LaptopPrice Predictor.ipynb

{
 "cells": [
  {
   "attachments": {
    "1.png": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "da1ce10b",
   "metadata": {},
   "source": [
    "![1.png](attachment:1.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c5629fe7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Impport the libraries and dataset\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import seaborn\n",
    "\n",
    "ld = pd.read_csv('laptop_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dfb82770",
   "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>Unnamed: 0</th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Inches</th>\n",
       "      <th>ScreenResolution</th>\n",
       "      <th>Cpu</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>699</th>\n",
       "      <td>699</td>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>IPS Panel Full HD 1920x1080</td>\n",
       "      <td>Intel Core i7 7500U 2.7GHz</td>\n",
       "      <td>4GB</td>\n",
       "      <td>500GB HDD</td>\n",
       "      <td>AMD Radeon 530</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.1kg</td>\n",
       "      <td>36709.9200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>78</td>\n",
       "      <td>Acer</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i3 7130U 2.7GHz</td>\n",
       "      <td>4GB</td>\n",
       "      <td>1TB HDD</td>\n",
       "      <td>Nvidia GeForce MX130</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.2kg</td>\n",
       "      <td>28238.4000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>766</td>\n",
       "      <td>Asus</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>17.3</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>Intel Pentium Quad Core N4200 1.1GHz</td>\n",
       "      <td>4GB</td>\n",
       "      <td>1TB HDD</td>\n",
       "      <td>Nvidia GeForce 920MX</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.8kg</td>\n",
       "      <td>25059.7152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1062</th>\n",
       "      <td>1062</td>\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>4GB</td>\n",
       "      <td>1TB HDD</td>\n",
       "      <td>Intel HD Graphics</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.25kg</td>\n",
       "      <td>29783.5200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>302</td>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>17.3</td>\n",
       "      <td>1600x900</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>6GB</td>\n",
       "      <td>1TB HDD</td>\n",
       "      <td>Nvidia GeForce GTX 940M</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.8kg</td>\n",
       "      <td>34578.7200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0 Company  TypeName  Inches             ScreenResolution  \\\n",
       "699          699      HP  Notebook    15.6  IPS Panel Full HD 1920x1080   \n",
       "78            78    Acer  Notebook    15.6            Full HD 1920x1080   \n",
       "766          766    Asus  Notebook    17.3                     1366x768   \n",
       "1062        1062    Dell  Notebook    15.6            Full HD 1920x1080   \n",
       "302          302  Lenovo  Notebook    17.3                     1600x900   \n",
       "\n",
       "                                       Cpu  Ram     Memory  \\\n",
       "699             Intel Core i7 7500U 2.7GHz  4GB  500GB HDD   \n",
       "78              Intel Core i3 7130U 2.7GHz  4GB    1TB HDD   \n",
       "766   Intel Pentium Quad Core N4200 1.1GHz  4GB    1TB HDD   \n",
       "1062            Intel Core i5 7200U 2.5GHz  4GB    1TB HDD   \n",
       "302             Intel Core i5 7200U 2.5GHz  6GB    1TB HDD   \n",
       "\n",
       "                          Gpu       OpSys  Weight       Price  \n",
       "699            AMD Radeon 530  Windows 10   2.1kg  36709.9200  \n",
       "78       Nvidia GeForce MX130  Windows 10   2.2kg  28238.4000  \n",
       "766      Nvidia GeForce 920MX  Windows 10   2.8kg  25059.7152  \n",
       "1062        Intel HD Graphics  Windows 10  2.25kg  29783.5200  \n",
       "302   Nvidia GeForce GTX 940M  Windows 10   2.8kg  34578.7200  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ld.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e747f9d",
   "metadata": {},
   "source": [
    "# Basic understanding for data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "adcef3a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1303, 12)\n"
     ]
    }
   ],
   "source": [
    "print(ld.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2a272679",
   "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",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(ld.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4de326ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "print(ld.duplicated().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "409c5feb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unnamed: 0          0\n",
      "Company             0\n",
      "TypeName            0\n",
      "Inches              0\n",
      "ScreenResolution    0\n",
      "Cpu                 0\n",
      "Ram                 0\n",
      "Memory              0\n",
      "Gpu                 0\n",
      "OpSys               0\n",
      "Weight              0\n",
      "Price               0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(ld.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d9e95534",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Drop unrequired column\n",
    "ld.drop(columns=['Unnamed: 0'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "69e6ca12",
   "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>64</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.86kg</td>\n",
       "      <td>36763.200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1104</th>\n",
       "      <td>Lenovo</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>16GB</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>Nvidia GeForce GTX 1050 Ti</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.4kg</td>\n",
       "      <td>63882.720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>17.3</td>\n",
       "      <td>1600x900</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>4GB</td>\n",
       "      <td>1TB HDD</td>\n",
       "      <td>Nvidia GeForce 920MX</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.79kg</td>\n",
       "      <td>31381.920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1120</th>\n",
       "      <td>Vero</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Atom X5-Z8350 1.44GHz</td>\n",
       "      <td>4GB</td>\n",
       "      <td>32GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics 400</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.35kg</td>\n",
       "      <td>10442.880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>483</th>\n",
       "      <td>Chuwi</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Atom x5-Z8350 1.44GHz</td>\n",
       "      <td>4GB</td>\n",
       "      <td>64GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.89kg</td>\n",
       "      <td>13261.392</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company  TypeName  Inches   ScreenResolution  \\\n",
       "64        HP  Notebook    15.6  Full HD 1920x1080   \n",
       "1104  Lenovo    Gaming    15.6  Full HD 1920x1080   \n",
       "321   Lenovo  Notebook    17.3           1600x900   \n",
       "1120    Vero  Notebook    13.3  Full HD 1920x1080   \n",
       "483    Chuwi  Notebook    15.6  Full HD 1920x1080   \n",
       "\n",
       "                              Cpu   Ram              Memory  \\\n",
       "64     Intel Core i5 7200U 2.5GHz   8GB           256GB SSD   \n",
       "1104  Intel Core i7 7700HQ 2.8GHz  16GB           512GB SSD   \n",
       "321    Intel Core i5 7200U 2.5GHz   4GB             1TB HDD   \n",
       "1120  Intel Atom X5-Z8350 1.44GHz   4GB  32GB Flash Storage   \n",
       "483   Intel Atom x5-Z8350 1.44GHz   4GB  64GB Flash Storage   \n",
       "\n",
       "                             Gpu       OpSys  Weight      Price  \n",
       "64         Intel HD Graphics 620  Windows 10  1.86kg  36763.200  \n",
       "1104  Nvidia GeForce GTX 1050 Ti  Windows 10   2.4kg  63882.720  \n",
       "321         Nvidia GeForce 920MX  Windows 10  2.79kg  31381.920  \n",
       "1120       Intel HD Graphics 400  Windows 10  1.35kg  10442.880  \n",
       "483            Intel HD Graphics  Windows 10  1.89kg  13261.392  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ld.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d525b22b",
   "metadata": {},
   "source": [
    "#### Now split the mixed varibels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "10cc67af",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Ram---\n",
    "ld['Ram'] = ld['Ram'].str.replace('GB','')\n",
    "\n",
    "#Weight---\n",
    "ld['Weight'] = ld['Weight'].str.replace('kg','')\n",
    "\n",
    "#Change data type----\n",
    "ld['Ram'] = ld['Ram'].astype('int32')\n",
    "ld['Weight'] = ld['Weight'].astype('float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1c85ebb4",
   "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>147</th>\n",
       "      <td>Asus</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Celeron Dual Core N3350 1.1GHz</td>\n",
       "      <td>4</td>\n",
       "      <td>1TB HDD</td>\n",
       "      <td>Intel HD Graphics 500</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.00</td>\n",
       "      <td>18328.3200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611</th>\n",
       "      <td>Dell</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>16</td>\n",
       "      <td>1TB HDD</td>\n",
       "      <td>Nvidia GeForce GTX 960&lt;U+039C&gt;</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.59</td>\n",
       "      <td>46833.6528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Dell</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i7 8650U 1.9GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD +  256GB SSD</td>\n",
       "      <td>Intel UHD Graphics 620</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.88</td>\n",
       "      <td>69157.4400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>966</th>\n",
       "      <td>Dell</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>4</td>\n",
       "      <td>500GB HDD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.90</td>\n",
       "      <td>43956.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>949</th>\n",
       "      <td>Acer</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>Intel Celeron Dual Core N3060 1.6GHz</td>\n",
       "      <td>4</td>\n",
       "      <td>32GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics 400</td>\n",
       "      <td>Chrome OS</td>\n",
       "      <td>1.68</td>\n",
       "      <td>18594.7200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Company   TypeName  Inches   ScreenResolution  \\\n",
       "147    Asus   Notebook    15.6  Full HD 1920x1080   \n",
       "611    Dell     Gaming    15.6  Full HD 1920x1080   \n",
       "28     Dell  Ultrabook    15.6  Full HD 1920x1080   \n",
       "966    Dell   Notebook    15.6           1366x768   \n",
       "949    Acer   Notebook    14.0           1366x768   \n",
       "\n",
       "                                      Cpu  Ram                  Memory  \\\n",
       "147  Intel Celeron Dual Core N3350 1.1GHz    4                 1TB HDD   \n",
       "611           Intel Core i7 6700HQ 2.6GHz   16                 1TB HDD   \n",
       "28             Intel Core i7 8650U 1.9GHz    8  256GB SSD +  256GB SSD   \n",
       "966            Intel Core i5 7200U 2.5GHz    4               500GB HDD   \n",
       "949  Intel Celeron Dual Core N3060 1.6GHz    4      32GB Flash Storage   \n",
       "\n",
       "                                Gpu       OpSys  Weight       Price  \n",
       "147           Intel HD Graphics 500  Windows 10    2.00  18328.3200  \n",
       "611  Nvidia GeForce GTX 960<U+039C>  Windows 10    2.59  46833.6528  \n",
       "28           Intel UHD Graphics 620  Windows 10    1.88  69157.4400  \n",
       "966           Intel HD Graphics 620  Windows 10    1.90  43956.0000  \n",
       "949           Intel HD Graphics 400   Chrome OS    1.68  18594.7200  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ld.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b0d56ef9",
   "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",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(ld.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04363b2f",
   "metadata": {},
   "source": [
    "# EDA with Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "768603f1",
   "metadata": {},
   "source": [
    "#### Price Distriution $"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d6e267a1",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(ld['Price'],\n",
    "             hist_kws={'color':'purple', 'edgecolor':'black',\n",
    "                       'linewidth':.5, 'linestyle':'-'})\n",
    "sns.set(rc={\"figure.figsize\":(7, 5)})\n",
    "plt.title(\"Price Distriution\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08ff0062",
   "metadata": {},
   "source": [
    "### Company 🏢"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e7450107",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Company wise laptops-------------\n",
    "sns.countplot(x ='Company', data = ld,palette ='PiYG')\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.title(\"Company wise laptops\")\n",
    "sns.set(rc={\"figure.figsize\":(8, 8)})\n",
    "plt.show()\n",
    "\n",
    "#Company with Price---------------\n",
    "sns.barplot(x=\"Company\", y=\"Price\", data=ld, palette=\"PiYG\")\n",
    "plt.xticks(rotation='vertical')\n",
    "sns.set(rc={\"figure.figsize\":(7, 7)})\n",
    "plt.title(\"Company wise Price\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e47ebce9",
   "metadata": {},
   "source": [
    "### TypeName 💻"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d81c97e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#TypeName wise laptops-------------\n",
    "sns.countplot(x ='TypeName', data = ld,palette ='PiYG')\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.title(\"TypeName wise laptops\")\n",
    "sns.set(rc={\"figure.figsize\":(8, 8)})\n",
    "plt.show()\n",
    "\n",
    "#TypeName with Price---------------\n",
    "sns.barplot(x=\"TypeName\", y=\"Price\", data=ld, palette=\"PiYG\")\n",
    "plt.xticks(rotation='vertical')\n",
    "sns.set(rc={\"figure.figsize\":(7, 7)})\n",
    "plt.title(\"TypeName wise Price\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "852cd55b",
   "metadata": {},
   "source": [
    "#### Inches "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "df6b8434",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#DIst plot---------\n",
    "sns.distplot(ld['Inches'],\n",
    "             hist_kws={'color':'blue', 'edgecolor':'#aaff00',\n",
    "                       'linewidth':.5, 'linestyle':'--'})\n",
    "sns.set(rc={\"figure.figsize\":(7, 5)})\n",
    "plt.title(\"Inches\")\n",
    "plt.show()\n",
    "\n",
    "#Scatter plot--------------\n",
    "seaborn.scatterplot(x=\"Inches\",\n",
    "                    y=\"Price\",\n",
    "                    hue=\"Inches\",\n",
    "                    marker = '+',\n",
    "                    data=ld)\n",
    "plt.title(\"Inches with Price\")\n",
    "sns.set(rc={\"figure.figsize\":(8, 8)})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1113e579",
   "metadata": {},
   "source": [
    "### Work with Screen Resolution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fdd9e5de",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Full HD 1920x1080                                507\n",
      "1366x768                                         281\n",
      "IPS Panel Full HD 1920x1080                      230\n",
      "IPS Panel Full HD / Touchscreen 1920x1080         53\n",
      "Full HD / Touchscreen 1920x1080                   47\n",
      "1600x900                                          23\n",
      "Touchscreen 1366x768                              16\n",
      "Quad HD+ / Touchscreen 3200x1800                  15\n",
      "IPS Panel 4K Ultra HD 3840x2160                   12\n",
      "IPS Panel 4K Ultra HD / Touchscreen 3840x2160     11\n",
      "4K Ultra HD / Touchscreen 3840x2160               10\n",
      "4K Ultra HD 3840x2160                              7\n",
      "Touchscreen 2560x1440                              7\n",
      "IPS Panel 1366x768                                 7\n",
      "IPS Panel Quad HD+ / Touchscreen 3200x1800         6\n",
      "IPS Panel Retina Display 2560x1600                 6\n",
      "IPS Panel Retina Display 2304x1440                 6\n",
      "Touchscreen 2256x1504                              6\n",
      "IPS Panel Touchscreen 2560x1440                    5\n",
      "IPS Panel Retina Display 2880x1800                 4\n",
      "IPS Panel Touchscreen 1920x1200                    4\n",
      "1440x900                                           4\n",
      "IPS Panel 2560x1440                                4\n",
      "IPS Panel Quad HD+ 2560x1440                       3\n",
      "Quad HD+ 3200x1800                                 3\n",
      "1920x1080                                          3\n",
      "Touchscreen 2400x1600                              3\n",
      "2560x1440                                          3\n",
      "IPS Panel Touchscreen 1366x768                     3\n",
      "IPS Panel Touchscreen / 4K Ultra HD 3840x2160      2\n",
      "IPS Panel Full HD 2160x1440                        2\n",
      "IPS Panel Quad HD+ 3200x1800                       2\n",
      "IPS Panel Retina Display 2736x1824                 1\n",
      "IPS Panel Full HD 1920x1200                        1\n",
      "IPS Panel Full HD 2560x1440                        1\n",
      "IPS Panel Full HD 1366x768                         1\n",
      "Touchscreen / Full HD 1920x1080                    1\n",
      "Touchscreen / Quad HD+ 3200x1800                   1\n",
      "Touchscreen / 4K Ultra HD 3840x2160                1\n",
      "IPS Panel Touchscreen 2400x1600                    1\n",
      "Name: ScreenResolution, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(ld['ScreenResolution'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8c7c7cc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Split the column based on Touchscreen\n",
    "ld['Touchscreen'] = ld['ScreenResolution'].apply(lambda x:1 if 'Touchscreen' in x else 0)\n",
    "ld['Ips'] = ld['ScreenResolution'].apply(lambda x:1 if 'IPS' in x else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8a5aa2c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Inches</th>\n",
       "      <th>ScreenResolution</th>\n",
       "      <th>Cpu</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>708</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i5 6300HQ 2.3GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 520</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.80</td>\n",
       "      <td>63456.48</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>Lenovo</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>128GB SSD</td>\n",
       "      <td>Intel HD Graphics 520</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.85</td>\n",
       "      <td>21498.48</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>Intel Pentium Quad Core N3710 1.6GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>2TB HDD</td>\n",
       "      <td>Intel HD Graphics 405</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.04</td>\n",
       "      <td>20725.92</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>17.3</td>\n",
       "      <td>IPS Panel Full HD 1920x1080</td>\n",
       "      <td>Intel Core i7 8550U 1.8GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>1TB HDD</td>\n",
       "      <td>Nvidia GeForce MX150</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.90</td>\n",
       "      <td>56423.52</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>927</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 6200U 2.3GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 520</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.31</td>\n",
       "      <td>62071.20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>741</th>\n",
       "      <td>Dell</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>Intel Core i3 6006U 2GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 520</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.00</td>\n",
       "      <td>35431.20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1113</th>\n",
       "      <td>HP</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i7 6500U 2.5GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>Intel HD Graphics 520</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.16</td>\n",
       "      <td>104908.32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>832</th>\n",
       "      <td>Asus</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>4</td>\n",
       "      <td>1TB HDD</td>\n",
       "      <td>Nvidia GeForce 920</td>\n",
       "      <td>Linux</td>\n",
       "      <td>2.10</td>\n",
       "      <td>27652.32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Asus</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>14.0</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>Intel UHD Graphics 620</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.40</td>\n",
       "      <td>50136.48</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</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>16</td>\n",
       "      <td>256GB SSD +  2TB HDD</td>\n",
       "      <td>AMD Radeon 530</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.80</td>\n",
       "      <td>69210.72</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company   TypeName  Inches             ScreenResolution  \\\n",
       "708   Lenovo   Notebook    14.0            Full HD 1920x1080   \n",
       "381   Lenovo   Notebook    15.6            Full HD 1920x1080   \n",
       "365       HP   Notebook    15.6                     1366x768   \n",
       "257       HP   Notebook    17.3  IPS Panel Full HD 1920x1080   \n",
       "927       HP   Notebook    15.6            Full HD 1920x1080   \n",
       "741     Dell   Notebook    15.6                     1366x768   \n",
       "1113      HP  Ultrabook    13.3            Full HD 1920x1080   \n",
       "832     Asus   Notebook    15.6            Full HD 1920x1080   \n",
       "63      Asus   Notebook    14.0            Full HD 1920x1080   \n",
       "60      Dell   Notebook    17.3            Full HD 1920x1080   \n",
       "\n",
       "                                       Cpu  Ram                Memory  \\\n",
       "708            Intel Core i5 6300HQ 2.3GHz    8             256GB SSD   \n",
       "381               Intel Core i3 6006U 2GHz    4             128GB SSD   \n",
       "365   Intel Pentium Quad Core N3710 1.6GHz    8               2TB HDD   \n",
       "257             Intel Core i7 8550U 1.8GHz   16               1TB HDD   \n",
       "927             Intel Core i5 6200U 2.3GHz    8             256GB SSD   \n",
       "741               Intel Core i3 6006U 2GHz    8             256GB SSD   \n",
       "1113            Intel Core i7 6500U 2.5GHz    8             512GB SSD   \n",
       "832             Intel Core i5 7200U 2.5GHz    4               1TB HDD   \n",
       "63              Intel Core i5 8250U 1.6GHz    8             256GB SSD   \n",
       "60              Intel Core i7 8550U 1.8GHz   16  256GB SSD +  2TB HDD   \n",
       "\n",
       "                         Gpu       OpSys  Weight      Price  Touchscreen  Ips  \n",
       "708    Intel HD Graphics 520  Windows 10    1.80   63456.48            0    0  \n",
       "381    Intel HD Graphics 520       No OS    1.85   21498.48            0    0  \n",
       "365    Intel HD Graphics 405  Windows 10    2.04   20725.92            0    0  \n",
       "257     Nvidia GeForce MX150  Windows 10    2.90   56423.52            0    1  \n",
       "927    Intel HD Graphics 520  Windows 10    2.31   62071.20            0    0  \n",
       "741    Intel HD Graphics 520  Windows 10    2.00   35431.20            0    0  \n",
       "1113   Intel HD Graphics 520  Windows 10    1.16  104908.32            0    0  \n",
       "832       Nvidia GeForce 920       Linux    2.10   27652.32            0    0  \n",
       "63    Intel UHD Graphics 620  Windows 10    1.40   50136.48            0    0  \n",
       "60            AMD Radeon 530  Windows 10    2.80   69210.72            0    0  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ld.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f4ac735a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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S/G3btmns2LEaO3asli9fLkkqKSlRSkqKRo8erby8PN+2Bw8eVFpamhITEzVv3jw1NzcHY2QAANq0gAf/7NmzWrp0qdavX69t27bpzTff1K5du5SVlaX8/HwVFRWpvLxcu3fvliTNmTNHCxYs0Pbt22WMUUFBQaBHBgCgzQt48D0ej7xer86ePavm5mY1NzcrOjpasbGx6tmzp8LCwpSSkqLi4mJVVlaqvr5egwYNkiSlpaWpuLg40CMDANDmhQV6h9HR0frpT3+q5ORkXXXVVRo6dKhqamrkdDp927hcLlVXV5+33ul0qrq6+pL2FxMT7bfZgfbI6ewY7BEABEDAg3/o0CFt2rRJf/3rX9WxY0c99thjOnLkiBwOh28bY4wcDoe8Xu9Xrr8Ux47Vyes1fpv/c3yTRHvhdp8K9ggA/CQkxHHBA92An9J/7bXXFBcXp5iYGEVERCgtLU2vv/663G63bxu32y2Xy6WuXbues/7o0aNyuVyBHhkAgDYv4MHv16+fSkpKdObMGRljtGvXLg0cOFCHDx9WRUWFPB6PCgsLFR8fr+7duysyMlL79++X9Nmr++Pj4wM9MgAAbV7AT+kPHz5c7777rtLS0hQeHq4BAwZo1qxZGjZsmGbNmqWGhgYlJCQoKSlJkpSbm6vs7GzV1dWpf//+ysjICPTIAAC0eQ5jjP8vcF9BWvMafm1Zhd8fFwikTkNjuYYPtCNX1DV8AAAQeAQfAAALEHwAACxA8AEAsADBBwDAAgQfAAALEHwAACxA8AEAsADBBwDAAgQfAAALEHwAACxA8AEAsADBBwDAAgQfAAALEHwAACxA8AEAsADBBwDAAgQfAAALEHwAACxA8AEAsADBBwDAAgQfAAALEHwAACxA8AEAsADBBwDAAgQfAAALEHwAACxA8AEAsADBBwDAAgQfAAALEHwAACxA8AEAsADBBwDAAgQfAAALEHwAACxA8AEAsADBBwDAAgQfAAALEHwAACxA8AEAsADBBwDAAgQfAAALEHwAACxA8AEAsADBBwDAAgQfAAALEHwAACxA8AEAsADBBwDAAgQfAAALEHwAACxA8AEAsADBBwDAAgQfAAALEHwAACxA8AEAsADBBwDAAgQfAAALEHwAACxA8AEAsADBBwDAAgQfAAALtCj41dXV5617//33/T4MAABoHRcNfm1trWpra/XjH/9Yn376qW/56NGjmjlzZqBmBAAAlynsYjc++uij2rt3ryTp1ltv/f87hYUpMTGxdScDAAB+c9Hgr1u3TpL0xBNPaNmyZX7b6a5du7R69WqdPXtWw4YNU3Z2tkpKSrRs2TI1NDQoOTlZs2fPliQdPHhQ8+bN0+nTpzVkyBAtWrRIYWEXHRsAAHxJi67hL1u2TJWVlXr33Xf1zjvv+P77Oj788EMtXLhQ+fn5eumll/Tuu+9q9+7dysrKUn5+voqKilReXq7du3dLkubMmaMFCxZo+/btMsaooKDga+0XAACbtehQeeXKlVq3bp1iYmJ86xwOh3bu3HnJO9yxY4fGjBmjrl27SpLy8vJUUVGh2NhY9ezZU5KUkpKi4uJi9enTR/X19Ro0aJAkKS0tTStXrtTkyZMveb8AANisRcHfunWrXnnlFXXp0uWyd1hRUaHw8HA98MADqqqq0h133KEbbrhBTqfTt43L5VJ1dbVqamrOWe90Or/yJwYAAMDFtSj43bp180vsJcnj8ejNN9/U+vXr1aFDBz344IOKioqSw+HwbWOMkcPhkNfr/cr1lyImJtovcwPtldPZMdgjAAiAFgU/Li5OK1as0Pe+9z1FRUX51vfv3/+Sd3jttdcqLi5OnTt3liSNGjVKxcXFCg0N9W3jdrvlcrnUtWtXud1u3/qjR4/K5XJd0v6OHauT12suec7/hG+SaC/c7lPBHgGAn4SEOC54oNui4G/evFmSVFxc7Fv3da/hjxw5UnPnztXJkyd19dVXa8+ePUpKStLatWtVUVGhHj16qLCwUOnp6erevbsiIyO1f/9+DR48WNu2bVN8fPwl7xMAANu1KPi7du3y2w4HDhyoe++9V5MnT1ZTU5OGDRumu+66S71799asWbPU0NCghIQEJSUlSZJyc3OVnZ2turo69e/fXxkZGX6bBQAAWziMMf/xfPevf/3rr1x/9913+30gf2vNU/q1ZRV+f1wgkDoNjeWUPtCOXPYp/ffee8/3cWNjo8rKyhQXF+ef6QAAQKtrUfC//C571dXVmjdvXqsMBAAA/O9r/XrcLl26qLKy0t+zAACAVtKiI/wvXsM3xqi8vPycd90DAABXtku+hi999kY8jz/+eKsMBAAA/O+SruFXVlaqublZsbGxrToUAADwrxYFv6KiQg899JBqamrk9Xp1zTXXaM2aNbr++utbez4AAOAHLXrR3uLFi3XvvfeqrKxM+/fv14MPPqhFixa19mwAAMBPWhT8Y8eOKTU11becnp6uEydOtNpQAADAv1oUfI/Ho9raWt/y8ePHW2seAADQClp0DX/q1Kn6wQ9+oOTkZDkcDhUVFWnatGmtPRsAAPCTFh3hJyQkSJKampr0wQcfqLq6Wv/93//dqoMBAAD/adERfmZmpqZMmaKMjAw1NDTohRdeUFZWlp5//vnWng8AAPhBi47wT5w44fu1tJGRkZo+fbrcbnerDgYAAPynxS/aq66u9i0fPXpULfitugAA4ArRolP606dP14QJEzRixAg5HA6VlJTw1roAALQhLQr+xIkTdfPNN2vfvn0KDQ3VPffcoxtvvLG1ZwMAAH7SouBLUr9+/dSvX7/WnAUAALSSFl3DBwAAbRvBBwDAAgQfAAALEHwAACxA8AEAsADBBwDAAgQfAAALEHwAACxA8AEAsADBBwDAAgQfAAALEHwAACxA8AEAsADBBwDAAgQfAAALEHwAACxA8AEAsADBBwDAAgQfAAALEHwAACxA8AEAsADBBwDAAgQfAAALEHwAACxA8AEAsADBBwDAAgQfAAALEHwAACxA8AEAsADBBwDAAgQfAAALEHwAACxA8AEAsADBBwDAAgQfAAALEHwAACxA8AEAsADBBwDAAgQfAAALEHwAACxA8AEAsADBBwDAAgQfAAALEHwAACxA8AEAsADBBwDAAgQfAAALEHwAACwQ1OAvX75cmZmZkqSSkhKlpKRo9OjRysvL821z8OBBpaWlKTExUfPmzVNzc3OwxgUAoM0KWvBLS0u1ZcsWSVJ9fb2ysrKUn5+voqIilZeXa/fu3ZKkOXPmaMGCBdq+fbuMMSooKAjWyAAAtFlBCX5tba3y8vL0wAMPSJIOHDig2NhY9ezZU2FhYUpJSVFxcbEqKytVX1+vQYMGSZLS0tJUXFwcjJEBAGjTwoKx0wULFmj27NmqqqqSJNXU1MjpdPpud7lcqq6uPm+90+lUdXX1Je0rJibaP0MD7ZTT2THYIwAIgIAH/8UXX1S3bt0UFxenzZs3S5K8Xq8cDodvG2OMHA7HBddfimPH6uT1Gv8M/wV8k0R74XafCvYIAPwkJMRxwQPdgAe/qKhIbrdb48eP16effqozZ86osrJSoaGhvm3cbrdcLpe6du0qt9vtW3/06FG5XK5AjwwAQJsX8OD/+te/9n28efNmvfHGG1q0aJFGjx6tiooK9ejRQ4WFhUpPT1f37t0VGRmp/fv3a/Dgwdq2bZvi4+MDPTIAAG1eUK7hf1lkZKRycnI0a9YsNTQ0KCEhQUlJSZKk3NxcZWdnq66uTv3791dGRkaQpwUAoO1xGGP8f4H7CtKa1/Bryyr8/rhAIHUaGss1fKAdudg1fN5pDwAACxB8AAAsQPABALAAwQcAwAIEHwAACxB8AAAsQPABALAAwQcAwAIEHwAACxB8AAAsQPABALAAwQcAwAIEHwAACxB8AAAsQPABALAAwQcAwAIEHwAACxB8AAAsQPABALAAwQcAwAIEHwAACxB8AAAsQPABALAAwQcAwAIEHwAACxB8AAAsQPABALAAwQcAwAIEHwAACxB8AAAsQPABALAAwQcAwAIEHwAACxB8AAAsQPABALAAwQcAwAIEHwAACxB8AAAsQPABALAAwQcAwAIEHwAACxB8AAAsQPABALAAwQcAwAIEHwAACxB8AAAsQPABALAAwQcAwAIEHwAACxB8AAAsQPABALAAwQcAwAIEHwAACxB8AAAsQPABALAAwQcAwAIEHwAACxB8AAAsQPABALAAwQcAwAIEHwAACxB8AAAsQPABALAAwQcAwAIEHwAACxB8AAAsEJTgr169WmPHjtXYsWO1YsUKSVJJSYlSUlI0evRo5eXl+bY9ePCg0tLSlJiYqHnz5qm5uTkYIwMA0KYFPPglJSV67bXXtGXLFm3dulXvvPOOCgsLlZWVpfz8fBUVFam8vFy7d++WJM2ZM0cLFizQ9u3bZYxRQUFBoEcGAKDNC3jwnU6nMjMzFRERofDwcF1//fU6cuSIYmNj1bNnT4WFhSklJUXFxcWqrKxUfX29Bg0aJElKS0tTcXFxoEcGAKDNCwv0Dm+44Qbfx0eOHNGf//xnTZ06VU6n07fe5XKpurpaNTU156x3Op2qrq6+pP3FxERf/tBAO+Z0dgz2CAACIODB/9w///lP3X///Xr88ccVGhqqI0eO+G4zxsjhcMjr9crhcJy3/lIcO1Ynr9f4a2wfvkmivXC7TwV7BAB+EhLiuOCBblBetLd//35Nnz5djz76qFJTU9W1a1e53W7f7W63Wy6X67z1R48elcvlCsbIAAC0aQE/wq+qqtKMGTOUl5enuLg4SdLAgQN1+PBhVVRUqEePHiosLFR6erq6d++uyMhI7d+/X4MHD9a2bdsUHx8f6JEBXEGu6XyVwkKDdnIS8ItmT7NOHD8b0H0G/Ktm3bp1amhoUE5Ojm/dpEmTlJOTo1mzZqmhoUEJCQlKSkqSJOXm5io7O1t1dXXq37+/MjIyAj0ygCtIWGiYSo9sDPYYwGWJ+68fBHyfDmOM/y9wX0Fa8xp+bVmF3x8XCKROQ2Pb3DV8p7MjwUebF/dfP2iVr70r7ho+AAAILIIPAIAFCD4AABYg+AAAWIDgAwBgAYIPAIAFCD4AABYg+AAAWIDgAwBgAYIPAIAFCD4AABYg+AAAWIDgAwBgAYIPAIAFCD4AABYg+AAAWIDgAwBgAYIPAIAFCD4AABYg+AAAWIDgAwBgAYIPAIAFCD4AABYg+AAAWIDgAwBgAYIPAIAFCD4AABYg+AAAWIDgAwBgAYIPAIAFCD4AABYg+AAAWIDgAwBgAYIPAIAFCD4AABYg+AAAWIDgAwBgAYIPAIAFCD4AABYg+AAAWIDgAwBgAYIPAIAFCD4AABYg+AAAWIDgAwBgAYIPAIAFCD4AABYg+AAAWIDgAwBgAYIPAIAFCD4AABYg+AAAWIDgAwBgAYIPAIAFCD4AABYg+AAAWIDgAwBgAYIPAIAFCD4AABYg+AAAWIDgAwBgAYIPAIAFCD4AABYg+AAAWIDgAwBggTYR/JdfflljxozR6NGjtWHDhmCPAwBAmxMW7AH+k+rqauXl5Wnz5s2KiIjQpEmTdOutt6pPnz7BHg0AgDbjig9+SUmJbrvtNnXq1EmSlJiYqOLiYs2cObNF9w8JcbTabCERoa322ECgtObXSGuJDOsQ7BGAy9YaX3sXe8wrPvg1NTVyOp2+ZZfLpQMHDrT4/tdcc3VrjCVJ+sbAHq322ECgxMREB3uES/bdHinBHgG4bIH+2rvir+F7vV45HP//LxZjzDnLAADgP7vig9+1a1e53W7fstvtlsvlCuJEAAC0PVd88G+//XaVlpbq+PHjOnv2rF555RXFx8cHeywAANqUK/4afpcuXTR79mxlZGSoqalJEydO1C233BLssQAAaFMcxhgT7CEAAEDruuJP6QMAgMtH8AEAsADBBwDAAgQfAAALEHwAACxA8AEAsMAV/3P4sMMHH3yg7du365NPPlFISIhcLpdGjBihAQMGBHs0AGgXOMJH0G3YsEGPPPKIJGnAgAHq37+/JGn+/Pn61a9+FczRAKDd4I13EHSJiYnaunWrrrrqqnPWnz17VqmpqSouLg7SZED79/HHH1/09uuuuy5Ak6C1cUofQRcWFqbm5ubz1tfX1ys8PDwIEwH2uP/++3XkyBG5XC59+fjP4XBo586dQZoM/kbwEXQPPPCAJkyYoLi4ODmdTjkcDtXU1Gjfvn2aPXt2sMcD2rUXXnhBkydP1sKFCzV48OBgj4NWxCl9XBGqq6tVWlqqmpoaeb1ede3aVXFxcerSpUuwRwPavQMHDujFF1/UkiVLgj0KWhHBBwDAArxKHwAACxB8AAAswIv2gHbmySefVFlZmaTP3tCoe/fuioqKkiRt3LjR9/Hl6Nu3r0pLS9W5c+fLfiwAgcE1fKAdu/POO/Wzn/3M7+9YSPCBtocjfMASP//5z/WnP/1JoaGh6tWrl+bPny+n06kf/vCHmjJlipKSkiTpnOW33npLTz75pM6ePavw8HA9/vjjiouLkyStWrVKb731lmpra3XPPfdoypQpcrvdmjt3rk6cOCFJSkhI0MMPPyxJWrNmjbZs2aKwsDDFxsYqJydHO3bs0B//+EedPXtW0dHRWr9+vV588UW98MIL8nq96tSpk+bPn6/rr79ejY2Nys3NVVlZmTwej7797W8rOztb0dHRuvPOO5WamqrS0lJVVVVp/Pjxvv0C+AzX8AELbNq0SXv27NEf//hHvfzyy7rhhhuUmZl50fs0NTVpxowZmjFjhgoLC7VkyRI99dRT8nq9kqSePXtq8+bNWr16tXJyctTU1KSCggL16NFDW7Zs0YYNG1RRUaFTp05p586d2rx5szZu3KjCwkL16NFDv/vd7yRJ77//vtavX6/169frjTfe0NatW7VhwwZt3bpV9957r2bOnClJWrt2rUJDQ7V582a99NJLcrlcys3N9c175swZ/f73v9cf/vAH/epXv9KHH37YSs8m0DZxhA9Y4G9/+5vS0tLUoUMHSVJGRoaee+45NTY2XvA+7733nkJCQnTHHXdIkm6++Wa9/PLLvtvHjRsnSbrpppvU2Niouro6jRgxQvfdd5+qqqp0++2369FHH1XHjh1VWlqqpKQkffOb35QkPfHEE5KkzZs3q2/fvoqOjpYkvfrqq6qoqNCkSZN8+zl58qRqa2v16quv6tSpUyopKZH02T9IYmJifNt973vfkyR16dJFMTEx+vTTT9WzZ8/Let6A9oTgAxbwer1yOBznLH/x7Yy/+FKepqYmSVJoaOg595E++0dA7969JX32lsiSfNsYY3TLLbdo586dKi0t1b59+/T9739fzz///HmPdfLkSZ08eVKSfP8I+Xyu8ePHa86cOb7lmpoaffOb35TX61VWVpYSEhIkSadPn1ZDQ4PvvpGRkb6PHQ7HeW8TC9iOU/qABUaMGKFNmzbpzJkzkqT169dr6NChioiIUOfOnVVeXi7ps9Pr//jHPyRJvXv3lsPh0N69eyVJ77zzjqZNm+Y7pf9VcnNzlZ+fr1GjRmnevHnq06eP/vnPf+r222/Xjh07VFdXJ+mz6/+/+c1vzrv/8OHD9ac//Uk1NTWSPnvb12nTpvlu27BhgxobG+X1ejV//nw988wz/nmCAAtwhA9YYOLEiaqqqtL3v/99eb1excbG+q5/P/jgg8rMzNTu3bvVu3dvDRkyRJIUERGhVatW6amnntKKFSsUHh6uVatWKSIi4oL7mTZtmjIzMzVu3DhFRESob9++Gjt2rCIiIvT+++/rrrvukiT16dNHS5Ys0SuvvHLO/YcPH64f//jH+tGPfiSHw6Ho6GitXr1aDodDDz30kJYvX67U1FR5PB7ddNNN//F1CAD+Hz+WBwCABTilDwCABQg+AAAWIPgAAFiA4AMAYAGCDwCABQg+AAAWIPgAAFjg/wDeZnd6eKmoCwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Touchscreen\n",
    "sns.countplot(x ='Touchscreen', data = ld,palette ='PiYG')\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.title(\"TypeName wise laptops\")\n",
    "sns.set(rc={\"figure.figsize\":(5, 5)})\n",
    "plt.show()\n",
    "\n",
    "sns.barplot(x=\"Touchscreen\", y=\"Price\", data=ld, palette=\"PiYG\")\n",
    "plt.xticks(rotation='vertical')\n",
    "sns.set(rc={\"figure.figsize\":(5, 5)})\n",
    "plt.title(\"TypeName wise Price\")\n",
    "plt.show()\n",
    "\n",
    "#IPs\n",
    "sns.countplot(x ='Ips', data = ld,palette ='PiYG')\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.title(\"TypeName wise laptops\")\n",
    "sns.set(rc={\"figure.figsize\":(5, 5)})\n",
    "plt.show()\n",
    "\n",
    "#TypeName with Price---------------\n",
    "sns.barplot(x=\"Ips\", y=\"Price\", data=ld, palette=\"PiYG\")\n",
    "plt.xticks(rotation='vertical')\n",
    "sns.set(rc={\"figure.figsize\":(5, 5)})\n",
    "plt.title(\"TypeName wise Price\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1c3d2999",
   "metadata": {},
   "outputs": [],
   "source": [
    "new = ld['ScreenResolution'].str.split('x',n=1,expand=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f6c97b12",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Again split screenresolution column\n",
    "ld['X_res'] = new[0]\n",
    "ld['Y_res'] = new[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "381a76f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Inches</th>\n",
       "      <th>ScreenResolution</th>\n",
       "      <th>Cpu</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>X_res</th>\n",
       "      <th>Y_res</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>17.3</td>\n",
       "      <td>1600x900</td>\n",
       "      <td>Intel Core i5 8250U 1.6GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Nvidia GeForce MX150</td>\n",
       "      <td>No OS</td>\n",
       "      <td>2.800</td>\n",
       "      <td>37189.4400</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1600</td>\n",
       "      <td>900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>835</th>\n",
       "      <td>Dell</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>16</td>\n",
       "      <td>256GB SSD +  1TB HDD</td>\n",
       "      <td>Nvidia GeForce GTX 1070</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>3.210</td>\n",
       "      <td>147832.2864</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Full HD 1920</td>\n",
       "      <td>1080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>Microsoft</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.5</td>\n",
       "      <td>Touchscreen 2256x1504</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>Windows 10 S</td>\n",
       "      <td>1.252</td>\n",
       "      <td>71395.2000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Touchscreen 2256</td>\n",
       "      <td>1504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>805</th>\n",
       "      <td>HP</td>\n",
       "      <td>Netbook</td>\n",
       "      <td>12.5</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>Intel Core i5 7300U 2.6GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.260</td>\n",
       "      <td>74005.9200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1366</td>\n",
       "      <td>768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1258</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i7 6500U 2.5GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>1.0TB Hybrid</td>\n",
       "      <td>AMD Radeon R5 M330</td>\n",
       "      <td>Windows 7</td>\n",
       "      <td>2.320</td>\n",
       "      <td>47685.6000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Full HD 1920</td>\n",
       "      <td>1080</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Company   TypeName  Inches       ScreenResolution  \\\n",
       "193      Lenovo   Notebook    17.3               1600x900   \n",
       "835        Dell     Gaming    15.6      Full HD 1920x1080   \n",
       "104   Microsoft  Ultrabook    13.5  Touchscreen 2256x1504   \n",
       "805          HP    Netbook    12.5               1366x768   \n",
       "1258     Lenovo   Notebook    15.6      Full HD 1920x1080   \n",
       "\n",
       "                              Cpu  Ram                Memory  \\\n",
       "193    Intel Core i5 8250U 1.6GHz    8             256GB SSD   \n",
       "835   Intel Core i7 7700HQ 2.8GHz   16  256GB SSD +  1TB HDD   \n",
       "104    Intel Core i5 7200U 2.5GHz    8             256GB SSD   \n",
       "805    Intel Core i5 7300U 2.6GHz    8             256GB SSD   \n",
       "1258   Intel Core i7 6500U 2.5GHz    8          1.0TB Hybrid   \n",
       "\n",
       "                          Gpu         OpSys  Weight        Price  Touchscreen  \\\n",
       "193      Nvidia GeForce MX150         No OS   2.800   37189.4400            0   \n",
       "835   Nvidia GeForce GTX 1070    Windows 10   3.210  147832.2864            0   \n",
       "104     Intel HD Graphics 620  Windows 10 S   1.252   71395.2000            1   \n",
       "805     Intel HD Graphics 620    Windows 10   1.260   74005.9200            0   \n",
       "1258       AMD Radeon R5 M330     Windows 7   2.320   47685.6000            0   \n",
       "\n",
       "      Ips             X_res Y_res  \n",
       "193     0              1600   900  \n",
       "835     0      Full HD 1920  1080  \n",
       "104     0  Touchscreen 2256  1504  \n",
       "805     0              1366   768  \n",
       "1258    0      Full HD 1920  1080  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ld.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "304d9a94",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Edit X-res\n",
    "ld['X_res'] = ld['X_res'].str.replace(',','').str.findall(r'(\\d+\\.?\\d+)').apply(lambda x:x[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5bcb1ed6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Inches</th>\n",
       "      <th>ScreenResolution</th>\n",
       "      <th>Cpu</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>X_res</th>\n",
       "      <th>Y_res</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>14.0</td>\n",
       "      <td>IPS Panel 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>Windows 10</td>\n",
       "      <td>1.90</td>\n",
       "      <td>49976.64</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1920</td>\n",
       "      <td>1080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>454</th>\n",
       "      <td>Dell</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i7 8550U 1.8GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB SSD +  1TB HDD</td>\n",
       "      <td>AMD Radeon 530</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.36</td>\n",
       "      <td>52161.12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1920</td>\n",
       "      <td>1080</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Company  TypeName  Inches             ScreenResolution  \\\n",
       "397  Lenovo  Notebook    14.0  IPS Panel Full HD 1920x1080   \n",
       "454    Dell  Notebook    15.6            Full HD 1920x1080   \n",
       "\n",
       "                            Cpu  Ram                Memory  \\\n",
       "397  Intel Core i5 7200U 2.5GHz    8             256GB SSD   \n",
       "454  Intel Core i7 8550U 1.8GHz    8  128GB SSD +  1TB HDD   \n",
       "\n",
       "                       Gpu       OpSys  Weight     Price  Touchscreen  Ips  \\\n",
       "397  Intel HD Graphics 620  Windows 10    1.90  49976.64            0    1   \n",
       "454         AMD Radeon 530  Windows 10    2.36  52161.12            0    0   \n",
       "\n",
       "    X_res Y_res  \n",
       "397  1920  1080  \n",
       "454  1920  1080  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ld.sample(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e512b3e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Change data type\n",
    "ld['X_res'] = ld['X_res'].astype('int')\n",
    "ld['Y_res'] = ld['Y_res'].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "162b00d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1303 entries, 0 to 1302\n",
      "Data columns (total 15 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Company           1303 non-null   object \n",
      " 1   TypeName          1303 non-null   object \n",
      " 2   Inches            1303 non-null   float64\n",
      " 3   ScreenResolution  1303 non-null   object \n",
      " 4   Cpu               1303 non-null   object \n",
      " 5   Ram               1303 non-null   int32  \n",
      " 6   Memory            1303 non-null   object \n",
      " 7   Gpu               1303 non-null   object \n",
      " 8   OpSys             1303 non-null   object \n",
      " 9   Weight            1303 non-null   float32\n",
      " 10  Price             1303 non-null   float64\n",
      " 11  Touchscreen       1303 non-null   int64  \n",
      " 12  Ips               1303 non-null   int64  \n",
      " 13  X_res             1303 non-null   int32  \n",
      " 14  Y_res             1303 non-null   int32  \n",
      "dtypes: float32(1), float64(2), int32(3), int64(2), object(7)\n",
      "memory usage: 132.5+ KB\n"
     ]
    }
   ],
   "source": [
    "ld.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03d9a30b",
   "metadata": {},
   "source": [
    "### Work with Price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6c6bcf22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Inches         0.068197\n",
       "Ram            0.743007\n",
       "Weight         0.210370\n",
       "Price          1.000000\n",
       "Touchscreen    0.191226\n",
       "Ips            0.252208\n",
       "X_res          0.556529\n",
       "Y_res          0.552809\n",
       "Name: Price, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ld.corr()['Price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "bdc47a00",
   "metadata": {},
   "outputs": [],
   "source": [
    "ld['ppi'] = (((ld['X_res']**2) + (ld['Y_res']**2))**0.5/ld['Inches']).astype('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5e9286c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Inches         0.068197\n",
       "Ram            0.743007\n",
       "Weight         0.210370\n",
       "Price          1.000000\n",
       "Touchscreen    0.191226\n",
       "Ips            0.252208\n",
       "X_res          0.556529\n",
       "Y_res          0.552809\n",
       "ppi            0.473487\n",
       "Name: Price, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ld.corr()['Price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "1dcb8194",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Inches</th>\n",
       "      <th>ScreenResolution</th>\n",
       "      <th>Cpu</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>X_res</th>\n",
       "      <th>Y_res</th>\n",
       "      <th>ppi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>2TB HDD</td>\n",
       "      <td>Nvidia GeForce 940MX</td>\n",
       "      <td>No OS</td>\n",
       "      <td>2.20</td>\n",
       "      <td>29250.7200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1366</td>\n",
       "      <td>768</td>\n",
       "      <td>100.454670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</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>1TB HDD</td>\n",
       "      <td>Intel UHD Graphics 620</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.10</td>\n",
       "      <td>42304.3200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1920</td>\n",
       "      <td>1080</td>\n",
       "      <td>141.211998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</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>4</td>\n",
       "      <td>500GB HDD</td>\n",
       "      <td>Intel HD Graphics 520</td>\n",
       "      <td>No OS</td>\n",
       "      <td>2.10</td>\n",
       "      <td>18594.7200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1920</td>\n",
       "      <td>1080</td>\n",
       "      <td>141.211998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>814</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>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>AMD Radeon R5 M420</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.18</td>\n",
       "      <td>42081.0768</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1920</td>\n",
       "      <td>1080</td>\n",
       "      <td>141.211998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>485</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>14.0</td>\n",
       "      <td>IPS Panel Full HD 1920x1080</td>\n",
       "      <td>Intel Core i5 8250U 1.6GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel UHD Graphics 620</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.75</td>\n",
       "      <td>55890.7200</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1920</td>\n",
       "      <td>1080</td>\n",
       "      <td>157.350512</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Company  TypeName  Inches             ScreenResolution  \\\n",
       "303  Lenovo  Notebook    15.6                     1366x768   \n",
       "223      HP  Notebook    15.6            Full HD 1920x1080   \n",
       "212      HP  Notebook    15.6            Full HD 1920x1080   \n",
       "814    Dell  Notebook    15.6            Full HD 1920x1080   \n",
       "485  Lenovo  Notebook    14.0  IPS Panel Full HD 1920x1080   \n",
       "\n",
       "                            Cpu  Ram     Memory                     Gpu  \\\n",
       "303  Intel Core i5 7200U 2.5GHz    8    2TB HDD    Nvidia GeForce 940MX   \n",
       "223  Intel Core i5 8250U 1.6GHz    8    1TB HDD  Intel UHD Graphics 620   \n",
       "212    Intel Core i3 6006U 2GHz    4  500GB HDD   Intel HD Graphics 520   \n",
       "814  Intel Core i5 7200U 2.5GHz    8  256GB SSD      AMD Radeon R5 M420   \n",
       "485  Intel Core i5 8250U 1.6GHz    8  256GB SSD  Intel UHD Graphics 620   \n",
       "\n",
       "          OpSys  Weight       Price  Touchscreen  Ips  X_res  Y_res  \\\n",
       "303       No OS    2.20  29250.7200            0    0   1366    768   \n",
       "223  Windows 10    2.10  42304.3200            0    0   1920   1080   \n",
       "212       No OS    2.10  18594.7200            0    0   1920   1080   \n",
       "814  Windows 10    2.18  42081.0768            0    0   1920   1080   \n",
       "485  Windows 10    1.75  55890.7200            0    1   1920   1080   \n",
       "\n",
       "            ppi  \n",
       "303  100.454670  \n",
       "223  141.211998  \n",
       "212  141.211998  \n",
       "814  141.211998  \n",
       "485  157.350512  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ld.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "fbf93173",
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop screenresolution, Inches, X_res and Y_res\n",
    "ld.drop(columns=['ScreenResolution','Inches','X_res','Y_res'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b1d4adb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Cpu</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>ppi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>Acer</td>\n",
       "      <td>2 in 1 Convertible</td>\n",
       "      <td>Intel Core i7 6500U 2.5GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 520</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.6</td>\n",
       "      <td>42038.4528</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>165.632118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541</th>\n",
       "      <td>Dell</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>Intel Core i3 6006U 2GHz</td>\n",
       "      <td>4</td>\n",
       "      <td>128GB SSD</td>\n",
       "      <td>Intel HD Graphics 520</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.6</td>\n",
       "      <td>39207.1536</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>157.350512</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Company            TypeName                         Cpu  Ram     Memory  \\\n",
       "413    Acer  2 in 1 Convertible  Intel Core i7 6500U 2.5GHz    8  256GB SSD   \n",
       "541    Dell            Notebook    Intel Core i3 6006U 2GHz    4  128GB SSD   \n",
       "\n",
       "                       Gpu       OpSys  Weight       Price  Touchscreen  Ips  \\\n",
       "413  Intel HD Graphics 520  Windows 10     1.6  42038.4528            1    1   \n",
       "541  Intel HD Graphics 520  Windows 10     1.6  39207.1536            0    0   \n",
       "\n",
       "            ppi  \n",
       "413  165.632118  \n",
       "541  157.350512  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ld.sample(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e67e5b1",
   "metadata": {},
   "source": [
    "### Work with CPU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "94d9d14d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intel Core i5 7200U 2.5GHz       190\n",
       "Intel Core i7 7700HQ 2.8GHz      146\n",
       "Intel Core i7 7500U 2.7GHz       134\n",
       "Intel Core i7 8550U 1.8GHz        73\n",
       "Intel Core i5 8250U 1.6GHz        72\n",
       "                                ... \n",
       "Intel Core M M3-6Y30 0.9GHz        1\n",
       "AMD A9-Series 9420 2.9GHz          1\n",
       "Intel Core i3 6006U 2.2GHz         1\n",
       "AMD A6-Series 7310 2GHz            1\n",
       "Intel Xeon E3-1535M v6 3.1GHz      1\n",
       "Name: Cpu, Length: 118, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ld['Cpu'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "765e68fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#split first 3-words\n",
    "ld['Cpu Name'] = ld['Cpu'].apply(lambda x:\" \".join(x.split()[0:3]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "2b94cb85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Cpu</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>ppi</th>\n",
       "      <th>Cpu Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>MSI</td>\n",
       "      <td>Gaming</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 1070</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.90</td>\n",
       "      <td>108105.12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>127.335675</td>\n",
       "      <td>Intel Core i7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>Dell</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>Intel Core i7 7500U 2.7GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>AMD Radeon R7 M445</td>\n",
       "      <td>Linux</td>\n",
       "      <td>2.36</td>\n",
       "      <td>47898.72</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Company  TypeName                          Cpu  Ram                Memory  \\\n",
       "148     MSI    Gaming  Intel Core i7 7700HQ 2.8GHz   16  256GB SSD +  1TB HDD   \n",
       "235    Dell  Notebook   Intel Core i7 7500U 2.7GHz   16             256GB SSD   \n",
       "\n",
       "                         Gpu       OpSys  Weight      Price  Touchscreen  Ips  \\\n",
       "148  Nvidia GeForce GTX 1070  Windows 10    2.90  108105.12            0    0   \n",
       "235       AMD Radeon R7 M445       Linux    2.36   47898.72            0    0   \n",
       "\n",
       "            ppi       Cpu Name  \n",
       "148  127.335675  Intel Core i7  \n",
       "235  141.211998  Intel Core i7  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ld.sample(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "1816ebca",
   "metadata": {},
   "outputs": [],
   "source": [
    "#CPU name extration function\n",
    "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": 37,
   "id": "601c485d",
   "metadata": {},
   "outputs": [],
   "source": [
    "ld['Cpu brand'] = ld['Cpu Name'].apply(fetch_processor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e252048e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Cpu</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>ppi</th>\n",
       "      <th>Cpu Name</th>\n",
       "      <th>Cpu brand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>543</th>\n",
       "      <td>Acer</td>\n",
       "      <td>Gaming</td>\n",
       "      <td>Intel Core i5 7300HQ 2.5GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>1TB HDD</td>\n",
       "      <td>Nvidia GeForce GTX 1050</td>\n",
       "      <td>Linux</td>\n",
       "      <td>2.40</td>\n",
       "      <td>42517.44</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>678</th>\n",
       "      <td>LG</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>Intel Core i7 8550U 1.8GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.09</td>\n",
       "      <td>122490.72</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>Intel Core i7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Company   TypeName                          Cpu  Ram     Memory  \\\n",
       "543    Acer     Gaming  Intel Core i5 7300HQ 2.5GHz    8    1TB HDD   \n",
       "678      LG  Ultrabook   Intel Core i7 8550U 1.8GHz    8  512GB SSD   \n",
       "\n",
       "                         Gpu       OpSys  Weight      Price  Touchscreen  Ips  \\\n",
       "543  Nvidia GeForce GTX 1050       Linux    2.40   42517.44            0    0   \n",
       "678    Intel HD Graphics 620  Windows 10    1.09  122490.72            0    1   \n",
       "\n",
       "            ppi       Cpu Name      Cpu brand  \n",
       "543  141.211998  Intel Core i5  Intel Core i5  \n",
       "678  141.211998  Intel Core i7  Intel Core i7  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ld.sample(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "6f99df73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Cpu brand\n",
    "sns.countplot(x ='Cpu brand', data = ld,palette ='PiYG')\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.title(\"Cpu brand wise laptops\")\n",
    "sns.set(rc={\"figure.figsize\":(5, 5)})\n",
    "plt.show()\n",
    "\n",
    "sns.barplot(x=\"Cpu brand\", y=\"Price\", data=ld, palette=\"PiYG\")\n",
    "plt.xticks(rotation='vertical')\n",
    "sns.set(rc={\"figure.figsize\":(5, 5)})\n",
    "plt.title(\"Cpu brand wise Price\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "63c01eda",
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop cpu and cpu name \n",
    "ld.drop(columns=['Cpu','Cpu Name'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "10c2a4aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>ppi</th>\n",
       "      <th>Cpu brand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>593</th>\n",
       "      <td>Samsung</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>16</td>\n",
       "      <td>128GB SSD +  1TB HDD</td>\n",
       "      <td>Nvidia GeForce GTX 1050</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.5</td>\n",
       "      <td>90522.72</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1252</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>6</td>\n",
       "      <td>128GB SSD</td>\n",
       "      <td>Nvidia GeForce 920MX</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.4</td>\n",
       "      <td>30316.32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Company  TypeName  Ram                Memory                      Gpu  \\\n",
       "593   Samsung  Notebook   16  128GB SSD +  1TB HDD  Nvidia GeForce GTX 1050   \n",
       "1252   Lenovo  Notebook    6             128GB SSD     Nvidia GeForce 920MX   \n",
       "\n",
       "           OpSys  Weight     Price  Touchscreen  Ips         ppi  \\\n",
       "593   Windows 10     2.5  90522.72            0    0  141.211998   \n",
       "1252  Windows 10     2.4  30316.32            0    0  141.211998   \n",
       "\n",
       "          Cpu brand  \n",
       "593   Intel Core i7  \n",
       "1252  Intel Core i3  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ld.sample(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95d4bbba",
   "metadata": {},
   "source": [
    "### Work with RAM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "77e3b3f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#RAM\n",
    "sns.countplot(x ='Ram', data = ld,palette ='PiYG')\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.title(\"Ram wise laptops\")\n",
    "sns.set(rc={\"figure.figsize\":(5, 5)})\n",
    "plt.show()\n",
    "\n",
    "sns.barplot(x=\"Ram\", y=\"Price\", data=ld, palette=\"PiYG\")\n",
    "plt.xticks(rotation='vertical')\n",
    "sns.set(rc={\"figure.figsize\":(5, 5)})\n",
    "plt.title(\"Ram wise Price\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dff9d3b",
   "metadata": {},
   "source": [
    "### Work with Memory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "70fb87c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "256GB SSD                        412\n",
      "1TB HDD                          223\n",
      "500GB HDD                        132\n",
      "512GB SSD                        118\n",
      "128GB SSD +  1TB HDD              94\n",
      "128GB SSD                         76\n",
      "256GB SSD +  1TB HDD              73\n",
      "32GB Flash Storage                38\n",
      "2TB HDD                           16\n",
      "64GB Flash Storage                15\n",
      "512GB SSD +  1TB HDD              14\n",
      "1TB SSD                           14\n",
      "256GB SSD +  2TB HDD              10\n",
      "1.0TB Hybrid                       9\n",
      "256GB Flash Storage                8\n",
      "16GB Flash Storage                 7\n",
      "32GB SSD                           6\n",
      "180GB SSD                          5\n",
      "128GB Flash Storage                4\n",
      "512GB SSD +  2TB HDD               3\n",
      "16GB SSD                           3\n",
      "512GB Flash Storage                2\n",
      "1TB SSD +  1TB HDD                 2\n",
      "256GB SSD +  500GB HDD             2\n",
      "128GB SSD +  2TB HDD               2\n",
      "256GB SSD +  256GB SSD             2\n",
      "512GB SSD +  256GB SSD             1\n",
      "512GB SSD +  512GB SSD             1\n",
      "64GB Flash Storage +  1TB HDD      1\n",
      "1TB HDD +  1TB HDD                 1\n",
      "32GB HDD                           1\n",
      "64GB SSD                           1\n",
      "128GB HDD                          1\n",
      "240GB SSD                          1\n",
      "8GB SSD                            1\n",
      "508GB Hybrid                       1\n",
      "1.0TB HDD                          1\n",
      "512GB SSD +  1.0TB Hybrid          1\n",
      "256GB SSD +  1.0TB Hybrid          1\n",
      "Name: Memory, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(ld['Memory'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "d924f5f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\AppData\\Local\\Temp/ipykernel_26944/2715484543.py:18: FutureWarning: The default value of regex will change from True to False in a future version.\n",
      "  ld['first'] = ld['first'].str.replace(r'\\D', '')\n",
      "C:\\Users\\HP\\AppData\\Local\\Temp/ipykernel_26944/2715484543.py:27: FutureWarning: The default value of regex will change from True to False in a future version.\n",
      "  ld['second'] = ld['second'].str.replace(r'\\D', '')\n"
     ]
    }
   ],
   "source": [
    "#Split the memory column based on SSD, HDD, TB, Hybrid and Flash storage\n",
    "\n",
    "ld['Memory'] = ld['Memory'].astype(str).replace('\\.0', '', regex=True)\n",
    "ld[\"Memory\"] = ld[\"Memory\"].str.replace('GB', '')\n",
    "ld[\"Memory\"] = ld[\"Memory\"].str.replace('TB', '000')\n",
    "new = ld[\"Memory\"].str.split(\"+\", n = 1, expand = True)\n",
    "\n",
    "ld[\"first\"]= new[0]\n",
    "ld[\"first\"]=ld[\"first\"].str.strip()\n",
    "\n",
    "ld[\"second\"]= new[1]\n",
    "\n",
    "ld[\"Layer1HDD\"] = ld[\"first\"].apply(lambda x: 1 if \"HDD\" in x else 0)\n",
    "ld[\"Layer1SSD\"] = ld[\"first\"].apply(lambda x: 1 if \"SSD\" in x else 0)\n",
    "ld[\"Layer1Hybrid\"] = ld[\"first\"].apply(lambda x: 1 if \"Hybrid\" in x else 0)\n",
    "ld[\"Layer1Flash_Storage\"] = ld[\"first\"].apply(lambda x: 1 if \"Flash Storage\" in x else 0)\n",
    "\n",
    "ld['first'] = ld['first'].str.replace(r'\\D', '')\n",
    "\n",
    "ld[\"second\"].fillna(\"0\", inplace = True)\n",
    "\n",
    "ld[\"Layer2HDD\"] = ld[\"second\"].apply(lambda x: 1 if \"HDD\" in x else 0)\n",
    "ld[\"Layer2SSD\"] = ld[\"second\"].apply(lambda x: 1 if \"SSD\" in x else 0)\n",
    "ld[\"Layer2Hybrid\"] = ld[\"second\"].apply(lambda x: 1 if \"Hybrid\" in x else 0)\n",
    "ld[\"Layer2Flash_Storage\"] = ld[\"second\"].apply(lambda x: 1 if \"Flash Storage\" in x else 0)\n",
    "\n",
    "ld['second'] = ld['second'].str.replace(r'\\D', '')\n",
    "\n",
    "ld[\"first\"] = ld[\"first\"].astype(int)\n",
    "ld[\"second\"] = ld[\"second\"].astype(int)\n",
    "\n",
    "ld[\"HDD\"]=(ld[\"first\"]*ld[\"Layer1HDD\"]+ld[\"second\"]*ld[\"Layer2HDD\"])\n",
    "ld[\"SSD\"]=(ld[\"first\"]*ld[\"Layer1SSD\"]+ld[\"second\"]*ld[\"Layer2SSD\"])\n",
    "ld[\"Hybrid\"]=(ld[\"first\"]*ld[\"Layer1Hybrid\"]+ld[\"second\"]*ld[\"Layer2Hybrid\"])\n",
    "ld[\"Flash_Storage\"]=(ld[\"first\"]*ld[\"Layer1Flash_Storage\"]+ld[\"second\"]*ld[\"Layer2Flash_Storage\"])\n",
    "\n",
    "ld.drop(columns=['first', 'second', 'Layer1HDD', 'Layer1SSD', 'Layer1Hybrid',\n",
    "       'Layer1Flash_Storage', 'Layer2HDD', 'Layer2SSD', 'Layer2Hybrid',\n",
    "       'Layer2Flash_Storage'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "b485bef9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>ppi</th>\n",
       "      <th>Cpu brand</th>\n",
       "      <th>HDD</th>\n",
       "      <th>SSD</th>\n",
       "      <th>Hybrid</th>\n",
       "      <th>Flash_Storage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>545</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>4</td>\n",
       "      <td>128 SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.10</td>\n",
       "      <td>37589.04</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i3</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>984</th>\n",
       "      <td>Toshiba</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>4</td>\n",
       "      <td>500 HDD</td>\n",
       "      <td>Intel HD Graphics 520</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.75</td>\n",
       "      <td>48751.20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111.935204</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>940</th>\n",
       "      <td>HP</td>\n",
       "      <td>Gaming</td>\n",
       "      <td>8</td>\n",
       "      <td>128 SSD +  1000 HDD</td>\n",
       "      <td>Nvidia GeForce GTX 965M</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.75</td>\n",
       "      <td>74538.72</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>127.335675</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>1000</td>\n",
       "      <td>128</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>470</th>\n",
       "      <td>HP</td>\n",
       "      <td>2 in 1 Convertible</td>\n",
       "      <td>8</td>\n",
       "      <td>512 SSD</td>\n",
       "      <td>Intel UHD Graphics 620</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.29</td>\n",
       "      <td>106506.72</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>331.264236</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>512</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>MSI</td>\n",
       "      <td>Gaming</td>\n",
       "      <td>16</td>\n",
       "      <td>256 SSD +  2000 HDD</td>\n",
       "      <td>Nvidia GeForce GTX 1070</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.43</td>\n",
       "      <td>130482.72</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>127.335675</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>2000</td>\n",
       "      <td>256</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company            TypeName  Ram               Memory  \\\n",
       "545       HP            Notebook    4              128 SSD   \n",
       "984  Toshiba            Notebook    4              500 HDD   \n",
       "940       HP              Gaming    8  128 SSD +  1000 HDD   \n",
       "470       HP  2 in 1 Convertible    8              512 SSD   \n",
       "58       MSI              Gaming   16  256 SSD +  2000 HDD   \n",
       "\n",
       "                         Gpu       OpSys  Weight      Price  Touchscreen  Ips  \\\n",
       "545    Intel HD Graphics 620  Windows 10    2.10   37589.04            0    0   \n",
       "984    Intel HD Graphics 520  Windows 10    1.75   48751.20            0    0   \n",
       "940  Nvidia GeForce GTX 965M  Windows 10    2.75   74538.72            0    1   \n",
       "470   Intel UHD Graphics 620  Windows 10    1.29  106506.72            1    1   \n",
       "58   Nvidia GeForce GTX 1070  Windows 10    2.43  130482.72            0    0   \n",
       "\n",
       "            ppi      Cpu brand   HDD  SSD  Hybrid  Flash_Storage  \n",
       "545  141.211998  Intel Core i3     0  128       0              0  \n",
       "984  111.935204  Intel Core i5   500    0       0              0  \n",
       "940  127.335675  Intel Core i5  1000  128       0              0  \n",
       "470  331.264236  Intel Core i7     0  512       0              0  \n",
       "58   127.335675  Intel Core i7  2000  256       0              0  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ld.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "e5e06dbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 6))\n",
    "colormap = sns.color_palette(\"PiYG\")\n",
    "sns.heatmap(ld.corr(), annot=True, cmap=colormap).set_title('Correlation Heatmap', fontdict={'fontsize':18}, pad=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "21c0dd4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "ld.drop(columns=['Memory','Hybrid','Flash_Storage'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "5c9c62e5",
   "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>Price</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>ppi</th>\n",
       "      <th>Cpu brand</th>\n",
       "      <th>HDD</th>\n",
       "      <th>SSD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1193</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>Intel HD Graphics 5300</td>\n",
       "      <td>Mac OS X</td>\n",
       "      <td>0.92</td>\n",
       "      <td>61964.640</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.415547</td>\n",
       "      <td>Other Intel Processor</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>Dell</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>4</td>\n",
       "      <td>AMD Radeon R5 M420X</td>\n",
       "      <td>Linux</td>\n",
       "      <td>2.18</td>\n",
       "      <td>24279.696</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.454670</td>\n",
       "      <td>Intel Core i3</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company   TypeName  Ram                     Gpu     OpSys  Weight  \\\n",
       "1193   Apple  Ultrabook    8  Intel HD Graphics 5300  Mac OS X    0.92   \n",
       "886     Dell   Notebook    4     AMD Radeon R5 M420X     Linux    2.18   \n",
       "\n",
       "          Price  Touchscreen  Ips         ppi              Cpu brand   HDD  \\\n",
       "1193  61964.640            0    1  226.415547  Other Intel Processor     0   \n",
       "886   24279.696            0    0  100.454670          Intel Core i3  1000   \n",
       "\n",
       "      SSD  \n",
       "1193    0  \n",
       "886     0  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ld.sample(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a1d7bb7",
   "metadata": {},
   "source": [
    "### Work with GPU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "106d53b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intel HD Graphics 620      281\n",
      "Intel HD Graphics 520      185\n",
      "Intel UHD Graphics 620      68\n",
      "Nvidia GeForce GTX 1050     66\n",
      "Nvidia GeForce GTX 1060     48\n",
      "                          ... \n",
      "AMD Radeon R5 520            1\n",
      "AMD Radeon R7                1\n",
      "Intel HD Graphics 540        1\n",
      "AMD Radeon 540               1\n",
      "ARM Mali T860 MP4            1\n",
      "Name: Gpu, Length: 110, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(ld['Gpu'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "96aebb20",
   "metadata": {},
   "outputs": [],
   "source": [
    "#split first 3-words\n",
    "ld['Gpu Brand'] = ld['Gpu'].apply(lambda x:x.split()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "42df207b",
   "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>Price</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>ppi</th>\n",
       "      <th>Cpu brand</th>\n",
       "      <th>HDD</th>\n",
       "      <th>SSD</th>\n",
       "      <th>Gpu Brand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>Intel Iris Plus Graphics 640</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>71378.6832</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>47895.5232</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>127.677940</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Ram                           Gpu  OpSys  Weight  \\\n",
       "0   Apple  Ultrabook    8  Intel Iris Plus Graphics 640  macOS    1.37   \n",
       "1   Apple  Ultrabook    8        Intel HD Graphics 6000  macOS    1.34   \n",
       "\n",
       "        Price  Touchscreen  Ips         ppi      Cpu brand  HDD  SSD Gpu Brand  \n",
       "0  71378.6832            0    1  226.983005  Intel Core i5    0  128     Intel  \n",
       "1  47895.5232            0    0  127.677940  Intel Core i5    0    0     Intel  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ld.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "48b076be",
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop GPU \n",
    "ld.drop(columns=['Gpu'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "b8e8778b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intel     722\n",
       "Nvidia    400\n",
       "AMD       180\n",
       "ARM         1\n",
       "Name: Gpu Brand, dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ld['Gpu Brand'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "cec91763",
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop ARM GPU\n",
    "ld = ld[ld['Gpu Brand'] != 'ARM']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "6fffaa78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"Gpu Brand\", y=\"Price\", data=ld, palette=\"PiYG\")\n",
    "plt.xticks(rotation='vertical')\n",
    "sns.set(rc={\"figure.figsize\":(5, 5)})\n",
    "plt.title(\"Gpu Brand wise Price\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccdd5b79",
   "metadata": {},
   "source": [
    "### Work with Ops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "27740818",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Windows 10      1072\n",
      "No OS             66\n",
      "Linux             62\n",
      "Windows 7         45\n",
      "Chrome OS         26\n",
      "macOS             13\n",
      "Mac OS X           8\n",
      "Windows 10 S       8\n",
      "Android            2\n",
      "Name: OpSys, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(ld['OpSys'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "3c01f2a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"OpSys\", y=\"Price\", data=ld, palette=\"PiYG\")\n",
    "plt.xticks(rotation='vertical')\n",
    "sns.set(rc={\"figure.figsize\":(5, 5)})\n",
    "plt.title(\"OpSys wise Price\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "da1b6f2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#OPs extrating function\n",
    "\n",
    "def cat_os(inp):\n",
    "    if inp == 'Windows 10' or inp == 'Windows 7' or inp == 'Windows 10 S':\n",
    "        return 'Windows'\n",
    "    elif inp == 'macOS' or inp == 'Mac OS X':\n",
    "        return 'Mac'\n",
    "    else:\n",
    "        return 'Others'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "524c49c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "ld['os'] = ld['OpSys'].apply(cat_os)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "7a20f4b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Ram</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>ppi</th>\n",
       "      <th>Cpu brand</th>\n",
       "      <th>HDD</th>\n",
       "      <th>SSD</th>\n",
       "      <th>Gpu Brand</th>\n",
       "      <th>os</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>71378.6832</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Mac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>47895.5232</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>127.677940</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Mac</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Ram  OpSys  Weight       Price  Touchscreen  Ips  \\\n",
       "0   Apple  Ultrabook    8  macOS    1.37  71378.6832            0    1   \n",
       "1   Apple  Ultrabook    8  macOS    1.34  47895.5232            0    0   \n",
       "\n",
       "          ppi      Cpu brand  HDD  SSD Gpu Brand   os  \n",
       "0  226.983005  Intel Core i5    0  128     Intel  Mac  \n",
       "1  127.677940  Intel Core i5    0    0     Intel  Mac  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ld.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "0dedf483",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"os\", y=\"Price\", data=ld, palette=\"PiYG\")\n",
    "plt.xticks(rotation='vertical')\n",
    "sns.set(rc={\"figure.figsize\":(5, 5)})\n",
    "plt.title(\"OpSys wise Price\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "affddb3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "ld.drop(columns=['OpSys'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "092ca9f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>ppi</th>\n",
       "      <th>Cpu brand</th>\n",
       "      <th>HDD</th>\n",
       "      <th>SSD</th>\n",
       "      <th>Gpu Brand</th>\n",
       "      <th>os</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>Xiaomi</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>1.95</td>\n",
       "      <td>63882.72</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>Nvidia</td>\n",
       "      <td>Others</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552</th>\n",
       "      <td>Dell</td>\n",
       "      <td>Gaming</td>\n",
       "      <td>16</td>\n",
       "      <td>4.42</td>\n",
       "      <td>143802.72</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>127.335675</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>1000</td>\n",
       "      <td>256</td>\n",
       "      <td>Nvidia</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Company  TypeName  Ram  Weight      Price  Touchscreen  Ips         ppi  \\\n",
       "184  Xiaomi  Notebook    8    1.95   63882.72            0    1  141.211998   \n",
       "552    Dell    Gaming   16    4.42  143802.72            0    1  127.335675   \n",
       "\n",
       "         Cpu brand   HDD  SSD Gpu Brand       os  \n",
       "184  Intel Core i5     0  256    Nvidia   Others  \n",
       "552  Intel Core i7  1000  256    Nvidia  Windows  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ld.sample(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95ba6198",
   "metadata": {},
   "source": [
    "### Work with Weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "58058d0f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Distplot------------\n",
    "sns.distplot(ld['Weight'],\n",
    "             hist_kws={'color':'purple', 'edgecolor':'black',\n",
    "                       'linewidth':.5, 'linestyle':'-'})\n",
    "sns.set(rc={\"figure.figsize\":(7, 5)})\n",
    "plt.title(\"Price Distriution\")\n",
    "plt.show()\n",
    "\n",
    "#Scatterplot------------\n",
    "seaborn.scatterplot(x=\"Weight\",\n",
    "                    y=\"Price\",\n",
    "                    hue=\"Price\",\n",
    "                    marker = '+',\n",
    "                    data=ld)\n",
    "plt.title(\"Inches with Price\")\n",
    "sns.set(rc={\"figure.figsize\":(8, 8)})\n",
    "plt.show()\n",
    "\n",
    "#Let's see corr on weight\n",
    "plt.figure(figsize=(16, 6))\n",
    "colormap = sns.color_palette(\"PiYG\")\n",
    "sns.heatmap(ld.corr(), annot=True, cmap=colormap).set_title('Correlation Heatmap', fontdict={'fontsize':18}, pad=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b20938f7",
   "metadata": {},
   "source": [
    "### Work with price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "a157af84",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Apply Log in price function\n",
    "sns.distplot(np.log(ld['Price']),hist_kws={'color':'purple', 'edgecolor':'black',\n",
    "                       'linewidth':.5, 'linestyle':'-'})\n",
    "sns.set(rc={\"figure.figsize\":(7, 5)})\n",
    "plt.title(\"Price Distriution\") #It's normal distribution\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c629dd38",
   "metadata": {},
   "source": [
    "# It's time to Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "f0dc7d85",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Divide the data for x and y\n",
    "X = ld.drop(columns=['Price'])\n",
    "y = np.log(ld['Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "8b392051",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>ppi</th>\n",
       "      <th>Cpu brand</th>\n",
       "      <th>HDD</th>\n",
       "      <th>SSD</th>\n",
       "      <th>Gpu Brand</th>\n",
       "      <th>os</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>1.37</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Mac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>1.34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>127.677940</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Mac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>1.86</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Others</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>16</td>\n",
       "      <td>1.83</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>220.534624</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>512</td>\n",
       "      <td>AMD</td>\n",
       "      <td>Mac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>1.37</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Mac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1298</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>2 in 1 Convertible</td>\n",
       "      <td>4</td>\n",
       "      <td>1.80</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>157.350512</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1299</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>2 in 1 Convertible</td>\n",
       "      <td>16</td>\n",
       "      <td>1.30</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>276.053530</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>512</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1300</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>2</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111.935204</td>\n",
       "      <td>Other Intel Processor</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1301</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>6</td>\n",
       "      <td>2.19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.454670</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>AMD</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1302</th>\n",
       "      <td>Asus</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>4</td>\n",
       "      <td>2.20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.454670</td>\n",
       "      <td>Other Intel Processor</td>\n",
       "      <td>500</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1302 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company            TypeName  Ram  Weight  Touchscreen  Ips         ppi  \\\n",
       "0      Apple           Ultrabook    8    1.37            0    1  226.983005   \n",
       "1      Apple           Ultrabook    8    1.34            0    0  127.677940   \n",
       "2         HP            Notebook    8    1.86            0    0  141.211998   \n",
       "3      Apple           Ultrabook   16    1.83            0    1  220.534624   \n",
       "4      Apple           Ultrabook    8    1.37            0    1  226.983005   \n",
       "...      ...                 ...  ...     ...          ...  ...         ...   \n",
       "1298  Lenovo  2 in 1 Convertible    4    1.80            1    1  157.350512   \n",
       "1299  Lenovo  2 in 1 Convertible   16    1.30            1    1  276.053530   \n",
       "1300  Lenovo            Notebook    2    1.50            0    0  111.935204   \n",
       "1301      HP            Notebook    6    2.19            0    0  100.454670   \n",
       "1302    Asus            Notebook    4    2.20            0    0  100.454670   \n",
       "\n",
       "                  Cpu brand   HDD  SSD Gpu Brand       os  \n",
       "0             Intel Core i5     0  128     Intel      Mac  \n",
       "1             Intel Core i5     0    0     Intel      Mac  \n",
       "2             Intel Core i5     0  256     Intel   Others  \n",
       "3             Intel Core i7     0  512       AMD      Mac  \n",
       "4             Intel Core i5     0  256     Intel      Mac  \n",
       "...                     ...   ...  ...       ...      ...  \n",
       "1298          Intel Core i7     0  128     Intel  Windows  \n",
       "1299          Intel Core i7     0  512     Intel  Windows  \n",
       "1300  Other Intel Processor     0    0     Intel  Windows  \n",
       "1301          Intel Core i7  1000    0       AMD  Windows  \n",
       "1302  Other Intel Processor   500    0     Intel  Windows  \n",
       "\n",
       "[1302 rows x 12 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "1fdfe2e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       11.175755\n",
       "1       10.776777\n",
       "2       10.329931\n",
       "3       11.814476\n",
       "4       11.473101\n",
       "          ...    \n",
       "1298    10.433899\n",
       "1299    11.288115\n",
       "1300     9.409283\n",
       "1301    10.614129\n",
       "1302     9.886358\n",
       "Name: Price, Length: 1302, dtype: float64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf7723b6",
   "metadata": {},
   "source": [
    "### Train_Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "3a18c9ef",
   "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": 70,
   "id": "60c06ae7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>ppi</th>\n",
       "      <th>Cpu brand</th>\n",
       "      <th>HDD</th>\n",
       "      <th>SSD</th>\n",
       "      <th>Gpu Brand</th>\n",
       "      <th>os</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>Toshiba</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.454670</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1141</th>\n",
       "      <td>MSI</td>\n",
       "      <td>Gaming</td>\n",
       "      <td>8</td>\n",
       "      <td>2.40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>1000</td>\n",
       "      <td>128</td>\n",
       "      <td>Nvidia</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1049</th>\n",
       "      <td>Asus</td>\n",
       "      <td>Netbook</td>\n",
       "      <td>4</td>\n",
       "      <td>1.20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>135.094211</td>\n",
       "      <td>Other Intel Processor</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Others</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1020</th>\n",
       "      <td>Dell</td>\n",
       "      <td>2 in 1 Convertible</td>\n",
       "      <td>4</td>\n",
       "      <td>2.08</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i3</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>878</th>\n",
       "      <td>Dell</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>4</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>1000</td>\n",
       "      <td>128</td>\n",
       "      <td>Nvidia</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466</th>\n",
       "      <td>Acer</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>4</td>\n",
       "      <td>2.20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.454670</td>\n",
       "      <td>Intel Core i3</td>\n",
       "      <td>500</td>\n",
       "      <td>0</td>\n",
       "      <td>Nvidia</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>Asus</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>16</td>\n",
       "      <td>1.63</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>512</td>\n",
       "      <td>Nvidia</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>Acer</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>2.20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.454670</td>\n",
       "      <td>AMD Processor</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>AMD</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>527</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>2.20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.454670</td>\n",
       "      <td>Intel Core i3</td>\n",
       "      <td>2000</td>\n",
       "      <td>0</td>\n",
       "      <td>Nvidia</td>\n",
       "      <td>Others</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1193</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.415547</td>\n",
       "      <td>Other Intel Processor</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Mac</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1106 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Company            TypeName  Ram  Weight  Touchscreen  Ips         ppi  \\\n",
       "183   Toshiba            Notebook    8    2.00            0    0  100.454670   \n",
       "1141      MSI              Gaming    8    2.40            0    0  141.211998   \n",
       "1049     Asus             Netbook    4    1.20            0    0  135.094211   \n",
       "1020     Dell  2 in 1 Convertible    4    2.08            1    1  141.211998   \n",
       "878      Dell            Notebook    4    2.18            0    0  141.211998   \n",
       "...       ...                 ...  ...     ...          ...  ...         ...   \n",
       "466      Acer            Notebook    4    2.20            0    0  100.454670   \n",
       "299      Asus           Ultrabook   16    1.63            0    0  141.211998   \n",
       "493      Acer            Notebook    8    2.20            0    0  100.454670   \n",
       "527    Lenovo            Notebook    8    2.20            0    0  100.454670   \n",
       "1193    Apple           Ultrabook    8    0.92            0    1  226.415547   \n",
       "\n",
       "                  Cpu brand   HDD  SSD Gpu Brand       os  \n",
       "183           Intel Core i5     0  128     Intel  Windows  \n",
       "1141          Intel Core i7  1000  128    Nvidia  Windows  \n",
       "1049  Other Intel Processor     0    0     Intel   Others  \n",
       "1020          Intel Core i3  1000    0     Intel  Windows  \n",
       "878           Intel Core i5  1000  128    Nvidia  Windows  \n",
       "...                     ...   ...  ...       ...      ...  \n",
       "466           Intel Core i3   500    0    Nvidia  Windows  \n",
       "299           Intel Core i7     0  512    Nvidia  Windows  \n",
       "493           AMD Processor  1000    0       AMD  Windows  \n",
       "527           Intel Core i3  2000    0    Nvidia   Others  \n",
       "1193  Other Intel Processor     0    0     Intel      Mac  \n",
       "\n",
       "[1106 rows x 12 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "6f10cc96",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>ppi</th>\n",
       "      <th>Cpu brand</th>\n",
       "      <th>HDD</th>\n",
       "      <th>SSD</th>\n",
       "      <th>Gpu Brand</th>\n",
       "      <th>os</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>4</td>\n",
       "      <td>1.49</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>165.632118</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>500</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>555</th>\n",
       "      <td>Asus</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>4</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.454670</td>\n",
       "      <td>Other Intel Processor</td>\n",
       "      <td>500</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Others</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1251</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>4</td>\n",
       "      <td>1.86</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.454670</td>\n",
       "      <td>AMD Processor</td>\n",
       "      <td>500</td>\n",
       "      <td>0</td>\n",
       "      <td>AMD</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>547</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>4</td>\n",
       "      <td>2.15</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>0</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Others</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>HP</td>\n",
       "      <td>2 in 1 Convertible</td>\n",
       "      <td>4</td>\n",
       "      <td>1.28</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>165.632118</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1129</th>\n",
       "      <td>HP</td>\n",
       "      <td>Gaming</td>\n",
       "      <td>8</td>\n",
       "      <td>3.35</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>127.335675</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>1000</td>\n",
       "      <td>128</td>\n",
       "      <td>Nvidia</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>4</td>\n",
       "      <td>2.20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.454670</td>\n",
       "      <td>AMD Processor</td>\n",
       "      <td>500</td>\n",
       "      <td>0</td>\n",
       "      <td>AMD</td>\n",
       "      <td>Others</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>Asus</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>157.350512</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>HP</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>1.11</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>165.632118</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>470</th>\n",
       "      <td>HP</td>\n",
       "      <td>2 in 1 Convertible</td>\n",
       "      <td>8</td>\n",
       "      <td>1.29</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>331.264236</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>512</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>196 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company            TypeName  Ram  Weight  Touchscreen  Ips         ppi  \\\n",
       "248       HP            Notebook    4    1.49            0    1  165.632118   \n",
       "555     Asus            Notebook    4    2.00            0    0  100.454670   \n",
       "1251      HP            Notebook    4    1.86            0    0  100.454670   \n",
       "547   Lenovo            Notebook    4    2.15            0    0  141.211998   \n",
       "885       HP  2 in 1 Convertible    4    1.28            1    0  165.632118   \n",
       "...      ...                 ...  ...     ...          ...  ...         ...   \n",
       "1129      HP              Gaming    8    3.35            0    1  127.335675   \n",
       "172   Lenovo            Notebook    4    2.20            0    0  100.454670   \n",
       "322     Asus            Notebook    8    2.00            0    0  157.350512   \n",
       "996       HP           Ultrabook    8    1.11            1    1  165.632118   \n",
       "470       HP  2 in 1 Convertible    8    1.29            1    1  331.264236   \n",
       "\n",
       "                  Cpu brand   HDD  SSD Gpu Brand       os  \n",
       "248           Intel Core i5   500    0     Intel  Windows  \n",
       "555   Other Intel Processor   500    0     Intel   Others  \n",
       "1251          AMD Processor   500    0       AMD  Windows  \n",
       "547           Intel Core i5  1000    0     Intel   Others  \n",
       "885           Intel Core i5     0  256     Intel  Windows  \n",
       "...                     ...   ...  ...       ...      ...  \n",
       "1129          Intel Core i5  1000  128    Nvidia  Windows  \n",
       "172           AMD Processor   500    0       AMD   Others  \n",
       "322           Intel Core i7     0  256     Intel  Windows  \n",
       "996           Intel Core i7     0  256     Intel  Windows  \n",
       "470           Intel Core i7     0  512     Intel  Windows  \n",
       "\n",
       "[196 rows x 12 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "bfb13323",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: xgboost in c:\\programdata\\anaconda3\\lib\\site-packages (1.7.2)\n",
      "Requirement already satisfied: scipy in c:\\programdata\\anaconda3\\lib\\site-packages (from xgboost) (1.7.1)\n",
      "Requirement already satisfied: numpy in c:\\programdata\\anaconda3\\lib\\site-packages (from xgboost) (1.20.3)\n"
     ]
    }
   ],
   "source": [
    "!pip install xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "9010273c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import all ML algoritham \n",
    "\n",
    "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,mean_squared_error\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\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": 74,
   "id": "6c4eb926",
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating dictionary for storing different models accuracy\n",
    "model_comparison={}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "019e49b7",
   "metadata": {},
   "source": [
    "### Model 1 : Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "bc678c6e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 Score : 80.73%\n",
      "MAE : 21.02%\n",
      "MSE : 7.37%\n",
      "Cross Val Accuracy: 81.79 %\n"
     ]
    }
   ],
   "source": [
    "step1 = ColumnTransformer(transformers=[\n",
    "    ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,7,10,11])\n",
    "],remainder='passthrough')\n",
    "\n",
    "step2 = LinearRegression()\n",
    "\n",
    "pipe = Pipeline([\n",
    "    ('step1',step1),\n",
    "    ('step2',step2)\n",
    "])\n",
    "\n",
    "pipe.fit(X_train,y_train)\n",
    "\n",
    "y_pred = pipe.predict(X_test)\n",
    "\n",
    "accuracies = cross_val_score(estimator = pipe, X = X_train, y = y_train, cv = 5)\n",
    "\n",
    "print(f\"R2 Score : {r2_score(y_test,y_pred)*100:.2f}%\") \n",
    "print(f\"MAE : {mean_absolute_error(y_test,y_pred)*100:.2f}%\")\n",
    "print(f\"MSE : {mean_squared_error(y_test,y_pred)*100:.2f}%\")\n",
    "print(\"Cross Val Accuracy: {:.2f} %\".format(accuracies.mean()*100))\n",
    "\n",
    "model_comparison['Linear Regression']=[r2_score(y_test,y_pred),mean_squared_error(y_test,y_pred),mean_absolute_error(y_test,y_pred),(accuracies.mean())]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b152b31",
   "metadata": {},
   "source": [
    "### Model 2 : Ridge Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "5ddc9db4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 Score : 81.27%\n",
      "MAE : 20.93%\n",
      "MSE : 7.16%\n",
      "Cross Val Accuracy: 80.76 %\n"
     ]
    }
   ],
   "source": [
    "step1 = ColumnTransformer(transformers=[\n",
    "    ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,7,10,11])\n",
    "],remainder='passthrough')\n",
    "\n",
    "step2 = Ridge(alpha=10)\n",
    "\n",
    "pipe = Pipeline([\n",
    "    ('step1',step1),\n",
    "    ('step2',step2)\n",
    "])\n",
    "\n",
    "pipe.fit(X_train,y_train)\n",
    "\n",
    "y_pred = pipe.predict(X_test)\n",
    "\n",
    "accuracies = cross_val_score(estimator = pipe, X = X_train, y = y_train, cv = 5)\n",
    "\n",
    "print(f\"R2 Score : {r2_score(y_test,y_pred)*100:.2f}%\") \n",
    "print(f\"MAE : {mean_absolute_error(y_test,y_pred)*100:.2f}%\")\n",
    "print(f\"MSE : {mean_squared_error(y_test,y_pred)*100:.2f}%\")\n",
    "print(\"Cross Val Accuracy: {:.2f} %\".format(accuracies.mean()*100))\n",
    "\n",
    "model_comparison['Linear Regression']=[r2_score(y_test,y_pred),mean_squared_error(y_test,y_pred),mean_absolute_error(y_test,y_pred),(accuracies.mean())]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5c77ff4",
   "metadata": {},
   "source": [
    "### Model 3 : Lasso Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "c8f799e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 Score : 80.72%\n",
      "MAE : 21.11%\n",
      "MSE : 7.38%\n",
      "Cross Val Accuracy: 80.92 %\n"
     ]
    }
   ],
   "source": [
    "step1 = ColumnTransformer(transformers=[\n",
    "    ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,7,10,11])\n",
    "],remainder='passthrough')\n",
    "\n",
    "step2 = Lasso(alpha=0.001)\n",
    "\n",
    "pipe = Pipeline([\n",
    "    ('step1',step1),\n",
    "    ('step2',step2)\n",
    "])\n",
    "\n",
    "pipe.fit(X_train,y_train)\n",
    "\n",
    "y_pred = pipe.predict(X_test)\n",
    "\n",
    "accuracies = cross_val_score(estimator = pipe, X = X_train, y = y_train, cv = 5)\n",
    "\n",
    "print(f\"R2 Score : {r2_score(y_test,y_pred)*100:.2f}%\") \n",
    "print(f\"MAE : {mean_absolute_error(y_test,y_pred)*100:.2f}%\")\n",
    "print(f\"MSE : {mean_squared_error(y_test,y_pred)*100:.2f}%\")\n",
    "print(\"Cross Val Accuracy: {:.2f} %\".format(accuracies.mean()*100))\n",
    "\n",
    "model_comparison['Lasso Regression']=[r2_score(y_test,y_pred),mean_squared_error(y_test,y_pred),mean_absolute_error(y_test,y_pred),(accuracies.mean())]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a27d9a98",
   "metadata": {},
   "source": [
    "### Model 4 : KNN Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "8fa076dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 Score : 80.22%\n",
      "MAE : 19.32%\n",
      "MSE : 7.57%\n",
      "Cross Val Accuracy: 76.48 %\n"
     ]
    }
   ],
   "source": [
    "step1 = ColumnTransformer(transformers=[\n",
    "    ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,7,10,11])\n",
    "],remainder='passthrough')\n",
    "\n",
    "step2 = KNeighborsRegressor(n_neighbors=3)\n",
    "\n",
    "pipe = Pipeline([\n",
    "    ('step1',step1),\n",
    "    ('step2',step2)\n",
    "])\n",
    "\n",
    "pipe.fit(X_train,y_train)\n",
    "\n",
    "y_pred = pipe.predict(X_test)\n",
    "\n",
    "accuracies = cross_val_score(estimator = pipe, X = X_train, y = y_train, cv = 5)\n",
    "\n",
    "print(f\"R2 Score : {r2_score(y_test,y_pred)*100:.2f}%\") \n",
    "print(f\"MAE : {mean_absolute_error(y_test,y_pred)*100:.2f}%\")\n",
    "print(f\"MSE : {mean_squared_error(y_test,y_pred)*100:.2f}%\")\n",
    "print(\"Cross Val Accuracy: {:.2f} %\".format(accuracies.mean()*100))\n",
    "\n",
    "model_comparison['KNN Regression']=[r2_score(y_test,y_pred),mean_squared_error(y_test,y_pred),mean_absolute_error(y_test,y_pred),(accuracies.mean())]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58eab09f",
   "metadata": {},
   "source": [
    "### Model 5 : DecisionTree Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "12ae3686",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 Score : 84.26%\n",
      "MAE : 18.13%\n",
      "MSE : 6.02%\n",
      "Cross Val Accuracy: 79.45 %\n"
     ]
    }
   ],
   "source": [
    "step1 = ColumnTransformer(transformers=[\n",
    "    ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,7,10,11])\n",
    "],remainder='passthrough')\n",
    "\n",
    "step2 = DecisionTreeRegressor(max_depth=8)\n",
    "\n",
    "pipe = Pipeline([\n",
    "    ('step1',step1),\n",
    "    ('step2',step2)\n",
    "])\n",
    "\n",
    "pipe.fit(X_train,y_train)\n",
    "\n",
    "y_pred = pipe.predict(X_test)\n",
    "\n",
    "accuracies = cross_val_score(estimator = pipe, X = X_train, y = y_train, cv = 5)\n",
    "\n",
    "print(f\"R2 Score : {r2_score(y_test,y_pred)*100:.2f}%\") \n",
    "print(f\"MAE : {mean_absolute_error(y_test,y_pred)*100:.2f}%\")\n",
    "print(f\"MSE : {mean_squared_error(y_test,y_pred)*100:.2f}%\")\n",
    "print(\"Cross Val Accuracy: {:.2f} %\".format(accuracies.mean()*100))\n",
    "\n",
    "model_comparison['DecisionTree Regression']=[r2_score(y_test,y_pred),mean_squared_error(y_test,y_pred),mean_absolute_error(y_test,y_pred),(accuracies.mean())]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51b5b109",
   "metadata": {},
   "source": [
    "### Model 6 : SVR Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "d9d7f989",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 Score : 80.83%\n",
      "MAE : 20.24%\n",
      "MSE : 7.33%\n",
      "Cross Val Accuracy: 80.24 %\n"
     ]
    }
   ],
   "source": [
    "step1 = ColumnTransformer(transformers=[\n",
    "    ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,7,10,11])\n",
    "],remainder='passthrough')\n",
    "\n",
    "step2 = SVR(kernel='rbf',C=10000,epsilon=0.1)\n",
    "\n",
    "pipe = Pipeline([\n",
    "    ('step1',step1),\n",
    "    ('step2',step2)\n",
    "])\n",
    "\n",
    "pipe.fit(X_train,y_train)\n",
    "\n",
    "y_pred = pipe.predict(X_test)\n",
    "\n",
    "accuracies = cross_val_score(estimator = pipe, X = X_train, y = y_train, cv = 5)\n",
    "\n",
    "print(f\"R2 Score : {r2_score(y_test,y_pred)*100:.2f}%\") \n",
    "print(f\"MAE : {mean_absolute_error(y_test,y_pred)*100:.2f}%\")\n",
    "print(f\"MSE : {mean_squared_error(y_test,y_pred)*100:.2f}%\")\n",
    "print(\"Cross Val Accuracy: {:.2f} %\".format(accuracies.mean()*100))\n",
    "\n",
    "model_comparison['SVR Regression']=[r2_score(y_test,y_pred),mean_squared_error(y_test,y_pred),mean_absolute_error(y_test,y_pred),(accuracies.mean())]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0134c66a",
   "metadata": {},
   "source": [
    "### Model 7 : RandomForest Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "89c7b211",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 Score : 89.49%\n",
      "MAE : 15.55%\n",
      "MSE : 4.02%\n",
      "Cross Val Accuracy: 85.91 %\n"
     ]
    }
   ],
   "source": [
    "step1 = ColumnTransformer(transformers=[\n",
    "    ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,7,10,11])\n",
    "],remainder='passthrough')\n",
    "\n",
    "step2 = RandomForestRegressor(n_estimators=100,\n",
    "                              random_state=3,\n",
    "                              max_samples=0.5,\n",
    "                              max_features=0.10,\n",
    "                              max_depth=15)\n",
    "\n",
    "pipe = Pipeline([\n",
    "    ('step1',step1),\n",
    "    ('step2',step2)\n",
    "])\n",
    "\n",
    "pipe.fit(X_train,y_train)\n",
    "\n",
    "y_pred = pipe.predict(X_test)\n",
    "\n",
    "accuracies = cross_val_score(estimator = pipe, X = X_train, y = y_train, cv = 5)\n",
    "\n",
    "print(f\"R2 Score : {r2_score(y_test,y_pred)*100:.2f}%\") \n",
    "print(f\"MAE : {mean_absolute_error(y_test,y_pred)*100:.2f}%\")\n",
    "print(f\"MSE : {mean_squared_error(y_test,y_pred)*100:.2f}%\")\n",
    "print(\"Cross Val Accuracy: {:.2f} %\".format(accuracies.mean()*100))\n",
    "\n",
    "model_comparison['RandomForest Regression']=[r2_score(y_test,y_pred),mean_squared_error(y_test,y_pred),mean_absolute_error(y_test,y_pred),(accuracies.mean())]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "374b59f4",
   "metadata": {},
   "source": [
    "### Model 8 : ExtraTrees Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "310e6f2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 Score : 87.54%\n",
      "MAE : 15.98%\n",
      "MSE : 4.77%\n",
      "Cross Val Accuracy: 85.70 %\n"
     ]
    }
   ],
   "source": [
    "step1 = ColumnTransformer(transformers=[\n",
    "    ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,7,10,11])\n",
    "],remainder='passthrough')\n",
    "\n",
    "step2 = ExtraTreesRegressor(n_estimators=100,\n",
    "                              random_state=3,\n",
    "                              max_samples=0.5,\n",
    "                              max_features=0.75,\n",
    "                              max_depth=15)\n",
    "\n",
    "pipe = Pipeline([\n",
    "    ('step1',step1),\n",
    "    ('step2',step2)\n",
    "])\n",
    "\n",
    "pipe.fit(X_train,y_train)\n",
    "\n",
    "y_pred = pipe.predict(X_test)\n",
    "\n",
    "accuracies = cross_val_score(estimator = pipe, X = X_train, y = y_train, cv = 5)\n",
    "\n",
    "print(f\"R2 Score : {r2_score(y_test,y_pred)*100:.2f}%\") \n",
    "print(f\"MAE : {mean_absolute_error(y_test,y_pred)*100:.2f}%\")\n",
    "print(f\"MSE : {mean_squared_error(y_test,y_pred)*100:.2f}%\")\n",
    "print(\"Cross Val Accuracy: {:.2f} %\".format(accuracies.mean()*100))\n",
    "\n",
    "model_comparison['ExtraTrees Regression']=[r2_score(y_test,y_pred),mean_squared_error(y_test,y_pred),mean_absolute_error(y_test,y_pred),(accuracies.mean())]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d714543",
   "metadata": {},
   "source": [
    "### Model 8 : AdaBoost Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "9cff3deb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 Score : 80.01%\n",
      "MAE : 22.80%\n",
      "MSE : 7.65%\n",
      "Cross Val Accuracy: 78.41 %\n"
     ]
    }
   ],
   "source": [
    "step1 = ColumnTransformer(transformers=[\n",
    "    ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,7,10,11])\n",
    "],remainder='passthrough')\n",
    "\n",
    "step2 = AdaBoostRegressor(n_estimators=15,learning_rate=1.0)\n",
    "\n",
    "pipe = Pipeline([\n",
    "    ('step1',step1),\n",
    "    ('step2',step2)\n",
    "])\n",
    "\n",
    "pipe.fit(X_train,y_train)\n",
    "\n",
    "y_pred = pipe.predict(X_test)\n",
    "\n",
    "accuracies = cross_val_score(estimator = pipe, X = X_train, y = y_train, cv = 5)\n",
    "\n",
    "print(f\"R2 Score : {r2_score(y_test,y_pred)*100:.2f}%\") \n",
    "print(f\"MAE : {mean_absolute_error(y_test,y_pred)*100:.2f}%\")\n",
    "print(f\"MSE : {mean_squared_error(y_test,y_pred)*100:.2f}%\")\n",
    "print(\"Cross Val Accuracy: {:.2f} %\".format(accuracies.mean()*100))\n",
    "\n",
    "model_comparison['AdaBoost Regression']=[r2_score(y_test,y_pred),mean_squared_error(y_test,y_pred),mean_absolute_error(y_test,y_pred),(accuracies.mean())]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40f87245",
   "metadata": {},
   "source": [
    "### Model 9 : GradientBoosting Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "e50d45a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 Score : 88.43%\n",
      "MAE : 15.88%\n",
      "MSE : 4.43%\n",
      "Cross Val Accuracy: 87.89 %\n"
     ]
    }
   ],
   "source": [
    "step1 = ColumnTransformer(transformers=[\n",
    "    ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,7,10,11])\n",
    "],remainder='passthrough')\n",
    "\n",
    "step2 = GradientBoostingRegressor(n_estimators=500)\n",
    "\n",
    "pipe = Pipeline([\n",
    "    ('step1',step1),\n",
    "    ('step2',step2)\n",
    "])\n",
    "\n",
    "pipe.fit(X_train,y_train)\n",
    "\n",
    "y_pred = pipe.predict(X_test)\n",
    "\n",
    "accuracies = cross_val_score(estimator = pipe, X = X_train, y = y_train, cv = 5)\n",
    "\n",
    "print(f\"R2 Score : {r2_score(y_test,y_pred)*100:.2f}%\") \n",
    "print(f\"MAE : {mean_absolute_error(y_test,y_pred)*100:.2f}%\")\n",
    "print(f\"MSE : {mean_squared_error(y_test,y_pred)*100:.2f}%\")\n",
    "print(\"Cross Val Accuracy: {:.2f} %\".format(accuracies.mean()*100))\n",
    "\n",
    "model_comparison['GradientBoosting Regression']=[r2_score(y_test,y_pred),mean_squared_error(y_test,y_pred),mean_absolute_error(y_test,y_pred),(accuracies.mean())]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d74ce0e9",
   "metadata": {},
   "source": [
    "### Model 10 : XGB Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "e22121b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 Score : 88.12%\n",
      "MAE : 16.50%\n",
      "MSE : 4.55%\n",
      "Cross Val Accuracy: 86.95 %\n"
     ]
    }
   ],
   "source": [
    "step1 = ColumnTransformer(transformers=[\n",
    "    ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,7,10,11])\n",
    "],remainder='passthrough')\n",
    "\n",
    "step2 = XGBRegressor(n_estimators=45,max_depth=5,learning_rate=0.5)\n",
    "\n",
    "pipe = Pipeline([\n",
    "    ('step1',step1),\n",
    "    ('step2',step2)\n",
    "])\n",
    "\n",
    "pipe.fit(X_train,y_train)\n",
    "\n",
    "y_pred = pipe.predict(X_test)\n",
    "\n",
    "accuracies = cross_val_score(estimator = pipe, X = X_train, y = y_train, cv = 5)\n",
    "\n",
    "print(f\"R2 Score : {r2_score(y_test,y_pred)*100:.2f}%\") \n",
    "print(f\"MAE : {mean_absolute_error(y_test,y_pred)*100:.2f}%\")\n",
    "print(f\"MSE : {mean_squared_error(y_test,y_pred)*100:.2f}%\")\n",
    "print(\"Cross Val Accuracy: {:.2f} %\".format(accuracies.mean()*100))\n",
    "\n",
    "model_comparison['XGB Regression']=[r2_score(y_test,y_pred),mean_squared_error(y_test,y_pred),mean_absolute_error(y_test,y_pred),(accuracies.mean())]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5888c35",
   "metadata": {},
   "source": [
    "### Model 11 : Voting Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "e068e3ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 Score : 89.11%\n",
      "MAE : 15.78%\n",
      "MSE : 4.17%\n",
      "Cross Val Accuracy: 87.66 %\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import VotingRegressor,StackingRegressor\n",
    "\n",
    "step1 = ColumnTransformer(transformers=[\n",
    "    ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,7,10,11])\n",
    "],remainder='passthrough')\n",
    "\n",
    "\n",
    "rf = RandomForestRegressor(n_estimators=350,random_state=3,max_samples=0.5,max_features=0.75,max_depth=15)\n",
    "gbdt = GradientBoostingRegressor(n_estimators=100,max_features=0.5)\n",
    "xgb = XGBRegressor(n_estimators=25,learning_rate=0.3,max_depth=5)\n",
    "et = ExtraTreesRegressor(n_estimators=100,random_state=3,max_samples=0.5,max_features=0.75,max_depth=10)\n",
    "\n",
    "step2 = VotingRegressor([('rf', rf), ('gbdt', gbdt), ('xgb',xgb), ('et',et)],weights=[5,1,1,1])\n",
    "\n",
    "pipe = Pipeline([\n",
    "    ('step1',step1),\n",
    "    ('step2',step2)\n",
    "])\n",
    "\n",
    "pipe.fit(X_train,y_train)\n",
    "\n",
    "y_pred = pipe.predict(X_test)\n",
    "\n",
    "accuracies = cross_val_score(estimator = pipe, X = X_train, y = y_train, cv = 5)\n",
    "\n",
    "print(f\"R2 Score : {r2_score(y_test,y_pred)*100:.2f}%\") \n",
    "print(f\"MAE : {mean_absolute_error(y_test,y_pred)*100:.2f}%\")\n",
    "print(f\"MSE : {mean_squared_error(y_test,y_pred)*100:.2f}%\")\n",
    "print(\"Cross Val Accuracy: {:.2f} %\".format(accuracies.mean()*100))\n",
    "\n",
    "model_comparison['Voting Regression']=[r2_score(y_test,y_pred),mean_squared_error(y_test,y_pred),mean_absolute_error(y_test,y_pred),(accuracies.mean())]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "346f6529",
   "metadata": {},
   "source": [
    "### Model 12 : Stacking Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "9f69b5f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 Score : 87.97%\n",
      "MAE : 16.74%\n",
      "MSE : 4.60%\n",
      "Cross Val Accuracy: 86.47 %\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import VotingRegressor,StackingRegressor\n",
    "\n",
    "step1 = ColumnTransformer(transformers=[\n",
    "    ('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,7,10,11])\n",
    "],remainder='passthrough')\n",
    "\n",
    "\n",
    "estimators = [\n",
    "    ('rf', RandomForestRegressor(n_estimators=350,random_state=3,max_samples=0.5,max_features=0.75,max_depth=15)),\n",
    "    ('gbdt',GradientBoostingRegressor(n_estimators=100,max_features=0.5)),\n",
    "    ('xgb', XGBRegressor(n_estimators=25,learning_rate=0.3,max_depth=5))\n",
    "]\n",
    "\n",
    "step2 = StackingRegressor(estimators=estimators, final_estimator=Ridge(alpha=100))\n",
    "\n",
    "pipe = Pipeline([\n",
    "    ('step1',step1),\n",
    "    ('step2',step2)\n",
    "])\n",
    "\n",
    "pipe.fit(X_train,y_train)\n",
    "\n",
    "y_pred = pipe.predict(X_test)\n",
    "\n",
    "accuracies = cross_val_score(estimator = pipe, X = X_train, y = y_train, cv = 5)\n",
    "\n",
    "print(f\"R2 Score : {r2_score(y_test,y_pred)*100:.2f}%\") \n",
    "print(f\"MAE : {mean_absolute_error(y_test,y_pred)*100:.2f}%\")\n",
    "print(f\"MSE : {mean_squared_error(y_test,y_pred)*100:.2f}%\")\n",
    "print(\"Cross Val Accuracy: {:.2f} %\".format(accuracies.mean()*100))\n",
    "\n",
    "model_comparison['Stacking Regression']=[r2_score(y_test,y_pred),mean_squared_error(y_test,y_pred),mean_absolute_error(y_test,y_pred),(accuracies.mean())]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3e74081",
   "metadata": {},
   "source": [
    "# Model Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "f9f568a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "Model_com_ld=pd.DataFrame(model_comparison).T\n",
    "Model_com_ld.columns=['R2 Score','MAE','MSE','Cross Val Accuracy']\n",
    "Model_com_ld=Model_com_ld.sort_values(by='R2 Score',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "15f2ec7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "Model = Model_com_ld.style.format(\"{:.2%}\").background_gradient(cmap='RdPu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "1a35ee78",
   "metadata": {},
   "outputs": [],
   "source": [
    "Max = Model_com_ld.style.highlight_max().set_caption(\"Maximum Score in each Column\").format(\"{:.2%}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "dd893cf5",
   "metadata": {},
   "outputs": [],
   "source": [
    "Min = Model_com_ld.style.highlight_min().set_caption(\"Minimum Score in each Column\").format(\"{:.2%}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "edd1959c",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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       "<table id=\"T_b50a2_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >R2 Score</th>\n",
       "      <th class=\"col_heading level0 col1\" >MAE</th>\n",
       "      <th class=\"col_heading level0 col2\" >MSE</th>\n",
       "      <th class=\"col_heading level0 col3\" >Cross Val Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_b50a2_level0_row0\" class=\"row_heading level0 row0\" >RandomForest Regression</th>\n",
       "      <td id=\"T_b50a2_row0_col0\" class=\"data row0 col0\" >89.49%</td>\n",
       "      <td id=\"T_b50a2_row0_col1\" class=\"data row0 col1\" >4.02%</td>\n",
       "      <td id=\"T_b50a2_row0_col2\" class=\"data row0 col2\" >15.55%</td>\n",
       "      <td id=\"T_b50a2_row0_col3\" class=\"data row0 col3\" >85.91%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b50a2_level0_row1\" class=\"row_heading level0 row1\" >Voting Regression</th>\n",
       "      <td id=\"T_b50a2_row1_col0\" class=\"data row1 col0\" >89.11%</td>\n",
       "      <td id=\"T_b50a2_row1_col1\" class=\"data row1 col1\" >4.17%</td>\n",
       "      <td id=\"T_b50a2_row1_col2\" class=\"data row1 col2\" >15.78%</td>\n",
       "      <td id=\"T_b50a2_row1_col3\" class=\"data row1 col3\" >87.66%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b50a2_level0_row2\" class=\"row_heading level0 row2\" >GradientBoosting Regression</th>\n",
       "      <td id=\"T_b50a2_row2_col0\" class=\"data row2 col0\" >88.43%</td>\n",
       "      <td id=\"T_b50a2_row2_col1\" class=\"data row2 col1\" >4.43%</td>\n",
       "      <td id=\"T_b50a2_row2_col2\" class=\"data row2 col2\" >15.88%</td>\n",
       "      <td id=\"T_b50a2_row2_col3\" class=\"data row2 col3\" >87.89%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b50a2_level0_row3\" class=\"row_heading level0 row3\" >XGB Regression</th>\n",
       "      <td id=\"T_b50a2_row3_col0\" class=\"data row3 col0\" >88.12%</td>\n",
       "      <td id=\"T_b50a2_row3_col1\" class=\"data row3 col1\" >4.55%</td>\n",
       "      <td id=\"T_b50a2_row3_col2\" class=\"data row3 col2\" >16.50%</td>\n",
       "      <td id=\"T_b50a2_row3_col3\" class=\"data row3 col3\" >86.95%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b50a2_level0_row4\" class=\"row_heading level0 row4\" >Stacking Regression</th>\n",
       "      <td id=\"T_b50a2_row4_col0\" class=\"data row4 col0\" >87.97%</td>\n",
       "      <td id=\"T_b50a2_row4_col1\" class=\"data row4 col1\" >4.60%</td>\n",
       "      <td id=\"T_b50a2_row4_col2\" class=\"data row4 col2\" >16.74%</td>\n",
       "      <td id=\"T_b50a2_row4_col3\" class=\"data row4 col3\" >86.47%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b50a2_level0_row5\" class=\"row_heading level0 row5\" >ExtraTrees Regression</th>\n",
       "      <td id=\"T_b50a2_row5_col0\" class=\"data row5 col0\" >87.54%</td>\n",
       "      <td id=\"T_b50a2_row5_col1\" class=\"data row5 col1\" >4.77%</td>\n",
       "      <td id=\"T_b50a2_row5_col2\" class=\"data row5 col2\" >15.98%</td>\n",
       "      <td id=\"T_b50a2_row5_col3\" class=\"data row5 col3\" >85.70%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b50a2_level0_row6\" class=\"row_heading level0 row6\" >DecisionTree Regression</th>\n",
       "      <td id=\"T_b50a2_row6_col0\" class=\"data row6 col0\" >84.26%</td>\n",
       "      <td id=\"T_b50a2_row6_col1\" class=\"data row6 col1\" >6.02%</td>\n",
       "      <td id=\"T_b50a2_row6_col2\" class=\"data row6 col2\" >18.13%</td>\n",
       "      <td id=\"T_b50a2_row6_col3\" class=\"data row6 col3\" >79.45%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b50a2_level0_row7\" class=\"row_heading level0 row7\" >Linear Regression</th>\n",
       "      <td id=\"T_b50a2_row7_col0\" class=\"data row7 col0\" >81.27%</td>\n",
       "      <td id=\"T_b50a2_row7_col1\" class=\"data row7 col1\" >7.16%</td>\n",
       "      <td id=\"T_b50a2_row7_col2\" class=\"data row7 col2\" >20.93%</td>\n",
       "      <td id=\"T_b50a2_row7_col3\" class=\"data row7 col3\" >80.76%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b50a2_level0_row8\" class=\"row_heading level0 row8\" >SVR Regression</th>\n",
       "      <td id=\"T_b50a2_row8_col0\" class=\"data row8 col0\" >80.83%</td>\n",
       "      <td id=\"T_b50a2_row8_col1\" class=\"data row8 col1\" >7.33%</td>\n",
       "      <td id=\"T_b50a2_row8_col2\" class=\"data row8 col2\" >20.24%</td>\n",
       "      <td id=\"T_b50a2_row8_col3\" class=\"data row8 col3\" >80.24%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b50a2_level0_row9\" class=\"row_heading level0 row9\" >Lasso Regression</th>\n",
       "      <td id=\"T_b50a2_row9_col0\" class=\"data row9 col0\" >80.72%</td>\n",
       "      <td id=\"T_b50a2_row9_col1\" class=\"data row9 col1\" >7.38%</td>\n",
       "      <td id=\"T_b50a2_row9_col2\" class=\"data row9 col2\" >21.11%</td>\n",
       "      <td id=\"T_b50a2_row9_col3\" class=\"data row9 col3\" >80.92%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b50a2_level0_row10\" class=\"row_heading level0 row10\" >KNN Regression</th>\n",
       "      <td id=\"T_b50a2_row10_col0\" class=\"data row10 col0\" >80.22%</td>\n",
       "      <td id=\"T_b50a2_row10_col1\" class=\"data row10 col1\" >7.57%</td>\n",
       "      <td id=\"T_b50a2_row10_col2\" class=\"data row10 col2\" >19.32%</td>\n",
       "      <td id=\"T_b50a2_row10_col3\" class=\"data row10 col3\" >76.48%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b50a2_level0_row11\" class=\"row_heading level0 row11\" >AdaBoost Regression</th>\n",
       "      <td id=\"T_b50a2_row11_col0\" class=\"data row11 col0\" >80.01%</td>\n",
       "      <td id=\"T_b50a2_row11_col1\" class=\"data row11 col1\" >7.65%</td>\n",
       "      <td id=\"T_b50a2_row11_col2\" class=\"data row11 col2\" >22.80%</td>\n",
       "      <td id=\"T_b50a2_row11_col3\" class=\"data row11 col3\" >78.41%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1c18d962730>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "6cfb4ab2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_c3601_row0_col1, #T_c3601_row0_col2, #T_c3601_row10_col3, #T_c3601_row11_col0 {\n",
       "  background-color: yellow;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_c3601_\">\n",
       "  <caption>Minimum Score in each Column</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >R2 Score</th>\n",
       "      <th class=\"col_heading level0 col1\" >MAE</th>\n",
       "      <th class=\"col_heading level0 col2\" >MSE</th>\n",
       "      <th class=\"col_heading level0 col3\" >Cross Val Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_c3601_level0_row0\" class=\"row_heading level0 row0\" >RandomForest Regression</th>\n",
       "      <td id=\"T_c3601_row0_col0\" class=\"data row0 col0\" >89.49%</td>\n",
       "      <td id=\"T_c3601_row0_col1\" class=\"data row0 col1\" >4.02%</td>\n",
       "      <td id=\"T_c3601_row0_col2\" class=\"data row0 col2\" >15.55%</td>\n",
       "      <td id=\"T_c3601_row0_col3\" class=\"data row0 col3\" >85.91%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c3601_level0_row1\" class=\"row_heading level0 row1\" >Voting Regression</th>\n",
       "      <td id=\"T_c3601_row1_col0\" class=\"data row1 col0\" >89.11%</td>\n",
       "      <td id=\"T_c3601_row1_col1\" class=\"data row1 col1\" >4.17%</td>\n",
       "      <td id=\"T_c3601_row1_col2\" class=\"data row1 col2\" >15.78%</td>\n",
       "      <td id=\"T_c3601_row1_col3\" class=\"data row1 col3\" >87.66%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c3601_level0_row2\" class=\"row_heading level0 row2\" >GradientBoosting Regression</th>\n",
       "      <td id=\"T_c3601_row2_col0\" class=\"data row2 col0\" >88.43%</td>\n",
       "      <td id=\"T_c3601_row2_col1\" class=\"data row2 col1\" >4.43%</td>\n",
       "      <td id=\"T_c3601_row2_col2\" class=\"data row2 col2\" >15.88%</td>\n",
       "      <td id=\"T_c3601_row2_col3\" class=\"data row2 col3\" >87.89%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c3601_level0_row3\" class=\"row_heading level0 row3\" >XGB Regression</th>\n",
       "      <td id=\"T_c3601_row3_col0\" class=\"data row3 col0\" >88.12%</td>\n",
       "      <td id=\"T_c3601_row3_col1\" class=\"data row3 col1\" >4.55%</td>\n",
       "      <td id=\"T_c3601_row3_col2\" class=\"data row3 col2\" >16.50%</td>\n",
       "      <td id=\"T_c3601_row3_col3\" class=\"data row3 col3\" >86.95%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c3601_level0_row4\" class=\"row_heading level0 row4\" >Stacking Regression</th>\n",
       "      <td id=\"T_c3601_row4_col0\" class=\"data row4 col0\" >87.97%</td>\n",
       "      <td id=\"T_c3601_row4_col1\" class=\"data row4 col1\" >4.60%</td>\n",
       "      <td id=\"T_c3601_row4_col2\" class=\"data row4 col2\" >16.74%</td>\n",
       "      <td id=\"T_c3601_row4_col3\" class=\"data row4 col3\" >86.47%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c3601_level0_row5\" class=\"row_heading level0 row5\" >ExtraTrees Regression</th>\n",
       "      <td id=\"T_c3601_row5_col0\" class=\"data row5 col0\" >87.54%</td>\n",
       "      <td id=\"T_c3601_row5_col1\" class=\"data row5 col1\" >4.77%</td>\n",
       "      <td id=\"T_c3601_row5_col2\" class=\"data row5 col2\" >15.98%</td>\n",
       "      <td id=\"T_c3601_row5_col3\" class=\"data row5 col3\" >85.70%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c3601_level0_row6\" class=\"row_heading level0 row6\" >DecisionTree Regression</th>\n",
       "      <td id=\"T_c3601_row6_col0\" class=\"data row6 col0\" >84.26%</td>\n",
       "      <td id=\"T_c3601_row6_col1\" class=\"data row6 col1\" >6.02%</td>\n",
       "      <td id=\"T_c3601_row6_col2\" class=\"data row6 col2\" >18.13%</td>\n",
       "      <td id=\"T_c3601_row6_col3\" class=\"data row6 col3\" >79.45%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c3601_level0_row7\" class=\"row_heading level0 row7\" >Linear Regression</th>\n",
       "      <td id=\"T_c3601_row7_col0\" class=\"data row7 col0\" >81.27%</td>\n",
       "      <td id=\"T_c3601_row7_col1\" class=\"data row7 col1\" >7.16%</td>\n",
       "      <td id=\"T_c3601_row7_col2\" class=\"data row7 col2\" >20.93%</td>\n",
       "      <td id=\"T_c3601_row7_col3\" class=\"data row7 col3\" >80.76%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c3601_level0_row8\" class=\"row_heading level0 row8\" >SVR Regression</th>\n",
       "      <td id=\"T_c3601_row8_col0\" class=\"data row8 col0\" >80.83%</td>\n",
       "      <td id=\"T_c3601_row8_col1\" class=\"data row8 col1\" >7.33%</td>\n",
       "      <td id=\"T_c3601_row8_col2\" class=\"data row8 col2\" >20.24%</td>\n",
       "      <td id=\"T_c3601_row8_col3\" class=\"data row8 col3\" >80.24%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c3601_level0_row9\" class=\"row_heading level0 row9\" >Lasso Regression</th>\n",
       "      <td id=\"T_c3601_row9_col0\" class=\"data row9 col0\" >80.72%</td>\n",
       "      <td id=\"T_c3601_row9_col1\" class=\"data row9 col1\" >7.38%</td>\n",
       "      <td id=\"T_c3601_row9_col2\" class=\"data row9 col2\" >21.11%</td>\n",
       "      <td id=\"T_c3601_row9_col3\" class=\"data row9 col3\" >80.92%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c3601_level0_row10\" class=\"row_heading level0 row10\" >KNN Regression</th>\n",
       "      <td id=\"T_c3601_row10_col0\" class=\"data row10 col0\" >80.22%</td>\n",
       "      <td id=\"T_c3601_row10_col1\" class=\"data row10 col1\" >7.57%</td>\n",
       "      <td id=\"T_c3601_row10_col2\" class=\"data row10 col2\" >19.32%</td>\n",
       "      <td id=\"T_c3601_row10_col3\" class=\"data row10 col3\" >76.48%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c3601_level0_row11\" class=\"row_heading level0 row11\" >AdaBoost Regression</th>\n",
       "      <td id=\"T_c3601_row11_col0\" class=\"data row11 col0\" >80.01%</td>\n",
       "      <td id=\"T_c3601_row11_col1\" class=\"data row11 col1\" >7.65%</td>\n",
       "      <td id=\"T_c3601_row11_col2\" class=\"data row11 col2\" >22.80%</td>\n",
       "      <td id=\"T_c3601_row11_col3\" class=\"data row11 col3\" >78.41%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1c18e391550>"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "8d7daebf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_bf6c0_row0_col0, #T_bf6c0_row2_col3, #T_bf6c0_row11_col1, #T_bf6c0_row11_col2 {\n",
       "  background-color: yellow;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_bf6c0_\">\n",
       "  <caption>Maximum Score in each Column</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >R2 Score</th>\n",
       "      <th class=\"col_heading level0 col1\" >MAE</th>\n",
       "      <th class=\"col_heading level0 col2\" >MSE</th>\n",
       "      <th class=\"col_heading level0 col3\" >Cross Val Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_bf6c0_level0_row0\" class=\"row_heading level0 row0\" >RandomForest Regression</th>\n",
       "      <td id=\"T_bf6c0_row0_col0\" class=\"data row0 col0\" >89.49%</td>\n",
       "      <td id=\"T_bf6c0_row0_col1\" class=\"data row0 col1\" >4.02%</td>\n",
       "      <td id=\"T_bf6c0_row0_col2\" class=\"data row0 col2\" >15.55%</td>\n",
       "      <td id=\"T_bf6c0_row0_col3\" class=\"data row0 col3\" >85.91%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bf6c0_level0_row1\" class=\"row_heading level0 row1\" >Voting Regression</th>\n",
       "      <td id=\"T_bf6c0_row1_col0\" class=\"data row1 col0\" >89.11%</td>\n",
       "      <td id=\"T_bf6c0_row1_col1\" class=\"data row1 col1\" >4.17%</td>\n",
       "      <td id=\"T_bf6c0_row1_col2\" class=\"data row1 col2\" >15.78%</td>\n",
       "      <td id=\"T_bf6c0_row1_col3\" class=\"data row1 col3\" >87.66%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bf6c0_level0_row2\" class=\"row_heading level0 row2\" >GradientBoosting Regression</th>\n",
       "      <td id=\"T_bf6c0_row2_col0\" class=\"data row2 col0\" >88.43%</td>\n",
       "      <td id=\"T_bf6c0_row2_col1\" class=\"data row2 col1\" >4.43%</td>\n",
       "      <td id=\"T_bf6c0_row2_col2\" class=\"data row2 col2\" >15.88%</td>\n",
       "      <td id=\"T_bf6c0_row2_col3\" class=\"data row2 col3\" >87.89%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bf6c0_level0_row3\" class=\"row_heading level0 row3\" >XGB Regression</th>\n",
       "      <td id=\"T_bf6c0_row3_col0\" class=\"data row3 col0\" >88.12%</td>\n",
       "      <td id=\"T_bf6c0_row3_col1\" class=\"data row3 col1\" >4.55%</td>\n",
       "      <td id=\"T_bf6c0_row3_col2\" class=\"data row3 col2\" >16.50%</td>\n",
       "      <td id=\"T_bf6c0_row3_col3\" class=\"data row3 col3\" >86.95%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bf6c0_level0_row4\" class=\"row_heading level0 row4\" >Stacking Regression</th>\n",
       "      <td id=\"T_bf6c0_row4_col0\" class=\"data row4 col0\" >87.97%</td>\n",
       "      <td id=\"T_bf6c0_row4_col1\" class=\"data row4 col1\" >4.60%</td>\n",
       "      <td id=\"T_bf6c0_row4_col2\" class=\"data row4 col2\" >16.74%</td>\n",
       "      <td id=\"T_bf6c0_row4_col3\" class=\"data row4 col3\" >86.47%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bf6c0_level0_row5\" class=\"row_heading level0 row5\" >ExtraTrees Regression</th>\n",
       "      <td id=\"T_bf6c0_row5_col0\" class=\"data row5 col0\" >87.54%</td>\n",
       "      <td id=\"T_bf6c0_row5_col1\" class=\"data row5 col1\" >4.77%</td>\n",
       "      <td id=\"T_bf6c0_row5_col2\" class=\"data row5 col2\" >15.98%</td>\n",
       "      <td id=\"T_bf6c0_row5_col3\" class=\"data row5 col3\" >85.70%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bf6c0_level0_row6\" class=\"row_heading level0 row6\" >DecisionTree Regression</th>\n",
       "      <td id=\"T_bf6c0_row6_col0\" class=\"data row6 col0\" >84.26%</td>\n",
       "      <td id=\"T_bf6c0_row6_col1\" class=\"data row6 col1\" >6.02%</td>\n",
       "      <td id=\"T_bf6c0_row6_col2\" class=\"data row6 col2\" >18.13%</td>\n",
       "      <td id=\"T_bf6c0_row6_col3\" class=\"data row6 col3\" >79.45%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bf6c0_level0_row7\" class=\"row_heading level0 row7\" >Linear Regression</th>\n",
       "      <td id=\"T_bf6c0_row7_col0\" class=\"data row7 col0\" >81.27%</td>\n",
       "      <td id=\"T_bf6c0_row7_col1\" class=\"data row7 col1\" >7.16%</td>\n",
       "      <td id=\"T_bf6c0_row7_col2\" class=\"data row7 col2\" >20.93%</td>\n",
       "      <td id=\"T_bf6c0_row7_col3\" class=\"data row7 col3\" >80.76%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bf6c0_level0_row8\" class=\"row_heading level0 row8\" >SVR Regression</th>\n",
       "      <td id=\"T_bf6c0_row8_col0\" class=\"data row8 col0\" >80.83%</td>\n",
       "      <td id=\"T_bf6c0_row8_col1\" class=\"data row8 col1\" >7.33%</td>\n",
       "      <td id=\"T_bf6c0_row8_col2\" class=\"data row8 col2\" >20.24%</td>\n",
       "      <td id=\"T_bf6c0_row8_col3\" class=\"data row8 col3\" >80.24%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bf6c0_level0_row9\" class=\"row_heading level0 row9\" >Lasso Regression</th>\n",
       "      <td id=\"T_bf6c0_row9_col0\" class=\"data row9 col0\" >80.72%</td>\n",
       "      <td id=\"T_bf6c0_row9_col1\" class=\"data row9 col1\" >7.38%</td>\n",
       "      <td id=\"T_bf6c0_row9_col2\" class=\"data row9 col2\" >21.11%</td>\n",
       "      <td id=\"T_bf6c0_row9_col3\" class=\"data row9 col3\" >80.92%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bf6c0_level0_row10\" class=\"row_heading level0 row10\" >KNN Regression</th>\n",
       "      <td id=\"T_bf6c0_row10_col0\" class=\"data row10 col0\" >80.22%</td>\n",
       "      <td id=\"T_bf6c0_row10_col1\" class=\"data row10 col1\" >7.57%</td>\n",
       "      <td id=\"T_bf6c0_row10_col2\" class=\"data row10 col2\" >19.32%</td>\n",
       "      <td id=\"T_bf6c0_row10_col3\" class=\"data row10 col3\" >76.48%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_bf6c0_level0_row11\" class=\"row_heading level0 row11\" >AdaBoost Regression</th>\n",
       "      <td id=\"T_bf6c0_row11_col0\" class=\"data row11 col0\" >80.01%</td>\n",
       "      <td id=\"T_bf6c0_row11_col1\" class=\"data row11 col1\" >7.65%</td>\n",
       "      <td id=\"T_bf6c0_row11_col2\" class=\"data row11 col2\" >22.80%</td>\n",
       "      <td id=\"T_bf6c0_row11_col3\" class=\"data row11 col3\" >78.41%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1c18e391d90>"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3139bdd3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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