LPP.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Laptop price predictor \n",
    "In this note book we will make a predictor to predict the price of a laptop giving some characteristics. In this project we will perform a lot of preprocessing and exploratory data analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sn\n",
    "import re\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importing the data from csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>laptop_ID</th>\n",
       "      <th>Company</th>\n",
       "      <th>Product</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_euros</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Apple</td>\n",
       "      <td>MacBook Pro</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 2.3GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>128GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 640</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37kg</td>\n",
       "      <td>1339.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Apple</td>\n",
       "      <td>Macbook Air</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>1440x900</td>\n",
       "      <td>Intel Core i5 1.8GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>128GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34kg</td>\n",
       "      <td>898.94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   laptop_ID Company      Product   TypeName  Inches  \\\n",
       "0          1   Apple  MacBook Pro  Ultrabook    13.3   \n",
       "1          2   Apple  Macbook Air  Ultrabook    13.3   \n",
       "\n",
       "                     ScreenResolution                   Cpu  Ram  \\\n",
       "0  IPS Panel Retina Display 2560x1600  Intel Core i5 2.3GHz  8GB   \n",
       "1                            1440x900  Intel Core i5 1.8GHz  8GB   \n",
       "\n",
       "                Memory                           Gpu  OpSys  Weight  \\\n",
       "0            128GB SSD  Intel Iris Plus Graphics 640  macOS  1.37kg   \n",
       "1  128GB Flash Storage        Intel HD Graphics 6000  macOS  1.34kg   \n",
       "\n",
       "   Price_euros  \n",
       "0      1339.69  \n",
       "1       898.94  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"laptop_data.csv\",encoding='latin-1')\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['laptop_ID', 'Company', 'Product', 'TypeName', 'Inches',\n",
       "       'ScreenResolution', 'Cpu', 'Ram', 'Memory', 'Gpu', 'OpSys', 'Weight',\n",
       "       'Price_euros'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1303, 13)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have 11 characteristics on this dataset, but some of them are really noicy, we will deal with them in the EDA."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets get some more information about this dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1303 entries, 0 to 1302\n",
      "Data columns (total 13 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   laptop_ID         1303 non-null   int64  \n",
      " 1   Company           1303 non-null   object \n",
      " 2   Product           1303 non-null   object \n",
      " 3   TypeName          1303 non-null   object \n",
      " 4   Inches            1303 non-null   float64\n",
      " 5   ScreenResolution  1303 non-null   object \n",
      " 6   Cpu               1303 non-null   object \n",
      " 7   Ram               1303 non-null   object \n",
      " 8   Memory            1303 non-null   object \n",
      " 9   Gpu               1303 non-null   object \n",
      " 10  OpSys             1303 non-null   object \n",
      " 11  Weight            1303 non-null   object \n",
      " 12  Price_euros       1303 non-null   float64\n",
      "dtypes: float64(2), int64(1), object(10)\n",
      "memory usage: 132.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like we have no null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "laptop_ID           0\n",
       "Company             0\n",
       "Product             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_euros         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So this is a near perfect dataset, its awsome for academic research and to get some knowledge on how things work, the only problem is that you rarely come across data like this as it usually needs to be cleaned and processed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now lets see the unique values that we have"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "laptop_ID colum has 1303 unique elements________________________________________ \n",
      "Unique values in laptop_ID:\n",
      " [   1    2    3 ... 1318 1319 1320]\n",
      "\n",
      "Company colum has 19 unique elements________________________________________ \n",
      "Unique values in Company:\n",
      " ['Apple' 'HP' 'Acer' 'Asus' 'Dell' 'Lenovo' 'Chuwi' 'MSI' 'Microsoft'\n",
      " 'Toshiba' 'Huawei' 'Xiaomi' 'Vero' 'Razer' 'Mediacom' 'Samsung' 'Google'\n",
      " 'Fujitsu' 'LG']\n",
      "\n",
      "Product colum has 618 unique elements________________________________________ \n",
      "Unique values in Product:\n",
      " ['MacBook Pro' 'Macbook Air' '250 G6' 'Aspire 3' 'ZenBook UX430UN'\n",
      " 'Swift 3' 'Inspiron 3567' 'MacBook 12\"' 'IdeaPad 320-15IKB' 'XPS 13'\n",
      " 'Vivobook E200HA' 'Legion Y520-15IKBN' '255 G6' 'Inspiron 5379'\n",
      " '15-BS101nv (i7-8550U/8GB/256GB/FHD/W10)' 'MacBook Air' 'Inspiron 5570'\n",
      " 'Latitude 5590' 'ProBook 470' 'LapBook 15.6\"'\n",
      " 'E402WA-GA010T (E2-6110/2GB/32GB/W10)'\n",
      " '17-ak001nv (A6-9220/4GB/500GB/Radeon' 'IdeaPad 120S-14IAP'\n",
      " 'Inspiron 5770' 'ProBook 450' 'X540UA-DM186 (i3-6006U/4GB/1TB/FHD/Linux)'\n",
      " 'Inspiron 7577' 'X542UQ-GO005 (i5-7200U/8GB/1TB/GeForce'\n",
      " 'Aspire A515-51G' 'Inspiron 7773' 'IdeaPad 320-15ISK' 'Rog Strix'\n",
      " 'X751NV-TY001T (N4200/4GB/1TB/GeForce' 'Yoga Book' 'ProBook 430'\n",
      " 'Inspiron 3576' '15-bs002nv (i3-6006U/4GB/128GB/FHD/W10)' 'VivoBook Max'\n",
      " 'GS73VR 7RG' 'X541UA-DM1897 (i3-6006U/4GB/256GB/FHD/Linux)' 'Vostro 5471'\n",
      " 'IdeaPad 520S-14IKB' 'UX410UA-GV350T (i5-8250U/8GB/256GB/FHD/W10)'\n",
      " 'ZenBook Pro' 'Stream 14-AX040wm' 'V310-15ISK (i5-7200U/4GB/1TB/FHD/W10)'\n",
      " 'FX753VE-GC093 (i7-7700HQ/12GB/1TB/GeForce' 'Surface Laptop'\n",
      " 'Inspiron 5370' 'GL72M 7RDX' 'Aspire E5-475'\n",
      " 'FX503VD-E4022T (i7-7700HQ/8GB/1TB/GeForce' 'IdeaPad 320-15IKBN'\n",
      " 'Aspire A515-51G-32MX' 'ProBook 440' 'IdeaPad 320-15AST'\n",
      " 'Pavilion 15-CK000nv' 'FX503VM-E4007T (i7-7700HQ/16GB/1TB'\n",
      " 'FX550IK-DM018T (FX-9830P/8GB/1TB/Radeon' 'Aspire 5' 'Probook 430'\n",
      " 'Zenbook UX430UA' 'Spin 5' 'X541UV-DM1439T (i3-7100U/6GB/256GB/GeForce'\n",
      " 'Omen 15-ce007nv' '15-bs017nv (i7-7500U/8GB/256GB/Radeon'\n",
      " '15-bw000nv (E2-9000e/4GB/500GB/Radeon' 'Envy 13-ad009n'\n",
      " 'Pavilion 14-BK001nv' 'Ideapad 310-15ISK'\n",
      " 'UX430UQ-GV209R (i7-7500U/8GB/256GB/GeForce' 'GP62M 7REX' 'Thinkpad T470'\n",
      " 'VivoBook S15' 'ThinkPad Yoga' 'Probook 440' 'Spectre x360'\n",
      " 'Inspiron 7570' 'X705UV-BX074T (i3-6006U/4GB/1TB/GeForce' 'Spin 3'\n",
      " 'GS63VR 7RG' 'Probook 470' 'E402WA-GA007T (E2-6110/4GB/64GB/W10'\n",
      " 'Inspiron 5567' 'Aspire A515-51G-37JS'\n",
      " '15-BS078nr (i7-7500U/8GB/1TB/W10)' 'V110-15IAP (N3350/4GB/1TB/No'\n",
      " 'FX753VD-GC086T (i5-7300HQ/8GB/1TB' 'Envy 13-AD007nv' 'ThinkPad E480'\n",
      " 'Satellite Pro' 'ZenBook UX430UA' 'EliteBook Folio'\n",
      " 'X541NA (N3350/4GB/1TB/FHD/W10)' 'GE72MVR 7RG' 'Aspire A315-51'\n",
      " 'Inspiron 5577' 'Inspiron 7567' 'V110-15IKB (i5-7200U/4GB/128GB/W10)'\n",
      " 'GE73VR 7RE' 'EliteBook 840' '15-BS103nv (i5-8250U/6GB/256GB/Radeon'\n",
      " 'Yoga 520-14IKB' 'ZenBook Flip' 'Inspiron 5579'\n",
      " 'X555BP-XX180T (A9-9420/4GB/1TB/Radeon' 'Aspire A517-51G'\n",
      " 'Aspire A315-31' 'GE63VR 7RE' 'MateBook X'\n",
      " '17-bs001nv (i5-7200U/6GB/2TB/Radeon' 'GT80S 6QF-074US'\n",
      " 'V310-15IKB (i5-7200U/8GB/1TB' 'Yoga 920-13IKB' 'Mi Notebook' 'XPS 15'\n",
      " 'Swift 7' 'Thinkpad Yoga' 'K147 (N3350/4GB/32GB/FHD/W10)'\n",
      " 'IdeaPad 320-17IKBR' 'Blade Pro' 'Omen 17-W295'\n",
      " 'V110-15ISK (i5-6200U/4GB/128GB/W10)' 'Aspire E5-576G'\n",
      " 'Legion Y720-15IKB' 'Precision 7520' 'Aspire 7' 'ROG GL703VD-GC028T'\n",
      " '15-bs018nq (i3-6006U/4GB/500GB/FHD/No' 'IdeaPad 320-17IKB'\n",
      " 'Latitude 5490' 'Portege Z30-C-16L' 'Alienware 17'\n",
      " 'Vivobook X541UV-DM1217T' 'K756UX-T4340T (i5-7200U/8GB/500GB' 'ZBook 15u'\n",
      " 'Pro P2540UA-XO0198T' '15-rb013nv (E2-9000e/4GB/500GB/W10)' 'Vostro 5468'\n",
      " 'Aspire R7' 'X555QG-DM242T (A10-9620P/4GB/1TB' 'ROG G703VI-E5062T'\n",
      " 'Nitro AN515-51' 'VivoBook Pro' 'F756UX-T4201D (i7-7500U/8GB/128GB'\n",
      " 'Yoga 910-13IKB' '15-bs015dx (i5-7200U/8GB/1TB/W10)' 'Rog G701VIK-BA060T'\n",
      " 'ROG G752VSK-GC493T' 'X505BP-BR019T (A9-9420/4GB/1TB/Radeon'\n",
      " 'Vostro 5370' '15-BW094nd (A6-9220/8GB/128GB/W10)' 'Envy 17-U275cl'\n",
      " 'GT73EVR 7RE' 'Yoga 720-15IKB' 'Vostro 3568'\n",
      " 'V330-15IKB (i7-8550U/8GB/256GB/FHD/W10)' 'ThinkPad X1'\n",
      " 'IdeaPad 320-17ISK' 'Ideapad 320-15IKBN'\n",
      " 'SP315-51 (i7-7500U/12GB/1TB/FHD/W10)' 'Thinkpad T570'\n",
      " 'Chromebook C910-C2ST' 'FX753VD-GC071T (i7-7700HQ/8GB/1TB/GeForce'\n",
      " '17-BS037cl (i3-6006U/8GB/1TB/W10)'\n",
      " 'V330-15IKB (i5-8250U/8GB/256GB/FHD/W10)' 'Aspire A715-71G'\n",
      " 'Precision 7720' 'IdeaPad 310-15ABR' 'ZenBook UX530UQ-PRO' 'VivoBook S14'\n",
      " 'Rog GL702VS-GC095T' 'GL553VE-FY082T (i7-7700HQ/8GB/1TB'\n",
      " 'IdeaPad 320-15IAP' 'EliteBook x360' 'IdeaPad 720S-13IKB' 'GE63VR 7RF'\n",
      " 'ES1-523-84K7 (A8-7410/8GB/256GB/FHD/W10)' 'VivoBook Flip' 'ThinkPad 13'\n",
      " 'ProBook 640' 'TravelMate B' 'Elitebook 840' 'ZenBook UX410UA-GV183T'\n",
      " 'Aspire E5-575' 'Elitebook 820' 'GL72M 7REX'\n",
      " 'UX510UX-CN269T (i7-7500U/8GB/256GB'\n",
      " 'V310-15ISK (i3-6006U/4GB/1TB/FHD/W10)'\n",
      " 'FX553VD-FY647T (i7-7700HQ/8GB/256GB/GeForce' 'Elitebook 850'\n",
      " 'X541NA (N3350/4GB/1TB/Linux)' 'Inspiron 3552' 'IdeaPad 320-15ABR'\n",
      " 'Stream 14-AX001nv' 'GP72MVR 7RFX' 'Zbook 15' 'Tecra A50-C-21G'\n",
      " 'Latitude 7480' 'Zenbook UX410UA-GV027T' '15-AY023na (N3710/8GB/2TB/W10)'\n",
      " 'Elitebook 1040' 'IdeaPad 110-17ACL'\n",
      " '15-bw003nv (A9-Series-9420/4GB/256GB/FHD/W10)' 'Yoga 11e'\n",
      " 'VivoBook E403NA' 'Omen 17-w212nv'\n",
      " 'V310-15ISK (i3-6006U/4GB/128GB/FHD/No' 'ROG Strix' 'IdeaPad 720S-14IKB'\n",
      " 'Zenbook Flip' 'Thinkpad X1' 'Ideapad 510S-13IKB' 'Precision 3510'\n",
      " 'Precision 5520' 'Rog GL753VD-GC042T' 'Rog GL753VE-GC070T'\n",
      " 'Leopard GP72M' '15-BW004nv (A9-9420/4GB/256GB/Radeon' 'ThinkPad E580'\n",
      " 'ThinkPad L470' 'Precision M5520' 'FX753VD-GC461T (i7-7700HQ/16GB/1TB'\n",
      " 'GE73VR 7RF' 'Zenbook 3' 'Portege Z30-C-16P' 'Lenovo IdeaPad'\n",
      " 'ThinkPad P51' 'Thinkpad T470p' '15-BS028nv (i3-6006U/4GB/1TB/Radeon'\n",
      " 'Latitude 3380' 'EliteBook 1040' 'LapBook 12.3' 'ProBook 650'\n",
      " 'X542UQ-DM117 (i3-7100U/8GB/1TB/GeForce' 'Latitude 5480' 'Omen 17-w207nv'\n",
      " 'FlexBook Edge' 'Chromebook 3' 'Thinkpad 13' 'IdeaPad 320s-14IKB'\n",
      " 'Thinkpad P51' '15-ra044nv (N3060/4GB/500GB/W10)' 'Pixelbook (Core'\n",
      " 'ThinkPad T470s' 'ThinkPad X270' 'Omen 15-AX205na' 'Aspire ES1-572'\n",
      " 'Precision 3520' 'GV62 7RD-1686NL' '15-bs024nv (i5-7200U/8GB/128GB/W10)'\n",
      " 'ThinkPad T470' 'Inspiron 3168' '17-BS092ND (i3-6006U/8GB/256GB/W10)'\n",
      " 'Pro P2540UA-AB51' 'IdeaPad 510s-14IKB'\n",
      " 'X541NA-PD1003Y (N4200/4GB/500GB/W10)' 'Omen 17-an006nv' 'Thinkpad T460s'\n",
      " 'Latitude 7390' 'Latitude E5470' 'Portege X30-D-10J' 'Lapbook 15,6'\n",
      " 'ThinkPad E570' 'Thinkpad X270' 'Zenbook UX390UA' 'Thinkpad E570'\n",
      " 'Portege X30-D-10L' 'Rog G752VL-UH71T' 'Thinkpad X260'\n",
      " 'Ideapad 520-15IKBR' 'ThinkPad L570' 'VivoBook E201NA'\n",
      " '15-BS026nv (i5-7200U/8GB/256GB/Radeon' 'IdeaPad 320-14IAP'\n",
      " 'Chromebook N23' 'ZenBook UX510UX-CN211T' 'Aspire A515-51G-59QF'\n",
      " 'Envy 13-AB002nv' 'Vostro 5568' 'VivoBook E12'\n",
      " '15-bs190od (i5-8250U/4GB/1TB/W10)' 'ROG Zephyrus' 'Probook 450'\n",
      " 'FX753VE-GC155T (i7-7700HQ/16GB/1TB' 'Spectre X360' 'Latitude 5580'\n",
      " 'Zenbook UX510UW-FI095T' 'SmartBook Edge' 'Omen 15-ce006nv'\n",
      " 'Thinkpad E470' 'Envy 13-AB020nr' 'VivoBook X540YA-XX519T'\n",
      " 'ThinkPad E470' 'V310-15ISK (i5-6200U/4GB/1TB/FHD/No' 'ThinkPad T570'\n",
      " '17-X047na (i3-6006U/8GB/1TB/W10)' 'A541NA-GO342 (N3350/4GB/500GB/Linux)'\n",
      " 'SmartBook 130' '15-bw007nv (A10-9620P/6GB/128GB/Radeon' 'Spin SP111-31'\n",
      " 'V330-15IKB (i3-7130U/4GB/128GB/FHD/W10)' 'EliteBook 1030' 'Thinkpad P71'\n",
      " 'FX553VD-DM627T (i5-7300HQ/8GB/1TB' 'Lifebook A557' 'ZBook 17'\n",
      " '14-am079na (N3710/8GB/2TB/W10)' '15-cd005nv (A9-9420/6GB/256GB/Radeon'\n",
      " 'V330-15IKB (i5-8250U/4GB/500GB/FHD/W10)' 'SmartBook 141'\n",
      " 'Tecra X40-D-10H' 'IdeaPad Y910-17ISK' 'GT73VR Titan' 'Chromebook 11'\n",
      " 'GT80S 6QE' 'Omen 17-AN010nv' 'Ideapad 320-15IKBR'\n",
      " 'TP501UA-CJ131T (i5-7200U/8GB/1TB/W10)' 'Inspiron 3179'\n",
      " 'Notebook Odyssey' 'V320-17ISK (i3-6006U/4GB/500GB/FHD/No'\n",
      " 'IdeaPad 110-15ISK' 'Latitude 5289' 'EliteBook 850' 'Aspire 1'\n",
      " 'Laptop MSI' 'GS63VR 7RF' 'Tecra Z50-C-144' 'IdeaPad 310-15IKB'\n",
      " 'Swift SF114-31-P5HY' 'Inspiron 7559' 'FX753VD-GC007T (i7-7700HQ/8GB/1TB'\n",
      " 'GT62VR 7RE' 'CB5-132T-C9KK (N3160/4GB/32GB/Chrome' 'LifeBook A557'\n",
      " 'SmartBook 140' 'Q304UA-BHI5T11 (i5-7200U/6GB/1TB/FHD/W10)' 'ZenBook 3'\n",
      " 'V330-15IKB (i5-8250U/4GB/256GB/FHD/W10)' 'Ideapad 320-15ISK'\n",
      " 'X541NA-GO414T (N3350/8GB/1TB/W10)' 'IdeaPad 100S-14IBR'\n",
      " '17-AK091ND (A9-9420/8GB/1TB/W10)' 'ROG GL553VE-FY022' 'Extensa EX2540'\n",
      " 'Portege Z30-C-16J' 'ROG G701VI'\n",
      " 'A715-71G-59DH (i5-7300HQ/8GB/1TB/GeForce' 'GL62M 7REX' 'Tecra A50-D-11M'\n",
      " 'IdeaPad Y700-15ISK' 'Latitude E7470' 'Ideapad 320-15IAP'\n",
      " '15-ay047nv (i3-6006U/6GB/1TB/Radeon' 'GP72VR Leopard' 'Latitude 3580'\n",
      " '15-bs012nv (i7-7500U/8GB/1TB/Radeon' 'Tecra Z50-D-10E'\n",
      " 'V310-15ISK (i5-7200U/8GB/1TB' 'Yoga 720-13IKB' 'Pavilion X360'\n",
      " 'GP62 7RDX' 'Chromebook X360' 'Gram 15Z975' 'Aspire VX5-591G' 'GV62M 7RD'\n",
      " 'L502NA-GO052T (N3350/4GB/128GB/W10)' 'Alienware 15' '17-bs000nv I3'\n",
      " 'Yoga 730' '17-Y002nv (A10-9600P/6GB/2TB/Radeon'\n",
      " 'V110-15ISK (3855U/4GB/500GB/W10)' 'Chromebook 14' 'IdeaPad 520s-14IKB'\n",
      " 'TravelMate B117-M' 'Chromebook Flip' 'Portege Z30T-C-133'\n",
      " '15-bs011nv (i7-7500U/4GB/500GB/Radeon'\n",
      " 'V310-15IKB (i5-7200U/4GB/1TB/FHD/W10)'\n",
      " 'V310-15ISK (i3-6006U/4GB/500GB/No' 'ThinkPad P51s' 'Thinkpad T460p'\n",
      " '17-ak002nv (A10-9620P/6GB/2TB/Radeon'\n",
      " '110-15ACL (A6-7310/4GB/500GB/W10)' 'Smartbook 142'\n",
      " 'V310-15IKB (i5-7200U/4GB/1TB/No' 'Inspiron 5378'\n",
      " '15-BW037na (A9-9420/4GB/1TB/Radeon' 'Predator 17'\n",
      " '15-BW091ND (A9-9420/6GB/1TB' 'Extensa EX2540-58KR'\n",
      " 'V310-15IKB (i7-7500U/4GB/1TB/FHD/W10)' 'ZBook 15' 'Inspiron 7560'\n",
      " 'Tecra X40-D-10G' 'Flex 5' 'Thinkpad P51s' 'Notebook 9' 'Zbook 17'\n",
      " 'N23 (N3060/4GB/128GB/W10)' 'X550VX-XX015D (i5-6300HQ/4GB/1TB/GeForce'\n",
      " 'Thinkpad T460' 'Pro P2540UA-XO0192R' 'Yoga 900-13ISK'\n",
      " '15-cb003na (i5-7300HQ/8GB/1TB' 'Latitude 7280' 'Zenbook UX330UA-AH5Q'\n",
      " 'TravelMate P238-M' 'X751NV-TY001 (N4200/4GB/1TB/GeForce'\n",
      " 'Tecra A40-C-1E5' 'EliteBook 820'\n",
      " 'Q524UQ-BHI7T15 (i7-7500U/12GB/2TB/GeForce' 'Thinkpad P50' 'Vivobook Max'\n",
      " 'Rog G752VS-BA171T' 'Tecra Z40-C-161' 'IdeaPad 110-15IBR' 'GS43VR 7RE'\n",
      " 'GL62M (i5-7300HQ/8GB/1TB' 'Predator G9-793'\n",
      " 'FX502VM-DM560T (i7-7700HQ/8GB/1TB' 'K146 (N3350/4GB/32GB/W10)'\n",
      " 'Yoga 510-15IKB' 'R417NA-RS01 (N3350/4GB/32GB/W10)' 'Pro P2540UA-XS51'\n",
      " 'Latitude 3180' '15-ba043na (A12-9700P/8GB/2TB/W10)' 'Omen 17-an012dx'\n",
      " 'Thinkpad T470s' 'Blade Stealth' 'Latitude 3480'\n",
      " 'V110-15ISK (i3-6006U/4GB/500GB/W10)' 'Tecra X40-D-10Z' 'GL62M 7RD'\n",
      " 'Rog GL702VS-BA023T' 'N42-20 Chromebook'\n",
      " 'R558UA-DM966T (i5-7200U/8GB/128GB/FHD/W10)' 'Rog GL702VM-GC017T'\n",
      " 'ZenBook UX310UQ-GL026T' 'Rog GL502VM-DS74' 'Inspiron 5767'\n",
      " 'ThinkPad T470p' 'K556UR-DM621T (i7-7500U/8GB/256GB/GeForce'\n",
      " 'X541NA (N4200/4GB/1TB/W10)' 'Inspiron 5368' 'Portege X30-D-10X'\n",
      " 'Portï¿½gï¿½ Z30-C-188' 'TMX349-G2-M-50FS (i5-7200U/8GB/256GB/FHD/W10)'\n",
      " 'Tecra A50-D-11D' 'X541NA-GO121 (N4200/4GB/1TB/Linux)' 'Pavilion x360'\n",
      " 'VivoBook L402NA' 'IdeaPad 510-15ISK' 'Rog GL753VD-GC082T'\n",
      " 'Chromebook C731-C78G' 'Probook 640' 'Envy x360' 'GS73VR Stealth'\n",
      " 'Portege X30-D-10V' 'G701VO-IH74K (i7-6820HK/32GB/2x' 'Gram 15Z970'\n",
      " 'Chromebook CB5-571-C1DZ' 'Gram 14Z970' 'Elitebook Folio'\n",
      " 'IdeaPad 510-15IKB' 'GE72VR 6RF' 'Envy 13-AB077cl' 'Tecra Z50-C-140'\n",
      " 'Probook 650' 'Tecra Z40-C-12X' 'GP62M Leopard' 'Omen 17-W006na'\n",
      " 'X751SV-TY001T (N3710/4GB/1TB/GeForce' 'TravelMate P259-G2'\n",
      " 'Tecra A50-C-1ZV' 'Yoga 700-11ISK' 'IdeaPad Y700-15ACZ' 'Insprion 5767'\n",
      " 'ZBook Studio' 'Portege Z30-C-1CW' 'ProBook x360' 'Chromebook C738T-C2EJ'\n",
      " 'Portege Z30-C-16Z' 'Aspire F5-573G-510L' 'Portege X20W-D-10V'\n",
      " 'Tecra A40-C-1DF' 'ThinkPad T460' 'Q534UX-BHI7T19 (i7-7500U/16GB/2TB'\n",
      " '15-bs053od (i7-7500U/6GB/1TB/W10)' 'Rog GL753VE-DS74' 'Inspiron 7579'\n",
      " 'Portege Z30-C-1CV' 'LifeBook A556' 'Tecra A40-C-1KF'\n",
      " '15-bs005nv (i3-6006U/4GB/1TB' 'V110-15IAP (N3350/4GB/128GB/No'\n",
      " 'ThinkPad T560' 'ZenBook UX310UA-FB485T' 'Spectre 13-V111dx'\n",
      " 'Aspire ES1-533' 'Rog GL553VE-DS74' 'Nitro 5' 'ENVY -'\n",
      " 'Portege Z30-C-16H' 'Portege A30-C-1CZ' 'ThinkPad P70' 'Tecra Z40-C-12Z'\n",
      " 'Inspiron 5568' 'Portï¿½gï¿½ Z30-C-16K' 'Spectre 13-V100nv'\n",
      " 'Latitude E5570' 'Tecra Z40-C-136' 'Yoga 500-15ISK'\n",
      " 'V142 (X5-Z8350/2GB/32GB/W10)' 'Tecra A50-C-218' 'Thinkpad L560'\n",
      " 'GT72S Dominator' 'IdeaPad Y900-17ISK' 'Chromebook C202SA' 'Noteb Pav'\n",
      " 'Inspiron 5578' '250 G5' 'Aspire ES1-523' 'Inspiron 7378' 'GT62VR 6RD'\n",
      " 'Rog G752VL-GC088D' 'GS63VR 6RF' 'ROG G701VO' 'Latitude 3570'\n",
      " 'IdeaPad 300-17ISK' 'Ideapad 700-15ISK' 'GT72VR Dominator'\n",
      " 'V110-15ISK (i5-6200U/4GB/500GB/W10)' 'Yoga 900S-12ISK' 'Chromebook 13'\n",
      " 'Rog GL702VM-GC354T' 'Aspire F5-573G' 'GS70 Stealth'\n",
      " 'G752VY-GC162T (i7-6700HQ/16GB/1TB' 'Latitude E5270' 'Chromebook 15'\n",
      " 'GE72 Apache' '15-bw011nv (A6-9220/4GB/1TB/FHD/W10)' 'Rog GL552VW-CN470T'\n",
      " 'Vostro 3559' 'V110-15ISK (i3-6006U/4GB/128GB/W10)' 'Spectre Pro'\n",
      " 'Portege X30-D-10K' 'Rog GL752VW-T4308T'\n",
      " 'V131 (X5-Z8350/4GB/32GB/FHD/W10)' 'Omen -'\n",
      " '15-bs078cl (i7-7500U/8GB/2TB/W10)' 'ThinkPad P40'\n",
      " 'L403NA-GA013TS (N3350/4GB/32GB/W10)' 'IdeaPad 500-15ISK' 'GP62M 7RDX'\n",
      " 'V110-15ISK (i3-6006U/4GB/1TB/No' '15-BA015wm (E2-7110/4GB/500GB/W10)'\n",
      " 'B51-80 (i5-6200U/8GB/1TB/Radeon' '15-bw002nv (A6-9220/4GB/256GB/Radeon'\n",
      " 'GP72M 7REX' 'ThinkPad T460s' 'B51-80 (i5-6200U/8GB/1008GB/Radeon'\n",
      " 'GS40 Phantom' 'Pavilion 15-cb003nv' 'IdeaPad 310-15ISK' '250 G4'\n",
      " '320-15ISK (i3-6006U/4GB/1TB/GeForce' 'Stream 14-AX000nv' 'PL60 7RD'\n",
      " 'X553SA-XX021T (N3050/4GB/500GB/W10)' 'V110-15ISK (i5-6200U/4GB/500GB/No'\n",
      " 'UX410UA-GV097T (i3-7100U/4GB/256GB/FHD/W10)'\n",
      " 'B51-80 (i7-6500U/4GB/1008GB/FHD/W7)' 'GS60 Ghost' 'Pavilion 15-BC000nv'\n",
      " 'Rog GL552VW-DM201T' 'Chromebook Plus' 'Pavilion Power'\n",
      " 'V110-15ISK (i3-6006U/4GB/1TB/Radeon' 'Rog G752VY-GC229T' 'GS73VR 7RF'\n",
      " 'FX502VM-DM105T (i7-6700HQ/8GB/1TB/GeForce'\n",
      " '15-bs025nv (i5-7200U/8GB/256GB/W10)' 'Aspire E5-774G'\n",
      " 'FX502VM-AS73 (i7-7700HQ/16GB/1TB' 'C740-C9QX (3205U/2GB/32GB/Chrome'\n",
      " 'E5 774G' 'SP714-51 (i7-7Y75/8GB/256GB/FHD/W10)' 'Thinkpad T560'\n",
      " 'GP62MVR 6RF' '15-bw009nv (A12-9720P/6GB/1TB/Radeon' 'Latitude E7270'\n",
      " 'X540SA-RBPDN09 (N3710/4GB/1TB/W10)' 'GL62M 7RDX' 'GE72VR Apache'\n",
      " '15-bs023nv (i3-6006U/4GB/1TB/FHD/W10)' 'GL62 6QF' 'ZenBook UX310UA-WB71'\n",
      " 'Inspiron 7779' 'Rog GL553VE-FY052T' 'Rog GL502VS'\n",
      " 'V510-15IKB (i5-7200U/8GB/256GB/FHD/No' 'ThinkPad L460'\n",
      " 'X541NA-GO020T (N3350/4GB/1TB/W10)' 'Rog G752VT-GC073T'\n",
      " 'B51-80 (i7-6500U/8GB/1008GB/Radeon' 'GE62 Apache' 'Yoga 500-14IBD'\n",
      " 'ZenBook UX305CA-UBM1' 'Aspire ES1-531' 'Pavilion 15-AW003nv'\n",
      " 'Stream 11-Y000na' 'X556UJ-XO044T (i7-6500U/4GB/500GB/GeForce'\n",
      " 'Yoga 500-14ISK' '15-AC110nv (i7-6500U/6GB/1TB/Radeon'\n",
      " 'X553SA-XX031T (N3050/4GB/500GB/W10)']\n",
      "\n",
      "TypeName colum has 6 unique elements________________________________________ \n",
      "Unique values in TypeName:\n",
      " ['Ultrabook' 'Notebook' 'Netbook' 'Gaming' '2 in 1 Convertible'\n",
      " 'Workstation']\n",
      "\n",
      "Inches colum has 18 unique elements________________________________________ \n",
      "Unique values in Inches:\n",
      " [13.3 15.6 15.4 14.  12.  11.6 17.3 10.1 13.5 12.5 13.  18.4 13.9 12.3\n",
      " 17.  15.  14.1 11.3]\n",
      "\n",
      "ScreenResolution colum has 40 unique elements________________________________________ \n",
      "Unique values in ScreenResolution:\n",
      " ['IPS Panel Retina Display 2560x1600' '1440x900' 'Full HD 1920x1080'\n",
      " 'IPS Panel Retina Display 2880x1800' '1366x768'\n",
      " 'IPS Panel Full HD 1920x1080' 'IPS Panel Retina Display 2304x1440'\n",
      " 'IPS Panel Full HD / Touchscreen 1920x1080'\n",
      " 'Full HD / Touchscreen 1920x1080' 'Touchscreen / Quad HD+ 3200x1800'\n",
      " 'IPS Panel Touchscreen 1920x1200' 'Touchscreen 2256x1504'\n",
      " 'Quad HD+ / Touchscreen 3200x1800' 'IPS Panel 1366x768'\n",
      " 'IPS Panel 4K Ultra HD / Touchscreen 3840x2160'\n",
      " 'IPS Panel Full HD 2160x1440' '4K Ultra HD / Touchscreen 3840x2160'\n",
      " 'Touchscreen 2560x1440' '1600x900' 'IPS Panel 4K Ultra HD 3840x2160'\n",
      " '4K Ultra HD 3840x2160' 'Touchscreen 1366x768'\n",
      " 'IPS Panel Full HD 1366x768' 'IPS Panel 2560x1440'\n",
      " 'IPS Panel Full HD 2560x1440' 'IPS Panel Retina Display 2736x1824'\n",
      " 'Touchscreen 2400x1600' '2560x1440' 'IPS Panel Quad HD+ 2560x1440'\n",
      " 'IPS Panel Quad HD+ 3200x1800'\n",
      " 'IPS Panel Quad HD+ / Touchscreen 3200x1800'\n",
      " 'IPS Panel Touchscreen 1366x768' '1920x1080'\n",
      " 'IPS Panel Full HD 1920x1200'\n",
      " 'IPS Panel Touchscreen / 4K Ultra HD 3840x2160'\n",
      " 'IPS Panel Touchscreen 2560x1440' 'Touchscreen / Full HD 1920x1080'\n",
      " 'Quad HD+ 3200x1800' 'Touchscreen / 4K Ultra HD 3840x2160'\n",
      " 'IPS Panel Touchscreen 2400x1600']\n",
      "\n",
      "Cpu colum has 118 unique elements________________________________________ \n",
      "Unique values in Cpu:\n",
      " ['Intel Core i5 2.3GHz' 'Intel Core i5 1.8GHz'\n",
      " 'Intel Core i5 7200U 2.5GHz' 'Intel Core i7 2.7GHz'\n",
      " 'Intel Core i5 3.1GHz' 'AMD A9-Series 9420 3GHz' 'Intel Core i7 2.2GHz'\n",
      " 'Intel Core i7 8550U 1.8GHz' 'Intel Core i5 8250U 1.6GHz'\n",
      " 'Intel Core i3 6006U 2GHz' 'Intel Core i7 2.8GHz'\n",
      " 'Intel Core M m3 1.2GHz' 'Intel Core i7 7500U 2.7GHz'\n",
      " 'Intel Core i7 2.9GHz' 'Intel Core i3 7100U 2.4GHz'\n",
      " 'Intel Atom x5-Z8350 1.44GHz' 'Intel Core i5 7300HQ 2.5GHz'\n",
      " 'AMD E-Series E2-9000e 1.5GHz' 'Intel Core i5 1.6GHz'\n",
      " 'Intel Core i7 8650U 1.9GHz' 'Intel Atom x5-Z8300 1.44GHz'\n",
      " 'AMD E-Series E2-6110 1.5GHz' 'AMD A6-Series 9220 2.5GHz'\n",
      " 'Intel Celeron Dual Core N3350 1.1GHz' 'Intel Core i3 7130U 2.7GHz'\n",
      " 'Intel Core i7 7700HQ 2.8GHz' 'Intel Core i5 2.0GHz'\n",
      " 'AMD Ryzen 1700 3GHz' 'Intel Pentium Quad Core N4200 1.1GHz'\n",
      " 'Intel Atom x5-Z8550 1.44GHz' 'Intel Celeron Dual Core N3060 1.6GHz'\n",
      " 'Intel Core i5 1.3GHz' 'AMD FX 9830P 3GHz' 'Intel Core i7 7560U 2.4GHz'\n",
      " 'AMD E-Series 6110 1.5GHz' 'Intel Core i5 6200U 2.3GHz'\n",
      " 'Intel Core M 6Y75 1.2GHz' 'Intel Core i5 7500U 2.7GHz'\n",
      " 'Intel Core i3 6006U 2.2GHz' 'AMD A6-Series 9220 2.9GHz'\n",
      " 'Intel Core i7 6920HQ 2.9GHz' 'Intel Core i5 7Y54 1.2GHz'\n",
      " 'Intel Core i7 7820HK 2.9GHz' 'Intel Xeon E3-1505M V6 3GHz'\n",
      " 'Intel Core i7 6500U 2.5GHz' 'AMD E-Series 9000e 1.5GHz'\n",
      " 'AMD A10-Series A10-9620P 2.5GHz' 'AMD A6-Series A6-9220 2.5GHz'\n",
      " 'Intel Core i5 2.9GHz' 'Intel Core i7 6600U 2.6GHz'\n",
      " 'Intel Core i3 6006U 2.0GHz' 'Intel Celeron Dual Core 3205U 1.5GHz'\n",
      " 'Intel Core i7 7820HQ 2.9GHz' 'AMD A10-Series 9600P 2.4GHz'\n",
      " 'Intel Core i7 7600U 2.8GHz' 'AMD A8-Series 7410 2.2GHz'\n",
      " 'Intel Celeron Dual Core 3855U 1.6GHz'\n",
      " 'Intel Pentium Quad Core N3710 1.6GHz' 'AMD A12-Series 9720P 2.7GHz'\n",
      " 'Intel Core i5 7300U 2.6GHz' 'AMD A12-Series 9720P 3.6GHz'\n",
      " 'Intel Celeron Quad Core N3450 1.1GHz'\n",
      " 'Intel Celeron Dual Core N3060 1.60GHz' 'Intel Core i5 6440HQ 2.6GHz'\n",
      " 'Intel Core i7 6820HQ 2.7GHz' 'AMD Ryzen 1600 3.2GHz'\n",
      " 'Intel Core i7 7Y75 1.3GHz' 'Intel Core i5 7440HQ 2.8GHz'\n",
      " 'Intel Core i7 7660U 2.5GHz' 'Intel Core i7 7700HQ 2.7GHz'\n",
      " 'Intel Core M m3-7Y30 2.2GHz' 'Intel Core i5 7Y57 1.2GHz'\n",
      " 'Intel Core i7 6700HQ 2.6GHz' 'Intel Core i3 6100U 2.3GHz'\n",
      " 'AMD A10-Series 9620P 2.5GHz' 'AMD E-Series 7110 1.8GHz'\n",
      " 'Intel Celeron Dual Core N3350 2.0GHz' 'AMD A9-Series A9-9420 3GHz'\n",
      " 'Intel Core i7 6820HK 2.7GHz' 'Intel Core M 7Y30 1.0GHz'\n",
      " 'Intel Xeon E3-1535M v6 3.1GHz' 'Intel Celeron Quad Core N3160 1.6GHz'\n",
      " 'Intel Core i5 6300U 2.4GHz' 'Intel Core i3 6100U 2.1GHz'\n",
      " 'AMD E-Series E2-9000 2.2GHz' 'Intel Celeron Dual Core N3050 1.6GHz'\n",
      " 'Intel Core M M3-6Y30 0.9GHz' 'AMD A9-Series 9420 2.9GHz'\n",
      " 'Intel Core i5 6300HQ 2.3GHz' 'AMD A6-Series 7310 2GHz'\n",
      " 'Intel Atom Z8350 1.92GHz' 'Intel Xeon E3-1535M v5 2.9GHz'\n",
      " 'Intel Core i5 6260U 1.8GHz' 'Intel Pentium Dual Core N4200 1.1GHz'\n",
      " 'Intel Celeron Quad Core N3710 1.6GHz' 'Intel Core M 1.2GHz'\n",
      " 'AMD A12-Series 9700P 2.5GHz' 'Intel Core i7 7500U 2.5GHz'\n",
      " 'Intel Pentium Dual Core 4405U 2.1GHz' 'AMD A4-Series 7210 2.2GHz'\n",
      " 'Intel Core i7 6560U 2.2GHz' 'Intel Core M m7-6Y75 1.2GHz'\n",
      " 'AMD FX 8800P 2.1GHz' 'Intel Core M M7-6Y75 1.2GHz'\n",
      " 'Intel Core i5 7200U 2.50GHz' 'Intel Core i5 7200U 2.70GHz'\n",
      " 'Intel Atom X5-Z8350 1.44GHz' 'Intel Core i5 7200U 2.7GHz'\n",
      " 'Intel Core M 1.1GHz' 'Intel Pentium Dual Core 4405Y 1.5GHz'\n",
      " 'Intel Pentium Quad Core N3700 1.6GHz' 'Intel Core M 6Y54 1.1GHz'\n",
      " 'Intel Core i7 6500U 2.50GHz' 'Intel Celeron Dual Core N3350 2GHz'\n",
      " 'Samsung Cortex A72&A53 2.0GHz' 'AMD E-Series 9000 2.2GHz'\n",
      " 'Intel Core M 6Y30 0.9GHz' 'AMD A9-Series 9410 2.9GHz']\n",
      "\n",
      "Ram colum has 9 unique elements________________________________________ \n",
      "Unique values in Ram:\n",
      " ['8GB' '16GB' '4GB' '2GB' '12GB' '6GB' '32GB' '24GB' '64GB']\n",
      "\n",
      "Memory colum has 39 unique elements________________________________________ \n",
      "Unique values in Memory:\n",
      " ['128GB SSD' '128GB Flash Storage' '256GB SSD' '512GB SSD' '500GB HDD'\n",
      " '256GB Flash Storage' '1TB HDD' '32GB Flash Storage'\n",
      " '128GB SSD +  1TB HDD' '256GB SSD +  256GB SSD' '64GB Flash Storage'\n",
      " '256GB SSD +  1TB HDD' '256GB SSD +  2TB HDD' '32GB SSD' '2TB HDD'\n",
      " '64GB SSD' '1.0TB Hybrid' '512GB SSD +  1TB HDD' '1TB SSD'\n",
      " '256GB SSD +  500GB HDD' '128GB SSD +  2TB HDD' '512GB SSD +  512GB SSD'\n",
      " '16GB SSD' '16GB Flash Storage' '512GB SSD +  256GB SSD'\n",
      " '512GB SSD +  2TB HDD' '64GB Flash Storage +  1TB HDD' '180GB SSD'\n",
      " '1TB HDD +  1TB HDD' '32GB HDD' '1TB SSD +  1TB HDD'\n",
      " '512GB Flash Storage' '128GB HDD' '240GB SSD' '8GB SSD' '508GB Hybrid'\n",
      " '1.0TB HDD' '512GB SSD +  1.0TB Hybrid' '256GB SSD +  1.0TB Hybrid']\n",
      "\n",
      "Gpu colum has 110 unique elements________________________________________ \n",
      "Unique values in Gpu:\n",
      " ['Intel Iris Plus Graphics 640' 'Intel HD Graphics 6000'\n",
      " 'Intel HD Graphics 620' 'AMD Radeon Pro 455'\n",
      " 'Intel Iris Plus Graphics 650' 'AMD Radeon R5' 'Intel Iris Pro Graphics'\n",
      " 'Nvidia GeForce MX150' 'Intel UHD Graphics 620' 'Intel HD Graphics 520'\n",
      " 'AMD Radeon Pro 555' 'AMD Radeon R5 M430' 'Intel HD Graphics 615'\n",
      " 'AMD Radeon Pro 560' 'Nvidia GeForce 940MX' 'Intel HD Graphics 400'\n",
      " 'Nvidia GeForce GTX 1050' 'AMD Radeon R2' 'AMD Radeon 530'\n",
      " 'Nvidia GeForce 930MX' 'Intel HD Graphics' 'Intel HD Graphics 500'\n",
      " 'Nvidia GeForce 930MX ' 'Nvidia GeForce GTX 1060' 'Nvidia GeForce 150MX'\n",
      " 'Intel Iris Graphics 540' 'AMD Radeon RX 580' 'Nvidia GeForce 920MX'\n",
      " 'AMD Radeon R4 Graphics' 'AMD Radeon 520' 'Nvidia GeForce GTX 1070'\n",
      " 'Nvidia GeForce GTX 1050 Ti' 'Nvidia GeForce MX130' 'AMD R4 Graphics'\n",
      " 'Nvidia GeForce GTX 940MX' 'AMD Radeon RX 560' 'Nvidia GeForce 920M'\n",
      " 'AMD Radeon R7 M445' 'AMD Radeon RX 550' 'Nvidia GeForce GTX 1050M'\n",
      " 'Intel HD Graphics 515' 'AMD Radeon R5 M420' 'Intel HD Graphics 505'\n",
      " 'Nvidia GTX 980 SLI' 'AMD R17M-M1-70' 'Nvidia GeForce GTX 1080'\n",
      " 'Nvidia Quadro M1200' 'Nvidia GeForce 920MX ' 'Nvidia GeForce GTX 950M'\n",
      " 'AMD FirePro W4190M ' 'Nvidia GeForce GTX 980M' 'Intel Iris Graphics 550'\n",
      " 'Nvidia GeForce 930M' 'Intel HD Graphics 630' 'AMD Radeon R5 430'\n",
      " 'Nvidia GeForce GTX 940M' 'Intel HD Graphics 510' 'Intel HD Graphics 405'\n",
      " 'AMD Radeon RX 540' 'Nvidia GeForce GT 940MX' 'AMD FirePro W5130M'\n",
      " 'Nvidia Quadro M2200M' 'AMD Radeon R4' 'Nvidia Quadro M620'\n",
      " 'AMD Radeon R7 M460' 'Intel HD Graphics 530' 'Nvidia GeForce GTX 965M'\n",
      " 'Nvidia GeForce GTX1080' 'Nvidia GeForce GTX1050 Ti'\n",
      " 'Nvidia GeForce GTX 960M' 'AMD Radeon R2 Graphics' 'Nvidia Quadro M620M'\n",
      " 'Nvidia GeForce GTX 970M' 'Nvidia GeForce GTX 960<U+039C>'\n",
      " 'Intel Graphics 620' 'Nvidia GeForce GTX 960' 'AMD Radeon R5 520'\n",
      " 'AMD Radeon R7 M440' 'AMD Radeon R7' 'Nvidia Quadro M520M'\n",
      " 'Nvidia Quadro M2200' 'Nvidia Quadro M2000M' 'Intel HD Graphics 540'\n",
      " 'Nvidia Quadro M1000M' 'AMD Radeon 540' 'Nvidia GeForce GTX 1070M'\n",
      " 'Nvidia GeForce GTX1060' 'Intel HD Graphics 5300' 'AMD Radeon R5 M420X'\n",
      " 'AMD Radeon R7 Graphics' 'Nvidia GeForce 920' 'Nvidia GeForce 940M'\n",
      " 'Nvidia GeForce GTX 930MX' 'AMD Radeon R7 M465' 'AMD Radeon R3'\n",
      " 'Nvidia GeForce GTX 1050Ti' 'AMD Radeon R7 M365X' 'AMD Radeon R9 M385'\n",
      " 'Intel HD Graphics 620 ' 'Nvidia Quadro 3000M' 'Nvidia GeForce GTX 980 '\n",
      " 'AMD Radeon R5 M330' 'AMD FirePro W4190M' 'AMD FirePro W6150M'\n",
      " 'AMD Radeon R5 M315' 'Nvidia Quadro M500M' 'AMD Radeon R7 M360'\n",
      " 'Nvidia Quadro M3000M' 'Nvidia GeForce 960M' 'ARM Mali T860 MP4']\n",
      "\n",
      "OpSys colum has 9 unique elements________________________________________ \n",
      "Unique values in OpSys:\n",
      " ['macOS' 'No OS' 'Windows 10' 'Mac OS X' 'Linux' 'Android' 'Windows 10 S'\n",
      " 'Chrome OS' 'Windows 7']\n",
      "\n",
      "Weight colum has 179 unique elements________________________________________ \n",
      "Unique values in Weight:\n",
      " ['1.37kg' '1.34kg' '1.86kg' '1.83kg' '2.1kg' '2.04kg' '1.3kg' '1.6kg'\n",
      " '2.2kg' '0.92kg' '1.22kg' '0.98kg' '2.5kg' '1.62kg' '1.91kg' '2.3kg'\n",
      " '1.35kg' '1.88kg' '1.89kg' '1.65kg' '2.71kg' '1.2kg' '1.44kg' '2.8kg'\n",
      " '2kg' '2.65kg' '2.77kg' '3.2kg' '0.69kg' '1.49kg' '2.4kg' '2.13kg'\n",
      " '2.43kg' '1.7kg' '1.4kg' '1.8kg' '1.9kg' '3kg' '1.252kg' '2.7kg' '2.02kg'\n",
      " '1.63kg' '1.96kg' '1.21kg' '2.45kg' '1.25kg' '1.5kg' '2.62kg' '1.38kg'\n",
      " '1.58kg' '1.85kg' '1.23kg' '1.26kg' '2.16kg' '2.36kg' '2.05kg' '1.32kg'\n",
      " '1.75kg' '0.97kg' '2.9kg' '2.56kg' '1.48kg' '1.74kg' '1.1kg' '1.56kg'\n",
      " '2.03kg' '1.05kg' '4.4kg' '1.90kg' '1.29kg' '2.0kg' '1.95kg' '2.06kg'\n",
      " '1.12kg' '1.42kg' '3.49kg' '3.35kg' '2.23kg' '4.42kg' '2.69kg' '2.37kg'\n",
      " '4.7kg' '3.6kg' '2.08kg' '4.3kg' '1.68kg' '1.41kg' '4.14kg' '2.18kg'\n",
      " '2.24kg' '2.67kg' '2.14kg' '1.36kg' '2.25kg' '2.15kg' '2.19kg' '2.54kg'\n",
      " '3.42kg' '1.28kg' '2.33kg' '1.45kg' '2.79kg' '1.84kg' '2.6kg' '2.26kg'\n",
      " '3.25kg' '1.59kg' '1.13kg' '1.78kg' '1.10kg' '1.15kg' '1.27kg' '1.43kg'\n",
      " '2.31kg' '1.16kg' '1.64kg' '2.17kg' '1.47kg' '3.78kg' '1.79kg' '0.91kg'\n",
      " '1.99kg' '4.33kg' '1.93kg' '1.87kg' '2.63kg' '3.4kg' '3.14kg' '1.94kg'\n",
      " '1.24kg' '4.6kg' '4.5kg' '2.73kg' '1.39kg' '2.29kg' '2.59kg' '2.94kg'\n",
      " '1.14kg' '3.8kg' '3.31kg' '1.09kg' '3.21kg' '1.19kg' '1.98kg' '1.17kg'\n",
      " '4.36kg' '1.71kg' '2.32kg' '4.2kg' '1.55kg' '0.81kg' '1.18kg' '2.72kg'\n",
      " '1.31kg' '0.920kg' '3.74kg' '1.76kg' '1.54kg' '2.83kg' '2.07kg' '2.38kg'\n",
      " '3.58kg' '1.08kg' '2.20kg' '2.75kg' '1.70kg' '2.99kg' '1.11kg' '2.09kg'\n",
      " '4kg' '3.0kg' '0.99kg' '3.52kg' '2.591kg' '2.21kg' '3.3kg' '2.191kg'\n",
      " '2.34kg' '4.0kg']\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for col in df.columns:\n",
    "    if col!='Price_euros':\n",
    "        print(f'{col} colum has {df[col].unique().size} unique elements'+'__'*20,f'\\nUnique values in {col}:\\n {df[col].unique()}\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "As we can observe; `Ram`, `Memory` and `Weight` are numerical data but have a unit attached, lets fix that"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Ram']=df['Ram'].str.replace('GB','')\n",
    "df['Ram']=df['Ram'].astype('int32')\n",
    "df=df.rename(columns={\"Ram\": \"Ram (GB)\"})\n",
    "df['Weight']=df['Weight'].str.replace('kg','')\n",
    "df['Weight']=df['Weight'].astype('float64')\n",
    "\n",
    "df=df.rename(columns={\"Weight\": \"Weight (kg)\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>laptop_ID</th>\n",
       "      <th>Company</th>\n",
       "      <th>Product</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Inches</th>\n",
       "      <th>ScreenResolution</th>\n",
       "      <th>Cpu</th>\n",
       "      <th>Ram (GB)</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight (kg)</th>\n",
       "      <th>Price_euros</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Apple</td>\n",
       "      <td>MacBook Pro</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 2.3GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 640</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>1339.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Apple</td>\n",
       "      <td>Macbook Air</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>1440x900</td>\n",
       "      <td>Intel Core i5 1.8GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>898.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>HP</td>\n",
       "      <td>250 G6</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>575.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Apple</td>\n",
       "      <td>MacBook Pro</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>15.4</td>\n",
       "      <td>IPS Panel Retina Display 2880x1800</td>\n",
       "      <td>Intel Core i7 2.7GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>2537.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Apple</td>\n",
       "      <td>MacBook Pro</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 3.1GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>1803.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   laptop_ID Company      Product   TypeName  Inches  \\\n",
       "0          1   Apple  MacBook Pro  Ultrabook    13.3   \n",
       "1          2   Apple  Macbook Air  Ultrabook    13.3   \n",
       "2          3      HP       250 G6   Notebook    15.6   \n",
       "3          4   Apple  MacBook Pro  Ultrabook    15.4   \n",
       "4          5   Apple  MacBook Pro  Ultrabook    13.3   \n",
       "\n",
       "                     ScreenResolution                         Cpu  Ram (GB)  \\\n",
       "0  IPS Panel Retina Display 2560x1600        Intel Core i5 2.3GHz         8   \n",
       "1                            1440x900        Intel Core i5 1.8GHz         8   \n",
       "2                   Full HD 1920x1080  Intel Core i5 7200U 2.5GHz         8   \n",
       "3  IPS Panel Retina Display 2880x1800        Intel Core i7 2.7GHz        16   \n",
       "4  IPS Panel Retina Display 2560x1600        Intel Core i5 3.1GHz         8   \n",
       "\n",
       "                Memory                           Gpu  OpSys  Weight (kg)  \\\n",
       "0            128GB SSD  Intel Iris Plus Graphics 640  macOS         1.37   \n",
       "1  128GB Flash Storage        Intel HD Graphics 6000  macOS         1.34   \n",
       "2            256GB SSD         Intel HD Graphics 620  No OS         1.86   \n",
       "3            512GB SSD            AMD Radeon Pro 455  macOS         1.83   \n",
       "4            256GB SSD  Intel Iris Plus Graphics 650  macOS         1.37   \n",
       "\n",
       "   Price_euros  \n",
       "0      1339.69  \n",
       "1       898.94  \n",
       "2       575.00  \n",
       "3      2537.45  \n",
       "4      1803.60  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Numerical data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>laptop_ID</th>\n",
       "      <th>Inches</th>\n",
       "      <th>Ram (GB)</th>\n",
       "      <th>Weight (kg)</th>\n",
       "      <th>Price_euros</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1303.000000</td>\n",
       "      <td>1303.000000</td>\n",
       "      <td>1303.000000</td>\n",
       "      <td>1303.000000</td>\n",
       "      <td>1303.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>660.155794</td>\n",
       "      <td>15.017191</td>\n",
       "      <td>8.382195</td>\n",
       "      <td>2.038734</td>\n",
       "      <td>1123.686992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>381.172104</td>\n",
       "      <td>1.426304</td>\n",
       "      <td>5.084665</td>\n",
       "      <td>0.665475</td>\n",
       "      <td>699.009043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.100000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.690000</td>\n",
       "      <td>174.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>331.500000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>599.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>659.000000</td>\n",
       "      <td>15.600000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2.040000</td>\n",
       "      <td>977.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>990.500000</td>\n",
       "      <td>15.600000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>1487.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1320.000000</td>\n",
       "      <td>18.400000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>4.700000</td>\n",
       "      <td>6099.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         laptop_ID       Inches     Ram (GB)  Weight (kg)  Price_euros\n",
       "count  1303.000000  1303.000000  1303.000000  1303.000000  1303.000000\n",
       "mean    660.155794    15.017191     8.382195     2.038734  1123.686992\n",
       "std     381.172104     1.426304     5.084665     0.665475   699.009043\n",
       "min       1.000000    10.100000     2.000000     0.690000   174.000000\n",
       "25%     331.500000    14.000000     4.000000     1.500000   599.000000\n",
       "50%     659.000000    15.600000     8.000000     2.040000   977.000000\n",
       "75%     990.500000    15.600000     8.000000     2.300000  1487.880000\n",
       "max    1320.000000    18.400000    64.000000     4.700000  6099.000000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Discrete data \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for variable in ['Inches','Ram (GB)']:\n",
    "       plt.figure(figsize=(15,7))\n",
    "       sn.countplot(x=variable,data = df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These graphs tell us that people prefere laptops with 15.6\" displays, and laptops with 8GB of Ram are the most bought."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Continuous data "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a plot the Price and weight counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1f39f86c1c0>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.displot(df['Price_euros'])\n",
    "plt.plot([df['Price_euros'].mean(), df['Price_euros'].mean()], [160, 0],color='red', linewidth=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1f3fbdfde80>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.displot(np.log1p(df['Price_euros']),kind='kde')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1f39f4f8c10>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.displot(df['Weight (kg)'])\n",
    "plt.plot([df['Weight (kg)'].mean(), df['Weight (kg)'].mean()], [180, 0],color='red' ,linewidth=2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We observer that weight is unevenly spread so the mean doesn't give a useful information, we can see that this data has a some laptops whos weight is more than 4kg but are still sold, we can probably say that these laptops are the workstation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Categorical data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot some of these variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Categorical=['Company', 'TypeName', 'OpSys']\n",
    "\n",
    "for variable in Categorical:\n",
    "       plt.figure(figsize=(20,7))\n",
    "       sn.countplot(x=variable,data = df, order =df[variable].value_counts().index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From these graphs we can see that windows 10 is the most popular OS in the dataset. We can also observe that Dell and Lenovo are fighting for the most popular brand, notebooks are the most popular type sold."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's explore more about each company and their prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Company', ylabel='Price_euros'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,7))\n",
    "plt.xticks(rotation=20)\n",
    "sn.barplot(x = df['Company'],y = df['Price_euros'],order=df['Company'].value_counts().index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We observe that Razor product have the most variation in price; peaking at 42244.11 MAD and reaching a bottom at 7127.26 MAD with a mean of 23176.72 MAD."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's see the price variation based on laptop type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='TypeName', ylabel='count'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,7))\n",
    "sn.countplot(x=df['TypeName'],order=df['TypeName'].value_counts().index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='TypeName', ylabel='Price_euros'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,7))\n",
    "x = df.groupby(['TypeName']).Price_euros.max().sort_values().keys()\n",
    "sn.barplot(x = 'TypeName',y= 'Price_euros',data=df,order=x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notebooks have the least amount of variation in price compared to other laptop types, this is probably a results from the high number of notebooks and competitors in the market.\n",
    "Also the high price of workstation and the low number of units sold shows that workstations are more of a niche product."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Deep dive to `ScreenResolution` column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Full HD 1920x1080                                507\n",
       "1366x768                                         281\n",
       "IPS Panel Full HD 1920x1080                      230\n",
       "IPS Panel Full HD / Touchscreen 1920x1080         53\n",
       "Full HD / Touchscreen 1920x1080                   47\n",
       "1600x900                                          23\n",
       "Touchscreen 1366x768                              16\n",
       "Quad HD+ / Touchscreen 3200x1800                  15\n",
       "IPS Panel 4K Ultra HD 3840x2160                   12\n",
       "IPS Panel 4K Ultra HD / Touchscreen 3840x2160     11\n",
       "4K Ultra HD / Touchscreen 3840x2160               10\n",
       "4K Ultra HD 3840x2160                              7\n",
       "Touchscreen 2560x1440                              7\n",
       "IPS Panel 1366x768                                 7\n",
       "IPS Panel Quad HD+ / Touchscreen 3200x1800         6\n",
       "IPS Panel Retina Display 2560x1600                 6\n",
       "IPS Panel Retina Display 2304x1440                 6\n",
       "Touchscreen 2256x1504                              6\n",
       "IPS Panel Touchscreen 2560x1440                    5\n",
       "IPS Panel Retina Display 2880x1800                 4\n",
       "IPS Panel Touchscreen 1920x1200                    4\n",
       "1440x900                                           4\n",
       "IPS Panel 2560x1440                                4\n",
       "IPS Panel Quad HD+ 2560x1440                       3\n",
       "Quad HD+ 3200x1800                                 3\n",
       "1920x1080                                          3\n",
       "Touchscreen 2400x1600                              3\n",
       "2560x1440                                          3\n",
       "IPS Panel Touchscreen 1366x768                     3\n",
       "IPS Panel Touchscreen / 4K Ultra HD 3840x2160      2\n",
       "IPS Panel Full HD 2160x1440                        2\n",
       "IPS Panel Quad HD+ 3200x1800                       2\n",
       "IPS Panel Retina Display 2736x1824                 1\n",
       "IPS Panel Full HD 1920x1200                        1\n",
       "IPS Panel Full HD 2560x1440                        1\n",
       "IPS Panel Full HD 1366x768                         1\n",
       "Touchscreen / Full HD 1920x1080                    1\n",
       "Touchscreen / Quad HD+ 3200x1800                   1\n",
       "Touchscreen / 4K Ultra HD 3840x2160                1\n",
       "IPS Panel Touchscreen 2400x1600                    1\n",
       "Name: ScreenResolution, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ScreenResolution'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This column has three type of data in it:\n",
    "- _TouchScreen_,\n",
    "- _display panel_ (IPS or TN Panels)\n",
    "- and _Max Screen Resolution_ (i.e. Full HD 2560x1440 ...).\n",
    "\n",
    "We need to separate this information into different columns. To do that we will one-hot encode some of this data, meaning we will convert each categorical (Touchscreen and Display) value into a new categorical column and assign a binary value of 'Yes' or 'No', for `TouchScreen` and 'TN' or 'IPS' for display panel.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Starting with _TouchScreen_:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>laptop_ID</th>\n",
       "      <th>Company</th>\n",
       "      <th>Product</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Inches</th>\n",
       "      <th>ScreenResolution</th>\n",
       "      <th>Cpu</th>\n",
       "      <th>Ram (GB)</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight (kg)</th>\n",
       "      <th>Price_euros</th>\n",
       "      <th>TouchScreen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1298</th>\n",
       "      <td>1316</td>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Yoga 500-14ISK</td>\n",
       "      <td>2 in 1 Convertible</td>\n",
       "      <td>14.0</td>\n",
       "      <td>IPS Panel Full HD 1920x1080</td>\n",
       "      <td>Intel Core i7 6500U 2.5GHz</td>\n",
       "      <td>4</td>\n",
       "      <td>128GB SSD</td>\n",
       "      <td>Intel HD Graphics 520</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.80</td>\n",
       "      <td>638.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1299</th>\n",
       "      <td>1317</td>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Yoga 900-13ISK</td>\n",
       "      <td>2 in 1 Convertible</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Quad HD+ 3200x1800</td>\n",
       "      <td>Intel Core i7 6500U 2.5GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>Intel HD Graphics 520</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.30</td>\n",
       "      <td>1499.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1300</th>\n",
       "      <td>1318</td>\n",
       "      <td>Lenovo</td>\n",
       "      <td>IdeaPad 100S-14IBR</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>Intel Celeron Dual Core N3050 1.6GHz</td>\n",
       "      <td>2</td>\n",
       "      <td>64GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.50</td>\n",
       "      <td>229.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1301</th>\n",
       "      <td>1319</td>\n",
       "      <td>HP</td>\n",
       "      <td>15-AC110nv (i7-6500U/6GB/1TB/Radeon</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>Intel Core i7 6500U 2.5GHz</td>\n",
       "      <td>6</td>\n",
       "      <td>1TB HDD</td>\n",
       "      <td>AMD Radeon R5 M330</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.19</td>\n",
       "      <td>764.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1302</th>\n",
       "      <td>1320</td>\n",
       "      <td>Asus</td>\n",
       "      <td>X553SA-XX031T (N3050/4GB/500GB/W10)</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>Intel Celeron Dual Core N3050 1.6GHz</td>\n",
       "      <td>4</td>\n",
       "      <td>500GB HDD</td>\n",
       "      <td>Intel HD Graphics</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.20</td>\n",
       "      <td>369.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      laptop_ID Company                              Product  \\\n",
       "1298       1316  Lenovo                       Yoga 500-14ISK   \n",
       "1299       1317  Lenovo                       Yoga 900-13ISK   \n",
       "1300       1318  Lenovo                   IdeaPad 100S-14IBR   \n",
       "1301       1319      HP  15-AC110nv (i7-6500U/6GB/1TB/Radeon   \n",
       "1302       1320    Asus  X553SA-XX031T (N3050/4GB/500GB/W10)   \n",
       "\n",
       "                TypeName  Inches              ScreenResolution  \\\n",
       "1298  2 in 1 Convertible    14.0   IPS Panel Full HD 1920x1080   \n",
       "1299  2 in 1 Convertible    13.3  IPS Panel Quad HD+ 3200x1800   \n",
       "1300            Notebook    14.0                      1366x768   \n",
       "1301            Notebook    15.6                      1366x768   \n",
       "1302            Notebook    15.6                      1366x768   \n",
       "\n",
       "                                       Cpu  Ram (GB)              Memory  \\\n",
       "1298            Intel Core i7 6500U 2.5GHz         4           128GB SSD   \n",
       "1299            Intel Core i7 6500U 2.5GHz        16           512GB SSD   \n",
       "1300  Intel Celeron Dual Core N3050 1.6GHz         2  64GB Flash Storage   \n",
       "1301            Intel Core i7 6500U 2.5GHz         6             1TB HDD   \n",
       "1302  Intel Celeron Dual Core N3050 1.6GHz         4           500GB HDD   \n",
       "\n",
       "                        Gpu       OpSys  Weight (kg)  Price_euros TouchScreen  \n",
       "1298  Intel HD Graphics 520  Windows 10         1.80        638.0         Yes  \n",
       "1299  Intel HD Graphics 520  Windows 10         1.30       1499.0         Yes  \n",
       "1300      Intel HD Graphics  Windows 10         1.50        229.0          No  \n",
       "1301     AMD Radeon R5 M330  Windows 10         2.19        764.0          No  \n",
       "1302      Intel HD Graphics  Windows 10         2.20        369.0          No  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"TouchScreen\"]=df['ScreenResolution'].apply(lambda x:'Yes' if 'Touchscreen' in x else 'No')\n",
    "df['ScreenResolution']=df['ScreenResolution'].replace(regex={r\"/* *Touchscreen /*\":\"\"})\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='TouchScreen', ylabel='count'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.countplot(x='TouchScreen',data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see most laptops in this dataset don't have a TouchScreen, just 192 have this functionality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='TouchScreen', ylabel='Price_euros'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.barplot(x = 'TouchScreen',y= 'Price_euros',data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Price of laptops with touchscreen is subjected to alot of variation avreging at 10009.37, in contrast to normal ones that have an average price of about 7753.11"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "let see how each type of df "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3, 4, 5]),\n",
       " [Text(0, 0, 'Ultrabook'),\n",
       "  Text(1, 0, 'Notebook'),\n",
       "  Text(2, 0, 'Netbook'),\n",
       "  Text(3, 0, 'Gaming'),\n",
       "  Text(4, 0, '2 in 1 Convertible'),\n",
       "  Text(5, 0, 'Workstation')])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.countplot(x=\"TypeName\", hue=\"TouchScreen\", data=df)\n",
    "plt.xticks(rotation=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>TypeName</th>\n",
       "      <th>2 in 1 Convertible</th>\n",
       "      <th>Gaming</th>\n",
       "      <th>Netbook</th>\n",
       "      <th>Notebook</th>\n",
       "      <th>Ultrabook</th>\n",
       "      <th>Workstation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TouchScreen</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>2</td>\n",
       "      <td>200</td>\n",
       "      <td>23</td>\n",
       "      <td>708</td>\n",
       "      <td>151</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>119</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>45</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "TypeName     2 in 1 Convertible  Gaming  Netbook  Notebook  Ultrabook  \\\n",
       "TouchScreen                                                             \n",
       "No                            2     200       23       708        151   \n",
       "Yes                         119       5        2        19         45   \n",
       "\n",
       "TypeName     Workstation  \n",
       "TouchScreen               \n",
       "No                    27  \n",
       "Yes                    2  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(index=df['TouchScreen'], columns=df['TypeName'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see most of the laptops that have a Touchscreen are 2 in 1 Convertible, we can also see the most popular laptop type don't usual come with touch screen.\n",
    "\n",
    "We could say that Touchscreen are mostly a luxury for laptop users"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Display panel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the display panel we will asign 1 to observations with IPS panles and 0 to TN panels we can flip them it doesn't matter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>laptop_ID</th>\n",
       "      <th>Company</th>\n",
       "      <th>Product</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Inches</th>\n",
       "      <th>ScreenResolution</th>\n",
       "      <th>Cpu</th>\n",
       "      <th>Ram (GB)</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight (kg)</th>\n",
       "      <th>Price_euros</th>\n",
       "      <th>TouchScreen</th>\n",
       "      <th>Display panel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1298</th>\n",
       "      <td>1316</td>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Yoga 500-14ISK</td>\n",
       "      <td>2 in 1 Convertible</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i7 6500U 2.5GHz</td>\n",
       "      <td>4</td>\n",
       "      <td>128GB SSD</td>\n",
       "      <td>Intel HD Graphics 520</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.80</td>\n",
       "      <td>638.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>IPS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1299</th>\n",
       "      <td>1317</td>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Yoga 900-13ISK</td>\n",
       "      <td>2 in 1 Convertible</td>\n",
       "      <td>13.3</td>\n",
       "      <td>Quad HD+ 3200x1800</td>\n",
       "      <td>Intel Core i7 6500U 2.5GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>Intel HD Graphics 520</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.30</td>\n",
       "      <td>1499.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>IPS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1300</th>\n",
       "      <td>1318</td>\n",
       "      <td>Lenovo</td>\n",
       "      <td>IdeaPad 100S-14IBR</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>Intel Celeron Dual Core N3050 1.6GHz</td>\n",
       "      <td>2</td>\n",
       "      <td>64GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.50</td>\n",
       "      <td>229.0</td>\n",
       "      <td>No</td>\n",
       "      <td>TN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1301</th>\n",
       "      <td>1319</td>\n",
       "      <td>HP</td>\n",
       "      <td>15-AC110nv (i7-6500U/6GB/1TB/Radeon</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>Intel Core i7 6500U 2.5GHz</td>\n",
       "      <td>6</td>\n",
       "      <td>1TB HDD</td>\n",
       "      <td>AMD Radeon R5 M330</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.19</td>\n",
       "      <td>764.0</td>\n",
       "      <td>No</td>\n",
       "      <td>TN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1302</th>\n",
       "      <td>1320</td>\n",
       "      <td>Asus</td>\n",
       "      <td>X553SA-XX031T (N3050/4GB/500GB/W10)</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>Intel Celeron Dual Core N3050 1.6GHz</td>\n",
       "      <td>4</td>\n",
       "      <td>500GB HDD</td>\n",
       "      <td>Intel HD Graphics</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.20</td>\n",
       "      <td>369.0</td>\n",
       "      <td>No</td>\n",
       "      <td>TN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      laptop_ID Company                              Product  \\\n",
       "1298       1316  Lenovo                       Yoga 500-14ISK   \n",
       "1299       1317  Lenovo                       Yoga 900-13ISK   \n",
       "1300       1318  Lenovo                   IdeaPad 100S-14IBR   \n",
       "1301       1319      HP  15-AC110nv (i7-6500U/6GB/1TB/Radeon   \n",
       "1302       1320    Asus  X553SA-XX031T (N3050/4GB/500GB/W10)   \n",
       "\n",
       "                TypeName  Inches    ScreenResolution  \\\n",
       "1298  2 in 1 Convertible    14.0   Full HD 1920x1080   \n",
       "1299  2 in 1 Convertible    13.3  Quad HD+ 3200x1800   \n",
       "1300            Notebook    14.0            1366x768   \n",
       "1301            Notebook    15.6            1366x768   \n",
       "1302            Notebook    15.6            1366x768   \n",
       "\n",
       "                                       Cpu  Ram (GB)              Memory  \\\n",
       "1298            Intel Core i7 6500U 2.5GHz         4           128GB SSD   \n",
       "1299            Intel Core i7 6500U 2.5GHz        16           512GB SSD   \n",
       "1300  Intel Celeron Dual Core N3050 1.6GHz         2  64GB Flash Storage   \n",
       "1301            Intel Core i7 6500U 2.5GHz         6             1TB HDD   \n",
       "1302  Intel Celeron Dual Core N3050 1.6GHz         4           500GB HDD   \n",
       "\n",
       "                        Gpu       OpSys  Weight (kg)  Price_euros TouchScreen  \\\n",
       "1298  Intel HD Graphics 520  Windows 10         1.80        638.0         Yes   \n",
       "1299  Intel HD Graphics 520  Windows 10         1.30       1499.0         Yes   \n",
       "1300      Intel HD Graphics  Windows 10         1.50        229.0          No   \n",
       "1301     AMD Radeon R5 M330  Windows 10         2.19        764.0          No   \n",
       "1302      Intel HD Graphics  Windows 10         2.20        369.0          No   \n",
       "\n",
       "     Display panel  \n",
       "1298           IPS  \n",
       "1299           IPS  \n",
       "1300            TN  \n",
       "1301            TN  \n",
       "1302            TN  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Display panel\"]=df['ScreenResolution'].apply(lambda x:'IPS' if 'IPS' in x else 'TN')\n",
    "df['ScreenResolution']=df['ScreenResolution'].replace(regex={r\"IPS Panel.[Retina Display ]*\":\"\"})\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While we at it lets also clean the `ScreenResolution`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ScreenResolution']=df['ScreenResolution'].replace(regex={r\"(4K)?[^0-9^x]*\":\"\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Display panel', ylabel='count'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.countplot(x='Display panel',data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most laptops in this dataset dont have IPS panel but TN ones insted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Display panel', ylabel='Price_euros'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.barplot(x = 'Display panel',y= 'Price_euros',data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "IPS display panels are more expencive than TN panels "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3, 4, 5]),\n",
       " [Text(0, 0, 'Ultrabook'),\n",
       "  Text(1, 0, 'Notebook'),\n",
       "  Text(2, 0, 'Netbook'),\n",
       "  Text(3, 0, 'Gaming'),\n",
       "  Text(4, 0, '2 in 1 Convertible'),\n",
       "  Text(5, 0, 'Workstation')])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.countplot(x=\"TypeName\", hue=\"Display panel\", data=df)\n",
    "plt.xticks(rotation=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>TypeName</th>\n",
       "      <th>2 in 1 Convertible</th>\n",
       "      <th>Gaming</th>\n",
       "      <th>Netbook</th>\n",
       "      <th>Notebook</th>\n",
       "      <th>Ultrabook</th>\n",
       "      <th>Workstation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Display panel</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>IPS</th>\n",
       "      <td>65</td>\n",
       "      <td>88</td>\n",
       "      <td>4</td>\n",
       "      <td>115</td>\n",
       "      <td>82</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TN</th>\n",
       "      <td>56</td>\n",
       "      <td>117</td>\n",
       "      <td>21</td>\n",
       "      <td>612</td>\n",
       "      <td>114</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "TypeName       2 in 1 Convertible  Gaming  Netbook  Notebook  Ultrabook  \\\n",
       "Display panel                                                             \n",
       "IPS                            65      88        4       115         82   \n",
       "TN                             56     117       21       612        114   \n",
       "\n",
       "TypeName       Workstation  \n",
       "Display panel               \n",
       "IPS                     11  \n",
       "TN                      18  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(index=df['Display panel'], columns=df['TypeName'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see here that most Notebooks and netbooks laptop types dont use TN panel probably because of the high-price, more than half of 2 in 1 convertible laptops use IPS panels, similarly for workstations, ultrabooks and gaming laptops, close to half of them have IPS, this is logical as these panel provide better view from an angel compared to TN counterpart so they are more of a premium adition."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### ScreenResolution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1920x1080    841\n",
       "1366x768     305\n",
       "3840x2160     43\n",
       "3200x1800     27\n",
       "1600x900      23\n",
       "2560x1440     18\n",
       "2256x1504      6\n",
       "2560x1600      6\n",
       "2304x1440      6\n",
       "560x1440       5\n",
       "920x1200       4\n",
       "1440x900       4\n",
       "2880x1800      4\n",
       "2400x1600      3\n",
       "366x768        3\n",
       "2160x1440      2\n",
       "2736x1824      1\n",
       "1920x1200      1\n",
       "400x1600       1\n",
       "Name: ScreenResolution, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ScreenResolution'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "        17, 18]),\n",
       " [Text(0, 0, '1920x1080'),\n",
       "  Text(1, 0, '1366x768'),\n",
       "  Text(2, 0, '3840x2160'),\n",
       "  Text(3, 0, '3200x1800'),\n",
       "  Text(4, 0, '1600x900'),\n",
       "  Text(5, 0, '2560x1440'),\n",
       "  Text(6, 0, '2256x1504'),\n",
       "  Text(7, 0, '2560x1600'),\n",
       "  Text(8, 0, '2304x1440'),\n",
       "  Text(9, 0, '560x1440'),\n",
       "  Text(10, 0, '920x1200'),\n",
       "  Text(11, 0, '1440x900'),\n",
       "  Text(12, 0, '2880x1800'),\n",
       "  Text(13, 0, '2400x1600'),\n",
       "  Text(14, 0, '366x768'),\n",
       "  Text(15, 0, '2160x1440'),\n",
       "  Text(16, 0, '2736x1824'),\n",
       "  Text(17, 0, '1920x1200'),\n",
       "  Text(18, 0, '400x1600')])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,7))\n",
    "sn.countplot(x='ScreenResolution',data=df,order=df['ScreenResolution'].value_counts().index)\n",
    "plt.xticks(rotation=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we observe that 1920x1080 (FHD) is the most popular display resolution across all dfs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df['ScreenResolution'].str.split(\"x\",expand=True)\n",
    "df['X_res']=X[0]\n",
    "df['Y_res']=X[1]\n",
    "for value in ['X_res','Y_res']:\n",
    "    df[value]=df[value].astype(\"int\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>laptop_ID</th>\n",
       "      <th>Company</th>\n",
       "      <th>Product</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Inches</th>\n",
       "      <th>ScreenResolution</th>\n",
       "      <th>Cpu</th>\n",
       "      <th>Ram (GB)</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight (kg)</th>\n",
       "      <th>Price_euros</th>\n",
       "      <th>TouchScreen</th>\n",
       "      <th>Display panel</th>\n",
       "      <th>X_res</th>\n",
       "      <th>Y_res</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>845</th>\n",
       "      <td>855</td>\n",
       "      <td>Dell</td>\n",
       "      <td>Vostro 3568</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>1920x1080</td>\n",
       "      <td>Intel Core i7 7500U 2.7GHz</td>\n",
       "      <td>4</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>739.0</td>\n",
       "      <td>No</td>\n",
       "      <td>TN</td>\n",
       "      <td>1920</td>\n",
       "      <td>1080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>50</td>\n",
       "      <td>Asus</td>\n",
       "      <td>X751NV-TY001T (N4200/4GB/1TB/GeForce</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>4</td>\n",
       "      <td>1TB HDD</td>\n",
       "      <td>Nvidia GeForce 920MX</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.80</td>\n",
       "      <td>466.0</td>\n",
       "      <td>No</td>\n",
       "      <td>TN</td>\n",
       "      <td>1366</td>\n",
       "      <td>768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>929</th>\n",
       "      <td>943</td>\n",
       "      <td>HP</td>\n",
       "      <td>ProBook 640</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>Intel Core i5 6200U 2.3GHz</td>\n",
       "      <td>4</td>\n",
       "      <td>500GB HDD</td>\n",
       "      <td>Intel HD Graphics 520</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.95</td>\n",
       "      <td>1149.0</td>\n",
       "      <td>No</td>\n",
       "      <td>TN</td>\n",
       "      <td>1366</td>\n",
       "      <td>768</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     laptop_ID Company                               Product  TypeName  \\\n",
       "845        855    Dell                           Vostro 3568  Notebook   \n",
       "49          50    Asus  X751NV-TY001T (N4200/4GB/1TB/GeForce  Notebook   \n",
       "929        943      HP                           ProBook 640  Notebook   \n",
       "\n",
       "     Inches ScreenResolution                                   Cpu  Ram (GB)  \\\n",
       "845    15.6        1920x1080            Intel Core i7 7500U 2.7GHz         4   \n",
       "49     17.3         1366x768  Intel Pentium Quad Core N4200 1.1GHz         4   \n",
       "929    14.0         1366x768            Intel Core i5 6200U 2.3GHz         4   \n",
       "\n",
       "        Memory                    Gpu       OpSys  Weight (kg)  Price_euros  \\\n",
       "845  256GB SSD     AMD Radeon R5 M420  Windows 10         2.18        739.0   \n",
       "49     1TB HDD   Nvidia GeForce 920MX  Windows 10         2.80        466.0   \n",
       "929  500GB HDD  Intel HD Graphics 520  Windows 10         1.95       1149.0   \n",
       "\n",
       "    TouchScreen Display panel  X_res  Y_res  \n",
       "845          No            TN   1920   1080  \n",
       "49           No            TN   1366    768  \n",
       "929          No            TN   1366    768  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pixel Per Inch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pixel density indicates how many pixels per inch (PPI) there are on a display. The higher the pixel density, the more detailed and spacious the picture is.\n",
    "\n",
    "In contrast, displays with low pixel density will have less screen space and more pixelated image quality."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inches and resolution give almost the same amount of information we can combine them into a single metric PixelPerInch or PPI for short its calculated like the following. At the end of the day, our goal is to improve the performance by having fewer features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "    PPI = \\frac{\\sqrt{X_{resolution}^2+Y_{resolution}^2}}{inches}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ppi'] = (((df['X_res']**2) + (df['Y_res']**2))**0.5/df['Inches']).astype('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Res_value\"]=df['X_res']*df['Y_res']\n",
    "df.drop(columns = ['X_res','Y_res'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       2560x1600\n",
       "1        1440x900\n",
       "2       1920x1080\n",
       "3       2880x1800\n",
       "4       2560x1600\n",
       "          ...    \n",
       "1298    1920x1080\n",
       "1299    3200x1800\n",
       "1300     1366x768\n",
       "1301     1366x768\n",
       "1302     1366x768\n",
       "Name: ScreenResolution, Length: 1303, dtype: object"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Convert=dict(zip(df[\"Res_value\"].value_counts().index, df[\"ScreenResolution\"].value_counts().index))\n",
    "df.pop('ScreenResolution')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1303 entries, 0 to 1302\n",
      "Data columns (total 16 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   laptop_ID      1303 non-null   int64  \n",
      " 1   Company        1303 non-null   object \n",
      " 2   Product        1303 non-null   object \n",
      " 3   TypeName       1303 non-null   object \n",
      " 4   Inches         1303 non-null   float64\n",
      " 5   Cpu            1303 non-null   object \n",
      " 6   Ram (GB)       1303 non-null   int32  \n",
      " 7   Memory         1303 non-null   object \n",
      " 8   Gpu            1303 non-null   object \n",
      " 9   OpSys          1303 non-null   object \n",
      " 10  Weight (kg)    1303 non-null   float64\n",
      " 11  Price_euros    1303 non-null   float64\n",
      " 12  TouchScreen    1303 non-null   object \n",
      " 13  Display panel  1303 non-null   object \n",
      " 14  ppi            1303 non-null   float64\n",
      " 15  Res_value      1303 non-null   int32  \n",
      "dtypes: float64(4), int32(2), int64(1), object(9)\n",
      "memory usage: 152.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Inches', ylabel='Price_euros'>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,7))\n",
    "sn.boxplot(y='Price_euros',x ='Inches',data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "df.drop(columns = ['Inches','X_res','Y_res'], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `CPU` column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The CPU column also contains lots of information, like CPU manifactuer and model and also it's speed (GHz) with 118 different categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Cpu'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we will put the CPU speed in it's own column and change its dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_GHz(CPU):\n",
    "    return re.search(r'\\d?\\.?\\d(?!(?!GHz))',CPU).group()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['GHz']=df['Cpu'].apply(get_GHz)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['GHz']=df['GHz'].astype(\"float\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will be clustering this column, so each df will either have an one of intel processors (Intel Xeon, i3, i5, i7 or Other Intel Processor) or AMD Processor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_processor(x):\n",
    "    match=re.search(r'Intel Core i[357]',x)\n",
    "    if match:\n",
    "        return match[0]\n",
    "    if 'xeon' in x.lower():\n",
    "        return 'Intel Xeon E3'\n",
    "    if 'intel' in x.lower():\n",
    "        return 'Other Intel Processor'\n",
    "    return 'AMD Processor'\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Cpu_brand']=df['Cpu'].apply(get_processor)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How does the price vary with processors?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                       Intel Core i5 2.3GHz\n",
       "1                       Intel Core i5 1.8GHz\n",
       "2                 Intel Core i5 7200U 2.5GHz\n",
       "3                       Intel Core i7 2.7GHz\n",
       "4                       Intel Core i5 3.1GHz\n",
       "                        ...                 \n",
       "1298              Intel Core i7 6500U 2.5GHz\n",
       "1299              Intel Core i7 6500U 2.5GHz\n",
       "1300    Intel Celeron Dual Core N3050 1.6GHz\n",
       "1301              Intel Core i7 6500U 2.5GHz\n",
       "1302    Intel Celeron Dual Core N3050 1.6GHz\n",
       "Name: Cpu, Length: 1303, dtype: object"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pop('Cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3, 4, 5]),\n",
       " [Text(0, 0, 'Intel Core i7'),\n",
       "  Text(1, 0, 'Intel Core i5'),\n",
       "  Text(2, 0, 'Other Intel Processor'),\n",
       "  Text(3, 0, 'Intel Core i3'),\n",
       "  Text(4, 0, 'AMD Processor'),\n",
       "  Text(5, 0, 'Intel Xeon E3')])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.countplot(x='Cpu_brand',data=df,palette='plasma',order=df['Cpu_brand'].value_counts().index)\n",
    "plt.xticks(rotation=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Price_euros', ylabel='Cpu_brand'>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(16,10))\n",
    "x = df.groupby(['Cpu_brand']).Price_euros.median().sort_values().keys()\n",
    "sn.barplot(x='Price_euros',y='Cpu_brand',data= df,order=x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Storage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Xeon processors are one if the best processors Intel offers which explains their very high price we can also see that i7 and i5 processors are the most popular and better than core i3 and AMD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "256GB SSD                        412\n",
       "1TB HDD                          223\n",
       "500GB HDD                        132\n",
       "512GB SSD                        118\n",
       "128GB SSD +  1TB HDD              94\n",
       "128GB SSD                         76\n",
       "256GB SSD +  1TB HDD              73\n",
       "32GB Flash Storage                38\n",
       "2TB HDD                           16\n",
       "64GB Flash Storage                15\n",
       "512GB SSD +  1TB HDD              14\n",
       "1TB SSD                           14\n",
       "256GB SSD +  2TB HDD              10\n",
       "1.0TB Hybrid                       9\n",
       "256GB Flash Storage                8\n",
       "16GB Flash Storage                 7\n",
       "32GB SSD                           6\n",
       "180GB SSD                          5\n",
       "128GB Flash Storage                4\n",
       "512GB SSD +  2TB HDD               3\n",
       "16GB SSD                           3\n",
       "512GB Flash Storage                2\n",
       "1TB SSD +  1TB HDD                 2\n",
       "256GB SSD +  500GB HDD             2\n",
       "128GB SSD +  2TB HDD               2\n",
       "256GB SSD +  256GB SSD             2\n",
       "512GB SSD +  256GB SSD             1\n",
       "512GB SSD +  512GB SSD             1\n",
       "64GB Flash Storage +  1TB HDD      1\n",
       "1TB HDD +  1TB HDD                 1\n",
       "32GB HDD                           1\n",
       "64GB SSD                           1\n",
       "128GB HDD                          1\n",
       "240GB SSD                          1\n",
       "8GB SSD                            1\n",
       "508GB Hybrid                       1\n",
       "1.0TB HDD                          1\n",
       "512GB SSD +  1.0TB Hybrid          1\n",
       "256GB SSD +  1.0TB Hybrid          1\n",
       "Name: Memory, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Memory'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First of all let's standardize the units and remove the decimal point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Memory']=df['Memory'].str.replace('.0','',regex=False)\n",
    "df['Memory']=df['Memory'].str.replace('GB','')\n",
    "df['Memory']=df['Memory'].str.replace('TB','000')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_memory(mem,StorageType='HDD'):\n",
    "  if StorageType in mem.split(' '):\n",
    "    i = mem.split(' ').index(StorageType)\n",
    "    return mem.split(' ')[i-1]\n",
    "  else:\n",
    "    return 0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "for type in ['HDD', 'SSD','Flash','Hybrid']:\n",
    "    df[type]=df['Memory'].apply(lambda x: get_memory(x,StorageType=type))\n",
    "    df[type]=df[type].astype(\"int\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>laptop_ID</th>\n",
       "      <th>Company</th>\n",
       "      <th>Product</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Inches</th>\n",
       "      <th>Ram (GB)</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight (kg)</th>\n",
       "      <th>Price_euros</th>\n",
       "      <th>TouchScreen</th>\n",
       "      <th>Display panel</th>\n",
       "      <th>ppi</th>\n",
       "      <th>Res_value</th>\n",
       "      <th>GHz</th>\n",
       "      <th>Cpu_brand</th>\n",
       "      <th>HDD</th>\n",
       "      <th>SSD</th>\n",
       "      <th>Flash</th>\n",
       "      <th>Hybrid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Apple</td>\n",
       "      <td>MacBook Pro</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>8</td>\n",
       "      <td>Intel Iris Plus Graphics 640</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>1339.69</td>\n",
       "      <td>No</td>\n",
       "      <td>IPS</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>4096000</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Apple</td>\n",
       "      <td>Macbook Air</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>8</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>898.94</td>\n",
       "      <td>No</td>\n",
       "      <td>TN</td>\n",
       "      <td>127.677940</td>\n",
       "      <td>1296000</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>HP</td>\n",
       "      <td>250 G6</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>8</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>575.00</td>\n",
       "      <td>No</td>\n",
       "      <td>TN</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>2073600</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Apple</td>\n",
       "      <td>MacBook Pro</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>15.4</td>\n",
       "      <td>16</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>2537.45</td>\n",
       "      <td>No</td>\n",
       "      <td>IPS</td>\n",
       "      <td>220.534624</td>\n",
       "      <td>5184000</td>\n",
       "      <td>2.7</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>512</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Apple</td>\n",
       "      <td>MacBook Pro</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>8</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>1803.60</td>\n",
       "      <td>No</td>\n",
       "      <td>IPS</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>4096000</td>\n",
       "      <td>3.1</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   laptop_ID Company      Product   TypeName  Inches  Ram (GB)  \\\n",
       "0          1   Apple  MacBook Pro  Ultrabook    13.3         8   \n",
       "1          2   Apple  Macbook Air  Ultrabook    13.3         8   \n",
       "2          3      HP       250 G6   Notebook    15.6         8   \n",
       "3          4   Apple  MacBook Pro  Ultrabook    15.4        16   \n",
       "4          5   Apple  MacBook Pro  Ultrabook    13.3         8   \n",
       "\n",
       "                            Gpu  OpSys  Weight (kg)  Price_euros TouchScreen  \\\n",
       "0  Intel Iris Plus Graphics 640  macOS         1.37      1339.69          No   \n",
       "1        Intel HD Graphics 6000  macOS         1.34       898.94          No   \n",
       "2         Intel HD Graphics 620  No OS         1.86       575.00          No   \n",
       "3            AMD Radeon Pro 455  macOS         1.83      2537.45          No   \n",
       "4  Intel Iris Plus Graphics 650  macOS         1.37      1803.60          No   \n",
       "\n",
       "  Display panel         ppi  Res_value  GHz      Cpu_brand  HDD  SSD  Flash  \\\n",
       "0           IPS  226.983005    4096000  2.3  Intel Core i5    0  128      0   \n",
       "1            TN  127.677940    1296000  1.8  Intel Core i5    0    0    128   \n",
       "2            TN  141.211998    2073600  2.5  Intel Core i5    0  256      0   \n",
       "3           IPS  220.534624    5184000  2.7  Intel Core i7    0  512      0   \n",
       "4           IPS  226.983005    4096000  3.1  Intel Core i5    0  256      0   \n",
       "\n",
       "   Hybrid  \n",
       "0       0  \n",
       "1       0  \n",
       "2       0  \n",
       "3       0  \n",
       "4       0  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pop('Memory')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gpu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intel HD Graphics 620      281\n",
       "Intel HD Graphics 520      185\n",
       "Intel UHD Graphics 620      68\n",
       "Nvidia GeForce GTX 1050     66\n",
       "Nvidia GeForce GTX 1060     48\n",
       "                          ... \n",
       "AMD Radeon R5 520            1\n",
       "AMD Radeon R7                1\n",
       "Intel HD Graphics 540        1\n",
       "AMD Radeon 540               1\n",
       "ARM Mali T860 MP4            1\n",
       "Name: Gpu, Length: 110, dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gpu'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[182,'Gpu']='AMD Radeon R7'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_Gpu(x):\n",
    "    if 'Intel' in x:\n",
    "        return 'Intel Iris Graphics' if 'Iris' in x else \"Intel HD Graphics\"\n",
    "    if  'AMD' in x:\n",
    "        match = re.search(r'R[579X]',x)\n",
    "        if match:\n",
    "            return 'AMD Radeon '+match.group()\n",
    "        if 'FirePro' in x:\n",
    "            return 'AMD FirePro'\n",
    "        else:\n",
    "            return \"Other AMD Radeon\"\n",
    "    # if not AMD or Intel then its Nvidia\n",
    "    if 'Nvidia' in x:\n",
    "        if 'Quadro' in x:\n",
    "            return 'Nvidia Quadro'\n",
    "        if 'GTX' in x:\n",
    "            return 'Nvidia GeForce GTX'\n",
    "        else:\n",
    "            return 'Nvidia GeForce GT'\n",
    "    return 'Other Gpu'\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Gpu_model']=df['Gpu'].apply(get_Gpu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Price_euros', ylabel='Gpu_model'>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(16,7))\n",
    "x = df.groupby(['Gpu_model']).Price_euros.median().sort_values().keys()\n",
    "sn.boxplot(x='Price_euros',y='Gpu_model',data= df,order=x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x1440 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(2,3,figsize=(25,20))\n",
    "companies=df[\"TypeName\"].unique()\n",
    "j=i=0\n",
    "for company in companies[0:12]:\n",
    "  data = df.groupby([\"TypeName\",\"Gpu_model\"]).count()['laptop_ID'][company]   \n",
    "  data.plot.pie (autopct=\"%.1f%%\",ax=ax[i][j])\n",
    "  ax[i][j].set_title(company)\n",
    "  j+=1\n",
    "  if j%3 == 0:\n",
    "    i+=1\n",
    "    j=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns = ['laptop_ID','Product','Inches', 'Gpu'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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 (GB)</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight (kg)</th>\n",
       "      <th>Price_euros</th>\n",
       "      <th>TouchScreen</th>\n",
       "      <th>Display panel</th>\n",
       "      <th>ppi</th>\n",
       "      <th>Res_value</th>\n",
       "      <th>GHz</th>\n",
       "      <th>Cpu_brand</th>\n",
       "      <th>HDD</th>\n",
       "      <th>SSD</th>\n",
       "      <th>Flash</th>\n",
       "      <th>Hybrid</th>\n",
       "      <th>Gpu_model</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>1339.69</td>\n",
       "      <td>No</td>\n",
       "      <td>IPS</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>4096000</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel Iris Graphics</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>898.94</td>\n",
       "      <td>No</td>\n",
       "      <td>TN</td>\n",
       "      <td>127.677940</td>\n",
       "      <td>1296000</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel HD Graphics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>575.00</td>\n",
       "      <td>No</td>\n",
       "      <td>TN</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>2073600</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel HD Graphics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>16</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>2537.45</td>\n",
       "      <td>No</td>\n",
       "      <td>IPS</td>\n",
       "      <td>220.534624</td>\n",
       "      <td>5184000</td>\n",
       "      <td>2.7</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>512</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Other AMD Radeon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>1803.60</td>\n",
       "      <td>No</td>\n",
       "      <td>IPS</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>4096000</td>\n",
       "      <td>3.1</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel Iris Graphics</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Ram (GB)  OpSys  Weight (kg)  Price_euros TouchScreen  \\\n",
       "0   Apple  Ultrabook         8  macOS         1.37      1339.69          No   \n",
       "1   Apple  Ultrabook         8  macOS         1.34       898.94          No   \n",
       "2      HP   Notebook         8  No OS         1.86       575.00          No   \n",
       "3   Apple  Ultrabook        16  macOS         1.83      2537.45          No   \n",
       "4   Apple  Ultrabook         8  macOS         1.37      1803.60          No   \n",
       "\n",
       "  Display panel         ppi  Res_value  GHz      Cpu_brand  HDD  SSD  Flash  \\\n",
       "0           IPS  226.983005    4096000  2.3  Intel Core i5    0  128      0   \n",
       "1            TN  127.677940    1296000  1.8  Intel Core i5    0    0    128   \n",
       "2            TN  141.211998    2073600  2.5  Intel Core i5    0  256      0   \n",
       "3           IPS  220.534624    5184000  2.7  Intel Core i7    0  512      0   \n",
       "4           IPS  226.983005    4096000  3.1  Intel Core i5    0  256      0   \n",
       "\n",
       "   Hybrid            Gpu_model  \n",
       "0       0  Intel Iris Graphics  \n",
       "1       0    Intel HD Graphics  \n",
       "2       0    Intel HD Graphics  \n",
       "3       0     Other AMD Radeon  \n",
       "4       0  Intel Iris Graphics  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1303 entries, 0 to 1302\n",
      "Data columns (total 17 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Company        1303 non-null   object \n",
      " 1   TypeName       1303 non-null   object \n",
      " 2   Ram (GB)       1303 non-null   int32  \n",
      " 3   OpSys          1303 non-null   object \n",
      " 4   Weight (kg)    1303 non-null   float64\n",
      " 5   Price_euros    1303 non-null   float64\n",
      " 6   TouchScreen    1303 non-null   object \n",
      " 7   Display panel  1303 non-null   object \n",
      " 8   ppi            1303 non-null   float64\n",
      " 9   Res_value      1303 non-null   int32  \n",
      " 10  GHz            1303 non-null   float64\n",
      " 11  Cpu_brand      1303 non-null   object \n",
      " 12  HDD            1303 non-null   int32  \n",
      " 13  SSD            1303 non-null   int32  \n",
      " 14  Flash          1303 non-null   int32  \n",
      " 15  Hybrid         1303 non-null   int32  \n",
      " 16  Gpu_model      1303 non-null   object \n",
      "dtypes: float64(4), int32(6), object(7)\n",
      "memory usage: 142.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Building"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1f3a1dbcd60>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.displot(df['Price_euros'],kind='kde')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1f3a2114250>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.displot(np.log(df['Price_euros']),kind='kde')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Price_euros']=df.pop('Price_euros')\n",
    "plt.figure(figsize=(20,12))\n",
    "sn.heatmap(df.corr(),annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Price_euros    1.000000\n",
       "Ram (GB)       0.743007\n",
       "SSD            0.676202\n",
       "Res_value      0.508977\n",
       "ppi            0.473927\n",
       "Weight (kg)    0.210370\n",
       "Hybrid         0.007989\n",
       "Flash         -0.040511\n",
       "GHz           -0.053240\n",
       "HDD           -0.095672\n",
       "Name: Price_euros, dtype: float64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()['Price_euros'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that have prepared our data and hold a better understanding of the dataset. let’s get started with Machine learning modeling! and finding the best algorithm with the best hyperparameters to achieve maximum accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "let's load the libraries that we will use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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 (GB)</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight (kg)</th>\n",
       "      <th>TouchScreen</th>\n",
       "      <th>Display panel</th>\n",
       "      <th>ppi</th>\n",
       "      <th>Res_value</th>\n",
       "      <th>GHz</th>\n",
       "      <th>Cpu_brand</th>\n",
       "      <th>HDD</th>\n",
       "      <th>SSD</th>\n",
       "      <th>Flash</th>\n",
       "      <th>Hybrid</th>\n",
       "      <th>Gpu_model</th>\n",
       "      <th>Price_euros</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>No</td>\n",
       "      <td>IPS</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>4096000</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel Iris Graphics</td>\n",
       "      <td>1339.69</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>No</td>\n",
       "      <td>TN</td>\n",
       "      <td>127.677940</td>\n",
       "      <td>1296000</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel HD Graphics</td>\n",
       "      <td>898.94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Ram (GB)  OpSys  Weight (kg) TouchScreen Display panel  \\\n",
       "0   Apple  Ultrabook         8  macOS         1.37          No           IPS   \n",
       "1   Apple  Ultrabook         8  macOS         1.34          No            TN   \n",
       "\n",
       "          ppi  Res_value  GHz      Cpu_brand  HDD  SSD  Flash  Hybrid  \\\n",
       "0  226.983005    4096000  2.3  Intel Core i5    0  128      0       0   \n",
       "1  127.677940    1296000  1.8  Intel Core i5    0    0    128       0   \n",
       "\n",
       "             Gpu_model  Price_euros  \n",
       "0  Intel Iris Graphics      1339.69  \n",
       "1    Intel HD Graphics       898.94  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn as sl\n",
    "from sklearn.compose import ColumnTransformer,make_column_selector\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "# models\n",
    "from sklearn.linear_model import LinearRegression,Ridge,BayesianRidge,SGDRegressor\n",
    "from lightgbm import LGBMRegressor\n",
    "from xgboost.sklearn import XGBRegressor\n",
    "from catboost import CatBoostRegressor\n",
    "from sklearn.ensemble import GradientBoostingRegressor,RandomForestRegressor,AdaBoostRegressor\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "from sklearn.metrics import mean_absolute_error,r2_score,mean_squared_error,max_error\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "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 (GB)</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight (kg)</th>\n",
       "      <th>TouchScreen</th>\n",
       "      <th>Display panel</th>\n",
       "      <th>ppi</th>\n",
       "      <th>Res_value</th>\n",
       "      <th>GHz</th>\n",
       "      <th>Cpu_brand</th>\n",
       "      <th>HDD</th>\n",
       "      <th>SSD</th>\n",
       "      <th>Flash</th>\n",
       "      <th>Hybrid</th>\n",
       "      <th>Gpu_model</th>\n",
       "      <th>Price_euros</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Chuwi</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>4</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.89</td>\n",
       "      <td>No</td>\n",
       "      <td>TN</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>2073600</td>\n",
       "      <td>44.0</td>\n",
       "      <td>Other Intel Processor</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel HD Graphics</td>\n",
       "      <td>244.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>Chuwi</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>6</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.40</td>\n",
       "      <td>No</td>\n",
       "      <td>IPS</td>\n",
       "      <td>267.338436</td>\n",
       "      <td>4990464</td>\n",
       "      <td>1.1</td>\n",
       "      <td>Other Intel Processor</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel HD Graphics</td>\n",
       "      <td>449.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>483</th>\n",
       "      <td>Chuwi</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>4</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.89</td>\n",
       "      <td>No</td>\n",
       "      <td>TN</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>2073600</td>\n",
       "      <td>44.0</td>\n",
       "      <td>Other Intel Processor</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel HD Graphics</td>\n",
       "      <td>248.90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Company  TypeName  Ram (GB)       OpSys  Weight (kg) TouchScreen  \\\n",
       "30    Chuwi  Notebook         4  Windows 10         1.89          No   \n",
       "421   Chuwi  Notebook         6  Windows 10         1.40          No   \n",
       "483   Chuwi  Notebook         4  Windows 10         1.89          No   \n",
       "\n",
       "    Display panel         ppi  Res_value   GHz              Cpu_brand  HDD  \\\n",
       "30             TN  141.211998    2073600  44.0  Other Intel Processor    0   \n",
       "421           IPS  267.338436    4990464   1.1  Other Intel Processor    0   \n",
       "483            TN  141.211998    2073600  44.0  Other Intel Processor    0   \n",
       "\n",
       "     SSD  Flash  Hybrid          Gpu_model  Price_euros  \n",
       "30     0     64       0  Intel HD Graphics       244.99  \n",
       "421    0     64       0  Intel HD Graphics       449.00  \n",
       "483    0     64       0  Intel HD Graphics       248.90  "
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Company']=='Chuwi']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1303 entries, 0 to 1302\n",
      "Data columns (total 17 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Company        1303 non-null   object \n",
      " 1   TypeName       1303 non-null   object \n",
      " 2   Ram (GB)       1303 non-null   int32  \n",
      " 3   OpSys          1303 non-null   object \n",
      " 4   Weight (kg)    1303 non-null   float64\n",
      " 5   TouchScreen    1303 non-null   object \n",
      " 6   Display panel  1303 non-null   object \n",
      " 7   ppi            1303 non-null   float64\n",
      " 8   Res_value      1303 non-null   int32  \n",
      " 9   GHz            1303 non-null   float64\n",
      " 10  Cpu_brand      1303 non-null   object \n",
      " 11  HDD            1303 non-null   int32  \n",
      " 12  SSD            1303 non-null   int32  \n",
      " 13  Flash          1303 non-null   int32  \n",
      " 14  Hybrid         1303 non-null   int32  \n",
      " 15  Gpu_model      1303 non-null   object \n",
      " 16  Price_euros    1303 non-null   float64\n",
      "dtypes: float64(4), int32(6), object(7)\n",
      "memory usage: 142.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getName(x):\n",
    "    return re.search(r\"\\w*(?!(?!'>))\",str(x)).group()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=np.log(df['Price_euros'])\n",
    "X=df.loc[:, df.columns != 'Price_euros']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "mapper = {i:value for i,value in enumerate(X_train.columns)}\n",
    "mapper"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We create the preprocessing pipelines for both numeric and categorical data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "numerical_features = make_column_selector(dtype_include=np.number)\n",
    "cat_features = make_column_selector(dtype_exclude=np.number)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "#numeric_features = ['Ram (GB)','Weight (kg)','ppi','Res_value','GHz','HDD','SSD','Flash','Hybrid']\n",
    "numeric_transformer = StandardScaler()\n",
    "\n",
    "#categorical_features = ['Company','TypeName','OpSys','Cpu_brand','Gpu_model']\n",
    "categorical_transformer = OneHotEncoder(sparse=False,drop='first',handle_unknown='ignore')\n",
    "\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        (\"num\", numeric_transformer, numerical_features),\n",
    "        (\"cat\", categorical_transformer, cat_features),\n",
    "    ],\n",
    "    remainder='passthrough'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "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": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Model_pipe(mdl):\n",
    "    return Pipeline(steps=[('preprocessor',preprocessor),(\"Reg\", mdl())])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After appending a regression model we have a full prediction pipeline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "#models=[LinearRegression,Ridge,SGDRegressor,DecisionTreeRegressor,RandomForestRegressor,GradientBoostingRegressor,\n",
    "#        SVR,BayesianRidge,LGBMRegressor,XGBRegressor,AdaBoostRegressor,CatBoostRegressor]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "def scores(md):\n",
    "    for score_type,X,y in zip(['+ Training accuracy','+ Test accuracy'],[X_train,X_test],[y_train,y_test]):\n",
    "        print(score_type)\n",
    "        y_pred = md.predict(X)\n",
    "        print('|            R2 Score: {:.3}%'.format(r2_score(y,y_pred)*100))\n",
    "        print('| Mean Absolute Error: {:.3}'.format(mean_squared_error(y,y_pred)))\n",
    "        print('|           Max Error: {:.3}'.format(max_error(y,y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "params=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate(model):\n",
    "    md=Model_pipe(model)\n",
    "    md.fit(X_train,y_train)\n",
    "    print('\\n------ {} ------'.format(getName(model)))\n",
    "    scores(md)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "scoring='max_error'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Linear Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Ordinary least squares Linear Regression.**\n",
    "\n",
    "$\\mathbf {y}$ is a $n\\times 1$, ($1303\\times 1$ in our case), vectors of the response variables, and ${\\mathrm  {X}}$ is an $n\\times p$, ($1303\\times 16$), matrix of regressors, whose $i$ th row is $\\mathbf {x} _{i}$ and contains the $i-th$ observations on all the explanatory variables. In a linear regression model, the response variable, $y_{i}$, is a linear function of the regressors:\n",
    "$$\n",
    "{\\displaystyle \\mathbf {y} =\\mathrm {X} {\\boldsymbol {\\beta }}+{\\boldsymbol {\\varepsilon }},\\,}\n",
    "$$\n",
    "where ${\\boldsymbol {\\beta }}$ is a $16\\times 1$ vector of unknown parameters and $\\boldsymbol{\\varepsilon}$ is a $1303\\times 1$ vectors of the errors of the $n$ observations.\n",
    "\n",
    "`LinearRegression` fits a linear model with coefficients $ \\boldsymbol {\\beta } = ({\\beta }_1, …, {\\beta }_p) $ to minimize the residual sum of squares between the observed targets in the dataset, and the targets predicted by the linear approximation.\n",
    "$$\n",
    "\\sum _{i=1}^{1303}{\\biggl |}y_{i}-\\sum _{j=1}^{16}X_{ij}\\beta _{j}{\\biggr |}^{2}={\\bigl \\|}\\mathbf {y} -\\mathrm {X} {\\boldsymbol {\\beta }}{\\bigr \\|}^{2}_2.\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "------ LinearRegression ------\n",
      "+ Training accuracy\n",
      "|            R2 Score: 85.5%\n",
      "| Mean Absolute Error: 0.0565\n",
      "|           Max Error: 1.16\n",
      "+ Test accuracy\n",
      "|            R2 Score: 85.1%\n",
      "| Mean Absolute Error: 0.0569\n",
      "|           Max Error: 0.733\n"
     ]
    }
   ],
   "source": [
    "evaluate(LinearRegression)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### GradientBoostingRegressor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "hel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "------ GradientBoostingRegressor ------\n",
      "+ Training accuracy\n",
      "|            R2 Score: 91.6%\n",
      "| Mean Absolute Error: 0.0326\n",
      "|           Max Error: 0.736\n",
      "+ Test accuracy\n",
      "|            R2 Score: 88.8%\n",
      "| Mean Absolute Error: 0.0429\n",
      "|           Max Error: 0.549\n"
     ]
    }
   ],
   "source": [
    "evaluate(GradientBoostingRegressor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "GBR = Model_pipe(GradientBoostingRegressor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_GBR = {\n",
    "    'Reg__learning_rate': [.01,.03, 0.05, .07],\n",
    "    'Reg__subsample'    : [0.7, 0.5, 0.2, 0.1],\n",
    "    'Reg__n_estimators' : [1000],\n",
    "    'Reg__max_depth'    : [4,6,8,10,12],\n",
    "    'Reg' : [GradientBoostingRegressor()]\n",
    "}\n",
    "params.append(param_GBR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### XGBRegressor "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "------ XGBRegressor ------\n",
      "+ Training accuracy\n",
      "|            R2 Score: 99.0%\n",
      "| Mean Absolute Error: 0.00396\n",
      "|           Max Error: 0.301\n",
      "+ Test accuracy\n",
      "|            R2 Score: 91.3%\n",
      "| Mean Absolute Error: 0.033\n",
      "|           Max Error: 0.53\n"
     ]
    }
   ],
   "source": [
    "evaluate(XGBRegressor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_XGBR = {\n",
    "    'Reg__learning_rate'    : [.01,.03, 0.05, .07],\n",
    "    'Reg__subsample'        : [0.7, 0.5, 0.2, 0.1],\n",
    "    'Reg__n_estimators'     : [1000],\n",
    "    'Reg__max_depth'        : [2,4,6,8,10],\n",
    "    'Reg'                   : [XGBRegressor()]\n",
    "}\n",
    "params.append(param_XGBR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "XGBR = Model_pipe(XGBRegressor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_XGBR = GridSearchCV(XGBR,param_XGBR,n_jobs=-1,scoring=scoring,error_score='raise')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(error_score=&#x27;raise&#x27;,\n",
       "             estimator=Pipeline(steps=[(&#x27;Reg&#x27;,\n",
       "                                        XGBRegressor(base_score=None,\n",
       "                                                     booster=None,\n",
       "                                                     callbacks=None,\n",
       "                                                     colsample_bylevel=None,\n",
       "                                                     colsample_bynode=None,\n",
       "                                                     colsample_bytree=None,\n",
       "                                                     early_stopping_rounds=None,\n",
       "                                                     enable_categorical=False,\n",
       "                                                     eval_metric=None,\n",
       "                                                     gamma=None, gpu_id=None,\n",
       "                                                     grow_policy=None,\n",
       "                                                     importance_type=None,\n",
       "                                                     interaction_constraints=None,\n",
       "                                                     l...\n",
       "                                              max_leaves=None,\n",
       "                                              min_child_weight=None,\n",
       "                                              missing=nan,\n",
       "                                              monotone_constraints=None,\n",
       "                                              n_estimators=1000, n_jobs=None,\n",
       "                                              num_parallel_tree=None,\n",
       "                                              predictor=None, random_state=None,\n",
       "                                              reg_alpha=None, reg_lambda=None, ...)],\n",
       "                         &#x27;Reg__learning_rate&#x27;: [0.01, 0.03, 0.05, 0.07],\n",
       "                         &#x27;Reg__max_depth&#x27;: [2, 4, 6, 8, 10],\n",
       "                         &#x27;Reg__n_estimators&#x27;: [1000],\n",
       "                         &#x27;Reg__subsample&#x27;: [0.7, 0.5, 0.2, 0.1]},\n",
       "             scoring=&#x27;max_error&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(error_score=&#x27;raise&#x27;,\n",
       "             estimator=Pipeline(steps=[(&#x27;Reg&#x27;,\n",
       "                                        XGBRegressor(base_score=None,\n",
       "                                                     booster=None,\n",
       "                                                     callbacks=None,\n",
       "                                                     colsample_bylevel=None,\n",
       "                                                     colsample_bynode=None,\n",
       "                                                     colsample_bytree=None,\n",
       "                                                     early_stopping_rounds=None,\n",
       "                                                     enable_categorical=False,\n",
       "                                                     eval_metric=None,\n",
       "                                                     gamma=None, gpu_id=None,\n",
       "                                                     grow_policy=None,\n",
       "                                                     importance_type=None,\n",
       "                                                     interaction_constraints=None,\n",
       "                                                     l...\n",
       "                                              max_leaves=None,\n",
       "                                              min_child_weight=None,\n",
       "                                              missing=nan,\n",
       "                                              monotone_constraints=None,\n",
       "                                              n_estimators=1000, n_jobs=None,\n",
       "                                              num_parallel_tree=None,\n",
       "                                              predictor=None, random_state=None,\n",
       "                                              reg_alpha=None, reg_lambda=None, ...)],\n",
       "                         &#x27;Reg__learning_rate&#x27;: [0.01, 0.03, 0.05, 0.07],\n",
       "                         &#x27;Reg__max_depth&#x27;: [2, 4, 6, 8, 10],\n",
       "                         &#x27;Reg__n_estimators&#x27;: [1000],\n",
       "                         &#x27;Reg__subsample&#x27;: [0.7, 0.5, 0.2, 0.1]},\n",
       "             scoring=&#x27;max_error&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;Reg&#x27;,\n",
       "                 XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "                              colsample_bylevel=None, colsample_bynode=None,\n",
       "                              colsample_bytree=None, early_stopping_rounds=None,\n",
       "                              enable_categorical=False, eval_metric=None,\n",
       "                              gamma=None, gpu_id=None, grow_policy=None,\n",
       "                              importance_type=None,\n",
       "                              interaction_constraints=None, learning_rate=None,\n",
       "                              max_bin=None, max_cat_to_onehot=None,\n",
       "                              max_delta_step=None, max_depth=None,\n",
       "                              max_leaves=None, min_child_weight=None,\n",
       "                              missing=nan, monotone_constraints=None,\n",
       "                              n_estimators=100, n_jobs=None,\n",
       "                              num_parallel_tree=None, predictor=None,\n",
       "                              random_state=None, reg_alpha=None,\n",
       "                              reg_lambda=None, ...))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, gamma=None,\n",
       "             gpu_id=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_to_onehot=None, max_delta_step=None, max_depth=None,\n",
       "             max_leaves=None, min_child_weight=None, missing=nan,\n",
       "             monotone_constraints=None, n_estimators=100, n_jobs=None,\n",
       "             num_parallel_tree=None, predictor=None, random_state=None,\n",
       "             reg_alpha=None, reg_lambda=None, ...)</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(error_score='raise',\n",
       "             estimator=Pipeline(steps=[('Reg',\n",
       "                                        XGBRegressor(base_score=None,\n",
       "                                                     booster=None,\n",
       "                                                     callbacks=None,\n",
       "                                                     colsample_bylevel=None,\n",
       "                                                     colsample_bynode=None,\n",
       "                                                     colsample_bytree=None,\n",
       "                                                     early_stopping_rounds=None,\n",
       "                                                     enable_categorical=False,\n",
       "                                                     eval_metric=None,\n",
       "                                                     gamma=None, gpu_id=None,\n",
       "                                                     grow_policy=None,\n",
       "                                                     importance_type=None,\n",
       "                                                     interaction_constraints=None,\n",
       "                                                     l...\n",
       "                                              max_leaves=None,\n",
       "                                              min_child_weight=None,\n",
       "                                              missing=nan,\n",
       "                                              monotone_constraints=None,\n",
       "                                              n_estimators=1000, n_jobs=None,\n",
       "                                              num_parallel_tree=None,\n",
       "                                              predictor=None, random_state=None,\n",
       "                                              reg_alpha=None, reg_lambda=None, ...)],\n",
       "                         'Reg__learning_rate': [0.01, 0.03, 0.05, 0.07],\n",
       "                         'Reg__max_depth': [2, 4, 6, 8, 10],\n",
       "                         'Reg__n_estimators': [1000],\n",
       "                         'Reg__subsample': [0.7, 0.5, 0.2, 0.1]},\n",
       "             scoring='max_error')"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_XGBR.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameter (CV score=-0.632):\n",
      "{'Reg': XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
      "             colsample_bylevel=None, colsample_bynode=None,\n",
      "             colsample_bytree=None, early_stopping_rounds=None,\n",
      "             enable_categorical=False, eval_metric=None, gamma=None,\n",
      "             gpu_id=None, grow_policy=None, importance_type=None,\n",
      "             interaction_constraints=None, learning_rate=0.05, max_bin=None,\n",
      "             max_cat_to_onehot=None, max_delta_step=None, max_depth=6,\n",
      "             max_leaves=None, min_child_weight=None, missing=nan,\n",
      "             monotone_constraints=None, n_estimators=1000, n_jobs=None,\n",
      "             num_parallel_tree=None, predictor=None, random_state=None,\n",
      "             reg_alpha=None, reg_lambda=None, ...), 'Reg__learning_rate': 0.05, 'Reg__max_depth': 6, 'Reg__n_estimators': 1000, 'Reg__subsample': 0.5}\n"
     ]
    }
   ],
   "source": [
    "print(\"Best parameter (CV score=%0.3f):\" % grid_XGBR.best_score_)\n",
    "print(grid_XGBR.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+ Training accuracy\n",
      "|            R2 Score: 99.4%\n",
      "| Mean Absolute Error: 0.00219\n",
      "|           Max Error: 0.283\n",
      "+ Test accuracy\n",
      "|            R2 Score: 92.0%\n",
      "| Mean Absolute Error: 0.0305\n",
      "|           Max Error: 0.666\n"
     ]
    }
   ],
   "source": [
    "scores(grid_XGBR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This model doesn't have Hyperparameters to tune."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Support vector machines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "------ SVR ------\n",
      "+ Training accuracy\n",
      "|            R2 Score: 93.4%\n",
      "| Mean Absolute Error: 0.0257\n",
      "|           Max Error: 0.645\n",
      "+ Test accuracy\n",
      "|            R2 Score: 88.2%\n",
      "| Mean Absolute Error: 0.0449\n",
      "|           Max Error: 1.14\n"
     ]
    }
   ],
   "source": [
    "evaluate(SVR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "SVMR=Model_pipe(SVR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_SVMR={\n",
    "    'Reg__C': [1.1, 5.4, 170, 1001],\n",
    "    'Reg__epsilon': [0.0003, 0.007, 0.0109, 0.019, 0.14, 0.05, 8, 0.2, 3, 2, 7],\n",
    "    'Reg__gamma': [0.7001, 0.008, 0.001, 3.1, 1, 1.3, 5],\n",
    "    'Reg' : [SVR()]\n",
    "}\n",
    "params.append(param_SVMR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_SVMR = GridSearchCV(SVMR,param_SVMR,scoring=scoring,error_score='raise')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(error_score=&#x27;raise&#x27;, estimator=Pipeline(steps=[(&#x27;Reg&#x27;, SVR())]),\n",
       "             n_jobs=60,\n",
       "             param_grid={&#x27;Reg&#x27;: [SVR(C=5.4, epsilon=0.05, gamma=0.008)],\n",
       "                         &#x27;Reg__C&#x27;: [1.1, 5.4, 170, 1001],\n",
       "                         &#x27;Reg__epsilon&#x27;: [0.0003, 0.007, 0.0109, 0.019, 0.14,\n",
       "                                          0.05, 8, 0.2, 3, 2, 7],\n",
       "                         &#x27;Reg__gamma&#x27;: [0.7001, 0.008, 0.001, 3.1, 1, 1.3, 5]},\n",
       "             scoring=&#x27;max_error&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(error_score=&#x27;raise&#x27;, estimator=Pipeline(steps=[(&#x27;Reg&#x27;, SVR())]),\n",
       "             n_jobs=60,\n",
       "             param_grid={&#x27;Reg&#x27;: [SVR(C=5.4, epsilon=0.05, gamma=0.008)],\n",
       "                         &#x27;Reg__C&#x27;: [1.1, 5.4, 170, 1001],\n",
       "                         &#x27;Reg__epsilon&#x27;: [0.0003, 0.007, 0.0109, 0.019, 0.14,\n",
       "                                          0.05, 8, 0.2, 3, 2, 7],\n",
       "                         &#x27;Reg__gamma&#x27;: [0.7001, 0.008, 0.001, 3.1, 1, 1.3, 5]},\n",
       "             scoring=&#x27;max_error&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;Reg&#x27;, SVR())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVR</label><div class=\"sk-toggleable__content\"><pre>SVR()</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(error_score='raise', estimator=Pipeline(steps=[('Reg', SVR())]),\n",
       "             n_jobs=60,\n",
       "             param_grid={'Reg': [SVR(C=5.4, epsilon=0.05, gamma=0.008)],\n",
       "                         'Reg__C': [1.1, 5.4, 170, 1001],\n",
       "                         'Reg__epsilon': [0.0003, 0.007, 0.0109, 0.019, 0.14,\n",
       "                                          0.05, 8, 0.2, 3, 2, 7],\n",
       "                         'Reg__gamma': [0.7001, 0.008, 0.001, 3.1, 1, 1.3, 5]},\n",
       "             scoring='max_error')"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_SVMR.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameter (CV score=-0.789):\n",
      "{'Reg': SVR(C=5.4, epsilon=0.05, gamma=0.008), 'Reg__C': 5.4, 'Reg__epsilon': 0.05, 'Reg__gamma': 0.008}\n"
     ]
    }
   ],
   "source": [
    "print(\"Best parameter (CV score=%0.3f):\" % grid_SVMR.best_score_)\n",
    "print(grid_SVMR.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+ Training accuracy\n",
      "|            R2 Score: 89.9%\n",
      "| Mean Absolute Error: 0.0394\n",
      "|           Max Error: 0.886\n",
      "+ Test accuracy\n",
      "|            R2 Score: 88.6%\n",
      "| Mean Absolute Error: 0.0434\n",
      "|           Max Error: 0.654\n"
     ]
    }
   ],
   "source": [
    "scores(grid_SVMR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "------ DecisionTreeRegressor ------\n",
      "+ Training accuracy\n",
      "|            R2 Score: 99.8%\n",
      "| Mean Absolute Error: 0.000847\n",
      "|           Max Error: 0.272\n",
      "+ Test accuracy\n",
      "|            R2 Score: 80.9%\n",
      "| Mean Absolute Error: 0.073\n",
      "|           Max Error: 1.19\n"
     ]
    }
   ],
   "source": [
    "evaluate(DecisionTreeRegressor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ccp_alpha': 0.0,\n",
       " 'criterion': 'squared_error',\n",
       " 'max_depth': None,\n",
       " 'max_features': None,\n",
       " 'max_leaf_nodes': None,\n",
       " 'min_impurity_decrease': 0.0,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 2,\n",
       " 'min_weight_fraction_leaf': 0.0,\n",
       " 'random_state': None,\n",
       " 'splitter': 'best'}"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DecisionTreeRegressor().get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "DTR=Model_pipe(DecisionTreeRegressor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_DTR={\n",
    "    \"Reg__splitter\":[\"best\",\"random\"],\n",
    "    \"Reg__max_depth\" : [1,3,5,7,9,11,12],\n",
    "    \"Reg__max_leaf_nodes\":[None,10,20,30,40,50,60,70,80,90],\n",
    "    \"Reg__min_samples_leaf\":[1,2,3,4,5,6,7,8,9,10],\n",
    "    \"Reg\" : [DecisionTreeRegressor()]\n",
    "}\n",
    "params.append(param_DTR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_DTR = GridSearchCV(DTR,param_DTR,scoring=scoring,error_score='raise')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(error_score=&#x27;raise&#x27;,\n",
       "             estimator=Pipeline(steps=[(&#x27;Reg&#x27;, DecisionTreeRegressor())]),\n",
       "             n_jobs=60,\n",
       "             param_grid={&#x27;Reg&#x27;: [DecisionTreeRegressor(max_depth=12,\n",
       "                                                       max_leaf_nodes=90,\n",
       "                                                       min_samples_leaf=2)],\n",
       "                         &#x27;Reg__max_depth&#x27;: [1, 3, 5, 7, 9, 11, 12],\n",
       "                         &#x27;Reg__max_leaf_nodes&#x27;: [None, 10, 20, 30, 40, 50, 60,\n",
       "                                                 70, 80, 90],\n",
       "                         &#x27;Reg__min_samples_leaf&#x27;: [1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
       "                                                   10],\n",
       "                         &#x27;Reg__splitter&#x27;: [&#x27;best&#x27;, &#x27;random&#x27;]},\n",
       "             scoring=&#x27;max_error&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(error_score=&#x27;raise&#x27;,\n",
       "             estimator=Pipeline(steps=[(&#x27;Reg&#x27;, DecisionTreeRegressor())]),\n",
       "             n_jobs=60,\n",
       "             param_grid={&#x27;Reg&#x27;: [DecisionTreeRegressor(max_depth=12,\n",
       "                                                       max_leaf_nodes=90,\n",
       "                                                       min_samples_leaf=2)],\n",
       "                         &#x27;Reg__max_depth&#x27;: [1, 3, 5, 7, 9, 11, 12],\n",
       "                         &#x27;Reg__max_leaf_nodes&#x27;: [None, 10, 20, 30, 40, 50, 60,\n",
       "                                                 70, 80, 90],\n",
       "                         &#x27;Reg__min_samples_leaf&#x27;: [1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
       "                                                   10],\n",
       "                         &#x27;Reg__splitter&#x27;: [&#x27;best&#x27;, &#x27;random&#x27;]},\n",
       "             scoring=&#x27;max_error&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;Reg&#x27;, DecisionTreeRegressor())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor()</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(error_score='raise',\n",
       "             estimator=Pipeline(steps=[('Reg', DecisionTreeRegressor())]),\n",
       "             n_jobs=60,\n",
       "             param_grid={'Reg': [DecisionTreeRegressor(max_depth=12,\n",
       "                                                       max_leaf_nodes=90,\n",
       "                                                       min_samples_leaf=2)],\n",
       "                         'Reg__max_depth': [1, 3, 5, 7, 9, 11, 12],\n",
       "                         'Reg__max_leaf_nodes': [None, 10, 20, 30, 40, 50, 60,\n",
       "                                                 70, 80, 90],\n",
       "                         'Reg__min_samples_leaf': [1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
       "                                                   10],\n",
       "                         'Reg__splitter': ['best', 'random']},\n",
       "             scoring='max_error')"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_DTR.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameter (CV score=-0.832):\n",
      "{'Reg': DecisionTreeRegressor(max_depth=12, max_leaf_nodes=90, min_samples_leaf=2), 'Reg__max_depth': 12, 'Reg__max_leaf_nodes': 90, 'Reg__min_samples_leaf': 2, 'Reg__splitter': 'best'}\n"
     ]
    }
   ],
   "source": [
    "print(\"Best parameter (CV score=%0.3f):\" % grid_DTR.best_score_)\n",
    "print(grid_DTR.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+ Training accuracy\n",
      "|            R2 Score: 92.0%\n",
      "| Mean Absolute Error: 0.0313\n",
      "|           Max Error: 0.718\n",
      "+ Test accuracy\n",
      "|            R2 Score: 83.7%\n",
      "| Mean Absolute Error: 0.0622\n",
      "|           Max Error: 0.842\n"
     ]
    }
   ],
   "source": [
    "scores(grid_DTR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bootstrap': True,\n",
       " 'ccp_alpha': 0.0,\n",
       " 'criterion': 'squared_error',\n",
       " 'max_depth': None,\n",
       " 'max_features': 1.0,\n",
       " 'max_leaf_nodes': None,\n",
       " 'max_samples': None,\n",
       " 'min_impurity_decrease': 0.0,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 2,\n",
       " 'min_weight_fraction_leaf': 0.0,\n",
       " 'n_estimators': 100,\n",
       " 'n_jobs': None,\n",
       " 'oob_score': False,\n",
       " 'random_state': None,\n",
       " 'verbose': 0,\n",
       " 'warm_start': False}"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RandomForestRegressor().get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "------ RandomForestRegressor ------\n",
      "+ Training accuracy\n",
      "|            R2 Score: 98.3%\n",
      "| Mean Absolute Error: 0.00658\n",
      "|           Max Error: 0.357\n",
      "+ Test accuracy\n",
      "|            R2 Score: 89.8%\n",
      "| Mean Absolute Error: 0.039\n",
      "|           Max Error: 0.809\n"
     ]
    }
   ],
   "source": [
    "evaluate(RandomForestRegressor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "RFR=Model_pipe(RandomForestRegressor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_RFR={\n",
    "    'Reg__bootstrap': [True, False],\n",
    "    'Reg__min_samples_leaf': [1, 2, 4],\n",
    "    'Reg__min_samples_split': [2, 5, 10],\n",
    "    \"Reg__n_estimators\":[1000,],\n",
    "    \"Reg\" : [RandomForestRegressor()]\n",
    "}\n",
    "params.append(param_RFR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_RFR = GridSearchCV(RFR,param_RFR,scoring='r2',error_score='raise')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(error_score=&#x27;raise&#x27;,\n",
       "             estimator=Pipeline(steps=[(&#x27;Reg&#x27;, RandomForestRegressor())]),\n",
       "             param_grid={&#x27;Reg&#x27;: [RandomForestRegressor(n_estimators=1000)],\n",
       "                         &#x27;Reg__bootstrap&#x27;: [True, False],\n",
       "                         &#x27;Reg__min_samples_leaf&#x27;: [1, 2, 4],\n",
       "                         &#x27;Reg__min_samples_split&#x27;: [2, 5, 10],\n",
       "                         &#x27;Reg__n_estimators&#x27;: [1000]},\n",
       "             scoring=&#x27;r2&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-25\" type=\"checkbox\" ><label for=\"sk-estimator-id-25\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(error_score=&#x27;raise&#x27;,\n",
       "             estimator=Pipeline(steps=[(&#x27;Reg&#x27;, RandomForestRegressor())]),\n",
       "             param_grid={&#x27;Reg&#x27;: [RandomForestRegressor(n_estimators=1000)],\n",
       "                         &#x27;Reg__bootstrap&#x27;: [True, False],\n",
       "                         &#x27;Reg__min_samples_leaf&#x27;: [1, 2, 4],\n",
       "                         &#x27;Reg__min_samples_split&#x27;: [2, 5, 10],\n",
       "                         &#x27;Reg__n_estimators&#x27;: [1000]},\n",
       "             scoring=&#x27;r2&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-26\" type=\"checkbox\" ><label for=\"sk-estimator-id-26\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;Reg&#x27;, RandomForestRegressor())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-27\" type=\"checkbox\" ><label for=\"sk-estimator-id-27\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(error_score='raise',\n",
       "             estimator=Pipeline(steps=[('Reg', RandomForestRegressor())]),\n",
       "             param_grid={'Reg': [RandomForestRegressor(n_estimators=1000)],\n",
       "                         'Reg__bootstrap': [True, False],\n",
       "                         'Reg__min_samples_leaf': [1, 2, 4],\n",
       "                         'Reg__min_samples_split': [2, 5, 10],\n",
       "                         'Reg__n_estimators': [1000]},\n",
       "             scoring='r2')"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_RFR.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameter (CV score=0.877):\n",
      "{'Reg': RandomForestRegressor(n_estimators=1000), 'Reg__bootstrap': True, 'Reg__min_samples_leaf': 1, 'Reg__min_samples_split': 2, 'Reg__n_estimators': 1000}\n"
     ]
    }
   ],
   "source": [
    "print(\"Best parameter (CV score=%0.3f):\" % grid_RFR.best_score_)\n",
    "print(grid_RFR.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Best parameter (CV score=0.794):\n",
    "{'Reg': RandomForestRegressor(criterion='mse', max_depth=8, max_leaf_nodes=100,\n",
    "                      min_samples_leaf=20, min_samples_split=40), 'Reg__criterion': 'mse', 'Reg__max_depth': 8, 'Reg__max_leaf_nodes': 100, 'Reg__min_samples_leaf': 20, 'Reg__min_samples_split': 40}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+ Training accuracy\n",
      "|            R2 Score: 98.3%\n",
      "| Mean Absolute Error: 0.00662\n",
      "|           Max Error: 0.386\n",
      "+ Test accuracy\n",
      "|            R2 Score: 89.6%\n",
      "| Mean Absolute Error: 0.0399\n",
      "|           Max Error: 0.822\n"
     ]
    }
   ],
   "source": [
    "scores(grid_RFR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ Training accuracy\n",
    "|            R2 Score: 82.9%\n",
    "| Mean Absolute Error: 0.0669\n",
    "|           Max Error: 0.982\n",
    "+ Test accuracy\n",
    "|            R2 Score: 83.1%\n",
    "| Mean Absolute Error: 0.0647\n",
    "|           Max Error: 0.707"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Performance\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the next step is to know how well our models performes for making predictions on the unknown test set. There are various metrics to check that. However, mean absolute error, mean squared error, and root mean squared error are three of the most common metrics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'NoneType' object has no attribute 'group'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32mc:\\Users\\amine\\OneDrive\\Documents\\GitHub\\Laptop_Price_predection\\LPP.ipynb Cell 196\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/amine/OneDrive/Documents/GitHub/Laptop_Price_predection/LPP.ipynb#ch0000195?line=0'>1</a>\u001b[0m md\u001b[39m=\u001b[39mModel_pipe(LinearRegression)\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/amine/OneDrive/Documents/GitHub/Laptop_Price_predection/LPP.ipynb#ch0000195?line=1'>2</a>\u001b[0m md\u001b[39m.\u001b[39mfit(X_train,y_train)\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/amine/OneDrive/Documents/GitHub/Laptop_Price_predection/LPP.ipynb#ch0000195?line=2'>3</a>\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m'\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m------ \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m ------\u001b[39m\u001b[39m'\u001b[39m\u001b[39m.\u001b[39mformat(getName(md)))\n",
      "\u001b[1;32mc:\\Users\\amine\\OneDrive\\Documents\\GitHub\\Laptop_Price_predection\\LPP.ipynb Cell 196\u001b[0m in \u001b[0;36mgetName\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/amine/OneDrive/Documents/GitHub/Laptop_Price_predection/LPP.ipynb#ch0000195?line=0'>1</a>\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mgetName\u001b[39m(x):\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/amine/OneDrive/Documents/GitHub/Laptop_Price_predection/LPP.ipynb#ch0000195?line=1'>2</a>\u001b[0m     \u001b[39mreturn\u001b[39;00m re\u001b[39m.\u001b[39;49msearch(\u001b[39mr\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39m\\\u001b[39;49m\u001b[39mw*(?!(?!\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39m>))\u001b[39;49m\u001b[39m\"\u001b[39;49m,\u001b[39mstr\u001b[39;49m(x))\u001b[39m.\u001b[39;49mgroup()\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'group'"
     ]
    }
   ],
   "source": [
    "md=Model_pipe(LinearRegression)\n",
    "md.fit(X_train,y_train)\n",
    "print('\\n------ {} ------'.format(getName(md)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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