laptop_price_predictor.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv(\"laptop_data.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Inches</th>\n",
       "      <th>ScreenResolution</th>\n",
       "      <th>Cpu</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 2.3GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>128GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 640</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37kg</td>\n",
       "      <td>71378.6832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>1440x900</td>\n",
       "      <td>Intel Core i5 1.8GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>128GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34kg</td>\n",
       "      <td>47895.5232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86kg</td>\n",
       "      <td>30636.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>15.4</td>\n",
       "      <td>IPS Panel Retina Display 2880x1800</td>\n",
       "      <td>Intel Core i7 2.7GHz</td>\n",
       "      <td>16GB</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83kg</td>\n",
       "      <td>135195.3360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 3.1GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37kg</td>\n",
       "      <td>96095.8080</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 Company   TypeName  Inches                    ScreenResolution  \\\n",
       "0           0   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "1           1   Apple  Ultrabook    13.3                            1440x900   \n",
       "2           2      HP   Notebook    15.6                   Full HD 1920x1080   \n",
       "3           3   Apple  Ultrabook    15.4  IPS Panel Retina Display 2880x1800   \n",
       "4           4   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "\n",
       "                          Cpu   Ram               Memory  \\\n",
       "0        Intel Core i5 2.3GHz   8GB            128GB SSD   \n",
       "1        Intel Core i5 1.8GHz   8GB  128GB Flash Storage   \n",
       "2  Intel Core i5 7200U 2.5GHz   8GB            256GB SSD   \n",
       "3        Intel Core i7 2.7GHz  16GB            512GB SSD   \n",
       "4        Intel Core i5 3.1GHz   8GB            256GB SSD   \n",
       "\n",
       "                            Gpu  OpSys  Weight        Price  \n",
       "0  Intel Iris Plus Graphics 640  macOS  1.37kg   71378.6832  \n",
       "1        Intel HD Graphics 6000  macOS  1.34kg   47895.5232  \n",
       "2         Intel HD Graphics 620  No OS  1.86kg   30636.0000  \n",
       "3            AMD Radeon Pro 455  macOS  1.83kg  135195.3360  \n",
       "4  Intel Iris Plus Graphics 650  macOS  1.37kg   96095.8080  "
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1303, 12)"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.shape\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1303 entries, 0 to 1302\n",
      "Data columns (total 12 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Unnamed: 0        1303 non-null   int64  \n",
      " 1   Company           1303 non-null   object \n",
      " 2   TypeName          1303 non-null   object \n",
      " 3   Inches            1303 non-null   float64\n",
      " 4   ScreenResolution  1303 non-null   object \n",
      " 5   Cpu               1303 non-null   object \n",
      " 6   Ram               1303 non-null   object \n",
      " 7   Memory            1303 non-null   object \n",
      " 8   Gpu               1303 non-null   object \n",
      " 9   OpSys             1303 non-null   object \n",
      " 10  Weight            1303 non-null   object \n",
      " 11  Price             1303 non-null   float64\n",
      "dtypes: float64(2), int64(1), object(9)\n",
      "memory usage: 122.3+ KB\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the first step we are going to remove unnammed column from our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Inches</th>\n",
       "      <th>ScreenResolution</th>\n",
       "      <th>Cpu</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 2.3GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>128GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 640</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37kg</td>\n",
       "      <td>71378.6832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>1440x900</td>\n",
       "      <td>Intel Core i5 1.8GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>128GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34kg</td>\n",
       "      <td>47895.5232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86kg</td>\n",
       "      <td>30636.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>15.4</td>\n",
       "      <td>IPS Panel Retina Display 2880x1800</td>\n",
       "      <td>Intel Core i7 2.7GHz</td>\n",
       "      <td>16GB</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83kg</td>\n",
       "      <td>135195.3360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 3.1GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37kg</td>\n",
       "      <td>96095.8080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1298</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>2 in 1 Convertible</td>\n",
       "      <td>14.0</td>\n",
       "      <td>IPS Panel Full HD / Touchscreen 1920x1080</td>\n",
       "      <td>Intel Core i7 6500U 2.5GHz</td>\n",
       "      <td>4GB</td>\n",
       "      <td>128GB SSD</td>\n",
       "      <td>Intel HD Graphics 520</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.8kg</td>\n",
       "      <td>33992.6400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1299</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>2 in 1 Convertible</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Quad HD+ / Touchscreen 3200x1800</td>\n",
       "      <td>Intel Core i7 6500U 2.5GHz</td>\n",
       "      <td>16GB</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>Intel HD Graphics 520</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.3kg</td>\n",
       "      <td>79866.7200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1300</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>Intel Celeron Dual Core N3050 1.6GHz</td>\n",
       "      <td>2GB</td>\n",
       "      <td>64GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.5kg</td>\n",
       "      <td>12201.1200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1301</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>Intel Core i7 6500U 2.5GHz</td>\n",
       "      <td>6GB</td>\n",
       "      <td>1TB HDD</td>\n",
       "      <td>AMD Radeon R5 M330</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.19kg</td>\n",
       "      <td>40705.9200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1302</th>\n",
       "      <td>Asus</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>Intel Celeron Dual Core N3050 1.6GHz</td>\n",
       "      <td>4GB</td>\n",
       "      <td>500GB HDD</td>\n",
       "      <td>Intel HD Graphics</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.2kg</td>\n",
       "      <td>19660.3200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1303 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company            TypeName  Inches  \\\n",
       "0      Apple           Ultrabook    13.3   \n",
       "1      Apple           Ultrabook    13.3   \n",
       "2         HP            Notebook    15.6   \n",
       "3      Apple           Ultrabook    15.4   \n",
       "4      Apple           Ultrabook    13.3   \n",
       "...      ...                 ...     ...   \n",
       "1298  Lenovo  2 in 1 Convertible    14.0   \n",
       "1299  Lenovo  2 in 1 Convertible    13.3   \n",
       "1300  Lenovo            Notebook    14.0   \n",
       "1301      HP            Notebook    15.6   \n",
       "1302    Asus            Notebook    15.6   \n",
       "\n",
       "                                ScreenResolution  \\\n",
       "0             IPS Panel Retina Display 2560x1600   \n",
       "1                                       1440x900   \n",
       "2                              Full HD 1920x1080   \n",
       "3             IPS Panel Retina Display 2880x1800   \n",
       "4             IPS Panel Retina Display 2560x1600   \n",
       "...                                          ...   \n",
       "1298   IPS Panel Full HD / Touchscreen 1920x1080   \n",
       "1299  IPS Panel Quad HD+ / Touchscreen 3200x1800   \n",
       "1300                                    1366x768   \n",
       "1301                                    1366x768   \n",
       "1302                                    1366x768   \n",
       "\n",
       "                                       Cpu   Ram               Memory  \\\n",
       "0                     Intel Core i5 2.3GHz   8GB            128GB SSD   \n",
       "1                     Intel Core i5 1.8GHz   8GB  128GB Flash Storage   \n",
       "2               Intel Core i5 7200U 2.5GHz   8GB            256GB SSD   \n",
       "3                     Intel Core i7 2.7GHz  16GB            512GB SSD   \n",
       "4                     Intel Core i5 3.1GHz   8GB            256GB SSD   \n",
       "...                                    ...   ...                  ...   \n",
       "1298            Intel Core i7 6500U 2.5GHz   4GB            128GB SSD   \n",
       "1299            Intel Core i7 6500U 2.5GHz  16GB            512GB SSD   \n",
       "1300  Intel Celeron Dual Core N3050 1.6GHz   2GB   64GB Flash Storage   \n",
       "1301            Intel Core i7 6500U 2.5GHz   6GB              1TB HDD   \n",
       "1302  Intel Celeron Dual Core N3050 1.6GHz   4GB            500GB HDD   \n",
       "\n",
       "                               Gpu       OpSys  Weight        Price  \n",
       "0     Intel Iris Plus Graphics 640       macOS  1.37kg   71378.6832  \n",
       "1           Intel HD Graphics 6000       macOS  1.34kg   47895.5232  \n",
       "2            Intel HD Graphics 620       No OS  1.86kg   30636.0000  \n",
       "3               AMD Radeon Pro 455       macOS  1.83kg  135195.3360  \n",
       "4     Intel Iris Plus Graphics 650       macOS  1.37kg   96095.8080  \n",
       "...                            ...         ...     ...          ...  \n",
       "1298         Intel HD Graphics 520  Windows 10   1.8kg   33992.6400  \n",
       "1299         Intel HD Graphics 520  Windows 10   1.3kg   79866.7200  \n",
       "1300             Intel HD Graphics  Windows 10   1.5kg   12201.1200  \n",
       "1301            AMD Radeon R5 M330  Windows 10  2.19kg   40705.9200  \n",
       "1302             Intel HD Graphics  Windows 10   2.2kg   19660.3200  \n",
       "\n",
       "[1303 rows x 11 columns]"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.drop(columns=[\"Unnamed: 0\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are going to remove GB from RAM column to get only numbers in the RAM Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['Ram'] = dataset['Ram'].str.replace('GB', '')\n",
    "dataset['Ram'] = dataset['Ram'].astype('int32')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are going to remove kg from weight column to get only numbers in the weight Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['Weight'] = dataset['Weight'].str.replace('kg', '')\n",
    "dataset['Weight'] = dataset['Weight'].astype('float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1303 entries, 0 to 1302\n",
      "Data columns (total 12 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Unnamed: 0        1303 non-null   int64  \n",
      " 1   Company           1303 non-null   object \n",
      " 2   TypeName          1303 non-null   object \n",
      " 3   Inches            1303 non-null   float64\n",
      " 4   ScreenResolution  1303 non-null   object \n",
      " 5   Cpu               1303 non-null   object \n",
      " 6   Ram               1303 non-null   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            1303 non-null   float32\n",
      " 11  Price             1303 non-null   float64\n",
      "dtypes: float32(1), float64(2), int32(1), int64(1), object(7)\n",
      "memory usage: 112.1+ KB\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are going to do a bit of analysis of data using Seaborn Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Zaid\\AppData\\Local\\Temp\\ipykernel_11012\\983781003.py:1: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(dataset['Price'])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Price', ylabel='Density'>"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(dataset['Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset['Company'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=dataset['Company'], y=dataset['Price'])\n",
    "plt.xticks(rotation = 'vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset['TypeName'].value_counts().plot(kind='bar')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=dataset['TypeName'], y=dataset['Price'])\n",
    "plt.xticks(rotation = 'vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Zaid\\AppData\\Local\\Temp\\ipykernel_11012\\850755342.py:1: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(dataset['Inches'])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Inches', ylabel='Density'>"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(dataset['Inches'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "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": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['ScreenResolution'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now as we can see that over here in this one column we have multiple information such as touchscreen, IPS panel, Resolution, etc. So we will split all this into different columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['Touch Screen'] = dataset['ScreenResolution'].apply(lambda x:1 if 'Touchscreen' in x else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Inches</th>\n",
       "      <th>ScreenResolution</th>\n",
       "      <th>Cpu</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "      <th>Touch Screen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 2.3GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 640</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>71378.6832</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>1440x900</td>\n",
       "      <td>Intel Core i5 1.8GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>47895.5232</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>30636.0000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>15.4</td>\n",
       "      <td>IPS Panel Retina Display 2880x1800</td>\n",
       "      <td>Intel Core i7 2.7GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>135195.3360</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 3.1GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>96095.8080</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 Company   TypeName  Inches                    ScreenResolution  \\\n",
       "0           0   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "1           1   Apple  Ultrabook    13.3                            1440x900   \n",
       "2           2      HP   Notebook    15.6                   Full HD 1920x1080   \n",
       "3           3   Apple  Ultrabook    15.4  IPS Panel Retina Display 2880x1800   \n",
       "4           4   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "\n",
       "                          Cpu  Ram               Memory  \\\n",
       "0        Intel Core i5 2.3GHz    8            128GB SSD   \n",
       "1        Intel Core i5 1.8GHz    8  128GB Flash Storage   \n",
       "2  Intel Core i5 7200U 2.5GHz    8            256GB SSD   \n",
       "3        Intel Core i7 2.7GHz   16            512GB SSD   \n",
       "4        Intel Core i5 3.1GHz    8            256GB SSD   \n",
       "\n",
       "                            Gpu  OpSys  Weight        Price  Touch Screen  \n",
       "0  Intel Iris Plus Graphics 640  macOS    1.37   71378.6832             0  \n",
       "1        Intel HD Graphics 6000  macOS    1.34   47895.5232             0  \n",
       "2         Intel HD Graphics 620  No OS    1.86   30636.0000             0  \n",
       "3            AMD Radeon Pro 455  macOS    1.83  135195.3360             0  \n",
       "4  Intel Iris Plus Graphics 650  macOS    1.37   96095.8080             0  "
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset['Touch Screen'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Touch Screen', ylabel='Price'>"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x = dataset['Touch Screen'], y = dataset['Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['IPS'] = dataset['ScreenResolution'].apply(lambda x:1 if 'IPS' in x else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset['Touch Screen'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [],
   "source": [
    "new  = dataset['ScreenResolution'].str.split('x', n=1, expand = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['X_res'] = new[0]\n",
    "dataset['Y_res'] = new[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Inches</th>\n",
       "      <th>ScreenResolution</th>\n",
       "      <th>Cpu</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "      <th>Touch Screen</th>\n",
       "      <th>IPS</th>\n",
       "      <th>X_res</th>\n",
       "      <th>Y_res</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 2.3GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 640</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>71378.6832</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>IPS Panel Retina Display 2560</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>1440x900</td>\n",
       "      <td>Intel Core i5 1.8GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>47895.5232</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1440</td>\n",
       "      <td>900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>30636.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Full HD 1920</td>\n",
       "      <td>1080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>15.4</td>\n",
       "      <td>IPS Panel Retina Display 2880x1800</td>\n",
       "      <td>Intel Core i7 2.7GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>135195.3360</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>IPS Panel Retina Display 2880</td>\n",
       "      <td>1800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 3.1GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>96095.8080</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>IPS Panel Retina Display 2560</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 Company   TypeName  Inches                    ScreenResolution  \\\n",
       "0           0   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "1           1   Apple  Ultrabook    13.3                            1440x900   \n",
       "2           2      HP   Notebook    15.6                   Full HD 1920x1080   \n",
       "3           3   Apple  Ultrabook    15.4  IPS Panel Retina Display 2880x1800   \n",
       "4           4   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "\n",
       "                          Cpu  Ram               Memory  \\\n",
       "0        Intel Core i5 2.3GHz    8            128GB SSD   \n",
       "1        Intel Core i5 1.8GHz    8  128GB Flash Storage   \n",
       "2  Intel Core i5 7200U 2.5GHz    8            256GB SSD   \n",
       "3        Intel Core i7 2.7GHz   16            512GB SSD   \n",
       "4        Intel Core i5 3.1GHz    8            256GB SSD   \n",
       "\n",
       "                            Gpu  OpSys  Weight        Price  Touch Screen  \\\n",
       "0  Intel Iris Plus Graphics 640  macOS    1.37   71378.6832             0   \n",
       "1        Intel HD Graphics 6000  macOS    1.34   47895.5232             0   \n",
       "2         Intel HD Graphics 620  No OS    1.86   30636.0000             0   \n",
       "3            AMD Radeon Pro 455  macOS    1.83  135195.3360             0   \n",
       "4  Intel Iris Plus Graphics 650  macOS    1.37   96095.8080             0   \n",
       "\n",
       "   IPS                          X_res Y_res  \n",
       "0    1  IPS Panel Retina Display 2560  1600  \n",
       "1    0                           1440   900  \n",
       "2    0                   Full HD 1920  1080  \n",
       "3    1  IPS Panel Retina Display 2880  1800  \n",
       "4    1  IPS Panel Retina Display 2560  1600  "
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['X_res'] = dataset['X_res'].str.replace(',', '').str.findall(r'(\\d+\\.?\\d+)').apply(lambda x: x[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
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       "      <th>TypeName</th>\n",
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       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>47895.5232</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1440</td>\n",
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       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>30636.0000</td>\n",
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       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>15.4</td>\n",
       "      <td>IPS Panel Retina Display 2880x1800</td>\n",
       "      <td>Intel Core i7 2.7GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>135195.3360</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1800</td>\n",
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       "      <th>4</th>\n",
       "      <td>4</td>\n",
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       "      <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",
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       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
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      "text/plain": [
       "   Unnamed: 0 Company   TypeName  Inches                    ScreenResolution  \\\n",
       "0           0   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "1           1   Apple  Ultrabook    13.3                            1440x900   \n",
       "2           2      HP   Notebook    15.6                   Full HD 1920x1080   \n",
       "3           3   Apple  Ultrabook    15.4  IPS Panel Retina Display 2880x1800   \n",
       "4           4   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "\n",
       "                          Cpu  Ram               Memory  \\\n",
       "0        Intel Core i5 2.3GHz    8            128GB SSD   \n",
       "1        Intel Core i5 1.8GHz    8  128GB Flash Storage   \n",
       "2  Intel Core i5 7200U 2.5GHz    8            256GB SSD   \n",
       "3        Intel Core i7 2.7GHz   16            512GB SSD   \n",
       "4        Intel Core i5 3.1GHz    8            256GB SSD   \n",
       "\n",
       "                            Gpu  OpSys  Weight        Price  Touch Screen  \\\n",
       "0  Intel Iris Plus Graphics 640  macOS    1.37   71378.6832             0   \n",
       "1        Intel HD Graphics 6000  macOS    1.34   47895.5232             0   \n",
       "2         Intel HD Graphics 620  No OS    1.86   30636.0000             0   \n",
       "3            AMD Radeon Pro 455  macOS    1.83  135195.3360             0   \n",
       "4  Intel Iris Plus Graphics 650  macOS    1.37   96095.8080             0   \n",
       "\n",
       "   IPS X_res Y_res  \n",
       "0    1  2560  1600  \n",
       "1    0  1440   900  \n",
       "2    0  1920  1080  \n",
       "3    1  2880  1800  \n",
       "4    1  2560  1600  "
      ]
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     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['X_res'] = dataset['X_res'].astype('int')\n",
    "dataset['Y_res'] = dataset['Y_res'].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = dataset.drop(columns=['Unnamed: 0'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Inches          0.068197\n",
       "Ram             0.743007\n",
       "Weight          0.210370\n",
       "Price           1.000000\n",
       "Touch Screen    0.191226\n",
       "IPS             0.252208\n",
       "X_res           0.556529\n",
       "Y_res           0.552809\n",
       "Name: Price, dtype: float64"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.corr()['Price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['ppi'] = ((dataset['X_res']**2 + dataset['Y_res']**2)**0.5 / dataset['Inches']).astype('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>Lenovo</td>\n",
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       "      <td>14.0</td>\n",
       "      <td>Intel Core i7 6500U 2.5GHz</td>\n",
       "      <td>4</td>\n",
       "      <td>128GB SSD</td>\n",
       "      <td>Intel HD Graphics 520</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.80</td>\n",
       "      <td>33992.6400</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1080</td>\n",
       "      <td>157.350512</td>\n",
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       "    <tr>\n",
       "      <th>1299</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>2 in 1 Convertible</td>\n",
       "      <td>13.3</td>\n",
       "      <td>Intel Core i7 6500U 2.5GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>Intel HD Graphics 520</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.30</td>\n",
       "      <td>79866.7200</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3200</td>\n",
       "      <td>1800</td>\n",
       "      <td>276.053530</td>\n",
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       "      <th>1300</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Intel Celeron Dual Core N3050 1.6GHz</td>\n",
       "      <td>2</td>\n",
       "      <td>64GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.50</td>\n",
       "      <td>12201.1200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1366</td>\n",
       "      <td>768</td>\n",
       "      <td>111.935204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1301</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Intel Core i7 6500U 2.5GHz</td>\n",
       "      <td>6</td>\n",
       "      <td>1TB HDD</td>\n",
       "      <td>AMD Radeon R5 M330</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.19</td>\n",
       "      <td>40705.9200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1366</td>\n",
       "      <td>768</td>\n",
       "      <td>100.454670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1302</th>\n",
       "      <td>Asus</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Intel Celeron Dual Core N3050 1.6GHz</td>\n",
       "      <td>4</td>\n",
       "      <td>500GB HDD</td>\n",
       "      <td>Intel HD Graphics</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.20</td>\n",
       "      <td>19660.3200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1366</td>\n",
       "      <td>768</td>\n",
       "      <td>100.454670</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1303 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company            TypeName  Inches  \\\n",
       "0      Apple           Ultrabook    13.3   \n",
       "1      Apple           Ultrabook    13.3   \n",
       "2         HP            Notebook    15.6   \n",
       "3      Apple           Ultrabook    15.4   \n",
       "4      Apple           Ultrabook    13.3   \n",
       "...      ...                 ...     ...   \n",
       "1298  Lenovo  2 in 1 Convertible    14.0   \n",
       "1299  Lenovo  2 in 1 Convertible    13.3   \n",
       "1300  Lenovo            Notebook    14.0   \n",
       "1301      HP            Notebook    15.6   \n",
       "1302    Asus            Notebook    15.6   \n",
       "\n",
       "                                       Cpu  Ram               Memory  \\\n",
       "0                     Intel Core i5 2.3GHz    8            128GB SSD   \n",
       "1                     Intel Core i5 1.8GHz    8  128GB Flash Storage   \n",
       "2               Intel Core i5 7200U 2.5GHz    8            256GB SSD   \n",
       "3                     Intel Core i7 2.7GHz   16            512GB SSD   \n",
       "4                     Intel Core i5 3.1GHz    8            256GB SSD   \n",
       "...                                    ...  ...                  ...   \n",
       "1298            Intel Core i7 6500U 2.5GHz    4            128GB SSD   \n",
       "1299            Intel Core i7 6500U 2.5GHz   16            512GB SSD   \n",
       "1300  Intel Celeron Dual Core N3050 1.6GHz    2   64GB Flash Storage   \n",
       "1301            Intel Core i7 6500U 2.5GHz    6              1TB HDD   \n",
       "1302  Intel Celeron Dual Core N3050 1.6GHz    4            500GB HDD   \n",
       "\n",
       "                               Gpu       OpSys  Weight        Price  \\\n",
       "0     Intel Iris Plus Graphics 640       macOS    1.37   71378.6832   \n",
       "1           Intel HD Graphics 6000       macOS    1.34   47895.5232   \n",
       "2            Intel HD Graphics 620       No OS    1.86   30636.0000   \n",
       "3               AMD Radeon Pro 455       macOS    1.83  135195.3360   \n",
       "4     Intel Iris Plus Graphics 650       macOS    1.37   96095.8080   \n",
       "...                            ...         ...     ...          ...   \n",
       "1298         Intel HD Graphics 520  Windows 10    1.80   33992.6400   \n",
       "1299         Intel HD Graphics 520  Windows 10    1.30   79866.7200   \n",
       "1300             Intel HD Graphics  Windows 10    1.50   12201.1200   \n",
       "1301            AMD Radeon R5 M330  Windows 10    2.19   40705.9200   \n",
       "1302             Intel HD Graphics  Windows 10    2.20   19660.3200   \n",
       "\n",
       "      Touch Screen  IPS  X_res  Y_res         ppi  \n",
       "0                0    1   2560   1600  226.983005  \n",
       "1                0    0   1440    900  127.677940  \n",
       "2                0    0   1920   1080  141.211998  \n",
       "3                0    1   2880   1800  220.534624  \n",
       "4                0    1   2560   1600  226.983005  \n",
       "...            ...  ...    ...    ...         ...  \n",
       "1298             1    1   1920   1080  157.350512  \n",
       "1299             1    1   3200   1800  276.053530  \n",
       "1300             0    0   1366    768  111.935204  \n",
       "1301             0    0   1366    768  100.454670  \n",
       "1302             0    0   1366    768  100.454670  \n",
       "\n",
       "[1303 rows x 15 columns]"
      ]
     },
     "execution_count": 230,
     "metadata": {},
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    }
   ],
   "source": [
    "dataset.drop(columns = ['ScreenResolution'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
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       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
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       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>30636.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
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       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
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       "      <td>Intel Core i7 2.7GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</td>\n",
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       "      <td>macOS</td>\n",
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       "      <td>Intel Core i5 3.1GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>96095.8080</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>Intel Core i7 6500U 2.5GHz</td>\n",
       "      <td>4</td>\n",
       "      <td>128GB SSD</td>\n",
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       "      <td>Lenovo</td>\n",
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       "      <td>IPS Panel Quad HD+ / Touchscreen 3200x1800</td>\n",
       "      <td>Intel Core i7 6500U 2.5GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>Intel HD Graphics 520</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.30</td>\n",
       "      <td>79866.7200</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>276.053530</td>\n",
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       "    <tr>\n",
       "      <th>1300</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>Intel Celeron Dual Core N3050 1.6GHz</td>\n",
       "      <td>2</td>\n",
       "      <td>64GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.50</td>\n",
       "      <td>12201.1200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111.935204</td>\n",
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       "    <tr>\n",
       "      <th>1301</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>Intel Core i7 6500U 2.5GHz</td>\n",
       "      <td>6</td>\n",
       "      <td>1TB HDD</td>\n",
       "      <td>AMD Radeon R5 M330</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.19</td>\n",
       "      <td>40705.9200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.454670</td>\n",
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       "    <tr>\n",
       "      <th>1302</th>\n",
       "      <td>Asus</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>Intel Celeron Dual Core N3050 1.6GHz</td>\n",
       "      <td>4</td>\n",
       "      <td>500GB HDD</td>\n",
       "      <td>Intel HD Graphics</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.20</td>\n",
       "      <td>19660.3200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.454670</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1303 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company            TypeName                            ScreenResolution  \\\n",
       "0      Apple           Ultrabook          IPS Panel Retina Display 2560x1600   \n",
       "1      Apple           Ultrabook                                    1440x900   \n",
       "2         HP            Notebook                           Full HD 1920x1080   \n",
       "3      Apple           Ultrabook          IPS Panel Retina Display 2880x1800   \n",
       "4      Apple           Ultrabook          IPS Panel Retina Display 2560x1600   \n",
       "...      ...                 ...                                         ...   \n",
       "1298  Lenovo  2 in 1 Convertible   IPS Panel Full HD / Touchscreen 1920x1080   \n",
       "1299  Lenovo  2 in 1 Convertible  IPS Panel Quad HD+ / Touchscreen 3200x1800   \n",
       "1300  Lenovo            Notebook                                    1366x768   \n",
       "1301      HP            Notebook                                    1366x768   \n",
       "1302    Asus            Notebook                                    1366x768   \n",
       "\n",
       "                                       Cpu  Ram               Memory  \\\n",
       "0                     Intel Core i5 2.3GHz    8            128GB SSD   \n",
       "1                     Intel Core i5 1.8GHz    8  128GB Flash Storage   \n",
       "2               Intel Core i5 7200U 2.5GHz    8            256GB SSD   \n",
       "3                     Intel Core i7 2.7GHz   16            512GB SSD   \n",
       "4                     Intel Core i5 3.1GHz    8            256GB SSD   \n",
       "...                                    ...  ...                  ...   \n",
       "1298            Intel Core i7 6500U 2.5GHz    4            128GB SSD   \n",
       "1299            Intel Core i7 6500U 2.5GHz   16            512GB SSD   \n",
       "1300  Intel Celeron Dual Core N3050 1.6GHz    2   64GB Flash Storage   \n",
       "1301            Intel Core i7 6500U 2.5GHz    6              1TB HDD   \n",
       "1302  Intel Celeron Dual Core N3050 1.6GHz    4            500GB HDD   \n",
       "\n",
       "                               Gpu       OpSys  Weight        Price  \\\n",
       "0     Intel Iris Plus Graphics 640       macOS    1.37   71378.6832   \n",
       "1           Intel HD Graphics 6000       macOS    1.34   47895.5232   \n",
       "2            Intel HD Graphics 620       No OS    1.86   30636.0000   \n",
       "3               AMD Radeon Pro 455       macOS    1.83  135195.3360   \n",
       "4     Intel Iris Plus Graphics 650       macOS    1.37   96095.8080   \n",
       "...                            ...         ...     ...          ...   \n",
       "1298         Intel HD Graphics 520  Windows 10    1.80   33992.6400   \n",
       "1299         Intel HD Graphics 520  Windows 10    1.30   79866.7200   \n",
       "1300             Intel HD Graphics  Windows 10    1.50   12201.1200   \n",
       "1301            AMD Radeon R5 M330  Windows 10    2.19   40705.9200   \n",
       "1302             Intel HD Graphics  Windows 10    2.20   19660.3200   \n",
       "\n",
       "      Touch Screen  IPS         ppi  \n",
       "0                0    1  226.983005  \n",
       "1                0    0  127.677940  \n",
       "2                0    0  141.211998  \n",
       "3                0    1  220.534624  \n",
       "4                0    1  226.983005  \n",
       "...            ...  ...         ...  \n",
       "1298             1    1  157.350512  \n",
       "1299             1    1  276.053530  \n",
       "1300             0    0  111.935204  \n",
       "1301             0    0  100.454670  \n",
       "1302             0    0  100.454670  \n",
       "\n",
       "[1303 rows x 13 columns]"
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     },
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   "source": [
    "dataset.drop(columns = ['X_res', 'Y_res', 'Inches'])"
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  },
  {
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   "execution_count": 232,
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    {
     "data": {
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       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>1.37</td>\n",
       "      <td>96095.8080</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2560</td>\n",
       "      <td>1600</td>\n",
       "      <td>226.983005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1298</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>2 in 1 Convertible</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Intel Core i7 6500U 2.5GHz</td>\n",
       "      <td>4</td>\n",
       "      <td>128GB SSD</td>\n",
       "      <td>Intel HD Graphics 520</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.80</td>\n",
       "      <td>33992.6400</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1920</td>\n",
       "      <td>1080</td>\n",
       "      <td>157.350512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1299</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>2 in 1 Convertible</td>\n",
       "      <td>13.3</td>\n",
       "      <td>Intel Core i7 6500U 2.5GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>Intel HD Graphics 520</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.30</td>\n",
       "      <td>79866.7200</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3200</td>\n",
       "      <td>1800</td>\n",
       "      <td>276.053530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1300</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Intel Celeron Dual Core N3050 1.6GHz</td>\n",
       "      <td>2</td>\n",
       "      <td>64GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.50</td>\n",
       "      <td>12201.1200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1366</td>\n",
       "      <td>768</td>\n",
       "      <td>111.935204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1301</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Intel Core i7 6500U 2.5GHz</td>\n",
       "      <td>6</td>\n",
       "      <td>1TB HDD</td>\n",
       "      <td>AMD Radeon R5 M330</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.19</td>\n",
       "      <td>40705.9200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1366</td>\n",
       "      <td>768</td>\n",
       "      <td>100.454670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1302</th>\n",
       "      <td>Asus</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Intel Celeron Dual Core N3050 1.6GHz</td>\n",
       "      <td>4</td>\n",
       "      <td>500GB HDD</td>\n",
       "      <td>Intel HD Graphics</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.20</td>\n",
       "      <td>19660.3200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1366</td>\n",
       "      <td>768</td>\n",
       "      <td>100.454670</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1303 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company            TypeName  Inches  \\\n",
       "0      Apple           Ultrabook    13.3   \n",
       "1      Apple           Ultrabook    13.3   \n",
       "2         HP            Notebook    15.6   \n",
       "3      Apple           Ultrabook    15.4   \n",
       "4      Apple           Ultrabook    13.3   \n",
       "...      ...                 ...     ...   \n",
       "1298  Lenovo  2 in 1 Convertible    14.0   \n",
       "1299  Lenovo  2 in 1 Convertible    13.3   \n",
       "1300  Lenovo            Notebook    14.0   \n",
       "1301      HP            Notebook    15.6   \n",
       "1302    Asus            Notebook    15.6   \n",
       "\n",
       "                                       Cpu  Ram               Memory  \\\n",
       "0                     Intel Core i5 2.3GHz    8            128GB SSD   \n",
       "1                     Intel Core i5 1.8GHz    8  128GB Flash Storage   \n",
       "2               Intel Core i5 7200U 2.5GHz    8            256GB SSD   \n",
       "3                     Intel Core i7 2.7GHz   16            512GB SSD   \n",
       "4                     Intel Core i5 3.1GHz    8            256GB SSD   \n",
       "...                                    ...  ...                  ...   \n",
       "1298            Intel Core i7 6500U 2.5GHz    4            128GB SSD   \n",
       "1299            Intel Core i7 6500U 2.5GHz   16            512GB SSD   \n",
       "1300  Intel Celeron Dual Core N3050 1.6GHz    2   64GB Flash Storage   \n",
       "1301            Intel Core i7 6500U 2.5GHz    6              1TB HDD   \n",
       "1302  Intel Celeron Dual Core N3050 1.6GHz    4            500GB HDD   \n",
       "\n",
       "                               Gpu       OpSys  Weight        Price  \\\n",
       "0     Intel Iris Plus Graphics 640       macOS    1.37   71378.6832   \n",
       "1           Intel HD Graphics 6000       macOS    1.34   47895.5232   \n",
       "2            Intel HD Graphics 620       No OS    1.86   30636.0000   \n",
       "3               AMD Radeon Pro 455       macOS    1.83  135195.3360   \n",
       "4     Intel Iris Plus Graphics 650       macOS    1.37   96095.8080   \n",
       "...                            ...         ...     ...          ...   \n",
       "1298         Intel HD Graphics 520  Windows 10    1.80   33992.6400   \n",
       "1299         Intel HD Graphics 520  Windows 10    1.30   79866.7200   \n",
       "1300             Intel HD Graphics  Windows 10    1.50   12201.1200   \n",
       "1301            AMD Radeon R5 M330  Windows 10    2.19   40705.9200   \n",
       "1302             Intel HD Graphics  Windows 10    2.20   19660.3200   \n",
       "\n",
       "      Touch Screen  IPS  X_res  Y_res         ppi  \n",
       "0                0    1   2560   1600  226.983005  \n",
       "1                0    0   1440    900  127.677940  \n",
       "2                0    0   1920   1080  141.211998  \n",
       "3                0    1   2880   1800  220.534624  \n",
       "4                0    1   2560   1600  226.983005  \n",
       "...            ...  ...    ...    ...         ...  \n",
       "1298             1    1   1920   1080  157.350512  \n",
       "1299             1    1   3200   1800  276.053530  \n",
       "1300             0    0   1366    768  111.935204  \n",
       "1301             0    0   1366    768  100.454670  \n",
       "1302             0    0   1366    768  100.454670  \n",
       "\n",
       "[1303 rows x 15 columns]"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.drop(columns=[\"ScreenResolution\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>ScreenResolution</th>\n",
       "      <th>Cpu</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
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       "      <th>ppi</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 2.3GHz</td>\n",
       "      <td>8</td>\n",
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       "      <td>macOS</td>\n",
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       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>1440x900</td>\n",
       "      <td>Intel Core i5 1.8GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>47895.5232</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>127.677940</td>\n",
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       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>30636.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>IPS Panel Retina Display 2880x1800</td>\n",
       "      <td>Intel Core i7 2.7GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>135195.3360</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>220.534624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 3.1GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>96095.8080</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1298</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>2 in 1 Convertible</td>\n",
       "      <td>IPS Panel Full HD / Touchscreen 1920x1080</td>\n",
       "      <td>Intel Core i7 6500U 2.5GHz</td>\n",
       "      <td>4</td>\n",
       "      <td>128GB SSD</td>\n",
       "      <td>Intel HD Graphics 520</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.80</td>\n",
       "      <td>33992.6400</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>157.350512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1299</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>2 in 1 Convertible</td>\n",
       "      <td>IPS Panel Quad HD+ / Touchscreen 3200x1800</td>\n",
       "      <td>Intel Core i7 6500U 2.5GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>Intel HD Graphics 520</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.30</td>\n",
       "      <td>79866.7200</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>276.053530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1300</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>Intel Celeron Dual Core N3050 1.6GHz</td>\n",
       "      <td>2</td>\n",
       "      <td>64GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.50</td>\n",
       "      <td>12201.1200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111.935204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1301</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>Intel Core i7 6500U 2.5GHz</td>\n",
       "      <td>6</td>\n",
       "      <td>1TB HDD</td>\n",
       "      <td>AMD Radeon R5 M330</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.19</td>\n",
       "      <td>40705.9200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.454670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1302</th>\n",
       "      <td>Asus</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>Intel Celeron Dual Core N3050 1.6GHz</td>\n",
       "      <td>4</td>\n",
       "      <td>500GB HDD</td>\n",
       "      <td>Intel HD Graphics</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.20</td>\n",
       "      <td>19660.3200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.454670</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1303 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company            TypeName                            ScreenResolution  \\\n",
       "0      Apple           Ultrabook          IPS Panel Retina Display 2560x1600   \n",
       "1      Apple           Ultrabook                                    1440x900   \n",
       "2         HP            Notebook                           Full HD 1920x1080   \n",
       "3      Apple           Ultrabook          IPS Panel Retina Display 2880x1800   \n",
       "4      Apple           Ultrabook          IPS Panel Retina Display 2560x1600   \n",
       "...      ...                 ...                                         ...   \n",
       "1298  Lenovo  2 in 1 Convertible   IPS Panel Full HD / Touchscreen 1920x1080   \n",
       "1299  Lenovo  2 in 1 Convertible  IPS Panel Quad HD+ / Touchscreen 3200x1800   \n",
       "1300  Lenovo            Notebook                                    1366x768   \n",
       "1301      HP            Notebook                                    1366x768   \n",
       "1302    Asus            Notebook                                    1366x768   \n",
       "\n",
       "                                       Cpu  Ram               Memory  \\\n",
       "0                     Intel Core i5 2.3GHz    8            128GB SSD   \n",
       "1                     Intel Core i5 1.8GHz    8  128GB Flash Storage   \n",
       "2               Intel Core i5 7200U 2.5GHz    8            256GB SSD   \n",
       "3                     Intel Core i7 2.7GHz   16            512GB SSD   \n",
       "4                     Intel Core i5 3.1GHz    8            256GB SSD   \n",
       "...                                    ...  ...                  ...   \n",
       "1298            Intel Core i7 6500U 2.5GHz    4            128GB SSD   \n",
       "1299            Intel Core i7 6500U 2.5GHz   16            512GB SSD   \n",
       "1300  Intel Celeron Dual Core N3050 1.6GHz    2   64GB Flash Storage   \n",
       "1301            Intel Core i7 6500U 2.5GHz    6              1TB HDD   \n",
       "1302  Intel Celeron Dual Core N3050 1.6GHz    4            500GB HDD   \n",
       "\n",
       "                               Gpu       OpSys  Weight        Price  \\\n",
       "0     Intel Iris Plus Graphics 640       macOS    1.37   71378.6832   \n",
       "1           Intel HD Graphics 6000       macOS    1.34   47895.5232   \n",
       "2            Intel HD Graphics 620       No OS    1.86   30636.0000   \n",
       "3               AMD Radeon Pro 455       macOS    1.83  135195.3360   \n",
       "4     Intel Iris Plus Graphics 650       macOS    1.37   96095.8080   \n",
       "...                            ...         ...     ...          ...   \n",
       "1298         Intel HD Graphics 520  Windows 10    1.80   33992.6400   \n",
       "1299         Intel HD Graphics 520  Windows 10    1.30   79866.7200   \n",
       "1300             Intel HD Graphics  Windows 10    1.50   12201.1200   \n",
       "1301            AMD Radeon R5 M330  Windows 10    2.19   40705.9200   \n",
       "1302             Intel HD Graphics  Windows 10    2.20   19660.3200   \n",
       "\n",
       "      Touch Screen  IPS         ppi  \n",
       "0                0    1  226.983005  \n",
       "1                0    0  127.677940  \n",
       "2                0    0  141.211998  \n",
       "3                0    1  220.534624  \n",
       "4                0    1  226.983005  \n",
       "...            ...  ...         ...  \n",
       "1298             1    1  157.350512  \n",
       "1299             1    1  276.053530  \n",
       "1300             0    0  111.935204  \n",
       "1301             0    0  100.454670  \n",
       "1302             0    0  100.454670  \n",
       "\n",
       "[1303 rows x 13 columns]"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.drop(columns= [\"Inches\", \"X_res\", \"Y_res\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = dataset.drop(columns= [\"ScreenResolution\", \"Inches\", \"X_res\", \"Y_res\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Touch Screen</th>\n",
       "      <th>IPS</th>\n",
       "      <th>ppi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>Intel Core i5 2.3GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 640</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>71378.6832</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>Intel Core i5 1.8GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>47895.5232</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>127.677940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>30636.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>Intel Core i7 2.7GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>135195.3360</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>220.534624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>Intel Core i5 3.1GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>96095.8080</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName                         Cpu  Ram               Memory  \\\n",
       "0   Apple  Ultrabook        Intel Core i5 2.3GHz    8            128GB SSD   \n",
       "1   Apple  Ultrabook        Intel Core i5 1.8GHz    8  128GB Flash Storage   \n",
       "2      HP   Notebook  Intel Core i5 7200U 2.5GHz    8            256GB SSD   \n",
       "3   Apple  Ultrabook        Intel Core i7 2.7GHz   16            512GB SSD   \n",
       "4   Apple  Ultrabook        Intel Core i5 3.1GHz    8            256GB SSD   \n",
       "\n",
       "                            Gpu  OpSys  Weight        Price  Touch Screen  \\\n",
       "0  Intel Iris Plus Graphics 640  macOS    1.37   71378.6832             0   \n",
       "1        Intel HD Graphics 6000  macOS    1.34   47895.5232             0   \n",
       "2         Intel HD Graphics 620  No OS    1.86   30636.0000             0   \n",
       "3            AMD Radeon Pro 455  macOS    1.83  135195.3360             0   \n",
       "4  Intel Iris Plus Graphics 650  macOS    1.37   96095.8080             0   \n",
       "\n",
       "   IPS         ppi  \n",
       "0    1  226.983005  \n",
       "1    0  127.677940  \n",
       "2    0  141.211998  \n",
       "3    1  220.534624  \n",
       "4    1  226.983005  "
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "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": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[\"Cpu\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset[\"Cpu Name\"] = dataset[\"Cpu\"].apply(lambda x: x.split()[0:3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Cpu</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "      <th>Touch Screen</th>\n",
       "      <th>IPS</th>\n",
       "      <th>ppi</th>\n",
       "      <th>Cpu Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>Intel Core i5 2.3GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 640</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>71378.6832</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>[Intel, Core, i5]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>Intel Core i5 1.8GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>47895.5232</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>127.677940</td>\n",
       "      <td>[Intel, Core, i5]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>30636.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>[Intel, Core, i5]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>Intel Core i7 2.7GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>135195.3360</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>220.534624</td>\n",
       "      <td>[Intel, Core, i7]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>Intel Core i5 3.1GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>96095.8080</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>[Intel, Core, i5]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName                         Cpu  Ram               Memory  \\\n",
       "0   Apple  Ultrabook        Intel Core i5 2.3GHz    8            128GB SSD   \n",
       "1   Apple  Ultrabook        Intel Core i5 1.8GHz    8  128GB Flash Storage   \n",
       "2      HP   Notebook  Intel Core i5 7200U 2.5GHz    8            256GB SSD   \n",
       "3   Apple  Ultrabook        Intel Core i7 2.7GHz   16            512GB SSD   \n",
       "4   Apple  Ultrabook        Intel Core i5 3.1GHz    8            256GB SSD   \n",
       "\n",
       "                            Gpu  OpSys  Weight        Price  Touch Screen  \\\n",
       "0  Intel Iris Plus Graphics 640  macOS    1.37   71378.6832             0   \n",
       "1        Intel HD Graphics 6000  macOS    1.34   47895.5232             0   \n",
       "2         Intel HD Graphics 620  No OS    1.86   30636.0000             0   \n",
       "3            AMD Radeon Pro 455  macOS    1.83  135195.3360             0   \n",
       "4  Intel Iris Plus Graphics 650  macOS    1.37   96095.8080             0   \n",
       "\n",
       "   IPS         ppi           Cpu Name  \n",
       "0    1  226.983005  [Intel, Core, i5]  \n",
       "1    0  127.677940  [Intel, Core, i5]  \n",
       "2    0  141.211998  [Intel, Core, i5]  \n",
       "3    1  220.534624  [Intel, Core, i7]  \n",
       "4    1  226.983005  [Intel, Core, i5]  "
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fetchprocessor(text):\n",
    "    text = \" \".join(text)\n",
    "    if text == \"Intel Core i7\" or text == \"Intel Core i5\" or text == \"Intel Core i3\":\n",
    "        return text\n",
    "    else: \n",
    "        if text.split()[0] == \"Intel\":\n",
    "            return \"Other Intel Processor\"\n",
    "        else :\n",
    "            return \"AMD Processor\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset[\"CPU Brand\"] = dataset[\"Cpu Name\"].apply(fetchprocessor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Cpu</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "      <th>Touch Screen</th>\n",
       "      <th>IPS</th>\n",
       "      <th>ppi</th>\n",
       "      <th>Cpu Name</th>\n",
       "      <th>CPU Brand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>Intel Core i5 2.3GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 640</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>71378.6832</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>[Intel, Core, i5]</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>Intel Core i5 1.8GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>47895.5232</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>127.677940</td>\n",
       "      <td>[Intel, Core, i5]</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>30636.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>[Intel, Core, i5]</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>Intel Core i7 2.7GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>135195.3360</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>220.534624</td>\n",
       "      <td>[Intel, Core, i7]</td>\n",
       "      <td>Intel Core i7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>Intel Core i5 3.1GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>96095.8080</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>[Intel, Core, i5]</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName                         Cpu  Ram               Memory  \\\n",
       "0   Apple  Ultrabook        Intel Core i5 2.3GHz    8            128GB SSD   \n",
       "1   Apple  Ultrabook        Intel Core i5 1.8GHz    8  128GB Flash Storage   \n",
       "2      HP   Notebook  Intel Core i5 7200U 2.5GHz    8            256GB SSD   \n",
       "3   Apple  Ultrabook        Intel Core i7 2.7GHz   16            512GB SSD   \n",
       "4   Apple  Ultrabook        Intel Core i5 3.1GHz    8            256GB SSD   \n",
       "\n",
       "                            Gpu  OpSys  Weight        Price  Touch Screen  \\\n",
       "0  Intel Iris Plus Graphics 640  macOS    1.37   71378.6832             0   \n",
       "1        Intel HD Graphics 6000  macOS    1.34   47895.5232             0   \n",
       "2         Intel HD Graphics 620  No OS    1.86   30636.0000             0   \n",
       "3            AMD Radeon Pro 455  macOS    1.83  135195.3360             0   \n",
       "4  Intel Iris Plus Graphics 650  macOS    1.37   96095.8080             0   \n",
       "\n",
       "   IPS         ppi           Cpu Name      CPU Brand  \n",
       "0    1  226.983005  [Intel, Core, i5]  Intel Core i5  \n",
       "1    0  127.677940  [Intel, Core, i5]  Intel Core i5  \n",
       "2    0  141.211998  [Intel, Core, i5]  Intel Core i5  \n",
       "3    1  220.534624  [Intel, Core, i7]  Intel Core i7  \n",
       "4    1  226.983005  [Intel, Core, i5]  Intel Core i5  "
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = dataset.drop(columns= [\"Cpu\", \"Cpu Name\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset[\"CPU Brand\"].value_counts().plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x = dataset[\"CPU Brand\"], y = dataset[\"Price\"])\n",
    "plt.xticks(rotation=\"vertical\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8     619\n",
       "4     375\n",
       "16    200\n",
       "6      41\n",
       "12     25\n",
       "2      22\n",
       "32     17\n",
       "24      3\n",
       "64      1\n",
       "Name: Ram, dtype: int64"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[\"Ram\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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H9Yc//EEhISFavny5ysvLtWjRInl5een2229XQUGBZs+ebYav9PR09e/fXxMmTJAkTZs2TdnZ2Zo/f74yMjJkGIbmzp2r1NRUPfLII5KkZcuWKTAwUJmZmRo0aJBVbwkAAHBTdf6aqk2bNikgIEC33XabxowZo3//+9/mWF5enpo2bWoGKkmKjo6Wh4eHtm7datb06tVLXl5eZk1sbKwKCwt1/PhxsyY6OtrpdWNjY5WXlydJOnTokOx2u1ONv7+/IiMjzZralJWVyeFwOC0AAKB+qtOhqn///lq2bJlycnL0+9//Xps3b1ZcXJyqqqokSXa7XQEBAU7PadCggZo3by673W7WBAYGOtXUrF+q5tzxc59XW01tpk+fLn9/f3MJDQ29ov0HAADuo07fp+rc02rh4eHq1q2b2rdvr02bNumBBx5wYWeXJyUlRcnJyea6w+EgWAEAUE/V6SNV/+nWW29Vy5YtdeDAAUlSUFCQjh496lRTWVmpY8eOmddhBQUFqbi42KmmZv1SNeeOn/u82mpq4+3tLT8/P6cFAADUT24Vqn788Uf9+9//VnBwsCQpKipKJ06cUH5+vlnz8ccfq7q6WpGRkWbNli1bVFFRYdZkZ2frtttuU7NmzcyanJwcp9fKzs5WVFSUJCksLExBQUFONQ6HQ1u3bjVrAADAzc2loer06dMqKChQQUGBpJ8vCC8oKFBRUZFOnz6tCRMm6IsvvtB3332nnJwcPfLII+rQoYNiY2MlSZ07d1b//v01cuRIbdu2TZ9//rmSkpI0aNAghYSESJKGDBkiLy8vjRgxQnv27NF7772n9PR0p9NyY8eOVVZWlmbNmqWvv/5akydP1vbt25WUlCRJstlsGjdunF599VV98MEH+uqrrzR06FCFhIQoISHhhr5nAACgbnLpNVXbt29X3759zfWaoDNs2DAtXLhQu3bt0tKlS3XixAmFhIQoJiZG06ZNc/pyyOXLlyspKUkPPPCAPDw8NGDAAM2bN88c9/f314YNG5SYmKiIiAi1bNlSaWlpTveyuueee7RixQqlpqbqpZdeUseOHZWZmamuXbuaNRMnTlRpaalGjRqlEydO6L777lNWVpZ8fHyu51sEAADchM0wDMPVTdwsHA6H/P39dfLkSa6vAgAXGzJkiHmtbGBgoFasWOGyXiZPnuyy174aV9LvqtV3X79GroPfDtx23rbL/f3tVtdUAQAA1FWEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAs4NJQtWXLFj300EMKCQmRzWZTZmamOVZRUaFJkyYpPDxcjRs3VkhIiIYOHarDhw87zdGuXTvZbDan5Y033nCq2bVrl+6//375+PgoNDRUM2bMOK+X1atXq1OnTvLx8VF4eLjWrVvnNG4YhtLS0hQcHCxfX19FR0dr//791r0ZAADArbk0VJWWlqp79+5asGDBeWNnzpzRjh079PLLL2vHjh16//33VVhYqIcffvi82qlTp+rIkSPm8vzzz5tjDodDMTExatu2rfLz8zVz5kxNnjxZb731llmTm5urwYMHa8SIEdq5c6cSEhKUkJCg3bt3mzUzZszQvHnzlJGRoa1bt6px48aKjY3V2bNnLX5XAACAO2rgyhePi4tTXFxcrWP+/v7Kzs522jZ//nzdfffdKioqUps2bcztTZo0UVBQUK3zLF++XOXl5Vq0aJG8vLx0++23q6CgQLNnz9aoUaMkSenp6erfv78mTJggSZo2bZqys7M1f/58ZWRkyDAMzZ07V6mpqXrkkUckScuWLVNgYKAyMzM1aNCga34vAACAe3Ora6pOnjwpm82mpk2bOm1/44031KJFC91xxx2aOXOmKisrzbG8vDz16tVLXl5e5rbY2FgVFhbq+PHjZk10dLTTnLGxscrLy5MkHTp0SHa73anG399fkZGRZk1tysrK5HA4nBYAAFA/ufRI1ZU4e/asJk2apMGDB8vPz8/c/sILL+jOO+9U8+bNlZubq5SUFB05ckSzZ8+WJNntdoWFhTnNFRgYaI41a9ZMdrvd3HZujd1uN+vOfV5tNbWZPn26pkyZcpV7DAAA3IlbhKqKigr99re/lWEYWrhwodNYcnKy+bhbt27y8vLSc889p+nTp8vb2/tGt+okJSXFqT+Hw6HQ0FAXdgQAAK6XOn/6ryZQff/998rOznY6SlWbyMhIVVZW6rvvvpMkBQUFqbi42KmmZr3mOqwL1Zw7fu7zaqupjbe3t/z8/JwWAABQP9XpUFUTqPbv36+NGzeqRYsWl3xOQUGBPDw8FBAQIEmKiorSli1bVFFRYdZkZ2frtttuU7NmzcyanJwcp3mys7MVFRUlSQoLC1NQUJBTjcPh0NatW80aAABwc3Pp6b/Tp0/rwIED5vqhQ4dUUFCg5s2bKzg4WI899ph27NihNWvWqKqqyrx+qXnz5vLy8lJeXp62bt2qvn37qkmTJsrLy9P48eP15JNPmoFpyJAhmjJlikaMGKFJkyZp9+7dSk9P15w5c8zXHTt2rHr37q1Zs2YpPj5eK1eu1Pbt283bLthsNo0bN06vvvqqOnbsqLCwML388ssKCQlRQkLCjXvDAABAneXSULV9+3b17dvXXK+5/mjYsGGaPHmyPvjgA0lSjx49nJ73ySefqE+fPvL29tbKlSs1efJklZWVKSwsTOPHj3e6jsnf318bNmxQYmKiIiIi1LJlS6WlpZm3U5Cke+65RytWrFBqaqpeeukldezYUZmZmeratatZM3HiRJWWlmrUqFE6ceKE7rvvPmVlZcnHx+d6vDUAAMDNuDRU9enTR4ZhXHD8YmOSdOedd+qLL7645Ot069ZNn3766UVrBg4cqIEDB15w3GazaerUqZo6deolXw8AANx86vQ1VQAAAO6CUAUAAGCBawpV5eXlKiwsdLqDOQAAwM3oqkLVmTNnNGLECDVq1Ei33367ioqKJEnPP/+83njjDUsbBAAAcAdXFapSUlL0j3/8Q5s2bXL69Ft0dLTee+89y5oDAABwF1f16b/MzEy99957+tWvfiWbzWZuv/3223Xw4EHLmgMAoMa+1z62dL6Kk2edHls9f+f/7WfpfKj7rupIVUlJiXnH8nOVlpY6hSwAAICbxVWFqp49e2rt2rXmek2Qeuedd/jaFgAAcFO6qtN/r7/+uuLi4rR3715VVlYqPT1de/fuVW5urjZv3mx1jwAAAHXeVR2puu+++1RQUKDKykqFh4drw4YNCggIUF5eniIiIqzuEQAAoM676q+pad++vd5++20rewEAAHBbV3Wkat26dVq/fv1529evX6+PPvrompsCAABwN1cVql588UVVVVWdt90wDL344ovX3BQAAIC7uapQtX//fnXp0uW87Z06ddKBAweuuSkAAAB3c1Whyt/fX99+++152w8cOKDGjRtfc1MAAADu5qpC1SOPPKJx48Y53T39wIED+t3vfqeHH37YsuYAAADcxVWFqhkzZqhx48bq1KmTwsLCFBYWps6dO6tFixb6wx/+YHWPAAAAdd5V3VLB399fubm5ys7O1j/+8Q/5+vqqW7du6tWrl9X9AQAAuIWrvk+VzWZTTEyMYmJirOwHAADALV12qJo3b55GjRolHx8fzZs376K1L7zwwjU3BgBwvbFjx6qkpESS1KpVK6Wnp7u4I6DuuuxQNWfOHD3xxBPy8fHRnDlzLlhns9kIVQBQT5SUlKi4uNjVbQBu4bJD1aFDh2p9DAAAgKv49F9FRYXat2+vffv2XY9+AAAA3NIVh6qGDRvq7Nmz16MXAAAAt3VV96lKTEzU73//e1VWVlrdDwAAgFu6qlsqfPnll8rJydGGDRsUHh5+3lfTvP/++5Y0BwAA4C6uKlQ1bdpUAwYMsLoXAAAAt3VFoaq6ulozZ87UN998o/LycvXr10+TJ0+Wr6/v9eoPAADALVzRNVWvvfaaXnrpJd1yyy36xS9+oXnz5ikxMfF69QYAAOA2ruhI1bJly/Tmm2/queeekyRt3LhR8fHxeuedd+ThcVXXvAMALPTak49ZOt/Jf50853GJ5fP/77t/tXQ+wJWuKAkVFRXpwQcfNNejo6Nls9l0+PDhq3rxLVu26KGHHlJISIhsNpsyMzOdxg3DUFpamoKDg+Xr66vo6Gjt37/fqebYsWN64okn5Ofnp6ZNm2rEiBE6ffq0U82uXbt0//33y8fHR6GhoZoxY8Z5vaxevVqdOnWSj4+PwsPDtW7duivuBQAA3LyuKFRVVlbKx8fHaVvDhg1VUVFxVS9eWlqq7t27a8GCBbWOz5gxQ/PmzVNGRoa2bt2qxo0bKzY21uk+WU888YT27Nmj7OxsrVmzRlu2bNGoUaPMcYfDoZiYGLVt21b5+fmaOXOmJk+erLfeesusyc3N1eDBgzVixAjt3LlTCQkJSkhI0O7du6+oFwAAcPO6otN/hmHo6aeflre3t7nt7NmzGj16tNNtFS73lgpxcXGKi4u74GvNnTtXqampeuSRRyT9fPoxMDBQmZmZGjRokPbt26esrCx9+eWX6tmzpyTpj3/8ox588EH94Q9/UEhIiJYvX67y8nItWrRIXl5euv3221VQUKDZs2eb4Ss9PV39+/fXhAkTJEnTpk1Tdna25s+fr4yMjMvqBQAA3Nyu6EjVsGHDFBAQIH9/f3N58sknFRIS4rTNCocOHZLdbld0dLS5zd/fX5GRkcrLy5Mk5eXlqWnTpmagkn4+Jenh4aGtW7eaNb169ZKXl5dZExsbq8LCQh0/ftysOfd1ampqXudyeqlNWVmZHA6H0wIAAOqnKzpStXjx4uvVx3nsdrskKTAw0Gl7YGCgOWa32xUQEOA03qBBAzVv3typJiws7Lw5asaaNWsmu91+yde5VC+1mT59uqZMmXLpnQUAAG6Pj+xdRykpKTp58qS5/PDDD65uCQAAXCd1NlQFBQVJkoqLi522FxcXm2NBQUE6evSo03hlZaWOHTvmVFPbHOe+xoVqzh2/VC+18fb2lp+fn9MCAADqpzobqsLCwhQUFKScnBxzm8Ph0NatWxUVFSVJioqK0okTJ5Sfn2/WfPzxx6qurlZkZKRZs2XLFqdPKGZnZ+u2225Ts2bNzJpzX6empuZ1LqcXAKiPvD1s8vH0kI+nh7w9bK5uB6jTruq7/6xy+vRpHThwwFw/dOiQCgoK1Lx5c7Vp00bjxo3Tq6++qo4dOyosLEwvv/yyQkJClJCQIEnq3Lmz+vfvr5EjRyojI0MVFRVKSkrSoEGDFBISIkkaMmSIpkyZohEjRmjSpEnavXu30tPTNWfOHPN1x44dq969e2vWrFmKj4/XypUrtX37dvO2Czab7ZK9AEB9FNGSI+zA5XJpqNq+fbv69u1rricnJ0v6+VOGS5Ys0cSJE1VaWqpRo0bpxIkTuu+++5SVleV0r6zly5crKSlJDzzwgDw8PDRgwADNmzfPHPf399eGDRuUmJioiIgItWzZUmlpaU73srrnnnu0YsUKpaam6qWXXlLHjh2VmZmprl27mjWX0wsAALh5uTRU9enTR4ZhXHDcZrNp6tSpmjp16gVrmjdvrhUrVlz0dbp166ZPP/30ojUDBw7UwIEDr6kXAABw86qz11QBAAC4E0IVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABao86GqXbt2stls5y2JiYmSpD59+pw3Nnr0aKc5ioqKFB8fr0aNGikgIEATJkxQZWWlU82mTZt05513ytvbWx06dNCSJUvO62XBggVq166dfHx8FBkZqW3btl23/QbgPsaOHashQ4ZoyJAhGjt2rKvbAeAiDVzdwKV8+eWXqqqqMtd3796t//qv/9LAgQPNbSNHjtTUqVPN9UaNGpmPq6qqFB8fr6CgIOXm5urIkSMaOnSoGjZsqNdff12SdOjQIcXHx2v06NFavny5cnJy9Oyzzyo4OFixsbGSpPfee0/JycnKyMhQZGSk5s6dq9jYWBUWFiogIOB6vw0A6rCSkhIVFxe7ug0ALlbnj1S1atVKQUFB5rJmzRq1b99evXv3NmsaNWrkVOPn52eObdiwQXv37tW7776rHj16KC4uTtOmTdOCBQtUXl4uScrIyFBYWJhmzZqlzp07KykpSY899pjmzJljzjN79myNHDlSw4cPV5cuXZSRkaFGjRpp0aJFN+7NAAAAdVadD1XnKi8v17vvvqtnnnlGNpvN3L58+XK1bNlSXbt2VUpKis6cOWOO5eXlKTw8XIGBgea22NhYORwO7dmzx6yJjo52eq3Y2Fjl5eWZr5ufn+9U4+HhoejoaLOmNmVlZXI4HE4LAACon+r86b9zZWZm6sSJE3r66afNbUOGDFHbtm0VEhKiXbt2adKkSSosLNT7778vSbLb7U6BSpK5brfbL1rjcDj0008/6fjx46qqqqq15uuvv75gv9OnT9eUKVOuen8BAID7cKtQ9ac//UlxcXEKCQkxt40aNcp8HB4eruDgYD3wwAM6ePCg2rdv74o2TSkpKUpOTjbXHQ6HQkNDXdgRAAC4XtwmVH3//ffauHGjeQTqQiIjIyVJBw4cUPv27RUUFHTep/RqLigNCgoy//ufF5kWFxfLz89Pvr6+8vT0lKenZ601NXPUxtvbW97e3pe3gwAAwK25zTVVixcvVkBAgOLj4y9aV1BQIEkKDg6WJEVFRemrr77S0aNHzZrs7Gz5+fmpS5cuZk1OTo7TPNnZ2YqKipIkeXl5KSIiwqmmurpaOTk5Zg0AALi5uUWoqq6u1uLFizVs2DA1aPD/Dq4dPHhQ06ZNU35+vr777jt98MEHGjp0qHr16qVu3bpJkmJiYtSlSxc99dRT+sc//qH169crNTVViYmJ5lGk0aNH69tvv9XEiRP19ddf680339SqVas0fvx487WSk5P19ttva+nSpdq3b5/GjBmj0tJSDR8+/Ma+GQAAoE5yi9N/GzduVFFRkZ555hmn7V5eXtq4caPmzp2r0tJShYaGasCAAUpNTTVrPD09tWbNGo0ZM0ZRUVFq3Lixhg0b5nRfq7CwMK1du1bjx49Xenq6WrdurXfeece8R5UkPf744yopKVFaWprsdrt69OihrKys8y5eBwAANye3CFUxMTEyDOO87aGhodq8efMln9+2bVutW7fuojV9+vTRzp07L1qTlJSkpKSkS74eAAC4+bjF6T8AAIC6jlAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAG3uKUCAABWa+rdpNbHwNUiVAG4IcaOHauSkhJJUqtWrZSenu6yXub/7kNL5zt17IzTY6vnT5r1kKXz4WfJEcNc3QLqGUIVgBuipKTkvC8lB4D6hGuqAAAALECoAgAAsAChCgAAwAKEKgAAAAtwoTqAWm3u1dvS+c428JRstp8f2+2Wz997y2ZL5wOAK8WRKgAAAAsQqgAAACxAqAIAALAA11QBuCH8DEkyznkMAPULoQrADTG8qsrVLQDAdcXpPwAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAswC0VAOAa+Xg1qfUxgJsLoQoArlHvjr91dQsA6gBO/wEAAFiAUAUAAGABQhUAAIAF6nSomjx5smw2m9PSqVMnc/zs2bNKTExUixYtdMstt2jAgAEqLi52mqOoqEjx8fFq1KiRAgICNGHCBFVWVjrVbNq0SXfeeae8vb3VoUMHLVmy5LxeFixYoHbt2snHx0eRkZHatm3bddlnAADgnup0qJKk22+/XUeOHDGXzz77zBwbP368PvzwQ61evVqbN2/W4cOH9eijj5rjVVVVio+PV3l5uXJzc7V06VItWbJEaWlpZs2hQ4cUHx+vvn37qqCgQOPGjdOzzz6r9evXmzXvvfeekpOT9corr2jHjh3q3r27YmNjdfTo0RvzJgAAgDqvzoeqBg0aKCgoyFxatmwpSTp58qT+9Kc/afbs2erXr58iIiK0ePFi5ebm6osvvpAkbdiwQXv37tW7776rHj16KC4uTtOmTdOCBQtUXl4uScrIyFBYWJhmzZqlzp07KykpSY899pjmzJlj9jB79myNHDlSw4cPV5cuXZSRkaFGjRpp0aJFN/4NAQAAdVKdD1X79+9XSEiIbr31Vj3xxBMqKiqSJOXn56uiokLR0dFmbadOndSmTRvl5eVJkvLy8hQeHq7AwECzJjY2Vg6HQ3v27DFrzp2jpqZmjvLycuXn5zvVeHh4KDo62qy5kLKyMjkcDqcFAADUT3U6VEVGRmrJkiXKysrSwoULdejQId1///06deqU7Ha7vLy81LRpU6fnBAYGym63S5LsdrtToKoZrxm7WI3D4dBPP/2kf/3rX6qqqqq1pmaOC5k+fbr8/f3NJTQ09IrfAwAA4B7q9M0/4+LizMfdunVTZGSk2rZtq1WrVsnX19eFnV2elJQUJScnm+sOh4NgBQBAPVWnj1T9p6ZNm+qXv/ylDhw4oKCgIJWXl+vEiRNONcXFxQoKCpIkBQUFnfdpwJr1S9X4+fnJ19dXLVu2lKenZ601NXNciLe3t/z8/JwWAABQP7lVqDp9+rQOHjyo4OBgRUREqGHDhsrJyTHHCwsLVVRUpKioKElSVFSUvvrqK6dP6WVnZ8vPz09dunQxa86do6amZg4vLy9FREQ41VRXVysnJ8esAQAAqNOh6n/+53+0efNmfffdd8rNzdVvfvMbeXp6avDgwfL399eIESOUnJysTz75RPn5+Ro+fLiioqL0q1/9SpIUExOjLl266KmnntI//vEPrV+/XqmpqUpMTJS3t7ckafTo0fr22281ceJEff3113rzzTe1atUqjR8/3uwjOTlZb7/9tpYuXap9+/ZpzJgxKi0t1fDhw13yvgAAgLqnTl9T9eOPP2rw4MH697//rVatWum+++7TF198oVatWkmS5syZIw8PDw0YMEBlZWWKjY3Vm2++aT7f09NTa9as0ZgxYxQVFaXGjRtr2LBhmjp1qlkTFhamtWvXavz48UpPT1fr1q31zjvvKDY21qx5/PHHVVJSorS0NNntdvXo0UNZWVnnXbwOAABuXnU6VK1cufKi4z4+PlqwYIEWLFhwwZq2bdtq3bp1F52nT58+2rlz50VrkpKSlJSUdNEaAABw86rTp/8AAADcBaEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALNDA1Q0A+H/Gjh2rkpISSVKrVq2Unp7u4o4AAJeLUAXUISUlJSouLnZ1GwCAq8DpPwAAAAsQqgAAACxAqAIAALAA11TB7XAxNwCgLiJUwe3UpYu57/3jvZbO5+3wlk02SZLdYbd8/s+f/9zS+QAA/w+n/wAAACxAqAIAALAAp/9w3RVNDbd0vsoTLSR5/v+PD1s+f5u0ryydDwBwcyBUAXWI4WvU+hgAUPcRqoA6pLxXuatbAABcJa6pAgAAsABHquB2mntX1foYAABXIlTB7bx0xwlXtwAAwHnq9Om/6dOn66677lKTJk0UEBCghIQEFRYWOtX06dNHNpvNaRk9erRTTVFRkeLj49WoUSMFBARowoQJqqysdKrZtGmT7rzzTnl7e6tDhw5asmTJef0sWLBA7dq1k4+PjyIjI7Vt2zbL9xkAALinOh2qNm/erMTERH3xxRfKzs5WRUWFYmJiVFpa6lQ3cuRIHTlyxFxmzJhhjlVVVSk+Pl7l5eXKzc3V0qVLtWTJEqWlpZk1hw4dUnx8vPr27auCggKNGzdOzz77rNavX2/WvPfee0pOTtYrr7yiHTt2qHv37oqNjdXRo0ev/xsBAADqvDp9+i8rK8tpfcmSJQoICFB+fr569eplbm/UqJGCgoJqnWPDhg3au3evNm7cqMDAQPXo0UPTpk3TpEmTNHnyZHl5eSkjI0NhYWGaNWuWJKlz58767LPPNGfOHMXGxkqSZs+erZEjR2r48OGSpIyMDK1du1aLFi3Siy++eD12HwAAuJE6faTqP508eVKS1Lx5c6fty5cvV8uWLdW1a1elpKTozJkz5lheXp7Cw8MVGBhobouNjZXD4dCePXvMmujoaKc5Y2NjlZeXJ0kqLy9Xfn6+U42Hh4eio6PNmtqUlZXJ4XA4LQAAoH6q00eqzlVdXa1x48bp3nvvVdeuXc3tQ4YMUdu2bRUSEqJdu3Zp0qRJKiws1Pvvvy9JstvtToFKkrlut9svWuNwOPTTTz/p+PHjqqqqqrXm66+/vmDP06dP15QpU65+pwEAgNtwm1CVmJio3bt367PPPnPaPmrUKPNxeHi4goOD9cADD+jgwYNq3779jW7TSUpKipKTk811h8Oh0NBQF3YEAACuF7cIVUlJSVqzZo22bNmi1q1bX7Q2MjJSknTgwAG1b99eQUFB531Kr7i4WJLM67CCgoLMbefW+Pn5ydfXV56envL09Ky15kLXckmSt7e3vL29L28nAQCAW6vT11QZhqGkpCT97W9/08cff6ywsLBLPqegoECSFBwcLEmKiorSV1995fQpvezsbPn5+alLly5mTU5OjtM82dnZioqKkiR5eXkpIiLCqaa6ulo5OTlmDQAAuLnV6SNViYmJWrFihf7+97+rSZMm5jVQ/v7+8vX11cGDB7VixQo9+OCDatGihXbt2qXx48erV69e6tatmyQpJiZGXbp00VNPPaUZM2bIbrcrNTVViYmJ5lGk0aNHa/78+Zo4caKeeeYZffzxx1q1apXWrl1r9pKcnKxhw4apZ8+euvvuuzV37lyVlpaanwYEAAA3tzodqhYuXCjp5xt8nmvx4sV6+umn5eXlpY0bN5oBJzQ0VAMGDFBqaqpZ6+npqTVr1mjMmDGKiopS48aNNWzYME2dOtWsCQsL09q1azV+/Hilp6erdevWeuedd8zbKUjS448/rpKSEqWlpclut6tHjx7Kyso67+J1AABwc6rTocowjIuOh4aGavPmzZecp23btlq3bt1Fa/r06aOdO3detCYpKUlJSUmXfD0AAHDzqdPXVAEAALgLQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFqjT96nC1Rk7dqxKSkokSa1atVJ6erqLOwIAoP4jVNVDJSUl5335MwAAuL44/QcAAGABQhUAAIAFOP1XB0RMWGbpfH7HT5tp+cjx05bPL0n5M4daPicAAO6MI1UAAAAWIFQBAABYgFAFAABgAa6pqoeqGzau9TEAALh+CFX10Onb4lzdAgAANx1O/wEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUHWFFixYoHbt2snHx0eRkZHatm2bq1sCAAB1AKHqCrz33ntKTk7WK6+8oh07dqh79+6KjY3V0aNHXd0aAABwMULVFZg9e7ZGjhyp4cOHq0uXLsrIyFCjRo20aNEiV7cGAABcrIGrG3AX5eXlys/PV0pKirnNw8ND0dHRysvLq/U5ZWVlKisrM9dPnjwpSXI4HE51VWU/XYeOr6//3IeLOXW26jp2Yr0r2bfKnyqvYyfWu5J9K62sv/v2U9mZ69iJ9a5k385WVFzHTqx3Jft2+mzpdezEeleyb+f+rnAHV7JvZ864/++Amm2GYVz8yQYuyz//+U9DkpGbm+u0fcKECcbdd99d63NeeeUVQxILCwsLCwtLPVh++OGHi2YFjlRdRykpKUpOTjbXq6urdezYMbVo0UI2m+26vrbD4VBoaKh++OEH+fn5XdfXcoX6vH/sm3ti39wT++aebvS+GYahU6dOKSQk5KJ1hKrL1LJlS3l6eqq4uNhpe3FxsYKCgmp9jre3t7y9vZ22NW3a9Hq1WCs/P79695fpXPV5/9g398S+uSf2zT3dyH3z9/e/ZA0Xql8mLy8vRUREKCcnx9xWXV2tnJwcRUVFubAzAABQF3Ck6gokJydr2LBh6tmzp+6++27NnTtXpaWlGj58uKtbAwAALkaougKPP/64SkpKlJaWJrvdrh49eigrK0uBgYGubu083t7eeuWVV847/Vhf1Of9Y9/cE/vmntg391RX981mGJf6fCAAAAAuhWuqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQquCW+HwFAKCuIVTBLXl7e2vfvn2ubgMAABP3qaonfvrpJ+Xn56t58+bq0qWL09jZs2e1atUqDR061EXdXb1zvzvxXFVVVXrjjTfUokULSdLs2bNvZFuW2bFjh5o1a6awsDBJ0p///GdlZGSoqKhIbdu2VVJSkgYNGuTiLq/e/PnztW3bNj344IMaNGiQ/vznP2v69Omqrq7Wo48+qqlTp6pBA/4Zgmv169dPixcvVtu2bV3dCq7ArbfeqvXr16tjx46ubsXEv2b1wDfffKOYmBgVFRXJZrPpvvvu08qVKxUcHCxJOnnypIYPH+6WoWru3Lnq3r37ed+ZaBiG9u3bp8aNG1/3L6e+noYPH65Zs2YpLCxM77zzjl544QWNHDlSTz31lAoLCzVy5EidOXNGzzzzjKtbvWKvvvqqZsyYoZiYGI0fP17ff/+9Zs6cqfHjx8vDw0Nz5sxRw4YNNWXKFFe3etV+/PFHNW3aVLfccovT9oqKCuXl5alXr14u6uza/Pjjj/Lx8VHLli0lSZ9++qlT2E9MTHTLr+f64IMPat2+ZcsWrVmzRqGhoZKkhx9++Ea2Zanq6mp5eJx/Eqq6ulo//vij2rRp44Kurs28efNq3V5UVKTFixeb37/7wgsv3Mi2amfA7SUkJBjx8fFGSUmJsX//fiM+Pt4ICwszvv/+e8MwDMNutxseHh4u7vLqTJ8+3QgLCzNycnKctjdo0MDYs2ePi7qyjq+vr/Hdd98ZhmEYd9xxh/HWW285jS9fvtzo0qWLK1q7Zu3btzf+7//9v4ZhGEZBQYHh6elpvPvuu+b4+++/b3To0MFV7V2Tw4cPG3fddZfh4eFheHp6Gk899ZRx6tQpc9yd/84ZhmHcfffdxocffmgYhmFkZmYaHh4exsMPP2xMmjTJ+M1vfmM0bNjQHHcnNpvN8PDwMGw22wUXd/25nTx50hg4cKDh4+NjBAQEGC+//LJRWVlpjrvzn0mbzWa0bt3aaNeundNis9mMX/ziF0a7du2MsLAwV7dpGIZhEKrqgYCAAGPXrl3menV1tTF69GijTZs2xsGDB936L5NhGMa2bduMX/7yl8bvfvc7o7y83DCM+hOqWrRoYWzfvt0wjJ9/jgUFBU7jBw4cMHx9fV3R2jXz9fU1g71hGEbDhg2N3bt3m+vfffed0ahRI1e0ds2GDh1qREZGGl9++aWRnZ1tREREGD179jSOHTtmGMbPv8BsNpuLu7x6jRs3Nr799lvDMAwjMjLSeOONN5zG//jHPxp33HGHK1q7Jv379zfi4+ON4uJip+314d+TF154wfjlL39prF692nj77beNtm3bGvHx8UZZWZlhGO79Z/K5554zevToYezdu9dpe138uXGhej3w008/OV2XYrPZtHDhQj300EPq3bu3vvnmGxd2d+3uuusu5efnq6SkRD179tTu3bvd+pTfueLi4rRw4UJJUu/evfXXv/7VaXzVqlXq0KGDK1q7ZkFBQdq7d68kaf/+/aqqqjLXJWnPnj0KCAhwVXvXZOPGjZo3b5569uyp6Ohoff755woODla/fv107NgxSXLrP6MNGjTQqVOnJEmHDh1SXFyc03hcXJwKCwtd0do1+eijj/TAAw+oZ8+eWrNmjavbsVRmZqb+z//5P3rsscf07LPPavv27SopKdFDDz2ksrIySe77ZzIjI0NpaWmKjY3V/PnzXd3Oxbk61eHa3XXXXcayZctqHUtMTDSaNm3q1keqzvWXv/zFCAwMNDw8POrc/6FcjX/+859Gu3btjF69ehnJycmGr6+vcd999xkjR440evXqZXh5eRlr1651dZtXJTU11WjVqpXx7LPPGmFhYcaLL75otGnTxli4cKGRkZFhhIaGGuPHj3d1m1elcePGxjfffOO0raKiwkhISDC6detm7Nq1y63/zj388MPGiy++aBiGYcTGxhrp6elO42+//bbRsWNHV7RmiZ07dxpdunQxRo0aZZSWltbJIx5XytfX1zy6WMPhcBhRUVFGv379jG+//dat/0wahmH8+OOPRr9+/Yz+/fsbR44cqZM/N0JVPfD6668bcXFxFxwfM2aM2x72rc0PP/xgZGZmGqdPn3Z1K5Y4fvy4MWnSJKNLly6Gj4+P4eXlZbRt29YYMmSI8eWXX7q6vatWVVVlvPbaa8avf/1r4/XXXzeqq6uNv/zlL0ZoaKjRokUL4+mnn3bbn2F4eLjx17/+9bztNcGqTZs2bv0LbO/evUaLFi2MoUOHGtOmTTNuueUW48knnzRee+01Y+jQoYa3t7exePFiV7d5Tc6cOWM899xzRseOHQ1PT88698v5St122221/g/YqVOnjKioKKN79+5u/WeyRnV1tfH6668bQUFBdfLnZjMM7qIIAFdi0qRJKigo0Pr1688bq6ys1IABA/Thhx+qurraBd1Z4+DBg0pNTdXatWt1+vRpST+fFrzrrrs0YcIEJSQkuLZBi3zwwQf65JNPlJKS4rano6WfP/l25MgRrV69+ryxU6dO6b/+67/05ZdfqqqqygXdWS8/P1+fffaZhg4dqmbNmrm6HROhCgCuUGVlpc6cOSM/P78Ljv/zn/+sF/c9MgxDR48eVXV1tVq2bKmGDRu6uiXU4vjx4zp8+LBuv/32WsdPnTqlHTt2qHfv3je4s5sLF6oDwBVq0KDBBQOVJB05csSt7791LpvNpsDAQAUHB5uB6ocffnDLe6dJP3+w57PPPnP60ESNs2fPatmyZS7o6to1a9ZMHh4eWrx4sb7++mtJ0tdff60xY8bomWee0Zdffum2gWrHjh06dOiQuf7nP/9Z9957r0JDQ837MtYVhCoAsNixY8e0dOlSV7dx3bjr/n3zzTfq3LmzevXqpfDwcPXu3VtHjhwxx2tulOyOsrKy1KNHD/3P//yP7rjjDmVlZalXr146cOCAvv/+e8XExOjjjz92dZtXZfjw4Tp48KAk6Z133tFzzz2nnj176n//93911113aeTIkVq0aJGLu/wZp/8A4Apd6M7cNb799lv97ne/c9vrV+rr/v3mN79RRUWFlixZohMnTmjcuHHau3evNm3apDZt2qi4uFghISFut1+SdM8996hfv3569dVXtXLlSv33f/+3xowZo9dee02SlJKSovz8fG3YsMHFnV65Ro0aad++fWrbtq3uvPNOjRkzRiNHjjTHV6xYoddee0179uxxYZc/I1QBwBXy8PCQzWbTxf75tNlsbvnLWaq/+xcYGKiNGzcqPDxc0s/Xi/33f/+31q1bp08++USNGzd221Dl7++v/Px8dejQQdXV1fL29ta2bdt0xx13SJJ2796t6Oho2e12F3d65Vq2bKn169crIiJCgYGB2rBhg7p3726OHzx4UOHh4Tpz5owLu/wZp/8A4AoFBwfr/fffV3V1da3Ljh07XN3iNamv+1ffb5Rcc3NPDw8P+fj4yN/f3xxr0qSJTp486arWrok73SSZL1QGgCsUERGh/Px8PfLII7WOX+ooT11XX/evU6dO2r59uzp37uy0veYu3e78Rcrt2rXT/v371b59e0lSXl6e05cnFxUVKTg42FXtXZPf//73uvfee9W7d2/17NlTs2bN0qZNm9S5c2cVFhbqiy++0N/+9jdXtymJI1UAcMUmTJige+6554LjHTp00CeffHIDO7JWfd2/3/zmN/rLX/5S69j8+fM1ePBgtwyLkjRmzBin05Zdu3Z1Oir30UcfqV+/fq5o7ZqFhIRo586dioqKUlZWlgzD0LZt27Rhwwa1bt1an3/+uR588EFXtymJa6oAAAAswZEqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgDgPzz99NOy2Wyy2Wxq2LChwsLCNHHiRJ09e9bVrQGow7j5JwDUon///lq8eLEqKiqUn5+vYcOGyWaz6fe//72rWwNQR3GkCgBq4e3traCgIIWGhiohIUHR0dHKzs6WJP373//W4MGD9Ytf/EKNGjVSeHj4eTeV7NOnj55//nmNGzdOzZo1U2BgoN5++22VlpZq+PDhatKkiTp06KCPPvrIFbsH4DogVAHAJezevVu5ubny8vKSJJ09e1YRERFau3atdu/erVGjRumpp57Stm3bnJ63dOlStWzZUtu2bdPzzz+vMWPGaODAgbrnnnu0Y8cOxcTE6KmnnqoTXwQL4NpxR3UA+A9PP/203n33Xfn4+KiyslJlZWXy8PDQqlWrNGDAgFqf8+tf/1qdOnXSH/7wB0k/H6mqqqrSp59+KkmqqqqSv7+/Hn30US1btkySZLfbFRwcrLy8PP3qV7+6MTsH4LrhmioAqEXfvn21cOFClZaWas6cOWrQoIEZqKqqqvT6669r1apV+uc//6ny8nKVlZWpUaNGTnN069bNfOzp6akWLVooPDzc3BYYGChJOnr06A3YIwDXG6f/AKAWjRs3VocOHdS9e3ctWrRIW7du1Z/+9CdJ0syZM5Wenq5Jkybpk08+UUFBgWJjY1VeXu40R8OGDZ3Waz5NeO66JFVXV1/nvQFwIxCqAOASPDw89NJLLyk1NVU//fSTPv/8cz3yyCN68skn1b17d91666365ptvXN0mABcjVAHAZRg4cKA8PT21YMECdezYUdnZ2crNzdW+ffv03HPPqbi42NUtAnAxrqkCgMvQoEEDJSUlacaMGdq5c6e+/fZbxcbGqlGjRho1apQSEhJ08uRJV7cJwIX49B8AAIAFOP0HAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAW+P8AQxEcd5bM4SIAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x = dataset[\"Ram\"], y = dataset[\"Price\"])\n",
    "plt.xticks(rotation = 'vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset[\"Memory\"].value_counts().plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                 128GB SSD\n",
       "1       128GB Flash Storage\n",
       "2                 256GB SSD\n",
       "3                 512GB SSD\n",
       "4                 256GB SSD\n",
       "               ...         \n",
       "1298              128GB SSD\n",
       "1299              512GB SSD\n",
       "1300     64GB Flash Storage\n",
       "1301                1TB HDD\n",
       "1302              500GB HDD\n",
       "Name: Memory, Length: 1303, dtype: object"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Memory\"] = df[\"Memory\"].astype(str).replace(\"\\.0\" , \"\",regex = True)\n",
    "df[\"Memory\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>128 SSD</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>128 Flash Storage</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>256 SSD</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>512 SSD</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>256 SSD</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1298</th>\n",
       "      <td>128 SSD</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1299</th>\n",
       "      <td>512 SSD</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1300</th>\n",
       "      <td>64 Flash Storage</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1301</th>\n",
       "      <td>1000 HDD</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1302</th>\n",
       "      <td>500 HDD</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1303 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      0     1\n",
       "0               128 SSD  None\n",
       "1     128 Flash Storage  None\n",
       "2               256 SSD  None\n",
       "3               512 SSD  None\n",
       "4               256 SSD  None\n",
       "...                 ...   ...\n",
       "1298            128 SSD  None\n",
       "1299            512 SSD  None\n",
       "1300   64 Flash Storage  None\n",
       "1301           1000 HDD  None\n",
       "1302            500 HDD  None\n",
       "\n",
       "[1303 rows x 2 columns]"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Memory\"] = df[\"Memory\"].str.replace('GB', \"\")\n",
    "df[\"Memory\"] = df[\"Memory\"].str.replace('TB', '000')\n",
    "new = df[\"Memory\"].str.split(\"+\", n = 1, expand = True)\n",
    "new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"first\"] = new[0]\n",
    "df[\"first\"] = df[\"first\"].str.strip()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"second\"] = new[1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                 128 SSD\n",
       "1       128 Flash Storage\n",
       "2                 256 SSD\n",
       "3                 512 SSD\n",
       "4                 256 SSD\n",
       "              ...        \n",
       "1298              128 SSD\n",
       "1299              512 SSD\n",
       "1300     64 Flash Storage\n",
       "1301             1000 HDD\n",
       "1302              500 HDD\n",
       "Name: first, Length: 1303, dtype: object"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"first\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Zaid\\AppData\\Local\\Temp\\ipykernel_11012\\2840207539.py:6: FutureWarning: The default value of regex will change from True to False in a future version.\n",
      "  df[\"first\"] = df[\"first\"].str.replace(r'\\D', \"\")  # this removes all the words from the string and only leaves the int part\n"
     ]
    }
   ],
   "source": [
    "df[\"Layer1HDD\"] = df[\"first\"].apply(lambda x: 1 if \"HDD\" in x else 0)\n",
    "df[\"Layer1SSD\"] = df[\"first\"].apply(lambda x: 1 if \"SSD\" in x else 0)\n",
    "df[\"Layer1Hybrid\"] = df[\"first\"].apply(lambda x: 1 if \"Hybrid\" in x else 0)\n",
    "df[\"Layer1Flash_Storage\"] = df[\"first\"].apply(lambda x: 1 if \"Flash Storage\" in x else 0)\n",
    "\n",
    "df[\"first\"] = df[\"first\"].str.replace(r'\\D', \"\")  # this removes all the words from the string and only leaves the int part\n",
    "\n",
    "#df[\"second\"] = df[\"second\"].fillna(\"0\", inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        128\n",
       "1        128\n",
       "2        256\n",
       "3        512\n",
       "4        256\n",
       "        ... \n",
       "1298     128\n",
       "1299     512\n",
       "1300      64\n",
       "1301    1000\n",
       "1302     500\n",
       "Name: first, Length: 1303, dtype: object"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"first\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['0', '  1000 HDD', '  256 SSD', '  2000 HDD', '  500 HDD',\n",
       "       '  512 SSD', '  1000 Hybrid'], dtype=object)"
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"second\"] = df[\"second\"].fillna(\"0\")\n",
    "df[\"second\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Zaid\\AppData\\Local\\Temp\\ipykernel_11012\\3408928854.py:6: FutureWarning: The default value of regex will change from True to False in a future version.\n",
      "  df[\"second\"] = df[\"second\"].str.replace(r'\\D', \"\")\n"
     ]
    }
   ],
   "source": [
    "df[\"Layer2HDD\"] = df[\"second\"].apply(lambda x: 1 if \"HDD\" in x else 0)\n",
    "df[\"Layer2SSD\"] = df[\"second\"].apply(lambda x: 1 if \"SSD\" in x else 0)\n",
    "df[\"Layer2Hybrid\"] = df[\"second\"].apply(lambda x: 1 if \"Hybrid\" in x else 0)\n",
    "df[\"Layer2Flash_Storage\"] = df[\"second\"].apply(lambda x: 1 if \"Flash Storage\" in x else 0)\n",
    "\n",
    "df[\"second\"] = df[\"second\"].str.replace(r'\\D', \"\")\n",
    "\n",
    "df[\"first\"] = df[\"first\"].astype(int)\n",
    "df[\"second\"] = df[\"second\"].astype(int)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1], dtype=int64)"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Layer2HDD\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        128\n",
       "1        128\n",
       "2        256\n",
       "3        512\n",
       "4        256\n",
       "        ... \n",
       "1298     128\n",
       "1299     512\n",
       "1300      64\n",
       "1301    1000\n",
       "1302     500\n",
       "Name: first, Length: 1303, dtype: int32"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"first\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"HDD\"] = (df[\"first\"]*df[\"Layer1HDD\"] + df[\"second\"]*df[\"Layer2HDD\"])\n",
    "df[\"SSD\"] = (df[\"first\"]*df[\"Layer1SSD\"] + df[\"second\"]*df[\"Layer2SSD\"])\n",
    "df[\"Hybrid\"] = (df[\"first\"]*df[\"Layer1Hybrid\"] + df[\"second\"]*df[\"Layer2Hybrid\"])\n",
    "df[\"Flash_Storage\"] = (df[\"first\"]*df[\"Layer1Flash_Storage\"] + df[\"second\"]*df[\"Layer2Flash_Storage\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   0,  500, 1000, 2000,   32,  128], dtype=int64)"
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"HDD\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "      <th>Touch Screen</th>\n",
       "      <th>IPS</th>\n",
       "      <th>...</th>\n",
       "      <th>Layer1Hybrid</th>\n",
       "      <th>Layer1Flash_Storage</th>\n",
       "      <th>Layer2HDD</th>\n",
       "      <th>Layer2SSD</th>\n",
       "      <th>Layer2Hybrid</th>\n",
       "      <th>Layer2Flash_Storage</th>\n",
       "      <th>HDD</th>\n",
       "      <th>SSD</th>\n",
       "      <th>Hybrid</th>\n",
       "      <th>Flash_Storage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>128 SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 640</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>71378.6832</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>128 Flash Storage</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>47895.5232</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>256 SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>30636.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>16</td>\n",
       "      <td>512 SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>135195.3360</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>512</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>256 SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>96095.8080</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Ram             Memory                           Gpu  \\\n",
       "0   Apple  Ultrabook    8            128 SSD  Intel Iris Plus Graphics 640   \n",
       "1   Apple  Ultrabook    8  128 Flash Storage        Intel HD Graphics 6000   \n",
       "2      HP   Notebook    8            256 SSD         Intel HD Graphics 620   \n",
       "3   Apple  Ultrabook   16            512 SSD            AMD Radeon Pro 455   \n",
       "4   Apple  Ultrabook    8            256 SSD  Intel Iris Plus Graphics 650   \n",
       "\n",
       "   OpSys  Weight        Price  Touch Screen  IPS  ...  Layer1Hybrid  \\\n",
       "0  macOS    1.37   71378.6832             0    1  ...             0   \n",
       "1  macOS    1.34   47895.5232             0    0  ...             0   \n",
       "2  No OS    1.86   30636.0000             0    0  ...             0   \n",
       "3  macOS    1.83  135195.3360             0    1  ...             0   \n",
       "4  macOS    1.37   96095.8080             0    1  ...             0   \n",
       "\n",
       "  Layer1Flash_Storage  Layer2HDD  Layer2SSD  Layer2Hybrid  \\\n",
       "0                   0          0          0             0   \n",
       "1                   1          0          0             0   \n",
       "2                   0          0          0             0   \n",
       "3                   0          0          0             0   \n",
       "4                   0          0          0             0   \n",
       "\n",
       "   Layer2Flash_Storage  HDD  SSD  Hybrid  Flash_Storage  \n",
       "0                    0    0  128       0              0  \n",
       "1                    0    0    0       0            128  \n",
       "2                    0    0  256       0              0  \n",
       "3                    0    0  512       0              0  \n",
       "4                    0    0  256       0              0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(columns= ['first', 'second' , 'Layer1HDD', \"Layer1SSD\", 'Layer1Hybrid', 'Layer1Flash_Storage', 'Layer2HDD', \"Layer2SSD\", 'Layer2Hybrid', 'Layer2Flash_Storage'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
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       "      <th>Memory</th>\n",
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       "      <th>OpSys</th>\n",
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       "      <th>IPS</th>\n",
       "      <th>ppi</th>\n",
       "      <th>CPU Brand</th>\n",
       "      <th>HDD</th>\n",
       "      <th>SSD</th>\n",
       "      <th>Hybrid</th>\n",
       "      <th>Flash_Storage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>773</th>\n",
       "      <td>Dell</td>\n",
       "      <td>Gaming</td>\n",
       "      <td>8</td>\n",
       "      <td>1000 HDD</td>\n",
       "      <td>Nvidia GeForce GTX 1050</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.62</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>828</th>\n",
       "      <td>Acer</td>\n",
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       "      <td>4</td>\n",
       "      <td>32 Flash Storage</td>\n",
       "      <td>Intel HD Graphics 400</td>\n",
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       "      <td>1.35</td>\n",
       "      <td>18914.40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>135.094211</td>\n",
       "      <td>Other Intel Processor</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
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       "    <tr>\n",
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       "      <td>4</td>\n",
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       "      <td>Intel HD Graphics 400</td>\n",
       "      <td>Chrome OS</td>\n",
       "      <td>1.25</td>\n",
       "      <td>20725.92</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>135.094211</td>\n",
       "      <td>Other Intel Processor</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>857</th>\n",
       "      <td>HP</td>\n",
       "      <td>2 in 1 Convertible</td>\n",
       "      <td>8</td>\n",
       "      <td>256 SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.28</td>\n",
       "      <td>136343.52</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>165.632118</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>963</th>\n",
       "      <td>Toshiba</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>512 SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.10</td>\n",
       "      <td>95371.20</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>176.232574</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company            TypeName  Ram            Memory  \\\n",
       "773     Dell              Gaming    8          1000 HDD   \n",
       "828     Acer             Netbook    4  32 Flash Storage   \n",
       "959     Acer  2 in 1 Convertible    4  32 Flash Storage   \n",
       "857       HP  2 in 1 Convertible    8           256 SSD   \n",
       "963  Toshiba           Ultrabook    8           512 SSD   \n",
       "\n",
       "                         Gpu       OpSys  Weight      Price  Touch Screen  \\\n",
       "773  Nvidia GeForce GTX 1050  Windows 10    2.62   58554.72             0   \n",
       "828    Intel HD Graphics 400   Chrome OS    1.35   18914.40             0   \n",
       "959    Intel HD Graphics 400   Chrome OS    1.25   20725.92             1   \n",
       "857    Intel HD Graphics 620  Windows 10    1.28  136343.52             1   \n",
       "963    Intel HD Graphics 620  Windows 10    1.10   95371.20             1   \n",
       "\n",
       "     IPS         ppi              CPU Brand   HDD  SSD  Hybrid  Flash_Storage  \n",
       "773    0  141.211998          Intel Core i7  1000    0       0              0  \n",
       "828    1  135.094211  Other Intel Processor     0    0       0             32  \n",
       "959    1  135.094211  Other Intel Processor     0    0       0             32  \n",
       "857    0  165.632118          Intel Core i7     0  256       0              0  \n",
       "963    0  176.232574          Intel Core i7     0  512       0              0  "
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(columns = \"Memory\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
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       "      <th>Ram</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
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       "      <th>Touch Screen</th>\n",
       "      <th>IPS</th>\n",
       "      <th>ppi</th>\n",
       "      <th>CPU Brand</th>\n",
       "      <th>HDD</th>\n",
       "      <th>SSD</th>\n",
       "      <th>Hybrid</th>\n",
       "      <th>Flash_Storage</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>Intel Iris Plus Graphics 640</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>71378.6832</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>47895.5232</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>127.677940</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>30636.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>16</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>135195.3360</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>220.534624</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>512</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>96095.8080</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Ram                           Gpu  OpSys  Weight  \\\n",
       "0   Apple  Ultrabook    8  Intel Iris Plus Graphics 640  macOS    1.37   \n",
       "1   Apple  Ultrabook    8        Intel HD Graphics 6000  macOS    1.34   \n",
       "2      HP   Notebook    8         Intel HD Graphics 620  No OS    1.86   \n",
       "3   Apple  Ultrabook   16            AMD Radeon Pro 455  macOS    1.83   \n",
       "4   Apple  Ultrabook    8  Intel Iris Plus Graphics 650  macOS    1.37   \n",
       "\n",
       "         Price  Touch Screen  IPS         ppi      CPU Brand  HDD  SSD  \\\n",
       "0   71378.6832             0    1  226.983005  Intel Core i5    0  128   \n",
       "1   47895.5232             0    0  127.677940  Intel Core i5    0    0   \n",
       "2   30636.0000             0    0  141.211998  Intel Core i5    0  256   \n",
       "3  135195.3360             0    1  220.534624  Intel Core i7    0  512   \n",
       "4   96095.8080             0    1  226.983005  Intel Core i5    0  256   \n",
       "\n",
       "   Hybrid  Flash_Storage  \n",
       "0       0              0  \n",
       "1       0            128  \n",
       "2       0              0  \n",
       "3       0              0  \n",
       "4       0              0  "
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ram              0.743007\n",
       "Weight           0.210370\n",
       "Price            1.000000\n",
       "Touch Screen     0.191226\n",
       "IPS              0.252208\n",
       "ppi              0.473487\n",
       "HDD             -0.096441\n",
       "SSD              0.670799\n",
       "Hybrid           0.007989\n",
       "Flash_Storage   -0.040511\n",
       "Name: Price, dtype: float64"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()[\"Price\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(columns = [\"Hybrid\", \"Flash_Storage\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "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": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Gpu\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Gpu Brand\"] = df[\"Gpu\"].apply(lambda x : x.split()[0]) # the purpose of lambda function is that it apply this to each element one by one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intel     722\n",
       "Nvidia    400\n",
       "AMD       180\n",
       "ARM         1\n",
       "Name: Gpu Brand, dtype: int64"
      ]
     },
     "execution_count": 286,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Gpu Brand\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>30636.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>Intel</td>\n",
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       "      <td>AMD Radeon Pro 455</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>220.534624</td>\n",
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       "      <td>512</td>\n",
       "      <td>AMD</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>96095.8080</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>Intel</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Ram                           Gpu  OpSys  Weight  \\\n",
       "0   Apple  Ultrabook    8  Intel Iris Plus Graphics 640  macOS    1.37   \n",
       "1   Apple  Ultrabook    8        Intel HD Graphics 6000  macOS    1.34   \n",
       "2      HP   Notebook    8         Intel HD Graphics 620  No OS    1.86   \n",
       "3   Apple  Ultrabook   16            AMD Radeon Pro 455  macOS    1.83   \n",
       "4   Apple  Ultrabook    8  Intel Iris Plus Graphics 650  macOS    1.37   \n",
       "\n",
       "         Price  Touch Screen  IPS         ppi      CPU Brand  HDD  SSD  \\\n",
       "0   71378.6832             0    1  226.983005  Intel Core i5    0  128   \n",
       "1   47895.5232             0    0  127.677940  Intel Core i5    0    0   \n",
       "2   30636.0000             0    0  141.211998  Intel Core i5    0  256   \n",
       "3  135195.3360             0    1  220.534624  Intel Core i7    0  512   \n",
       "4   96095.8080             0    1  226.983005  Intel Core i5    0  256   \n",
       "\n",
       "  Gpu Brand  \n",
       "0     Intel  \n",
       "1     Intel  \n",
       "2     Intel  \n",
       "3       AMD  \n",
       "4     Intel  "
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ram             0.743007\n",
       "Weight          0.210370\n",
       "Price           1.000000\n",
       "Touch Screen    0.191226\n",
       "IPS             0.252208\n",
       "ppi             0.473487\n",
       "HDD            -0.096441\n",
       "SSD             0.670799\n",
       "Name: Price, dtype: float64"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()[\"Price\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df[\"Gpu Brand\"] != \"ARM\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
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       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>16</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>135195.3360</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>220.534624</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>512</td>\n",
       "      <td>AMD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>96095.8080</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Ram                           Gpu  OpSys  Weight  \\\n",
       "0   Apple  Ultrabook    8  Intel Iris Plus Graphics 640  macOS    1.37   \n",
       "1   Apple  Ultrabook    8        Intel HD Graphics 6000  macOS    1.34   \n",
       "2      HP   Notebook    8         Intel HD Graphics 620  No OS    1.86   \n",
       "3   Apple  Ultrabook   16            AMD Radeon Pro 455  macOS    1.83   \n",
       "4   Apple  Ultrabook    8  Intel Iris Plus Graphics 650  macOS    1.37   \n",
       "\n",
       "         Price  Touch Screen  IPS         ppi      CPU Brand  HDD  SSD  \\\n",
       "0   71378.6832             0    1  226.983005  Intel Core i5    0  128   \n",
       "1   47895.5232             0    0  127.677940  Intel Core i5    0    0   \n",
       "2   30636.0000             0    0  141.211998  Intel Core i5    0  256   \n",
       "3  135195.3360             0    1  220.534624  Intel Core i7    0  512   \n",
       "4   96095.8080             0    1  226.983005  Intel Core i5    0  256   \n",
       "\n",
       "  Gpu Brand  \n",
       "0     Intel  \n",
       "1     Intel  \n",
       "2     Intel  \n",
       "3       AMD  \n",
       "4     Intel  "
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x = df[\"Gpu Brand\"], y = df[\"Price\"])\n",
    "plt.xticks(rotation = 'vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(columns = \"Gpu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Windows 10      1072\n",
       "No OS             66\n",
       "Linux             62\n",
       "Windows 7         45\n",
       "Chrome OS         26\n",
       "macOS             13\n",
       "Mac OS X           8\n",
       "Windows 10 S       8\n",
       "Android            2\n",
       "Name: OpSys, dtype: int64"
      ]
     },
     "execution_count": 294,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"OpSys\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x = df[\"OpSys\"], y = df[\"Price\"])\n",
    "plt.xticks(rotation = 'vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Ram</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "      <th>Touch Screen</th>\n",
       "      <th>IPS</th>\n",
       "      <th>ppi</th>\n",
       "      <th>CPU Brand</th>\n",
       "      <th>HDD</th>\n",
       "      <th>SSD</th>\n",
       "      <th>Gpu Brand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>71378.6832</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>47895.5232</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>127.677940</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>30636.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>16</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>135195.3360</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>220.534624</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>512</td>\n",
       "      <td>AMD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>96095.8080</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Ram  OpSys  Weight        Price  Touch Screen  IPS  \\\n",
       "0   Apple  Ultrabook    8  macOS    1.37   71378.6832             0    1   \n",
       "1   Apple  Ultrabook    8  macOS    1.34   47895.5232             0    0   \n",
       "2      HP   Notebook    8  No OS    1.86   30636.0000             0    0   \n",
       "3   Apple  Ultrabook   16  macOS    1.83  135195.3360             0    1   \n",
       "4   Apple  Ultrabook    8  macOS    1.37   96095.8080             0    1   \n",
       "\n",
       "          ppi      CPU Brand  HDD  SSD Gpu Brand  \n",
       "0  226.983005  Intel Core i5    0  128     Intel  \n",
       "1  127.677940  Intel Core i5    0    0     Intel  \n",
       "2  141.211998  Intel Core i5    0  256     Intel  \n",
       "3  220.534624  Intel Core i7    0  512       AMD  \n",
       "4  226.983005  Intel Core i5    0  256     Intel  "
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [],
   "source": [
    "def oos(text):\n",
    "    if text == \"Windows 10\" or text == \"Windows 7\" or text == \"Windows 10 S\":\n",
    "        return \"Windows\"\n",
    "    elif text == \"macOS\" or text == \"Mac OS X\":\n",
    "        return \"Mac\"\n",
    "    else :\n",
    "        return \"Other\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"os\"] = df[\"OpSys\"].apply(oos)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Ram</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "      <th>Touch Screen</th>\n",
       "      <th>IPS</th>\n",
       "      <th>ppi</th>\n",
       "      <th>CPU Brand</th>\n",
       "      <th>HDD</th>\n",
       "      <th>SSD</th>\n",
       "      <th>Gpu Brand</th>\n",
       "      <th>os</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>71378.6832</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Mac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>47895.5232</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>127.677940</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Mac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>30636.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>16</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>135195.3360</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>220.534624</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>512</td>\n",
       "      <td>AMD</td>\n",
       "      <td>Mac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>96095.8080</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Mac</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Ram  OpSys  Weight        Price  Touch Screen  IPS  \\\n",
       "0   Apple  Ultrabook    8  macOS    1.37   71378.6832             0    1   \n",
       "1   Apple  Ultrabook    8  macOS    1.34   47895.5232             0    0   \n",
       "2      HP   Notebook    8  No OS    1.86   30636.0000             0    0   \n",
       "3   Apple  Ultrabook   16  macOS    1.83  135195.3360             0    1   \n",
       "4   Apple  Ultrabook    8  macOS    1.37   96095.8080             0    1   \n",
       "\n",
       "          ppi      CPU Brand  HDD  SSD Gpu Brand     os  \n",
       "0  226.983005  Intel Core i5    0  128     Intel    Mac  \n",
       "1  127.677940  Intel Core i5    0    0     Intel    Mac  \n",
       "2  141.211998  Intel Core i5    0  256     Intel  Other  \n",
       "3  220.534624  Intel Core i7    0  512       AMD    Mac  \n",
       "4  226.983005  Intel Core i5    0  256     Intel    Mac  "
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Mac', 'Other', 'Windows'], dtype=object)"
      ]
     },
     "execution_count": 305,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"os\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(columns = \"OpSys\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 308,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aAwBQLlTAVqBRKvyf6tHR0bpw4YIaNmyowMBA+3hUVJTOnj1rXz5CkmbMmKGnnnpKCQkJatasmXr27KlVq1apXr16RV67Y8eOWrRokebOnavIyEitWbNG48ePl7e39zXFOGHCBLm7uyssLEwBAQFKT0+//hsGAABljsVmo/5npBEjRujnn3/Wxo0bXf5dhyPvcPl3lIabkl4zOwRDrG7xpNkhlNjo3J/MDsEQ/au1MDuEElv2+w9mh2CIvX8NNjsEQ4z+1t/sEErsX7984vLvuPDGBMOu5TN8jmHXKgsqfEvR1ebMmaM77rhDVapU0Zdffqlly5bplVdeMTssAABKH8tCOEXCVUJbt27V7NmzdfbsWdWvX18LFizQgw8+aHZYAACUOpuVppkzJFwl9OGHH5odAgAAKONIuAAAgDF4StEpEi4AAGAM5nA5VeGXhQAAADAbFS4AAGAMJs07RcIFAACMwRwup2gpAgAAuBgVLgAAYAwqXE6RcAEAAGPwtkCnaCkCAAC4GBUuAABgDFqKTpFwAQAAY7AshFMkXAAAwBisNO8Uc7gAAABcjAoXAAAwBi1Fp0i4yrGgJ24zOwRDrG7xpNkhGOLOXc+YHUKJ5YU/ZXYIhqhxIc/sEEpsYudAs0MwxIPf+podgiGWj61pdgjlgo1J807RUgQAAHAxKlwAAMAYtBSdIuECAADG4ClFp2gpAgAAuBgVLgAAYAxaik6RcAEAAGPwlKJTtBQBAABcjAoXAAAwBi1Fp0i4AACAMXhK0SlaigAAwBhWm3HbNVq4cKFCQ0Pl7e2tdu3aaevWrVc8/syZMxo1apTq1KkjLy8vNW7cWKtXr77eO78qKlwAAKBc++CDDxQXF6dFixapXbt2mj9/vnr06KG9e/eqdu3ahY7Pzc3VHXfcodq1a+vf//636tatq19++UX+/v4ui5GECwAAGMLIdynm5OQoJyfHYczLy0teXl6Fjp07d65GjBihYcOGSZIWLVqkVatWaenSpZo0aVKh45cuXarTp09r06ZN8vDwkCSFhoYaFntRaCkCAABjGNhSTEhIkJ+fn8OWkJBQ6Ctzc3O1Y8cOxcTE2Mfc3NwUExOjzZs3FxnmihUr1L59e40aNUqBgYFq0aKFnn32WRUUFLjsR0OFCwAAlDnx8fGKi4tzGCuqunXy5EkVFBQoMDDQYTwwMFA///xzkdc+dOiQvv76aw0cOFCrV6/WgQMH9OijjyovL09Tp0417ib+gIQLAAAYw8BlIZy1D41gtVpVu3Ztvf7663J3d1fr1q3166+/6vnnnyfhAgAAZZwJy0LUqlVL7u7uOnbsmMP4sWPHFBQUVOQ5derUkYeHh9zd3e1jzZo1U0ZGhnJzc+Xp6Wl4nMzhMkhoaKjmz59vdhgAANxQPD091bp1ayUlJdnHrFarkpKS1L59+yLP6dixow4cOCDrHyb579u3T3Xq1HFJsiWRcBVp6NChslgsslgs8vT0VMOGDTV9+nTl5+c7PWfbtm166KGHSjFKAADKGJPW4YqLi9PixYu1bNkypaWlaeTIkcrOzrY/tTh48GDFx8fbjx85cqROnz6tsWPHat++fVq1apWeffZZjRo1ytAfxx/RUnSiZ8+eevPNN5WTk6PVq1dr1KhR8vDwcPgXJsleegwICDApUgAAygabSa/2ue+++3TixAlNmTJFGRkZatmypdasWWOfSJ+eni43t//VmEJCQrR27VqNHz9eERERqlu3rsaOHauJEye6LEYqXE54eXkpKChIt9xyi0aOHKmYmBitWLFCQ4cOVZ8+fTRz5kwFBwerSZMmkgq3FM+cOaOHH35YgYGB8vb2VosWLbRy5Ur7/m+//VadOnWSj4+PQkJCNGbMGGVnZ5f2bQIAUCHExsbql19+UU5OjrZs2aJ27drZ9yUnJ+utt95yOL59+/b67rvvdPHiRR08eFCTJ092mNNlNCpcxeTj46NTp05JkpKSklStWjUlJiYWeazValWvXr109uxZvf3222rQoIH27Nlj/xd58OBB9ezZU88884yWLl2qEydOKDY2VrGxsXrzzTdL7Z4AADAUL692ioTrKmw2m5KSkrR27VqNHj1aJ06cUJUqVbRkyRKnE+vWrVunrVu3Ki0tTY0bN5Yk1a9f374/ISFBAwcO1Lhx4yRJjRo10oIFCxQVFaVXX31V3t7eha5Z1Iq71rx8eXnwrxAAUEYYuNJ8RUNL0YmVK1fK19dX3t7e6tWrl+677z5NmzZNkhQeHn7FpxhSU1N100032ZOtP9u5c6feeust+fr62rcePXrIarXq8OHDRZ5T1Iq7z69IKfF9AgBgGBNfXl3WUR5xIjo6Wq+++qo8PT0VHBysSpX+96OqUqXKFc/18fG54v5z587p4Ycf1pgxYwrtu/nmm4s8p6gVd62fFH7FAQAAKHtIuJyoUqWKGjZseF3nRkRE6L///a/27dtXZJXr1ltv1Z49e67p+kWtuHuBdiIAoCypgJUpo9BSdIGoqCh17txZffv2VWJiog4fPqwvv/xSa9askSRNnDhRmzZtUmxsrFJTU7V//359/vnnio2NNTlyAACun81mM2yraEi4XOTjjz/WX/7yFw0YMEBhYWF64okn7G8hj4iI0IYNG7Rv3z516tRJrVq10pQpUxQcHGxy1AAAwBXoSRXhz2t1FGffkSNHHD7XqFFDS5cudXqdv/zlL/rqq6+uIzoAAMooWopOkXABAABjkHA5RUsRAADAxahwAQAAQ5j1LsXygIQLAAAYg4TLKVqKAAAALkaFCwAAGINXKTpFwgUAAAzBHC7nSLgAAIAxSLicYg4XAACAi1HhAgAAxmAOl1MkXAAAwBDM4XKOliIAAICLUeECAADGoKXoFAkXAAAwBC1F52gpAgAAuBgVLgAAYAxaik6RcAEAAEPYSLicoqUIAADgYlS4yrE3Jh02OwRDzM79yewQDJEX/pTZIZTYPT/NMDsEQ7wdOcXsEEosItfsCIzxS94Zs0MwxNZZXmaHUGJd4krhS6hwOUXCBQAADEFL0TkSLgAAYAwSLqeYwwUAAOBiVLgAAIAhaCk6R8IFAAAMQcLlHC1FAAAAF6PCBQAADEGFyzkSLgAAYAybxewIyixaigAAAC5GhQsAABiClqJzJFwAAMAQNistRWdoKQIAALgYFS4AAGAIWorOkXABAABD2HhK0SlaigAAwBA2q3HbtVq4cKFCQ0Pl7e2tdu3aaevWrcU67/3335fFYlGfPn2u/UuvQblJuLp06aJx48aZHQYAAChjPvjgA8XFxWnq1Kn6/vvvFRkZqR49euj48eNXPO/IkSOaMGGCOnXq5PIYi51wWSyWK27Tpk1zYZjX5/z584qPj1eDBg3k7e2tgIAARUVF6fPPPzc7NAAAKhyb1WLYdi3mzp2rESNGaNiwYQoLC9OiRYtUuXJlLV261Ok5BQUFGjhwoJ5++mnVr1+/pLd+VcWew3X06FH7rz/44ANNmTJFe/futY/5+voaG5kBHnnkEW3ZskUvvfSSwsLCdOrUKW3atEmnTp26ruvZbDYVFBSoUiWmvgEA8Gc2m3HXysnJUU5OjsOYl5eXvLy8HMZyc3O1Y8cOxcfH28fc3NwUExOjzZs3O73+9OnTVbt2bQ0fPlwbN240LnAnil3hCgoKsm9+fn6yWCz2z7Vr19bcuXN10003ycvLSy1bttSaNWvs5yYnJ8tisejMmTP2sdTUVFksFh05csQ+lpKSoi5duqhy5cqqXr26evTood9//92+32q16oknnlCNGjUUFBR01araihUrNHnyZN15550KDQ1V69atNXr0aP3zn/+0H5OTk6OJEycqJCREXl5eatiwod544w2HuL/88ku1bt1aXl5e+vbbb2W1WpWQkKB69erJx8dHkZGR+ve//+3w3bt27VKvXr3k6+urwMBADRo0SCdPnrTv79Kli8aMGXNN9wMAwI0iISFBfn5+DltCQkKh406ePKmCggIFBgY6jAcGBiojI6PIa3/77bd64403tHjxYpfEXhRD5nC9+OKLeuGFFzRnzhz9+OOP6tGjh+6++27t37+/2NdITU1Vt27dFBYWps2bN+vbb79V7969VVBQYD9m2bJlqlKlirZs2aLZs2dr+vTpSkxMdHrNoKAgrV69WmfPnnV6zODBg/Xee+9pwYIFSktL02uvvVaoWjdp0iQ999xzSktLU0REhBISErR8+XItWrRIu3fv1vjx4/XAAw9ow4YNkqQzZ86oa9euatWqlbZv3641a9bo2LFj+sc//uFw3Wu9HwAAyjIjW4rx8fHKzMx02P5YxbpeZ8+e1aBBg7R48WLVqlXLgLsuHkN6Y3PmzNHEiRPVv39/SdKsWbO0fv16zZ8/XwsXLizWNWbPnq02bdrolVdesY81b97c4ZiIiAhNnTpVktSoUSO9/PLLSkpK0h133FHkNV9//XUNHDhQNWvWVGRkpG6//Xbde++96tixoyRp3759+vDDD5WYmKiYmBhJKrKPO336dPt35OTk6Nlnn9W6devUvn17+znffvutXnvtNUVFRenll19Wq1at9Oyzz9qvsXTpUoWEhGjfvn1q3Ljxdd0PAABlmZErzRfVPixKrVq15O7urmPHjjmMHzt2TEFBQYWOP3jwoI4cOaLevXvbx6zWS49FVqpUSXv37lWDBg1KGH1hJa5wZWVl6bfffrMnMZd17NhRaWlpxb7O5QrXlURERDh8rlOnzhWfQOjcubMOHTqkpKQk3Xvvvdq9e7c6deqkGTNm2L/T3d1dUVFRV/zeNm3a2H994MABnT9/XnfccYd8fX3t2/Lly3Xw4EFJ0s6dO7V+/XqH/U2bNpUk+zHXej85OTnKyspy2PJsBUUeCwDAjcLT01OtW7dWUlKSfcxqtSopKcleGPmjpk2b6qefflJqaqp9u/vuuxUdHa3U1FSFhIS4JM5Smf3t5nYpr7P9YTZdXl6ewzE+Pj5XvY6Hh4fDZ4vFYs9Kr3ROp06d1KlTJ02cOFHPPPOMpk+frokTJxbrOyWpSpUq9l+fO3dOkrRq1SrVrVvX4bjLmfi5c+fUu3dvzZo1q9C16tSpc133k5CQoKefftphrFfVcN3pF1Hk8QAAlDYjJ81fi7i4OA0ZMkRt2rRR27ZtNX/+fGVnZ2vYsGGSLk0fqlu3rhISEuTt7a0WLVo4nO/v7y9JhcaNVOKEq1q1agoODlZKSopDpSglJUVt27aVJAUEBEi69KRj9erVJV2qLv1RRESEkpKSCiUVRgsLC1N+fr4uXryo8PBwWa1Wbdiwwd5SLM75Xl5eSk9Pd1oZu/XWW/Xxxx8rNDTUsCca4+PjFRcX5zD2RtjDhlwbAAAjmPXy6vvuu08nTpzQlClTlJGRYX947/JE+vT0dHvxxyyGZAOPP/64pk6dqgYNGqhly5Z68803lZqaqnfeeUeS1LBhQ4WEhGjatGmaOXOm9u3bpxdeeMHhGvHx8QoPD9ejjz6qRx55RJ6enlq/fr369et33ZPaunTpogEDBqhNmzaqWbOm9uzZo8mTJys6OlrVqlVTtWrVNGTIEP3zn//UggULFBkZqV9++UXHjx8vNMH9sqpVq2rChAkaP368rFarbr/9dmVmZiolJcV+vVGjRmnx4sUaMGCA/SnEAwcO6P3339eSJUvk7u5+zfdSVC/bw3Lt1wEAoCKKjY1VbGxskfuSk5OveO5bb71lfEB/YkjCNWbMGGVmZuqxxx7T8ePHFRYWphUrVqhRo0aSLrXO3nvvPY0cOVIRERH6y1/+omeeeUb9+vWzX6Nx48b66quvNHnyZLVt21Y+Pj5q166dBgwYcN1x9ejRQ8uWLdPkyZN1/vx5BQcH669//aumTJliP+bVV1/V5MmT9eijj+rUqVO6+eabNXny5Cted8aMGQoICFBCQoIOHTokf39/3XrrrfbzLlf8Jk6cqO7duysnJ0e33HKLevbsaXqGDQCAq/AuRecsNptZHVeU1MshD5gdgiFmZ+80OwRDzPUq//Pp7vlphtkhGOLtyClXP6iM693iP2aHYIheP1WMP2KeLwi8+kFlXJdjH7n8Ow6E9TDsWg33rDXsWmUBS6YDAABDWKlwOUV/CwAAwMWocAEAAEMwh8s5Ei4AAGAIs5aFKA9oKQIAALgYFS4AAGAI1j1wjoQLAAAYgpaic7QUAQAAXIwKFwAAMATrcDlHwgUAAAzBshDO0VIEAABwMSpcAADAEDyl6BwJFwAAMARzuJwj4QIAAIZgDpdzzOECAABwMSpcAADAEMzhco6ECwAAGII5XM7RUgQAAHAxKlzlWEie1ewQDNG/WguzQzBEjQt5ZodQYm9HTjE7BEM8sHO62SGUWGrkY2aHYIhQj4rx+9T5AnezQygXmDTvHAkXAAAwBC1F52gpAgAAuBgVLgAAYAgeUnSOhAsAABiClqJztBQBAABcjAoXAAAwBE8pOkfCBQAADFExFgFxDRIuAABgCJuocDnDHC4AAAAXo8IFAAAMYWVdCKdIuAAAgCGstBSdoqUIAADgYlS4AACAIZg07xwJFwAAMATLQjhHSxEAAMDFqHABAABD0FJ0jgqXQYYOHao+ffrYf22xWGSxWOTp6amGDRtq+vTpys/Ptx+/ePFiRUZGytfXV/7+/mrVqpUSEhJMih4AgJKzGrhVNFS4XKRnz5568803lZOTo9WrV2vUqFHy8PBQfHy8li5dqnHjxmnBggWKiopSTk6OfvzxR+3atcvssAEAuG4VMVEyCgmXi3h5eSkoKEiSNHLkSH366adasWKF4uPjtWLFCv3jH//Q8OHD7cc3b97crFABAICL0VIsJT4+PsrNzZUkBQUF6bvvvtMvv/xiclQAABjHJothW0VDwuViNptN69at09q1a9W1a1dJ0tSpU+Xv76/Q0FA1adJEQ4cO1YcffiirlWIsAKD8slqM2yoaEi4XWblypXx9feXt7a1evXrpvvvu07Rp0yRJderU0ebNm/XTTz9p7Nixys/P15AhQ9SzZ0+nSVdOTo6ysrIctjxbQSneEQAAZdfChQsVGhoqb29vtWvXTlu3bnV67OLFi9WpUydVr15d1atXV0xMzBWPNwIJl4tER0crNTVV+/fv14ULF7Rs2TJVqVLF4ZgWLVro0Ucf1dtvv63ExEQlJiZqw4YNRV4vISFBfn5+Dtu/s/eUxq0AAFAsVlkM267FBx98oLi4OE2dOlXff/+9IiMj1aNHDx0/frzI45OTkzVgwACtX79emzdvVkhIiLp3765ff/3ViB9DkUi4XKRKlSpq2LChbr75ZlWqdPVnE8LCwiRJ2dnZRe6Pj49XZmamw3ZvlTBDYwYAoCRsBm7XYu7cuRoxYoSGDRumsLAwLVq0SJUrV9bSpUuLPP6dd97Ro48+qpYtW6pp06ZasmSJrFarkpKSrvWWi42nFE0wcuRIBQcHq2vXrrrpppt09OhRPfPMMwoICFD79u2LPMfLy0teXl4OYx4W99IIFwCAUpeTk6OcnByHsaL+LMzNzdWOHTsUHx9vH3Nzc1NMTIw2b95crO86f/688vLyVKNGjZIH7gQVLhPExMTou+++U79+/dS4cWP17dtX3t7eSkpKUs2aNc0ODwCA62LkwqdFTaUpaoHwkydPqqCgQIGBgQ7jgYGBysjIKFbcEydOVHBwsGJiYq79pouJCpdB3nrrrSJ/XZS+ffuqb9++rg0IAIBSZrUY93hhfHy84uLiHMb+XN0ywnPPPaf3339fycnJ8vb2Nvz6l5FwAQCAMqeo9mFRatWqJXd3dx07dsxh/NixY/YFyJ2ZM2eOnnvuOa1bt04RERElivdqaCkCAABDmDFp3tPTU61bt3aY8H55AryzedGSNHv2bM2YMUNr1qxRmzZtruEbrw8VLgAAYAizlu+Oi4vTkCFD1KZNG7Vt21bz589Xdna2hg0bJkkaPHiw6tata58DNmvWLE2ZMkXvvvuuQkND7XO9fH195evr65IYSbgAAIAhzFoh/r777tOJEyc0ZcoUZWRkqGXLllqzZo19In16errc3P7X1Hv11VeVm5ure++91+E6U6dOtS9SbjQSLgAAUO7FxsYqNja2yH3JyckOn48cOeL6gP6EhAsAABjiWleIv5GQcAEAAENc6wrxNxKeUgQAAHAxKlwAAMAQZk2aLw9IuAAAgCHMWhaiPKClCAAA4GJUuAAAgCGYNO8cCRcAADAEc7ico6UIAADgYlS4AACAIZg07xwJFwAAMAQJl3MkXAAAwBA25nA5xRwuAAAAF6PCVY6FeGSbHYIhlv2+1+wQDDGxc6DZIZRYRK7ZERgjNfIxs0MosZY7XzA7BENsaXyP2SEYYnGPWmaHUC7QUnSOhAsAABiChMs5WooAAAAuRoULAAAYgpXmnSPhAgAAhmCleedoKQIAALgYFS4AAGAIJs07R8IFAAAMQcLlHC1FAAAAF6PCBQAADMFTis6RcAEAAEPwlKJzJFwAAMAQzOFyjjlcAAAALkaFCwAAGII5XM6RcAEAAENYSbmcoqUIAADgYlS4AACAIZg07xwVrjLCYrHos88+MzsMAACum83AraKhwlVGHD16VNWrVzc7DAAA4AIkXGVEUFCQ2SEAAFAitBSdo6VogC5duig2NlaxsbHy8/NTrVq19NRTT8lmu1QUDQ0N1YwZMzRgwABVqVJFdevW1cKFCx2uQUsRAFDeWS3GbRUNCZdBli1bpkqVKmnr1q168cUXNXfuXC1ZssS+//nnn1dkZKR++OEHTZo0SWPHjlViYqKJEQMAgNJCS9EgISEhmjdvniwWi5o0aaKffvpJ8+bN04gRIyRJHTt21KRJkyRJjRs3VkpKiubNm6c77rijWNfPyclRTk6Ow1iurUCeFndjbwQAgOvEOlzOUeEyyG233SaL5X810Pbt22v//v0qKCiwf/6j9u3bKy0trdjXT0hIkJ+fn8P2ZtZ+Y4IHAMAAPKXoHAlXOREfH6/MzEyHbVi1RmaHBQCAndXAraKhpWiQLVu2OHz+7rvv1KhRI7m7u9s//3l/s2bNin19Ly8veXl5OYzRTgQAoHwg4TJIenq64uLi9PDDD+v777/XSy+9pBdeeMG+PyUlRbNnz1afPn2UmJiojz76SKtWrTIxYgAAjMUcLudIuAwyePBgXbhwQW3btpW7u7vGjh2rhx56yL7/scce0/bt2/X000+rWrVqmjt3rnr06GFixAAAGIt0yzkSLoN4eHho/vz5evXVV4vcX61aNX344YdOz7+8ZhcAAKh4mDQPAAAMYeak+YULFyo0NFTe3t5q166dtm7desXjP/roIzVt2lTe3t4KDw/X6tWrr+Nbi4+ECwAAGMIqm2Hbtfjggw8UFxenqVOn6vvvv1dkZKR69Oih48ePF3n8pk2bNGDAAA0fPlw//PCD+vTpoz59+mjXrl1G/BiKZLHRyyq3vg+5x+wQDNEra6/ZIRhiT+dAs0MoMVuu2REY4/CuGmaHUGItd75w9YPKgQaNK8bvUz/2qGV2CCXm/956l39HXGh/w64198j7xT62Xbt2+stf/qKXX35ZkmS1WhUSEqLRo0fbFx3/o/vuu0/Z2dlauXKlfey2225Ty5YttWjRopIHXwQqXAAAwBBGLnyak5OjrKwsh+3Pb1yRpNzcXO3YsUMxMTH2MTc3N8XExGjz5s1Fxrl582aH4yWpR48eTo83AgkXAAAwhJFzuIp6w0pCQkKh7zx58qQKCgoUGOjYZQgMDFRGRkaRcWZkZFzT8UbgKUUAAFDmxMfHKy4uzmHszwuAlyckXAAAwBA2A1fiKuoNK0WpVauW3N3ddezYMYfxY8eOKSgoqMhzgoKCrul4I9BSBAAAhjBjWQhPT0+1bt1aSUlJ/4vDalVSUpLat29f5Dnt27d3OF6SEhMTnR5vBCpcAADAEGa92icuLk5DhgxRmzZt1LZtW82fP1/Z2dkaNmyYpEtvg6lbt659DtjYsWMVFRWlF154QXfddZfef/99bd++Xa+//rrLYiThAgAA5dp9992nEydOaMqUKcrIyFDLli21Zs0a+8T49PR0ubn9r6nXoUMHvfvuu3ryySc1efJkNWrUSJ999platGjhshhJuAAAgCHMXNgzNjZWsbGxRe5LTk4uNNavXz/169fPxVH9DwkXAAAwhFktxfKASfMAAAAuRoULAAAY4npeOn2jIOECAACGMHIdroqGliIAAICLUeECAACGoKXoHAlXObbeUtXsEAyx96/BZodgiAe/9TU7hBL7Je+M2SEYItSj/P+2v6XxPWaHYIiD+z43OwRD+AR3MjuEEssvhe+gpegcLUUAAAAXo8IFAAAMUf5ry65DwgUAAAxhtdFSdIaECwAAGIJ0yznmcAEAALgYFS4AAGAI3qXoHAkXAAAwBMtCOEdLEQAAwMWocAEAAEOwLIRzJFwAAMAQzOFyjpYiAACAi1HhAgAAhmDSvHMkXAAAwBDM4XKOliIAAICLUeECAACGsPEuRadIuAAAgCF4StE5Ei4AAGAI5nA5xxyuKxg6dKj69OlTaDw5OVkWi0Vnzpyx/9piscjNzU1+fn5q1aqVnnjiCR09etThvGnTptmPrVSpkmrVqqXOnTtr/vz5ysnJKaW7AgAApY2EyyB79+7Vb7/9pm3btmnixIlat26dWrRooZ9++snhuObNm+vo0aNKT0/X+vXr1a9fPyUkJKhDhw46e/asSdEDAFByNgP/qWhIuAxSu3ZtBQUFqXHjxurfv79SUlIUEBCgkSNHOhxXqVIlBQUFKTg4WOHh4Ro9erQ2bNigXbt2adasWSZFDwBAyVllM2yraEi4XMTHx0ePPPKIUlJSdPz48Sse27RpU/Xq1UuffPJJKUUHAABKE5Pmr2LlypXy9fV1GCsoKCjWuU2bNpUkHTlyRLVr177qsV999ZXT/Tk5OYXmeeXbClTJ4l6sWAAAcDWWhXCOCtdVREdHKzU11WFbsmRJsc69/B+exWIp1rFXOi4hIUF+fn4OW1LW7uLdBAAApcBq4FbRUOG6iipVqqhhw4YOY//973+LdW5aWpokKTQ0tFjH1qtXz+n++Ph4xcXFOYy92vzhYsUBAADMRcLlIhcuXNDrr7+uzp07KyAg4IrH/vzzz1qzZo3i4+OdHuPl5SUvLy+HMdqJAICypCI+XWgUEi6DHD9+XBcvXtTZs2e1Y8cOzZ49WydPniw0ET4/P18ZGRmyWq06deqUkpOT9cwzz6hly5Z6/PHHTYoeAICSq4hPFxqFhMsgTZo0kcVika+vr+rXr6/u3bsrLi5OQUFBDsft3r1bderUkbu7u/z8/BQWFqb4+HiNHDmyUAULAABUDBYbjxSUWy/c/IDZIRhieKffzA7BEA9+63v1g8q4X/LOmB2CIUI9qpsdQoltOXvI7BAMcXDf52aHYAif4E5mh1Bi+bm/uvw7ut3U3bBrJf3X+ZP75REVLgAAYAhais6RcAEAAEMwad451uECAABwMSpcAADAEFamhTtFwgUAAAxBuuUcLUUAAAAXo8IFAAAMwVOKzlHhAgAAhrDKZtjmKqdPn9bAgQNVrVo1+fv7a/jw4Tp37twVjx89erSaNGkiHx8f3XzzzRozZowyMzOv6XtJuAAAwA1j4MCB2r17txITE7Vy5Up98803euihh5we/9tvv+m3337TnDlztGvXLr311ltas2aNhg8ffk3fS0sRAAAYoqy/vCYtLU1r1qzRtm3b1KZNG0nSSy+9pDvvvFNz5sxRcHBwoXNatGihjz/+2P65QYMGmjlzph544AHl5+erUqXipVJUuAAAgCGMbCnm5OQoKyvLYcvJySlRfJs3b5a/v7892ZKkmJgYubm5acuWLcW+TmZmpqpVq1bsZEsi4QIAAGVQQkKC/Pz8HLaEhIQSXTMjI0O1a9d2GKtUqZJq1KihjIyMYl3j5MmTmjFjxhXbkEUh4QIAAIawGfhPfHy8MjMzHbb4+Pgiv3fSpEmyWCxX3H7++ecS319WVpbuuusuhYWFadq0add0LnO4AACAIYycw+Xl5SUvL69iHfvYY49p6NChVzymfv36CgoK0vHjxx3G8/Pzdfr0aQUFBV3x/LNnz6pnz56qWrWqPv30U3l4eBQrtstIuAAAgCHMWocrICBAAQEBVz2uffv2OnPmjHbs2KHWrVtLkr7++mtZrVa1a9fO6XlZWVnq0aOHvLy8tGLFCnl7e19zjLQUAQDADaFZs2bq2bOnRowYoa1btyolJUWxsbHq37+//QnFX3/9VU2bNtXWrVslXUq2unfvruzsbL3xxhvKyspSRkaGMjIyVFBQUOzvpsIFAAAMUdaXhZCkd955R7GxserWrZvc3NzUt29fLViwwL4/Ly9Pe/fu1fnz5yVJ33//vf0JxoYNGzpc6/DhwwoNDS3W91ps5eGngyKdi+9rdgiGGPluxfhP8LWxNc0OocS2zjpjdgiGOC93s0MosQ5dj5kdgiFqfbzP7BAMceG3jWaHUGIeteq7/DsigzoYdq2dGZsMu1ZZQEsRAADAxWgpAgAAQ9h4ebVTJFwAAMAQVmYpOUVLEQAAwMWocAEAAEPQUnSOhAsAABiClqJztBQBAABcjAoXAAAwBC1F50i4AACAIWgpOkfCBQAADEGFyznmcAEAALgYFS4AAGAIWorOkXABAABD0FJ0jpYiAACAi1HhAgAAhrDZrGaHUGaRcAEAAENYaSk6RUsRAADAxUi4rsGJEyc0cuRI3XzzzfLy8lJQUJB69OihlJQUSdLOnTt19913q3bt2vL29lZoaKjuu+8+HT9+XJJ05MgRWSwW+1a1alU1b95co0aN0v79+828NQAASsxmsxm2VTS0FK9B3759lZubq2XLlql+/fo6duyYkpKSdOrUKZ04cULdunXTX//6V61du1b+/v46cuSIVqxYoezsbIfrrFu3Ts2bN9f58+f1008/6cUXX1RkZKS++OILdevWzaS7AwCgZGgpOkfCVUxnzpzRxo0blZycrKioKEnSLbfcorZt20qSPvvsM2VmZmrJkiWqVOnSj7VevXqKjo4udK2aNWsqKChIklS/fn317t1b3bp10/Dhw3Xw4EG5u7uX0l0BAIDSQEuxmHx9feXr66vPPvtMOTk5hfYHBQUpPz9fn3766TWXQt3c3DR27Fj98ssv2rFjh1EhAwBQqmgpOkfCVUyVKlXSW2+9pWXLlsnf318dO3bU5MmT9eOPP0qSbrvtNk2ePFn333+/atWqpV69eun555/XsWPHinX9pk2bSro0z6soOTk5ysrKcthy8gsMuTcAAIxgtdkM2yoaEq5r0LdvX/32229asWKFevbsqeTkZN1666166623JEkzZ85URkaGFi1apObNm2vRokVq2rSpfvrpp6te+3I2b7FYityfkJAgPz8/h+2FzXsNuzcAAErKZuA/FQ0J1zXy9vbWHXfcoaeeekqbNm3S0KFDNXXqVPv+mjVrql+/fpozZ47S0tIUHBysOXPmXPW6aWlpki7N+ypKfHy8MjMzHbbH2jcx5qYAAIBLkXCVUFhYWKGnEC/z9PRUgwYNnO6/zGq1asGCBapXr55atWpV5DFeXl6qVq2aw+ZVicn1AICygzlczvGUYjGdOnVK/fr10z//+U9FRESoatWq2r59u2bPnq177rlHK1eu1Pvvv6/+/furcePGstls+uKLL7R69Wq9+eabha6VkZGh8+fPa9euXZo/f762bt2qVatW8YQiAKDcYlkI50i4isnX11ft2rXTvHnzdPDgQeXl5SkkJEQjRozQ5MmTdfToUVWuXFmPPfaY/vOf/8jLy0uNGjXSkiVLNGjQIIdrxcTESJIqV66sW265RdHR0Xr99dfVsGFDM24NAAC4GAlXMXl5eSkhIUEJCQlF7q9fv75ef/31K14jNDS0QpZJAQCQxJ9xV0DCBQAADFERl3MwCpPmAQAAXIwKFwAAMAQtRedIuAAAgCF4StE5WooAAAAuRoULAAAYgpaicyRcAADAEDyl6BwJFwAAMERFfOm0UZjDBQAA4GJUuAAAgCFoKTpHwgUAAAzBpHnnaCkCAAC4GBUuAABgCCbNO0eFCwAAGMJmsxm2ucrp06c1cOBAVatWTf7+/ho+fLjOnTtX7Pvr1auXLBaLPvvss2v6XhIuAABwwxg4cKB2796txMRErVy5Ut98840eeuihYp07f/58WSyW6/peWooAAMAQZX3SfFpamtasWaNt27apTZs2kqSXXnpJd955p+bMmaPg4GCn56ampuqFF17Q9u3bVadOnWv+bipcAADAEDYDt5ycHGVlZTlsOTk5JYpv8+bN8vf3tydbkhQTEyM3Nzdt2bLF6Xnnz5/X/fffr4ULFyooKOi6vpuECwAAlDkJCQny8/Nz2BISEkp0zYyMDNWuXdthrFKlSqpRo4YyMjKcnjd+/Hh16NBB99xzz/V/uQ1w4uLFi7apU6faLl68aHYoJVIR7qMi3IPNxn2UJRXhHmy2inEfFeEeXOHixYu2zMxMh83Zz2jixIlXLZqlpaXZZs6caWvcuHGh8wMCAmyvvPJKkdf+/PPPbQ0bNrSdPXvWPibJ9umnn17T/Vj+/4lAIVlZWfLz81NmZqaqVatmdjjXrSLcR0W4B4n7KEsqwj1IFeM+KsI9mO3EiRM6derUFY+pX7++3n77bT322GP6/fff7eP5+fny9vbWRx99pL/97W+Fzhs3bpwWLFggN7f/NQULCgrk5uamTp06KTk5uVgxMmkeAACUawEBAQoICLjqce3bt9eZM2e0Y8cOtW7dWpL09ddfy2q1ql27dkWeM2nSJD344IMOY+Hh4Zo3b5569+5d7BhJuAAAwA2hWbNm6tmzp0aMGKFFixYpLy9PsbGx6t+/v/0JxV9//VXdunXT8uXL1bZtWwUFBRU5Uf7mm29WvXr1iv3dTJoHAAA3jHfeeUdNmzZVt27ddOedd+r222/X66+/bt+fl5envXv36vz584Z+LxUuOOXl5aWpU6fKy8vL7FBKpCLcR0W4B4n7KEsqwj1IFeM+KsI9lCc1atTQu+++63R/aGjoVdcTu57p70yaBwAAcDFaigAAAC5GwgUAAOBiJFwAAAAuRsIFAADgYiRcqHDS09OLfILEZrMpPT3dhIgAADc6nlJEhePu7q6jR48WekHpqVOnVLt2bRUUFJgU2Y0pKSlJSUlJOn78uKxWq8O+pUuXmhQVYC6bzaYdO3boyJEjslgsqlevnlq1aiWLxWJ2aHAR1uFCkS5evKgff/yxyD8k7777bpOiKh6bzVbkb1rnzp2Tt7e3CRGVXG5urg4fPqwGDRqoUqXy87/t008/renTp6tNmzaqU6dOhfrDZMOGDcrOzlb79u1VvXp1s8O5ohUrVqhXr17y8PDQihUrrnhsWf//uyJYv369hg8frl9++cVejb+cdC1dulSdO3c2OUK4AhUuFLJmzRoNHjxYJ0+eLLTPYrGU2QpRXFycJOnFF1/UiBEjVLlyZfu+goICbdmyRe7u7kpJSTErxGt2/vx5jR49WsuWLZMk7du3T/Xr19fo0aNVt25dTZo0yeQIr6xOnTqaPXu2Bg0aZHYo123WrFk6d+6cZsyYIelSQt+rVy999dVXkqTatWsrKSlJzZs3NzPMK3Jzc1NGRoZq167t8ALePyvL/39flp2drVmzZumTTz5xqA7de++9mjBhgsP/92XRgQMHFBkZqXbt2mns2LFq2rSpbDab9uzZowULFmj79u368ccfVb9+fbNDhcFIuFBIo0aN1L17d02ZMkWBgYFmh1Ns0dHRki5VHtq3by9PT0/7Pk9PT4WGhmrChAlq1KiRWSFes7FjxyolJUXz589Xz5497b8Rf/7555o2bZp++OEHs0O8opo1a2rr1q1q0KCB2aFct1tvvVUTJ07UfffdJ0n66KOPNGTIECUmJqpZs2YaPHiwKleurA8//NDkSCu+3NxcdejQQbt27VKvXr3syUpaWprWrFmjW2+9Vd988408PDzMDtWp2NhYpaWlKSkpqdA+m82mmJgYhYWF6aWXXjIhOriUDfiTqlWr2g4cOGB2GNdt6NChtszMTLPDMMTNN99s27x5s81ms9l8fX1tBw8etNlsNtv+/fttVatWNTO0YnniiSds06dPNzuMEvH397ft2bPH/nno0KG2QYMG2T9v3rzZdtNNN5kR2g1n/vz5tsDAQNvPP/9caF9aWpotMDDQtmDBAhMiK77mzZvbVqxY4XT/ihUrbM2bNy/FiFBays9kEJSae++9V8nJyeW2KvHmm2+aHYJhTpw4UWjyv3SprVIe5kNdvHhRr7/+utatW6eIiIhClYe5c+eaFFnx5efnO7zjbvPmzRo3bpz9c3BwcJHt97IsKSlJ8+bNU1pamiSpWbNmGjdunGJiYkyO7Mo++eQTPfXUU2rSpEmhfU2bNtX//d//6d///rdGjx5tQnTFk56ervDwcKf7W7RooV9++aUUI0JpIeFCIS+//LL69eunjRs3Kjw8vNAfkmPGjDEpsuLJzs7Wc8895/TJuEOHDpkU2bVr06aNVq1aZf8D5HKStWTJErVv397M0Irlxx9/VMuWLSVJu3btcthXHhJGSWrQoIG++eYb1a9fX+np6dq3b5/DpOb//ve/qlmzpokRXptXXnlFY8eO1b333quxY8dKkr777jvdeeedmjdvnkaNGmVyhM7t2bNHXbp0cbo/Ojpa06dPL72ArsO5c+euOM+scuXKOn/+fClGhNJCwoVC3nvvPX311Vfy9vZWcnKywx+MFoulzCdcDz74oDZs2KBBgwaV+yfjnn32WfXq1Ut79uxRfn6+XnzxRe3Zs0ebNm3Shg0bzA7vqtavX292CCU2atQoxcbGauPGjfruu+/Uvn17hYWF2fd//fXXatWqlYkRXptnn31W8+bNU2xsrH1szJgx6tixo5599tkynXCdOXPmisltzZo1lZmZWYoRXZ89e/YoIyOjyH3lrVqK4mPSPAoJCgrSmDFjNGnSpCs+0VRW+fv7a9WqVerYsaPZoRji4MGDeu6557Rz506dO3fOPon7Sm2JsubAgQM6ePCgOnfuLB8fH6dLd5RVS5cu1RdffKGgoCBNnTpVQUFB9n2PPvqo7rjjDv3tb38zMcLi8/X1VWpqqho2bOgwvn//frVq1Urnzp0zKbKrc3d3V0ZGhgICAorcf+zYMQUHB5fpJy3d3NxksViKXJz58nh5eFoU146EC4XUqFFD27ZtK7dzuOrVq6fVq1erWbNmZodywzt16pT+8Y9/aP369bJYLNq/f7/q16+vf/7zn6pevbpeeOEFs0O84dx///1q1aqVHn/8cYfxOXPmaPv27Xr//fdNiuzq3Nzc1KJFC6dr0eXn52v37t1lOlkp7vysW265xcWRoLSRcKGQ8ePHKyAgQJMnTzY7lOvy9ttv6/PPP9eyZcvK/Jo8V7N69Wq5u7urR48eDuNr166V1WpVr169TIqseAYPHqzjx49ryZIlatasmXbu3Kn69etr7dq1iouL0+7du80O8aoKCgo0Z84crVixQrm5uerWrZumTp0qHx8fs0O7Ls8884zmzJmjjh072ucBfvfdd0pJSdFjjz2matWq2Y8ta9MHnn766WIdN3XqVBdHAlw7Ei4UMmbMGC1fvlyRkZHl5smyP78S48CBA7LZbAoNDS0U//fff1/a4V23iIgIPffcc7rzzjsdxtesWaOJEydq586dJkVWPEFBQVq7dq0iIyNVtWpVe8J16NAhRURElOn21WUzZszQtGnTFBMTIx8fH61du1YDBgwot68lqlevXrGOs1gs5eoBk/Li5MmTys7Odqhg7d69W3PmzFF2drb69Omj+++/38QI4SpMmkchP/30k30ScHl5sqxPnz5mh+AS+/fvd5igfVnTpk114MABEyK6NtnZ2UVWGU+fPu2w1EJZtnz5cr3yyit6+OGHJUnr1q3TXXfdpSVLlpTLOY6HDx+2/9r2h9fKlDcnT560rzQfGhpabp4UHT16tIKDg+3t9OPHj6tTp04KDg5WgwYNNHToUBUUFJTrtzPACVNW/wJQLIGBgbakpKRC44mJibaAgAATIro2vXr1sj355JM2m+3Swq2HDh2yFRQU2Pr162fr27evydEVj6enpy09Pd1hzMvLy/af//zHpIhKbsmSJbbmzZvbPD09bZ6enrbmzZvbFi9ebHZYxbJr1y5bp06dbG5ubg5bdHR0kQuiljWhoaG25ORk++fnn3/e1qBBA1teXp79c7t27cwKDy5EhQsow+655x6NGzdOn376qf0hhgMHDuixxx4rFy8Znj17trp166bt27crNzdXTzzxhHbv3q3Tp0+Xm3da5ufnF3rpuYeHh/Ly8kyKqGSmTJmiuXPnavTo0fY5XJs3b9b48eOVnp5eptexysjIUFRUlAICAjR37lyH9xAuXrxYnTp10q5du4pcLLisyMjIUGhoqP3z119/rb///e/2BwHuvvtuJSQkmBQdXIk5XCjS9u3b9eGHHyo9PV25ubkO+z755BOToiqe6tWrF9kisVgs8vb2VsOGDTV06FANGzbMhOiuTWZmpnr27Knt27frpptuknRpoc1OnTrpk08+kb+/v7kBFkNmZqZeeukl/fjjj/ZlLUaNGqU6deqYHVqxuLm5qVevXg4t0C+++EJdu3ZVlSpV7GNl/f+LywICArRgwQINGDDAYfy9997T6NGjy/Q6UBMnTtS6deuUkpJSKAm+cOGCbr/9dnXv3r1MJyyBgYH66quvFBkZKUmqVauWXnvtNfXt21dS+VieA9eHChcKef/99zV48GD16NFDX331lbp37659+/bp2LFj5WKtoSlTpmjmzJnq1auX2rZtK0naunWr1qxZo1GjRunw4cMaOXKk8vPzNWLECJOjvTI/Pz9t2rRJiYmJ2rlzp3x8fBQREeGw0nlZ5+fnpyeffNLsMK7b4MGDCyXwDzzwgEnRlFxeXp7atGlTaLx169bKz883IaLiS0xM1KRJkwolW5Lk4+Ojxx9/XLNnzy7TCddtt92mBQsWaPHixfrkk0909uxZde3a1b5/3759CgkJMTFCuIzJLU2UQeHh4baXX37ZZrP974XJVqvVNmLECNuUKVNMju7q/v73v9teffXVQuOLFi2y/f3vf7fZbDbbggULbC1atCjt0G5I33zzjW3gwIG29u3b2/773//abDabbfny5baNGzeaHNmNKTY21jZ+/PhC44899pjt0UcfNSGi4vPz87Pt37/f6f79+/fb/Pz8Si+g65CammqrVauWzdPT0+bm5maf43jZAw88YHv44YdNig6uREsRhVSpUkW7d++2P/mTnJys8PBwpaWlqWvXrjp69KjZIV6Rs5W0Dxw4oJYtW+rcuXM6ePCgIiIilJ2dbVKUzi1YsEAPPfSQvL29tWDBgiseW9bWSfqzjz/+WIMGDdLAgQP1r3/9S3v27FH9+vX18ssva/Xq1Vq9erXZIV7V3//+96seY7FY9PHHH5dCNCU3evRoLV++XCEhIbrtttskSVu2bFF6eroGDx7ssIxKWVsCxt3dXUePHnU6R+vYsWOqW7duma/UnTx5UikpKQoKClK7du0c9q1atUphYWHFXr4D5QctRRRSvXp1nT17VpJUt25d7dq1S+Hh4Tpz5ky5eKlqjRo19MUXX2j8+PEO41988YVq1Kgh6dJyBVWrVjUjvKuaN2+eBg4cKG9vb82bN8/pceXhvZbPPPOMFi1apMGDBzusYN6xY0c988wzJkZWfH5+fmaHYKhdu3bp1ltvlXTptVHSpXlEtWrVclgGpqwuFXH27NkiW4qSlJWVVeQrc8qSzZs369SpU7rnnnvsY8uXL9fUqVPt63DFxMSYGCFchYQLhXTu3FmJiYkKDw9Xv379NHbsWH399ddKTEx0mGtQVj311FMaOXKk1q9fb5/DtW3bNq1evVqLFi2SdGkuSFRUlJlhOvXHdZL++OvyaO/evUXON/Pz89OZM2dKP6Dr8Oabb5odgqHK8wvFbTabGjdufMX9ZTVRvGz69Onq0qWL/vrXv0q6tO7h8OHDNXToUDVr1kzPP/+8goODNW3aNHMDheFIuFDIyy+/rIsXL0qS/u///k8eHh7atGmT+vbtqwkTJpgc3dWNGDFCYWFhevnll+1PjjVp0kQbNmxQhw4dJEmPPfaYmSEWS15enpo2baqVK1eW2/dCBgUF6cCBAw6PwUvSt99+q/r165sTFMqt8pwsXpaamqoZM2bYP7///vtq166dFi9eLEkKCQnR1KlTSbgqIBIuFHK57SZdeiR+0qRJunjxohYuXKhWrVopIyPDxOiKp2PHjurYsaPZYZSIh4eHPfEtr0aMGKGxY8dq6dKlslgs+u2337R582ZNmDBBTz31lNnhoZy5/AaM8uz3339XYGCg/fOGDRsc3on6l7/8Rf/5z3/MCA0uRsIFu5ycHE2bNk2JiYny9PTUE088oT59+ujNN9/Uk08+KXd390LzosqKrKws+0t3s7KyrnjsH1/OW9aNGjVKs2bN0pIlS+wLI5YnkyZNktVqVbdu3XT+/Hl17txZXl5emjBhgkaPHm12eChn/P39i9UyLCgoKIVork9gYKAOHz6skJAQ5ebm6vvvv3d4KffZs2cLvf8VFQNPKcJu4sSJeu211xQTE6NNmzbpxIkTGjZsmL777jtNnjxZ/fr1k7u7u9lhFumPTy+5ubkV+Zvy5fkdZfk34z/729/+pqSkJPn6+io8PNxhoU2pbC+2WVBQoJSUFEVERKhy5co6cOCAzp07p7CwMPn6+podHsqhDRs22H9ts9l05513asmSJapbt67DcWV1fqYkjRw5Ujt37tSsWbP02WefadmyZfrtt9/k6ekpSXrnnXc0f/58bdu2zeRIYbTy91dmuMxHH32k5cuX6+6779auXbsUERGh/Px87dy5s8xPRP3666/trdCKMM/jMn9/f/sK1OWNu7u7unfvrrS0NPn7+xf5Em7gWvw5kXJ3d9dtt91WruYDzpgxQ3//+98VFRUlX19fLVu2zJ5sSdLSpUvVvXt3EyOEq1Dhgp2np6cOHz5s/9uij4+Ptm7dqvDwcJMju/FYrVY9//zzWrFihXJzc9W1a1dNmzZNPj4+Zod2Tdq0aaNZs2apW7duZoeCCqhq1arauXNnuUq4LsvMzJSvr2+hrsHp06fl6+vrkIShYnAzOwCUHQUFBQ7/k1eqVKnctn42btyoBx54QB06dNCvv/4qSfrXv/6lb7/91uTIimfmzJmaPHmyfH19VbduXS1YsECjRo0yO6xr9swzz2jChAlauXKljh49qqysLIcNuFH5+fkVOUWjRo0aJFsVFBUu2P35Jb1FvaBXKtvzhqSKsbp5o0aNNGHCBD388MOSpHXr1umuu+7ShQsX5OZWfv6e9MdY/9iWLo/z6VD2VK1aVT/++COrsqNcIOGC3bBhw4p1XFlfCLJVq1YaP368Bg8e7NBy+OGHH9SrV69ysayFl5eXDhw44PASW29vbx04cEA33XSTiZFdmz9Oci5KWZ7cjLLnz69ZKq9/KcSNiUnzsCvriVRxVYTVzfPz8wu9vsTDw0N5eXkmRXR9SKhgpD+/ZumBBx4wKRLg2pFwocKpCKub22w2DR061N7elaSLFy/qkUcecfjbfFn/m/ybb74pX19f9evXz2H8o48+0vnz5zVkyBCTIkN5VFH+UogbU/mZDAIU0+XVzbds2WJf3fydd97RhAkTNHLkSLPDK5YhQ4aodu3a8vPzs28PPPCAgoODHcbKuoSEBNWqVavQeO3atfXss8+aEBEAmIM5XKgwDh8+rHr16slms+nZZ59VQkKCzp8/L0n21c3/+A4zuJ63t7d+/vnnQtXGI0eOqFmzZrpw4YI5gQFAKaOliAqjQYMGuuWWWxQdHa3o6GilpaXp7NmzrG5uotq1a+vHH38slHDt3LlTNWvWNCcoADABCRcqjK+//lrJyclKTk7We++9p9zcXNWvX19du3ZV165d1aVLF4eXxsL1BgwYoDFjxqhq1ar2Bxk2bNigsWPHqn///iZHBwClh5YiKqSLFy9q06ZN9gRs69atysvLU9OmTbV7926zw7th5ObmatCgQfroo4/sL9+2Wq0aPHiwFi1axAKPAG4YJFyo0HJzc5WSkqIvv/xSr732ms6dO8dimybYv3+/UlNT5ePjo/DwcN1yyy1mhwQApYqECxVKbm6uvvvuO61fv17JycnasmWLQkJC1LlzZ3Xu3FlRUVG6+eabzQ7zhpWfn6+LFy8ynw7ADYeECxVG165dtWXLFtWrV09RUVHq1KmToqKiVKdOHbNDu+F88cUXOnXqlIYOHWofmzlzpmbMmKH8/Hx17dpVH3zwgapXr25ekABQiliHCxXGxo0bVbNmTXXt2lXdunXTHXfcQbJlkrlz5yo7O9v+edOmTZoyZYqeeuopffjhh/rPf/7DEh0AbihUuFBhZGdna+PGjUpOTtb69euVmpqqxo0bKyoqSl26dFFUVJQCAgLMDvOGULt2ba1du1atWrWSJMXFxWnPnj1as2aNJGn16tUaO3as9u/fb2aYAFBqSLhQYZ09e1bffvutfT7Xzp071ahRI+3atcvs0Co8Hx8f7d271z5frm3bturXr58ef/xxSdIvv/yisLAwhyoYAFRktBRRYVWpUkU1atRQjRo1VL16dVWqVElpaWlmh3VDqFu3rv1nfe7cOe3cuVMdOnSw7z916pQqV65sVngAUOpY+BQVhtVq1fbt2+0txZSUFGVnZ6tu3bqKjo7WwoULFR0dbXaYN4R+/fpp3Lhxmjx5slavXq2goCDddttt9v3bt29XkyZNTIwQAEoXCRcqDH9/f2VnZysoKEjR0dGaN2+eunTpogYNGpgd2g1nypQp+vXXXzVmzBgFBQXp7bfflru7u33/e++9p969e5sYIQCULuZwocJ47bXXFB0drcaNG5sdCgAADki4AAAAXIxJ8wAAAC5GwgUAAOBiJFwAAAAuRsIFAADgYiwLAcClkpKSlJSUpOPHj8tqtTrsW7p0qUlRAUDpIuEC4DJPP/20pk+frjZt2qhOnTqyWCxmhwQApmBZCAAuU6dOHc2ePVuDBg0yOxQAMBVzuAC4TG5ursM7FAHgRkXCBcBlHnzwQb377rtmhwEApqOlCMBQcXFx9l9brVYtW7ZMERERioiIkIeHh8Oxc+fOLe3wAMAUJFwADBUdHV2s4ywWi77++msXRwMAZQMJFwAAgIsxhwuAy2RmZur06dOFxk+fPq2srCwTIgIAc5BwAXCZ/v376/333y80/uGHH6p///4mRAQA5qClCMBlatSooZSUFDVr1sxh/Oeff1bHjh116tQpkyIDgNJFhQuAy+Tk5Cg/P7/QeF5eni5cuGBCRABgDhIuAC7Ttm1bvf7664XGFy1apNatW5sQEQCYg3cpAnCZZ555RjExMdq5c6e6desm6dLLrLdt26avvvrK5OgAoPQwhwuAS6Wmpur5559XamqqfHx8FBERofj4eDVq1Mjs0ACg1JBwAQAAuBgtRQAuk56efsX9N998cylFAgDmosIFwGXc3NxksVic7i8oKCjFaADAPFS4ALjMDz/84PA5Ly9PP/zwg+bOnauZM2eaFBUAlD4qXABK3apVq/T8888rOTnZ7FAAoFSwDheAUtekSRNt27bN7DAAoNTQUgTgMn9+QbXNZtPRo0c1bdo0loUAcEMh4QLgMv7+/oUmzdtsNoWEhBT5UmsAqKiYwwXAZTZs2ODw2c3NTQEBAWrYsKEqVeLvewBuHCRcAAAALsZfMQG41MGDBzV//nylpaVJksLCwjR27Fg1aNDA5MgAoPTwlCIAl1m7dq3CwsK0detWRUREKCIiQlu2bFHz5s2VmJhodngAUGpoKQJwmVatWqlHjx567rnnHMYnTZqkr776St9//71JkQFA6SLhAuAy3t7e+umnnwotAbFv3z5FRETo4sWLJkUGAKWLliIAlwkICFBqamqh8dTUVNWuXbv0AwIAkzBpHoDhpk+frgkTJmjEiBF66KGHdOjQIXXo0EGSlJKSolmzZikuLs7kKAGg9NBSBGA4d3d3HT16VAEBAZo/f75eeOEF/fbbb5Kk4OBgPf744xozZkyhRVEBoKIi4QJgODc3N2VkZDi0Dc+ePStJqlq1qllhAYBpaCkCcIk/V69ItADcyKhwATCcm5ub/Pz8rtoyPH36dClFBADmosIFwCWefvpp+fn5mR0GAJQJVLgAGK6oOVwAcCNjHS4AhuPpQwBwRMIFwHAUzgHAES1FAAAAF6PCBQAA4GIkXAAAAC5GwgUAAOBiJFwAAAAuRsIFAADgYiRcAAAALkbCBQAA4GL/D5LccjQsWp+SAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df.drop(columns = \"Price\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df[\"Price\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = np.log(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       11.175755\n",
       "1       10.776777\n",
       "2       10.329931\n",
       "3       11.814476\n",
       "4       11.473101\n",
       "          ...    \n",
       "1298    10.433899\n",
       "1299    11.288115\n",
       "1300     9.409283\n",
       "1301    10.614129\n",
       "1302     9.886358\n",
       "Name: Price, Length: 1302, dtype: float64"
      ]
     },
     "execution_count": 312,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size = 0.15, random_state= 2 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.linear_model import LinearRegression, Ridge, Lasso\n",
    "from sklearn.metrics import r2_score, mean_absolute_error\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 Score 0.8073277448418622\n",
      "MAE 0.21017827976428838\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "\n",
    "step1 = ColumnTransformer(transformers= [\n",
    "    ('col_tnf', OneHotEncoder(sparse = False, drop = 'first'), [0,1,7,10,11])\n",
    "], remainder='passthrough')\n",
    "step2 = LinearRegression()\n",
    "\n",
    "pipe  = Pipeline([('step1', step1), ('step2', step2)])\n",
    "pipe.fit(x_train, y_train)\n",
    "y_pred = pipe.predict(x_test)\n",
    "\n",
    "print (\"R2 Score\", r2_score(y_test, y_pred))\n",
    "print (\"MAE\", mean_absolute_error(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 Score 0.9017845063495822\n",
      "MAE 0.1479789694098753\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "step1 = ColumnTransformer(transformers= [\n",
    "    ('col_tnf', OneHotEncoder(sparse = False, drop = 'first'), [0,1,7,10,11])\n",
    "], remainder='passthrough')\n",
    "step2 = RandomForestRegressor(n_estimators=300, random_state = 3, max_samples = 0.5, max_features= 0.1, max_depth = 20)\n",
    "\n",
    "pipe  = Pipeline([('step1', step1), ('step2', step2)])\n",
    "pipe.fit(x_train, y_train)\n",
    "y_pred = pipe.predict(x_test)\n",
    "\n",
    "print (\"R2 Score\", r2_score(y_test, y_pred))\n",
    "print (\"MAE\", mean_absolute_error(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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