Laptop Price Prediction Model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e4fa648a-aff7-4dc0-9245-dea0d7e7970b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b3d97cb2-2202-45b6-8280-6733b546cab8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('laptop_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "68f221ec-002b-43f8-8334-05a850e0e3e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Inches</th>\n",
       "      <th>ScreenResolution</th>\n",
       "      <th>Cpu</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 2.3GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>128GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 640</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37kg</td>\n",
       "      <td>71378.6832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>1440x900</td>\n",
       "      <td>Intel Core i5 1.8GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>128GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34kg</td>\n",
       "      <td>47895.5232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86kg</td>\n",
       "      <td>30636.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>15.4</td>\n",
       "      <td>IPS Panel Retina Display 2880x1800</td>\n",
       "      <td>Intel Core i7 2.7GHz</td>\n",
       "      <td>16GB</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83kg</td>\n",
       "      <td>135195.3360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 3.1GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37kg</td>\n",
       "      <td>96095.8080</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 Company   TypeName  Inches                    ScreenResolution  \\\n",
       "0           0   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "1           1   Apple  Ultrabook    13.3                            1440x900   \n",
       "2           2      HP   Notebook    15.6                   Full HD 1920x1080   \n",
       "3           3   Apple  Ultrabook    15.4  IPS Panel Retina Display 2880x1800   \n",
       "4           4   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "\n",
       "                          Cpu   Ram               Memory  \\\n",
       "0        Intel Core i5 2.3GHz   8GB            128GB SSD   \n",
       "1        Intel Core i5 1.8GHz   8GB  128GB Flash Storage   \n",
       "2  Intel Core i5 7200U 2.5GHz   8GB            256GB SSD   \n",
       "3        Intel Core i7 2.7GHz  16GB            512GB SSD   \n",
       "4        Intel Core i5 3.1GHz   8GB            256GB SSD   \n",
       "\n",
       "                            Gpu  OpSys  Weight        Price  \n",
       "0  Intel Iris Plus Graphics 640  macOS  1.37kg   71378.6832  \n",
       "1        Intel HD Graphics 6000  macOS  1.34kg   47895.5232  \n",
       "2         Intel HD Graphics 620  No OS  1.86kg   30636.0000  \n",
       "3            AMD Radeon Pro 455  macOS  1.83kg  135195.3360  \n",
       "4  Intel Iris Plus Graphics 650  macOS  1.37kg   96095.8080  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "76141098-41ed-4935-ab12-f496e4aa42be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1303, 12)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f9206096-490d-4aa6-aef4-33b98897796f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1303 entries, 0 to 1302\n",
      "Data columns (total 12 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Unnamed: 0        1303 non-null   int64  \n",
      " 1   Company           1303 non-null   object \n",
      " 2   TypeName          1303 non-null   object \n",
      " 3   Inches            1303 non-null   float64\n",
      " 4   ScreenResolution  1303 non-null   object \n",
      " 5   Cpu               1303 non-null   object \n",
      " 6   Ram               1303 non-null   object \n",
      " 7   Memory            1303 non-null   object \n",
      " 8   Gpu               1303 non-null   object \n",
      " 9   OpSys             1303 non-null   object \n",
      " 10  Weight            1303 non-null   object \n",
      " 11  Price             1303 non-null   float64\n",
      "dtypes: float64(2), int64(1), object(9)\n",
      "memory usage: 122.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2f2d8fd-d0cc-4428-9317-b7726a97d328",
   "metadata": {},
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e291071-ea27-4360-9c45-136e39872f88",
   "metadata": {},
   "source": [
    "### Checking for duplicated values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "509e974a-0dc2-4fd6-ad1c-4cc00bdb5b49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.int64(0)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbab9c34-d3fa-4ccc-a0c1-8d927ef24ff9",
   "metadata": {},
   "source": [
    "### Checking for missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cb987104-63fa-4166-be3a-9671cc125c0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0          0\n",
       "Company             0\n",
       "TypeName            0\n",
       "Inches              0\n",
       "ScreenResolution    0\n",
       "Cpu                 0\n",
       "Ram                 0\n",
       "Memory              0\n",
       "Gpu                 0\n",
       "OpSys               0\n",
       "Weight              0\n",
       "Price               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05e98ebb-8ac9-4504-a3b9-ecf01adff07e",
   "metadata": {},
   "source": [
    "We can the drop the first column unamed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "eab2f647-d372-4cfb-8371-df633032ced7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['Unnamed: 0'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a44b27ac-6b83-48de-ae31-9dbb31b132b1",
   "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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Inches                    ScreenResolution  \\\n",
       "0   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "1   Apple  Ultrabook    13.3                            1440x900   \n",
       "2      HP   Notebook    15.6                   Full HD 1920x1080   \n",
       "3   Apple  Ultrabook    15.4  IPS Panel Retina Display 2880x1800   \n",
       "4   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "\n",
       "                          Cpu   Ram               Memory  \\\n",
       "0        Intel Core i5 2.3GHz   8GB            128GB SSD   \n",
       "1        Intel Core i5 1.8GHz   8GB  128GB Flash Storage   \n",
       "2  Intel Core i5 7200U 2.5GHz   8GB            256GB SSD   \n",
       "3        Intel Core i7 2.7GHz  16GB            512GB SSD   \n",
       "4        Intel Core i5 3.1GHz   8GB            256GB SSD   \n",
       "\n",
       "                            Gpu  OpSys  Weight        Price  \n",
       "0  Intel Iris Plus Graphics 640  macOS  1.37kg   71378.6832  \n",
       "1        Intel HD Graphics 6000  macOS  1.34kg   47895.5232  \n",
       "2         Intel HD Graphics 620  No OS  1.86kg   30636.0000  \n",
       "3            AMD Radeon Pro 455  macOS  1.83kg  135195.3360  \n",
       "4  Intel Iris Plus Graphics 650  macOS  1.37kg   96095.8080  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26546473-bf8a-43ea-a829-305648dff104",
   "metadata": {},
   "source": [
    "We can convert the \"RAM\" and \"Weight\" columns into numerical columns by removing the \"GB\" and \"KG\" units"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d3ed41fc-4864-410f-a28c-93ec5367fafd",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Ram'] = df['Ram'].str.replace('GB','')\n",
    "df['Weight'] = df['Weight'].str.replace('kg','')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "878bb3d4-5f44-4a90-95cf-0fe3ed00b77b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Ram'] = df['Ram'].astype('int32')\n",
    "df['Weight'] = df['Weight'].astype('float32')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f8c4e3e-1551-4ccb-9c15-0d76e1999c17",
   "metadata": {},
   "source": [
    "# EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b430bc77-f107-4016-9dc0-8f72de99d7a2",
   "metadata": {},
   "source": [
    "## Statistical Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c9e0f889-bd62-4742-b5c3-0e49226c56a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>Inches</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1303.000000</td>\n",
       "      <td>1303.000000</td>\n",
       "      <td>1303.000000</td>\n",
       "      <td>1303.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>15.017191</td>\n",
       "      <td>8.382195</td>\n",
       "      <td>2.038734</td>\n",
       "      <td>59870.042910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.426304</td>\n",
       "      <td>5.084665</td>\n",
       "      <td>0.665475</td>\n",
       "      <td>37243.201786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>10.100000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.690000</td>\n",
       "      <td>9270.720000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>14.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>31914.720000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>15.600000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2.040000</td>\n",
       "      <td>52054.560000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>15.600000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>79274.246400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>18.400000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>4.700000</td>\n",
       "      <td>324954.720000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Inches          Ram       Weight          Price\n",
       "count  1303.000000  1303.000000  1303.000000    1303.000000\n",
       "mean     15.017191     8.382195     2.038734   59870.042910\n",
       "std       1.426304     5.084665     0.665475   37243.201786\n",
       "min      10.100000     2.000000     0.690000    9270.720000\n",
       "25%      14.000000     4.000000     1.500000   31914.720000\n",
       "50%      15.600000     8.000000     2.040000   52054.560000\n",
       "75%      15.600000     8.000000     2.300000   79274.246400\n",
       "max      18.400000    64.000000     4.700000  324954.720000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77e67126-9a21-4515-a501-6e7caa315b39",
   "metadata": {},
   "source": [
    "Most laptops seem to be equipped with 8GB RAM, which is considered the standard for general-purpose laptops. However, there are high-end laptops with up to 64GB RAM.\n",
    "\n",
    "The dataset includes both ultrabooks and heavier laptops, with a significant portion being lightweight (around 2kg or less), which could imply a focus on portability.\n",
    "\n",
    "The dataset includes a variety of laptops from budget-friendly to high-end models. The high maximum price suggests the presence of premium or specialized laptops."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4eefb2bb-d757-4d63-949b-e739bf6e5703",
   "metadata": {},
   "source": [
    "## Univariate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5fa87a61-6782-4929-ba01-e5d688bd7746",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Company'>"
      ]
     },
     "execution_count": 13,
     "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": [
    "df['Company'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e66eb2ea-f5bd-418f-9ffc-c825662b367d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='TypeName'>"
      ]
     },
     "execution_count": 14,
     "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": [
    "df['TypeName'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "33f6870e-1b02-4742-8bf6-e21d6846b6f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='OpSys'>"
      ]
     },
     "execution_count": 15,
     "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": [
    "df['OpSys'].value_counts().plot(kind='bar')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "07d325cb-eefc-4265-9a54-21564fb0a252",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# List of continuous variables\n",
    "continuous_vars = ['Inches', 'Ram','Weight', 'Price']\n",
    "\n",
    "# Set up the matplotlib figure\n",
    "plt.figure(figsize=(10, 15))\n",
    "\n",
    "# Plot Histograms\n",
    "for i, var in enumerate(continuous_vars):\n",
    "    plt.subplot(5, 2, 2*i + 1)\n",
    "    sns.histplot(df[var], kde=True)\n",
    "    plt.title(f'Histogram of {var}')\n",
    "    plt.xlabel(var)\n",
    "    plt.ylabel('Frequency')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "158a51a5-172e-4271-a5f5-9e5c5fb30a4e",
   "metadata": {},
   "source": [
    "**Inches:**  Notable peaks around 13, 15, and 17 inches suggest these are popular screen sizes, with 15 inches being particularly common.\n",
    "\n",
    "**RAM:**   Significant peaks at 4GB, 8GB, and 16GB, indicating these are the standard RAM sizes in the market.here is a right skew, meaning higher RAM options like 32GB and 64GB are less common.Higher RAM configurations are likely found in premium or high-performance laptops, whereas 4GB and 8GB configurations cater to budget or mid-range devices.\n",
    "\n",
    "**Weight** The majority of laptops weigh between 1.5kg and 2.5kg, with a peak around 2kg.The slight skew indicates that most laptops are designed to be lightweight and portable, but there are some heavier models, likely due to larger screens or additional features\n",
    "\n",
    "**Price** There is a high concentration of laptops in the lower to mid-price range (below 1 lakh), with fewer models in the premium range.The skew suggests that while the majority of laptops are affordable and cater to a mass market, there is also a niche market for high-end, expensive laptops\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c707726-4dd5-4314-b9ba-a032dae85f09",
   "metadata": {},
   "source": [
    "## Bivariate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "75b85713-e8bb-44c0-951c-84e8ac90da91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 4))\n",
    "sns.boxplot(data=df, x='Company', y='Price')\n",
    "plt.xticks(rotation=45)\n",
    "plt.title('Box Plot of Price by Company')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fad19ef-6301-494a-9ce4-be83de6de291",
   "metadata": {},
   "source": [
    "**Apple:** Apple laptops have a higher median price compared to most other companies, with a relatively narrow IQR. This indicates that Apple's pricing is generally high and consistent across its models.\n",
    "\n",
    "**HP, Dell, Lenovo:** These companies have a moderate median price with wider IQRs, suggesting a diverse range of laptop prices. This likely reflects a variety of models ranging from budget to premium.\n",
    "\n",
    "**Razer:** Razer shows the highest median price, with a very wide IQR, indicating that Razer laptops are generally expensive, but there is significant variability, possibly due to different models targeting both high-end gaming and more affordable segments.\n",
    "\n",
    "**Acer:** Acer has a lower median price with a narrower IQR, suggesting that Acer primarily targets the budget and mid-range market segments with less variability in pricing.\n",
    "\n",
    "**Chuwi and Vero:** These companies show the lowest median prices with minimal variability, indicating they primarily offer budget-friendly laptops.\n",
    "\n",
    "\n",
    "* Razer's wide IQR and outliers suggest a strategy of catering to different consumer segments, from high-end gaming enthusiasts to more affordable gaming options.\n",
    "\n",
    "* Apple maintains consistent high pricing with fewer outliers, reflecting its brand positioning as a premium manufacturer.\n",
    "\n",
    "* Companies like Vero and Chuwi focus on the lower end of the market with minimal price variation, potentially indicating a strategy of offering affordable, entry-level laptops."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9a82e080-73ba-41c9-8fd4-3b29a1688cec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0xNq1azWms+3fvx8XL16Ej49PiT2G/6p3794QQmDu3LmF2tQjMb169YJCocDcuXM1qiSq+zx48KBYxwoJCdG4rU6W1O8BtcGDB+Pff//FmDFjClUd/a8GDx6MgwcPYvny5ahWrVqhY6utX78e+fn50u01a9agoKBA6u/t7Q1TU1N89tlnGv3UUlJSXhhH27Zt0a5dO3zxxRcaSxsAj5NWmUymUdHyxo0b2LNnT3EfZrE0bdoUderUwZIlS6QTLE972WMgInoejrAREemI/fv3SyNl9+7dw7Zt23D16lVMnz5duobnvffeQ/v27TFr1izcuHEDjRs3xsGDB/HTTz9h4sSJ0ll+9fU6kZGRqFKlClxdXREYGIiAgAD06dNHI3kqSYsXL0aXLl3g4eGBESNGIDs7G1999RXMzMwwZ84cqV/Tpk0BPC7FP2DAAOjr6+O9994r8vo2fX19fPHFFxg+fDjatm2LgQMHIjk5GStWrEDt2rVLNQl9Ve3bt8fgwYOxcuVKXL16FZ07d4ZKpcKxY8fQvn17jB8/HnXq1MGCBQswY8YM3LhxAz169ECVKlWQkJCAH3/8EaNHj8Ynn3zy0mMlJCSge/fu6Ny5M6Kjo7F161Z88MEHhdZec3d3R6NGjbBz5040aNAATZo0KbHH+8EHH+DTTz/Fjz/+CD8/v+def5eXl4eOHTuiX79+uHz5MlavXo02bdqge/fuAB6PSq1ZswaDBw9GkyZNMGDAANSoUQOJiYnYu3cvWrduXegkxbOCgoI0RvHUfHx8sHTpUnTu3BkffPAB7t27h5CQENStWxdxcXH//Un4f3K5HBs2bECXLl3g7OyM4cOH46233sI///yDw4cPw9TUFL/88kuJHY+I3iBaq09JRERCiKLL+hsZGQk3NzexZs0ajRLhQjwugT5p0iRha2sr9PX1xdtvvy0WL14s9YuJiRF6enpiwoQJGvcrKCgQzZs3F7a2tuLff/99bjzqsv47d+58YdxFlfUXQojffvtNtG7dWhgbGwtTU1Px3nvviQsXLhS6//z588Vbb70l5HJ5sUr879ixQ7i7uwtDQ0NhYWEhfH19pdL7aq9T1v9lfYcOHSpMTEye2/Z0WX8hHj/PixcvFk5OTsLAwEDUqFFDdOnSRcTExGj02717t2jTpo0wMTERJiYmwsnJSfj7+4vLly+/MB51WfsLFy6IPn36iCpVqoiqVauK8ePHayx78DR1Gf3PPvvshft+nmfL+j+ta9euAoA4ceJEoTb1c3z06FExevRoUbVqVVG5cmXh6+ursfSD2uHDh4W3t7cwMzMTRkZGok6dOmLYsGHi9OnThR5/SkpKofu3bdtWAChU1v+bb74Rb7/9tjA0NBROTk5i48aN0n6eBkD4+/trbFO/zxcvXlwo1qJ+T86ePSt69eolqlWrJgwNDUWtWrVEv379RGRkZJHPHxHRy8iEeGY+BhEREVUoK1aswKRJk3Djxo1CVRD/q549e+LcuXO4du1aobbQ0FAMHz4cf/7550sLuxARUdF4DRsREVEFJoTAN998g7Zt25Z4snb37l3s3bv3Py8TQEREz8dr2IiIiCqgzMxM/Pzzzzh8+DDOnTuHn376qcT2nZCQgN9//x0bNmyAvr4+xowZU2L7JiIiTUzYiIiIKqCUlBR88MEHMDc3x8yZM6UCHyXh6NGjGD58OOzt7bFp06Yi104jIqKSwWvYiIiIiIiIdBSvYSMiIiIiItJRnBJZhlQqFe7cuYMqVapAJpNpOxwiIiIiItISIQQePnwIW1tbyOXPH0djwlaG7ty5Azs7O22HQUREREREOuLWrVuoWbPmc9uZsJWhKlWqAHj8opiammo5GiIiIiIi0paMjAzY2dlJOcLzMGErQ+ppkKampkzYiIiIiIjopZdKsegIERERERGRjmLCRkREREREpKOYsBEREREREekoJmxEREREREQ6igkbERERERGRjmLCRkREREREpKNY1p+IiIjKjFKpRFxcHFJTU2FhYQFXV1coFApth0VEpLOYsBEREVGZiIqKwurVq5GUlCRts7a2xrhx4+Dp6anFyIiIdBenRBIREVGpi4qKQlBQEBwdHRESEoJ9+/YhJCQEjo6OCAoKQlRUlLZDJCLSSTIhhNB2EG+KjIwMmJmZIT09HaamptoOh4iIqEwolUr4+vrC0dERCxYsgFz+5HyxSqVCQEAAEhISsHXrVk6PJKI3RnFzA46wERERUamKi4tDUlISfH19NZI1AJDL5fD19cXdu3cRFxenpQiJiHQXEzYiIiIqVampqQAABweHItvV29X9iIjoCSZsREREVKosLCwAAAkJCUW2q7er+xER0RNM2IiIiKhUubq6wtraGmFhYVCpVBptKpUKYWFhsLGxgaurq5YiJCLSXUzYiIiIqFQpFAqMGzcO0dHRCAgIQHx8PLKyshAfH4+AgABER0fDz8+PBUeIiIrAKpFliFUiiYjoTVbUOmw2Njbw8/PjOmxE9MYpbm7AhK0MMWEjIqI3nVKpRFxcHFJTU2FhYQFXV1eOrBHRG6m4uYFeGcZEREREbziFQgF3d3dth0FEVG7wGjYiIiIiIiIdxYSNiIiIiIhIRzFhIyIiIiIi0lFM2IiIiIiIiHQUEzYiIiIiIiIdxYSNiIiIiIhIRzFhIyIiIiIi0lFM2IiIiIiIiHQUEzYiIiIiIiIdxYSNiIiIiIhIRzFhIyIiIiIi0lFM2IiIiIiIiHSUVhO2NWvWwNXVFaampjA1NYWHhwf2798vtefk5MDf3x/VqlVD5cqV0bt3byQnJ2vsIzExET4+PqhUqRIsLS0xdepUFBQUaPQ5cuQImjRpAkNDQ9StWxehoaGFYgkJCUHt2rVhZGSEli1b4o8//tBoL04sREREREREJUmrCVvNmjXx+eefIyYmBqdPn0aHDh3w/vvvIz4+HgAwadIk/PLLL9i5cyeOHj2KO3fuoFevXtL9lUolfHx8kJeXhxMnTmDTpk0IDQ1FYGCg1CchIQE+Pj5o3749YmNjMXHiRIwcORLh4eFSnx07dmDy5MkICgrCmTNn0LhxY3h7e+PevXtSn5fFQkREREREVNJkQgih7SCeZmFhgcWLF6NPnz6oUaMGtm3bhj59+gAALl26hAYNGiA6OhqtWrXC/v370a1bN9y5cwdWVlYAgLVr12LatGlISUmBgYEBpk2bhr179+L8+fPSMQYMGIC0tDQcOHAAANCyZUs0b94cq1atAgCoVCrY2dlhwoQJmD59OtLT018aS3FkZGTAzMwM6enpMDU1LbHnjIiIiIiIypfi5gY6cw2bUqnE9u3bkZmZCQ8PD8TExCA/Px9eXl5SHycnJ9jb2yM6OhoAEB0dDRcXFylZAwBvb29kZGRIo3TR0dEa+1D3Ue8jLy8PMTExGn3kcjm8vLykPsWJpSi5ubnIyMjQ+CEiIiIiIiourSds586dQ+XKlWFoaIixY8fixx9/RMOGDZGUlAQDAwOYm5tr9LeyskJSUhIAICkpSSNZU7er217UJyMjA9nZ2bh//z6USmWRfZ7ex8tiKcrChQthZmYm/djZ2RXvSSEiIiIiIoIOJGz169dHbGwsTp06BT8/PwwdOhQXLlzQdlglYsaMGUhPT5d+bt26pe2QiIiItEqpVOLs2bOIjIzE2bNnoVQqtR0SEZFO09N2AAYGBqhbty4AoGnTpvjzzz+xYsUK9O/fH3l5eUhLS9MY2UpOToa1tTUAwNraulA1R3Xlxqf7PFvNMTk5GaampjA2NoZCoYBCoSiyz9P7eFksRTE0NIShoeErPBtEREQVV1RUFFavXq0xO8Xa2hrjxo2Dp6enFiMjItJdWh9he5ZKpUJubi6aNm0KfX19REZGSm2XL19GYmIiPDw8AAAeHh44d+6cRjXHiIgImJqaomHDhlKfp/eh7qPeh4GBAZo2barRR6VSITIyUupTnFiIiIjo+aKiohAUFARHR0eEhIRg3759CAkJgaOjI4KCghAVFaXtEImIdJJWq0TOmDEDXbp0gb29PR4+fIht27bhiy++QHh4ON599134+flh3759CA0NhampKSZMmAAAOHHiBIDH0yrc3Nxga2uLRYsWISkpCYMHD8bIkSPx2WefAXhc1r9Ro0bw9/fHhx9+iEOHDuGjjz7C3r174e3tDeBxWf+hQ4di3bp1aNGiBZYvX47vv/8ely5dkq5te1ksxcEqkURE9CZSKpXw9fWFo6MjFixYALn8yflilUqFgIAAJCQkYOvWrVAoFFqMlIio7BQ3N9DqlMh79+5hyJAhuHv3LszMzODq6iolawCwbNkyyOVy9O7dG7m5ufD29sbq1aul+ysUCvz666/w8/ODh4cHTExMMHToUMybN0/q4+DggL1792LSpElYsWIFatasiQ0bNkjJGgD0798fKSkpCAwMRFJSEtzc3HDgwAGNQiQvi4WIiIiKFhcXh6SkJMyePVsjWQMeV2b29fWFv78/4uLi4O7urqUoiYh0k86tw1aRcYSNiIjeRJGRkZg/fz727duHSpUqFWrPyspC165dMXv2bHTs2FELERIRlb1ytw4bERERVUwWFhYAHl+mUBT1dnU/IiJ6ggkbERERlSpXV1dYW1sjLCwMKpVKo02lUiEsLAw2NjZwdXXVUoRERLqLCRsRERGVKoVCgXHjxiE6OhoBAQGIj49HVlYW4uPjERAQgOjoaPj5+bHgCBFREXgNWxniNWxERPQmK2odNhsbG/j5+XEdNiJ64xQ3N2DCVoaYsBER0ZtOqVQiLi4OqampsLCwgKurK0fWiOiNVC7K+hMREdGbRaFQsHQ/EdEr4DVsREREREREOooJGxERERERkY5iwkZERERERKSjmLARERERERHpKCZsREREREREOooJGxERERERkY5iwkZERERERKSjmLARERERERHpKCZsREREREREOooJGxERERERkY5iwkZERERERKSjmLARERERERHpKCZsREREREREOooJGxERERERkY5iwkZERERERKSjmLARERERERHpKD1tB0BEbw6lUom4uDikpqbCwsICrq6uUCgU2g6LiIiISGcxYSOiMhEVFYXVq1cjKSlJ2mZtbY1x48bB09NTi5ERERER6S5OiSSiUhcVFYWgoCA4OjoiJCQE+/btQ0hICBwdHREUFISoqChth0hERESkk2RCCKHtIN4UGRkZMDMzQ3p6OkxNTbUdDlGZUCqV8PX1haOjIxYsWAC5/Ml5IpVKhYCAACQkJGDr1q2cHklERERvjOLmBhxhI6JSFRcXh6SkJPj6+mokawAgl8vh6+uLu3fvIi4uTksREhEREekuJmxEVKpSU1MBAA4ODkW2q7er+xERERHRE0zYiKhUWVhYAAASEhKKbFdvV/cjIiIioieYsBFRqXJ1dYW1tTXCwsKgUqk02lQqFcLCwmBjYwNXV1ctRUhERESku5iwEVGpUigUGDduHKKjoxEQEID4+HhkZWUhPj4eAQEBiI6Ohp+fHwuOEBERERWBVSLLEKtE0pusqHXYbGxs4Ofnx3XYiIiI6I1T3NyACVsZYsJGbzqlUom4uDikpqbCwsICrq6uHFkjIiKiN1K5KOu/cOFCNG/eHFWqVIGlpSV69OiBy5cva/Rp164dZDKZxs/YsWM1+iQmJsLHxweVKlWCpaUlpk6dioKCAo0+R44cQZMmTWBoaIi6desiNDS0UDwhISGoXbs2jIyM0LJlS/zxxx8a7Tk5OfD390e1atVQuXJl9O7dG8nJySXzZBC9ARQKBdzd3dGxY0e4u7szWSMiIiJ6Ca0mbEePHoW/vz9OnjyJiIgI5Ofno1OnTsjMzNToN2rUKNy9e1f6WbRokdSmVCrh4+ODvLw8nDhxAps2bUJoaCgCAwOlPgkJCfDx8UH79u0RGxuLiRMnYuTIkQgPD5f67NixA5MnT0ZQUBDOnDmDxo0bw9vbG/fu3ZP6TJo0Cb/88gt27tyJo0eP4s6dO+jVq1cpPkNERERERPQm06kpkSkpKbC0tMTRo0ela1ratWsHNzc3LF++vMj77N+/H926dcOdO3dgZWUFAFi7di2mTZuGlJQUGBgYYNq0adi7dy/Onz8v3W/AgAFIS0vDgQMHAAAtW7ZE8+bNsWrVKgCPq9fZ2dlhwoQJmD59OtLT01GjRg1s27YNffr0AQBcunQJDRo0QHR0NFq1avXSx8cpkUREREREBJSTKZHPSk9PB1B4PaawsDBUr14djRo1wowZM5CVlSW1RUdHw8XFRUrWAMDb2xsZGRmIj4+X+nh5eWns09vbG9HR0QCAvLw8xMTEaPSRy+Xw8vKS+sTExCA/P1+jj5OTE+zt7aU+z8rNzUVGRobGDxERERERUXHpaTsANZVKhYkTJ6J169Zo1KiRtP2DDz5ArVq1YGtri7i4OEybNg2XL1/GDz/8AABISkrSSNYASLfV1eie1ycjIwPZ2dn4999/oVQqi+xz6dIlaR8GBgYwNzcv1OfpqndPW7hwIebOnfuKzwQREREREdFjOpOw+fv74/z58zh+/LjG9tGjR0v/d3FxgY2NDTp27Ijr16+jTp06ZR3mK5kxYwYmT54s3c7IyICdnZ0WIyIiIiIiovJEJ6ZEjh8/Hr/++isOHz6MmjVrvrBvy5YtAQDXrl0DAFhbWxeq1Ki+bW1t/cI+pqamMDY2RvXq1aFQKIrs8/Q+8vLykJaW9tw+zzI0NISpqanGDxERERERUXFpNWETQmD8+PH48ccfcejQITg4OLz0PrGxsQAeL7gLAB4eHjh37pxGNceIiAiYmpqiYcOGUp/IyEiN/URERMDDwwMAYGBggKZNm2r0UalUiIyMlPo0bdoU+vr6Gn0uX76MxMREqQ8REREREVFJ0uqUSH9/f2zbtg0//fQTqlSpIl0LZmZmBmNjY1y/fh3btm1D165dUa1aNcTFxWHSpEnw9PSEq6srAKBTp05o2LAhBg8ejEWLFiEpKQkBAQHw9/eHoaEhAGDs2LFYtWoVPv30U3z44Yc4dOgQvv/+e+zdu1eKZfLkyRg6dCiaNWuGFi1aYPny5cjMzMTw4cOlmEaMGIHJkyfDwsICpqammDBhAjw8PIpVIZKIiIiIiOhVabWsv0wmK3L7xo0bMWzYMNy6dQuDBg3C+fPnkZmZCTs7O/Ts2RMBAQEa0wtv3rwJPz8/HDlyBCYmJhg6dCg+//xz6Ok9yUePHDmCSZMm4cKFC6hZsyZmz56NYcOGaRx31apVWLx4MZKSkuDm5oaVK1dKUzCBxwtnT5kyBd999x1yc3Ph7e2N1atXP3dK5LNY1p+IiIiIiIDi5wY6tQ5bRceEjYiIiIiIgHK6DhsRERERERE9wYSNiIiIiIhIR+nMOmxERERERFTxKZVKxMXFITU1FRYWFnB1dYVCodB2WDqLCRsREREREZWJqKgorF69WqoODzxe73jcuHHw9PTUYmS6i1MiiYiIiIio1EVFRSEoKAiOjo4ICQnBvn37EBISAkdHRwQFBSEqKkrbIeokVoksQ6wSSURERERvIqVSCV9fXzg6OmLBggWQy5+MG6lUKgQEBCAhIQFbt259Y6ZHskokERERERHphLi4OCQlJcHX11cjWQMAuVwOX19f3L17F3FxcVqKUHcxYSMiIiIiolKVmpoKAHBwcCiyXb1d3Y+eYMJGRERERESlysLCAgCQkJBQZLt6u7ofPcGEjYiIiIiISpWrqyusra0RFhYGlUql0aZSqRAWFgYbGxu4urpqKULdxYSNiIiIiIhKlUKhwLhx4xAdHY2AgADEx8cjKysL8fHxCAgIQHR0NPz8/N6YgiOvglUiyxCrRBIRERHRm6yoddhsbGzg5+f3xq3DVtzcgAlbGWLCRkRERERvOqVSibi4OKSmpsLCwgKurq5v5MhacXMDvTKMiYiIiIiI3nAKhQLu7u7aDqPc4DVsREREREREOooJGxERERERkY5iwkZERERERKSjmLARERERERHpKCZsREREREREOooJGxERERERkY5iwkZERERERKSjmLARERERERHpKC6cTURERGVGqVQiLi4OqampsLCwgKurKxQKhbbDIiLSWUzYiIiIqExERUVh9erVSEpKkrZZW1tj3Lhx8PT01GJkRFSWeOLm1TBhIyIiolIXFRWFoKAgeHh4YPbs2XBwcEBCQgLCwsIQFBSEuXPnMmkjegPwxM2rkwkhhLaDeFNkZGTAzMwM6enpMDU11XY4REREZUKpVMLX1xeOjo5YsGAB5PInl9CrVCoEBAQgISEBW7du5Vl2ogrs6RM3vr6+GiduoqOj37gTN8XNDVh0hIiIiEpVXFwckpKS4OvrCyEEzp49i8jISJw9exZCCPj6+uLu3buIi4vTdqhEVEqUSiVWr14NDw8PLFiwAM7OzqhUqRKcnZ2xYMECeHh4YM2aNVAqldoOVedwSiQRERGVqtTUVADAnTt3MH/+/EJToUaMGKHRj4gqHvWJm9mzZ2uMsgOAXC6Hr68v/P39ERcXB3d3dy1FqZuYsBEREVGpsrCwAAAEBwfjf//7X6Fr2IKDgzX6EVHFoz4h4+DgUGS7ejtP3BTGKZFERERUqpydnaFQKFC1alXMmzdPYyrUvHnzULVqVSgUCjg7O2s7VCIqJeoTMgkJCUW2q7fzxE1hTNiIiIioVMXHx0OpVCItLQ2BgYGIj49HVlYW4uPjERgYiLS0NCiVSsTHx2s7VCIqJa6urrC2tkZYWBhUKpVGm0qlQlhYGGxsbODq6qqlCHUXEzYiIiIqVeopTjNnzsTff/8Nf39/dO3aFf7+/khISMDMmTM1+hFRxaNQKDBu3DhER0cjICBA48RNQEAAoqOj4efnx0qxReA1bERERFSq1FOcbG1tERYWVmjB3EuXLmn0I6KKydPTE3PnzsXq1avh7+8vbbexsXnjSvq/CiZsREREVKqengq1YMECjQpwnApF9Gbx9PRE69atC5244cja82l1SuTChQvRvHlzVKlSBZaWlujRowcuX76s0ScnJwf+/v6oVq0aKleujN69eyM5OVmjT2JiInx8fFCpUiVYWlpi6tSpKCgo0Ohz5MgRNGnSBIaGhqhbty5CQ0MLxRMSEoLatWvDyMgILVu2xB9//PHKsRAREZEmToUioqcpFAq4u7ujY8eOcHd35+/+S2g1YTt69Cj8/f1x8uRJREREID8/H506dUJmZqbUZ9KkSfjll1+wc+dOHD16FHfu3EGvXr2kdqVSCR8fH+Tl5eHEiRPYtGkTQkNDERgYKPVJSEiAj48P2rdvj9jYWEycOBEjR45EeHi41GfHjh2YPHkygoKCcObMGTRu3Bje3t64d+9esWMhIiKioqmnQhV1DRunQhERPZ9MCCG0HYRaSkoKLC0tcfToUXh6eiI9PR01atTAtm3b0KdPHwDApUuX0KBBA0RHR6NVq1bYv38/unXrhjt37sDKygoAsHbtWkybNg0pKSkwMDDAtGnTsHfvXpw/f1461oABA5CWloYDBw4AAFq2bInmzZtj1apVAB5P0bCzs8OECRMwffr0YsXyrNzcXOTm5kq3MzIyYGdnh/T0dJiampbOk0hERKTDlEolp0IREeFxbmBmZvbS3ECnqkSmp6cDeHLRcUxMDPLz8+Hl5SX1cXJygr29PaKjowEA0dHRcHFxkZI1APD29kZGRoZUHjg6OlpjH+o+6n3k5eUhJiZGo49cLoeXl5fUpzixPGvhwoUwMzOTfuzs7F7viSEiIqogOBWKiOjV6EzCplKpMHHiRLRu3RqNGjUCACQlJcHAwADm5uYafa2srJCUlCT1eTpZU7er217UJyMjA9nZ2bh//z6USmWRfZ7ex8tiedaMGTOQnp4u/dy6dauYzwYREREREZEOVYn09/fH+fPncfz4cW2HUmIMDQ1haGio7TCIiIiIiKic0okRtvHjx+PXX3/F4cOHUbNmTWm7tbU18vLykJaWptE/OTkZ1tbWUp9nKzWqb7+sj6mpKYyNjVG9enUoFIoi+zy9j5fFQkREREREVJK0OsImhMCECRPw448/4siRI3BwcNBob9q0KfT19REZGYnevXsDAC5fvozExER4eHgAADw8PBAcHIx79+7B0tISABAREQFTU1M0bNhQ6rNv3z6NfUdEREj7MDAwQNOmTREZGYkePXoAeDxFMzIyEuPHjy92LEREROVZTk4OEhMTtR1GibO3t4eRkZG2wyAiei1aTdj8/f2xbds2/PTTT6hSpYp0LZiZmRmMjY1hZmaGESNGYPLkybCwsICpqSkmTJgADw8PqSpjp06d0LBhQwwePBiLFi1CUlISAgIC4O/vL01HHDt2LFatWoVPP/0UH374IQ4dOoTvv/8ee/fulWKZPHkyhg4dimbNmqFFixZYvnw5MjMzMXz4cCmml8VCRERUniUmJmL06NHaDqPErV+/HvXq1dN2GEREr0WrZf1lMlmR2zdu3Ihhw4YBeHy2b8qUKfjuu++Qm5sLb29vrF69WmMa4s2bN+Hn54cjR47AxMQEQ4cOxeeffw49vSf56JEjRzBp0iRcuHABNWvWxOzZs6VjqK1atQqLFy9GUlIS3NzcsHLlSrRs2VJqL04sL1Lc0p1ERETaUFYjbDdv3kRwcDBmzZqFWrVqlfrxOMJGRLqouLmBTq3DVtExYSMiIgKuXLmC0aNHc+SLiN5o5XIdNiIiIiIiInqCCRsREREREZGOYsJGRERERESko5iwERERERER6SgmbERERERERDqKCRsREREREZGOYsJGRERERESko5iwERERERER6aj/lLDl5eXh8uXLKCgoKKl4iIiIiIiI6P+9VsKWlZWFESNGoFKlSnB2dkZiYiIAYMKECfj8889LNEAiIiIiIqI31WslbDNmzMBff/2FI0eOwMjISNru5eWFHTt2lFhwREREREREbzK917nTnj17sGPHDrRq1QoymUza7uzsjOvXr5dYcERERERERG+y1xphS0lJgaWlZaHtmZmZGgkcERERERERvb7XGmFr1qwZ9u7diwkTJgCAlKRt2LABHh4eJRcdERERERGVmZycHKk+RUVib2+vcSlXefJaCdtnn32GLl264MKFCygoKMCKFStw4cIFnDhxAkePHi3pGImIiIiIqAwkJiZi9OjR2g6jxK1fvx716tXTdhiv5bUStjZt2iA2Nhaff/45XFxccPDgQTRp0gTR0dFwcXEp6RiJiIiIiKgM2NvbY/369aV+nJs3byI4OBizZs1CrVq1Sv149vb2pX6M0vJaCRsA1KlTB19//XVJxkJERERERFpkZGRUpiNRtWrVKrcjX2XltYqO7Nu3D+Hh4YW2h4eHY//+/f85KCIiIiIiInrNhG369OlQKpWFtgshMH369P8cFFU8SqUSZ8+eRWRkJM6ePVvk+4eIiIiIiDS91pTIq1evomHDhoW2Ozk54dq1a/85KKpYoqKisHr1aiQlJUnbrK2tMW7cOHh6emoxMiIiIiIi3fZaI2xmZmb4+++/C22/du0aTExM/nNQVHFERUUhKCgIjo6OCAkJwb59+xASEgJHR0cEBQUhKipK2yESEREREems10rY3n//fUycOBHXr1+Xtl27dg1TpkxB9+7dSyw4Kt+USiVWr14NDw8PLFiwAM7OzqhUqRKcnZ2xYMECeHh4YM2aNZweSURERET0HK+VsC1atAgmJiZwcnKCg4MDHBwc0KBBA1SrVg1Lliwp6RipnIqLi0NSUhJ8fX0hl2u+1eRyOXx9fXH37l3ExcVpKUIiIiIiIt32WtewmZmZ4cSJE4iIiMBff/0FY2NjuLq68nok0pCamgoAcHBwKLJdvV3djyo+pVKJuLg4pKamwsLCAq6urlAoFNoOi4iIiEhnvfY6bDKZDJ06dUKnTp1KMh6qQCwsLAAACQkJcHZ2LtSekJCg0Y8qtqioKISEhCA5OVnaZmVlBX9/f57sISIiInqOYidsK1euxOjRo2FkZISVK1e+sO9HH330nwOj8s/V1RXW1tYICwvDggULNKZFqlQqhIWFwcbGBq6urlqMkspCVFQUAgMDYWhoqLE9LS0NgYGBmDdvHpM2IiIioiIUO2FbtmwZfH19YWRkhGXLlj23n0wmY8JGAACFQoFx48YhKCgIAQEB8PX1hYODAxISEhAWFobo6GjMnTuXU+IqOKVSiaVLlwIAmjRpgkGDBknvg61btyI6OhpLly5F69at+V4gIiIiekaxEzb19LVn/0/0Ip6enpg7dy5Wr14Nf39/abuNjQ3mzp3LUZU3QGxsLNLS0uDi4oLg4GBppNXZ2RnBwcH4+OOPce7cOcTGxqJp06ZajpaIiIhIt7zyNWz5+flwcnLCr7/+igYNGpRGTFTBeHp6onXr1iw28YaKjY0FAAwfPrzIaqHDhg3DlClTmLARERERFeGVEzZ9fX3k5OSURixUgSkUCri7u2s7DNIiIYS2QyAiIiIqd15rHTZ/f3988cUXKCgoKOl4iKiCcXNzAwCEhoZCpVJptKlUKoSGhmr0IyIiIqInXqus/59//onIyEgcPHgQLi4uMDEx0Wj/4YcfSiQ4Iir/3NzcYG5ujnPnzmHWrFmFio6cO3cOVatWZcJGREREVITXStjMzc3Ru3fvko6FiCoghUKByZMnIygoCGfOnEF0dLTUZmhoCJlMhkmTJvGaRiIiIqIivNKUSJVKhS+++AJXrlzB+fPnYWlpidWrV2Pjxo0aP8UVFRWF9957D7a2tpDJZNizZ49G+7BhwyCTyTR+OnfurNEnNTUVvr6+MDU1hbm5OUaMGIFHjx5p9ImLi8M777wDIyMj2NnZYdGiRYVi2blzJ5ycnGBkZAQXFxfs27dPo10IgcDAQNjY2MDY2BheXl64evVqsR8r0ZtMXS20atWqGtstLCxYLZSIiIjoBV5phC04OBhz5syBl5cXjI2NsXLlSqSkpODbb799rYNnZmaicePG+PDDD9GrV68i+3Tu3FkjCXx24V1fX1/cvXsXERERyM/Px/DhwzF69Ghs27YNAJCRkYFOnTrBy8sLa9euxblz5/Dhhx/C3Nwco0ePBgCcOHECAwcOxMKFC9GtWzds27YNPXr0wJkzZ9CoUSMAwKJFi7By5Ups2rQJDg4OmD17Nry9vXHhwgUYGRm91uPXJTk5OUhMTNR2GCXK3t6+Qrw2FQWrhRIRERG9uldK2DZv3ozVq1djzJgxAIDffvsNPj4+2LBhQ6Fy3cXRpUsXdOnS5YV9DA0NYW1tXWTbxYsXceDAAfz5559o1qwZAOCrr75C165dsWTJEtja2iIsLAx5eXn49ttvYWBgAGdnZ8TGxmLp0qVSwrZixQp07twZU6dOBQDMnz8fERERWLVqFdauXQshBJYvX46AgAC8//770nNhZWWFPXv2YMCAAa/82HVNYmKi9HxUFOvXr0e9evW0HQY9hdVCiYh0W0U8gQvwJC6Vb6+UsCUmJqJr167SbS8vL8hkMty5cwc1a9Ys8eAA4MiRI7C0tETVqlXRoUMHLFiwANWqVQMAREdHw9zcXErW1DHJ5XKcOnUKPXv2RHR0NDw9PWFgYCD18fb2xhdffIF///0XVatWRXR0NCZPnqxxXG9vb2mKZkJCApKSkuDl5SW1m5mZoWXLloiOjn5uwpabm4vc3FzpdkZGxn9+PkqLvb091q9fX6rHuHnzJoKDgzFr1izUqlWrVI8FPH5MREREVHwV8QQuwJO4VL69UsJWUFBQ6OyEvr4+8vPzSzQotc6dO6NXr15wcHDA9evXMXPmTHTp0gXR0dFQKBRISkqCpaWlxn309PRgYWGBpKQkAEBSUhIcHBw0+lhZWUltVatWRVJSkrTt6T5P7+Pp+xXVpygLFy7E3LlzX+ORlz0jI6My+0NWq1Yt/tEkIiLSQWVxAhfgSVyiV/FKCZsQAsOGDdO4jiwnJwdjx47VKO1fUmX9nx65cnFxgaurK+rUqYMjR46gY8eOJXKM0jRjxgyNkbuMjAzY2dlpMSIiIiKi5yvLE7gAT+ISFccrJWxDhw4ttG3QoEElFszLODo6onr16rh27Ro6duwIa2tr3Lt3T6NPQUEBUlNTpeverK2tkZycrNFHfftlfZ5uV2+zsbHR6POitaMMDQ0LFUkhIiIiIiIqrldK2F6lZH9puH37Nh48eCAlTR4eHkhLS0NMTAyaNm0KADh06BBUKhVatmwp9Zk1axby8/Ohr68PAIiIiED9+vWlEuMeHh6IjIzExIkTpWNFRETAw8MDAODg4ABra2tERkZKCVpGRgZOnToFPz+/snjoRBWCUqlklUgiIiKiV/BaC2eXlEePHuHatWvS7YSEBMTGxsLCwkJan6l3796wtrbG9evX8emnn6Ju3brw9vYGADRo0ACdO3fGqFGjsHbtWuTn52P8+PEYMGAAbG1tAQAffPAB5s6dixEjRmDatGk4f/48VqxYgWXLlknH/fjjj9G2bVt8+eWX8PHxwfbt23H69GlpDrdMJsPEiROxYMECvP3221JZf1tbW/To0aPsnjCiciwqKgohISEao9lWVlbw9/fnOmxEREREz/HqtfhL0OnTp+Hu7i6V+Z48eTLc3d0RGBgIhUKBuLg4dO/eHfXq1cOIESPQtGlTHDt2TGOaYVhYGJycnNCxY0d07doVbdq00bhY1szMDAcPHkRCQgKaNm2KKVOmIDAwUKMC0v/+9z9s27YN69evR+PGjbFr1y7s2bNHWoMNAD799FNMmDABo0ePRvPmzfHo0SMcOHCAJWKJiiEqKgqBgYFIS0vT2J6WlobAwEBERUVpJzAiIiIiHafVEbZ27dpBCPHc9vDw8Jfuw8LCQlok+3lcXV1x7NixF/bp27cv+vbt+9x2mUyGefPmYd68eS+NiYieUCqVWLp0KQCgSZMmGDRoEBwcHJCQkICtW7ciOjoaS5cuRevWrTk9koiIiOgZWh1hI6KKLzY2FmlpaXBxcUFwcDCcnZ1RqVIlODs7Izg4GC4uLkhLS0NsbKy2QyUiIiLSOUzYiKhUqROx4cOHQy7X/JMjl8sxbNgwjX5ERERE9AQTNiIqEy+a/kxERERERWPCRkSlSr0URmhoKFQqlUabSqVCaGioRj8iIiIieoIJGxGVKjc3N5ibm+PcuXOYNWsW4uPjkZWVhfj4eMyaNQvnzp1D1apVmbARERERFUGrVSKJqOJTKBSYPHkygoKCEBMTg+joaKnN0NAQMpkMkyZNYoVIIiIioiJwhI2ISp2npyf69+8PpVKpsb2goAD9+/fnwtlEREREz8ERNiIqdVFRUdixYwdatGgBQ0NDPHr0CJUrV0Zubi527NiBhg0bMmkjIiIiKgITNiIqVUqlEqtXr4aNjQ3+/PNPjcIjcrkcNjY2WLNmDRfOJiIiIioCEzYiKlVxcXFISkoCAJibm6NTp06wtbXFnTt3cPDgQdy5c0fq5+7urs1QiYiIdF5ycjLS09O1HcZ/dvPmTY1/KwIzMzNYWVmV+H6ZsBFRqbp37x4AoFKlSjAyMsL3338vtVlbW6NSpUrIysqS+hEREVHRkpOTMWjwEOTn5Wo7lBITHBys7RBKjL6BIbZu2VziSRsTNiIqVRcvXgQAZGVlwc3NDbNnz4aDgwMSEhIQFhaGEydOSP28vb21GSoREZFOS09PR35eLrId20JlZKbtcOgp8px04O+jSE9PZ8JGROWLEALA42kC8+bNg57e4z87zs7OmDdvHnr37o309HSpHxEREb2YysgMKpPq2g6DygjL+hNRqZLJZAAenxUMDAzUWDg7MDBQmoev7kdERERET3CEjYhKVYMGDbBnzx5UqlQJ165dg7+/v9RmZWUlXcPWoEEDLUZJREREpJuYsBFRqbK0tATw+Bq2nJwcjbaUlBSpzL+6HxERERE9wSmRRFSqXF1dYW5uDgAaa7A9fdvc3Byurq5lHRoRERGRzuMIGxGVuvz8fACPEzM3NzcYGRkhJycHsbGxSEtLk9qJiIiISBMTNiIqVbGxscjMzIS9vT1yc3Nx5MgRqc3a2hr29vZITExEbGwsmjZtqr1AiYiIiHQQEzYiKlWxsbEAgI8//hhubm6Ii4tDamoqLCws4OrqitjYWEyZMoUJGxEREVERmLARUZmQyWRQKBRwd3fXdihERERE5QYTNiIqVW5ubtiyZQs2btwIFxcXnD9/Xhpha9SoEUJDQ6V+RPR8ycnJ0rqF5d3Nmzc1/q0IzMzMYGVlpe0wiKgCYsJGRKXKzc0N5ubmOHfuHLp164bc3FypzdDQELm5uVIxEiIqWnJyMgYNHoL8vNyXdy5HgoODtR1CidE3MMTWLZuZtBFRiWPCRkSlSqFQoHPnzti+fbtGsgZAut25c2coFApthEdULqSnpyM/LxfZjm2hMjLTdjj0DHlOOvD3UaSnpzNhI6ISx4SNiEqVUqnEgQMHAAD6+voaJfzVt8PDwzFq1CgmbUQvoTIyg8qkurbDICKiMsSFs4moVKnXWrO3t0fVqlU12qpWrQp7e3v8+++/UjVJIiIiInqCCRsRlSp1IpaYmFioYEJ6ejoSExM1+hERERHRE5wSSUSlSggh/d/d3R0tW7aUio2cOnUKJ0+eLNSPiIiInk+enabtEOgZpfmaMGEjolJlYmICADAwMEBCQoKUoAGAlZUVDAwMkJeXJ/UjIiKiFzNOiNJ2CFSGmLARUal69OgRACAvLw8pKSkabSkpKVCpVBr9iIiI6MWyHTyhMjbXdhj0FHl2Wqkl0kzYiIiIiIjKEZWxOSvGvkGYsBFRqapSpQqAx4tkV6lSBffv35faqlWrhvT0dOTl5Un9qGJTKpWIi4tDamoqLCws4OrqyuUciIiIXoAJGxGVqocPHwJ4vEj2swtnPz1FUt2PKq6oqCisXr0aSUlJ0jZra2uMGzcOnp6eWoyMiIhId2m1rH9UVBTee+892NraQiaTYc+ePRrtQggEBgbCxsYGxsbG8PLywtWrVzX6pKamwtfXF6ampjA3N8eIESMKXQsTFxeHd955B0ZGRrCzs8OiRYsKxbJz5044OTnByMgILi4u2Ldv3yvHQkSFyeXF+zNT3H5UPkVFRSEoKAiOjo4ICQnBvn37EBISAkdHRwQFBSEqihfQExERFUWr35AyMzPRuHFjhISEFNm+aNEirFy5EmvXrsWpU6dgYmICb29v5OTkSH18fX0RHx+PiIgI/Prrr4iKisLo0aOl9oyMDHTq1Am1atVCTEwMFi9ejDlz5mD9+vVSnxMnTmDgwIEYMWIEzp49ix49eqBHjx44f/78K8VCRIU1atQIwOOErHp1zfn2NWrUkBI1dT+qeJRKJVavXg0PDw8sWLAAzs7OqFSpEpydnbFgwQJ4eHhgzZo1UCqV2g6ViIhI52g1YevSpQsWLFiAnj17FmoTQmD58uUICAjA+++/D1dXV2zevBl37tyRRuIuXryIAwcOYMOGDWjZsiXatGmDr776Ctu3b8edO3cAAGFhYcjLy8O3334LZ2dnDBgwAB999BGWLl0qHWvFihXo3Lkzpk6digYNGmD+/Plo0qQJVq1aVexYiKhoN27cAACoVCpkZGRotKWnp0tVItX9qOKJi4tDUlISfH19C42kyuVy+Pr64u7du4iLi9NShERERLpLZ69hS0hIQFJSEry8vKRtZmZmaNmyJaKjozFgwABER0fD3NwczZo1k/p4eXlBLpfj1KlT6NmzJ6Kjo+Hp6QkDAwOpj7e3N7744gv8+++/qFq1KqKjozF58mSN43t7e0vJWHFiKcqz1+w8+2W1uJKTk5Genv5a99UlN2/e1Pi3IjAzM4OVlZW2w9BpT1+vlJeXp9H29O2n+1HFkpqaCgBwcHAosl29Xd2PiIiIntDZhE395e3ZL8NWVlZSW1JSEiwtLTXa9fT0YGFhodHn2S8J6n0mJSWhatWqSEpKeulxXhZLURYuXIi5c+e+/MG+QHJyMgYNHoL8vNyXdy4ngoODtR1CidE3MMTWLZuZtL2AtbW19H/1ItlF3X66H1UsFhYWAB6f/HJ2di7UnpCQoNGPiIheTJ5T/k/kVzSl+ZrobMJWEcyYMUNj5C4jIwN2dnavtI/09HTk5+Ui27EtVEZmJR0i/QfynHTg76NIT09nwvYCtWvXBgAoFAps374dX375Je7cuQNbW1tMmTIFffv2hVKplPpRxePq6gpra2uEhYVhwYIFGtMiVSoVwsLCYGNjA1dXVy1GSUSk+8zMzKBvYAj8fVTboVAR9A0MYWZW8t/XdTZhU59tT05Oho2NjbQ9OTkZbm5uUp979+5p3K+goACpqanS/a2trZGcnKzRR337ZX2ebn9ZLEUxNDSEoaFhsR7vy6iMzLhAIpVL6uI9SqUSvXr1krYnJCTg999/1+jXsmXLMo+PSp9CocC4ceMQFBSEgIAA+Pr6wsHBAQkJCQgLC0N0dDTmzp3L9diIiF7CysoKW7dsrjCXygQHB2PWrFmoVauWtsMpEaV1qYzOJmwODg6wtrZGZGSklBRlZGTg1KlT8PPzAwB4eHggLS0NMTExaNq0KQDg0KFDUKlU0hc/Dw8PzJo1C/n5+dDX1wcAREREoH79+qhatarUJzIyEhMnTpSOHxERAQ8Pj2LHQkREz+fp6Ym5c+di9erV8Pf3l7bb2Nhg7ty5XIeNqBgqyjXtAK9r/y+srKwq1MyeWrVqoV69etoOQ6dpNWF79OgRrl27Jt1OSEhAbGwsLCwsYG9vj4kTJ2LBggV4++234eDggNmzZ8PW1hY9evQAADRo0ACdO3fGqFGjsHbtWuTn52P8+PEYMGAAbG1tAQAffPAB5s6dixEjRmDatGk4f/48VqxYgWXLlknH/fjjj9G2bVt8+eWX8PHxwfbt23H69Gmp9L9MJntpLERUtIYNG0r///nnnxEeHi5NifT29kb37t0L9aOKydPTE61bt0ZcXBxSU1NhYWEBV1dXjqwRFUNFvKYd4HXtRMWh1YTt9OnTaN++vXRbfb3X0KFDERoaik8//RSZmZkYPXo00tLS0KZNGxw4cABGRkbSfcLCwjB+/Hh07NgRcrkcvXv3xsqVK6V2MzMzHDx4EP7+/mjatCmqV6+OwMBAjbXa/ve//2Hbtm0ICAjAzJkz8fbbb2PPnj0a60IVJxYiKiw6Olr6/8KFCzFo0CD4+PggISEBCxcu1OinHtUmIiJNvKZdt/G6dipNWk3Y2rVrByHEc9tlMhnmzZuHefPmPbePhYUFtm3b9sLjuLq64tixYy/s07dvX/Tt2/c/xUJUnuXk5CAxMbHE9/v0KPrp06c1Erinl9u4du0arly5UqLHtre350kVHRIVFYWQkBCNa4atrKzg7+/PKZFExcRr2onePDp7DRsRla3ExESNkefSkJ+fr3H76RL/Fy5cKPHjr1+/nvPidURUVBQCAwMLFWJKS0tDYGAg5s2bx6SNiIioCEzYiAjA49Eo9XWbJSkvLw/jx4+HQqHAsmXL8Mcff2Dr1q0YNGgQWrRogUmTJkGpVGLVqlUaI24lwd7evkT3R69HqVRi6dKlAAB3d3e0bNkSRkZGyMnJwalTp3Dy5EksXboUrVu35vVsREREz2DCRkQAACMjo1IbjWrdujV+//13TJkyBR06dAAApKSkYMqUKVAqlWjdurXGNaNUscTGxiItLQ329vZISEjAyZMnpTYrKyvY29sjMTERsbGxUsVfIiIiekz+8i5ERP9NcHAwWrdujfz8fISHhwMAwsPDkZ+fj9atW1eoKmFUWGxsLIDH027r1KmDkJAQ7Nu3DyEhIahTp4507aS6HxERET3BhI2IykRwcDD279+Pdu3aAXhcdGj//v1M1t4AKpUKwOOlGxYsWABnZ2dUqlQJzs7OWLBggbSkg7ofERERPcGEjYjKjLGxMT744AMAj9dINDY21nJEVBZMTU0BALm5Ra8flZOTo9GPiIiInmDCRkREpcrCwgIAcP36dcyaNQvx8fHIyspCfHw8Zs2ahb///lujHxERET3BoiNERFSqqld/smbUmTNnNNbie7rM/9P9iIiI6DEmbEREVKpcXV1hbW0NuVyOpKQkjbb8/HzY2tpCCAFXV1ctRUhERKS7mLAREVGpUigUaNeuHbZv3w5zc3N06tQJtra2uHPnDg4ePIg7d+5gwIABXIONiIioCEzYygl5dpq2Q6Bn8DUhKh6lUokjR46gfv36SE9Px/fffy+12djYwMrKCkePHsWoUaOYtL0E/+7oJr4uRFSamLCVE8YJUdoOgYjotcTFxSEpKQmzZ8+Gk5MT4uLikJqaCgsLC7i6uuLSpUvw9/dHXFwc3N3dtR2uTuNnARHRm4cJWzmR7eAJlbG5tsOgp8iz0/jliagYUlNTAQAODg5QKBSFkjIHBweNfvR8/CzQTfw8IKLSxIStnFAZm0NlwgpqRFT+qMv1JyQkwNnZuVB7QkKCRj96Pn4WEBG9ebgOGxERlSp1lciwsDCoVCqNNpVKhbCwMNjY2LBKJBERURE4wkZERACAnJwcJCYmlsq+e/TogXXr1mHixIno0qUL3nrrLfzzzz/Yv38/zp07hzFjxuD69eulcmx7e3sYGRmVyr6JiIhKGxM2IiICACQmJmL06NGleoy4uDjExcUV2r527dpSO+b69etRr169Uts/EVFFUpon75528+ZNjX9LW3k+eceEjYiIADz+MFu/fn2pHkOlUuH48ePYunUrBg0ahDZt2kAuL93Z+fb29qW6fyKiiqQsTt49LTg4uEyOU55P3jFhIyIiAICRkVGZfJjJ5XJs3boVnp6e5fbDk4iooiqLk3faUJ5P3jFhIyIiIiIiAGV38o6Kj1UiiYiIiIiIdBQTNiIiIiIiIh3FhI2IiIiIiEhHMWEjIiIiIiLSUSw6QkRERFROyLPTtB0CFYGvC5UmJmxERETlhDwnXdshUBHK8nUxTogqs2MRkW5gwlZO8ENa9/A1IaKyYmZmBn0DQ+Dvo9oOhZ5D38AQZmZmpX6cbAdPqIzNS/049Grk2WlMpqnUMGHTcfyQ1m1l9QFNRG82KysrbN2yGenpFeNE0c2bNxEcHIxZs2ahVq1a2g6nRJiZmcHKyqrUj6MyNofKpHqpH4eIdAcTNh1XkT6k+QFNRPT6rKysKtzfm1q1anGBXiKil2DCVg5UtA9pfkATERERERUPy/oTERERERHpKCZsREREREREOooJGxERERERkY5iwkZERERERKSjdLroyJw5czB37lyNbfXr18elS5cAADk5OZgyZQq2b9+O3NxceHt7Y/Xq1RoFOhITE+Hn54fDhw+jcuXKGDp0KBYuXAg9vScP/ciRI5g8eTLi4+NhZ2eHgIAADBs2TOO4ISEhWLx4MZKSktC4cWN89dVXaNGiRek9eKKnJCcnV4hKocDjaqFP/1vesVIoERERlSadTtgAwNnZGb/99pt0++lEa9KkSdi7dy927twJMzMzjB8/Hr169cLvv/8OAFAqlfDx8YG1tTVOnDiBu3fvYsiQIdDX18dnn30GAEhISICPjw/Gjh2LsLAwREZGYuTIkbCxsYG3tzcAYMeOHZg8eTLWrl2Lli1bYvny5fD29sbly5dhaWlZhs8GvYmSk5MxaPAQ5OflajuUEhUcHKztEEqEvoEhtm7ZzKSNiIiISoXOJ2x6enqwtrYutD09PR3ffPMNtm3bhg4dOgAANm7ciAYNGuDkyZNo1aoVDh48iAsXLuC3336DlZUV3NzcMH/+fEybNg1z5syBgYEB1q5dCwcHB3z55ZcAgAYNGuD48eNYtmyZlLAtXboUo0aNwvDhwwEAa9euxd69e/Htt99i+vTpz409NzcXublPvmRnZGSU2PNCb4709HTk5+Ui27EtVEZcpFuXyHPSgb+PIj09nQkbERERlQqdT9iuXr0KW1tbGBkZwcPDAwsXLoS9vT1iYmKQn58PLy8vqa+TkxPs7e0RHR2NVq1aITo6Gi4uLhpfpLy9veHn54f4+Hi4u7sjOjpaYx/qPhMnTgQA5OXlISYmBjNmzJDa5XI5vLy8EB0d/cLYFy5cWGhKJ9HrUhmZQWVSXdthEBEREVEZ0umiIy1btkRoaCgOHDiANWvWICEhAe+88w4ePnyIpKQkGBgYwNzcXOM+VlZWSEpKAgAkJSUVOuutvv2yPhkZGcjOzsb9+/ehVCqL7KPex/PMmDED6enp0s+tW7de+TkgIiIiIqI3l06PsHXp0kX6v6urK1q2bIlatWrh+++/h7GxsRYjKx5DQ0MYGhpqOwwiIiIiIiqndHqE7Vnm5uaoV68erl27Bmtra+Tl5SEtLU2jT3JysnTNm7W1NZKTkwu1q9te1MfU1BTGxsaoXr06FApFkX2KuraOiIiIiIiopJSrhO3Ro0e4fv06bGxs0LRpU+jr6yMyMlJqv3z5MhITE+Hh4QEA8PDwwLlz53Dv3j2pT0REBExNTdGwYUOpz9P7UPdR78PAwABNmzbV6KNSqRAZGSn1ISIiIiIiKg06nbB98sknOHr0KG7cuIETJ06gZ8+eUCgUGDhwIMzMzDBixAhMnjwZhw8fRkxMDIYPHw4PDw+0atUKANCpUyc0bNgQgwcPxl9//YXw8HAEBATA399fmqo4duxY/P333/j0009x6dIlrF69Gt9//z0mTZokxTF58mR8/fXX2LRpEy5evAg/Pz9kZmZKVSOJiIiIiIhKg05fw3b79m0MHDgQDx48QI0aNdCmTRucPHkSNWrUAAAsW7YMcrkcvXv31lg4W02hUODXX3+Fn58fPDw8YGJigqFDh2LevHlSHwcHB+zduxeTJk3CihUrULNmTWzYsEEq6Q8A/fv3R0pKCgIDA5GUlAQ3NzccOHCAZbyJiIioTMlz0rUdAhWBrwuVJp1O2LZv3/7CdiMjI4SEhCAkJOS5fWrVqoV9+/a9cD/t2rXD2bNnX9hn/PjxGD9+/Av7EBEREZUGMzMz6BsYAn8f1XYo9Bz6BoYwM+N6qVTydDphIyIiIqLHywlt3bIZ6ekVYyTn5s2bCA4OxqxZs1CrVi1th1MizMzMOPuKSgUTNiKiciA5OblCfVF7+t/yjl/SqKxYWVlVuPdarVq1UK9ePW2HQaTTmLAREem45ORkDBo8BPl5udoOpUQFBwdrO4QSoW9giK1bNle4L9JERKQbmLARlRPy7DRth0DPKKvXJD09Hfl5uch2bAuVEa+P0CXynHTg76NIT0+vEAlbTk4OEhMTS/04ZT3Kam9vDyMjozI5FhFRSWPCRlROGCdEaTsE0jKVkRlUJtW1HQZVYImJiRg9enSZHa+sRlnXr1/PaXdEVG4xYSMqJ7IdPKEyNtd2GPQUeXYaE2mqUOzt7bF+/Xpth1Hi7O3ttR0CEdFrY8JGVE6ojM05ukJEpcrIyIgjUUREOkau7QCIiIiIiIioaEzYiIiIiIiIdBQTNiIiIiIiIh3FhI2IiIiIiEhHMWEjIiIiIiLSUawSSQDKZrFULpRKRERERPRqmLARgLJdLJULpRIRERERFQ8TNgJQMRdLrWgLpcpz0rUdAj2DrwkRERGVNiZsBICLpeoyMzMz6BsYAn8f1XYoVAR9A0OYmZlpOwwiIiKqoJiwEek4KysrbN2yGenpFWM05+bNmwgODsasWbNQq1YtbYfzn5mZmcHKykrbYRAREVEFxYSNqBywsrKqcElBrVq1OKpLRERE9BIs609ERERERKSjmLARERERERHpKE6JJCIqJ+TZadoOgZ7B14SIiEobEzYionLCOCFK2yEQERFRGWPCRkRUTmQ7eEJlbK7tMOgp8uw0JtJERFSqmLAREZUXMpm2I6Bn8TWhCiYnJweJiYmlfpybN29q/Fva7O3tYWRkVCbHIippTNiIiHQcF0/XbVw8nSqSxMREjB49usyOFxwcXCbHWb9+PZeSoXKLCRsRkY7j4um6jYunU0Vib2+P9evXazuMEmdvb6/tEIheGxM2IgJQMafBVKQpMFw8nYjKgpGREX8viXQMEzYiAlAxp8FwCsyrqYhJO1CxEnciInrzyIQQQttBvCkyMjJgZmaG9PR0mJqaajscIg1l9WW9LPGL+qu5cuVKmSbtZYWJOxER6aLi5gZM2MoQEzYi0mUVMWkHmLgTEZFuKm5uwCmRREQEgNeuEBER6SK5tgMgIiIiIiKiojFhIyIiIiIi0lFM2F5RSEgIateuDSMjI7Rs2RJ//PGHtkMiIiIiIqIKignbK9ixYwcmT56MoKAgnDlzBo0bN4a3tzfu3bun7dCIiIiIiKgCYsL2CpYuXYpRo0Zh+PDhaNiwIdauXYtKlSrh22+/1XZoRERERERUATFhK6a8vDzExMTAy8tL2iaXy+Hl5YXo6Ogi75Obm4uMjAyNHyIiIiIiouJiwlZM9+/fh1KphJWVlcZ2KysrJCUlFXmfhQsXwszMTPqxs7Mri1CJiIiIiKiCYMJWimbMmIH09HTp59atW9oOiYiIiIiIyhEunF1M1atXh0KhQHJyssb25ORkWFtbF3kfQ0NDGBoalkV4RERERERUAXGErZgMDAzQtGlTREZGSttUKhUiIyPh4eGhxciIiIiIiKii4gjbK5g8eTKGDh2KZs2aoUWLFli+fDkyMzMxfPjwYt1fCAEALD5CRERERPSGU+cE6hzheZiwvYL+/fsjJSUFgYGBSEpKgpubGw4cOFCoEMnzPHz4EABYfISIiIiIiAA8zhHMzMye2y4TL0vpqMSoVCrcuXMHVapUgUwm03Y4ZS4jIwN2dna4desWTE1NtR0OaQnfB8T3APE9QHwPEN8Dj0fWHj58CFtbW8jlz79SjSNsZUgul6NmzZraDkPrTE1N39hfTHqC7wPie4D4HiC+B+hNfw+8aGRNjUVHiIiIiIiIdBQTNiIiIiIiIh3FhI3KjKGhIYKCgrg23RuO7wPie4D4HiC+B4jvgeJj0REiIiIiIiIdxRE2IiIiIiIiHcWEjYiIiIiISEcxYSMiIiIiItJRTNiIiIiIiIh0FBM2IiIiIiIiHcWEjYiIiIiISEcxYSMiIiKdp1KptB0CEZFWMGEjonKJS0i+mfi6v7nk8sdfWe7cuQOACRyVPPXfl7y8POTm5mo5GqInmLBRufP0F7a//voLGRkZWoyGtEUmk2H//v0IDw/XdihUBpRKJYDHr/uhQ4ewa9cuLUdEZeXpxCwiIgI1a9bEmTNnIJfLmcBTiRFCQCaTYd++fRgxYgSaN2+OmTNn4qefftJ2aPQaivrbUJ5P8jBho3IjPT0dwOMvbABw7do1eHl5ITk5WZthURn566+/kJmZCeDxH91Hjx4hMDAQOTk5Wo6MStOxY8cAAAqFAnl5ecjOzsbYsWOlBI4qNpVKJY2sffPNN4iJiQEAdOvWDX/++SdkMhmTNioRMpkMP//8M/r06YO3334bfn5+iI+Px5gxY3Du3Dlth0evQJ18R0ZGYsaMGZg+fTquXr0q/S0pj8pv5PRGWbJkCfr06YOEhARpW3Z2NszMzPDWW2/xA7sCE0Lg0KFDcHd3R1hYGLKzsyGXy1GpUiVkZmZCX1+fr38Fdfz4cfTt2xcBAQEAAAMDAxgYGEBPTw8WFhZajo7KgvoL1vTp0xEYGIiqVasiICAAderUQceOHXHq1CkmbfSfqEddHjx4gOXLl2PhwoUIDAyEr68voqOjMXDgQLi4uGg5SnoVMpkMv/zyC7p3746TJ0/il19+gZubGyIiIrQd2mtjwkblQocOHXDs2DFMmzYNf//9N4DHCZuxsTEqVarED+wKTCaToUOHDpg0aRImTpyIsLAwPHr0CDKZDPr6+qhRowZf/wrKwcEBY8aMwU8//YTAwEAAj0fa9PT0YGZmBgAoKCgo19Nc6OVu3LiB3bt3Y/ny5RgzZgzmzZuHzZs3o1OnTujUqRNiYmIgk8n4PqBi27RpE+bNmwfgyUkBhUKBe/fuoUOHDkhMTETDhg3Ro0cPLFu2DACwb98+XL16VWsxU/FlZmYiLi4OK1aswOHDh3HkyBEMHjwY77//Pg4cOKDt8F4LEzbSeUqlEk2aNMGff/6J/fv3Y+rUqUhKSsLDhw+hVCo1rm2hikf9+n755ZcYP348xo0bh61bt+L27dtQKBQwNzcHoPn6c7pcxfDWW2/Bz88Pffv2xc6dOzF79mwolUrk5eVJCbqenp70hYtJe8WUk5OD27dvo3r16tK22rVrY968eTA3N0e3bt0QGxsLuVzOpI1e6tGjR4iMjMTPP/+MpUuXStuzsrJgZmaG2NhYtG/fHl26dMHatWsBAAkJCdi5cyeuXbumrbCpmM6cOQNbW1v88ssvqF27NgCgRo0aWLZsGYYOHYqePXuWy2vf9bQdANHLKBQKKJVKuLi44Pfff0fr1q0xffp0eHp6Qk9PDz///DMUCgWqV6+OgoIC3L59G25ubmjYsKG2Q6cSoFAoUFBQAD09PSxatAgymQyTJ09GTk4O0tLSMHLkSDRr1gwKhQIPHz6EEAKNGjXCuHHjyvV8dXrM2toao0aNghACO3bsQGpqKoDHCby1tTXy8vJgYGCAvLw81KxZU5o+SeWT+tqTpzk5OaFVq1bYvHkzmjdvjsqVK0Mmk6FevXpwdXXFxYsX0b59e5w5cwYODg5aipzKi8qVK2PhwoX4/PPP8f3330OlUuGTTz6Bra0tWrZsKX2p//rrr6X7fP311zh9+rQ0Kke6y9LSEp06dcLu3buRlZUF4PG0V2NjYyxbtgx6enro0qULDh48CC8vLy1HW3wywVOSVM7ExsaiTZs2EEKgevXqMDExQVZWFkxNTZGVlYXs7GwcO3YMjo6O2g6V/qOivrwBwOTJk7F8+XI4Ozujfv36qFmzJrKzs5GamgoTExNMnjwZrq6uWoiYSkJRr/s///yDDRs2YMeOHbh27RrGjh2LjIwM5OTkQKFQICcnB/PmzYOzs7OWoqb/6ukCI3fv3oVKpcJbb70FAFi2bBl27NgBHx8fzJw5EwqFAllZWRg8eDCGDx+OxYsXo1GjRlixYgUUCgVnXNBzqf++/PPPP/jss88QExODnj17Ytq0aQCAQYMG4aeffsL8+fORn5+PhIQEbN26FceOHUPjxo21HD0Vxz///IPx48cjKioKR48eRaNGjaTXPTs7GwEBARg1ahScnJy0HWqxMWEjnaX+5UpMTMT9+/dhaWmJSpUqwcLCAufPn4enpyeaNm2KpUuXwsHBQUrcAMDExETL0dN/pX79//zzT5w9exZKpRINGzZE27ZtAQCzZ8/G559/jrVr12L48OEcTasg1K/7H3/8gXPnziE1NRU+Pj5o2LAhHjx4gFWrVmHPnj0YMGCA9AWLKpaZM2fi119/xZ07d+Dr64tZs2ahWrVqmDVrFiIiImBoaIi2bdvi0KFDEELg5MmT6Nu3LwBg9+7dWo6edN3TJ4Ru3bqFhQsXIiYmBn379sUnn3wCpVKJadOm4dixY1AqlahXrx5mzpyJRo0aaTlyepb6tbxw4QLu3buH3NxctG7dGpUrV0ZqaiqGDBmCkydP4siRI2jUqJHGSaFyRxDpIJVKJYQQYvfu3cLR0VG89dZbwsHBQXTp0kWcPXtWCCHE2bNnhYmJiejXr5+4du2aFqOl0rJr1y5RuXJl0b59e2FpaSkaNWokRowYIbVPmjRJVKpUSSxfvlw8evRIi5FSSdq5c6cwMzMTTZs2FfXr1xdGRkZiyZIl4uHDhyIlJUXMmTNHODs7i48++ki6j/pvBpU/SqVS+v+3334r7OzsxLfffitWrVolzMzMRI8ePcSNGzeEUqkUe/bsEUOGDBGdOnUSH374ocjJyRFCCNG/f3/x8ccfi4KCAr4XqJC8vDzpfXH//n2Rk5MjMjIyhBBC3Lp1S4wbN040b95cLF68WLrPvXv3RF5ensjOztZKzPRi6tdz165dokaNGsLFxUXIZDLRrl078e233wohhHjw4IHo1q2bsLa2FrGxsdoM9z9jwkY669ixY6JSpUpi5cqV4sKFCyI0NFR069ZN1KtXT/rF++uvv4RMJhODBw8WeXl5Wo6YStLly5eFra2tCAkJESqVSty7d0+sXr1a1K9fX4waNUrq5+/vL2rUqCH+/fdf7QVLJebChQvC2tpabNy4UWRmZgohhAgODhbVqlUTy5YtE0IIcfv2bfHpp5+Kli1bipSUFC1GS//Fs3+zo6KiRHBwsNi6dau0LSYmRlhbW4sePXqIq1evStvz8/OFEELk5uaKTz/9VFSrVk1cvHixbAKncuO7774Td+7ckW7/+OOPws3NTTRu3Fi0aNFC/Prrr0IIIe7evSvGjRsnWrRooZG0ke55+gTP6dOnRdWqVcXXX38tkpOTxdWrV0W/fv2Ep6enCA0NFUIIcefOHdGuXTvh6OgocnNztRX2f8aEjXTWggULxPvvv6+x7Y8//hBdu3YVvXv3Funp6UIIIeLj4/lBXQFFREQIBwcH8c8//0jb0tLSxMqVK0Xjxo3F6dOnpe3JycnaCJFKwfHjx0XdunXF9evXNT6Y582bJypVqiSuX78uhBAiKSmJyVo5NnToUBEeHi6EePwF7Nq1a0ImkwmZTCaWL18uhHhyBj0mJkbY2NiIPn36iJiYGGkf169fF6NGjRJOTk7izJkzZf8gSKfFx8cLFxcX0aFDB5GWliYSEhKEkZGRmD9/vli2bJkYOXKkkMlk4ssvvxRCCJGYmCgmTJgg6tevL7766istR0/P2r59u0hNTdXYtmHDBuHm5iaysrKkvxc3btwQvXr1Eu+++64oKCgQQjxO2m7dulXmMZekcjqRk94EOTk5uHz5MnJycqRtzZs3h4+PD86ePYu8vDwAQMOGDcvVhaP0YuL/L6utVq0a8vLycOHCBanNzMwMPXr0wPXr13H58mVpe40aNco8TiodGRkZuH37NgwNDSGXy5GdnQ3g8cLJNWrUwJEjRwAAVlZWGmXeqfxQKpWoUqUK2rdvD+BxsZE6derg0KFDMDQ0xJEjR3D79m1pfcUmTZpg79692L17N3744QdpP46Ojhg9ejQiIiLg7u6urYdDOsrJyQkzZ86EUqlE//798csvv+Djjz9GQEAAJk6ciK+//hqLFy/GJ598goMHD8LOzg5TpkzBe++9h27dumk7fHrK4cOH8fXXX+PRo0ca2xUKBbKzs5GVlQWZTIaCggLUqlULc+bMwW+//YaTJ08CAGxsbFCzZk1thF5imLCRznJxcQEAREREIDc3V9retGlTAMC///6rlbio5Imnah+pLwavUaMGLC0tERYWhhs3bkjt1apVQ6NGjWBoaFjoPlT+denSBY0bN4avry8yMzNhbGwMIQQePXqEypUrSwtmU/mkUqmgUCjw1VdfQV9fH19//TXCwsKQlZWFdu3a4eeff8Yvv/yC+fPn4+7du1LS5u7ujkuXLmHu3LnSfgCgWbNm5f6LGJWsadOmYcuWLZDL5ejfvz/Gjh0LpVKJefPmSd8bCgoKIITAlClT4Ovri4ULFyIrKwu1atXC559/Lq3fRbqhXbt2+O6772BnZ4fz58/jwYMHAABXV1dcvXoVoaGhAB6vywkAxsbGaNiwYYUqQMd12EjrxP9X+UlISEBBQQGysrLQuHFj9OvXD5s3b8b06dNRUFCADh06oEqVKti5cydMTEx4dr2CEC+oCrhixQq89957UCqV6N27Nxo2bIgNGzbg+vXraNasmbZDp/9A/bqfPn0a586dgxACDRs2RKtWrbBw4UJ8+umn6Ny5M7755hvk5OTghx9+wIMHD/i6l3NPV2gTQmDTpk1IT0+HsbExunXrhnfffRd79+6Fj48PZDIZ5syZA2trawBAvXr1ADweoVMoFFqJn3Rbeno6DAwMpPL7MpkM/fr1gxACX375Jfbv34/k5GRYWVlJ63vWqVMHCQkJMDY2BgC+t3SM+nWqUaMGbt26hWHDhqFBgwZYunQpmjRpgmXLlmHKlCkoKCjAwIEDYWpqio0bNyIzM1P621EhaGcmJtFjT1f5cXR0FA4ODqJy5cpiwIAB0nzj9957TzRo0EC89dZbokOHDqJq1aq8XqGCebYqoKGhoVi0aJEQ4nEhAg8PD2FtbS3q1q0r6taty9e/gti1a5eoUqWK8PT0FNWrVxcNGzYUn376qRBCiKNHj4rWrVsLIyMj8fbbb4u6detqXL9E5c/T1ySq5efni27dugk3Nzexfft2qdDMgQMHhIGBgejXr5+4f/9+WYdK5Zi6IE14eLj45ptvhBCP33s7d+4UTZs2FW3bttW47nnMmDGibdu2rDSsQ9R/K9TXoAnxuCCVEEJ8/vnnwtPTU4wcOVL627Bq1SphZGQkateuLRo0aCBsbGwq3OcFEzbSuqioKGFiYiLWr18voqOjxaFDh4SdnZ3o2LGjVN1p3759YsmSJWL16tUs4V/BPK8qoIWFhVi6dKkQ4nGBicuXL4s//viDBUYqiIsXLwpra2uxevVqoVQqxT///COWL18uHB0dxfTp06V+x48fF+fOnRN3797VYrT0Xz2drF28eFEkJSWJtLQ0IcTjL9hdunQplLTt2bNHvPPOO0UmekRq6hO/KpVK473y8ccfC5lMJlULVCqV4rvvvhPu7u7C1tZW9O/fX0yYMEFUrlxZWi6IdMe1a9fEwIEDhRBCfP/996Jq1ariypUrQqVSicWLF4tWrVqJ0aNHS0nbuXPnxJ49e8SuXbvEzZs3tRl6qWDCRlo3f/584eXlJYR48of377//Fra2tmLIkCHaDI3KQHGrAlLFsn//flG3bl2NBDw1NVUsWbJENG7cWMTFxWkxOiot06ZNE46OjsLS0lJMmDBBHD9+XAjxJGlzd3cXO3bsKDTawaSNiqL+zvDgwQPpb8kPP/wgrly5IlJSUsT06dNFlSpVpHW51CNt//vf/4SJiYkIDQ0t99UDK6q4uDhhZGQkWrVqpZF4CyE0kraRI0eKe/fuaTHSssGiI6Q14v8LTSQlJUmVIGUyGXJycuDg4IB169bh119/xdWrV6ULzKniKW5VQKoY1L/3FhYWyMrKwsWLF6W2qlWromfPnrh27RquXLmirRCplISHh2P37t1Yt24dJk2ahAsXLmDBggU4dOgQ9PT08PPPP+Ott97CpEmTpOpu6vfL09e+EanJZDLcv38fHTp0wI4dO/Dtt9+id+/eOH36NKpXr47x48dj7Nix+Pjjj7Fx40bI5XL06tULI0eORKdOndChQwcWrdFRLi4umDNnDk6dOgV3d3cMGjQIwOOCQzKZDJ988gn69OmDK1eu4KOPPkJqaqqWIy5d/AtIWqOu7Ne5c2ecOnUKu3fvBgAYGRkBAPT19VG9enVUrlyZH9YVgFKp1KgGqcaqgBWX+vUWRVQBtbS0RLVq1bBt2zbcunVLare0tISzszMv/K8Anj3RplKp0LdvX3h5eWH69OmYPHkyZDIZFi1ahMOHD0NPTw8//PADBg4ciHbt2gFgBVh6uerVq8PHxwcLFizAyJEj8dVXX2HgwIEAgLfeegsff/yxlLRt2rQJcrkcQ4cORWhoKOzs7LQcPb2Ig4MDAgMDkZSUhO7du+PBgweQy+VQKpUAgClTpqBr1664d++eRjXxiohVIqnUqVQq6RdMoVDg77//RkpKCmrWrAlTU1N069YNH374IaZPnw4hBPr06YOCggIcO3YMxsbGMDAw0PZDoP8gJSUFNWrUkL6AR0dH49SpU6hUqRKcnZ3RunVrVgWsgNS/93l5eTAwMMCJEyfw119/4dGjR+jZsyfq1q2LJUuWoE+fPlCpVOjVqxecnJywbt063LhxA02aNNH2Q6D/SH2ibeXKlTh//jzS09Ph6OgotXft2hUymQyrVq3C4sWLkZOTgy5dumDJkiUAWA2SXk5dQbBHjx7Ytm0bzMzMoFKppEqQwJOkTU9PD8OHD4dCocCgQYNgamqq5ejpWeL/qwer9evXDwDQs2dPeHt7Y8iQIQgLC4O5uTkA4I8//sC0adMwZswYaVuFpbXJmPRG2LBhg9i8ebPIzs4WQgixY8cOYW1tLaysrETdunXF+++/LxITE0VKSorw8/MTenp6ws3NTbRs2VJYWFiwGmA599VXX4levXqJ2NhYIYQQP//8s1AoFOKdd94RVlZWomHDhmLq1KlCCFYFrEg2bNggGjRoIHJycoQQQmzfvl0YGxsLNzc3UadOHVGpUiWxYsUKIYQQBw8eFM2bNxeWlpbi7bffFo6Ojvy9L+eevt4sKChImJmZiW7duolatWoJU1NTERERodF///79okWLFmLSpElCiCfXJREVx44dO4STk5M4evSomDdvnrCzsxNffPGFSEpK0uiXmpoqZs2aJS5evKilSOlF1L/3J0+eFOvXrxfz588X//zzj9QeGxsrrK2tRZcuXcTp06fFrFmzhJWV1RtTkIoJG5UalUol2rRpIxo1aiR27twpYmNjRf369cVXX30lLly4IDZs2CDeffdd4ezsLBITE4UQQhw6dEgEBQWJVatWiatXr2r5EdB/9fPPPwtbW1sxbNgwcezYMdG1a1exevVqIYQQ//zzj1ixYoVwcHAQ06ZNk+7DqoDlm0qlEnv37hXOzs7inXfeEampqWLYsGHim2++EVlZWUIIIWbNmiWqVasmVq1aJYQQ4vbt2+L8+fMiOjq60JcsKr8uXbok5s+fL06cOCGEEOLEiROib9++wtXVVURGRmr0jY6OZmERKjb1l/vExETRqlUrsXbtWqlt1qxZws7OTixZskT6e/LFF1+IK1euaCVWejn167l7925Ro0YN0bp1a9GsWTNRo0YNsWfPHqkI0YULF8Rbb70l6tWrJ2xtbcXp06e1GXaZYsJGpUL9y5efny+6d+8uWrRoIb744gsxZMgQaY0UIR5/gHt5eYn+/ftzDZQKRv0eCA8PF/b29uLDDz8UHTp0EJcuXZL6pKamii+//FK4urpKo2k8u17+FRQUiIiICOHq6ipcXV3FO++8U2jUbMaMGaJKlSrSyRqqWH799Vchk8lE7dq1xblz56TtJ06cEAMGDCgyaROC1SCp+GJiYsSoUaNE7969RUZGhjSiL8TjpM3R0VGMGDFCjBo1SshkMpbu13HHjh0TNWrUkCp6pqamCplMJmxsbMS2bduk5T4yMzPF8ePH37iTuqzkQKVCJpNBqVRCT08Pu3fvRrVq1TB79mzExMRIF4sCgIeHB7p3746YmBhkZWVpMWIqaTKZDEIIdOrUCevWrcPBgwdx+PBhXLhwQeqjrgp448YNXLp0SboflS/PFpdQKBTw8vLCqlWrYG5ujhMnTkBfXx8ApN/zuXPnwtzcHOHh4WUeL5W+2rVrY8yYMbhz5w4SEhKk7R4eHvj444/RqFEjfPDBB4iJidG4HwtMUXHk5+cjNDQUe/fuxfnz51GlShUYGhpKhScWLFgAPz8/pKWl4dq1a4iNjYWbm5t2gyYN4qliVEIInD59GmPGjMHw4cNx48YNuLu746OPPkKnTp0wbtw47N27F+np6ahUqRJat24Na2trLUavBVpOGKmCU69SX1BQIPr37y/Mzc3FmjVrNEbT/vjjD1GzZk2Ns7BU8URGRoqaNWuK7t27a0xjyM3NFU2bNhUbN27UXnD0n926dUtERUUJIYTYvHmzdD1SeHi4aNiwoXB1ddUYXf/3339FvXr1xNatW7USL5Wc542KXbx4UQwePFhUrlxZ/PbbbxptR44cEYGBgdJnBNGrunv3rpg2bZqoWrWq+PTTT6Xtubm50v8zMzOlqdikfeq/FU//zTh//rzIzs4WcXFxIi4uTjx8+FB4enqKkSNHCiEef7aYmJgIQ0NDsWvXLq3ErQtYJZJKnHiqyo/6bKlCocDWrVvRs2dPrFmzBkqlEoMHD4ZMJsOOHTtgYGDw5p0tqaDUr//58+eRmJiIzMxMab2bb775BqNGjUJQUBB8fX1Rr1497N69G5cuXYKnp6e2Q6fXIIRAfn4+fH19IZPJ0KFDB8yZMwerV68GAHTs2BErV67EhAkT4O7ujvXr10OlUuHAgQO4f/8+PDw8tPwI6L9QVwMFHleAVS/f8c4778DJyQkzZ86EQqFA3759sWvXLnTo0AEA0LZtW7Rt2xYAq0HSy6k/V/755x8Aj0fYateujZkzZ6KgoACHDx/GggULEBAQAAMDA+Tn50NfXx+VKlXScuT0NLlcjoSEBIwbNw779+/Hjz/+iI8//hh79+6Fi4sLAOCvv/5CRkYGRowYAQB49OgRBgwYAIVCAWdnZ22Gr11aTRepwlFff/Tbb7+JKVOmCB8fH/Hjjz+K69evCyEeX9PWrVs3UblyZVGvXj3pWgZWA6xYdu3aJRwcHETjxo1FixYtRNWqVcXvv/8uhHhcFbBmzZpCJpOJd999V/Tq1Uv89ddfWo6Y/qu0tDRRt25dIZPJxIwZMzTaCgoKRGRkpGjUqJHQ09MTXbp0EWPHjuXvfTn39PWmM2fOFG+//baoXbu2qFu3rvjoo4+ktosXL4rhw4eLGjVqiL1792ojVCrH1O+zPXv2iEaNGgkXFxdRtWpVMWPGDHH37l2RmpoqJk6cKFq2bCmCg4O1HC29zNmzZ4WNjY1wcXERMpms0CyL/fv3C2NjY3Hs2DGRkZEhAgMDxXvvvffGj8YzYaMS98MPP4gqVaqI4cOHi0GDBon69euLcePGSaXd8/Pzha+vr5DJZGLNmjXizp07Wo6YSlJ0dLQwNzcXX3/9tRBCiLi4OCGTycQXX3whffAePXpUVKpUSUyePFmkpaVpM1wqATk5OSI9PV04OTkJOzs74ePjIw4dOqTRR12IpFGjRsLT01Nj2hKVb8HBwcLS0lIcP35cZGRkiJkzZwqZTCZNaRLiccXI999/X3Tu3FmLkVJ5FRERIUxMTERISIhIT08Xy5cvFzKZTPz4449CCCFSUlLEJ598IurVqycWL16s3WDppZYsWSJkMpmoX7++tO3phMzHx0fo6emJRo0aCXNzc57cE0zYqITFxMSIWrVqiQ0bNgghHs8lNzExkaoEqq9Ty8/PF7179xY3b97UZrhUCjZt2iQGDx4shBDi77//FnZ2dsLPz09qV19PEB4ezqUbKpjc3Fxx+/Zt4eLiIt59990iqwBGR0dLI+5UPj09snblyhXRrVs3sW/fPiHE4+qQZmZmYuzYscLExESMHj1a6nvz5k1WgaTXMmHCBDFu3DghxOPPlXr16olRo0Zp9ElOThYzZ84UCQkJWoiQXkb9d0OlUokDBw6I4OBgUb9+feHh4SFV+Hy60uemTZvEN998w+8J/4/XsNF/Iv6/yo9MJkNBQQEyMjLQu3dvjBgxAjdu3ED79u0xbNgwuLi4YOLEiVAoFBg9ejSaNWuGXbt2aTl6el1PX7fyrCtXruDevXv4559/0K5dO3Tp0gUhISEAgF27duHUqVMIDg5Gp06dyjJkKmHi/68pOXPmDM6ePYv8/Hy0atUKbm5u+PHHH9GrVy8sXrwYQgh07NgR06dPR0FBAZYsWaLt0Ok/iIqKwp9//gmZTIahQ4fCzs4OPj4+aNWqFY4fP46xY8fi888/x9ixYyGEwPr16/HgwQPs2rUL9vb2AF7894MI0LwWvqCgAFeuXEH//v2Rn5+PNm3a4L333sOaNWsAABs3bkSdOnXg6emJ+fPn872lg9Sv59GjR3HlyhV069YN3t7e8Pb2xsCBA9G+fXtERUXB0NAQAHDixAn07dsXxsbGWo5cd/BdTf+JTCaDTCbDTz/9hB07dqBevXoYN24cCgoKMHHiRLRv3x7Lly/HmDFjUL9+fezZswdbt25Fbm6uRklXKl/kcjlu376NY8eOAQDCwsLg7+8PAOjUqRPy8vLg6uoKLy8vrFu3Tnqtjx8/juTkZOTl5WktdioZMpkMu3fvxnvvvYfQ0FD89NNPaNKkCTZv3ow6dergxx9/xP379zF16lS0b98ea9asQe/evbUdNv0HmzdvxqhRo3D79m1UrlwZ1apVg5GREcaOHYuqVavip59+Qrt27TBkyBAAQM2aNdG9e3fk5ORoLP3AL9T0MjKZDL///juysrKgp6eH5s2b46uvvkKtWrXQq1cvfPXVV9KJ4vDwcBw4cAAFBQVcFkYHqZO1H374Ae+99x7u3LmDhw8fAgCaNGmC7du34969e/D09MSFCxcwc+ZMDB48GGlpadoNXNdobWyPyj318Pb58+eFTCbTKMt+//594erqKjZv3iyEeFyQYNCgQWL+/Pnixo0b2giXSohKpRK5ubnC09NTtG3bVsydO1fIZDKxbt06IcTj17p///7irbfeEqGhoUKlUomkpCQxY8YMUaNGDREfH6/lR0Cv6ulpbOrS/GfPnhU1atSQXve///5byGQyERAQIF2LcOPGDREcHCymTZsmLly4UPaBU4nZvHmzMDY2Frt27dKYtrR06VLxww8/CCGE6N69u+jUqZMQ4vHU5549e0qL4ArBRbGp+DIzM4Wrq6v48MMPhRCPp1K3adNGvP3229J17/n5+WLmzJnCzs5OXLlyRZvh0kscO3ZMVK1atdDyPdnZ2UIIIeLj40WDBg1ErVq1RK1atcSff/6phSh1m0wIDnPQ6zt58iRSU1Nx8uRJzJs3T9p+48YN9OzZE926dcP777+PX3/9FXv27MGRI0dgbm6uvYCpxKSnp6NZs2a4fv06pk+fjs8++0xqu3//PgYNGoR//vkHd+/eRcOGDXHr1i388MMPcHd312LU9Lpu3rwJe3t76Qz23r17sX79evz0009ISEiAp6cnunXrJk1TunPnDmxtbaFSqaSReCqfLl68iP79+8Pf3x9jxoyRtvfr1w+7du3Cu+++i6lTpyIvLw/9+/dH48aNkZmZCaVSiTNnzkBPT09jihvRy6hUKgQHB+PYsWPYtWsXTE1NsW7dOoSGhiI5ORnNmjXDw4cPERMTg/DwcH6u6LjFixcjMjISBw4cQE5ODo4ePYpvvvkGSqUSnTt3xqhRo6BUKhEdHY26detymacicF4CvTKlUgkAyMzMxOjRo9GtWzdcvnxZo0/t2rXRr18/hIWFoXfv3tiwYQO+/fZbJmsVRF5eHuRyOfT09FCzZk3ExcXh8OHDUnv16tXx/fff4+uvv8bs2bMRGBiIqKgofqiWU7m5uRgwYAAcHR2l6a137tzBP//8gwsXLqB9+/bo2rWrdK1ieHg4Zs+ejdTUVMjlcn5RL+du3bqFhw8fom3bttLURn9/f5w9exa//vorVCoVVq5ciQcPHmD37t1wcnKCt7e3lKwplUq+B+iVyOVyDB8+HH/++ad0EmjMmDFYunQphg0bBmNjY3h6euL333/n54oOevpzQn07KSkJ69atQ9++ffHVV18hIyMDNWrUwKJFixAfHw+FQoE2bdowWXsOjrDRS6kvEM/IyICpqSkA4PTp03B2dsaVK1cwdepUXLlyBSdOnICtra20YKW6n0qlgq2tLWrWrKnNh0Gl4OHDh3j48CE6d+4MGxsbTJs2TVoYlyoOIQR+//13+Pn5QU9PD2fOnEFSUhL69OmDCxcu4P3330doaKg0ijJlyhQkJCRg48aNMDMz03b49B8FBwdj2bJluH//vrTt7t27UCqVqFmzJi5cuIDRo0cDAPbs2YPq1atL/QoKCqCnx/pm9HIXL16Eqakp3nrrLWnbkiVLsHPnTmzatAlOTk5ajI6KS/2dMTIyEgsXLsRvv/2G27dvY+zYsUhMTESzZs0wePBgtG/fHidOnMCECROwZ88e2NnZaTt0ncYRNnopdYGJoUOHYv/+/di1axdatGiBv/76C40bN8bSpUthbm6OLl264OHDh9DX10d+fj4AoFmzZmjRogWTtQpAfW7n8uXLCA8Px+nTp5GWlgZbW1vs3LkTd+/elaY9AMDMmTMxYcIEbYZMr+npAhHA4wIA//vf//D1118jOzsbLVu2hI2NDd577z3IZDI0aNAA9+7dw82bNzF9+nSEhoZi/vz5TNYqiLp16yI7OxsRERHSNhsbG9SsWRMqlQoNGzbEe++9BzMzM6nKmxqTNXoZIQQePHiAli1bwtfXFxMnTkR2djYAoHPnzkhNTcW5c+cAQPpuQbpl3bp1WLRoEYAnRYVu3LiBKlWqAHhcgGjXrl04ePAgvv32W7Rv3x4AsG/fPgBgNchi4F9SeqHMzEyYmJggMzMT6enpmDFjBi5duoTQ0FC0atUKANCoUSOEhYXhgw8+wDvvvINjx46hSpUqLN1cgahHTnbv3o2PP/4Y+vr6EELAyMgI69evh6enJ3bt2oUPPvgAU6dOhZmZGc6cOYPw8HBth06vSP17m5SUhBs3bki/53K5HE2bNsXmzZsxYMAA6Xc9LS0NW7ZsQVBQEBo3boz09HT89ttvcHZ21vIjoZLSvHlz6OnpYd26dahXrx5q1aoltcnlcjx8+BDHjx9H/fr1pS9oRMUlk8lQrVo1HD9+HJGRkfj666/xyy+/YNCgQfDz88OQIUMQEBCALl26oHLlytoOl56RlpaGs2fPIiIiAiYmJlLF6Bs3bkgnetXfF9TTHQ8cOICIiAh88803OHLkiMaoPD1H2dc5ofJi8+bNolevXuL27dtCCCG2bNkiFAqFaNCggfjll18K9T9//rxo0qSJsLe3Fw8fPizrcKkEFVUV8NSpU6JKlSpi7dq14vbt2+LIkSNi0KBBwsjISERFRQkhhEhISGBVwAogMTFRVKtWTchkMtGuXTsxY8YMERkZKdLT04UQQvzxxx/CxcVFtG7dWgghxL1798T27dtFTEyMuHv3rjZDp1Kybds2YWhoKD744ANx9uxZafuNGzfEu+++Kxo3biz9rXh6YW2ioqjfI1evXhVnz54VN2/e1GifPXu28PHxESYmJqJfv35CX19fbNu2TRuhUjFcu3ZNTJ06VdSvX18sX75cCCHE3LlzRf/+/YUQT75HKJVKkZ6eLsaMGSPat28vzp07p7WYyxtew0aFqM+wL1myBN999x0aN26Mzz//HH///Tfi4+OxZ88eZGVlYfTo0ejfv7/GfePi4uDn54ctW7bA0dFRS4+ASsLTVQGVSiVCQ0MRFhaG3377TRo5TUpKwpQpU3Dx4kXs378fVlZWAMCKcOXczZs30aNHD2RnZ6NKlSpwdnbGjh074OTkBBcXF3Tr1g0ymQwzZsyAg4MDDh48yNe7glMqldi4cSPGjRsHKysrNGrUCAUFBdJ6SseOHYO+vj6USiUUCoWWo6Xy4IcffsCoUaNgbm6OlJQUrF69Gn369IGRkREAIDU1FXv37sWyZcuQnJyMY8eO8XuFjlFPn5fL5YiPj0doaCh++eUXfPrppxBC4OrVq5gwYQKMjY1hamqK7Oxs6XMlOzsb/9fe/UfVnOd/AH/eCun2Q66IOsnPiIuSDmrVjmjtjmVnJ0TkRyuJncqyOqNj1FL5VW5jdmozaMaPMWMZttJix67IjxFDo6RIqTSrkn5Iv97fP8y96w7ma/zoc9XzcU7n1Ofzvt3X53Sq+7zv9/v16dq1q8RX8OZgYKOnKi8vh0KhwJ49exATE4Nhw4Zh3bp16N69O7KzsxESEoKGhgYsWrQIXl5eAB6tRX7rrbegp6eHjh07SnwF9DIePnwId3d33LlzBzdu3IBMJkNMTAwiIiJw48YNdOnSRRPKkpOTERAQgLS0NAwePFjq0ukVycvLw4oVK9DS0oLQ0FD07NkTp0+fxocffojGxkZkZWWhX79+yMrKwpQpU3DgwAEG9Xbg0qVLSExMRG5uLmxsbODo6Ah/f3/o6+uzwQj9v9R/I4qKiuDh4YGQkBAMHz4cycnJWLduHWJjY+Hn56e1p6m4uBiGhoZQKBQSVk4/Zd++fSgsLMT48eOxa9cuJCcno7CwEIaGhujatStqamo0b+iYmJjg5MmTsLCwkLrsNwoDGz2hrq5OE7xOnz6Nzz77DCqVCvb29li7di2srKyQk5ODkJAQNDY2YsKECaitrUVERAQKCgpgY2Mj9SXQSxJP6Qp48+ZNTJ48GX5+fpg3b57mFg25ubmYNGkS9uzZA2dnZ2kLp1fq2rVreO+99zT3RBo1ahSAR3sWDh8+jJycHKSmpmLbtm1srd3OcWaNntfx48eRl5eH7777DiqVSnN8/fr1CA0NRWxsLBYuXPhEAxvSLerwXVhYCKVSiaioKAQEBCA/Px/x8fFITk6Gvb094uLi8P3336OyshJyuRwWFhZa+2DpOUmyEJN0WmNjo/jqq6/EkCFDxKRJk4QQj/avOTs7C19fX82etpycHDFr1iwxatQoYW9vLy5cuCBl2fQSHt+z9vixjIwMYWdnJ0aNGiWEEOL9998XSqVSrF+/Xty5c0dUV1eLP//5z6J///6irKystcumVpCbmys8PT2Fp6enOHHixBPn1XsTqP3gHjV6GYGBgUImkwlHR0fNvli16Oho0alTJxEdHS0ePnwoUYX0vI4fPy527Nghli1bpvV34fr162L58uViwIABYtu2bRJW2HYwsJEW9Qv3xsZGkZqaKuzs7H4ytJWXl4s7d+6Iu3fvSlYzvRz1z7y0tFRkZGRonWtoaBBnz54Vffr0EePGjRNCPNoMPnToUGFoaChGjx4tLCwsRGZmZqvXTa0nNzdX/OpXvxKenp7i1KlTUpdDRG+wlpYWsWrVKqGvry/27t37xPnVq1cLhUIhKioqJKiOnldDQ4OYPn26kMlkwsXFRTQ1NWmdz8/PFytWrBAWFhYiPj5eoirbDi6JbOfUDUYev9m1+lhTUxOOHj2K4OBg9O3bFykpKUhKSsJHH32EIUOGYM2aNby/WhtRVFQEBwcHVFRUwM3NDWPGjIGHhwecnJxgamqK8+fPY8GCBTA1NUV6ejru3LmDlJQUmJubw9HRkcsb2oHr168jJCQEd+/eRUxMjKbdPxHRs4gfls3V1dXh4cOHMDc315xbvHixppnV7373O63HqffRk24rKipCVFQUtm3bhtTUVPzyl7/UWh59/fp1fPrpp/D19UW/fv0krvbNxsBGuHXrFmJjYzFv3jwMGzYMgHZoO3bsGEJCQuDk5ISkpCQkJCRgy5YtcHd3h0ql4r6FNuDndAXs27cv0tLS2FyiHcrJyUFYWBg2bdrEvapE9JPUYe3w4cOIi4tDXl4eRo0aBTc3NyxevBgAEBAQgJ07d2LPnj2YMmWKxBXTs6ijgkwmQ2NjIxobG2FkZAQAqKyshL+/P44cOYLjx49j1KhRWqGNzYheDQY2wpUrVzBlyhR4enpiyZIlmhveqkNbfX099u3bh+joaGzbtg2jR49GfHw8PD09YWtrK23x9MqwKyA9j4aGBnaBJaLnkpqaiqlTp2L58uXo2bMnjh8/jlu3bsHNzQ2bN28GAAQFBUGlUuHQoUN4++23Ja6Ynkb9vz4lJQWJiYm4ceMGnJ2d8dvf/hZvv/02qqurMX/+fKSlpeFf//oXnJyc2IjoFWNgIwCPWjUvWLAAjo6OCAoK0oQ29S9cVVUVBg4ciNDQUAQFBUlbLL027ApIREQvSwiBuro6zJkzBwMHDkRkZCQAoKqqCgkJCdi7dy8CAwMxf/58AMDKlSsxd+5cDBo0SMqy6QfqN+wfPnyo6db5j3/8A7///e8REBAAhUKBQ4cOoWPHjvDy8kJQUBDu3buHwMBA7NmzBxcuXOBrhFeMgY00Ll68CD8/Pzg6OiI4OBj29vYAHk1n19fX491338WiRYswdepUzqy0YeobXQJAaGgo3NzctM5zeQMREampX9w/ePAATU1NAAATExMAwLhx46BUKrF161bN+Orqanh5ecHS0hI7duyQomR6Drdv34anpydSU1NhY2MDd3d3jB8/HmFhYQCA//73v1izZg0yMzMRHh4ODw8PlJSUICwsDCtWrICdnZ3EV9C26EldAOkOBwcHJCYmIjMzExs3bsTFixcBPHqnbOPGjcjNzYWjoyMAMKy1YQMGDEBcXBxkMhkiIyNx+vRprfMMa0REBPwvrF27dg2BgYGYMWMGUlJS8ODBAzQ2NsLW1hbFxcWorKxES0sLgEdhztXVFVlZWairq5P4CuhZhBCor6/H6tWrNT9PtZaWFlhYWGDNmjWoqalBcnIyAKBXr15ISEhgWHsNGNhIi4ODAz755BPk5eXBx8cH77zzDmbOnIlt27bh73//OxsNtBMDBgyASqVChw4dsGzZMpw5c0bqkoiISIeow9qVK1fg7u6Obt26wcfHB9OnT0fnzp3RoUMHBAYGIiUlBX/5y19w7949zWPz8/PRp08fTXdqkt6PF9z16tUL/v7++Oabb3Do0CF06NABeXl5mvMtLS1QKBQYP348rly5ogl03Lf2enBJJD1VYWEhDhw4gJMnT8LBwQFeXl4YOHCg1GVRK2NXQCIiepbCwkK4u7tj6tSpmiYiwKP97zKZDHp6epq9T+PHj4e5uTk6duyIL7/8EqdOndJ0piZpqcN3ZWWl1q0Xqqqq4OrqCicnJ/j7++MXv/gF1q5dixUrVmjGTJ8+HXK5HImJidDT4zzQ68LARkQ/iV0BiYjoceoX+PHx8fjyyy+xY8cOWFlZPTFOvd89MzMTO3bswM2bN9G9e3eEhIRompuRbsjPz8fo0aPh4uKChIQEGBsbw8jICGfPnoWrqys2btyIHj16YNasWZg2bRp69eqF2tpa7Nq1CxkZGRg6dKjUl9CmMbARERER0c/m6+uLGzdu4OTJk0+cU4e6+/fvw9DQUPPGX2NjI5dC6qDr16/D2dkZVVVVmDBhAn79619j/PjxGDp0KEJCQpCeno6EhATU1NRgw4YNqK6uhrm5OT744AMolUqpy2/zGNiIiIiI6LmpZ85mzZqFoqIi/Oc//9E6/rjly5fD2dkZXl5ezxxD0lCHanX3Z5VKhYKCAhgZGaG8vBwXLlxAeHg4unbtijlz5mD69OlYs2YNamtrIZfLUV9fD0NDQ6kvo13gYlMiIiIi+tkmTJigWe4IPOogrW7tDwA1NTXIz8/XakTBsCY99VyNukunuvvz8OHDkZ2dDRcXF2zevBlz5syBt7c3Tp06BVtbW2zZsgWXL1+GXC4HAM092uj1Y2AjIiIiouemDl1jxoyBnZ0dYmNj8cUXXwD434v/x28J5OzsLFmt9CSZTIY7d+7A3t4e77//PgoLCwEAbm5ucHFxwZw5c1BRUYElS5bg8OHDyMrKgoGBAe7fv49Vq1ahublZ832odXBJJBERERG9kGPHjmHhwoUQQmDmzJmYMWMGrl69iqNHj2L//v34+uuvMWLECKnLpB+5d+8eVCoVNm/ejJEjR2Ly5MkICgoCAMydOxcAsGXLFpiZmaGsrAxXr17Fpk2bEBkZyT1rEmBgIyIiIqIXlp6ejr/+9a9ITk5GU1MTevTogcGDByMqKordA3Xc1atXsXr1aly6dAnW1tb4+OOPcfnyZSQnJ8PHxwceHh6asdx/KB0GNiIiIiLS+HEzimd5/AV8TU0N7t+/j7y8PPTr1w+mpqYwMTFprZLpJVRUVCAjIwNhYWGoqqrCtGnTcPToUYwcORLx8fFSl0dgYCMiIiKiH6jD2vXr1/H555/D29sb/fr1k7osaiXBwcHIycnBlStXUFJSgoSEBPj5+UldVrv37LdNiIiIiKhd0dPTQ15eHsaOHYvy8nI8ePAAf/jDH2Brayt1afQaqWdLY2JicOLECRw5cgQfffQRXF1dpS6NwBk2IiIiIvpBbW0tAgICIITA4MGDsXXrVvj4+CAgIIChrY378R61+/fvw9TUVMKKSI0zbERERESk4erqCmNjY8ycORMKhQLh4eEAwNDWxv24oQjDmu5gYCMiIiIiAIBcLse0adNgZmYGAPD394cQAhEREQCAxYsXo3fv3mhubkZpaSmsra2lLJeoXWBgIyIiIiKNLl26AACam5uhr6+PRYsWQQiBtWvXQiaTYcGCBYiPj8elS5dw+PBhGBoast070WvEPWxERERE9FTqrpEA8PHHHyMyMhJGRkYoKCjA6dOn4eDgIHGFRG0fAxsRERERPdPjzShcXFyQk5ODEydOQKlUSlwZUfvAJZFERERE7dSPOwM+jUwmQ1NTE9577z1kZGTg22+/ZVgjakV6UhdARERERK1LvcCqublZ63hLS8tTxxsYGMDJyQnnz59nWCNqZVwSSURERNSOqGfVjh8/ji+++AJyuRxTp06Fi4sL9PT0tPatEZH0+NtIRERE1I7IZDIcO3YMnp6euH//Pvbt24eVK1ciJiZGE9aeNdNGRK2PgY2IiIiojRNCaJZB3r59G2lpaYiLi8Pu3bvx3XffQalUYv/+/diwYQOam5sZ2oh0CAMbERERURuVlJSEW7duQSaTQSaTITMzEwsWLMA///lPDBw4EABgamqKyMhIDB8+HAcPHsTmzZs1oY2IpMffRCIiIqI26MyZM9i9e7fWMYVCAT09PeTl5SE9PV1z3NzcHFFRUXBycsInn3yCDz/8sLXLJaJnYNMRIiIiojaqvLwcCoUCly5dglwux4ABA1BSUoKlS5eiuLgYAQEB8PX11YyvrKzE2rVrsWTJEtja2kpXOBFpMLARERERtTGPd3q8ffs2ZsyYAYVCgU2bNqF///4oLCzEH//4R1RWVmL+/Plaoe157s1GRK2HSyKJiIiI2pjH959ZW1tj5syZqK6uRlhYGPLy8mBjYwOVSgVzc3MkJSUhPj5eM55hjUi3MLARERERtVHqTo+LFy/GzJkzUVxcrBXa4uLiIITA4cOHUVVVJXG1RPQ0XBJJRERE1IY9vjwyMTERSUlJsLKyQkREBPr374/bt28DeDQTR0S6h4GNiIiI6A31eBh73nGJiYnYvXs3OnfujLi4OPTt2/d1l0lEL8FA6gKIiIiI6OdTh7C8vDzEx8ejoKAA9vb2WLJkCSwsLLTGqm+EraenBz8/P9TX1yM1NRWdOnWSqHoiel6cYSMiIiJ6w6jDV1ZWFiZMmIAxY8ZALpfjq6++wuTJk7Fr166ffBwA3Lt3D126dGnFqonoRTCwEREREb2BiouLMWnSJEycOBEbN24EAFy+fBljx45FcnIy3Nzcnvq4511GSUS6gb+tRERERG+gY8eOoXv37ggODgYANDU1oXfv3rCxsUF9ff0zH8ewRvRm4R42IiIiojfQuHHjkJ+fDysrKwCAvr4+zMzMYGRkhLKyMomrI6JXhW+xEBEREb2B+vTpg/DwcACAEELrhte1tbWazz///HOcP3++1esjoleDgY2IiIjoDSeTydDU1AQA6Ny5M8zMzAAAq1atgre3NxQKhZTlEdFLYGAjIiIiagPUM2wtLS3o1KkT1q1bh5iYGJw7d473WiN6g7FLJBEREVEb4uHhgezsbJSXlyM9PR1OTk5Sl0REL4FNR4iIiIjaACEEHj58iIqKCpSWluLKlSsYMmSI1GUR0UviDBsRERFRG5KdnQ0hBOzt7aUuhYheAQY2IiIiIiIiHcWmI0RERERERDqKgY2IiIiIiEhHMbARERERERHpKAY2IiIiIiIiHcXARkREREREpKMY2IiIiIiIiHQUAxsREREREZGOYmAjIiIiIiLSUQxsRETU5slksp/8+OCDD157DXPnzoVMJkNUVJTW8YMHD0Imk7325yciojcTAxsREbV5paWlmo/Y2FiYmppqHfvTn/7UKnUYGhoiOjoalZWVrfJ8RET05mNgIyKiNs/S0lLzYWZmBplMBktLS5iYmGDgwIE4cuSI1viDBw9CLpejuroaBQUFkMlk2Lt3L8aOHQtDQ0MMHToU//73v7Uek5WVhUmTJsHY2Bg9evTA7NmzcffuXa0xHh4esLS0RGRk5DNrLS8vh7e3N6ysrGBkZASlUok9e/ZojXF3d8fSpUsRFBQEc3Nz9OjRA3/7299QW1uLefPmwcTEBP3790dqaurPrpGIiHQLAxsREbVbcrkcM2bMwPbt27WOb9++He+++y5MTEw0x5YvX45ly5bh4sWLGDNmDCZPnozy8nIAwL179/DWW2/BwcEB33zzDY4cOYKysjJMmzZN6/vq6+tj3bp1iIuLw+3bt59aU319PUaOHInk5GRkZWVh4cKFmD17Ns6dO6c1bufOnejWrRvOnTuHpUuXIiAgAF5eXhg7diwyMzMxceJEzJ49G3V1dT+rRiIi0jGCiIioHdm+fbswMzPTfH327Fmhr68vSkpKhBBClJWVCQMDA3HixAkhhBA3b94UAERUVJTmMY2NjcLa2lpER0cLIYSIiIgQEydO1HqeoqIiAUBcu3ZNCCGEr6+vmDJlihBCiNGjR4v58+cLIYQ4cOCA+P/+Hf/mN78Ry5Yt03zt5uYmXF1dNV83NTUJuVwuZs+erTlWWloqAIiMjIznrpGIiHQPZ9iIiKhdc3Z2xpAhQ7Bz504AwGeffYbevXtj3LhxWuPGjBmj+dzAwABOTk7Izs4GAHz77bf4+uuvYWxsrPkYNGgQACA/P/+J54yOjsbOnTs1j39cc3MzIiIioFQq0bVrVxgbGyMtLQ2FhYVa44YNG6b5XF9fHwqFAkqlUnOsR48eAIDvv//+hWokIiLdYCB1AURERFLz8/PD1q1bsXLlSmzfvh3z5s37WZ0ba2pqMHnyZERHRz9xrmfPnk8cGzduHDw9PREaGoq5c+dqnduwYQO2bNmC2NhYKJVKyOVyBAUFoaGhQWtchw4dtL6WyWRax9T1t7S0vFCNRESkGxjYiIio3fPx8cGKFSugUqlw9epV+Pr6PjHmzJkzmlm3pqYmXLhwAUuWLAEAODo6Yv/+/bC1tYWBwfP9a42KisKIESNgZ2endfzUqVOYMmUKfHx8ADwKXLm5ubC3t3+ZS3yhGomISHpcEklERO2eubk53nnnHSxfvhwTJ06EtbX1E2O2bt2KAwcOICcnB4GBgaisrMT8+fMBAIGBgaioqIC3tzfOnz+P/Px8pKWlYd68eWhubn7qcyqVSsyaNQsqlUrr+IABA3D06FGcPn0a2dnZ8Pf3R1lZ2Utf44vUSERE0mNgIyIiArBgwQI0NDRoQtiPRUVFISoqCsOHD0d6ejoOHTqEbt26AQB69eqFU6dOobm5GRMnToRSqURQUBC6dOkCPb1n/6sNDw/XLFlUW7VqFRwdHeHp6Ql3d3dYWlpi6tSpL319L1ojERFJSyaEEFIXQUREJLVPP/0UwcHBKCkpQceOHTXHCwoK0KdPH1y8eBEjRoyQrkAiImqXuIidiIjatbq6OpSWliIqKgr+/v5aYY2IiEhqXANBRETt2vr16zFo0CBYWloiNDRU6nKIiIi0cEkkERERERGRjuIMGxERERERkY5iYCMiIiIiItJRDGxEREREREQ6ioGNiIiIiIhIRzGwERERERER6SgGNiIiIiIiIh3FwEZERERERKSjGNiIiIiIiIh01P8BpeW9jbkyl1YAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 4))\n",
    "sns.boxplot(data=df, x='TypeName', y='Price')\n",
    "plt.xticks(rotation=45)\n",
    "plt.title('Box Plot of Price by TypeName')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16efc6dd-6a34-447c-a4f8-722b40f123d7",
   "metadata": {},
   "source": [
    "**Ultrabook:** Ultrabooks have a relatively high median price, with a moderately wide IQR. This suggests that Ultrabooks are generally priced higher, likely due to their premium build, portability, and performance.\n",
    "\n",
    "**Notebook:** Notebooks show a lower median price with a wide IQR, indicating a diverse range of prices. This reflects the broad market coverage of notebooks, from budget models to high-end ones.\n",
    "\n",
    "**Netbook:** Netbooks have the lowest median price with a narrow IQR, indicating that they are typically budget-friendly and have little price variation. This makes sense as netbooks are designed to be low-cost, lightweight laptops with minimal features.\n",
    "\n",
    "**Gaming:** Gaming laptops have one of the highest median prices and a wide IQR. This reflects the premium nature of gaming laptops, which require powerful hardware and tend to be more expensive. The presence of outliers suggests that some gaming laptops are exceptionally high-priced, possibly due to advanced features like top-tier GPUs.\n",
    "\n",
    "**2 in 1 Convertible:** These laptops have a moderate to high median price with a relatively wide IQR. The variability suggests that 2-in-1 convertibles cater to both mid-range and high-end segments, appealing to users looking for versatility in a device that can function as both a laptop and a tablet.\n",
    "\n",
    "**Workstation:** Workstations have a high median price with a moderately wide IQR, indicating that they are typically expensive. This is expected as workstations are designed for professional use with high-performance components, suitable for tasks like 3D rendering and video editing.\n",
    "\n",
    "\n",
    "* The plot reveals clear stratification in pricing based on the type of laptop. Gaming laptops, workstations, and ultrabooks are consistently more expensive, while notebooks and netbooks occupy the lower price ranges.\n",
    "\n",
    "* Notebooks and 2-in-1 convertibles show significant price variability, suggesting that these categories cover a broad spectrum of market needs, from basic functionality to more advanced features.\n",
    "\n",
    "* The outliers in gaming, ultrabooks, and 2-in-1 convertibles suggest that certain models within these categories have specialized or premium features that significantly increase their prices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e4610902-553e-4547-a449-a9d1bf541763",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 4))\n",
    "sns.boxplot(data=df, x='OpSys', y='Price')\n",
    "plt.xticks(rotation=45)\n",
    "plt.title('Box Plot of Price by OpSys')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa2931a2-287f-4329-b041-c50fffc7683a",
   "metadata": {},
   "source": [
    "**macOS:** Laptops with macOS have a relatively high median price, with a moderately narrow IQR. This suggests that Apple products are consistently priced at a premium, reflecting their positioning as high-end devices.\n",
    "\n",
    "**No OS:** Laptops without an operating system have a low median price with a narrow IQR. This is likely because these laptops are often sold at lower prices, targeting users who prefer to install their own operating system or those seeking budget options.\n",
    "\n",
    "**Windows 10:** This category shows a moderate median price with a wide IQR and a significant number of outliers. The wide price range indicates that Windows 10 laptops cover a broad market spectrum, from budget to premium models.\n",
    "\n",
    "**Mac OS X:** Similar to macOS, laptops with Mac OS X show a high median price with a narrow IQR, reinforcing the premium pricing of Apple products.\n",
    "Linux: Laptops running Linux have a low median price with a narrow IQR, suggesting they are positioned as budget or specialized devices, often favored by developers or tech enthusiasts looking for cost-effective options.\n",
    "\n",
    "**Android:** Laptops running Android show a low median price with a narrow IQR, indicating that these devices are likely inexpensive and cater to a niche market.\n",
    "\n",
    "**Windows 10 S:** This category has a moderate median price with a relatively narrow IQR. Windows 10 S is typically found in mid-range devices, offering a streamlined version of Windows 10.\n",
    "\n",
    "**Chrome OS:** Laptops with Chrome OS have a low median price with a narrow IQR, reflecting their positioning as budget-friendly devices, often targeted at educational markets or users with basic computing needs.\n",
    "\n",
    "**Windows 7:** Laptops with Windows 7 show a relatively high median price with a wider IQR, which may indicate that these devices are either older high-end models or specialized systems still in use in certain sectors.\n",
    "\n",
    "Apple’s macOS and Mac OS X dominate the premium segment, while Linux, Chrome OS, and Android are linked with budget devices. Windows 10 demonstrates significant price variability, catering to a broad range of market needs. The presence of outliers, especially in the Windows 10 category, suggests that certain models with advanced features or specific use cases can command much higher prices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4168e61d-36cb-4f71-8366-eebe2e0049bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Inches', ylabel='Price'>"
      ]
     },
     "execution_count": 20,
     "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.scatterplot(x=df['Inches'],y=df['Price'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8366c833-474f-4015-8791-fcf753bdc44e",
   "metadata": {},
   "source": [
    "There is a general trend where the price increases with screen size (measured in inches). Laptops with larger screens, such as 15-17 inches, tend to be priced higher.\n",
    "\n",
    "Some outliers exist, where laptops with smaller screen sizes still have high prices. This could indicate premium models with advanced features or brand-specific pricing strategies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5a3507b6-b26a-4f05-83b9-2ad545c1ddbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Ram', ylabel='Price'>"
      ]
     },
     "execution_count": 21,
     "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.scatterplot(x=df['Ram'],y=df['Price'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06fea008-28c2-4d4e-81e0-f8bca3cd929f",
   "metadata": {},
   "source": [
    "As the amount of RAM increases, the price generally increases as well. Laptops with higher RAM configurations, such as 16 GB and above, tend to have significantly higher prices.\n",
    "\n",
    "There's a noticeable cluster of laptops with 8 GB RAM around the mid-price range, which might indicate that this is a common configuration for mid-range laptops.\n",
    "\n",
    "A few outliers show very high prices, even with high RAM values, possibly reflecting high-performance or specialized laptops.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "364cc345-8b95-40cc-9a00-085a40d6ef11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Weight', ylabel='Price'>"
      ]
     },
     "execution_count": 22,
     "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.scatterplot(x=df['Weight'],y=df['Price'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "055a641e-00c5-4bc0-8d9c-e32895410a57",
   "metadata": {},
   "source": [
    "The relationship between weight and price is less clear compared to the other two features. Most laptops are clustered around a weight of 1 to 3 kg, with prices distributed across a wide range.\n",
    "\n",
    "Heavier laptops (around 4 kg or more) seem to include some of the higher-priced models. However, there are also lightweight laptops with high prices, likely ultrabooks or premium models."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b9edc9a-dc4e-4c92-a8cb-c7f2e6f37400",
   "metadata": {},
   "source": [
    "# Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4bd3824-f3f4-4af1-889c-b89ba3acc247",
   "metadata": {},
   "source": [
    "## Creating New Features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2ddd0ab-03db-4e3b-8d44-e4cc19ac3cb7",
   "metadata": {},
   "source": [
    "#### Touchscreen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ff9bb242-25b7-4782-a040-67e4966998ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ScreenResolution\n",
       "Full HD 1920x1080                                507\n",
       "1366x768                                         281\n",
       "IPS Panel Full HD 1920x1080                      230\n",
       "IPS Panel Full HD / Touchscreen 1920x1080         53\n",
       "Full HD / Touchscreen 1920x1080                   47\n",
       "1600x900                                          23\n",
       "Touchscreen 1366x768                              16\n",
       "Quad HD+ / Touchscreen 3200x1800                  15\n",
       "IPS Panel 4K Ultra HD 3840x2160                   12\n",
       "IPS Panel 4K Ultra HD / Touchscreen 3840x2160     11\n",
       "4K Ultra HD / Touchscreen 3840x2160               10\n",
       "IPS Panel 1366x768                                 7\n",
       "Touchscreen 2560x1440                              7\n",
       "4K Ultra HD 3840x2160                              7\n",
       "IPS Panel Retina Display 2304x1440                 6\n",
       "IPS Panel Retina Display 2560x1600                 6\n",
       "Touchscreen 2256x1504                              6\n",
       "IPS Panel Quad HD+ / Touchscreen 3200x1800         6\n",
       "IPS Panel Touchscreen 2560x1440                    5\n",
       "IPS Panel Retina Display 2880x1800                 4\n",
       "1440x900                                           4\n",
       "IPS Panel Touchscreen 1920x1200                    4\n",
       "IPS Panel 2560x1440                                4\n",
       "IPS Panel Quad HD+ 2560x1440                       3\n",
       "IPS Panel Touchscreen 1366x768                     3\n",
       "Quad HD+ 3200x1800                                 3\n",
       "1920x1080                                          3\n",
       "2560x1440                                          3\n",
       "Touchscreen 2400x1600                              3\n",
       "IPS Panel Quad HD+ 3200x1800                       2\n",
       "IPS Panel Full HD 2160x1440                        2\n",
       "IPS Panel Touchscreen / 4K Ultra HD 3840x2160      2\n",
       "IPS Panel Full HD 1366x768                         1\n",
       "Touchscreen / Quad HD+ 3200x1800                   1\n",
       "IPS Panel Retina Display 2736x1824                 1\n",
       "IPS Panel Full HD 2560x1440                        1\n",
       "IPS Panel Full HD 1920x1200                        1\n",
       "Touchscreen / Full HD 1920x1080                    1\n",
       "Touchscreen / 4K Ultra HD 3840x2160                1\n",
       "IPS Panel Touchscreen 2400x1600                    1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ScreenResolution'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0be6dfc4-b956-4ac2-8f49-57dfb91fdc7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Touchscreen'] = df['ScreenResolution'].apply(lambda x:1 if 'Touchscreen' in x else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f288588b-5c94-4cd6-8f68-cee2d95e5675",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Inches</th>\n",
       "      <th>ScreenResolution</th>\n",
       "      <th>Cpu</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "      <th>Touchscreen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1089</th>\n",
       "      <td>Acer</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>AMD A8-Series 7410 2.2GHz</td>\n",
       "      <td>4</td>\n",
       "      <td>500GB HDD</td>\n",
       "      <td>AMD Radeon R5</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.40</td>\n",
       "      <td>20619.3600</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1023</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i7 7500U 2.7GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Nvidia GeForce 930MX</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.64</td>\n",
       "      <td>63159.7104</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603</th>\n",
       "      <td>MSI</td>\n",
       "      <td>Gaming</td>\n",
       "      <td>17.3</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i7 6820HK 2.7GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>128GB SSD +  1TB HDD</td>\n",
       "      <td>Nvidia GeForce GTX 970M</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>4.14</td>\n",
       "      <td>117162.7200</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384</th>\n",
       "      <td>Asus</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>Full HD / Touchscreen 1920x1080</td>\n",
       "      <td>Intel Core i7 7500U 2.7GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.10</td>\n",
       "      <td>93181.3920</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1243</th>\n",
       "      <td>Dell</td>\n",
       "      <td>2 in 1 Convertible</td>\n",
       "      <td>17.3</td>\n",
       "      <td>Full HD / Touchscreen 1920x1080</td>\n",
       "      <td>Intel Core i7 7500U 2.7GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>Nvidia GeForce 940MX</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.77</td>\n",
       "      <td>95850.7200</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company            TypeName  Inches                 ScreenResolution  \\\n",
       "1089    Acer            Notebook    15.6                         1366x768   \n",
       "1023      HP            Notebook    14.0                Full HD 1920x1080   \n",
       "603      MSI              Gaming    17.3                Full HD 1920x1080   \n",
       "384     Asus           Ultrabook    13.3  Full HD / Touchscreen 1920x1080   \n",
       "1243    Dell  2 in 1 Convertible    17.3  Full HD / Touchscreen 1920x1080   \n",
       "\n",
       "                              Cpu  Ram                Memory  \\\n",
       "1089    AMD A8-Series 7410 2.2GHz    4             500GB HDD   \n",
       "1023   Intel Core i7 7500U 2.7GHz    8             256GB SSD   \n",
       "603   Intel Core i7 6820HK 2.7GHz   16  128GB SSD +  1TB HDD   \n",
       "384    Intel Core i7 7500U 2.7GHz   16             512GB SSD   \n",
       "1243   Intel Core i7 7500U 2.7GHz   16             512GB SSD   \n",
       "\n",
       "                          Gpu       OpSys  Weight        Price  Touchscreen  \n",
       "1089            AMD Radeon R5  Windows 10    2.40   20619.3600            0  \n",
       "1023     Nvidia GeForce 930MX  Windows 10    1.64   63159.7104            0  \n",
       "603   Nvidia GeForce GTX 970M  Windows 10    4.14  117162.7200            0  \n",
       "384     Intel HD Graphics 620  Windows 10    1.10   93181.3920            1  \n",
       "1243     Nvidia GeForce 940MX  Windows 10    2.77   95850.7200            1  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60ad0f70-601c-428e-9cf9-63c45f814d7e",
   "metadata": {},
   "source": [
    "#### Ips"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a052905e-ca1d-4756-89bb-c75b541f8f09",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Ips'] = df['ScreenResolution'].apply(lambda x:1 if 'IPS' in x else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8aa641fa-755a-4382-9acb-bf5f7e7c5098",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Inches</th>\n",
       "      <th>ScreenResolution</th>\n",
       "      <th>Cpu</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 2.3GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 640</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>71378.6832</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>1440x900</td>\n",
       "      <td>Intel Core i5 1.8GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>47895.5232</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>30636.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>15.4</td>\n",
       "      <td>IPS Panel Retina Display 2880x1800</td>\n",
       "      <td>Intel Core i7 2.7GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>135195.3360</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 3.1GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>96095.8080</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Inches                    ScreenResolution  \\\n",
       "0   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "1   Apple  Ultrabook    13.3                            1440x900   \n",
       "2      HP   Notebook    15.6                   Full HD 1920x1080   \n",
       "3   Apple  Ultrabook    15.4  IPS Panel Retina Display 2880x1800   \n",
       "4   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "\n",
       "                          Cpu  Ram               Memory  \\\n",
       "0        Intel Core i5 2.3GHz    8            128GB SSD   \n",
       "1        Intel Core i5 1.8GHz    8  128GB Flash Storage   \n",
       "2  Intel Core i5 7200U 2.5GHz    8            256GB SSD   \n",
       "3        Intel Core i7 2.7GHz   16            512GB SSD   \n",
       "4        Intel Core i5 3.1GHz    8            256GB SSD   \n",
       "\n",
       "                            Gpu  OpSys  Weight        Price  Touchscreen  Ips  \n",
       "0  Intel Iris Plus Graphics 640  macOS    1.37   71378.6832            0    1  \n",
       "1        Intel HD Graphics 6000  macOS    1.34   47895.5232            0    0  \n",
       "2         Intel HD Graphics 620  No OS    1.86   30636.0000            0    0  \n",
       "3            AMD Radeon Pro 455  macOS    1.83  135195.3360            0    1  \n",
       "4  Intel Iris Plus Graphics 650  macOS    1.37   96095.8080            0    1  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "638c6ef4-7ca5-43a2-9572-711662db3535",
   "metadata": {},
   "source": [
    "#### X_res & Y_res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e6759e0c-ab44-45d6-a81d-14fe07837ab9",
   "metadata": {},
   "outputs": [],
   "source": [
    "new = df['ScreenResolution'].str.split('x',n=1,expand=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9ffb3ea8-4f02-4e00-942a-a003d1d3782b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['X_res'] = new[0]\n",
    "df['Y_res'] = new[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "11eb70fa-f594-41b0-bcc3-eef34cb30de2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\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>Inches</th>\n",
       "      <th>ScreenResolution</th>\n",
       "      <th>Cpu</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>X_res</th>\n",
       "      <th>Y_res</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1262</th>\n",
       "      <td>Dell</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>Intel Core i3 7100U 2.4GHz</td>\n",
       "      <td>6</td>\n",
       "      <td>1TB HDD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.30</td>\n",
       "      <td>24455.5200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1366</td>\n",
       "      <td>768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>895</th>\n",
       "      <td>Toshiba</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.05</td>\n",
       "      <td>78588.0000</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>611</th>\n",
       "      <td>Dell</td>\n",
       "      <td>Gaming</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i7 6700HQ 2.6GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>1TB HDD</td>\n",
       "      <td>Nvidia GeForce GTX 960&lt;U+039C&gt;</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.59</td>\n",
       "      <td>46833.6528</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Full HD 1920</td>\n",
       "      <td>1080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>707</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Workstation</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i7 6500U 2.5GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>Nvidia Quadro M520M</td>\n",
       "      <td>Windows 7</td>\n",
       "      <td>2.18</td>\n",
       "      <td>98834.4000</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>271</th>\n",
       "      <td>Asus</td>\n",
       "      <td>Gaming</td>\n",
       "      <td>17.3</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>AMD Ryzen 1700 3GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>256GB SSD +  1TB HDD</td>\n",
       "      <td>AMD Radeon RX 580</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>3.20</td>\n",
       "      <td>82530.7200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Full HD 1920</td>\n",
       "      <td>1080</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Company     TypeName  Inches   ScreenResolution  \\\n",
       "1262     Dell     Notebook    15.6           1366x768   \n",
       "895   Toshiba     Notebook    13.3  Full HD 1920x1080   \n",
       "611      Dell       Gaming    15.6  Full HD 1920x1080   \n",
       "707    Lenovo  Workstation    15.6  Full HD 1920x1080   \n",
       "271      Asus       Gaming    17.3  Full HD 1920x1080   \n",
       "\n",
       "                              Cpu  Ram                Memory  \\\n",
       "1262   Intel Core i3 7100U 2.4GHz    6               1TB HDD   \n",
       "895    Intel Core i5 7200U 2.5GHz    8             256GB SSD   \n",
       "611   Intel Core i7 6700HQ 2.6GHz   16               1TB HDD   \n",
       "707    Intel Core i7 6500U 2.5GHz   16             512GB SSD   \n",
       "271           AMD Ryzen 1700 3GHz   16  256GB SSD +  1TB HDD   \n",
       "\n",
       "                                 Gpu       OpSys  Weight       Price  \\\n",
       "1262           Intel HD Graphics 620  Windows 10    2.30  24455.5200   \n",
       "895            Intel HD Graphics 620  Windows 10    1.05  78588.0000   \n",
       "611   Nvidia GeForce GTX 960<U+039C>  Windows 10    2.59  46833.6528   \n",
       "707              Nvidia Quadro M520M   Windows 7    2.18  98834.4000   \n",
       "271                AMD Radeon RX 580  Windows 10    3.20  82530.7200   \n",
       "\n",
       "      Touchscreen  Ips         X_res Y_res  \n",
       "1262            0    0          1366   768  \n",
       "895             0    0  Full HD 1920  1080  \n",
       "611             0    0  Full HD 1920  1080  \n",
       "707             0    0  Full HD 1920  1080  \n",
       "271             0    0  Full HD 1920  1080  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "64795285-9b46-4329-99ea-7deb8698203e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['X_res'] = df['X_res'].str.replace(',','').str.findall(r'(\\d+\\.?\\d+)').apply(lambda x:x[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a572c9e4-3e0c-47c9-a86e-f636340f02c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>TypeName</th>\n",
       "      <th>Inches</th>\n",
       "      <th>ScreenResolution</th>\n",
       "      <th>Cpu</th>\n",
       "      <th>Ram</th>\n",
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       "      <th>Gpu</th>\n",
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       "      <th>Touchscreen</th>\n",
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       "      <th>Y_res</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 2.3GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 640</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>71378.6832</td>\n",
       "      <td>0</td>\n",
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       "      <td>2560</td>\n",
       "      <td>1600</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>1440x900</td>\n",
       "      <td>Intel Core i5 1.8GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>47895.5232</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1440</td>\n",
       "      <td>900</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>30636.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1920</td>\n",
       "      <td>1080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>15.4</td>\n",
       "      <td>IPS Panel Retina Display 2880x1800</td>\n",
       "      <td>Intel Core i7 2.7GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>135195.3360</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2880</td>\n",
       "      <td>1800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 3.1GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>96095.8080</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2560</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "  Company   TypeName  Inches                    ScreenResolution  \\\n",
       "0   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "1   Apple  Ultrabook    13.3                            1440x900   \n",
       "2      HP   Notebook    15.6                   Full HD 1920x1080   \n",
       "3   Apple  Ultrabook    15.4  IPS Panel Retina Display 2880x1800   \n",
       "4   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "\n",
       "                          Cpu  Ram               Memory  \\\n",
       "0        Intel Core i5 2.3GHz    8            128GB SSD   \n",
       "1        Intel Core i5 1.8GHz    8  128GB Flash Storage   \n",
       "2  Intel Core i5 7200U 2.5GHz    8            256GB SSD   \n",
       "3        Intel Core i7 2.7GHz   16            512GB SSD   \n",
       "4        Intel Core i5 3.1GHz    8            256GB SSD   \n",
       "\n",
       "                            Gpu  OpSys  Weight        Price  Touchscreen  Ips  \\\n",
       "0  Intel Iris Plus Graphics 640  macOS    1.37   71378.6832            0    1   \n",
       "1        Intel HD Graphics 6000  macOS    1.34   47895.5232            0    0   \n",
       "2         Intel HD Graphics 620  No OS    1.86   30636.0000            0    0   \n",
       "3            AMD Radeon Pro 455  macOS    1.83  135195.3360            0    1   \n",
       "4  Intel Iris Plus Graphics 650  macOS    1.37   96095.8080            0    1   \n",
       "\n",
       "  X_res Y_res  \n",
       "0  2560  1600  \n",
       "1  1440   900  \n",
       "2  1920  1080  \n",
       "3  2880  1800  \n",
       "4  2560  1600  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "283bd1f3-2c40-4414-b16f-9c5683ed5b19",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['X_res'] = df['X_res'].astype('int')\n",
    "df['Y_res'] = df['Y_res'].astype('int')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6485c41a-f905-43ec-a901-aa8f4091ac53",
   "metadata": {},
   "source": [
    "#### Ppi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b8fa72d4-113f-4044-8c60-61a11c87427a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ppi'] = (((df['X_res']**2) + (df['Y_res']**2))**0.5/df['Inches']).astype('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d07cba22-1187-459e-b673-3178ab2a3f54",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['Inches','X_res','Y_res','ScreenResolution'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6a4b905a-0d5d-4250-8398-91e4be61a33e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>128GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
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       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
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       "      <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",
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       "    <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",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>220.534624</td>\n",
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       "    <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  Touchscreen  Ips  \\\n",
       "0  Intel Iris Plus Graphics 640  macOS    1.37   71378.6832            0    1   \n",
       "1        Intel HD Graphics 6000  macOS    1.34   47895.5232            0    0   \n",
       "2         Intel HD Graphics 620  No OS    1.86   30636.0000            0    0   \n",
       "3            AMD Radeon Pro 455  macOS    1.83  135195.3360            0    1   \n",
       "4  Intel Iris Plus Graphics 650  macOS    1.37   96095.8080            0    1   \n",
       "\n",
       "          ppi  \n",
       "0  226.983005  \n",
       "1  127.677940  \n",
       "2  141.211998  \n",
       "3  220.534624  \n",
       "4  226.983005  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fff5fda-8f09-4419-9b8e-59b053756821",
   "metadata": {},
   "source": [
    "#### CPU Brand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "15b0cebc-575c-4f9b-800f-9eebd6ae580e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cpu\n",
       "Intel Core i5 7200U 2.5GHz       190\n",
       "Intel Core i7 7700HQ 2.8GHz      146\n",
       "Intel Core i7 7500U 2.7GHz       134\n",
       "Intel Core i7 8550U 1.8GHz        73\n",
       "Intel Core i5 8250U 1.6GHz        72\n",
       "                                ... \n",
       "Intel Core i5 7200U 2.70GHz        1\n",
       "Intel Core M M7-6Y75 1.2GHz        1\n",
       "Intel Core M 6Y54 1.1GHz           1\n",
       "AMD E-Series 9000 2.2GHz           1\n",
       "Samsung Cortex A72&A53 2.0GHz      1\n",
       "Name: count, Length: 118, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Cpu'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "99196db5-ce5c-4a16-8097-f09689bc22f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Cpu Name'] = df['Cpu'].apply(lambda x:\" \".join(x.split()[0:3]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "6dd3a2ff-a064-4342-886e-9d504626d674",
   "metadata": {},
   "outputs": [
    {
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       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
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       "      <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  Touchscreen  Ips  \\\n",
       "0  Intel Iris Plus Graphics 640  macOS    1.37   71378.6832            0    1   \n",
       "1        Intel HD Graphics 6000  macOS    1.34   47895.5232            0    0   \n",
       "2         Intel HD Graphics 620  No OS    1.86   30636.0000            0    0   \n",
       "3            AMD Radeon Pro 455  macOS    1.83  135195.3360            0    1   \n",
       "4  Intel Iris Plus Graphics 650  macOS    1.37   96095.8080            0    1   \n",
       "\n",
       "          ppi       Cpu Name  \n",
       "0  226.983005  Intel Core i5  \n",
       "1  127.677940  Intel Core i5  \n",
       "2  141.211998  Intel Core i5  \n",
       "3  220.534624  Intel Core i7  \n",
       "4  226.983005  Intel Core i5  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "82c2cc6d-db60-4e27-a416-85798ef0bcd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fetch_processor(text):\n",
    "    if text == 'Intel Core i7' or text == 'Intel Core i5' or text == 'Intel Core i3':\n",
    "        return text\n",
    "    else:\n",
    "        if text.split()[0] == 'Intel':\n",
    "            return 'Other Intel Processor'\n",
    "        else:\n",
    "            return 'AMD Processor'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "875e326c-77a7-4c9d-a4b6-40bcba2cc5ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Cpu brand'] = df['Cpu Name'].apply(fetch_processor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "d9aad9a7-ac12-4173-b9f3-fcd31cc92494",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['Cpu','Cpu Name'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "522fd95e-f4ca-43f6-b597-bdb4b194e6e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>ppi</th>\n",
       "      <th>Cpu brand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</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>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>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>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>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  Ram               Memory                           Gpu  \\\n",
       "0   Apple  Ultrabook    8            128GB SSD  Intel Iris Plus Graphics 640   \n",
       "1   Apple  Ultrabook    8  128GB Flash Storage        Intel HD Graphics 6000   \n",
       "2      HP   Notebook    8            256GB SSD         Intel HD Graphics 620   \n",
       "3   Apple  Ultrabook   16            512GB SSD            AMD Radeon Pro 455   \n",
       "4   Apple  Ultrabook    8            256GB SSD  Intel Iris Plus Graphics 650   \n",
       "\n",
       "   OpSys  Weight        Price  Touchscreen  Ips         ppi      Cpu brand  \n",
       "0  macOS    1.37   71378.6832            0    1  226.983005  Intel Core i5  \n",
       "1  macOS    1.34   47895.5232            0    0  127.677940  Intel Core i5  \n",
       "2  No OS    1.86   30636.0000            0    0  141.211998  Intel Core i5  \n",
       "3  macOS    1.83  135195.3360            0    1  220.534624  Intel Core i7  \n",
       "4  macOS    1.37   96095.8080            0    1  226.983005  Intel Core i5  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bae3d1bf-f73c-4db6-8f1f-f25125100f3f",
   "metadata": {},
   "source": [
    "#### GPU Brand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "cb20adfe-910f-4732-be3b-0c137b59f0f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gpu\n",
       "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",
       "Nvidia Quadro M500M          1\n",
       "AMD Radeon R7 M360           1\n",
       "Nvidia Quadro M3000M         1\n",
       "Nvidia GeForce 960M          1\n",
       "ARM Mali T860 MP4            1\n",
       "Name: count, Length: 110, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gpu'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "a1d632e8-ed47-4bed-a984-b6a4672d0c53",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Gpu brand'] = df['Gpu'].apply(lambda x:x.split()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "d862ab57-22b4-410b-aa65-dfeb608bf730",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gpu brand\n",
       "Intel     722\n",
       "Nvidia    400\n",
       "AMD       180\n",
       "ARM         1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gpu brand'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "772b6c9a-342f-4fb0-ac5b-e97d5458a2fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df['Gpu brand'] != 'ARM']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "4e30fd48-0b4f-42a7-94d3-1a6add582d5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gpu brand\n",
       "Intel     722\n",
       "Nvidia    400\n",
       "AMD       180\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gpu brand'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "6b92dc81-075a-4bc2-aab0-365a21a21892",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['Gpu'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "df49394f-5f4b-4567-b602-2784a2b9a062",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
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       "      <td>macOS</td>\n",
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       "      <td>47895.5232</td>\n",
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       "      <td>0</td>\n",
       "      <td>127.677940</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>Intel</td>\n",
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       "      <th>2</th>\n",
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       "      <td>256GB SSD</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</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>AMD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</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</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Ram               Memory  OpSys  Weight        Price  \\\n",
       "0   Apple  Ultrabook    8            128GB SSD  macOS    1.37   71378.6832   \n",
       "1   Apple  Ultrabook    8  128GB Flash Storage  macOS    1.34   47895.5232   \n",
       "2      HP   Notebook    8            256GB SSD  No OS    1.86   30636.0000   \n",
       "3   Apple  Ultrabook   16            512GB SSD  macOS    1.83  135195.3360   \n",
       "4   Apple  Ultrabook    8            256GB SSD  macOS    1.37   96095.8080   \n",
       "\n",
       "   Touchscreen  Ips         ppi      Cpu brand Gpu brand  \n",
       "0            0    1  226.983005  Intel Core i5     Intel  \n",
       "1            0    0  127.677940  Intel Core i5     Intel  \n",
       "2            0    0  141.211998  Intel Core i5     Intel  \n",
       "3            0    1  220.534624  Intel Core i7       AMD  \n",
       "4            0    1  226.983005  Intel Core i5     Intel  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17effac4-492c-4db2-8643-7e3ffd29c0b3",
   "metadata": {},
   "source": [
    "#### OS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "46a7d305-bba2-4ef2-a5dc-4e7ee5b316b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OpSys\n",
       "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: count, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['OpSys'].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "3326f24d-d00a-49bb-bba0-ceeec274243e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def cat_os(inp):\n",
    "    if inp == 'Windows 10' or inp == 'Windows 7' or inp == 'Windows 10 S':\n",
    "        return 'Windows'\n",
    "    elif inp == 'macOS' or inp == 'Mac OS X':\n",
    "        return 'Mac'\n",
    "    else:\n",
    "        return 'Others/No OS/Linux'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "2c12a519-573c-4942-89be-2b7c32742e7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['os'] = df['OpSys'].apply(cat_os)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "5e5b64a6-ff39-44a2-8e85-73e6c436892b",
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB SSD</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</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>128GB Flash Storage</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</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>256GB SSD</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</td>\n",
       "      <td>Others/No OS/Linux</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</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>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>256GB SSD</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</td>\n",
       "      <td>Mac</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Ram               Memory  OpSys  Weight        Price  \\\n",
       "0   Apple  Ultrabook    8            128GB SSD  macOS    1.37   71378.6832   \n",
       "1   Apple  Ultrabook    8  128GB Flash Storage  macOS    1.34   47895.5232   \n",
       "2      HP   Notebook    8            256GB SSD  No OS    1.86   30636.0000   \n",
       "3   Apple  Ultrabook   16            512GB SSD  macOS    1.83  135195.3360   \n",
       "4   Apple  Ultrabook    8            256GB SSD  macOS    1.37   96095.8080   \n",
       "\n",
       "   Touchscreen  Ips         ppi      Cpu brand Gpu brand                  os  \n",
       "0            0    1  226.983005  Intel Core i5     Intel                 Mac  \n",
       "1            0    0  127.677940  Intel Core i5     Intel                 Mac  \n",
       "2            0    0  141.211998  Intel Core i5     Intel  Others/No OS/Linux  \n",
       "3            0    1  220.534624  Intel Core i7       AMD                 Mac  \n",
       "4            0    1  226.983005  Intel Core i5     Intel                 Mac  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "eb8ccacb-f9a9-4429-bbe3-69f386d40a2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['OpSys'],inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0a6ab20-1893-4334-842e-1fe7fbdc3aab",
   "metadata": {},
   "source": [
    "#### Memory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "bd7b0186-99ab-4f20-835e-5517a1ef3a29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Memory\n",
       "256GB SSD                        412\n",
       "1TB HDD                          223\n",
       "500GB HDD                        132\n",
       "512GB SSD                        118\n",
       "128GB SSD +  1TB HDD              94\n",
       "128GB SSD                         76\n",
       "256GB SSD +  1TB HDD              73\n",
       "32GB Flash Storage                37\n",
       "2TB HDD                           16\n",
       "64GB Flash Storage                15\n",
       "1TB SSD                           14\n",
       "512GB SSD +  1TB HDD              14\n",
       "256GB SSD +  2TB HDD              10\n",
       "1.0TB Hybrid                       9\n",
       "256GB Flash Storage                8\n",
       "16GB Flash Storage                 7\n",
       "32GB SSD                           6\n",
       "180GB SSD                          5\n",
       "128GB Flash Storage                4\n",
       "16GB SSD                           3\n",
       "512GB SSD +  2TB HDD               3\n",
       "128GB SSD +  2TB HDD               2\n",
       "256GB SSD +  256GB SSD             2\n",
       "512GB Flash Storage                2\n",
       "1TB SSD +  1TB HDD                 2\n",
       "256GB SSD +  500GB HDD             2\n",
       "64GB SSD                           1\n",
       "512GB SSD +  512GB SSD             1\n",
       "64GB Flash Storage +  1TB HDD      1\n",
       "1TB HDD +  1TB HDD                 1\n",
       "512GB SSD +  256GB SSD             1\n",
       "32GB HDD                           1\n",
       "128GB HDD                          1\n",
       "240GB SSD                          1\n",
       "8GB SSD                            1\n",
       "508GB Hybrid                       1\n",
       "1.0TB HDD                          1\n",
       "512GB SSD +  1.0TB Hybrid          1\n",
       "256GB SSD +  1.0TB Hybrid          1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Memory'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "efbed61c-6a6f-467e-886e-4040ba897ec3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<>:4: SyntaxWarning: invalid escape sequence '\\.'\n",
      "<>:4: SyntaxWarning: invalid escape sequence '\\.'\n",
      "C:\\Users\\freev\\AppData\\Local\\Temp\\ipykernel_4124\\2834611207.py:4: SyntaxWarning: invalid escape sequence '\\.'\n",
      "  df['Memory'] = df['Memory'].astype(str).replace('\\.0', '', regex=True)\n",
      "C:\\Users\\freev\\AppData\\Local\\Temp\\ipykernel_4124\\2834611207.py:16: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  df[\"second\"].fillna(\"0\", inplace=True)  # Ensures that 'second' is not None\n"
     ]
    },
    {
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       "    <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>Weight</th>\n",
       "      <th>Price</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>ppi</th>\n",
       "      <th>Cpu brand</th>\n",
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       "      <th>SSD</th>\n",
       "      <th>Hybrid</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
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       "      <td>0</td>\n",
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       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>128 Flash Storage</td>\n",
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       "      <td>0</td>\n",
       "      <td>127.677940</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
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       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>256 SSD</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",
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       "      <td>Others/No OS/Linux</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>Ultrabook</td>\n",
       "      <td>16</td>\n",
       "      <td>512 SSD</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>AMD</td>\n",
       "      <td>Mac</td>\n",
       "      <td>0</td>\n",
       "      <td>512</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>256 SSD</td>\n",
       "      <td>1.37</td>\n",
       "      <td>96095.8080</td>\n",
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       "      <td>Mac</td>\n",
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      "text/plain": [
       "  Company   TypeName  Ram             Memory  Weight        Price  \\\n",
       "0   Apple  Ultrabook    8            128 SSD    1.37   71378.6832   \n",
       "1   Apple  Ultrabook    8  128 Flash Storage    1.34   47895.5232   \n",
       "2      HP   Notebook    8            256 SSD    1.86   30636.0000   \n",
       "3   Apple  Ultrabook   16            512 SSD    1.83  135195.3360   \n",
       "4   Apple  Ultrabook    8            256 SSD    1.37   96095.8080   \n",
       "\n",
       "   Touchscreen  Ips         ppi      Cpu brand Gpu brand                  os  \\\n",
       "0            0    1  226.983005  Intel Core i5     Intel                 Mac   \n",
       "1            0    0  127.677940  Intel Core i5     Intel                 Mac   \n",
       "2            0    0  141.211998  Intel Core i5     Intel  Others/No OS/Linux   \n",
       "3            0    1  220.534624  Intel Core i7       AMD                 Mac   \n",
       "4            0    1  226.983005  Intel Core i5     Intel                 Mac   \n",
       "\n",
       "   HDD  SSD  Hybrid  Flash_Storage  \n",
       "0    0  128       0              0  \n",
       "1    0    0       0            128  \n",
       "2    0  256       0              0  \n",
       "3    0  512       0              0  \n",
       "4    0  256       0              0  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Replace '.0' and remove 'GB' and 'TB'\n",
    "df['Memory'] = df['Memory'].astype(str).replace('\\.0', '', regex=True)\n",
    "df[\"Memory\"] = df[\"Memory\"].str.replace('GB', '')\n",
    "df[\"Memory\"] = df[\"Memory\"].str.replace('TB', '000')\n",
    "\n",
    "# Split the 'Memory' column into two parts\n",
    "new = df[\"Memory\"].str.split(\"+\", n=1, expand=True)\n",
    "\n",
    "# Clean and strip spaces\n",
    "df[\"first\"] = new[0].str.strip()\n",
    "\n",
    "# Check if the second part exists and replace None with \"0\"\n",
    "df[\"second\"] = new[1].str.strip() if new.shape[1] > 1 else \"0\"\n",
    "df[\"second\"].fillna(\"0\", inplace=True)  # Ensures that 'second' is not None\n",
    "\n",
    "# Create binary flags for storage types before numeric conversion\n",
    "df[\"Layer1HDD\"] = df[\"first\"].apply(lambda x: 1 if \"HDD\" in x else 0)\n",
    "df[\"Layer1SSD\"] = df[\"first\"].apply(lambda x: 1 if \"SSD\" in x else 0)\n",
    "df[\"Layer1Hybrid\"] = df[\"first\"].apply(lambda x: 1 if \"Hybrid\" in x else 0)\n",
    "df[\"Layer1Flash_Storage\"] = df[\"first\"].apply(lambda x: 1 if \"Flash Storage\" in x else 0)\n",
    "\n",
    "df[\"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",
    "# Replace non-numeric characters in 'first' and 'second'\n",
    "df[\"first\"] = df[\"first\"].str.replace(r'\\D', '', regex=True)\n",
    "df[\"second\"] = df[\"second\"].str.replace(r'\\D', '', regex=True)\n",
    "\n",
    "# Convert the cleaned 'first' and 'second' columns to integers\n",
    "df[\"first\"] = df[\"first\"].astype(int)\n",
    "df[\"second\"] = df[\"second\"].astype(int)\n",
    "\n",
    "# Calculate the total capacities for each storage type\n",
    "df[\"HDD\"] = df[\"first\"] * df[\"Layer1HDD\"] + df[\"second\"] * df[\"Layer2HDD\"]\n",
    "df[\"SSD\"] = df[\"first\"] * df[\"Layer1SSD\"] + df[\"second\"] * df[\"Layer2SSD\"]\n",
    "df[\"Hybrid\"] = df[\"first\"] * df[\"Layer1Hybrid\"] + df[\"second\"] * df[\"Layer2Hybrid\"]\n",
    "df[\"Flash_Storage\"] = df[\"first\"] * df[\"Layer1Flash_Storage\"] + df[\"second\"] * df[\"Layer2Flash_Storage\"]\n",
    "\n",
    "# Drop unnecessary columns\n",
    "df.drop(columns=['first', 'second', 'Layer1HDD', 'Layer1SSD', 'Layer1Hybrid',\n",
    "                 'Layer1Flash_Storage', 'Layer2HDD', 'Layer2SSD', 'Layer2Hybrid',\n",
    "                 'Layer2Flash_Storage'], inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "2e23fe99-c48b-4665-a431-5b51e724b95f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['Memory'],inplace=True)\n",
    "df.drop(columns=['Hybrid','Flash_Storage'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "9392db83-0537-4c7d-be74-f8732f91ab30",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
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       "      <td>Ultrabook</td>\n",
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       "      <td>1</td>\n",
       "      <td>220.534624</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>AMD</td>\n",
       "      <td>Mac</td>\n",
       "      <td>0</td>\n",
       "      <td>512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</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</td>\n",
       "      <td>Mac</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Ram  Weight        Price  Touchscreen  Ips         ppi  \\\n",
       "0   Apple  Ultrabook    8    1.37   71378.6832            0    1  226.983005   \n",
       "1   Apple  Ultrabook    8    1.34   47895.5232            0    0  127.677940   \n",
       "2      HP   Notebook    8    1.86   30636.0000            0    0  141.211998   \n",
       "3   Apple  Ultrabook   16    1.83  135195.3360            0    1  220.534624   \n",
       "4   Apple  Ultrabook    8    1.37   96095.8080            0    1  226.983005   \n",
       "\n",
       "       Cpu brand Gpu brand                  os  HDD  SSD  \n",
       "0  Intel Core i5     Intel                 Mac    0  128  \n",
       "1  Intel Core i5     Intel                 Mac    0    0  \n",
       "2  Intel Core i5     Intel  Others/No OS/Linux    0  256  \n",
       "3  Intel Core i7       AMD                 Mac    0  512  \n",
       "4  Intel Core i5     Intel                 Mac    0  256  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d0d5371-af77-4ddf-9a05-255366dad509",
   "metadata": {},
   "source": [
    "## Correlation Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "23f50064-886d-4f58-ba99-ac3b9f3eb0d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Select only numeric columns\n",
    "numeric_df = df.select_dtypes(include=['number'])\n",
    "\n",
    "# Calculate the correlation matrix\n",
    "correlation_matrix = numeric_df.corr()\n",
    "\n",
    "# Plot the Correlation Heatmap\n",
    "plt.figure(figsize=(8, 6))  # Set the size of the figure\n",
    "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt='.2f', linewidths=0.5)\n",
    "plt.title('Correlation Heatmap')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca14f742-38e5-42a7-aeec-7e1f3317f19a",
   "metadata": {},
   "source": [
    "**Strong Positive Correlations:**\n",
    "\n",
    "`Ram and Price (0.74):` There is a strong positive correlation between the amount of RAM and the price of the laptop. As the RAM increases, the price also tends to increase. This suggests that RAM is a significant determinant of the laptop's cost.\n",
    "\n",
    "`SSD and Price (0.67):` Laptops with larger SSDs are priced higher. SSDs tend to be more expensive, so this makes sense.\n",
    "\n",
    "`Ram and SSD (0.60):` Laptops with more RAM tend to have larger SSD storage. This reflects the trend in modern laptops where high-performance machines often combine more RAM with SSDs for faster performance.\n",
    "\n",
    "`ppi and Price (0.48):` Laptops with higher pixel density (ppi) screens are generally more expensive, which indicates that display quality is also a cost driver.\n",
    "    \n",
    "**Weak or Moderate Correlations:**\n",
    "\n",
    "`Weight and SSD (0.51):` Laptops with SSDs tend to be lighter. SSDs are physically smaller and lighter compared to HDDs, which likely contributes to this relationship.\n",
    "\n",
    "`ppi and Touchscreen (0.46):` Laptops with touchscreens also tend to have higher pixel density, likely because touchscreen devices may require better display quality for a good user experience.\n",
    "\n",
    "`Touchscreen and Price (0.19):` There is a weak positive correlation between touchscreen and price, indicating that while touchscreens slightly increase the price, they are not a primary driver of cost.\n",
    "\n",
    "`HDD and Weight (0.51):` Laptops with HDDs tend to be heavier, which is understandable given that HDDs are bulkier compared to SSDs.\n",
    "    \n",
    "**Negative Correlations:**\n",
    "\n",
    "`Weight and ppi (-0.32):` Laptops with higher pixel density screens tend to be lighter. This might be due to modern, lightweight laptops focusing on high-resolution displays for better portability and user experience.\n",
    "\n",
    "`Weight and SSD (-0.06):` There is a slightly negative correlation, meaning lighter laptops tend to have SSDs over HDDs.\n",
    "    \n",
    "**No Significant Correlation:***\n",
    "\n",
    "`Touchscreen and Ram (0.12):` There is little correlation between the presence of a touchscreen and the amount of RAM, suggesting that RAM does not heavily impact whether or not a laptop has a touchscreen.\n",
    "\n",
    "`Ips (In-Plane Switching technology for displays) and Price (0.25):` While IPS screens are positively correlated with price, the correlation is weaker, indicating that this factor plays a less significant role in determining the price."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9542eab-d897-4e9d-aa58-3a95baf46470",
   "metadata": {},
   "source": [
    "Laptops with high RAM, SSD, and higher ppi (display resolution) tend to be higher priced. This reflects market demand for performance and quality, with laptops combining these features commanding a premium price.\n",
    "\n",
    "Laptops that are lighter often have SSDs instead of HDDs and high-resolution displays (ppi). This indicates a trend toward portability, where consumers may prefer lighter, high-resolution machines over heavier ones with older technology like HDDs.\n",
    "\n",
    "While the heatmap doesn’t indicate severe multicollinearity, we should still check this in more detail to ensure robust model performance.RAM and SSD size are highly correlated with price, meaning these should be prioritized if cost is a primary factor in purchasing decisions."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c0c54ab-b6b8-4648-b9a9-cecffe1f3b47",
   "metadata": {},
   "source": [
    "## Checking Multicollinearity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "94136c44-655e-4ffd-aa7a-1fab573703f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Feature       VIF\n",
      "0          Ram  3.067345\n",
      "1       Weight  2.051582\n",
      "2        Price  2.996994\n",
      "3  Touchscreen  1.315171\n",
      "4          Ips  1.130486\n",
      "5          ppi  2.018742\n",
      "6          HDD  1.789804\n",
      "7          SSD  2.815358\n"
     ]
    }
   ],
   "source": [
    "# Import necessary libraries\n",
    "import pandas as pd\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Assuming 'df' is your DataFrame containing the dataset\n",
    "# First, you should only select the numeric columns to calculate VIF\n",
    "\n",
    "# Example dataframe of numeric features\n",
    "numeric_columns = ['Ram', 'Weight', 'Price', 'Touchscreen', 'Ips', 'ppi', 'HDD', 'SSD']\n",
    "\n",
    "# Standardizing the features (VIF works better when features are scaled)\n",
    "scaler = StandardScaler()\n",
    "scaled_data = scaler.fit_transform(df[numeric_columns])\n",
    "\n",
    "# Create a new DataFrame with scaled data\n",
    "scaled_df = pd.DataFrame(scaled_data, columns=numeric_columns)\n",
    "\n",
    "# Calculate VIF for each feature\n",
    "vif_data = pd.DataFrame()\n",
    "vif_data[\"Feature\"] = scaled_df.columns\n",
    "vif_data[\"VIF\"] = [variance_inflation_factor(scaled_df.values, i) for i in range(scaled_df.shape[1])]\n",
    "\n",
    "# Display the VIF values\n",
    "print(vif_data)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2254ec0-1821-40f5-949a-ce28e2b61731",
   "metadata": {},
   "source": [
    "Since none of the VIF values exceed 5, multicollinearity doesn't seem to be a major problem in this dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abe12a3c-4e19-43a0-9eb4-3627adb7a5bc",
   "metadata": {},
   "source": [
    "## Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "46557be9-9db9-4dc0-a163-c25ba8fa9b35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Rows containing outliers in column 'Ram':\n",
      "     Company            TypeName  Ram  Weight        Price  Touchscreen  Ips  \\\n",
      "3      Apple           Ultrabook   16    1.83  135195.3360            0    1   \n",
      "6      Apple           Ultrabook   16    2.04  114017.6016            0    1   \n",
      "8       Asus           Ultrabook   16    1.30   79653.6000            0    0   \n",
      "12     Apple           Ultrabook   16    1.83  130001.6016            0    1   \n",
      "17     Apple           Ultrabook   16    1.83  152274.2400            0    1   \n",
      "...      ...                 ...  ...     ...          ...          ...  ...   \n",
      "1247    Asus              Gaming   16    2.34  123876.0000            0    1   \n",
      "1256    Asus              Gaming   16    4.00  101232.0000            0    1   \n",
      "1271  Lenovo  2 in 1 Convertible   16    1.30   79866.7200            1    1   \n",
      "1285  Lenovo  2 in 1 Convertible   16    1.30   79866.7200            1    1   \n",
      "1299  Lenovo  2 in 1 Convertible   16    1.30   79866.7200            1    1   \n",
      "\n",
      "             ppi      Cpu brand Gpu brand       os   HDD  SSD  \n",
      "3     220.534624  Intel Core i7       AMD      Mac     0  512  \n",
      "6     220.534624  Intel Core i7     Intel      Mac     0    0  \n",
      "8     157.350512  Intel Core i7    Nvidia  Windows     0  512  \n",
      "12    220.534624  Intel Core i7       AMD      Mac     0  256  \n",
      "17    220.534624  Intel Core i7       AMD      Mac     0  512  \n",
      "...          ...            ...       ...      ...   ...  ...  \n",
      "1247  141.211998  Intel Core i7    Nvidia  Windows  1000  256  \n",
      "1256  127.335675  Intel Core i7    Nvidia  Windows  1000  128  \n",
      "1271  276.053530  Intel Core i7     Intel  Windows     0  512  \n",
      "1285  276.053530  Intel Core i7     Intel  Windows     0  512  \n",
      "1299  276.053530  Intel Core i7     Intel  Windows     0  512  \n",
      "\n",
      "[221 rows x 13 columns]\n",
      "\n",
      "Rows containing outliers in column 'Weight':\n",
      "     Company  TypeName  Ram  Weight        Price  Touchscreen  Ips  \\\n",
      "177      MSI    Gaming   32    4.40  149130.7200            0    0   \n",
      "224     Dell    Gaming   16    4.42  130873.7952            0    1   \n",
      "238     Asus    Gaming   32    4.70  207259.2000            0    0   \n",
      "247     Asus    Gaming   16    3.60  159786.7200            0    0   \n",
      "251     Asus    Gaming   16    4.30   95850.7200            0    0   \n",
      "258      MSI    Gaming   16    4.14  133146.7200            0    0   \n",
      "424     Dell    Gaming   16    4.42  149130.7200            0    1   \n",
      "456     Dell  Notebook    8    4.42  109010.8800            0    1   \n",
      "468       HP    Gaming   12    3.78   90522.7200            0    1   \n",
      "494     Asus    Gaming   24    4.33   67612.3200            1    1   \n",
      "530     Dell    Gaming   16    4.42  160520.3856            0    1   \n",
      "552     Dell    Gaming   16    4.42  143802.7200            0    1   \n",
      "577   Lenovo    Gaming   32    4.60  141884.6400            0    1   \n",
      "578      MSI    Gaming   16    4.14  145401.1200            0    0   \n",
      "585      MSI    Gaming   16    4.50  125154.7200            0    0   \n",
      "586       HP    Gaming   16    3.78   79813.4400            0    1   \n",
      "603      MSI    Gaming   16    4.14  117162.7200            0    0   \n",
      "650     Asus    Gaming   32    3.80  149130.7200            0    1   \n",
      "659     Dell    Gaming   32    4.42  167691.8736            0    1   \n",
      "723     Dell    Gaming   32    4.36  194972.8320            0    0   \n",
      "730     Acer    Gaming   16    4.20  103096.8000            0    1   \n",
      "758     Dell    Gaming   16    4.42  152859.7872            0    1   \n",
      "780     Dell    Gaming   32    4.42  191211.2640            0    1   \n",
      "781     Asus    Gaming   16    4.30  125208.0000            0    1   \n",
      "788     Acer    Gaming   16    4.20  122490.7200            0    1   \n",
      "810       HP    Gaming   12    3.74   93186.7200            0    1   \n",
      "818     Dell    Gaming   16    4.36  133467.4656            0    1   \n",
      "841     Dell    Gaming   32    4.42  163723.5792            0    1   \n",
      "901     Asus    Gaming   32    3.58   68145.1200            0    1   \n",
      "939     Dell    Gaming   16    4.36  146946.2400            0    0   \n",
      "955     Dell    Gaming   16    4.36  168045.1200            0    1   \n",
      "968     Dell    Gaming   32    4.42  167778.7200            0    1   \n",
      "972     Dell    Gaming   32    4.42  149184.0000            0    0   \n",
      "1047     MSI    Gaming   16    3.78   82351.6992            0    1   \n",
      "1048  Lenovo    Gaming   16    4.60  122010.6672            0    1   \n",
      "1061    Asus    Gaming   16    4.00   53173.4400            0    1   \n",
      "1066    Asus    Gaming   64    3.58  211788.0000            0    1   \n",
      "1077     MSI    Gaming   16    3.78  133146.7200            0    0   \n",
      "1081  Lenovo    Gaming   32    4.60  172627.2000            0    1   \n",
      "1099    Asus    Gaming   16    4.30  122490.7200            0    1   \n",
      "1116  Lenovo    Gaming   16    4.60  146519.4672            0    1   \n",
      "1119    Asus    Gaming    8    3.52   69929.4672            0    0   \n",
      "1138     MSI    Gaming   16    3.78  127818.7200            0    0   \n",
      "1189    Acer    Gaming   16    4.20  138474.7200            0    1   \n",
      "1197    Asus    Gaming   16    4.30  114552.0000            0    1   \n",
      "1256    Asus    Gaming   16    4.00  101232.0000            0    1   \n",
      "\n",
      "             ppi      Cpu brand Gpu brand                  os   HDD   SSD  \n",
      "177   119.723216  Intel Core i7    Nvidia             Windows  1000   512  \n",
      "224   127.335675  Intel Core i7    Nvidia             Windows  1000   256  \n",
      "238   127.335675  Intel Core i7    Nvidia             Windows  1000   512  \n",
      "247   127.335675  Intel Core i7    Nvidia             Windows     0   256  \n",
      "251   127.335675  Intel Core i7    Nvidia             Windows  1000   256  \n",
      "258   127.335675  Intel Core i7    Nvidia             Windows  1000   512  \n",
      "424   169.780899  Intel Core i7    Nvidia             Windows  1000   256  \n",
      "456   127.335675  Intel Core i7    Nvidia             Windows  1000     0  \n",
      "468   127.335675  Intel Core i7    Nvidia             Windows  1000     0  \n",
      "494   127.335675  Intel Core i7    Nvidia             Windows  1000   256  \n",
      "530   127.335675  Intel Core i7    Nvidia             Windows  1000   128  \n",
      "552   127.335675  Intel Core i7    Nvidia             Windows  1000   256  \n",
      "577   127.335675  Intel Core i7    Nvidia             Windows  1000   256  \n",
      "578   127.335675  Intel Core i7    Nvidia             Windows  1000   512  \n",
      "585   127.335675  Intel Core i7    Nvidia             Windows  1000   128  \n",
      "586   127.335675  Intel Core i7    Nvidia             Windows  1000   256  \n",
      "603   127.335675  Intel Core i7    Nvidia             Windows  1000   128  \n",
      "650   127.335675  Intel Core i7    Nvidia             Windows     0   512  \n",
      "659   254.671349  Intel Core i7    Nvidia             Windows  1000   512  \n",
      "723   254.671349  Intel Core i7    Nvidia             Windows  1000  1000  \n",
      "730   127.335675  Intel Core i7    Nvidia             Windows  1000   128  \n",
      "758   282.423996  Intel Core i7    Nvidia             Windows  1000   256  \n",
      "780   127.335675  Intel Core i7    Nvidia             Windows  1000  1000  \n",
      "781   127.335675  Intel Core i7    Nvidia             Windows  1000   256  \n",
      "788   127.335675  Intel Core i7    Nvidia             Windows  1000   256  \n",
      "810   127.335675  Intel Core i7       AMD             Windows  1000     0  \n",
      "818   127.335675  Intel Core i7    Nvidia             Windows  1000   128  \n",
      "841   127.335675  Intel Core i7    Nvidia             Windows  1000   512  \n",
      "901   127.335675  Intel Core i7    Nvidia             Windows     0   512  \n",
      "939   169.780899  Intel Core i7    Nvidia             Windows  1000   512  \n",
      "955   254.671349  Intel Core i7    Nvidia             Windows  1000   512  \n",
      "968   127.335675  Intel Core i7    Nvidia             Windows  1000   256  \n",
      "972   127.335675  Intel Core i7    Nvidia             Windows  1000   256  \n",
      "1047  127.335675  Intel Core i7    Nvidia             Windows  1000   256  \n",
      "1048  127.335675  Intel Core i7    Nvidia             Windows  1000   128  \n",
      "1061  127.335675  Intel Core i7    Nvidia  Others/No OS/Linux  1000     0  \n",
      "1066  127.335675  Intel Core i7    Nvidia             Windows     0  1000  \n",
      "1077  127.335675  Intel Core i7    Nvidia             Windows  1000   256  \n",
      "1081  127.335675  Intel Core i7    Nvidia             Windows     0   512  \n",
      "1099  127.335675  Intel Core i7    Nvidia             Windows  1000   128  \n",
      "1116  127.335675  Intel Core i7    Nvidia             Windows     0   256  \n",
      "1119  127.335675  Intel Core i7    Nvidia             Windows  1000   128  \n",
      "1138  127.335675  Intel Core i7    Nvidia             Windows  1000   256  \n",
      "1189  127.335675  Intel Core i7    Nvidia             Windows  1000   256  \n",
      "1197  127.335675  Intel Core i7    Nvidia             Windows  1000   512  \n",
      "1256  127.335675  Intel Core i7    Nvidia             Windows  1000   128  \n",
      "\n",
      "Rows containing outliers in column 'Price':\n",
      "     Company            TypeName  Ram  Weight        Price  Touchscreen  Ips  \\\n",
      "17     Apple           Ultrabook   16    1.83  152274.2400            0    1   \n",
      "190   Lenovo  2 in 1 Convertible   16    1.42  150462.7200            1    0   \n",
      "196    Razer              Gaming   32    3.49  324954.7200            1    0   \n",
      "204     Dell         Workstation   16    2.80  162770.4000            0    0   \n",
      "238     Asus              Gaming   32    4.70  207259.2000            0    0   \n",
      "247     Asus              Gaming   16    3.60  159786.7200            0    0   \n",
      "297     Dell         Workstation   16    3.42  153705.3408            0    0   \n",
      "517     Asus              Gaming   24    2.24  158135.0400            0    0   \n",
      "530     Dell              Gaming   16    4.42  160520.3856            0    1   \n",
      "563   Lenovo            Notebook    8    3.40  159786.7200            0    1   \n",
      "610   Lenovo            Notebook   32    2.50  261018.7200            0    1   \n",
      "659     Dell              Gaming   32    4.42  167691.8736            0    1   \n",
      "723     Dell              Gaming   32    4.36  194972.8320            0    0   \n",
      "744   Lenovo         Workstation   16    2.50  175770.7200            0    1   \n",
      "749       HP         Workstation   16    3.00  233845.9200            0    1   \n",
      "758     Dell              Gaming   16    4.42  152859.7872            0    1   \n",
      "778    Razer              Gaming   16    1.95  154458.7200            0    0   \n",
      "780     Dell              Gaming   32    4.42  191211.2640            0    1   \n",
      "830    Razer              Gaming   32    3.49  292986.7200            1    0   \n",
      "841     Dell              Gaming   32    4.42  163723.5792            0    1   \n",
      "911       HP           Ultrabook    8    1.09  165168.0000            1    0   \n",
      "955     Dell              Gaming   16    4.36  168045.1200            0    1   \n",
      "968     Dell              Gaming   32    4.42  167778.7200            0    1   \n",
      "1017  Lenovo            Notebook   16    2.40  158135.0400            0    1   \n",
      "1066    Asus              Gaming   64    3.58  211788.0000            0    1   \n",
      "1081  Lenovo              Gaming   32    4.60  172627.2000            0    1   \n",
      "1103      HP         Workstation    8    3.00  154458.7200            0    1   \n",
      "1136      HP         Workstation    8    3.00  210424.0320            0    1   \n",
      "1231   Razer              Gaming   16    1.95  186426.7200            0    0   \n",
      "\n",
      "             ppi              Cpu brand Gpu brand       os   HDD   SSD  \n",
      "17    220.534624          Intel Core i7       AMD      Mac     0   512  \n",
      "190   209.800683          Intel Core i7     Intel  Windows     0  1000  \n",
      "196   254.671349          Intel Core i7    Nvidia  Windows     0  1000  \n",
      "204   282.423996  Other Intel Processor    Nvidia  Windows  1000   256  \n",
      "238   127.335675          Intel Core i7    Nvidia  Windows  1000   512  \n",
      "247   127.335675          Intel Core i7    Nvidia  Windows     0   256  \n",
      "297   127.335675          Intel Core i7    Nvidia  Windows     0   256  \n",
      "517   141.211998          Intel Core i7    Nvidia  Windows     0   512  \n",
      "530   127.335675          Intel Core i7    Nvidia  Windows  1000   128  \n",
      "563   127.335675          Intel Core i7    Nvidia  Windows     0   256  \n",
      "610   282.423996  Other Intel Processor    Nvidia  Windows     0  1000  \n",
      "659   254.671349          Intel Core i7    Nvidia  Windows  1000   512  \n",
      "723   254.671349          Intel Core i7    Nvidia  Windows  1000  1000  \n",
      "744   282.423996          Intel Core i7    Nvidia  Windows     0  1000  \n",
      "749   127.335675  Other Intel Processor    Nvidia  Windows     0   256  \n",
      "758   282.423996          Intel Core i7    Nvidia  Windows  1000   256  \n",
      "778   157.350512          Intel Core i7    Nvidia  Windows     0   512  \n",
      "780   127.335675          Intel Core i7    Nvidia  Windows  1000  1000  \n",
      "830   254.671349          Intel Core i7    Nvidia  Windows     0   512  \n",
      "841   127.335675          Intel Core i7    Nvidia  Windows  1000   512  \n",
      "911   352.465147  Other Intel Processor     Intel  Windows     0   240  \n",
      "955   254.671349          Intel Core i7    Nvidia  Windows  1000   512  \n",
      "968   127.335675          Intel Core i7    Nvidia  Windows  1000   256  \n",
      "1017  254.671349          Intel Core i7    Nvidia  Windows     0   512  \n",
      "1066  127.335675          Intel Core i7    Nvidia  Windows     0  1000  \n",
      "1081  127.335675          Intel Core i7    Nvidia  Windows     0   512  \n",
      "1103  127.335675          Intel Core i7       AMD  Windows  1000     0  \n",
      "1136  127.335675          Intel Core i7    Nvidia  Windows     0   256  \n",
      "1231  157.350512          Intel Core i7    Nvidia  Windows     0  1000  \n",
      "\n",
      "Rows containing outliers in column 'Touchscreen':\n",
      "     Company            TypeName  Ram  Weight     Price  Touchscreen  Ips  \\\n",
      "19      Dell           Ultrabook    8    1.22  52161.12            1    1   \n",
      "23      Dell  2 in 1 Convertible    8    1.62  43636.32            1    0   \n",
      "33      Dell           Ultrabook   16    1.20  99580.32            1    0   \n",
      "44      Dell  2 in 1 Convertible   12    2.77  53226.72            1    0   \n",
      "50    Lenovo  2 in 1 Convertible    4    0.69  16996.32            1    1   \n",
      "...      ...                 ...  ...     ...       ...          ...  ...   \n",
      "1271  Lenovo  2 in 1 Convertible   16    1.30  79866.72            1    1   \n",
      "1284  Lenovo  2 in 1 Convertible    4    1.80  33992.64            1    1   \n",
      "1285  Lenovo  2 in 1 Convertible   16    1.30  79866.72            1    1   \n",
      "1298  Lenovo  2 in 1 Convertible    4    1.80  33992.64            1    1   \n",
      "1299  Lenovo  2 in 1 Convertible   16    1.30  79866.72            1    1   \n",
      "\n",
      "             ppi              Cpu brand Gpu brand                  os   HDD  \\\n",
      "19    165.632118          Intel Core i5     Intel             Windows     0   \n",
      "23    165.632118          Intel Core i5     Intel             Windows     0   \n",
      "33    276.053530          Intel Core i7     Intel             Windows     0   \n",
      "44    127.335675          Intel Core i5    Nvidia             Windows  1000   \n",
      "50    224.173809  Other Intel Processor     Intel  Others/No OS/Linux     0   \n",
      "...          ...                    ...       ...                 ...   ...   \n",
      "1271  276.053530          Intel Core i7     Intel             Windows     0   \n",
      "1284  157.350512          Intel Core i7     Intel             Windows     0   \n",
      "1285  276.053530          Intel Core i7     Intel             Windows     0   \n",
      "1298  157.350512          Intel Core i7     Intel             Windows     0   \n",
      "1299  276.053530          Intel Core i7     Intel             Windows     0   \n",
      "\n",
      "      SSD  \n",
      "19    128  \n",
      "23    256  \n",
      "33    512  \n",
      "44      0  \n",
      "50      0  \n",
      "...   ...  \n",
      "1271  512  \n",
      "1284  128  \n",
      "1285  512  \n",
      "1298  128  \n",
      "1299  512  \n",
      "\n",
      "[191 rows x 13 columns]\n",
      "\n",
      "Rows containing outliers in column 'Ips':\n",
      "No outliers detected.\n",
      "\n",
      "Rows containing outliers in column 'ppi':\n",
      "     Company            TypeName  Ram  Weight        Price  Touchscreen  Ips  \\\n",
      "0      Apple           Ultrabook    8    1.37   71378.6832            0    1   \n",
      "3      Apple           Ultrabook   16    1.83  135195.3360            0    1   \n",
      "4      Apple           Ultrabook    8    1.37   96095.8080            0    1   \n",
      "6      Apple           Ultrabook   16    2.04  114017.6016            0    1   \n",
      "12     Apple           Ultrabook   16    1.83  130001.6016            0    1   \n",
      "...      ...                 ...  ...     ...          ...          ...  ...   \n",
      "1236      HP           Ultrabook    8    1.54  117119.5632            0    0   \n",
      "1249    Dell  2 in 1 Convertible    8    1.24   96596.6400            1    0   \n",
      "1271  Lenovo  2 in 1 Convertible   16    1.30   79866.7200            1    1   \n",
      "1285  Lenovo  2 in 1 Convertible   16    1.30   79866.7200            1    1   \n",
      "1299  Lenovo  2 in 1 Convertible   16    1.30   79866.7200            1    1   \n",
      "\n",
      "             ppi      Cpu brand Gpu brand       os  HDD  SSD  \n",
      "0     226.983005  Intel Core i5     Intel      Mac    0  128  \n",
      "3     220.534624  Intel Core i7       AMD      Mac    0  512  \n",
      "4     226.983005  Intel Core i5     Intel      Mac    0  256  \n",
      "6     220.534624  Intel Core i7     Intel      Mac    0    0  \n",
      "12    220.534624  Intel Core i7       AMD      Mac    0  256  \n",
      "...          ...            ...       ...      ...  ...  ...  \n",
      "1236  209.800683  Intel Core i7     Intel  Windows    0  256  \n",
      "1249  276.053530  Intel Core i5     Intel  Windows    0  256  \n",
      "1271  276.053530  Intel Core i7     Intel  Windows    0  512  \n",
      "1285  276.053530  Intel Core i7     Intel  Windows    0  512  \n",
      "1299  276.053530  Intel Core i7     Intel  Windows    0  512  \n",
      "\n",
      "[115 rows x 13 columns]\n",
      "\n",
      "Rows containing outliers in column 'HDD':\n",
      "No outliers detected.\n",
      "\n",
      "Rows containing outliers in column 'SSD':\n",
      "     Company            TypeName  Ram  Weight        Price  Touchscreen  Ips  \\\n",
      "190   Lenovo  2 in 1 Convertible   16    1.42  150462.7200            1    0   \n",
      "196    Razer              Gaming   32    3.49  324954.7200            1    0   \n",
      "266     Asus  2 in 1 Convertible    8    1.10   79866.7200            1    0   \n",
      "311     Dell           Ultrabook   16    1.21  133146.7200            1    1   \n",
      "316       HP  2 in 1 Convertible   16    1.29  130482.7200            1    1   \n",
      "329     Dell            Notebook   32    2.06  140605.9200            1    0   \n",
      "347     Dell           Ultrabook   16    1.88   85672.1088            0    0   \n",
      "399   Lenovo           Ultrabook   16    1.13  139860.0000            0    1   \n",
      "497       HP            Notebook    8    1.48   93186.7200            0    0   \n",
      "610   Lenovo            Notebook   32    2.50  261018.7200            0    1   \n",
      "639     Dell            Notebook   16    2.06  127818.7200            1    0   \n",
      "723     Dell              Gaming   32    4.36  194972.8320            0    0   \n",
      "744   Lenovo         Workstation   16    2.50  175770.7200            0    1   \n",
      "780     Dell              Gaming   32    4.42  191211.2640            0    1   \n",
      "933   Lenovo  2 in 1 Convertible   16    1.30   95850.7200            1    1   \n",
      "1054    Dell           Ultrabook   16    1.20  120093.1200            1    0   \n",
      "1066    Asus              Gaming   64    3.58  211788.0000            0    1   \n",
      "1231   Razer              Gaming   16    1.95  186426.7200            0    0   \n",
      "\n",
      "             ppi              Cpu brand Gpu brand       os   HDD   SSD  \n",
      "190   209.800683          Intel Core i7     Intel  Windows     0  1000  \n",
      "196   254.671349          Intel Core i7    Nvidia  Windows     0  1000  \n",
      "266   165.632118          Intel Core i7     Intel  Windows     0  1024  \n",
      "311   331.264236          Intel Core i7     Intel  Windows     0  1000  \n",
      "316   331.264236          Intel Core i7     Intel  Windows     0  1000  \n",
      "329   282.423996          Intel Core i7    Nvidia  Windows     0  1000  \n",
      "347   141.211998          Intel Core i7     Intel  Windows     0   768  \n",
      "399   209.800683          Intel Core i7     Intel  Windows     0  1000  \n",
      "497   157.350512          Intel Core i7     Intel  Windows     0  1000  \n",
      "610   282.423996  Other Intel Processor    Nvidia  Windows     0  1000  \n",
      "639   282.423996          Intel Core i7    Nvidia  Windows     0  1000  \n",
      "723   254.671349          Intel Core i7    Nvidia  Windows  1000  1000  \n",
      "744   282.423996          Intel Core i7    Nvidia  Windows     0  1000  \n",
      "780   127.335675          Intel Core i7    Nvidia  Windows  1000  1000  \n",
      "933   276.053530          Intel Core i7     Intel  Windows     0  1000  \n",
      "1054  276.053530          Intel Core i7     Intel  Windows     0  1000  \n",
      "1066  127.335675          Intel Core i7    Nvidia  Windows     0  1000  \n",
      "1231  157.350512          Intel Core i7    Nvidia  Windows     0  1000  \n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df_numeric = df[['Ram', 'Weight', 'Price', 'Touchscreen', 'Ips', 'ppi', 'HDD', 'SSD']]  # Select relevant numeric columns from the DataFrame\n",
    "\n",
    "# Step 2: Apply the IQR method for detecting outliers\n",
    "Q1 = df_numeric.quantile(0.25)\n",
    "Q3 = df_numeric.quantile(0.75)\n",
    "IQR = Q3 - Q1\n",
    "\n",
    "# Step 3: Detect outliers for each column separately\n",
    "for col in df_numeric.columns:\n",
    "    print(f\"\\nRows containing outliers in column '{col}':\")\n",
    "    # Define outlier condition for the current column\n",
    "    outlier_condition = (df_numeric[col] < (Q1[col] - 1.5 * IQR[col])) | (df_numeric[col] > (Q3[col] + 1.5 * IQR[col]))\n",
    "    \n",
    "    # Extract rows with outliers in the current column\n",
    "    outlier_rows = df[outlier_condition]\n",
    "    \n",
    "    # Print outlier rows if any exist\n",
    "    if not outlier_rows.empty:\n",
    "        print(outlier_rows)\n",
    "    else:\n",
    "        print(\"No outliers detected.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "fdad427f-d574-4253-a7a1-a2c4cf203e94",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\freev\\AppData\\Local\\Temp\\ipykernel_4124\\3556049916.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(np.log(df['Price']))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Price', ylabel='Density'>"
      ]
     },
     "execution_count": 63,
     "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(np.log(df['Price']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b24390e-cad5-4a24-ac50-e4a8d3f9b20b",
   "metadata": {},
   "source": [
    "# Modelling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fff4ea5a-fa99-4a98-bba7-9677dd96f19b",
   "metadata": {},
   "source": [
    "## Split the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "c2f21764-acdd-47d0-b08d-35c88cbba25c",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(columns=['Price'])\n",
    "y = np.log(df['Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "c47e644a-b693-4e31-bfee-9ff8e946cee3",
   "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": 79,
   "id": "15c76931-23c8-46cd-8912-7cd1c0ff0456",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.metrics import r2_score,mean_absolute_error\n",
    "\n",
    "from sklearn.linear_model import LinearRegression,Ridge,Lasso\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor,GradientBoostingRegressor,AdaBoostRegressor,ExtraTreesRegressor\n",
    "from sklearn.svm import SVR\n",
    "from xgboost import XGBRegressor\n",
    "from sklearn.ensemble import VotingRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor, StackingRegressor"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bb341b2-545e-404e-b7e6-bc57af5b9e17",
   "metadata": {},
   "source": [
    "## One Hot Encoding & Normalizing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "786eabd4-6e35-43ba-b127-18731c7e57e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
    "\n",
    "# List of categorical features\n",
    "categorical_features = ['Company', 'TypeName', 'Cpu brand', 'Gpu brand', 'os']\n",
    "\n",
    "# Define ColumnTransformer with OneHotEncoder for categorical features and StandardScaler for numerical features\n",
    "step1 = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('cat_tnf', OneHotEncoder(sparse_output=False, drop='first'), categorical_features),\n",
    "        ('num_tnf', StandardScaler(), ['Ram', 'Weight', 'Touchscreen', 'Ips', 'ppi', 'HDD', 'SSD'])  # Standardize numeric features\n",
    "    ],\n",
    "    remainder='passthrough'\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1ca31fb-6daf-4e53-b9e4-d243a7cd2e87",
   "metadata": {},
   "source": [
    "## Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "30514216-e8f5-450a-aeaa-ea7a59ecf9bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score: 0.807452955953007\n",
      "MAE: 0.20953335384794006\n"
     ]
    }
   ],
   "source": [
    "# Define Linear Regression Model\n",
    "step2 = LinearRegression()\n",
    "\n",
    "# Create the Pipeline\n",
    "lr = Pipeline([\n",
    "    ('step1', step1),\n",
    "    ('step2', step2)\n",
    "])\n",
    "\n",
    "# Fit the pipeline with training data\n",
    "lr.fit(X_train, y_train)\n",
    "\n",
    "# Predict the test set\n",
    "y_pred = lr.predict(X_test)\n",
    "\n",
    "# Evaluate the model\n",
    "print('R2 score:', r2_score(y_test, y_pred))\n",
    "print('MAE:', mean_absolute_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e49c8c9-fa75-446b-bde8-16817164fde8",
   "metadata": {},
   "source": [
    "## Ridge Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "e7f198c4-afeb-461d-a0b8-6043b59bf9fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ridge R2 score: 0.8093995078710143\n",
      "Ridge MAE: 0.2096542042345226\n"
     ]
    }
   ],
   "source": [
    "# Define Ridge Regression model\n",
    "ridge_model = Ridge()\n",
    "\n",
    "# Create the Pipeline\n",
    "ridge_pipe = Pipeline([\n",
    "    ('step1', step1),  # Use the same step1 from the previous example for preprocessing\n",
    "    ('step2', ridge_model)\n",
    "])\n",
    "\n",
    "# Fit and evaluate the model\n",
    "ridge_pipe.fit(X_train, y_train)\n",
    "y_pred_ridge = ridge_pipe.predict(X_test)\n",
    "\n",
    "print('Ridge R2 score:', r2_score(y_test, y_pred_ridge))\n",
    "print('Ridge MAE:', mean_absolute_error(y_test, y_pred_ridge))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "407c9d7d-f034-4b5d-8619-3bf77d3de75b",
   "metadata": {},
   "source": [
    "## Lasso Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "2f033980-0a89-469e-b5da-118ddc1b9566",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lasso R2 score: -0.004410669401108347\n",
      "Lasso MAE: 0.5027070079762014\n"
     ]
    }
   ],
   "source": [
    "# Define Lasso Regression model\n",
    "lasso_model = Lasso()\n",
    "\n",
    "# Create the Pipeline\n",
    "lasso_pipe = Pipeline([\n",
    "    ('step1', step1),\n",
    "    ('step2', lasso_model)\n",
    "])\n",
    "\n",
    "# Fit and evaluate the model\n",
    "lasso_pipe.fit(X_train, y_train)\n",
    "y_pred_lasso = lasso_pipe.predict(X_test)\n",
    "\n",
    "print('Lasso R2 score:', r2_score(y_test, y_pred_lasso))\n",
    "print('Lasso MAE:', mean_absolute_error(y_test, y_pred_lasso))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae426b0a-a9fe-4b55-a51c-d09498dc5dce",
   "metadata": {},
   "source": [
    "## KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "438e0517-bef8-4405-8109-957c1e4be9cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN R2 score: 0.8408846467575076\n",
      "KNN MAE: 0.17987570089636074\n"
     ]
    }
   ],
   "source": [
    "# Define K-Nearest Neighbors model\n",
    "knn_model = KNeighborsRegressor(n_neighbors=5)  # You can adjust the number of neighbors\n",
    "\n",
    "# Create the Pipeline\n",
    "knn_pipe = Pipeline([\n",
    "    ('step1', step1),\n",
    "    ('step2', knn_model)\n",
    "])\n",
    "\n",
    "# Fit and evaluate the model\n",
    "knn_pipe.fit(X_train, y_train)\n",
    "y_pred_knn = knn_pipe.predict(X_test)\n",
    "\n",
    "print('KNN R2 score:', r2_score(y_test, y_pred_knn))\n",
    "print('KNN MAE:', mean_absolute_error(y_test, y_pred_knn))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb92a83c-87ed-44c1-992a-fef1c85788dc",
   "metadata": {},
   "source": [
    "## Decison Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "6f363334-e562-4b1f-9a9b-cf27cad2a316",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree R2 score: 0.7834354503190992\n",
      "Decision Tree MAE: 0.20753028506481627\n"
     ]
    }
   ],
   "source": [
    "# Define Decision Tree model\n",
    "tree_model = DecisionTreeRegressor(random_state=42)\n",
    "\n",
    "# Create the Pipeline\n",
    "tree_pipe = Pipeline([\n",
    "    ('step1', step1),\n",
    "    ('step2', tree_model)\n",
    "])\n",
    "\n",
    "# Fit and evaluate the model\n",
    "tree_pipe.fit(X_train, y_train)\n",
    "y_pred_tree = tree_pipe.predict(X_test)\n",
    "\n",
    "print('Decision Tree R2 score:', r2_score(y_test, y_pred_tree))\n",
    "print('Decision Tree MAE:', mean_absolute_error(y_test, y_pred_tree))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbeadcad-f002-43cd-a877-765c9363470b",
   "metadata": {},
   "source": [
    "## SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "ba293a16-710e-4b5f-94b6-d25126516127",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM R2 score: 0.8855291177001197\n",
      "SVM MAE: 0.15982024328717423\n"
     ]
    }
   ],
   "source": [
    "# Define Support Vector Machine model\n",
    "svm_model = SVR(kernel='rbf')  # You can change the kernel to 'linear' or 'poly'\n",
    "\n",
    "# Create the Pipeline\n",
    "svm_pipe = Pipeline([\n",
    "    ('step1', step1),\n",
    "    ('step2', svm_model)\n",
    "])\n",
    "\n",
    "# Fit and evaluate the model\n",
    "svm_pipe.fit(X_train, y_train)\n",
    "y_pred_svm = svm_pipe.predict(X_test)\n",
    "\n",
    "print('SVM R2 score:', r2_score(y_test, y_pred_svm))\n",
    "print('SVM MAE:', mean_absolute_error(y_test, y_pred_svm))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d1a7ba4-91ff-4d7b-922c-028470a4e9b6",
   "metadata": {},
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "ac82e140-1555-45c9-afd5-34a4d6942d31",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest R2 score: 0.8862640967507117\n",
      "Random Forest MAE: 0.1558719085659208\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.metrics import r2_score, mean_absolute_error\n",
    "\n",
    "# Define Random Forest model\n",
    "rf_model = RandomForestRegressor()  \n",
    "\n",
    "# Create the Pipeline\n",
    "rf_pipe = Pipeline([\n",
    "    ('step1', step1),\n",
    "    ('step2', rf_model)\n",
    "])\n",
    "\n",
    "# Fit and evaluate the model\n",
    "rf_pipe.fit(X_train, y_train)\n",
    "y_pred_rf = rf_pipe.predict(X_test)\n",
    "\n",
    "print('Random Forest R2 score:', r2_score(y_test, y_pred_rf))\n",
    "print('Random Forest MAE:', mean_absolute_error(y_test, y_pred_rf))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23f7f6a6-5f3b-4706-b453-99d7dd67973d",
   "metadata": {},
   "source": [
    "## Adaboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "36f20532-f400-4bd8-943d-1be0281b08e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.807452955953007\n",
      "MAE 0.20953335384794006\n"
     ]
    }
   ],
   "source": [
    "ada_model = AdaBoostRegressor(n_estimators=15,learning_rate=1.0)\n",
    "\n",
    "ada_pipe = Pipeline([\n",
    "    ('step1',step1),\n",
    "    ('step2',step2)\n",
    "])\n",
    "\n",
    "ada_pipe.fit(X_train,y_train)\n",
    "\n",
    "y_pred_ada = ada_pipe.predict(X_test)\n",
    "\n",
    "print('R2 score',r2_score(y_test,y_pred))\n",
    "print('MAE',mean_absolute_error(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2c9a29b-adec-40a5-bbe8-9c353bd7e319",
   "metadata": {},
   "source": [
    "## Gradient Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "3b666a4d-bdec-4ea7-809e-f3977f494c47",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.807452955953007\n",
      "MAE 0.20953335384794006\n"
     ]
    }
   ],
   "source": [
    "gb_model = GradientBoostingRegressor(n_estimators=500)\n",
    "\n",
    "gb_pipe = Pipeline([\n",
    "    ('step1',step1),\n",
    "    ('step2',step2)\n",
    "])\n",
    "\n",
    "gb_pipe.fit(X_train,y_train)\n",
    "\n",
    "y_pred_gb = gb_pipe.predict(X_test)\n",
    "\n",
    "print('R2 score',r2_score(y_test,y_pred))\n",
    "print('MAE',mean_absolute_error(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0148472-aed9-4498-b7a4-f0a065df1983",
   "metadata": {},
   "source": [
    "## XgBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "67077b4a-0c80-4591-87e3-3d967d327d46",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.807452955953007\n",
      "MAE 0.20953335384794006\n"
     ]
    }
   ],
   "source": [
    "xgb_model = XGBRegressor(n_estimators=45,max_depth=5,learning_rate=0.5)\n",
    "\n",
    "xgb = Pipeline([\n",
    "    ('step1',step1),\n",
    "    ('step2',step2)\n",
    "])\n",
    "\n",
    "xgb.fit(X_train,y_train)\n",
    "\n",
    "y_pred_xgb = xgb.predict(X_test)\n",
    "\n",
    "print('R2 score',r2_score(y_test,y_pred))\n",
    "print('MAE',mean_absolute_error(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f9e901a-1e91-48da-93ca-9cd90c479ed9",
   "metadata": {},
   "source": [
    "## Voting Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "1fe563d0-5a2b-470e-8c49-c66c676dba77",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score: 0.8979207291293257\n",
      "MAE: 0.149831116236925\n"
     ]
    }
   ],
   "source": [
    "rf = RandomForestRegressor(n_estimators=350, random_state=3, max_samples=0.5, max_features=0.75, max_depth=15)\n",
    "svr = SVR(C=100, kernel='rbf', gamma='scale')\n",
    "\n",
    "# Voting Regressor: Including RandomForest and SVR\n",
    "step2 = VotingRegressor([('rf', rf), ('svr', svr)], weights=[1, 1])\n",
    "\n",
    "# Pipeline\n",
    "pipe_voting = Pipeline([\n",
    "    ('step1', step1),\n",
    "    ('step2', step2)\n",
    "])\n",
    "\n",
    "# Train the model\n",
    "pipe_voting.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions\n",
    "y_pred = pipe_voting.predict(X_test)\n",
    "\n",
    "# Metrics\n",
    "print('R2 score:', r2_score(y_test, y_pred))\n",
    "print('MAE:', mean_absolute_error(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c373f7c6-81a9-42fd-ba73-fb60902cff2a",
   "metadata": {},
   "source": [
    "## Stacking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "f423efda-9a36-416b-8fbf-a7f0a8911060",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score: 0.8888033308273803\n",
      "MAE: 0.15822551842243876\n"
     ]
    }
   ],
   "source": [
    "# Regressors\n",
    "rf = RandomForestRegressor(n_estimators=350, random_state=3, max_samples=0.5, max_features=0.75, max_depth=15)\n",
    "svr = SVR(C=100, kernel='rbf', gamma='scale')\n",
    "\n",
    "# Define the estimators for the Stacking Regressor\n",
    "estimators = [('rf', rf), ('svr', svr)]  # List of base regressors\n",
    "\n",
    "# Stacking Regressor with Ridge as the final estimator\n",
    "step2 = StackingRegressor(estimators=estimators, final_estimator=Ridge(alpha=100))\n",
    "\n",
    "# Pipeline\n",
    "pipe = Pipeline([\n",
    "    ('step1', step1),\n",
    "    ('step2', step2)\n",
    "])\n",
    "\n",
    "# Train the model\n",
    "pipe.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions\n",
    "y_pred = pipe.predict(X_test)\n",
    "\n",
    "# Metrics\n",
    "print('R2 score:', r2_score(y_test, y_pred))\n",
    "print('MAE:', mean_absolute_error(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "730f3ad9-7d63-403a-8f97-d7347d0f1d4b",
   "metadata": {},
   "source": [
    "## Exporting the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "3628df86-922e-4376-ad89-ee28892d049c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "pickle.dump(df,open('df.pkl','wb'))\n",
    "pickle.dump(pipe_voting,open('pipe_voting.pkl','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "19ad2ce8-5b00-4cd5-b907-88279224491f",
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1301</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>6</td>\n",
       "      <td>2.19</td>\n",
       "      <td>40705.9200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.454670</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>AMD</td>\n",
       "      <td>Windows</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1302</th>\n",
       "      <td>Asus</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>4</td>\n",
       "      <td>2.20</td>\n",
       "      <td>19660.3200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.454670</td>\n",
       "      <td>Other Intel Processor</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Windows</td>\n",
       "      <td>500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1302 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company            TypeName  Ram  Weight        Price  Touchscreen  Ips  \\\n",
       "0      Apple           Ultrabook    8    1.37   71378.6832            0    1   \n",
       "1      Apple           Ultrabook    8    1.34   47895.5232            0    0   \n",
       "2         HP            Notebook    8    1.86   30636.0000            0    0   \n",
       "3      Apple           Ultrabook   16    1.83  135195.3360            0    1   \n",
       "4      Apple           Ultrabook    8    1.37   96095.8080            0    1   \n",
       "...      ...                 ...  ...     ...          ...          ...  ...   \n",
       "1298  Lenovo  2 in 1 Convertible    4    1.80   33992.6400            1    1   \n",
       "1299  Lenovo  2 in 1 Convertible   16    1.30   79866.7200            1    1   \n",
       "1300  Lenovo            Notebook    2    1.50   12201.1200            0    0   \n",
       "1301      HP            Notebook    6    2.19   40705.9200            0    0   \n",
       "1302    Asus            Notebook    4    2.20   19660.3200            0    0   \n",
       "\n",
       "             ppi              Cpu brand Gpu brand                  os   HDD  \\\n",
       "0     226.983005          Intel Core i5     Intel                 Mac     0   \n",
       "1     127.677940          Intel Core i5     Intel                 Mac     0   \n",
       "2     141.211998          Intel Core i5     Intel  Others/No OS/Linux     0   \n",
       "3     220.534624          Intel Core i7       AMD                 Mac     0   \n",
       "4     226.983005          Intel Core i5     Intel                 Mac     0   \n",
       "...          ...                    ...       ...                 ...   ...   \n",
       "1298  157.350512          Intel Core i7     Intel             Windows     0   \n",
       "1299  276.053530          Intel Core i7     Intel             Windows     0   \n",
       "1300  111.935204  Other Intel Processor     Intel             Windows     0   \n",
       "1301  100.454670          Intel Core i7       AMD             Windows  1000   \n",
       "1302  100.454670  Other Intel Processor     Intel             Windows   500   \n",
       "\n",
       "      SSD  \n",
       "0     128  \n",
       "1       0  \n",
       "2     256  \n",
       "3     512  \n",
       "4     256  \n",
       "...   ...  \n",
       "1298  128  \n",
       "1299  512  \n",
       "1300    0  \n",
       "1301    0  \n",
       "1302    0  \n",
       "\n",
       "[1302 rows x 13 columns]"
      ]
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