notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from numpy import median\n",
    "from scipy import stats as st"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('laptop_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1303, 12)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'Company', 'TypeName', 'Inches', 'ScreenResolution',\n",
       "       'Cpu', 'Ram', 'Memory', 'Gpu', 'OpSys', 'Weight', 'Price'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1303 entries, 0 to 1302\n",
      "Data columns (total 12 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Unnamed: 0        1303 non-null   int64  \n",
      " 1   Company           1303 non-null   object \n",
      " 2   TypeName          1303 non-null   object \n",
      " 3   Inches            1303 non-null   float64\n",
      " 4   ScreenResolution  1303 non-null   object \n",
      " 5   Cpu               1303 non-null   object \n",
      " 6   Ram               1303 non-null   object \n",
      " 7   Memory            1303 non-null   object \n",
      " 8   Gpu               1303 non-null   object \n",
      " 9   OpSys             1303 non-null   object \n",
      " 10  Weight            1303 non-null   object \n",
      " 11  Price             1303 non-null   float64\n",
      "dtypes: float64(2), int64(1), object(9)\n",
      "memory usage: 122.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop('Unnamed: 0', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>Dell</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>17.3</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i7 8550U 1.8GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>128GB SSD +  1TB HDD</td>\n",
       "      <td>AMD Radeon 530</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.8kg</td>\n",
       "      <td>57808.8000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>Dell</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>17.3</td>\n",
       "      <td>Full HD / Touchscreen 1920x1080</td>\n",
       "      <td>Intel Core i7 8550U 1.8GHz</td>\n",
       "      <td>16GB</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>Nvidia GeForce 150MX</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.77kg</td>\n",
       "      <td>82530.7200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>14.0</td>\n",
       "      <td>IPS Panel Full HD 2560x1440</td>\n",
       "      <td>Intel Core i7 7700HQ 2.8GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>Nvidia GeForce GT 940MX</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.7kg</td>\n",
       "      <td>103523.0400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>991</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i3 6006U 2GHz</td>\n",
       "      <td>4GB</td>\n",
       "      <td>128GB SSD +  1TB HDD</td>\n",
       "      <td>AMD Radeon 520</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.1kg</td>\n",
       "      <td>26586.7200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1008</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>14.0</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>Nvidia GeForce 930MX</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.64kg</td>\n",
       "      <td>55904.5728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1070</th>\n",
       "      <td>Dell</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>Intel Core i3 6100U 2.3GHz</td>\n",
       "      <td>4GB</td>\n",
       "      <td>500GB HDD</td>\n",
       "      <td>Intel HD Graphics 520</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.06kg</td>\n",
       "      <td>25679.8944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>705</th>\n",
       "      <td>Dell</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>Windows 10</td>\n",
       "      <td>2.18kg</td>\n",
       "      <td>42357.6000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i3 6006U 2GHz</td>\n",
       "      <td>4GB</td>\n",
       "      <td>128GB SSD</td>\n",
       "      <td>Intel HD Graphics 520</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.91kg</td>\n",
       "      <td>23389.9200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>713</th>\n",
       "      <td>Asus</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Celeron Dual Core N3350 1.1GHz</td>\n",
       "      <td>4GB</td>\n",
       "      <td>32GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics 500</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.5kg</td>\n",
       "      <td>15877.4400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>648</th>\n",
       "      <td>Toshiba</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i5 6200U 2.3GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 520</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.2kg</td>\n",
       "      <td>64628.6400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Company   TypeName  Inches                 ScreenResolution  \\\n",
       "189      Dell   Notebook    17.3                Full HD 1920x1080   \n",
       "185      Dell   Notebook    17.3  Full HD / Touchscreen 1920x1080   \n",
       "411    Lenovo  Ultrabook    14.0      IPS Panel Full HD 2560x1440   \n",
       "991        HP   Notebook    15.6                Full HD 1920x1080   \n",
       "1008       HP   Notebook    14.0                Full HD 1920x1080   \n",
       "1070     Dell   Notebook    15.6                         1366x768   \n",
       "705      Dell   Notebook    15.6                Full HD 1920x1080   \n",
       "56         HP   Notebook    15.6                Full HD 1920x1080   \n",
       "713      Asus   Notebook    14.0                Full HD 1920x1080   \n",
       "648   Toshiba   Notebook    13.3                Full HD 1920x1080   \n",
       "\n",
       "                                       Cpu   Ram                Memory  \\\n",
       "189             Intel Core i7 8550U 1.8GHz   8GB  128GB SSD +  1TB HDD   \n",
       "185             Intel Core i7 8550U 1.8GHz  16GB             512GB SSD   \n",
       "411            Intel Core i7 7700HQ 2.8GHz   8GB             512GB SSD   \n",
       "991               Intel Core i3 6006U 2GHz   4GB  128GB SSD +  1TB HDD   \n",
       "1008            Intel Core i5 7200U 2.5GHz   8GB             256GB SSD   \n",
       "1070            Intel Core i3 6100U 2.3GHz   4GB             500GB HDD   \n",
       "705             Intel Core i5 7200U 2.5GHz   8GB             256GB SSD   \n",
       "56                Intel Core i3 6006U 2GHz   4GB             128GB SSD   \n",
       "713   Intel Celeron Dual Core N3350 1.1GHz   4GB    32GB Flash Storage   \n",
       "648             Intel Core i5 6200U 2.3GHz   8GB             256GB SSD   \n",
       "\n",
       "                          Gpu       OpSys  Weight        Price  \n",
       "189            AMD Radeon 530  Windows 10   2.8kg   57808.8000  \n",
       "185      Nvidia GeForce 150MX  Windows 10  2.77kg   82530.7200  \n",
       "411   Nvidia GeForce GT 940MX  Windows 10   1.7kg  103523.0400  \n",
       "991            AMD Radeon 520  Windows 10   2.1kg   26586.7200  \n",
       "1008     Nvidia GeForce 930MX  Windows 10  1.64kg   55904.5728  \n",
       "1070    Intel HD Graphics 520  Windows 10  2.06kg   25679.8944  \n",
       "705     Intel HD Graphics 620  Windows 10  2.18kg   42357.6000  \n",
       "56      Intel HD Graphics 520  Windows 10  1.91kg   23389.9200  \n",
       "713     Intel HD Graphics 500  Windows 10   1.5kg   15877.4400  \n",
       "648     Intel HD Graphics 520  Windows 10   1.2kg   64628.6400  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Company'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 12,
     "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==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['TypeName'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEFCAYAAAAYKqc0AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAUkklEQVR4nO3dfbBc933X8ffHciKSOHFk+0pVLRm5VLS1y+Shd5QwgebBJRaYVobBU4WhiOAiGJw0hkKR28yk/KFiGJ76R10qkiYqaatRE4LVBNJqVDsNUCLLD7Ujy6pE7Fh3pFhK0jTQBAcpX/7Y43R7fa/u7r3nXq1+vF8zO3v2t7/z3e+eu/u5R3v3HKWqkCS15YpL3YAkqX+GuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSg6681A0AXHfddbVp06ZL3YYkXVYefvjhL1bV1Fz3TUS4b9q0iSNHjlzqNiTpspLk8/Pd58cyktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ1yHCXpAZNxEFMc9m06xMLznnm3ttWoBNJuvy45y5JDTLcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KDDHdJatBI4Z7k1Uk+kuSpJMeS/Nkk1yQ5mOREd71maP49SU4mOZ7k1uVrX5I0l1H33H8W+GRVfTfwGuAYsAs4VFWbgUPdbZLcBGwHbga2AvclWdV345Kk+S0Y7kleBXw/8AGAqvpGVX0F2Abs7abtBW7vlrcB+6rq+ap6GjgJbOm3bUnSxYyy5/4dwDngg0keTfL+JK8A1lXVGYDuem03/3rg1ND6M92YJGmFjBLuVwKvB36+ql4H/CHdRzDzyBxj9aJJyc4kR5IcOXfu3EjNSpJGM0q4zwAzVfWZ7vZHGIT9c0nWA3TXZ4fmbxxafwNwenbRqtpTVdNVNT01NbXY/iVJc1gw3KvqC8CpJN/VDd0CPAkcAHZ0YzuA+7vlA8D2JKuT3AhsBg732rUk6aKuHHHeu4FfTvJS4HPAOxn8Ytif5E7gWeAOgKo6mmQ/g18A54G7qupC751LkuY1UrhX1WPA9Bx33TLP/N3A7sW3JUlaCo9QlaQGGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDRop3JM8k+SJJI8lOdKNXZPkYJIT3fWaofn3JDmZ5HiSW5ereUnS3MbZc39rVb22qqa727uAQ1W1GTjU3SbJTcB24GZgK3BfklU99ixJWsBSPpbZBuztlvcCtw+N76uq56vqaeAksGUJjyNJGtOo4V7AbyZ5OMnObmxdVZ0B6K7XduPXA6eG1p3pxiRJK+TKEee9qapOJ1kLHEzy1EXmZo6xetGkwS+JnQA33HDDiG1IkkYx0p57VZ3urs8CH2PwMctzSdYDdNdnu+kzwMah1TcAp+eouaeqpqtqempqavHPQJL0IguGe5JXJHnlC8vA24HPAgeAHd20HcD93fIBYHuS1UluBDYDh/tuXJI0v1E+llkHfCzJC/N/pao+meQhYH+SO4FngTsAqupokv3Ak8B54K6qurAs3UuS5rRguFfV54DXzDH+JeCWedbZDexecneSpEXxCFVJapDhLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktSgkcM9yaokjyb5eHf7miQHk5zortcMzb0nyckkx5PcuhyNS5LmN86e+3uAY0O3dwGHqmozcKi7TZKbgO3AzcBW4L4kq/ppV5I0ipHCPckG4Dbg/UPD24C93fJe4Pah8X1V9XxVPQ2cBLb00q0kaSSj7rn/W+AngG8Oja2rqjMA3fXabvx64NTQvJluTJK0QhYM9yR/GThbVQ+PWDNzjNUcdXcmOZLkyLlz50YsLUkaxSh77m8CfijJM8A+4G1JPgw8l2Q9QHd9tps/A2wcWn8DcHp20araU1XTVTU9NTW1hKcgSZptwXCvqnuqakNVbWLwh9Lfqqq/ARwAdnTTdgD3d8sHgO1JVie5EdgMHO69c0nSvK5cwrr3AvuT3Ak8C9wBUFVHk+wHngTOA3dV1YUldypJGtlY4V5VDwIPdstfAm6ZZ95uYPcSe5MkLZJHqEpSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ1yHCXpAYtGO5J/kSSw0l+N8nRJP+0G78mycEkJ7rrNUPr3JPkZJLjSW5dzicgSXqxUfbcnwfeVlWvAV4LbE3yRmAXcKiqNgOHutskuQnYDtwMbAXuS7JqGXqXJM1jwXCvgf/d3XxJdylgG7C3G98L3N4tbwP2VdXzVfU0cBLY0mfTkqSLG+kz9ySrkjwGnAUOVtVngHVVdQagu17bTb8eODW0+kw3JklaISOFe1VdqKrXAhuALUm+9yLTM1eJF01KdiY5kuTIuXPnRmpWkjSasb4tU1VfAR5k8Fn6c0nWA3TXZ7tpM8DGodU2AKfnqLWnqqaranpqamr8ziVJ8xrl2zJTSV7dLb8M+AHgKeAAsKObtgO4v1s+AGxPsjrJjcBm4HDPfUuSLuLKEeasB/Z233i5AthfVR9P8jvA/iR3As8CdwBU1dEk+4EngfPAXVV1YXnalyTNZcFwr6rHgdfNMf4l4JZ51tkN7F5yd5KkRfEIVUlqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1KAFwz3JxiQPJDmW5GiS93Tj1yQ5mOREd71maJ17kpxMcjzJrcv5BCRJLzbKnvt54Mer6nuANwJ3JbkJ2AUcqqrNwKHuNt1924Gbga3AfUlWLUfzkqS5LRjuVXWmqh7plv8XcAy4HtgG7O2m7QVu75a3Afuq6vmqeho4CWzpuW9J0kWM9Zl7kk3A64DPAOuq6gwMfgEAa7tp1wOnhlab6cYkSStk5HBPchXwUeDuqvrqxabOMVZz1NuZ5EiSI+fOnRu1DUnSCK4cZVKSlzAI9l+uqv/YDT+XZH1VnUmyHjjbjc8AG4dW3wCcnl2zqvYAewCmp6dfFP592rTrEwvOeebe25azBUlaUaN8WybAB4BjVfWvh+46AOzolncA9w+Nb0+yOsmNwGbgcH8tS5IWMsqe+5uAHwGeSPJYN/aTwL3A/iR3As8CdwBU1dEk+4EnGXzT5q6qutB345Kk+S0Y7lX1X5n7c3SAW+ZZZzewewl9SZKWwCNUJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoNGOkJVA30e6epRs5KWk3vuktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDTLcJalBhrskNWjBcE/yi0nOJvns0Ng1SQ4mOdFdrxm6754kJ5McT3LrcjUuSZrfKHvuHwK2zhrbBRyqqs3Aoe42SW4CtgM3d+vcl2RVb91KkkayYLhX1W8DX541vA3Y2y3vBW4fGt9XVc9X1dPASWBLP61Kkka12M/c11XVGYDuem03fj1wamjeTDcmSVpBff9BNXOM1ZwTk51JjiQ5cu7cuZ7bkKT/vy023J9Lsh6guz7bjc8AG4fmbQBOz1WgqvZU1XRVTU9NTS2yDUnSXBYb7geAHd3yDuD+ofHtSVYnuRHYDBxeWouSpHFdudCEJL8KvAW4LskM8D7gXmB/kjuBZ4E7AKrqaJL9wJPAeeCuqrqwTL1LkuaxYLhX1TvmueuWeebvBnYvpSlJ0tJ4hKokNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KDDHdJatCCp/zVZNu06xMjzXvm3tuWuRNJk8Q9d0lqkHvu+pZR/hXgvwCky4N77pLUIMNdkhpkuEtSgwx3SWqQ4S5JDTLcJalByxbuSbYmOZ7kZJJdy/U4kqQXW5ZwT7IK+DngLwI3Ae9IctNyPJYk6cWW6yCmLcDJqvocQJJ9wDbgyWV6PE2Yvg6I6vPAqpXsqc9ak/j8PJht8qWq+i+a/DVga1X9aHf7R4A3VNW7hubsBHZ2N78LOD5C6euAL/bQYl91+qw1iT31WcueVr6WPa18rZXu6U9W1dRcdyzXnnvmGPtjv0Wqag+wZ6yiyZGqml5KY33Wab2nPmvZ08rXsqeVrzVJPS3XH1RngI1DtzcAp5fpsSRJsyxXuD8EbE5yY5KXAtuBA8v0WJKkWZblY5mqOp/kXcBvAKuAX6yqoz2UHutjnBWo02etSeypz1r2tPK17Gnla01MT8vyB1VJ0qXlEaqS1CDDXZIaZLhLUoMMd0lqkOEuSQ2a2P8gO8kbgGNV9dUkLwN2Aa9ncH6an6mqPxij1nczOLfN9QyOlD0NHKiqY4voawtQVfVQdzK0rcBTVfWfL2WtoZp/jsG5fT5bVb855rp9bvM/BfwVBgeznQdOAL86To156i76+XXr9/JaWI6f3VDtX6qqv7nUOkP13llVHxxznT7fM73VmlW31+3U1RxrW01qtsAEfxUyyVHgNd135vcAXwM+AtzSjf/VEev8E+AdwD4GR87C4IjZ7cC+qrp3jJ7ex+BMl1cCB4E3AA8CPwD8RlXtXulaSQ5X1ZZu+e8AdwEfA94O/PqYz6+vbf5jwA8CnwL+EvAY8PsMwv7vV9WDY/TU5/Pr5bXQ8+tg9sF9Ad4K/BZAVf3QqLUu8hjPVtUNY8zv8z3T1zZf9u3UPc7I22pSs+VbqmoiLwz2IF9YfmTWfY+NUef3gJfMMf5S4MSYPT3B4KCslwNfBV7Vjb8MePxS1AIeHVp+CJjqll8BPHGJtvkTwKpu+eXAg93yDcP9XoLn18troefXwSPAh4G3AG/urs90y28eo87j81yeAJ6/FNup523ey3bqc1tNara8cJnYj2WAzw79E+l3k0xX1ZEkfxr4v2PU+Sbw7cDnZ42v7+4bx/mqugB8Lcn/rKqvAlTV15NcqlpXJFnD4O8nqapzXZ0/THJ+zJ762uYw2AO5AKwGXtn19GySl4xZp8/n19droc/XwTTwHuCngH9cVY8l+XpVfWrMOuuAWxn8C2lYgP8+Zq0+3zN91eprO0F/22pSswWY4M/cgR8FfjbJexmc9vJ3kpwCTnX3jepu4FCSE926MNiD/E7gXfOtNI9vJHl5VX0N+L4XBpNczfg/zL5qXQ08zOCFWUm+raq+kOQq5j4758X0tc3fDzyU5H8A3w/8c4AkU8CXx+ypz+d3N/28Fnp7HVTVN4F/k+TXuuvnWNz78uPAVVX12Ow7kjw4Zq276e8900utHrcT9Let7mYys2WwbrfrP7GSvBL4DgY/yJmqem4RNa5g8Ee46xkEwgzwUPebcpw6q6vq+TnGrwPWV9UTl6LWPPVfDqyrqqcXsW4f2/xm4HsY/OHzqXHXH6H+op5fH6+F5fzZJbkNeFNV/eRia/Shr/dM37WGaja1nZbjNTXx4S5JGt9l+T33JB+fpDqTWsueVr6WPa18LXuaZ73Lcc89yfqqOjMpdSa1lj2tfC17Wvla9jTPepdjuC9VkrVVdfZS9yFpMrSYCRP7sUySrUPLVyf5QJLHk/xKknVj1Llm1uVa4HCSNUmuGbOnb0vy80l+Lsm1SX46yRNJ9idZP2atR5K8N4MjORctyauS/LMk/yHJX591331LqT2r1n8ZY26f26nPWr1sqz63eY+v86uT3JvkqSRf6i7HurFXX4qeuvV7+fklmU7yQJIPJ9mY5GCSP0jyUJLXjdlTL5nQ13t4hMcZ+b03bGLDHfiZoeV/xeCAhR9kcCDLL4xR54sMvkr3wuUIg79sP9Itj+NDDA7FPwU8AHwduA34NPDvxqy1Bng18ECSw0n+QZJvH7MGwAcZ/JX+o8D2JB9Nsrq7743jFEry+nku3we8doxSH6K/7dRnrb62VW/bnP5e5/sZfG/7LVV1bVVdy+AIzt8Hfu0S9QT9/fzuA/4F8AkG30X/haq6msEpMsbdiekrE/p6D/f53vsjiznyaSUuDB0hyayjI2ffXqDOPwI+CfyZobGnF9nTo0PLzy62pzme359n8AL9AoM3wM4x6szeNj8F/DfgWmYdZTpCrQsMDud+YI7L1y/RduqzVi/bqudt3tfr/Phi7lvOnvr8+S1Q59FR63Tze8mEvt7D3fq9vPeGL5N8ENPaJP+QwR7Sq5Kkuq3AGP/iqKp/mWQfgwMfTgHvY3CCn8UYftxfmnXfqkXWpKo+DXw6ybuBvwD8MKP//4mrk1xRg4M8qKrdSWaA3wauGrOVY8DfraoTs+/ott2o+txOfdbqa1v1uc17eZ0Dn0/yE8De6o5L6D5C+Vv80QE2K93T7PlL+fn9nyRvZ3BQWyW5var+U5I3MwjGkfWcCS/UXMp7GPp7733LJH8s8+8ZHLZ+FbAXuA4Gn+ExOBHVyKpqpqruYPBb8CCD8zcsxv0ZHBlJVb33hcEk3wkcH7PW783R54Wq+mRVvXOMOr8OvG1Wnb3AjwPfGLOnn2b+18S7x6jT53bqs1Zf26rPbd7X6/yHGfzL4VNJvpzkywxOPHUNcMcl6gn6+/n9PQbb928zOHXAW5N8hcHe8o+N2VNfmdDXexj6e+/9sWYuuwvwziWs+zLge5dap8+elquWPfn8JrWnSXp+y5EJk7CdLsuvQmbMU5gud51JrWVPK1/Lnla+lj3NbWI/c0/y+Hx3MTir24rWmdRa9rTytexp5WvZ0/gmNtzp77ScfZ4KdRJr2dPK17Knla9lT2Oa5HDv67ScfZ4KdRJr2dPK17Knla9lT2O6LD9zlyRd3CR/FVKStEiGuyQ1yHCXpAYZ7pLUIMNdkhr0/wCfiZQDV1Y03wAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Inches'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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",
       "IPS Panel 1366x768                                 7\n",
       "4K Ultra HD 3840x2160                              7\n",
       "Touchscreen 2560x1440                              7\n",
       "IPS Panel Quad HD+ / Touchscreen 3200x1800         6\n",
       "IPS Panel Retina Display 2560x1600                 6\n",
       "Touchscreen 2256x1504                              6\n",
       "IPS Panel Retina Display 2304x1440                 6\n",
       "IPS Panel Touchscreen 2560x1440                    5\n",
       "IPS Panel Touchscreen 1920x1200                    4\n",
       "1440x900                                           4\n",
       "IPS Panel Retina Display 2880x1800                 4\n",
       "IPS Panel 2560x1440                                4\n",
       "1920x1080                                          3\n",
       "IPS Panel Quad HD+ 2560x1440                       3\n",
       "2560x1440                                          3\n",
       "Quad HD+ 3200x1800                                 3\n",
       "Touchscreen 2400x1600                              3\n",
       "IPS Panel Touchscreen 1366x768                     3\n",
       "IPS Panel Touchscreen / 4K Ultra HD 3840x2160      2\n",
       "IPS Panel Quad HD+ 3200x1800                       2\n",
       "IPS Panel Full HD 2160x1440                        2\n",
       "IPS Panel Full HD 2560x1440                        1\n",
       "IPS Panel Retina Display 2736x1824                 1\n",
       "IPS Panel Full HD 1366x768                         1\n",
       "Touchscreen / 4K Ultra HD 3840x2160                1\n",
       "Touchscreen / Quad HD+ 3200x1800                   1\n",
       "IPS Panel Touchscreen 2400x1600                    1\n",
       "Touchscreen / Full HD 1920x1080                    1\n",
       "IPS Panel Full HD 1920x1200                        1\n",
       "Name: ScreenResolution, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ScreenResolution'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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 i5 6440HQ 2.6GHz               1\n",
       "Intel Celeron Dual Core N3350 2.0GHz      1\n",
       "Intel Core i5 7200U 2.70GHz               1\n",
       "Intel Core i7 2.7GHz                      1\n",
       "AMD A9-Series 9420 2.9GHz                 1\n",
       "Name: Cpu, Length: 118, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Cpu'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Ram'] = df['Ram'].str.replace('GB', '')\n",
    "df['Weight'] = df['Weight'].str.replace('kg', '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>761</th>\n",
       "      <td>Dell</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>12.5</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i7 7600U 2.8GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.18</td>\n",
       "      <td>99047.5200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>749</th>\n",
       "      <td>HP</td>\n",
       "      <td>Workstation</td>\n",
       "      <td>17.3</td>\n",
       "      <td>IPS Panel Full HD 1920x1080</td>\n",
       "      <td>Intel Xeon E3-1535M v5 2.9GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Nvidia Quadro M2000M</td>\n",
       "      <td>Windows 7</td>\n",
       "      <td>3</td>\n",
       "      <td>233845.9200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1204</th>\n",
       "      <td>Asus</td>\n",
       "      <td>Gaming</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i7 6700HQ 2.6GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>1TB HDD</td>\n",
       "      <td>Nvidia GeForce GTX 1060</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.2</td>\n",
       "      <td>62284.3200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1242</th>\n",
       "      <td>Acer</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>Intel Core i3 6006U 2.0GHz</td>\n",
       "      <td>4</td>\n",
       "      <td>500GB HDD</td>\n",
       "      <td>Intel HD Graphics 520</td>\n",
       "      <td>Linux</td>\n",
       "      <td>2.4</td>\n",
       "      <td>19276.7040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Dell</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i3 6006U 2GHz</td>\n",
       "      <td>4</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>AMD Radeon R5 M430</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.2</td>\n",
       "      <td>26581.3920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>Dell</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>16</td>\n",
       "      <td>128GB SSD +  1TB HDD</td>\n",
       "      <td>Nvidia GeForce GTX 1050 Ti</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.65</td>\n",
       "      <td>71874.7200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>576</th>\n",
       "      <td>Toshiba</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Full HD / Touchscreen 1920x1080</td>\n",
       "      <td>Intel Core i7 7500U 2.7GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.24</td>\n",
       "      <td>99367.2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i7 7500U 2.7GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>1TB HDD</td>\n",
       "      <td>Nvidia GeForce 940MX</td>\n",
       "      <td>No OS</td>\n",
       "      <td>2.2</td>\n",
       "      <td>35112.0528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>595</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>Intel Core i3 6100U 2.3GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>1TB HDD</td>\n",
       "      <td>Intel HD Graphics 520</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.29</td>\n",
       "      <td>24455.5200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580</th>\n",
       "      <td>Mediacom</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>14.0</td>\n",
       "      <td>IPS Panel Full HD 1920x1080</td>\n",
       "      <td>Intel Celeron Quad Core N3450 1.1GHz</td>\n",
       "      <td>4</td>\n",
       "      <td>32GB SSD</td>\n",
       "      <td>Intel HD Graphics 500</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.45</td>\n",
       "      <td>20725.9200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Company     TypeName  Inches                 ScreenResolution  \\\n",
       "761       Dell    Ultrabook    12.5                Full HD 1920x1080   \n",
       "749         HP  Workstation    17.3      IPS Panel Full HD 1920x1080   \n",
       "1204      Asus       Gaming    15.6                Full HD 1920x1080   \n",
       "1242      Acer     Notebook    15.6                         1366x768   \n",
       "13        Dell     Notebook    15.6                Full HD 1920x1080   \n",
       "85        Dell       Gaming    15.6      IPS Panel Full HD 1920x1080   \n",
       "576    Toshiba    Ultrabook    14.0  Full HD / Touchscreen 1920x1080   \n",
       "289     Lenovo     Notebook    15.6                Full HD 1920x1080   \n",
       "595     Lenovo     Notebook    15.6                         1366x768   \n",
       "580   Mediacom     Notebook    14.0      IPS Panel Full HD 1920x1080   \n",
       "\n",
       "                                       Cpu Ram                Memory  \\\n",
       "761             Intel Core i7 7600U 2.8GHz  16             256GB SSD   \n",
       "749          Intel Xeon E3-1535M v5 2.9GHz  16             256GB SSD   \n",
       "1204           Intel Core i7 6700HQ 2.6GHz   8               1TB HDD   \n",
       "1242            Intel Core i3 6006U 2.0GHz   4             500GB HDD   \n",
       "13                Intel Core i3 6006U 2GHz   4             256GB SSD   \n",
       "85             Intel Core i7 7700HQ 2.8GHz  16  128GB SSD +  1TB HDD   \n",
       "576             Intel Core i7 7500U 2.7GHz  16             512GB SSD   \n",
       "289             Intel Core i7 7500U 2.7GHz   8               1TB HDD   \n",
       "595             Intel Core i3 6100U 2.3GHz   8               1TB HDD   \n",
       "580   Intel Celeron Quad Core N3450 1.1GHz   4              32GB SSD   \n",
       "\n",
       "                             Gpu       OpSys Weight        Price  \n",
       "761        Intel HD Graphics 620  Windows 10   1.18   99047.5200  \n",
       "749         Nvidia Quadro M2000M   Windows 7      3  233845.9200  \n",
       "1204     Nvidia GeForce GTX 1060  Windows 10    2.2   62284.3200  \n",
       "1242       Intel HD Graphics 520       Linux    2.4   19276.7040  \n",
       "13            AMD Radeon R5 M430  Windows 10    2.2   26581.3920  \n",
       "85    Nvidia GeForce GTX 1050 Ti  Windows 10   2.65   71874.7200  \n",
       "576        Intel HD Graphics 620  Windows 10   1.24   99367.2000  \n",
       "289         Nvidia GeForce 940MX       No OS    2.2   35112.0528  \n",
       "595        Intel HD Graphics 520  Windows 10   2.29   24455.5200  \n",
       "580        Intel HD Graphics 500  Windows 10   1.45   20725.9200  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Ram'] = df['Ram'].astype('int32')\n",
    "df['Weight'] = df['Weight'].astype('float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1303 entries, 0 to 1302\n",
      "Data columns (total 11 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Company           1303 non-null   object \n",
      " 1   TypeName          1303 non-null   object \n",
      " 2   Inches            1303 non-null   float64\n",
      " 3   ScreenResolution  1303 non-null   object \n",
      " 4   Cpu               1303 non-null   object \n",
      " 5   Ram               1303 non-null   int32  \n",
      " 6   Memory            1303 non-null   object \n",
      " 7   Gpu               1303 non-null   object \n",
      " 8   OpSys             1303 non-null   object \n",
      " 9   Weight            1303 non-null   float32\n",
      " 10  Price             1303 non-null   float64\n",
      "dtypes: float32(1), float64(2), int32(1), object(7)\n",
      "memory usage: 101.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Hind\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: 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": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "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": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Company'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('Company')['Price'].mean().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# df.groupby('TypeName')['Price'].mean().plot(kind='bar')\n",
    "sns.barplot(x=df['TypeName'], y = df['Price'])\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=df['Inches'], y = df['Price'])\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Touchscreen'] = df['ScreenResolution'].apply(lambda x:1 if 'Touchscreen' in x else 0 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['IPS Panel'] = df['ScreenResolution'].apply(lambda x:1 if 'IPS Panel' in x else 0 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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 Panel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>39</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 8250U 1.6GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Nvidia GeForce 930MX</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.10</td>\n",
       "      <td>46833.1200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1063</th>\n",
       "      <td>Dell</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i7 7500U 2.7GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>2TB HDD</td>\n",
       "      <td>AMD Radeon R7 M445</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.32</td>\n",
       "      <td>52746.6672</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>Asus</td>\n",
       "      <td>Gaming</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i7 7700HQ 2.8GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB SSD +  1TB HDD</td>\n",
       "      <td>Nvidia GeForce GTX 1050 Ti</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.50</td>\n",
       "      <td>67399.2000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>Dell</td>\n",
       "      <td>Gaming</td>\n",
       "      <td>17.3</td>\n",
       "      <td>IPS Panel Full HD 1920x1080</td>\n",
       "      <td>Intel Core i7 7700HQ 2.8GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>256GB SSD +  1TB HDD</td>\n",
       "      <td>Nvidia GeForce GTX 1060</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>4.42</td>\n",
       "      <td>130873.7952</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1294</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>AMD A9-Series 9410 2.9GHz</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0TB Hybrid</td>\n",
       "      <td>AMD Radeon R7 M440</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.04</td>\n",
       "      <td>29303.4672</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>917</th>\n",
       "      <td>MSI</td>\n",
       "      <td>Gaming</td>\n",
       "      <td>17.3</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i7 7700HQ 2.8GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>256GB SSD +  1TB HDD</td>\n",
       "      <td>Nvidia GeForce GTX 1050 Ti</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.70</td>\n",
       "      <td>85194.7200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>901</th>\n",
       "      <td>Asus</td>\n",
       "      <td>Gaming</td>\n",
       "      <td>17.3</td>\n",
       "      <td>IPS Panel Full HD 1920x1080</td>\n",
       "      <td>Intel Core i7 6820HK 2.7GHz</td>\n",
       "      <td>32</td>\n",
       "      <td>256GB SSD +  256GB SSD</td>\n",
       "      <td>Nvidia GeForce GTX 980M</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>3.58</td>\n",
       "      <td>68145.1200</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>Dell</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i7 7700HQ 2.8GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>Nvidia GeForce GTX 1050</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.06</td>\n",
       "      <td>101178.7200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1037</th>\n",
       "      <td>Toshiba</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>14.0</td>\n",
       "      <td>IPS Panel Full HD 1920x1080</td>\n",
       "      <td>Intel Core i7 6600U 2.6GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 520</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.47</td>\n",
       "      <td>91908.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>443</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Pentium Quad Core N4200 1.1GHz</td>\n",
       "      <td>4</td>\n",
       "      <td>128GB SSD</td>\n",
       "      <td>Intel HD Graphics 505</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.20</td>\n",
       "      <td>18594.7200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Company   TypeName  Inches             ScreenResolution  \\\n",
       "39         HP   Notebook    15.6            Full HD 1920x1080   \n",
       "1063     Dell   Notebook    15.6            Full HD 1920x1080   \n",
       "304      Asus     Gaming    15.6            Full HD 1920x1080   \n",
       "224      Dell     Gaming    17.3  IPS Panel Full HD 1920x1080   \n",
       "1294       HP   Notebook    15.6            Full HD 1920x1080   \n",
       "917       MSI     Gaming    17.3            Full HD 1920x1080   \n",
       "901      Asus     Gaming    17.3  IPS Panel Full HD 1920x1080   \n",
       "487      Dell   Notebook    15.6            Full HD 1920x1080   \n",
       "1037  Toshiba  Ultrabook    14.0  IPS Panel Full HD 1920x1080   \n",
       "443    Lenovo   Notebook    15.6            Full HD 1920x1080   \n",
       "\n",
       "                                       Cpu  Ram                  Memory  \\\n",
       "39              Intel Core i5 8250U 1.6GHz    8               256GB SSD   \n",
       "1063            Intel Core i7 7500U 2.7GHz   16                 2TB HDD   \n",
       "304            Intel Core i7 7700HQ 2.8GHz    8    128GB SSD +  1TB HDD   \n",
       "224            Intel Core i7 7700HQ 2.8GHz   16    256GB SSD +  1TB HDD   \n",
       "1294             AMD A9-Series 9410 2.9GHz    6            1.0TB Hybrid   \n",
       "917            Intel Core i7 7700HQ 2.8GHz   16    256GB SSD +  1TB HDD   \n",
       "901            Intel Core i7 6820HK 2.7GHz   32  256GB SSD +  256GB SSD   \n",
       "487            Intel Core i7 7700HQ 2.8GHz   16               512GB SSD   \n",
       "1037            Intel Core i7 6600U 2.6GHz    8               256GB SSD   \n",
       "443   Intel Pentium Quad Core N4200 1.1GHz    4               128GB SSD   \n",
       "\n",
       "                             Gpu       OpSys  Weight        Price  \\\n",
       "39         Nvidia GeForce 930MX   Windows 10    2.10   46833.1200   \n",
       "1063          AMD Radeon R7 M445  Windows 10    2.32   52746.6672   \n",
       "304   Nvidia GeForce GTX 1050 Ti  Windows 10    2.50   67399.2000   \n",
       "224      Nvidia GeForce GTX 1060  Windows 10    4.42  130873.7952   \n",
       "1294          AMD Radeon R7 M440  Windows 10    2.04   29303.4672   \n",
       "917   Nvidia GeForce GTX 1050 Ti  Windows 10    2.70   85194.7200   \n",
       "901      Nvidia GeForce GTX 980M  Windows 10    3.58   68145.1200   \n",
       "487      Nvidia GeForce GTX 1050  Windows 10    2.06  101178.7200   \n",
       "1037       Intel HD Graphics 520  Windows 10    1.47   91908.0000   \n",
       "443        Intel HD Graphics 505  Windows 10    2.20   18594.7200   \n",
       "\n",
       "      Touchscreen  IPS Panel  \n",
       "39              0          0  \n",
       "1063            0          0  \n",
       "304             0          0  \n",
       "224             0          1  \n",
       "1294            0          0  \n",
       "917             0          0  \n",
       "901             0          1  \n",
       "487             0          0  \n",
       "1037            0          1  \n",
       "443             0          0  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "scr_df = df['ScreenResolution'].str.split('x',n=1,expand=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IPS Panel Retina Display 2560</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1440</td>\n",
       "      <td>900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Full HD 1920</td>\n",
       "      <td>1080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IPS Panel Retina Display 2880</td>\n",
       "      <td>1800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1298</th>\n",
       "      <td>IPS Panel Full HD / Touchscreen 1920</td>\n",
       "      <td>1080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1299</th>\n",
       "      <td>IPS Panel Quad HD+ / Touchscreen 3200</td>\n",
       "      <td>1800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1300</th>\n",
       "      <td>1366</td>\n",
       "      <td>768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1301</th>\n",
       "      <td>1366</td>\n",
       "      <td>768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1302</th>\n",
       "      <td>1366</td>\n",
       "      <td>768</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1303 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          0     1\n",
       "0             IPS Panel Retina Display 2560  1600\n",
       "1                                      1440   900\n",
       "2                              Full HD 1920  1080\n",
       "3             IPS Panel Retina Display 2880  1800\n",
       "4             IPS Panel Retina Display 2560  1600\n",
       "...                                     ...   ...\n",
       "1298   IPS Panel Full HD / Touchscreen 1920  1080\n",
       "1299  IPS Panel Quad HD+ / Touchscreen 3200  1800\n",
       "1300                                   1366   768\n",
       "1301                                   1366   768\n",
       "1302                                   1366   768\n",
       "\n",
       "[1303 rows x 2 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scr_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_res = scr_df[0]\n",
    "y_res = scr_df[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['x_res'] = x_res\n",
    "df['y_res'] = y_res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['x_res'] = df['x_res'].astype(float)\n",
    "df['y_res'] = df['y_res'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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 Panel         1303 non-null   int64  \n",
      " 13  x_res             1303 non-null   float64\n",
      " 14  y_res             1303 non-null   float64\n",
      "dtypes: float32(1), float64(4), int32(1), int64(2), object(7)\n",
      "memory usage: 142.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ppi'] = (((df['x_res']**2) + (df['y_res']**2))**0.5/df['Inches']).astype('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Inches</th>\n",
       "      <th>ScreenResolution</th>\n",
       "      <th>Cpu</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>IPS Panel</th>\n",
       "      <th>x_res</th>\n",
       "      <th>y_res</th>\n",
       "      <th>ppi</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",
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       "      <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>2560.0</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>226.983005</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>1440.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>127.677940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>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>1920.0</td>\n",
       "      <td>1080.0</td>\n",
       "      <td>141.211998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>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>2880.0</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>220.534624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>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>2560.0</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>226.983005</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",
       "      <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>1920.0</td>\n",
       "      <td>1080.0</td>\n",
       "      <td>157.350512</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>3200.0</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>276.053530</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>1366.0</td>\n",
       "      <td>768.0</td>\n",
       "      <td>111.935204</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>1366.0</td>\n",
       "      <td>768.0</td>\n",
       "      <td>100.454670</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>1366.0</td>\n",
       "      <td>768.0</td>\n",
       "      <td>100.454670</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1303 rows × 16 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 Panel   x_res   y_res         ppi  \n",
       "0               0          1  2560.0  1600.0  226.983005  \n",
       "1               0          0  1440.0   900.0  127.677940  \n",
       "2               0          0  1920.0  1080.0  141.211998  \n",
       "3               0          1  2880.0  1800.0  220.534624  \n",
       "4               0          1  2560.0  1600.0  226.983005  \n",
       "...           ...        ...     ...     ...         ...  \n",
       "1298            1          1  1920.0  1080.0  157.350512  \n",
       "1299            1          1  3200.0  1800.0  276.053530  \n",
       "1300            0          0  1366.0   768.0  111.935204  \n",
       "1301            0          0  1366.0   768.0  100.454670  \n",
       "1302            0          0  1366.0   768.0  100.454670  \n",
       "\n",
       "[1303 rows x 16 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(['x_res','y_res'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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 i5 6440HQ 2.6GHz               1\n",
       "Intel Celeron Dual Core N3350 2.0GHz      1\n",
       "Intel Core i5 7200U 2.70GHz               1\n",
       "Intel Core i7 2.7GHz                      1\n",
       "AMD A9-Series 9420 2.9GHz                 1\n",
       "Name: Cpu, Length: 118, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Cpu'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['cpu_name'] = df['Cpu'].apply(lambda x : ' '.join(x.split()[:3]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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 Panel</th>\n",
       "      <th>ppi</th>\n",
       "      <th>cpu_name</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>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>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>127.677940</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>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>141.211998</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>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>220.534624</td>\n",
       "      <td>Intel Core i7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>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>226.983005</td>\n",
       "      <td>Intel Core i5</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>157.350512</td>\n",
       "      <td>Intel Core i7</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>276.053530</td>\n",
       "      <td>Intel Core i7</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>111.935204</td>\n",
       "      <td>Intel Celeron Dual</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>100.454670</td>\n",
       "      <td>Intel Core i7</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>100.454670</td>\n",
       "      <td>Intel Celeron Dual</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 Panel         ppi            cpu_name  \n",
       "0               0          1  226.983005       Intel Core i5  \n",
       "1               0          0  127.677940       Intel Core i5  \n",
       "2               0          0  141.211998       Intel Core i5  \n",
       "3               0          1  220.534624       Intel Core i7  \n",
       "4               0          1  226.983005       Intel Core i5  \n",
       "...           ...        ...         ...                 ...  \n",
       "1298            1          1  157.350512       Intel Core i7  \n",
       "1299            1          1  276.053530       Intel Core i7  \n",
       "1300            0          0  111.935204  Intel Celeron Dual  \n",
       "1301            0          0  100.454670       Intel Core i7  \n",
       "1302            0          0  100.454670  Intel Celeron Dual  \n",
       "\n",
       "[1303 rows x 15 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fetch_processor(text):\n",
    "    if text == 'Intel Core i7' or text == 'Intel Core i5' or text == 'Intel Core i3':\n",
    "        return text\n",
    "    else:\n",
    "        if text.split()[0] == 'Intel':\n",
    "            return 'Other Intel Processor'\n",
    "        else:\n",
    "            return 'AMD Processor'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['cpu_name'] = df['cpu_name'].apply(lambda x: fetch_processor(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAFXCAYAAABZbA7IAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAbnUlEQVR4nO3df7RndV3v8efL4YfEj4AYCAEdtNGCSrQRMcpVUEGZYhnesfBylRa3e0lJb7cLlYXVJMuC5ZUbFpXeKX/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==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['cpu_name'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=df['cpu_name'],y=df['Price'], estimator = median)\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns='Cpu',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>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 Panel</th>\n",
       "      <th>ppi</th>\n",
       "      <th>cpu_name</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>8</td>\n",
       "      <td>128GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 640</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>71378.6832</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>1440x900</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>47895.5232</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>127.677940</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>30636.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>15.4</td>\n",
       "      <td>IPS Panel Retina Display 2880x1800</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>135195.3360</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>220.534624</td>\n",
       "      <td>Intel Core i7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>96095.8080</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "    <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",
       "    </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>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>157.350512</td>\n",
       "      <td>Intel Core i7</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>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>276.053530</td>\n",
       "      <td>Intel Core i7</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>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>111.935204</td>\n",
       "      <td>Other Intel Processor</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>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>100.454670</td>\n",
       "      <td>Intel Core i7</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>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>100.454670</td>\n",
       "      <td>Other Intel Processor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1303 rows × 14 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  Ram               Memory  \\\n",
       "0             IPS Panel Retina Display 2560x1600    8            128GB SSD   \n",
       "1                                       1440x900    8  128GB Flash Storage   \n",
       "2                              Full HD 1920x1080    8            256GB SSD   \n",
       "3             IPS Panel Retina Display 2880x1800   16            512GB SSD   \n",
       "4             IPS Panel Retina Display 2560x1600    8            256GB SSD   \n",
       "...                                          ...  ...                  ...   \n",
       "1298   IPS Panel Full HD / Touchscreen 1920x1080    4            128GB SSD   \n",
       "1299  IPS Panel Quad HD+ / Touchscreen 3200x1800   16            512GB SSD   \n",
       "1300                                    1366x768    2   64GB Flash Storage   \n",
       "1301                                    1366x768    6              1TB HDD   \n",
       "1302                                    1366x768    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 Panel         ppi               cpu_name  \n",
       "0               0          1  226.983005          Intel Core i5  \n",
       "1               0          0  127.677940          Intel Core i5  \n",
       "2               0          0  141.211998          Intel Core i5  \n",
       "3               0          1  220.534624          Intel Core i7  \n",
       "4               0          1  226.983005          Intel Core i5  \n",
       "...           ...        ...         ...                    ...  \n",
       "1298            1          1  157.350512          Intel Core i7  \n",
       "1299            1          1  276.053530          Intel Core i7  \n",
       "1300            0          0  111.935204  Other Intel Processor  \n",
       "1301            0          0  100.454670          Intel Core i7  \n",
       "1302            0          0  100.454670  Other Intel Processor  \n",
       "\n",
       "[1303 rows x 14 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Ram'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=df['Ram'],y=df['Price'])\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Ram'] = df['Ram'].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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",
       "1TB SSD                           14\n",
       "512GB SSD +  1TB HDD              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",
       "256GB SSD +  500GB HDD             2\n",
       "512GB Flash Storage                2\n",
       "128GB SSD +  2TB HDD               2\n",
       "256GB SSD +  256GB SSD             2\n",
       "1TB SSD +  1TB HDD                 2\n",
       "512GB SSD +  512GB SSD             1\n",
       "512GB SSD +  256GB SSD             1\n",
       "256GB SSD +  1.0TB Hybrid          1\n",
       "32GB HDD                           1\n",
       "128GB HDD                          1\n",
       "1.0TB HDD                          1\n",
       "508GB Hybrid                       1\n",
       "64GB Flash Storage +  1TB HDD      1\n",
       "64GB SSD                           1\n",
       "512GB SSD +  1.0TB Hybrid          1\n",
       "1TB HDD +  1TB HDD                 1\n",
       "240GB SSD                          1\n",
       "8GB SSD                            1\n",
       "Name: Memory, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Memory'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Memory'] = df['Memory'].astype(str).replace('\\.0', '', regex=True)\n",
    "df[\"Memory\"] = df[\"Memory\"].str.replace('GB', '')\n",
    "df[\"Memory\"] = df[\"Memory\"].str.replace('TB', '000')\n",
    "new = df[\"Memory\"].str.split(\"+\", n = 1, expand = True)\n",
    "\n",
    "df[\"first\"]= new[0]\n",
    "df[\"first\"]=df[\"first\"].str.strip()\n",
    "\n",
    "df[\"second\"]= new[1]\n",
    "\n",
    "df[\"Layer1HDD\"] = df[\"first\"].apply(lambda x: 1 if \"HDD\" in x else 0)\n",
    "df[\"Layer1SSD\"] = df[\"first\"].apply(lambda x: 1 if \"SSD\" in x else 0)\n",
    "df[\"Layer1Hybrid\"] = df[\"first\"].apply(lambda x: 1 if \"Hybrid\" in x else 0)\n",
    "df[\"Layer1Flash_Storage\"] = df[\"first\"].apply(lambda x: 1 if \"Flash Storage\" in x else 0)\n",
    "\n",
    "df['first'] = df['first'].str.replace(r'\\D', '')\n",
    "\n",
    "df[\"second\"].fillna(\"0\", inplace = True)\n",
    "\n",
    "df[\"Layer2HDD\"] = df[\"second\"].apply(lambda x: 1 if \"HDD\" in x else 0)\n",
    "df[\"Layer2SSD\"] = df[\"second\"].apply(lambda x: 1 if \"SSD\" in x else 0)\n",
    "df[\"Layer2Hybrid\"] = df[\"second\"].apply(lambda x: 1 if \"Hybrid\" in x else 0)\n",
    "df[\"Layer2Flash_Storage\"] = df[\"second\"].apply(lambda x: 1 if \"Flash Storage\" in x else 0)\n",
    "\n",
    "df['second'] = df['second'].str.replace(r'\\D', '')\n",
    "\n",
    "df[\"first\"] = df[\"first\"].astype(int)\n",
    "df[\"second\"] = df[\"second\"].astype(int)\n",
    "\n",
    "df[\"HDD\"]=(df[\"first\"]*df[\"Layer1HDD\"]+df[\"second\"]*df[\"Layer2HDD\"])\n",
    "df[\"SSD\"]=(df[\"first\"]*df[\"Layer1SSD\"]+df[\"second\"]*df[\"Layer2SSD\"])\n",
    "df[\"Hybrid\"]=(df[\"first\"]*df[\"Layer1Hybrid\"]+df[\"second\"]*df[\"Layer2Hybrid\"])\n",
    "df[\"Flash_Storage\"]=(df[\"first\"]*df[\"Layer1Flash_Storage\"]+df[\"second\"]*df[\"Layer2Flash_Storage\"])\n",
    "\n",
    "df.drop(columns=['first', 'second', 'Layer1HDD', 'Layer1SSD', 'Layer1Hybrid',\n",
    "       'Layer1Flash_Storage', 'Layer2HDD', 'Layer2SSD', 'Layer2Hybrid',\n",
    "       'Layer2Flash_Storage'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1291\n",
       "1000      11\n",
       "508        1\n",
       "Name: Hybrid, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Hybrid'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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 Panel        0.252208\n",
       "ppi              0.473487\n",
       "HDD             -0.096441\n",
       "SSD              0.670799\n",
       "Hybrid           0.007989\n",
       "Flash_Storage   -0.040511\n",
       "Name: Price, dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()['Price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['Hybrid','Flash_Storage'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>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 Panel</th>\n",
       "      <th>ppi</th>\n",
       "      <th>cpu_name</th>\n",
       "      <th>HDD</th>\n",
       "      <th>SSD</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>8</td>\n",
       "      <td>128 SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 640</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>71378.6832</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "    </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>8</td>\n",
       "      <td>128 Flash Storage</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>47895.5232</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>127.677940</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </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>8</td>\n",
       "      <td>256 SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>30636.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "    </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>16</td>\n",
       "      <td>512 SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>135195.3360</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>220.534624</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>512</td>\n",
       "    </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>8</td>\n",
       "      <td>256 SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>96095.8080</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Inches                    ScreenResolution  Ram  \\\n",
       "0   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600    8   \n",
       "1   Apple  Ultrabook    13.3                            1440x900    8   \n",
       "2      HP   Notebook    15.6                   Full HD 1920x1080    8   \n",
       "3   Apple  Ultrabook    15.4  IPS Panel Retina Display 2880x1800   16   \n",
       "4   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600    8   \n",
       "\n",
       "              Memory                           Gpu  OpSys  Weight  \\\n",
       "0            128 SSD  Intel Iris Plus Graphics 640  macOS    1.37   \n",
       "1  128 Flash Storage        Intel HD Graphics 6000  macOS    1.34   \n",
       "2            256 SSD         Intel HD Graphics 620  No OS    1.86   \n",
       "3            512 SSD            AMD Radeon Pro 455  macOS    1.83   \n",
       "4            256 SSD  Intel Iris Plus Graphics 650  macOS    1.37   \n",
       "\n",
       "         Price  Touchscreen  IPS Panel         ppi       cpu_name  HDD  SSD  \n",
       "0   71378.6832            0          1  226.983005  Intel Core i5    0  128  \n",
       "1   47895.5232            0          0  127.677940  Intel Core i5    0    0  \n",
       "2   30636.0000            0          0  141.211998  Intel Core i5    0  256  \n",
       "3  135195.3360            0          1  220.534624  Intel Core i7    0  512  \n",
       "4   96095.8080            0          1  226.983005  Intel Core i5    0  256  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Gpu'] = df['Gpu'].apply(lambda x : x.split()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6899999976158142"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Weight'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df['Gpu'] !='ARM']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZIAAAEcCAYAAADtODJSAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAaVElEQVR4nO3df7Bf9V3n8eeLxPKjNsiPQNmEGlriD6CWlizLiqNro5JqLazCmvoj0Y0TZdCtq24Ex1GrZqfU7bKlK+xgUQJWIVK7ZN2hygR/bB0WGmqVAmXIgoUAgRQojVVoA+/94/u505vLNzf35uT7Pbm5z8fMd8457+/5nLzPXLive358zzdVhSRJB+qIvhuQJM1tBokkqRODRJLUiUEiSerEIJEkdWKQSJI6Wdh3A+N24okn1rJly/puQ5LmlHvvvffzVbV42HvzLkiWLVvGtm3b+m5DkuaUJJ/b13ue2pIkdWKQSJI6MUgkSZ0YJJKkTgwSSVInBokkqRODRJLUyciCJMk3Jvn0pNcXk/xckuOT3JHk4TY9btKYK5JsT/JQkgsm1c9Jcl977+okafUjk9zS6ncnWTaq/ZGkfdmwYQNr1qxhw4YNfbfSi5EFSVU9VFVnV9XZwDnAPwEfAy4HtlbVcmBrWybJGcBq4ExgFXBNkgVtc9cC64Hl7bWq1dcBz1fV6cBVwJWj2h9J2pedO3fyxBNPsHPnzr5b6cW4Tm2tBP5fVX0OuBDY1OqbgIva/IXAzVX1UlU9CmwHzk1yCrCoqu6qwdc53jhlzMS2bgVWThytSJLGY1xBshr4ozZ/clU9BdCmJ7X6EuDxSWN2tNqSNj+1vteYqtoDvACcMPUfT7I+ybYk23bt2nVQdkiSNDDyIEnyGuBdwB/vb9UhtZqmPt2YvQtV11XViqpasXjx0GeOSZIO0DiOSN4BfKqqnm7LT7fTVbTpM62+Azh10rilwJOtvnRIfa8xSRYCxwLPjWAfJEn7MI4geTdfPa0FsAVY2+bXArdNqq9ud2KdxuCi+j3t9NfuJOe16x9rpoyZ2NbFwJ3tOookaUxG+hj5JMcA3w381KTy+4DNSdYBjwGXAFTV/Uk2Aw8Ae4DLqurlNuZS4AbgaOD29gK4HrgpyXYGRyKrR7k/kqRXG2mQVNU/MeXid1U9y+AurmHrbwQ2DqlvA84aUn+RFkSSpH74yXZJUicGiSSpE4NEktSJQSJJ6sQgkSR1YpBIkjoZ6e2/knQgzv/Q+X23MCuv+cJrOIIjePwLj8+p3v/mZ//moGzHIxJJUicGiSSpE4NEktSJQSJJ6sQgkSR1YpBIkjoxSCRJnRgkkqRODBJJUicGiSSpEx+RIkkd1THFK7xCHVN9t9ILg0SSOvrK+V/pu4VeeWpLktTJSIMkydcluTXJZ5M8mORfJzk+yR1JHm7T4yatf0WS7UkeSnLBpPo5Se5r712dJK1+ZJJbWv3uJMtGuT+SpFcb9RHJB4GPV9U3AW8BHgQuB7ZW1XJga1smyRnAauBMYBVwTZIFbTvXAuuB5e21qtXXAc9X1enAVcCVI94fSdIUIwuSJIuAbweuB6iqL1fVF4ALgU1ttU3ARW3+QuDmqnqpqh4FtgPnJjkFWFRVd1VVATdOGTOxrVuBlRNHK5Kk8RjlEckbgV3A7yf52yQfTvJa4OSqegqgTU9q6y8BHp80fkerLWnzU+t7jamqPcALwAlTG0myPsm2JNt27dp1sPZPksRog2Qh8Dbg2qp6K/Al2mmsfRh2JFHT1Kcbs3eh6rqqWlFVKxYvXjx915KkWRllkOwAdlTV3W35VgbB8nQ7XUWbPjNp/VMnjV8KPNnqS4fU9xqTZCFwLPDcQd8TSdI+jSxIqmon8HiSb2yllcADwBZgbautBW5r81uA1e1OrNMYXFS/p53+2p3kvHb9Y82UMRPbuhi4s11HkSSNyag/kPizwEeSvAZ4BPgJBuG1Ock64DHgEoCquj/JZgZhswe4rKpebtu5FLgBOBq4vb1gcCH/piTbGRyJrB7x/kiSphhpkFTVp4EVQ95auY/1NwIbh9S3AWcNqb9ICyJJUj/8ZLskqRODRJLUiUEiSerEIJEkdWKQSJI6MUgkSZ0YJJKkTgwSSVInBokkqRODRJLUiUEiSerEIJEkdWKQSJI6MUgkSZ0YJJKkTgwSSVInBokkqZNRf9WuxmjDhg3s3LmT17/+9bz//e/vux1J84RBchjZuXMnTzzxRN9tSJpnPLUlSepkpEGS5B+S3Jfk00m2tdrxSe5I8nCbHjdp/SuSbE/yUJILJtXPadvZnuTqJGn1I5Pc0up3J1k2yv2RJL3aOI5IvrOqzq6qFW35cmBrVS0HtrZlkpwBrAbOBFYB1yRZ0MZcC6wHlrfXqlZfBzxfVacDVwFXjmF/JEmT9HFq60JgU5vfBFw0qX5zVb1UVY8C24Fzk5wCLKqqu6qqgBunjJnY1q3AyomjFUnSeIw6SAr48yT3JlnfaidX1VMAbXpSqy8BHp80dkerLWnzU+t7jamqPcALwAkj2A9J0j6M+q6t86vqySQnAXck+ew06w47kqhp6tON2XvDgxBbD/CGN7xh+o4lSbMy0iOSqnqyTZ8BPgacCzzdTlfRps+01XcAp04avhR4stWXDqnvNSbJQuBY4LkhfVxXVSuqasXixYsPzs5JkoARBkmS1yZ53cQ88D3AZ4AtwNq22lrgtja/BVjd7sQ6jcFF9Xva6a/dSc5r1z/WTBkzsa2LgTvbdRRJ0piM8tTWycDH2rXvhcAfVtXHk3wS2JxkHfAYcAlAVd2fZDPwALAHuKyqXm7buhS4ATgauL29AK4HbkqyncGRyOoR7o8kaYiRBUlVPQK8ZUj9WWDlPsZsBDYOqW8DzhpSf5EWRJKkfvjJdklSJz5raxrn/Kcb+25hVl73+d0sAB77/O451fu9v72m7xYkdeARiSSpE4NEktSJQSJJ6sQgkSR1YpBIkjoxSCRJnRgkkqRODBJJUicGiSSpE4NEktSJQSJJ6sRnbR1GXnnNa/eaStI4GCSHkS8t/56+W5A0D3lqS5LUiUEiSerEIJEkdWKQSJI6MUgkSZ2MPEiSLEjyt0n+tC0fn+SOJA+36XGT1r0iyfYkDyW5YFL9nCT3tfeuTpJWPzLJLa1+d5Jlo94fSdLexnFE8h7gwUnLlwNbq2o5sLUtk+QMYDVwJrAKuCbJgjbmWmA9sLy9VrX6OuD5qjoduAq4crS7IkmaaqRBkmQp8H3AhyeVLwQ2tflNwEWT6jdX1UtV9SiwHTg3ySnAoqq6q6oKuHHKmIlt3QqsnDhakSSNx4yCJMk3JNma5DNt+VuS/MoMhv43YAPwyqTayVX1FECbntTqS4DHJ623o9WWtPmp9b3GVNUe4AXghJnskyTp4JjpEcnvAlcAXwGoqr9ncBpqn5K8E3imqu6d4b8x7EiipqlPN2ZqL+uTbEuybdeuXTNsRxqfDRs2sGbNGjZs2NB3K9KszfQRKcdU1T1Tzhrt2c+Y84F3Jfle4ChgUZI/AJ5OckpVPdVOWz3T1t8BnDpp/FLgyVZfOqQ+ecyOJAuBY4HnpjZSVdcB1wGsWLHiVUEj9W3nzp088cQTfbchHZCZHpF8PsmbaH/tJ7kYeGq6AVV1RVUtraplDI5e7qyqHwW2AGvbamuB29r8FmB1uxPrNAYX1e9pp792JzmvXf9YM2XMxLYubv+GQSFJYzTTI5LLGPxF/01JngAeBX70AP/N9wGbk6wDHgMuAaiq+5NsBh5gcLRzWVW93MZcCtwAHA3c3l4A1wM3JdnO4Ehk2tNtkqSDb0ZBUlWPAN+V5LXAEVW1ezb/SFX9JfCXbf5ZYOU+1tsIbBxS3wacNaT+Ii2IJEn9mOldW/85yddV1ZeqaneS45L81qibkyQd+mZ6jeQdVfWFiYWqeh743pF0JEmaU2YaJAuSHDmxkORo4Mhp1pckzRMzvdj+B8DWJL/P4M6tf89XP1EuSZrHZnqx/f1J7mNwkTzAb1bVn420M0nSnDDj72yvqsm33UqSBOwnSJJ8oqq+Lclu9n70SICqqkUj7U6SdMibNkiq6tva9HXjaUc6eB77jTf33cKM7XnueGAhe5773Jzq+w2/el/fLegQsN+7tpIcMfHUX0mSptpvkFTVK8DfJXnDGPqRJM0xM73Yfgpwf5J7gC9NFKvqXSPpSpI0Z8w0SN470i4kSXPW/u7aOgr4aeB04D7g+vZNhJIkAfu/RrIJWMEgRN4BfGDkHUmS5pT9ndo6o6reDJDkeuCe0bckSZpL9ndE8pWJGU9pSZKG2d8RyVuSfLHNBzi6LfvJdukgOvGoV4A9bSrNLfv7ZPuCcTUizWe/+C1f6LsF6YDN9PtIJEkayiCRJHVikEiSOhlZkCQ5Ksk9Sf4uyf1J3tvqxye5I8nDbXrcpDFXJNme5KEkF0yqn5Pkvvbe1UnS6kcmuaXV706ybFT7I0kabpRHJC8Bb6+qtwBnA6uSnAdcDmytquXA1rZMkjOA1cCZwCrgmiQTF/uvBdYDy9trVauvA56vqtOBq4ArR7g/kqQhRhYkNfCPbfFr2quAC/nq971vAi5q8xcCN1fVS1X1KLAdODfJKcCiqrqrqgq4ccqYiW3dCqycOFqRJI3HSK+RJFmQ5NPAM8AdVXU3cHJVPQXQpie11ZcAj08avqPVlrT5qfW9xrQPTL4AnDCkj/VJtiXZtmvXroO0d5IkGHGQVNXLVXU2sJTB0cVZ06w+7EiipqlPN2ZqH9dV1YqqWrF48eL9dC1Jmo2x3LVVVV8A/pLBtY2n2+kq2vSZttoO4NRJw5YCT7b60iH1vcYkWQgcCzw3in2QJA03yru2Fif5ujZ/NPBdwGeBLcDattpa4LY2vwVY3e7EOo3BRfV72umv3UnOa9c/1kwZM7Gti4E723UUSdKYzPSLrQ7EKcCmdufVEcDmqvrTJHcBm5OsAx4DLgGoqvuTbAYeAPYAl1XVy21blwI3AEcDt7cXwPXATUm2MzgSWT3C/ZEkDTGyIKmqvwfeOqT+LLByH2M2AhuH1LcBr7q+UlUv0oJIktQPP9kuSerEIJEkdWKQSJI6MUgkSZ0YJJKkTgwSSVInBokkqRODRJLUiUEiSerEIJEkdWKQSJI6MUgkSZ0YJJKkTgwSSVInBokkqRODRJLUiUEiSerEIJEkdWKQSJI6MUgkSZ2MLEiSnJrkL5I8mOT+JO9p9eOT3JHk4TY9btKYK5JsT/JQkgsm1c9Jcl977+okafUjk9zS6ncnWTaq/ZEkDTfKI5I9wC9U1TcD5wGXJTkDuBzYWlXLga1tmfbeauBMYBVwTZIFbVvXAuuB5e21qtXXAc9X1enAVcCVI9wfSdIQIwuSqnqqqj7V5ncDDwJLgAuBTW21TcBFbf5C4OaqeqmqHgW2A+cmOQVYVFV3VVUBN04ZM7GtW4GVE0crkqTxGMs1knbK6a3A3cDJVfUUDMIGOKmttgR4fNKwHa22pM1Pre81pqr2AC8AJ4xkJyRJQ408SJJ8LfBR4Oeq6ovTrTqkVtPUpxsztYf1SbYl2bZr1679tSxJmoWRBkmSr2EQIh+pqj9p5afb6Sra9JlW3wGcOmn4UuDJVl86pL7XmCQLgWOB56b2UVXXVdWKqlqxePHig7FrkqRmlHdtBbgeeLCq/uukt7YAa9v8WuC2SfXV7U6s0xhcVL+nnf7aneS8ts01U8ZMbOti4M52HUWSNCYLR7jt84EfA+5L8ulW+2XgfcDmJOuAx4BLAKrq/iSbgQcY3PF1WVW93MZdCtwAHA3c3l4wCKqbkmxncCSyeoT7I0kaYmRBUlWfYPg1DICV+xizEdg4pL4NOGtI/UVaEEmS+uEn2yVJnRgkkqRODBJJUicGiSSpE4NEktSJQSJJ6sQgkSR1YpBIkjoxSCRJnRgkkqRODBJJUicGiSSpE4NEktSJQSJJ6sQgkSR1YpBIkjoxSCRJnRgkkqRODBJJUicGiSSpk5EFSZLfS/JMks9Mqh2f5I4kD7fpcZPeuyLJ9iQPJblgUv2cJPe1965OklY/MsktrX53kmWj2hdJ0r6N8ojkBmDVlNrlwNaqWg5sbcskOQNYDZzZxlyTZEEbcy2wHljeXhPbXAc8X1WnA1cBV45sTyRJ+zSyIKmqvwaem1K+ENjU5jcBF02q31xVL1XVo8B24NwkpwCLququqirgxiljJrZ1K7By4mhFkjQ+475GcnJVPQXQpie1+hLg8Unr7Wi1JW1+an2vMVW1B3gBOGFknUuShjpULrYPO5KoaerTjXn1xpP1SbYl2bZr164DbFGSNMy4g+TpdrqKNn2m1XcAp05abynwZKsvHVLfa0yShcCxvPpUGgBVdV1VraiqFYsXLz5IuyJJgvEHyRZgbZtfC9w2qb663Yl1GoOL6ve001+7k5zXrn+smTJmYlsXA3e26yiSpDFaOKoNJ/kj4N8AJybZAfwa8D5gc5J1wGPAJQBVdX+SzcADwB7gsqp6uW3qUgZ3gB0N3N5eANcDNyXZzuBIZPWo9kWStG8jC5Kqevc+3lq5j/U3AhuH1LcBZw2pv0gLIklSfw6Vi+2SpDnKIJEkdWKQSJI6MUgkSZ0YJJKkTgwSSVInBokkqRODRJLUiUEiSerEIJEkdWKQSJI6MUgkSZ0YJJKkTgwSSVInBokkqRODRJLUiUEiSerEIJEkdWKQSJI6MUgkSZ0YJJKkTuZ8kCRZleShJNuTXN53P5I038zpIEmyAPgd4B3AGcC7k5zRb1eSNL/M6SABzgW2V9UjVfVl4Gbgwp57kqR5JVXVdw8HLMnFwKqq+sm2/GPAv6qqn5my3npgfVv8RuChsTY6XicCn++7CR0Qf3Zz2+H+8/v6qlo87I2F4+7kIMuQ2quSsaquA64bfTv9S7Ktqlb03Ydmz5/d3Daff35z/dTWDuDUSctLgSd76kWS5qW5HiSfBJYnOS3Ja4DVwJaee5KkeWVOn9qqqj1Jfgb4M2AB8HtVdX/PbfVtXpzCO0z5s5vb5u3Pb05fbJck9W+un9qSJPXMIJEkdWKQSJI6MUgkSZ3M6bu25rskb5vu/ar61Lh60ewlOQH4YeCbWulB4I+q6tn+utJsJFkM/BKDZ/0dNVGvqrf31lQPDJK57QPTvFfAvPqPeS5J8s3AnQxuXf9bBk9p+JfALyd5e1V9ts/+NGMfAW4Bvg/4aWAtsKvXjnrg7b9SD5LcCmyuqs1T6j8I/HBV/WA/nWk2ktxbVeck+fuq+pZW+6uq+o6+exsnr5EcBpIck+RXklzXlpcneWfffWlab54aIgBV9VHgrB760YH5Sps+leT7kryVwaOa5hVPbR0efh+4F/jWtrwD+GPgT3vrSPvzpQN8T4eW30pyLPALwIeARcB/7Lel8TNIDg9vqqofSvJugKr65yTDnoysQ8dJSX5+SD3A0Ed169BTVRN/rL0AfGefvfTJIDk8fDnJ0bRH6Cd5E/BSvy1pP34XeN0+3vvwOBvR7CXZUFXvT/Ihhn91xX/ooa3eGCSHh18HPg6cmuQjwPnAT/TakaZVVe/tuwd18mCbbuu1i0OEd20dJtpnEs5jcGrk/1bV4fxNbXNekqune3++/UWruc0gOQwk2VpVK/dX06EjyZeBzwCbGXwZ217XtKpqUx99aWaS/C+GnNKaUFXvGmM7vfPU1hyW5CjgGODEJMfx1V9Gi4B/0VtjmolTgEuAHwL2MPhQ20er6vleu9JM/Zc2/QHg9cAftOV3A//QR0N98ohkDkvyHuDnGITGE3w1SL4I/G5V/feeWtMsJFnC4BfQzwO/VFU39dySZijJX1fVt++vdrjziGQOq6oPAh9M8rNV9aG++9HsteelvRv4buB2Bp8H0tyxOMkbq+oRgCSnMQ9v3/aI5DCR5FuBZUz646CqbuytIU0ryXuBdzK4++dm4ONVtaffrjRbSVYx+IrdR1ppGfBTVfVnvTXVA4PkMJDkJuBNwKeBl1u5vPPn0JXkFQa/fP65lSb+RwzwSlW9pZfGNGtJjuSrT3D+bFXNu89weWrr8LACOKP8q2AuOW1ILQye0/TLY+5Fs9Se0Hxnkh+Y8tabklBVf9JLYz0xSA4Pn2Fw58hTfTeimamqz03MJzmbwfeS/DvgUeCjPbWlmfsOBl8D8P1D3itgXgWJp7YOA0n+AjgbuIdJj0aZb/eyzyVJvgFYzeBC+7MMbv/9xar6+l4b06wkWVBVL+9/zcObQXIYSDL0uw+q6q/G3Ytmpl0j+T/Auqra3mqPVNUb++1Ms5HkMQaPJ7oFuHO+nl42SKQeJPm3DI5IvpXBL6KbgQ9X1bBrJzpEtYelfj+Dn+XbGHx1w81V9YleGxszg2QOS7Kb4Y9pCIO7thaNuSXNUpLXAhcxOMX1dmAT8LGq+vM++9LstadLfBD4kapa0Hc/42SQSIeIJMfTHptSVW/vux/NTDu1/EPAO4BPAre0b7qcNwwSSTpASR5l8PmtzcCWqpqX325pkEjSAUqyqKq+2HcffTNIJGmWkvzqNG9XVf3m2Jo5BBgkkjRLSX5hSPkY4CeBE6rqa8fcUq8MEknqIMnrgPcA6xhcK/lAVT3Tb1fj5SNSJOkAtLvsfh74EQa3bb9tvn4xmUEiSbOU5LcZfDvidcCbq+ofe26pV57akqRZao+4eYnB1yRP/iU6Lz8MbJBIkjo5ou8GJElzm0EiSerEIJHGJMnJSf4wySNJ7k1yV3sKsDSnGSTSGCQJ8D+Bv66qN1bVOQwePb6018akg8Agkcbj7cCXq+p/TBSq6nNV9aEkP57ktiQfT/JQkl8DSLIsyWcm1k/yi0l+ffytS9PzcyTSeJwJfGqa988FzgL+Cfhkkv8NfH4cjUldeUQi9SDJ7yT5uySfbKU7qurZqvpn4E+Ab+uxPWlWDBJpPO5n8FWsAFTVZcBKYPFEacr6xeDDbpP/Hz1qlA1KB8ogkcbjTuCoJJdOqh0zaf67kxzfvgP8IuBvgKeBk5KckORI4J1j61aaBa+RSGNQVZXkIuCqJBuAXcCXgF8CjgY+AdwEnA78YVVtA0jyG8DdwKPAZ3toXdovH5Ei9SzJjwMrqupn+u5FOhCe2pIkdeIRiSSpE49IJEmdGCSSpE4MEklSJwaJJKkTg0SS1IlBIknq5P8D4tLdrl9nmX4AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=df['Gpu'],y=df['Price'],estimator=np.median)\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-59-ed90327d7b80>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['OpSys'] = df['OpSys'].apply(lambda x : x.split()[0])\n"
     ]
    }
   ],
   "source": [
    "df['OpSys'] = df['OpSys'].apply(lambda x : x.split()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Windows    1125\n",
       "No           66\n",
       "Linux        62\n",
       "Chrome       26\n",
       "macOS        13\n",
       "Mac           8\n",
       "Android       2\n",
       "Name: OpSys, dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['OpSys'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cat_os(inp):\n",
    "    if inp == 'Windows':\n",
    "        return 'Windows'\n",
    "    elif inp == 'macOS' or inp == 'Mac':\n",
    "        return 'Mac'\n",
    "    else:\n",
    "        return 'Others/No OS/Linux'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-62-11ac4ab7f180>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['OpSys'] = df['OpSys'].apply(cat_os)\n"
     ]
    }
   ],
   "source": [
    "df['OpSys'] = df['OpSys'].apply(cat_os)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Windows               1125\n",
       "Others/No OS/Linux     156\n",
       "Mac                     21\n",
       "Name: OpSys, dtype: int64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['OpSys'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vHsWJSZJ+aOQBU1U3VdUX2+87gauBlcAxwLlts3OBl7XfxwAfrKq7q+o6YANweJJ9gOVV9dmqKuC8KftMHOsC4KiJ3o0kaTQGHYNpl66eBVwGPLaqboIuhIC92mYrgRsm7baxta1sv6e232+fqtoGfAd4TC8nIUma1mABk+SRwIeBX6uqO2badJq2mqF9pn2m1nBCkvVJ1m/ZsmV7JUuS5mCQgEnyMLpweX9VfaQ139wue9G+b2ntG4H9Ju2+L3Bja993mvb77ZNkKbAbsHVqHVV1ZlWtqqpVK1aseChOTZLUDHEXWYCzgKur6m2TVl0IHNd+Hwd8bFL7mnZn2IF0g/mfb5fR7kxyRDvmsVP2mTjWK4BL2jiNJGlElg7wv3kk8AvAFUm+3Np+B/hT4PwkxwPXA68EqKork5wPXEV3B9pJVXVv2+9E4BxgGXBR+0AXYO9NsoGu57Km53OSJE0x8oCpqk8z/RgJwFEPsM9pwGnTtK8HDp2m/S5aQEmShuGT/JKkXhgwkqReGDCSpF4YMJKkXhgwkqReGDCSpF4YMJKkXhgwkqReGDCSpF4YMJKkXhgwkqReGDCSpF4YMJKkXhgwkqReGDCSpF4YMJKkXhgwkqReGDCSpF4YMJKkXhgwkqReGDCSpF4YMJKkXhgwkqReGDCSpF4YMJKkXhgwkqReGDCSpF4YMJKkXhgwkqReGDCSpF4YMJKkXhgwkqReGDCSpF4YMJKkXizogEmyOsk1STYkeePQ9UjSYrJgAybJEuAvgBcDBwOvSnLwsFVJ0uKxYAMGOBzYUFXfrKofAB8Ejhm4JklaNBZywKwEbpi0vLG1SZJGYOnQBfQo07TV/TZITgBOaIvfTXJN71UNZ0/g1qGLmIu89bihSxgn8+vvd+p0//ktWvPrbwfkdXP6+z3ugVYs5IDZCOw3aXlf4MbJG1TVmcCZoyxqKEnWV9WqoevQjvHvN38t5r/dQr5E9gXgoCQHJtkZWANcOHBNkrRoLNgeTFVtS/IrwCeAJcB7qurKgcuSpEVjwQYMQFX9I/CPQ9cxJhbFpcAFzL/f/LVo/3apqu1vJUnSHC3kMRhJ0oAMGElSLwwYaQxNN61Rkh8ffSXSjjNgFrAkJyV59KTl3ZP88oAlafbOT/KGdJYleQfwv4cuSnOXZKcky4euYwgGzML2S1V1+8RCVd0G/NJw5WgOnkv3oPC/0D3TdSNw5KAVadaS/E2S5Ul2Ba4Crkny20PXNWoGzMK2U5L/mPOhzTC984D1aPbuAb4PLAMeDlxXVfcNW5Lm4OCqugN4Gd2jEvsDvzBoRQMwYBa2T9BdajkqyQuBDwAfH7gmzc4X6ALmOcDz6V43ccGwJWkOHpbkYXQB87GquocpcyEuBgv6QUvxBuB/ACfSTf75SeDdg1ak2Tq+qta335uBY5Isun8Bz2N/Bfwb8BXgU0keB9wxaEUD8EFLaQwl2X+69qq6ftS16MFrl6qXVNW2oWsZJQNmAUtyEN2dRwfTXccHoKoeP1hRmpUkV9BdUgnd3+5A4JqqOmTQwjQrSb4BfA74f8CnquqqgUsahJfIFrazgVOB04GfAF7L9O/J0ZipqqdNXk7ybLrLnZofDqa7E/BHgbcmeQrwlar6uWHLGi0H+Re2ZVW1jq6n+q2qehPwwoFr0g6oqi/SDfhrfriX7k7Ae4H7gJuBWwataAD2YBa2u5LsBFzbXl2wCdhr4Jo0C0l+Y9LiTsCzgS0DlaO5uwO4Angb8NdV9e2B6xmEYzALWJLnAFcDjwb+CNgNWFtVnxuyLm1fklMnLW6juyPpw1V11zAVaS6SHEN3e/nhwA/oHpj9VLuisGgYMJLUkzb28mLg14C9qmrZsBWNlgGzACWZ8dXQVfXSUdWiHZPkScBvAQcw6VJ2VTmGNg8k+TDwTGAD8GngU8Bli60HasAsQEm2ADfQPbl/GVPuHKuqfx6iLs1ekq8AfwlcTjdQDEBVXT5YUZq1dnn6i1V173Y3XsAMmAWozTn2U8CrgKcD/wB8oKquHLQwzVqSy6vqsKHr0I5p08ScCPxYa/pn4C/blDGLhgGzwCXZhS5o3gL8YVW9Y+CSNAtJ3kR3W+tHgbsn2qtq61A1afaSvBt4GHBua/oF4N6q+u/DVTV6BswC1YLlZ+jC5QDgQuA9VbVpyLo0O0mum6a5nIVhfkjylap6xvbaFjqfg1mAkpwLHApcBPxBVX1t4JI0R1V14NA16EG5N8kTquobAEkez6SxtMXCHswClOQ+4HttcfIfOHT/Cl6Ub9ebD5K8sKouSfJfp1tfVR8ZdU2auyRH0U3V9E26/+4eB7y2qv5p0MJGzB7MAlRVTgE0f70AuAT42WnWFWDAzANVta5NNvtkuoD5elXdvZ3dFhx7MNI8keTlVfXhoevQA3ugnueExdYDNWCkeSLJ9VU17XtiNB6SnN1+7gX8CLCOrgfzE8ClVTVjAC00XiKT5g9ftTDmquq1AEn+Hji4qm5qy/sAfzFkbUPwWr00f3i5Yf44YCJcmpuBJw1VzFDswUhjZNKbLP/TKuCxIy5HO+7SJJ+gm66pgDXAorqDDByDkcZKksfNtL6qvjWqWvTgtAH/H22Ln6qqjw5ZzxAMGGmMtH/1fhy4qKq+PnQ90oNhwEhjJMnewOr2eRLdbNgfB9ZV1XeHrE2z13ovb6a7myws0oecDRhpTLXXXT+X7oVVRwHfBz5ZVWsHLUzblWQD8LNVdfXQtQzJgJHmiSR7AkdX1fuHrkUzS/KZqjpy6DqGZsBIYyTJL9E9kHdtkgBnAS8HvgUcV1VfGrRAzUqStwN7A3/H/V+3sKie5Pc2ZWm8vB44p/1+FfAM4PHAs4Az+OFdSRpvy4F/B140qW3RzSVnD0YaI0m+XFXPbL//hu497m9vy1+qqmcNWZ80F/ZgpPFyX5tW5Da6gf3TJq17+DAlabaSnFxVa5O8g2kemK2q1w1Q1mAMGGm8/D6wHlgCXFhVVwIkeQHdu0U03nZJ8hzgK8APWOTzx3mJTBozSZYCj6qq2ya17Ur336vPwoyxJG+lm0X5qXQh8y/AZ4DPVtXWIWsbggEjjZkkewEnAYfQXWa5CnhnVd08aGGatSQ7A6vowuZ57XN7VR08aGEj5mzK0hhJciTwhbZ4HvC+9vuytk7zwzK6O8l2a58b6WZlWFTswUhjJMnngBOnPu+S5JnAX1XVcwcpTLOS5Ey6nueddIHyOeBzky93Lib2YKTxsny6hymr6svAo0ZfjuZof2AXYDOwCdgI3D5kQUPyLjJpvCTJ7lP/xZtkD/wH4dirqtVtBoZD6MZffhM4NMlWuoH+UwctcMT8P6w0Xk4HPpnkBUke1T4/DlzU1mnMVedrwD/S/d0+AzyBbpaGRcUxGGnMJHkJcDL3v4vsLVX1fwctTNuV5HV0PZcjgXtotyi37yuq6r4Byxs5A0aSHiJJ3kZ79qWqbhq6nqEZMJKkXjgGI0nqhQEjSeqFASONoSS7JTk9yfr2+bMkuw1dlzQXBow0nt4D3AH8t/a5Azh70IqkOXKQXxpDk188NlObNM7swUjj6ftJnj+x0Ca6/P6A9UhzZg9GGkNtcstz6WbiDbAVeE1VfWXIuqS5MGCkMZZkOUBV3TF0LdJcGTDSGEly7Ezrq+q8UdUiPVgGjDRGkrxjumbgZ4GVVeUM6Jo3DBhpTLVp338eeAPdhJenVdVXh61Kmj3/NSSNmSRLgdfQvUvkMuAVVXXNoEVJO8CAkcZIkpPo3huyDlhdVd8auCRph3mJTBojSe4DbgG20L0L5j9W0b3L6umDFCbtAHsw0ng5cOgCpIeKPRhpjCT5BPBx4KKq+vrQ9UgPhgEjjZEkewOr2+dJdIP8HwfWVdV3h6xNmisDRhpTSXYCngu8GDiKbi6yT1bV2kELk2bJgJHmiSR7AkdX1fuHrkWaDWdTlsZQkrVJlid5WJJ1SW6lu23ZcNG8YcBI4+lFbYLLlwAb6cZjfnvYkqS5MWCk8fSw9v3TwAeqauuQxUg7wudgpPF0YZKv0w3s/3KSFcBdA9ckzYmD/NKYaXePHQFcDdxRVfcm2RV4VFVtHrY6afYMGGkMJflsVT1v6DqkB8MxGGk8fTLJy9uU/dK8ZA9GGkNJ7gR2Be6lG4eZmOxy+aCFSXNgwEiSeuElMmkMpfPqJL/XlvdLcvjQdUlzYQ9GGkNJ3gXcB7ywqp6aZHe6ecieM3Bp0qz5HIw0np5bVc9O8iWAqrotyc5DFyXNhZfIpPF0T5IltLdatgct7xu2JGluDBhpPJ0BfBTYK8lpwKeBPxm2JGluHIORxlSSp9C9ByZ0Lxy7euCSpDkxYKQx1S6RPZZJY6VVdf1wFUlz4yC/NIaS/CpwKnAz3cOWoRuPefqQdUlzYQ9GGkNJNtDdSfbtoWuRdpSD/NJ4ugH4ztBFSA+GPRhpjCT5jfbzEODJwD8Ad0+sr6q3DVGXtCMcg5HGy6Pa9/Xts3P7QHsmRpov7MFIYyjJK6vqb7fXJo0zA0YaQ0m+WFXP3l6bNM68RCaNkSQvBn4aWJnkjEmrlgPbhqlK2jEGjDRebgQuB17avifcCfz6IBVJO8hLZNIYSvJI4AC6gf1vVNVdw1YkzZ3PwUhjJMnSJGuB64BzgfcBNyRZm+Rhw1YnzY0BI42XtwB7AI+vqsOq6lnAE4BHA28dsjBprrxEJo2RJNcCT6op/2G2iS+/XlUHDVOZNHf2YKTxUlPDpTXeiw9aap4xYKTxclWSY6c2Jnk18PUB6pF2mJfIpDGSZCXwEeD7dLcpF/AcYBnwc1W1acDypDkxYKQxlOSFdBNeBriyqtYNXJI0ZwaMJKkXjsFIknphwEiSemHASCOUZN8kH0tybZJvJHl7kp23s8//SnJlkq8m+XKS546qXunBMGCkEUkSujvE/q49MPkk4JHAaTPs8zzgJcCzq+rpwE/SvU5ZGnsGjDQ6LwTuqqqz4T8envx14BeT/HLr2Xw8yTVJTm377APcWlV3t31uraobkxyV5KMTB07yU0k+kmRJknOSfC3JFUmcgVmDMWCk0TmE+0/BT1XdQfdq5KXA4cDPA88EXplkFfBJYL8k/5rknUle0Ha9BHhqkhVt+bXA2W3flVV1aFU9rbVJgzBgpNEJ00/3MtF+cVV9u6q+T3cp7flV9V3gMOAEYAvwoSSvadPJvBd4dZJHA88DLgK+CTw+yTuSrAbu6PukpAdiwEijcyWwanJDkuXAfsB0c40VdJfSqurSqjoV+BXg5W392cCrgVcBf1tV26rqNuAZwKXAScC7+zkVafsMGGl01gGPmJhrrM2Q/GfAOcC/Az+VZI8ky4CXAZ9J8uQkk2dQfibwLYCqupHuDZi/245Bkj2Bnarqw8DvAc/u/aykB2DASCPSLmv9HN34yrXAvwJ3Ab/TNvk03WWvLwMfrqr1dHeZnZvkqiRfBQ4G3jTpsO8Hbqiqq9rySuDSJF+mC51TejwlaUZOFSONgSSvAVZV1a/Mcb8/B75UVWf1Upj0ICwdugBJOybJ5cD3gN8cuhZpOvZgJEm9cAxGktQLA0aS1AsDRpLUCwNGktQLA0aS1AsDRpLUi/8PjDb/eMeEH2cAAAAASUVORK5CYII=",
      "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": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Hind\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:4163: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  return super().drop(\n"
     ]
    }
   ],
   "source": [
    "df.drop(columns=['ScreenResolution','Memory','Inches'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Weight', ylabel='Price'>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=df['Weight'],y=df['Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(columns=['Price'])\n",
    "y = np.log(df['Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.15,random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Notebook              621\n",
       "Gaming                174\n",
       "Ultrabook             167\n",
       "2 in 1 Convertible     98\n",
       "Netbook                24\n",
       "Workstation            22\n",
       "Name: TypeName, dtype: int64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.TypeName.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>IPS Panel</th>\n",
       "      <th>ppi</th>\n",
       "      <th>cpu_name</th>\n",
       "      <th>HDD</th>\n",
       "      <th>SSD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>Toshiba</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Windows</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.454670</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1141</th>\n",
       "      <td>MSI</td>\n",
       "      <td>Gaming</td>\n",
       "      <td>8</td>\n",
       "      <td>Nvidia</td>\n",
       "      <td>Windows</td>\n",
       "      <td>2.40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>1000</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1049</th>\n",
       "      <td>Asus</td>\n",
       "      <td>Netbook</td>\n",
       "      <td>4</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Others/No OS/Linux</td>\n",
       "      <td>1.20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>135.094211</td>\n",
       "      <td>Other Intel Processor</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1020</th>\n",
       "      <td>Dell</td>\n",
       "      <td>2 in 1 Convertible</td>\n",
       "      <td>4</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Windows</td>\n",
       "      <td>2.08</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i3</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>878</th>\n",
       "      <td>Dell</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>4</td>\n",
       "      <td>Nvidia</td>\n",
       "      <td>Windows</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>1000</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Company            TypeName  Ram     Gpu               OpSys  Weight  \\\n",
       "183   Toshiba            Notebook    8   Intel             Windows    2.00   \n",
       "1141      MSI              Gaming    8  Nvidia             Windows    2.40   \n",
       "1049     Asus             Netbook    4   Intel  Others/No OS/Linux    1.20   \n",
       "1020     Dell  2 in 1 Convertible    4   Intel             Windows    2.08   \n",
       "878      Dell            Notebook    4  Nvidia             Windows    2.18   \n",
       "\n",
       "      Touchscreen  IPS Panel         ppi               cpu_name   HDD  SSD  \n",
       "183             0          0  100.454670          Intel Core i5     0  128  \n",
       "1141            0          0  141.211998          Intel Core i7  1000  128  \n",
       "1049            0          0  135.094211  Other Intel Processor     0    0  \n",
       "1020            1          1  141.211998          Intel Core i3  1000    0  \n",
       "878             0          0  141.211998          Intel Core i5  1000  128  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 196 entries, 248 to 470\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   Company      196 non-null    object \n",
      " 1   TypeName     196 non-null    object \n",
      " 2   Ram          196 non-null    int32  \n",
      " 3   Gpu          196 non-null    object \n",
      " 4   OpSys        196 non-null    object \n",
      " 5   Weight       196 non-null    float32\n",
      " 6   Touchscreen  196 non-null    int64  \n",
      " 7   IPS Panel    196 non-null    int64  \n",
      " 8   ppi          196 non-null    float64\n",
      " 9   cpu_name     196 non-null    object \n",
      " 10  HDD          196 non-null    int64  \n",
      " 11  SSD          196 non-null    int64  \n",
      "dtypes: float32(1), float64(1), int32(1), int64(4), object(5)\n",
      "memory usage: 18.4+ KB\n"
     ]
    }
   ],
   "source": [
    "X_test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.metrics import r2_score,mean_absolute_error\n",
    "from sklearn.linear_model import LinearRegression,Ridge,Lasso\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor,GradientBoostingRegressor,AdaBoostRegressor,ExtraTreesRegressor\n",
    "from sklearn.svm import SVR\n",
    "from xgboost import XGBRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.8029050077622659\n",
      "MAE 0.19307790621011486\n"
     ]
    }
   ],
   "source": [
    "# step1 = ColumnTransformer(transformers=[\n",
    "#     ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,3,4,9])\n",
    "# ],remainder='passthrough')\n",
    "\n",
    "# step2 = KNeighborsRegressor(n_neighbors=3)\n",
    "\n",
    "# pipe_knn = Pipeline([\n",
    "#     ('step1',step1),\n",
    "#     ('step2',step2)\n",
    "# ])\n",
    "\n",
    "# pipe_knn.fit(X_train,y_train)\n",
    "\n",
    "# y_pred = pipe_knn.predict(X_test)\n",
    "\n",
    "# print('R2 score',r2_score(y_test,y_pred))\n",
    "# print('MAE',mean_absolute_error(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.8353175563571511\n",
      "MAE 0.18554264314114646\n"
     ]
    }
   ],
   "source": [
    "# step1 = ColumnTransformer(transformers=[\n",
    "#     ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,3,4,9])\n",
    "# ],remainder='passthrough')\n",
    "\n",
    "# step2 = DecisionTreeRegressor(max_depth=8)\n",
    "\n",
    "# pipe_dt = Pipeline([\n",
    "#     ('step1',step1),\n",
    "#     ('step2',step2)\n",
    "# ])\n",
    "\n",
    "# pipe_dt.fit(X_train,y_train)\n",
    "\n",
    "# y_pred = pipe_dt.predict(X_test)\n",
    "\n",
    "# print('R2 score',r2_score(y_test,y_pred))\n",
    "# print('MAE',mean_absolute_error(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.8083180902283282\n",
      "MAE 0.2023905942725167\n"
     ]
    }
   ],
   "source": [
    "# step1 = ColumnTransformer(transformers=[\n",
    "#     ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,3,4,9])\n",
    "# ],remainder='passthrough')\n",
    "\n",
    "# step2 = SVR(kernel='rbf',C=10000,epsilon=0.1)\n",
    "\n",
    "# pipe_svr = Pipeline([\n",
    "#     ('step1',step1),\n",
    "#     ('step2',step2)\n",
    "# ])\n",
    "\n",
    "# pipe_svr.fit(X_train,y_train)\n",
    "\n",
    "# y_pred = pipe_svr.predict(X_test)\n",
    "\n",
    "# print('R2 score',r2_score(y_test,y_pred))\n",
    "# print('MAE',mean_absolute_error(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.885852128776591\n",
      "MAE 0.16024987416370595\n"
     ]
    }
   ],
   "source": [
    "step1 = ColumnTransformer(transformers=[\n",
    "    ('col_tnf',OneHotEncoder(sparse=False,drop='first',handle_unknown='error'),[0,1,3,4,9])\n",
    "],remainder='passthrough')\n",
    "\n",
    "step2 = RandomForestRegressor(n_estimators=100,\n",
    "                              random_state=3,\n",
    "                              max_samples=0.5,\n",
    "                              max_features=0.75,\n",
    "                              max_depth=15,)\n",
    "\n",
    "pipe_rf = Pipeline([\n",
    "    ('step1',step1),\n",
    "    ('step2',step2)\n",
    "])\n",
    "\n",
    "pipe_rf.fit(X_train,y_train)\n",
    "\n",
    "y_pred = pipe_rf.predict(X_test)\n",
    "\n",
    "print('R2 score',r2_score(y_test,y_pred))\n",
    "print('MAE',mean_absolute_error(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "step1 = ColumnTransformer(transformers=[\n",
    "    ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,3,4,9])\n",
    "],remainder='passthrough')\n",
    "\n",
    "step2 = XGBRegressor()\n",
    "\n",
    "pipe_xgb = Pipeline([\n",
    "    ('step1',step1),\n",
    "    ('step2',step2)\n",
    "])\n",
    "grid_param = [{\n",
    "    'step2__n_estimators':[30,45,60,100],\n",
    "    'step2__learning_rate':[0.1],\n",
    "    'step2__max_depth' :[3,5,10,18],\n",
    "    'step2__reg_lambda' :[0,1,1.5,2,3]\n",
    "}]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gridsearch_xg = GridSearchCV(pipe_xgb, grid_param, cv=5, verbose=0,n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_model_xg = gridsearch_xg.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('step1',\n",
       "                 ColumnTransformer(remainder='passthrough',\n",
       "                                   transformers=[('col_tnf',\n",
       "                                                  OneHotEncoder(drop='first',\n",
       "                                                                sparse=False),\n",
       "                                                  [0, 1, 3, 4, 9])])),\n",
       "                ('step2',\n",
       "                 XGBRegressor(base_score=0.5, booster='gbtree',\n",
       "                              colsample_bylevel=1, colsample_bynode=1,\n",
       "                              colsample_bytree=1, enable_categorical=False,\n",
       "                              gamma=0, gpu_id=-1, importance_type=None,\n",
       "                              interaction_constraints='', learning_rate=0.1,\n",
       "                              max_delta_step=0, max_depth=5, min_child_weight=1,\n",
       "                              missing=nan, monotone_constraints='()',\n",
       "                              n_estimators=100, n_jobs=4, num_parallel_tree=1,\n",
       "                              predictor='auto', random_state=0, reg_alpha=0,\n",
       "                              reg_lambda=1, scale_pos_weight=1, subsample=1,\n",
       "                              tree_method='exact', validate_parameters=1,\n",
       "                              verbosity=None))])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_model_xg.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean accuracy of the model is: 0.8852831651230358\n"
     ]
    }
   ],
   "source": [
    "print(\"The mean accuracy of the model is:\",best_model_xg.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = best_model_xg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.8852831651230358\n",
      "MAE 0.1634642140265213\n"
     ]
    }
   ],
   "source": [
    "print('R2 score',r2_score(y_test,y_pred))\n",
    "print('MAE',mean_absolute_error(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "step1 = ColumnTransformer(transformers=[\n",
    "    ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,3,4,9])\n",
    "],remainder='passthrough')\n",
    "\n",
    "step2 = RandomForestRegressor()\n",
    "\n",
    "pipe_rfg = Pipeline([\n",
    "    ('step1',step1),\n",
    "    ('step2',step2)\n",
    "])\n",
    "grid_param = [{\n",
    "    'step2__n_estimators':[30,40,50,100],\n",
    "    'step2__max_samples':[0.8,1],\n",
    "    'step2__min_samples_leaf':[2],\n",
    "    'step2__min_samples_split':[4,6,8,10],\n",
    "    'step2__max_depth' :[18,25,40],\n",
    "    'step2__random_state':[0,3,10,42]\n",
    "}]\n",
    "gridsearch_rf = GridSearchCV(pipe_rfg, grid_param, cv=5, verbose=0,n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_model_rf = gridsearch_rf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('step1',\n",
       "                 ColumnTransformer(remainder='passthrough',\n",
       "                                   transformers=[('col_tnf',\n",
       "                                                  OneHotEncoder(drop='first',\n",
       "                                                                sparse=False),\n",
       "                                                  [0, 1, 3, 4, 9])])),\n",
       "                ('step2',\n",
       "                 RandomForestRegressor(max_depth=18, max_samples=0.8,\n",
       "                                       min_samples_leaf=2, min_samples_split=4,\n",
       "                                       n_estimators=50, random_state=3))])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_model_rf.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean accuracy of the model is: 0.8842261288615668\n"
     ]
    }
   ],
   "source": [
    "print(\"The mean accuracy of the model is:\",best_model_rf.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = best_model_rf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.8842261288615668\n",
      "MAE 0.16093523443218033\n"
     ]
    }
   ],
   "source": [
    "print('R2 score',r2_score(y_test,y_pred))\n",
    "print('MAE',mean_absolute_error(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "pickle.dump(df,open('df.pkl','wb'))\n",
    "pickle.dump(pipe_rf,open('pipe_rf.pkl','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'TypeName'"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.columns.values[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ary = np.array(['Toshiba','Notebook', 8, 'Intel', 'Windows',2.00,0, 0\t,100.454670\t,'Intel Core i5',0,128]).reshape(1,12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42055.13248684187"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp(pipe_rf.predict(ary)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "64d5365f749ca27007a5b874caa31bdd49d8c5f06f084f58926337f5d56198e5"
  },
  "kernelspec": {
   "display_name": "Python 3.8.5 ('base')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
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}