtZX0HcBcwEZgNLC2HLQXmlPXZwLW2d9reCGwAzpA0ATjC9q22DSxrK9N3rpXAzL7WSkREjIwR6SMpt5xOB9YA421vhSrZAMeVwyYCD7QU21xiE8t6e3yPMrZ3AU8Ax3T4/IWSeiT19Pb2DtNVRUQEjEAikfQC4J+BD9r+xUCHdoh5gPhAZfYM2EtsT7c9fdy4cYNVOSIi9kGjiUTS86iSyBdtf7WEHyq3qyjL7SW+GTihpfgk4MESn9QhvkcZSWOBI4FHh/9KIiKiP00+tSXgKuAu23/XsmsVML+szweua4nPK09inUjVqb623P7aIWlGOed5bWX6zjUXuKn0o0RExAgZ2+C5zwT+BFgn6Y4S+0vgYmCFpAXAJuBsANvrJa0A7qR64usC27tLufOBq4HDgOvLD1SJ6hpJG6haIvMavJ6IiOigsURi+xY692EAzOynzGJgcYd4D3Bqh/iTlEQUERHdkTfbIyKiliSSiIioJYkkIiJqSSKJiIhakkgiIqKWJJKIiKgliSQiImpJIomIiFqSSCIiopYkkoiIqCWJJCIiakkiiYiIWpJIIiKiliSSiIioJYkkIiJqSSKJiIhampxq93OStkv6aUvsaEk3Srq3LI9q2XeRpA2S7pF0Vkt8mqR1Zd/lZbpdypS8y0t8jaTJTV1LRET0r8kWydXArLbYhcBq21OA1WUbSVOppsk9pZS5QtKYUuZKYCHVHO5TWs65AHjM9knAZcAljV1JRET0q7FEYvv7VPOot5oNLC3rS4E5LfFrbe+0vRHYAJwhaQJwhO1bbRtY1lam71wrgZl9rZWIiBg5I91HMt72VoCyPK7EJwIPtBy3ucQmlvX2+B5lbO8CngCO6fShkhZK6pHU09vbO0yXEhERsP90tndqSXiA+EBl9g7aS2xPtz193Lhxz7KKERHRyUgnkofK7SrKcnuJbwZOaDluEvBgiU/qEN+jjKSxwJHsfSstIiIaNtKJZBUwv6zPB65ric8rT2KdSNWpvrbc/tohaUbp/zivrUzfueYCN5V+lIiIGEFjmzqxpC8DbwSOlbQZ+DhwMbBC0gJgE3A2gO31klYAdwK7gAts7y6nOp/qCbDDgOvLD8BVwDWSNlC1ROY1dS0REdG/xhKJ7XP62TWzn+MXA4s7xHuAUzvEn6QkooiI6J79pbM9IiJGqSSSiIioJYkkIiJqSSKJiIhakkgiIqKWJJKIiKgliSQiImpJIomIiFqSSCIiopYkkoiIqCWJJCIiakkiiYiIWpJIIiKiliSSiIioJYkkIiJqSSKJiIhaRn0ikTRL0j2SNki6sNv1iYg40IzqRCJpDPBZ4O3AVOAcSVO7W6uIiAPLqE4kwBnABtv32f4tcC0wu8t1iog4oMh2t+vwrEmaC8yy/adl+0+AP7D9vrbjFgILy+bLgXtGtKLPzrHAw92uxHNIvs/hk+9yeI2W7/PFtsd12jF2pGsyzNQhtldmtL0EWNJ8dYaPpB7b07tdj+eKfJ/DJ9/l8HoufJ+j/dbWZuCElu1JwINdqktExAFptCeSHwJTJJ0o6WBgHrCqy3WKiDigjOpbW7Z3SXof8C1gDPA52+u7XK3hMqpuxY0C+T6HT77L4TXqv89R3dkeERHdN9pvbUVERJclkURERC1JJCNM0i+HcMwHJR0+hONuljSqHxusS9K7JVnSyWV7ctn+m5ZjjpX0lKTPlO1PSNoi6Q5J90r66oE4IkL5nj7Vsv0RSZ8YpMwnJb2lQ/yNkr5R1t+V4Yoq/fx+/qb87t0paZmk55V9byzHLmgpf3qJfaRb1zAUSST7pw8CgyaSAOAc4BaqJ/b63Ae8o2X7bKD9IYzLbJ9mewqwHLhJUseXrZ7DdgJ/JOnYoRaw/THb3xnkmFW2L65du+eGTr+fP7N9GvAKqlcW/rhl3zrgPS3b84AfN1zH2pJIuqT89XGzpJWS7pb0RVXeD/we8F1J3y3Hvk3SrZJul/QVSS/obu33D+V7OBNYwJ7/UH8D3NXSWnsPsKK/89heDnwb+A8NVXV/tYvqiaEPtQYlHSnp55IOKtuHS3pA0vMkXV1GlOgbMPVuSbcAf9RS/r0trb93Sloj6UeSviNp/IhdXZcN8PsJgO3dwFpgYkt4E3CopPGSBMwCrh+B6taSRNJdp1O1PqYCLwHOtH051UuVb7L9pvLX4l8Bb7H9aqAH+C9dqu/+Zg5wg+3/Bzwq6dUt+64F5kmaBOxm8BdVbwdObqSW+7fPAv9R0pF9AdtPUP0V/IYSeifwLdtP9R0j6VDgH8u+1wHH93P+W4AZtk+n+m+yaNivYP81h/5/P/u+wz8Abmgrt5KqFf0aqt/Lnc1XtZ4kku5aa3uz7aeBO4DJHY6ZQZVofiDpDmA+8OKRquB+7hyq/zlRlue07LsBeGuJLR/CuToNt/OcZ/sXwDLg/W27lvPMLZZ57P0dngxstH2vq3cIvtDPR0wCviVpHfAXwCnDUvHRob/fz5eWf8uPAJts/6St3AqqRHIO8OURqGdto/qFxOeA1r80dtP5v4eAG22f02HfAUvSMcCbgVMlmeqFVANXANj+raTbgA9T/c/rnYOc8nSq1t6B6O+p/vL9fEtsFfDfJR0NTANu6lBuKC+hfRr4O9urJL0R+ESdio4Wg/x+/sz2aZImADdLepft343IYXubpKeo/hD6AFXLZL+WFsn+aQfwwrL+r8CZkk6C392vflnXarb/mAsss/1i25NtnwBspPoLuM+ngI/afmSgE0n698DbGCV//Q03249S/RW8oCX2S6r79/8AfKPcz291N3CipJeW7f7+0DkS2FLW5w9bpfd/g/5+2t4KXAhc1KH8x6h+d9u/9/1SEsn+aQlwvaTv2u4F3gt8WdJPqBLLgXgvv905wNfaYv8M/GXfhu31tpf2U/5DfY//AucCby7f9YHqU1TDmbdaTvXd7HVr0PaTVFMzfLN0tt/fz3k/AXxF0r8wOoZKHy6D/n4WXwcOl/S61qDt/2v7643VbphliJSIiKglLZKIiKgliSQiImpJIomIiFqSSCIiopYkkoiIqCWJJGIIythHX5J0n6Tbythn7x6mc7eORny3pCv7xrkabuWz9uuRZGP0SSKJGEQZPO/rwPdtv8T2NKphQyYNWHDfXFZGhJ1KNSrsG9oPkJSRKGK/lEQSMbg3A7+1/b/6Arbvt/1p+N1ot9dJukHSPZI+XuKTJf20r8xQ5vsADgYOBR4rZW6W9LeSvgd8oL/RdEtL43Pl+PvKKNJ9n/tfS72+A7x8WL6RiBb5CydicKdQjUU1kDOAU4FfAz+U9E327U3uD0k6l2pAzutt39Gy70W23wAg6Siq0XQt6U+pRtP9cDnuZOBNVMPr3CPpSuCVVK2n06n+vd8O3LYP9YoYVFokEftI0mcl/VjSD1vCN9p+xPZvgK8Cr93H0/bd2joOeL6k1vkrWocoGWg03W/a3mn7YWA7MJ5qiPev2f51Gel3FRHDLIkkYnDrgd/NJWH7AmAm0DqjYvtYQ6aaOKr139ihg31QmfPjBuD1LeFftax/GviM7VcAf9Z2zv5Gk844SNGoJJKIwd1ENWvd+S2x9qmQ3yrpaEmHUU1o9APgIeA4ScdIOoQ9p//tqHTsvwb4WT+H7Otout8H3i3pMEkvZPDh9CP2WfpIIgZR+iPmAJdJWgT0UrUSPtpy2C3ANcBJwJds9wBI+iSwhmoI8bsH+Ji+PpLnAT+hzKvSwSeoRtPdQjUS9ImD1P12ScupJk67H/iXgY6PeDYy+m9ETZLeC0y3/b5u1yWiG3JrKyIiakmLJCIiakmLJCIiakkiiYiIWpJIIiKiliSSiIioJYkkIiJq+f/l8ONcfS3hiwAAAABJRU5ErkJggg==\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'] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "ac274672",
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove 'ARM ' row it is not worth it\n",
    "df = df[df['Gpu Brand']!='ARM']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "064ddec7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['Gpu'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "7d8924d3",
   "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": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['OpSys'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "499a35d6",
   "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": 84,
   "id": "f9893a3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# reduce the number of columns\n",
    "def define_os(cat):\n",
    "    if cat == \"Windows 10\" or cat==\"Windows 7\" or cat == \"Windows 10 S\":\n",
    "        return \"Windows\"\n",
    "    elif cat==\"macOS\" or cat=='Mac OS X':\n",
    "        return \"Mac\"\n",
    "    else:\n",
    "        return \"Other/No OS/Linux\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "bfffcad5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['OS'] = df['OpSys'].apply(define_os)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "a28dcb15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Windows              1125\n",
       "Other/No OS/Linux     156\n",
       "Mac                    21\n",
       "Name: OS, dtype: int64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['OS'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "8b1e585a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['OpSys'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "d9d1f4cc",
   "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": 89,
   "id": "ab60caac",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\Umang\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Weight', ylabel='Density'>"
      ]
     },
     "execution_count": 89,
     "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": [
    "# weight columns\n",
    "# it was strong relation on sum of the point\n",
    "sns.distplot(df['Weight'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "c9dc2d58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Weight', ylabel='Price'>"
      ]
     },
     "execution_count": 90,
     "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": [
    "# it has a very lite linear relationship\n",
    "sns.scatterplot(x=df['Weight'],y=df['Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "b559e70a",
   "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           -0.094106\n",
       "SSD            0.670660\n",
       "Name: Price, dtype: float64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()['Price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "72556cda",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6UnntI8BnJF1EUSuY8K/A2hOd+cALpijjhLKD/zqKvpp5wFeA3wLXlp9xaPtDtu8CDgNOk3QtRdLZqhxm/SrgE+Wzc4E9l/P7joiohce727qwiOKP9gkbAne23bML8G1Jt1L83vyipJdNJ/6um8tsH9dyuFOH6xcBW3Y4/yDwxg7nZ7bsfxf4brn/ig7FLwXeU26tn7FJ2/HP6VDFsz0X2LvD+ee37N9N+mQiomHGe7d22RXAFpI2Be6g6Ct/3B/stjed2Jd0KvAj2z+YTqGZ8R8R0WDuUZKxvVTSURSjxmYAJ9u+XtIR5fWTJv2AFZQkExHRYL2cjGn7HOCctnMdk4vtw3pRZpJMRESDdTu6rKmSZCIiGmxsbLkXZmmUJJmIiAbrVZ9MXZJkIiIaLM1lERHRNz0cwlyLJJmIiAZLc1lERPTN2IC/TyZJZjlt+Ej9DaQHr71j3SEAsO5DS+sOga/udGzdIQDw5ms+WncIXLHd++sOAYANVlky9U19tvjhGXWH0DOpyURERN+kTyYiIvqm/raT6UmSiYhosNRkIiKib9InExERfTPW8V1jgyNJJiKiwcYHvFMmSSYiosHGU5OJiIh+cZJMRET0y3jdAUzT0CcZSQ/anll3HBERKyI1mYiI6Jv6F2+ansF+5dpykPR8SRdK+r6kGySdJGklSTMknSrpOknzJR1dd6wREROMutqaatRqMrsCWwO3AecCrwBuATawvS2ApLVqiy4ios2AL8I8OjWZ0uW2F9oeA04D9gIWAptJ+pyk/YH72x+SNEvSlZKu/OniBRWHHBGjbBx1tTXVqCWZ9mlNtv1HYAdgDnAk8JW/esiebXsX27vs+4Rn9D/KiIiSu9y6IWl/STdJWiDpmA7XXyvp2nL7paQdphv/yDWXSdqUornsIGC2pKcCD9v+nqT/BU6tM8CIiFZL1ZtaiqQZwBeA/YBFwBWSzrZ9Q8tttwDPs/1HSQcAs4HdplPuqCWZS4Hjge2AC4Hvl/unSJqo1f1LTbFFRPyVHq4qsyuwwPZCAEnfBg4EHk0ytn/Zcv9lwIbTLXTok0zbHJnFtg9qu2UesHOFIUVEdK2HkzE3AG5vOV7E5LWUNwM/nm6hQ59kIiIGWbejyyTNAma1nJpte3brLR0e61hRkvQCiiSzV3elL9vIJBnbcyg69yMiBka3I8fKhDJ7klsWARu1HG8I3Nl+k6TtKQZAHWD7nu4j7WzURpdFRAyUHo4uuwLYQtKmklYFDgbObr1B0sbAmcDrbf+mF/GPTE0mImIQLe3RFBjbSyUdBZwHzABOtn29pCPK6ycBxwJPAb6oYlTbUtu7TKfcJJmIiAbr5TvLbJ8DnNN27qSW/bcAb+lhkUkyERFNNujLyiTJREQ0WN4nExERfZMkExERfeM0l42WjfVQ3SFwyl1z6w4BgA/ttdHUN/XZDg/XHUHhiu3eX3cIPGf+CXWHAMDVW7y07hDYe9916w6hZwb9pWVJMhERDdbL0WV1SJKJiGiwjC6LiIi+Scd/RET0TZJMRET0zViayyIiol9Sk4mIiL7J6LKIiOib8QFPM0kypXK568W2v153LBERE9JcNiRal7uOiGiKwa7HDHGSkbQJcC7wK2An4DfAG4AbgNOBF5S3Hmp7gaTjgAdtf7L6aCMiOuvVS8vqMuyvX34mMNv29sD9wNvL8/fb3hX4PPDpmmKLiJjSOO5qa6phTzK3276k3P8GsFe5f1rL1z2m+hBJsyRdKenKHyy+pQ9hRkR05i63phr2JNP+s3eH81P+97E92/Yutnd52RM27VlwERFTGe9ya6phTzIbS5qoqRwCXFzuH9Ty9dLKo4qI6FKay5rtRuCNkq4F1gG+VJ5fTdKvgHcBR9cVXETEVMa63JpqaEeXlcZtH9F6QhLAF2x/pPW87eMqjCsioitNrqV0Y9iTTETEQBvsFDPEzWW2b7W9bYfzm9i+u46YIiKWVy87/iXtL+kmSQskHdPhuiR9trx+raSdpxv/0CaZiIhh4C7/NxVJM4AvAAcAWwOHSNq67bYDgC3KbRaP9WOvsCSZiIgG62FNZldgge2Fth8Gvg0c2HbPgcDXXbgMWEvS+tOJP0kmIqLBxnBXWxc2AG5vOV5Unlvee5ZLkkxERIN1O0+mdWWScpvV9lGdVkFrz07d3LNcMrosIqLBuu3Utz0bmD3JLYuAjVqONwTuXIF7lktqMhERDdarjn/gCmALSZtKWhU4GDi77Z6zgTeUo8x2B+6z/bvpxJ+aTEREg/VqXTLbSyUdBZwHzABOtn19+cLGiXdqnQO8GFgALAYOn265STLL6fxV1qg7BBbutdHUN1Xg8BufWHcI3PHIn+oOAYANVllSdwhcvcVL6w4BgFtv/mHdIbDG0/+u7hAAWNqDz+iyltLdZ9nnUCSS1nMntewbOLJnBZIkExHRaEs92HP+k2QiIhpssFNMkkxERKNlgcyIiOibXvbJ1CFJJiKiwZr81stuJMlERDTY2ICnmSSZiIgGG+wUkyQTEdFoHvAhzAO9rIykB9uOD5P0+XL/OEl3SJor6WZJZ7a+O0HSnPLlPddK+rWkz0taq+JvISJiUt0ukNlUA51kunCi7R1tbwGcDvxc0rot119re3tge2AJcFYdQUZELEsv34xZh2FPMo+yfTpwPnBoh2sPA/8EbCxph6pji4hYlh4ukFmLQe+TWUPS3JbjdfjrVUVbXQ1s1emC7TFJ88rr83oWYUTENIy5yfWUqQ16TeahsjlsR9s7AsdOcX+nF/JMeb31ZUBXPrhgReKMiFghaS4bLDsBN3a6IGkGsF2n67Zn297F9i67zHxGn0OMiHjMoDeXjUySkfRK4EXAaR2urQL8O3C77Wurji0iYlkGfXTZoPfJTOVoSa8D1gSuA/axfVfL9W9KWgKsBvwUOLCGGCMilmnQ58kMdJKxPbPt+FTg1HL/OOC4SZ59ft8Ci4jokSbXUrox0EkmImLYDfrosiSZiIgGG+x6TJJMRESjpbksIiL6JkkmIiL6JqPLIiKibwb9pWUjMxkzImIQ2e5qmy5J60j6SflqlJ9IWrvDPRtJukDSjZKul/SuqT43SSYiosEqnPF/DPCz8tUoPyuP2y0F3mv7WcDuwJGt7+nqJEkmIqLBqqrJUKx48rVy/2vAyzrE8jvbV5f7D1Cs9bjBZB+aPpnldPRrFtcdAod/Z626QwDg9HeuO/VNfXbJCWvWHQIAix+eUXcI7L1v/f89ANZ4+t/VHQIP3XlR3SH0TLe1FEmzgFktp2bbnr0cRa1n+3dQJBNJT5uivE0oFh3+1WT3JclERDRYtysslwll0qQi6afA33S49IHliUnSTOB7wLtt3z/ZvUkyEREN1stlZWzvu6xrkn4vaf2yFrM+8Idl3LcKRYL5pu0zpyozfTIREQ02bne19cDZwBvL/TcCZ7XfIEnAV4EbbX+qmw9NkomIaLAKX1p2PLCfpJuB/cpjJD1d0jnlPc8FXg/sI2luub14sg9Nc1lERIP1qJYyJdv3AC/scP5O4MXl/sVM/Rr7x0mSiYhosCa/WrkbSTIREQ1WVU2mX5JkIiIabNxjdYcwLUPT8S/pA+VaOteWnVG7SXqJpGskzZN0g6R/LO89TtId5X03SzpzqqURIiLqUOGyMn0xFDUZSXsALwF2tr1E0lOBNYHvA7vaXiRpNWCTlsdOtP3J8vmDgJ9L2s72XRWHHxGxTIO+1P+w1GTWB+62vQTA9t3AAxRJ9J7y3BLbN3V62PbpwPnAodWEGxHRnUGvyQxLkjkf2EjSbyR9UdLzbN9LMbnoNkmnSXqtpMm+36uBrSqJNiKiSxUukNkXQ5FkbD8IPJticbi7gNMlHWb7LRTjvi8H3gecPMnHLHPst6RZkq6UdOXJ827tXeAREVMY83hXW1MNRZ8MgO0xYA4wR9J8imURTrU9H5gv6b+BW4DDlvEROwFXLuOzH1147sH3v7y5fzJExNBpci2lG0NRk5H0TElbtJzaEfi9pOe3nbttGc+/EngRcFp/IoyIWDGD3iczLDWZmcDnJK1F8ea2BcC7gC9L+jLwEPBnHl+LOVrS6yhGoV0H7JORZRHRNINekxmKJGP7KmDPDpc6Ltxm+zjguD6GFBHRE5nxHxERfZOaTERE9E2TR451I0kmIqLB0lwWERF9k6X+IyKib1KTiYiIvknHf0RE9E2ayyIiom/GxzO6LCIi+mSw6zGgQW/vG0SSZpWLbo50DE2JowkxNCWOJsTQlDiaEMMwGIoFMgfQrLoDoBkxQDPiaEIM0Iw4mhADNCOOJsQw8JJkIiKib5JkIiKib5Jk6tGEdt4mxADNiKMJMUAz4mhCDNCMOJoQw8BLx39ERPRNajIREdE3STIREdE3STIREdE3mfEfI0fScylev/23FP8fEGDbm9UZV8QwSsd/RSStBbwB2ISW5G77nRWV/wnb/zzVuYpi2RL4ErCe7W0lbQ/8g+1/raj8XwNHA1cBYxPnbd9TRfltsbwLOAV4APgKsBNwjO3zKyj7dba/Iek9na7b/lS/Y2iLZztgq/LwRtvXVVz+U4BDW2MATqvj38UwSXNZdc6hSDDzKX65TWxV2a/DuQMqLL/VfwH/AjwCYPta4OAKy7/P9o9t/8H2PRNbheW3epPt+4EXAesChwPHV1T2muXXJy5jq4SkJ0uaA/yA4pf8a4GzJF0g6UkVxfAs4Drg2cBvgJuB5wDzJW012bMxuTSXVWd12x3/YuwnSW8D3g5sJunalktPBC6pOp7SE2xfLqn13NIKy79A0gnAmcCSiZO2r64whgkTP4QXA6fYnqe2H0y/2P5y+fUjVZQ3iY8BVwL72MUL7SWtRJFsPw68o6IY3mX7O60nJb2yjOGVFcQwlNJcVhFJRwMPAj/i8b/Y7u1zuU8G1gb+HTim5dID/S57kph+DBwFnGF7Z0mvAt5su5KalaQLOpy27X2qKL8tllOADYBNgR2AGcAc28+uMIbNgM8Au1Ms+nspcLTthRWVfwOwve2lbedXBubbflYFMdxk+5nLey2mlppMdR4GTgA+wGOrdxvoa2ez7fuA+4BDJM0A1qP47z5T0kzbv+1n+ctwJMVs6q0k3QHcAryuqsJtv6CqsrrwZmBHYKHtxWW/wOEVx/At4AvAy8vjg4HTgN0qKv/h9gQDYHuppCWdHuiDP6/gtZhCkkx13gM8w/bddRQu6SiKEVW/BybegmRg+6pjKf9C3lfSmsBKth+osnxJ6wH/Bjzd9gGStgb2sP3VKuMAsD0uaRPgdZIMXGz7+xWHIdv/3XL8jfLfS1VWl7QTjzUdPhoXsFpFMTxtGQMgRNFXFisozWUVkXQ2cLDtxTWVvwDYrQkjZST9G/Aftv9UHq8NvNf2Bysq/8cUI7o+YHuHslnmGtvbVVF+WyxfBJ5BUXMAOAj4X9tHVhjD8RS13dMo/vA4iOKX+xegkibdTs2Xj6qi5inpw1PEUHe/1cBKkqmIpO8D2wAX8Pg+maqGMF8A7NepWaJqkq6xvVPbuatt71xR+VfYfk5rHJLm2t6xivLbYrke2Nbl/xHLDu/5trepMIZbyt2JXwatNYrMH4ppSXNZdX5QbpVqaQJYCMyR9D88PslVOheiNEPSaraXlDGuQXXNIgB/Lvs+Jn6x707xl3wdbgI2Bm4rjzcCrl327X2xNcUIxL0ofiYXAV+y/ZeqAljGHJVvVTU4RdJbKQZc3FyO7vsqxYiy24A32r6mijiGUZJMRWx/raaiJ+Y7/LbcVi23On0D+Fk5ssrAm4Aqfz7vAc4GNpd0CUWb+6sqLL/VU4AbJV1eHj8HuLRsXsX2P1QQw9eA+4HPlseHAF8HXlNB2RNzVH4OnAdcQ1GTeg7wfyXtY/vXFYTxLuDUcv8QipF+m1FMjv0s8HcVxDCU0lxWEUlbUAwj3hpYfeL8qDZFSDoAeCHFL5TzbZ9XcfkrA88sy7/J9iNVlt8Sx/Mmu277FxXEMM/2DlOd62P53wW+s4w5Kofa7vscldbmUknfAn5l+zPlcWVNucMoNZnqnAJ8GDgReAHFMNVKJt0BSPohj7W5T7iPYhLcl6tsGgGw/WPgx1WWOUHSEyhqM39r+62StpD0TNs/qjqWKpJIF66RtLvtywAk7Ua1E3W3s/1XNUnb3ysHiVRhXNL6wB8p/vj5eMu1NSqKYShlWZnqrGH7ZxS1x9tsHwdUOflvIcVk0P8qt/sphjNvWR73naSLy68PSLq/ZXtA0v1VxFA6hWLe0h7l8SKgknXTJnT4GdT1s4BiPswvJd0q6VaKyZjPkzS/bZWIfmnCHJVjKf7guhU42/b18GhNs5JJqcMqNZnq/KUcOXRzOQfhDuBpFZa/k+29W45/KOlC23uXI5z6zvZe5dfK1sVahs1tHyTpkDKeh6paymVCA34GrfavufwmzFH5PcUfHQ/Y/qOkN1B0/P8emFVRDEMpNZnqvBt4AvBOikX4Xk+xKnNV1pW08cRBuf/U8vDhqoKQtJKkSlfX7eDhckTbxOiyzWkZcTdqypr1MrcKQvgvOi/QOZNiZeoqfBl4sEwwe1Osm/Z1iiTzmYpiGEqpyVTE9hXl7oPA4WXH80HAryoK4b3AxZL+l+IvxE2Bt5ez7isb2VXOcJ8naeOalrSBom/sXGAjSd8EngscVlMsI68hEx1ntAyXPgiYbft7wPckza0vrMGXJNNnKpYqP5JiEcSzgZ+Ux+8D5gHfrCIO2+eUI9y2okgyv27p7P90FTG0WB+4vhy2+2ibexXDdcsmy7WBV1AsCCmK1XdrWe4nQNJnJ7te0YTlGZJWLicrv5DHN5Hl9+Q05IfXf/9NMWLlUuAtwPsp5qm8zPbcfhdezjP4uaRXtF3aTBK2z+x3DB3U9pdrWZM6qhwu+z91xRGP0/pepY9Q1DSrdhrwC0l3Aw9RTEhF0jOob6LuUMg8mT6TNH9iTaxyFeS7gY2rWhRS0kdsf7ic+NjOtt9URRxlLKsDR1Cs1TUf+Gody9xI+hDFL5LTeXxNqpZXH8RjOi05VGHZu1PUss+3/efy3JbATNfzrqGhkCTTZ+0TuUZ5Ypek0ynehnkRxVs5b7P9rhriuKXD6azR1QCj/P+PYZUk02eSxnjsr2VRTOxaXO7bdlWvl619efu2Wt3KwOX5hRKtkmSGT4Yw95ntGbafVG5PtL1yy34lCaZ0KsXaUE8vj39DMay6So8u3VLnatCSjpS0Vsvx2pLeXlc8o651Yiqwfc0TU6PHUpMZEWrA8vYNqtX91fddZ19AxDDL6LLRUfvy9rZnVFneJFaSpJZ3uMyg/pWpI4ZSksyQk/RuisUO/wk4i2Lo8sTy9q+uMbQ6nQd8R9JJFEn3CIrJmRHRY2kuG3KSPgnsSTEJ89cUa6bNAU4f1QmI5YTMWcC+lK8aAL5ie6zWwCKGUJLMiJC0KrALRcLZo9z+ZHvrWgOrmaR1gA1tV/02yoiRkNFlo2MN4EnAk8vtTqpbN61RJM2R9KQywcwFTpFUx2uoI4ZeajJDTtJsYBvgAYqkchlwme0/1hpYjSZGkkl6C7BRuSLCtba3rzu2iGGTmszw2xhYDfh/FP0xi4A/1RlQA6xcvgXxNUDlb8OMGCUZXTbkbO9fvpBrG4r+mPcC20q6F7jUdh2LEdbtoxQjzC62fYWkzYCba44pYiiluWyESNqQ4t0pewIvAZ5ie61ag4qIoZYkM+QkvZMiqTyXYlmXSyheO3AJMN/2eI3h1ULSusBbgU1oqc1XuSJ1xKhIc9nw2wT4LnC07d/VHEtTnEWxEvRPgcyNieij1GRi5FS9ZlvEKMvoshhFP5L04rqDiBgFqcnEyJD0AMVaZQLWBJZQ9FNVugp0xChJkomIiL5Jc1mMHEkvl/TkluO1JL2sxpAihlZqMjFy8tKyiOqkJhOjqNO/+wznj+iDJJkYRVdK+pSkzSVtJulE4Kq6g4oYRkkyMYreATwMnA6cAfwFOLLWiCKGVPpkIiKib9IOHSNH0gUU82Uex/Y+NYQTMdSSZGIUva9lf3XglcDSmmKJGGppLosAJP3C9vPqjiNi2KQmEyNH0jothysBzwb+pqZwIoZakkyMoqt4bA2zpcAtwJtrjShiSKW5LCIi+iY1mRg5klYB3gbsXZ6aA3zZ9iO1BRUxpFKTiZEj6SvAKsDXylOvB8Zsv6W+qCKGU5JMjAxJK9teKmme7R3arv3VuYiYviwrE6Pk8vLrmKTNJ05K2gwYqyekiOGWPpkYJSq/vg+4QNLC8ngT4PBaIooYcmkui5EhaRHwqfJwDWAG8GeKWf8P2f7Usp6NiBWTmkyMkhnATB6r0VAeAzyx+nAihl9qMjEyJF1te+e644gYJen4j1GiqW+JiF5KTSZGhqR1bN9bdxwRoyRJJiIi+ibNZRER0TdJMhER0TdJMhER0TdJMhER0TdJMhER0Tf/H3YBeQwBILrhAAAAAElFTkSuQmCC\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": 93,
   "id": "ebcf3ded",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\Umang\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Price', ylabel='Density'>"
      ]
     },
     "execution_count": 93,
     "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": [
    "# we can see that we have target column which is right skewd so after apply \n",
    "sns.distplot(df['Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "7337ba32",
   "metadata": {},
   "outputs": [],
   "source": [
    "# what if we apply box-cox and yeo-jhonson \n",
    "from sklearn.preprocessing import PowerTransformer\n",
    "pt = PowerTransformer(method ='box-cox')\n",
    "\n",
    "x_price = pt.fit_transform(df['Price'].to_numpy().reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "71bee580",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\Umang\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "execution_count": 95,
     "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_price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "e1db940f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# we can also apply log transformation\n",
    "y = pd.DataFrame(x_price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "de971508",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = np.log(df['Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "8e7fc8b2",
   "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": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "20819efe",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(columns=['Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "62e22795",
   "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>Other/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  Other/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": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ff69895",
   "metadata": {},
   "source": [
    "### From here test has started"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "e9e230bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# so now we have X,y as well. Now perform train_test_split\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "3a2b6031",
   "metadata": {},
   "outputs": [],
   "source": [
    "# now we have to convert all the categorical columns to the numerical columns so we can train our models\n",
    "# here we have all nominal categorical data so we are going use OHE\n",
    "\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "ohe = OneHotEncoder(drop=\"first\")\n",
    "\n",
    "X_train_new =ohe.fit_transform(X_train[['Company','TypeName','Cpu Brand','Gpu Brand','OS']]).toarray()\n",
    "X_test_new =ohe.fit_transform(X_test[['Company','TypeName','Cpu Brand','Gpu Brand','OS']]).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "e43f32d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1041, 31)"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_new.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "2b0de35a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Windows              1125\n",
       "Other/No OS/Linux     156\n",
       "Mac                    21\n",
       "Name: OS, dtype: int64"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['OS'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "a80c9ec2",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_new1 = np.hstack((X_train[['Ram','Weight','Touchscreen','Ips','ppi','HDD','SSD']].values,X_train_new))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "ae6922fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1041, 38)"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_new1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "40735139",
   "metadata": {},
   "outputs": [],
   "source": [
    "df3 = pd.DataFrame(X_train_new1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b880007d",
   "metadata": {},
   "source": [
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "97d32e0b",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>1040</th>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>1041 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        0     1    2    3           4       5      6    7    8    9   ...  \\\n",
       "0      8.0  2.20  0.0  0.0  141.211998  1000.0    0.0  0.0  0.0  0.0  ...   \n",
       "1      8.0  2.50  0.0  1.0  141.211998  1000.0    0.0  0.0  0.0  0.0  ...   \n",
       "2     16.0  4.42  0.0  1.0  127.335675  1000.0  256.0  0.0  0.0  0.0  ...   \n",
       "3      4.0  2.04  0.0  0.0  141.211998   500.0    0.0  0.0  0.0  0.0  ...   \n",
       "4      8.0  2.04  0.0  0.0  100.454670  2000.0    0.0  0.0  0.0  0.0  ...   \n",
       "...    ...   ...  ...  ...         ...     ...    ...  ...  ...  ...  ...   \n",
       "1036   4.0  2.20  0.0  0.0  100.454670   500.0    0.0  0.0  0.0  0.0  ...   \n",
       "1037  16.0  1.63  0.0  0.0  141.211998     0.0  512.0  0.0  1.0  0.0  ...   \n",
       "1038   8.0  2.20  0.0  0.0  100.454670  1000.0    0.0  0.0  0.0  0.0  ...   \n",
       "1039   8.0  2.20  0.0  0.0  100.454670  2000.0    0.0  0.0  0.0  0.0  ...   \n",
       "1040   8.0  0.92  0.0  1.0  226.415547     0.0    0.0  1.0  0.0  0.0  ...   \n",
       "\n",
       "       28   29   30   31   32   33   34   35   36   37  \n",
       "0     0.0  0.0  0.0  0.0  1.0  0.0  0.0  1.0  0.0  1.0  \n",
       "1     0.0  0.0  0.0  1.0  0.0  0.0  0.0  1.0  1.0  0.0  \n",
       "2     0.0  0.0  0.0  0.0  1.0  0.0  0.0  1.0  0.0  1.0  \n",
       "3     0.0  0.0  1.0  0.0  0.0  0.0  1.0  0.0  0.0  1.0  \n",
       "4     0.0  0.0  0.0  0.0  0.0  1.0  1.0  0.0  0.0  1.0  \n",
       "...   ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  \n",
       "1036  0.0  0.0  1.0  0.0  0.0  0.0  0.0  1.0  0.0  1.0  \n",
       "1037  1.0  0.0  0.0  0.0  1.0  0.0  0.0  1.0  0.0  1.0  \n",
       "1038  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  \n",
       "1039  0.0  0.0  1.0  0.0  0.0  0.0  0.0  1.0  1.0  0.0  \n",
       "1040  1.0  0.0  0.0  0.0  0.0  1.0  1.0  0.0  0.0  0.0  \n",
       "\n",
       "[1041 rows x 38 columns]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29be2feb",
   "metadata": {},
   "source": [
    "###  From here the actual pipeline is going to start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "6d95580a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import for pipelines\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "# import for matrix\n",
    "from sklearn.metrics import r2_score,mean_absolute_error\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "ff6419be",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import all algorithams\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor,GradientBoostingRegressor,AdaBoostRegressor\n",
    "from sklearn.svm import SVR\n",
    "from xgboost import XGBRegressor"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8146eaa0",
   "metadata": {},
   "source": [
    "### Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "94bccfad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 Score - 0.8002900196837185\n",
      "MAE - 0.20441224420943246\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-17d50579-9854-4e54-8cff-56da37f71f8d {color: black;background-color: white;}#sk-17d50579-9854-4e54-8cff-56da37f71f8d pre{padding: 0;}#sk-17d50579-9854-4e54-8cff-56da37f71f8d div.sk-toggleable {background-color: white;}#sk-17d50579-9854-4e54-8cff-56da37f71f8d label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-17d50579-9854-4e54-8cff-56da37f71f8d label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-17d50579-9854-4e54-8cff-56da37f71f8d label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-17d50579-9854-4e54-8cff-56da37f71f8d div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-17d50579-9854-4e54-8cff-56da37f71f8d div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-17d50579-9854-4e54-8cff-56da37f71f8d div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-17d50579-9854-4e54-8cff-56da37f71f8d input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-17d50579-9854-4e54-8cff-56da37f71f8d input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-17d50579-9854-4e54-8cff-56da37f71f8d div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-17d50579-9854-4e54-8cff-56da37f71f8d div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-17d50579-9854-4e54-8cff-56da37f71f8d input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-17d50579-9854-4e54-8cff-56da37f71f8d div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-17d50579-9854-4e54-8cff-56da37f71f8d div.sk-estimator:hover {background-color: #d4ebff;}#sk-17d50579-9854-4e54-8cff-56da37f71f8d div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-17d50579-9854-4e54-8cff-56da37f71f8d div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-17d50579-9854-4e54-8cff-56da37f71f8d div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-17d50579-9854-4e54-8cff-56da37f71f8d div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-17d50579-9854-4e54-8cff-56da37f71f8d div.sk-item {z-index: 1;}#sk-17d50579-9854-4e54-8cff-56da37f71f8d div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-17d50579-9854-4e54-8cff-56da37f71f8d div.sk-parallel::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-17d50579-9854-4e54-8cff-56da37f71f8d div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-17d50579-9854-4e54-8cff-56da37f71f8d div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-17d50579-9854-4e54-8cff-56da37f71f8d div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-17d50579-9854-4e54-8cff-56da37f71f8d div.sk-parallel-item:only-child::after {width: 0;}#sk-17d50579-9854-4e54-8cff-56da37f71f8d div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;position: relative;}#sk-17d50579-9854-4e54-8cff-56da37f71f8d div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-17d50579-9854-4e54-8cff-56da37f71f8d div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-17d50579-9854-4e54-8cff-56da37f71f8d div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-17d50579-9854-4e54-8cff-56da37f71f8d div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-17d50579-9854-4e54-8cff-56da37f71f8d\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;step1&#x27;,\n",
       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;col_tnf&#x27;,\n",
       "                                                  OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                sparse=False),\n",
       "                                                  [0, 1, 7, 10, 11])])),\n",
       "                (&#x27;step2&#x27;, LinearRegression())])</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"df69b4cf-27b5-46ed-8397-1578ab44e5f9\" type=\"checkbox\" ><label for=\"df69b4cf-27b5-46ed-8397-1578ab44e5f9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;step1&#x27;,\n",
       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;col_tnf&#x27;,\n",
       "                                                  OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                sparse=False),\n",
       "                                                  [0, 1, 7, 10, 11])])),\n",
       "                (&#x27;step2&#x27;, LinearRegression())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"44f6cd4a-c5f1-4792-af12-4e7cc5d6e488\" type=\"checkbox\" ><label for=\"44f6cd4a-c5f1-4792-af12-4e7cc5d6e488\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">step1: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                  transformers=[(&#x27;col_tnf&#x27;,\n",
       "                                 OneHotEncoder(drop=&#x27;first&#x27;, sparse=False),\n",
       "                                 [0, 1, 7, 10, 11])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"b069454f-6d2b-49ac-9940-e1a876b28c7d\" type=\"checkbox\" ><label for=\"b069454f-6d2b-49ac-9940-e1a876b28c7d\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">col_tnf</label><div class=\"sk-toggleable__content\"><pre>[0, 1, 7, 10, 11]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"51dbbf61-d765-4510-b694-dcc69a5196b6\" type=\"checkbox\" ><label for=\"51dbbf61-d765-4510-b694-dcc69a5196b6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(drop=&#x27;first&#x27;, sparse=False)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"c7f10770-7fb1-4e3d-a450-718070e403d2\" type=\"checkbox\" ><label for=\"c7f10770-7fb1-4e3d-a450-718070e403d2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Ram&#x27;, &#x27;Weight&#x27;, &#x27;Touchscreen&#x27;, &#x27;Ips&#x27;, &#x27;ppi&#x27;, &#x27;HDD&#x27;, &#x27;SSD&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"7e010a09-fa79-4b14-89e0-b7301e4eb520\" type=\"checkbox\" ><label for=\"7e010a09-fa79-4b14-89e0-b7301e4eb520\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"3bfc66a9-e49d-4759-ae72-dbf0844bb192\" type=\"checkbox\" ><label for=\"3bfc66a9-e49d-4759-ae72-dbf0844bb192\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('step1',\n",
       "                 ColumnTransformer(remainder='passthrough',\n",
       "                                   transformers=[('col_tnf',\n",
       "                                                  OneHotEncoder(drop='first',\n",
       "                                                                sparse=False),\n",
       "                                                  [0, 1, 7, 10, 11])])),\n",
       "                ('step2', LinearRegression())])"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create pipeline and columtransformer\n",
    "\n",
    "from sklearn import set_config\n",
    "\n",
    "step1=ColumnTransformer(transformers=[\n",
    "    ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,7,10,11])\n",
    "],remainder='passthrough')\n",
    "\n",
    "step2 = LinearRegression()\n",
    "\n",
    "pipe = Pipeline([\n",
    "    ('step1',step1),\n",
    "    ('step2',step2)\n",
    "])\n",
    "\n",
    "pipe.fit(X_train,y_train)\n",
    "\n",
    "\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))\n",
    "\n",
    "set_config(display='diagram')\n",
    "pipe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "e7ff49e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.388517495536914"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp(0.328236628)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "97218c9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-c07bc73e-7608-4221-8214-6d3a4c5a0bf7 {color: black;background-color: white;}#sk-c07bc73e-7608-4221-8214-6d3a4c5a0bf7 pre{padding: 0;}#sk-c07bc73e-7608-4221-8214-6d3a4c5a0bf7 div.sk-toggleable {background-color: white;}#sk-c07bc73e-7608-4221-8214-6d3a4c5a0bf7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-c07bc73e-7608-4221-8214-6d3a4c5a0bf7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-c07bc73e-7608-4221-8214-6d3a4c5a0bf7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-c07bc73e-7608-4221-8214-6d3a4c5a0bf7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-c07bc73e-7608-4221-8214-6d3a4c5a0bf7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-c07bc73e-7608-4221-8214-6d3a4c5a0bf7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-c07bc73e-7608-4221-8214-6d3a4c5a0bf7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-c07bc73e-7608-4221-8214-6d3a4c5a0bf7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-c07bc73e-7608-4221-8214-6d3a4c5a0bf7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-c07bc73e-7608-4221-8214-6d3a4c5a0bf7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-c07bc73e-7608-4221-8214-6d3a4c5a0bf7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-c07bc73e-7608-4221-8214-6d3a4c5a0bf7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-c07bc73e-7608-4221-8214-6d3a4c5a0bf7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-c07bc73e-7608-4221-8214-6d3a4c5a0bf7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-c07bc73e-7608-4221-8214-6d3a4c5a0bf7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-c07bc73e-7608-4221-8214-6d3a4c5a0bf7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-c07bc73e-7608-4221-8214-6d3a4c5a0bf7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-c07bc73e-7608-4221-8214-6d3a4c5a0bf7 div.sk-item {z-index: 1;}#sk-c07bc73e-7608-4221-8214-6d3a4c5a0bf7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-c07bc73e-7608-4221-8214-6d3a4c5a0bf7 div.sk-parallel::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-c07bc73e-7608-4221-8214-6d3a4c5a0bf7 div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-c07bc73e-7608-4221-8214-6d3a4c5a0bf7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-c07bc73e-7608-4221-8214-6d3a4c5a0bf7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-c07bc73e-7608-4221-8214-6d3a4c5a0bf7 div.sk-parallel-item:only-child::after {width: 0;}#sk-c07bc73e-7608-4221-8214-6d3a4c5a0bf7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;position: relative;}#sk-c07bc73e-7608-4221-8214-6d3a4c5a0bf7 div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-c07bc73e-7608-4221-8214-6d3a4c5a0bf7 div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-c07bc73e-7608-4221-8214-6d3a4c5a0bf7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-c07bc73e-7608-4221-8214-6d3a4c5a0bf7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-c07bc73e-7608-4221-8214-6d3a4c5a0bf7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                  transformers=[(&#x27;col_tnf&#x27;,\n",
       "                                 OneHotEncoder(drop=&#x27;first&#x27;, sparse=False),\n",
       "                                 [0, 1, 7, 10, 11])])</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"b2fbd50a-ba58-4aa5-8617-aed999a5c9ec\" type=\"checkbox\" ><label for=\"b2fbd50a-ba58-4aa5-8617-aed999a5c9ec\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                  transformers=[(&#x27;col_tnf&#x27;,\n",
       "                                 OneHotEncoder(drop=&#x27;first&#x27;, sparse=False),\n",
       "                                 [0, 1, 7, 10, 11])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"c4764c9e-bb02-4a63-ad5f-5414ec6b0790\" type=\"checkbox\" ><label for=\"c4764c9e-bb02-4a63-ad5f-5414ec6b0790\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">col_tnf</label><div class=\"sk-toggleable__content\"><pre>[0, 1, 7, 10, 11]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"38466f42-3f68-4de5-9a71-91e19c40b4a4\" type=\"checkbox\" ><label for=\"38466f42-3f68-4de5-9a71-91e19c40b4a4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(drop=&#x27;first&#x27;, sparse=False)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"61fa6441-d904-4947-a912-88d4e49495a3\" type=\"checkbox\" ><label for=\"61fa6441-d904-4947-a912-88d4e49495a3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Ram&#x27;, &#x27;Weight&#x27;, &#x27;Touchscreen&#x27;, &#x27;Ips&#x27;, &#x27;ppi&#x27;, &#x27;HDD&#x27;, &#x27;SSD&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"d1532c89-2551-4ead-b31b-30bf296b55ef\" type=\"checkbox\" ><label for=\"d1532c89-2551-4ead-b31b-30bf296b55ef\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "ColumnTransformer(remainder='passthrough',\n",
       "                  transformers=[('col_tnf',\n",
       "                                 OneHotEncoder(drop='first', sparse=False),\n",
       "                                 [0, 1, 7, 10, 11])])"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "step1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "568a6d45",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "684e3a6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# here we can not able to use LogisticRegression because the target column does not containsthe binary value."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3316d888",
   "metadata": {},
   "source": [
    "### SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "caf54290",
   "metadata": {},
   "outputs": [],
   "source": [
    "# SVM works on non-linear data also"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "14710e54",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 Score - 0.783069620132929\n",
      "MAE - 0.20309339916915692\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",
    "\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))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "a1e80c8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.5779870250687718"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp( 0.45615)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae093637",
   "metadata": {},
   "source": [
    "### KNN "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "9f98fd8d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 Score - 0.7903138560974095\n",
      "MAE - 0.20199964156146163\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=5)\n",
    "\n",
    "pipe = Pipeline([\n",
    "    ('step1',step1),\n",
    "    ('step2',step2)\n",
    "])\n",
    "\n",
    "pipe.fit(X_train,y_train)\n",
    "\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))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "466a4a91",
   "metadata": {},
   "source": [
    "###  Decision Tree\n",
    "Lets try with Hyperparameter Tuning using GridSearhCV as well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "d5bc7555",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 Score - 0.8062561746746524\n",
      "MAE - 0.1978151385988938\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "param_grid = {\n",
    "    'max_depth':[2,4,8,10,None],\n",
    "    'criterion':['mse','mae'],\n",
    "    'max_features':[0.25,0.5,1.0],\n",
    "    'min_samples_split':[0.25,0.5,1.0]              \n",
    "}\n",
    "\n",
    "step1=ColumnTransformer(transformers=[\n",
    "    ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,7,10,11])\n",
    "],remainder='passthrough')\n",
    "\n",
    "# step2 = GridSearchCV(DecisionTreeRegressor(),param_grid=param_grid)\n",
    "step2 = DecisionTreeRegressor(max_depth=8)\n",
    "\n",
    "pipe = Pipeline([\n",
    "    ('step1',step1),\n",
    "    ('step2',step2)\n",
    "])\n",
    "\n",
    "\n",
    "pipe.fit(X_train,y_train)\n",
    "# print(step2.best_score_)\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))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c2b922b",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "8a6be14e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 Score - 0.8659524020923084\n",
      "MAE - 0.16534391581739144\n"
     ]
    }
   ],
   "source": [
    "\n",
    "step1=ColumnTransformer(transformers=[\n",
    "    ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,7,10,11])\n",
    "],remainder='passthrough')\n",
    "\n",
    "\n",
    "step2 = RandomForestRegressor(n_estimators=100,\n",
    "                              random_state=3,\n",
    "                              max_samples=0.50,\n",
    "                              max_features=0.75,\n",
    "                              max_depth=20)\n",
    "\n",
    "pipe = Pipeline([\n",
    "    ('step1',step1),\n",
    "    ('step2',step2)\n",
    "])\n",
    "\n",
    "\n",
    "pipe.fit(X_train,y_train)\n",
    "# print(step2.best_score_)\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))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f380da80",
   "metadata": {},
   "source": [
    "### AddaBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "74ed693f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.7852773681818224\n",
      "MAE 0.2253707262623307\n"
     ]
    }
   ],
   "source": [
    "step1 = ColumnTransformer(transformers=[\n",
    "    ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,7,10,11])\n",
    "],remainder='passthrough')\n",
    "\n",
    "step2 = AdaBoostRegressor(n_estimators=15,learning_rate=1.0)\n",
    "\n",
    "pipe = Pipeline([\n",
    "    ('step1',step1),\n",
    "    ('step2',step2)\n",
    "])\n",
    "\n",
    "pipe.fit(X_train,y_train)\n",
    "\n",
    "y_pred = pipe.predict(X_test)\n",
    "\n",
    "print('R2 score',r2_score(y_test,y_pred))\n",
    "print('MAE',mean_absolute_error(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "384add5c",
   "metadata": {},
   "source": [
    "### Grandient Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "ea34259d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.8645422087909728\n",
      "MAE 0.16252384164886727\n"
     ]
    }
   ],
   "source": [
    "step1 = ColumnTransformer(transformers=[\n",
    "    ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,7,10,11])\n",
    "],remainder='passthrough')\n",
    "\n",
    "step2 = GradientBoostingRegressor(n_estimators=500)\n",
    "\n",
    "pipe = Pipeline([\n",
    "    ('step1',step1),\n",
    "    ('step2',step2)\n",
    "])\n",
    "\n",
    "pipe.fit(X_train,y_train)\n",
    "\n",
    "y_pred = pipe.predict(X_test)\n",
    "\n",
    "print('R2 score',r2_score(y_test,y_pred))\n",
    "print('MAE',mean_absolute_error(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2e0e372",
   "metadata": {},
   "source": [
    "### XgBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "406b34e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.8604272065621432\n",
      "MAE 0.16454434681639316\n"
     ]
    }
   ],
   "source": [
    "step1 = ColumnTransformer(transformers=[\n",
    "    ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,7,10,11])\n",
    "],remainder='passthrough')\n",
    "\n",
    "step2 = XGBRegressor(n_estimators=45,max_depth=5,learning_rate=0.5)\n",
    "\n",
    "pipe = Pipeline([\n",
    "    ('step1',step1),\n",
    "    ('step2',step2)\n",
    "])\n",
    "\n",
    "pipe.fit(X_train,y_train)\n",
    "\n",
    "y_pred = pipe.predict(X_test)\n",
    "\n",
    "print('R2 score',r2_score(y_test,y_pred))\n",
    "print('MAE',mean_absolute_error(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10e84203",
   "metadata": {},
   "source": [
    "### Voting Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "46c6a152",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.873507119300922\n",
      "MAE 0.16248308578673268\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",
    "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 = VotingRegressor([('rf', rf), ('gbdt', gbdt), ('xgb',xgb)],weights=[5,1,1])\n",
    "\n",
    "pipe = Pipeline([\n",
    "    ('step1',step1),\n",
    "    ('step2',step2)\n",
    "])\n",
    "\n",
    "pipe.fit(X_train,y_train)\n",
    "\n",
    "y_pred = pipe.predict(X_test)\n",
    "\n",
    "print('R2 score',r2_score(y_test,y_pred))\n",
    "print('MAE',mean_absolute_error(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8d5c036",
   "metadata": {},
   "source": [
    "### Exporting Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "3f53f3c3",
   "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": 126,
   "id": "56ce7fac",
   "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>91</th>\n",
       "      <td>Acer</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>2.20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>Nvidia</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>863</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Gaming</td>\n",
       "      <td>8</td>\n",
       "      <td>2.50</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>Nvidia</td>\n",
       "      <td>Other/No OS/Linux</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552</th>\n",
       "      <td>Dell</td>\n",
       "      <td>Gaming</td>\n",
       "      <td>16</td>\n",
       "      <td>4.42</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>127.335675</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>1000</td>\n",
       "      <td>256</td>\n",
       "      <td>Nvidia</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>967</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>4</td>\n",
       "      <td>2.04</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i3</td>\n",
       "      <td>500</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>2.04</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.454670</td>\n",
       "      <td>Other Intel Processor</td>\n",
       "      <td>2000</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466</th>\n",
       "      <td>Acer</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>4</td>\n",
       "      <td>2.20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.454670</td>\n",
       "      <td>Intel Core i3</td>\n",
       "      <td>500</td>\n",
       "      <td>0</td>\n",
       "      <td>Nvidia</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>Asus</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>16</td>\n",
       "      <td>1.63</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>512</td>\n",
       "      <td>Nvidia</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>Acer</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>2.20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.454670</td>\n",
       "      <td>AMD Processor</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>AMD</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>527</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>2.20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.454670</td>\n",
       "      <td>Intel Core i3</td>\n",
       "      <td>2000</td>\n",
       "      <td>0</td>\n",
       "      <td>Nvidia</td>\n",
       "      <td>Other/No OS/Linux</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1193</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.415547</td>\n",
       "      <td>Other Intel Processor</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Mac</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1041 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company   TypeName  Ram  Weight  Touchscreen  Ips         ppi  \\\n",
       "91      Acer   Notebook    8    2.20            0    0  141.211998   \n",
       "863   Lenovo     Gaming    8    2.50            0    1  141.211998   \n",
       "552     Dell     Gaming   16    4.42            0    1  127.335675   \n",
       "967       HP   Notebook    4    2.04            0    0  141.211998   \n",
       "365       HP   Notebook    8    2.04            0    0  100.454670   \n",
       "...      ...        ...  ...     ...          ...  ...         ...   \n",
       "466     Acer   Notebook    4    2.20            0    0  100.454670   \n",
       "299     Asus  Ultrabook   16    1.63            0    0  141.211998   \n",
       "493     Acer   Notebook    8    2.20            0    0  100.454670   \n",
       "527   Lenovo   Notebook    8    2.20            0    0  100.454670   \n",
       "1193   Apple  Ultrabook    8    0.92            0    1  226.415547   \n",
       "\n",
       "                  Cpu Brand   HDD  SSD Gpu Brand                 OS  \n",
       "91            Intel Core i7  1000    0    Nvidia            Windows  \n",
       "863           Intel Core i5  1000    0    Nvidia  Other/No OS/Linux  \n",
       "552           Intel Core i7  1000  256    Nvidia            Windows  \n",
       "967           Intel Core i3   500    0     Intel            Windows  \n",
       "365   Other Intel Processor  2000    0     Intel            Windows  \n",
       "...                     ...   ...  ...       ...                ...  \n",
       "466           Intel Core i3   500    0    Nvidia            Windows  \n",
       "299           Intel Core i7     0  512    Nvidia            Windows  \n",
       "493           AMD Processor  1000    0       AMD            Windows  \n",
       "527           Intel Core i3  2000    0    Nvidia  Other/No OS/Linux  \n",
       "1193  Other Intel Processor     0    0     Intel                Mac  \n",
       "\n",
       "[1041 rows x 12 columns]"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "95221f51",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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