Laptop_price_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0d6e548b-aac0-435b-bd63-b12da9adb2f9",
   "metadata": {},
   "source": [
    "<div style=\"border-radius:10px; padding: 15px; background-color: #ffeacc; font-size:130%; text-align:left\">\n",
    "\n",
    "<h2 align=\"left\"><font color=#ff6200>Laptop Price Prediction:</font></h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7d006b12-2ad4-413b-9730-ae4accc8df82",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ignore warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.graph_objects as go\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cfb586f7-2cfd-4b46-9b06-965a22a38611",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('laptop_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7be318c2-b395-43f9-be4d-0698954d2164",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Inches</th>\n",
       "      <th>ScreenResolution</th>\n",
       "      <th>Cpu</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 2.3GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>128GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 640</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37kg</td>\n",
       "      <td>71378.6832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>1440x900</td>\n",
       "      <td>Intel Core i5 1.8GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>128GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34kg</td>\n",
       "      <td>47895.5232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86kg</td>\n",
       "      <td>30636.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>15.4</td>\n",
       "      <td>IPS Panel Retina Display 2880x1800</td>\n",
       "      <td>Intel Core i7 2.7GHz</td>\n",
       "      <td>16GB</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83kg</td>\n",
       "      <td>135195.3360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 3.1GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37kg</td>\n",
       "      <td>96095.8080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1298</th>\n",
       "      <td>1298</td>\n",
       "      <td>Lenovo</td>\n",
       "      <td>2 in 1 Convertible</td>\n",
       "      <td>14.0</td>\n",
       "      <td>IPS Panel Full HD / Touchscreen 1920x1080</td>\n",
       "      <td>Intel Core i7 6500U 2.5GHz</td>\n",
       "      <td>4GB</td>\n",
       "      <td>128GB SSD</td>\n",
       "      <td>Intel HD Graphics 520</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.8kg</td>\n",
       "      <td>33992.6400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1299</th>\n",
       "      <td>1299</td>\n",
       "      <td>Lenovo</td>\n",
       "      <td>2 in 1 Convertible</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Quad HD+ / Touchscreen 3200x1800</td>\n",
       "      <td>Intel Core i7 6500U 2.5GHz</td>\n",
       "      <td>16GB</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>Intel HD Graphics 520</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.3kg</td>\n",
       "      <td>79866.7200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1300</th>\n",
       "      <td>1300</td>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>Intel Celeron Dual Core N3050 1.6GHz</td>\n",
       "      <td>2GB</td>\n",
       "      <td>64GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.5kg</td>\n",
       "      <td>12201.1200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1301</th>\n",
       "      <td>1301</td>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>Intel Core i7 6500U 2.5GHz</td>\n",
       "      <td>6GB</td>\n",
       "      <td>1TB HDD</td>\n",
       "      <td>AMD Radeon R5 M330</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.19kg</td>\n",
       "      <td>40705.9200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1302</th>\n",
       "      <td>1302</td>\n",
       "      <td>Asus</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>Intel Celeron Dual Core N3050 1.6GHz</td>\n",
       "      <td>4GB</td>\n",
       "      <td>500GB HDD</td>\n",
       "      <td>Intel HD Graphics</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.2kg</td>\n",
       "      <td>19660.3200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1303 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0 Company            TypeName  Inches  \\\n",
       "0              0   Apple           Ultrabook    13.3   \n",
       "1              1   Apple           Ultrabook    13.3   \n",
       "2              2      HP            Notebook    15.6   \n",
       "3              3   Apple           Ultrabook    15.4   \n",
       "4              4   Apple           Ultrabook    13.3   \n",
       "...          ...     ...                 ...     ...   \n",
       "1298        1298  Lenovo  2 in 1 Convertible    14.0   \n",
       "1299        1299  Lenovo  2 in 1 Convertible    13.3   \n",
       "1300        1300  Lenovo            Notebook    14.0   \n",
       "1301        1301      HP            Notebook    15.6   \n",
       "1302        1302    Asus            Notebook    15.6   \n",
       "\n",
       "                                ScreenResolution  \\\n",
       "0             IPS Panel Retina Display 2560x1600   \n",
       "1                                       1440x900   \n",
       "2                              Full HD 1920x1080   \n",
       "3             IPS Panel Retina Display 2880x1800   \n",
       "4             IPS Panel Retina Display 2560x1600   \n",
       "...                                          ...   \n",
       "1298   IPS Panel Full HD / Touchscreen 1920x1080   \n",
       "1299  IPS Panel Quad HD+ / Touchscreen 3200x1800   \n",
       "1300                                    1366x768   \n",
       "1301                                    1366x768   \n",
       "1302                                    1366x768   \n",
       "\n",
       "                                       Cpu   Ram               Memory  \\\n",
       "0                     Intel Core i5 2.3GHz   8GB            128GB SSD   \n",
       "1                     Intel Core i5 1.8GHz   8GB  128GB Flash Storage   \n",
       "2               Intel Core i5 7200U 2.5GHz   8GB            256GB SSD   \n",
       "3                     Intel Core i7 2.7GHz  16GB            512GB SSD   \n",
       "4                     Intel Core i5 3.1GHz   8GB            256GB SSD   \n",
       "...                                    ...   ...                  ...   \n",
       "1298            Intel Core i7 6500U 2.5GHz   4GB            128GB SSD   \n",
       "1299            Intel Core i7 6500U 2.5GHz  16GB            512GB SSD   \n",
       "1300  Intel Celeron Dual Core N3050 1.6GHz   2GB   64GB Flash Storage   \n",
       "1301            Intel Core i7 6500U 2.5GHz   6GB              1TB HDD   \n",
       "1302  Intel Celeron Dual Core N3050 1.6GHz   4GB            500GB HDD   \n",
       "\n",
       "                               Gpu       OpSys  Weight        Price  \n",
       "0     Intel Iris Plus Graphics 640       macOS  1.37kg   71378.6832  \n",
       "1           Intel HD Graphics 6000       macOS  1.34kg   47895.5232  \n",
       "2            Intel HD Graphics 620       No OS  1.86kg   30636.0000  \n",
       "3               AMD Radeon Pro 455       macOS  1.83kg  135195.3360  \n",
       "4     Intel Iris Plus Graphics 650       macOS  1.37kg   96095.8080  \n",
       "...                            ...         ...     ...          ...  \n",
       "1298         Intel HD Graphics 520  Windows 10   1.8kg   33992.6400  \n",
       "1299         Intel HD Graphics 520  Windows 10   1.3kg   79866.7200  \n",
       "1300             Intel HD Graphics  Windows 10   1.5kg   12201.1200  \n",
       "1301            AMD Radeon R5 M330  Windows 10  2.19kg   40705.9200  \n",
       "1302             Intel HD Graphics  Windows 10   2.2kg   19660.3200  \n",
       "\n",
       "[1303 rows x 12 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
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    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "70290e4e-a147-409a-84ce-60b4ff931284",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1303 entries, 0 to 1302\n",
      "Data columns (total 12 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Unnamed: 0        1303 non-null   int64  \n",
      " 1   Company           1303 non-null   object \n",
      " 2   TypeName          1303 non-null   object \n",
      " 3   Inches            1303 non-null   float64\n",
      " 4   ScreenResolution  1303 non-null   object \n",
      " 5   Cpu               1303 non-null   object \n",
      " 6   Ram               1303 non-null   object \n",
      " 7   Memory            1303 non-null   object \n",
      " 8   Gpu               1303 non-null   object \n",
      " 9   OpSys             1303 non-null   object \n",
      " 10  Weight            1303 non-null   object \n",
      " 11  Price             1303 non-null   float64\n",
      "dtypes: float64(2), int64(1), object(9)\n",
      "memory usage: 122.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bbc9c890-6725-4f7d-81b3-ac95dcc1e5c5",
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       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
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       "      <td>128GB SSD</td>\n",
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       "      <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",
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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>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",
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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   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  "
      ]
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     "metadata": {},
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    }
   ],
   "source": [
    "df = df.drop(['Unnamed: 0'], axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8b1fea3f-3bc1-4da0-9550-7b28ce792d04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Company', 'TypeName', 'Inches', 'ScreenResolution', 'Cpu', 'Ram',\n",
       "       'Memory', 'Gpu', 'OpSys', 'Weight', 'Price'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "610720e4-de71-4f68-8570-eb14f88f3744",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Inches</th>\n",
       "      <td>1303.0</td>\n",
       "      <td>15.017191</td>\n",
       "      <td>1.426304</td>\n",
       "      <td>10.10</td>\n",
       "      <td>14.00</td>\n",
       "      <td>15.60</td>\n",
       "      <td>15.6000</td>\n",
       "      <td>18.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Price</th>\n",
       "      <td>1303.0</td>\n",
       "      <td>59870.042910</td>\n",
       "      <td>37243.201786</td>\n",
       "      <td>9270.72</td>\n",
       "      <td>31914.72</td>\n",
       "      <td>52054.56</td>\n",
       "      <td>79274.2464</td>\n",
       "      <td>324954.72</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         count          mean           std      min       25%       50%  \\\n",
       "Inches  1303.0     15.017191      1.426304    10.10     14.00     15.60   \n",
       "Price   1303.0  59870.042910  37243.201786  9270.72  31914.72  52054.56   \n",
       "\n",
       "               75%        max  \n",
       "Inches     15.6000      18.40  \n",
       "Price   79274.2464  324954.72  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# Summary statistics for numerical variables\n",
    "df.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "163de9ed-3461-4695-9805-daa353da8dc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Company</th>\n",
       "      <td>1303</td>\n",
       "      <td>19</td>\n",
       "      <td>Dell</td>\n",
       "      <td>297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TypeName</th>\n",
       "      <td>1303</td>\n",
       "      <td>6</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ScreenResolution</th>\n",
       "      <td>1303</td>\n",
       "      <td>40</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cpu</th>\n",
       "      <td>1303</td>\n",
       "      <td>118</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ram</th>\n",
       "      <td>1303</td>\n",
       "      <td>9</td>\n",
       "      <td>8GB</td>\n",
       "      <td>619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Memory</th>\n",
       "      <td>1303</td>\n",
       "      <td>39</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gpu</th>\n",
       "      <td>1303</td>\n",
       "      <td>110</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OpSys</th>\n",
       "      <td>1303</td>\n",
       "      <td>9</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weight</th>\n",
       "      <td>1303</td>\n",
       "      <td>179</td>\n",
       "      <td>2.2kg</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 count unique                         top  freq\n",
       "Company           1303     19                        Dell   297\n",
       "TypeName          1303      6                    Notebook   727\n",
       "ScreenResolution  1303     40           Full HD 1920x1080   507\n",
       "Cpu               1303    118  Intel Core i5 7200U 2.5GHz   190\n",
       "Ram               1303      9                         8GB   619\n",
       "Memory            1303     39                   256GB SSD   412\n",
       "Gpu               1303    110       Intel HD Graphics 620   281\n",
       "OpSys             1303      9                  Windows 10  1072\n",
       "Weight            1303    179                       2.2kg   121"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summary statistics for categorical variables\n",
    "df.describe(include='object').T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "08e6a8f5-9fbb-4f69-b0b9-eb0a0c03d83e",
   "metadata": {},
   "outputs": [],
   "source": [
    "missing_data = df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3495dd05-ecb7-4479-b87e-b44d38c184cb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Company             0\n",
       "TypeName            0\n",
       "Inches              0\n",
       "ScreenResolution    0\n",
       "Cpu                 0\n",
       "Ram                 0\n",
       "Memory              0\n",
       "Gpu                 0\n",
       "OpSys               0\n",
       "Weight              0\n",
       "Price               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6b25704-c3cd-4502-9cf6-50d6ad03fda7",
   "metadata": {},
   "source": [
    "Hence no null value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bafdcc8d-0cbb-4420-9a4c-b858953087db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1303, 11)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5f6fecfd-1258-4383-ad40-bbe35ecadde8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1303 entries, 0 to 1302\n",
      "Data columns (total 11 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Company           1303 non-null   object \n",
      " 1   TypeName          1303 non-null   object \n",
      " 2   Inches            1303 non-null   float64\n",
      " 3   ScreenResolution  1303 non-null   object \n",
      " 4   Cpu               1303 non-null   object \n",
      " 5   Ram               1303 non-null   object \n",
      " 6   Memory            1303 non-null   object \n",
      " 7   Gpu               1303 non-null   object \n",
      " 8   OpSys             1303 non-null   object \n",
      " 9   Weight            1303 non-null   object \n",
      " 10  Price             1303 non-null   float64\n",
      "dtypes: float64(2), object(9)\n",
      "memory usage: 112.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f569df56-53a7-41ad-b022-afbdeb433e6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "## remove gb and kg from Ram and weight and convert the cols to numeric\n",
    "df['Ram'] = df['Ram'].str.replace(\"GB\", \"\")\n",
    "df['Weight'] = df['Weight'].str.replace(\"kg\", \"\")\n",
    "df['Ram'] = df['Ram'].astype('int32')\n",
    "df['Weight'] = df['Weight'].astype('float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "20da94c0-f7e4-4140-aed8-583dcb2893d8",
   "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",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>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>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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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  \n",
       "0  Intel Iris Plus Graphics 640  macOS    1.37   71378.6832  \n",
       "1        Intel HD Graphics 6000  macOS    1.34   47895.5232  \n",
       "2         Intel HD Graphics 620  No OS    1.86   30636.0000  \n",
       "3            AMD Radeon Pro 455  macOS    1.83  135195.3360  \n",
       "4  Intel Iris Plus Graphics 650  macOS    1.37   96095.8080  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e38a996d-3d3f-4347-b4ab-7d3717c2edd0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1303 entries, 0 to 1302\n",
      "Data columns (total 11 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Company           1303 non-null   object \n",
      " 1   TypeName          1303 non-null   object \n",
      " 2   Inches            1303 non-null   float64\n",
      " 3   ScreenResolution  1303 non-null   object \n",
      " 4   Cpu               1303 non-null   object \n",
      " 5   Ram               1303 non-null   int32  \n",
      " 6   Memory            1303 non-null   object \n",
      " 7   Gpu               1303 non-null   object \n",
      " 8   OpSys             1303 non-null   object \n",
      " 9   Weight            1303 non-null   float32\n",
      " 10  Price             1303 non-null   float64\n",
      "dtypes: float32(1), float64(2), int32(1), object(7)\n",
      "memory usage: 101.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45c935b8-a1d6-450b-bfb3-760f2d4508d7",
   "metadata": {},
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b98d0c8c-e510-42dd-892a-c7a4733c72d8",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Price'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "22dc41be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='count', ylabel='Company'>"
      ]
     },
     "execution_count": 17,
     "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.countplot(y=df['Company'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6ad40083-053c-48d6-9983-56fe8f176651",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# avg price of each brand\n",
    "\n",
    "sns.barplot(x=df['Company'], y=df['Price'],palette='plasma')\n",
    "\n",
    "plt.xticks(rotation=\"vertical\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4bede7c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='TypeName'>"
      ]
     },
     "execution_count": 19,
     "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": 20,
   "id": "d18e1a76-5415-428a-b4e8-34511a369106",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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o0SLt3r1bkvTcc88pIyNDsbGxZp6GDRuqbt26mjFjxlVluRrp6eny8/NTWlqafH19LdluAADciF9jW9odoVAo//iyS6672n+/C+05VYcOHVJycrLCw8PNZX5+fmrQoIHWrl0rSVq7dq38/f3NQiVJ4eHhcnNz07p168wxzZo1MwuVJEVERGjPnj06deqUOebiz8kbk/c5V5OlIOfPn1d6errTAwAA3J4KbalKTk6WJAUEBDgtDwgIMNclJyerbNmyTuvd3d1VqlQppzEFvcfFn3GpMRevv1KWgowePVp+fn7mo0KFClf41gAAwFUV2lJ1Oxg8eLDS0tLMx9GjR+2OBAAAbhJ3uwNcSmBgoCQpJSVF5cqVM5enpKSobt265pjjx487vS47O1snT540Xx8YGKiUlBSnMXnPrzTm4vVXylIQT09PeXp6XtX3BYCbqXfv3jpx4oQkqUyZMoqJibE5EXD7KbR7qoKDgxUYGKiEhARzWXp6utatW6ewsDBJUlhYmFJTU5WYmGiOWb58uXJzc9WgQQNzzKpVq3ThwgVzTHx8vKpVq6aSJUuaYy7+nLwxeZ9zNVkAoDA7ceKEUlJSlJKSYpYrANaytVSdOXNGSUlJSkpKkvTXCeFJSUk6cuSIHA6H+vTpo3feeUeLFy/Wtm3b9OKLLyooKMi8QrBGjRpq1aqVunfvrvXr12v16tWKjo5Whw4dFBQUJEnq1KmTPDw81K1bN+3YsUMLFixQTEyM+vXrZ+bo3bu34uLiNGHCBO3evVvDhw/Xxo0bFR0dLUlXlQUAANzZbD38t3HjRjVv3tx8nld0OnfurNmzZ2vgwIHKyMhQjx49lJqaqiZNmiguLk5eXl7ma+bOnavo6Gg98sgjcnNz09NPP60pU6aY6/38/LRs2TJFRUWpXr16uuuuuzR06FCnuawaNWqkefPmaciQIXrzzTdVtWpVLVq0SLVr1zbHXE0WAABw5yo081TdCZinCoBdOnXqZJ47GhAQoHnz5tmcCIUF81T95baepwoAAMCVUKoAAAAsQKkCAACwAKUKAADAApQqAAAAC1CqAAAALECpAgAAsAClCgAAwAKUKgAAAAtQqgAAACxAqQIAALAApQoAAMAClCoAAAALuNsdAADuFBt7DrTts7NOnnL62c4soe+Ps+2zgZuJPVUAAAAWoFQBAABYgFIFAABgAUoVAACABShVAAAAFqBUAQAAWIBSBQAAYAFKFQAAgAUoVQAAABagVAEAAFiAUgUAAGABShUAAIAFuKEyTL1799aJEyckSWXKlFFMTIzNiQAAcB2UKphOnDihlJQUu2MAAOCSKFUAcAfwcy9a4M8ArEOpAoA7wGvB1e2OANz2OFEdAADAApQqAAAAC1CqAAAALECpAgAAsAClCgAAwAKUKgAAAAtQqgAAACxAqQIAALAAk38WMs8ETLbts9PuS5c8/vr5xNF0W7N8ndLHts8GAOB6sKcKAADAAoW6VOXk5Oitt95ScHCwvL29de+99+rtt9+WYRjmGMMwNHToUJUrV07e3t4KDw/Xvn37nN7n5MmTioyMlK+vr/z9/dWtWzedOXPGaczWrVvVtGlTeXl5qUKFCho3bly+PF999ZWqV68uLy8vhYSE6Lvvvrs5XxwAALicQl2qxo4dq+nTp2vq1KnatWuXxo4dq3Hjxun99983x4wbN05TpkzRjBkztG7dOvn4+CgiIkKZmZnmmMjISO3YsUPx8fGKjY3VqlWr1KNHD3N9enq6WrZsqYoVKyoxMVHjx4/X8OHD9dFHH5lj1qxZo44dO6pbt27avHmz2rVrp3bt2mn79u23ZmMAAIBCrVCXqjVr1qht27Zq06aNKlWqpGeeeUYtW7bU+vXrJf21l2ry5MkaMmSI2rZtqzp16uizzz7TsWPHtGjRIknSrl27FBcXp48//lgNGjRQkyZN9P7772v+/Pk6duyYJGnu3LnKysrSp59+qlq1aqlDhw7q1auXJk6caGaJiYlRq1atNGDAANWoUUNvv/22HnzwQU2dOvWWbxcAAFD4FOpS1ahRIyUkJGjv3r2SpC1btujnn3/WY489Jkk6dOiQkpOTFR4ebr7Gz89PDRo00Nq1ayVJa9eulb+/v0JDQ80x4eHhcnNz07p168wxzZo1k4eHhzkmIiJCe/bs0alTp8wxF39O3pi8zynI+fPnlZ6e7vQAAAC3p0J99d+gQYOUnp6u6tWrq0iRIsrJydG7776ryMhISVJycrIkKSAgwOl1AQEB5rrk5GSVLVvWab27u7tKlSrlNCY4ODjfe+StK1mypJKTky/7OQUZPXq0RowYca1fGwAAuKBCvafqyy+/1Ny5czVv3jxt2rRJc+bM0Xvvvac5c+bYHe2qDB48WGlpaebj6NGjdkcCAAA3SaHeUzVgwAANGjRIHTp0kCSFhITo8OHDGj16tDp37qzAwEBJUkpKisqVK2e+LiUlRXXr1pUkBQYG6vjx407vm52drZMnT5qvDwwMVEpKitOYvOdXGpO3viCenp7y9PS81q8NAABcUKHeU3X27Fm5uTlHLFKkiHJzcyVJwcHBCgwMVEJCgrk+PT1d69atU1hYmCQpLCxMqampSkxMNMcsX75cubm5atCggTlm1apVunDhgjkmPj5e1apVU8mSJc0xF39O3pi8zwEAAHe2Ql2qnnjiCb377rtasmSJ/vvf/2rhwoWaOHGinnrqKUmSw+FQnz599M4772jx4sXatm2bXnzxRQUFBaldu3aSpBo1aqhVq1bq3r271q9fr9WrVys6OlodOnRQUFCQJKlTp07y8PBQt27dtGPHDi1YsEAxMTHq16+fmaV3796Ki4vThAkTtHv3bg0fPlwbN25UdHT0Ld8uN4vjgpfcsrzlluUtxwUvu+MAAOBSCvXhv/fff19vvfWW/vGPf+j48eMKCgrSK6+8oqFDh5pjBg4cqIyMDPXo0UOpqalq0qSJ4uLi5OX1f6Vg7ty5io6O1iOPPCI3Nzc9/fTTmjJlirnez89Py5YtU1RUlOrVq6e77rpLQ4cOdZrLqlGjRpo3b56GDBmiN998U1WrVtWiRYtUu3btW7MxbgHfQ03sjgAAgMtyGBdPT46bKj09XX5+fkpLS5Ovr2+BY+y8315hwr3/cDva2HOg3REKhdD389+xAvb5Nbal3REKhfKPL7vkuqv591sq5If/AAAAXMUNlaqsrCzt2bNH2dnZVuUBAABwSddVqs6ePatu3bqpWLFiqlWrlo4cOSJJ6tmzp8aMGWNpQAAAAFdwXaVq8ODB2rJli1auXOl0Qnh4eLgWLFhgWTgAAABXcV1X/y1atEgLFixQw4YN5XA4zOW1atXSgQMHLAsHAADgKq5rT9WJEyfy3U9PkjIyMpxKFgAAwJ3iukpVaGiolixZYj7PK1Iff/wxM4wDAIA70nUd/hs1apQee+wx7dy5U9nZ2YqJidHOnTu1Zs0a/fjjj1ZnBACg0Ojdu7dOnDghSSpTpoxiYmJsToTC4rr2VDVp0kRJSUnKzs5WSEiIli1bprJly2rt2rWqV6+e1RkBACg0Tpw4oZSUFKWkpJjlCpBu4DY19957r2bOnGllFgAAAJd1XXuqvvvuOy1dujTf8qVLl+r777+/4VAAAACu5rpK1aBBg5STk5NvuWEYGjRo0A2HAgAAcDXXVar27dunmjVr5ltevXp17d+//4ZDAQAAuJrrKlV+fn46ePBgvuX79++Xj4/PDYcCAABwNddVqtq2bas+ffo4zZ6+f/9+9e/fX08++aRl4QAAAFzFdZWqcePGycfHR9WrV1dwcLCCg4NVo0YNlS5dWu+9957VGQEAAAq965pSwc/PT2vWrFF8fLy2bNkib29v1alTR82aNbM6HwAAgEu47nmqHA6HWrZsqZYtW1qZBwAAwCVddamaMmWKevToIS8vL02ZMuWyY3v16nXDwQAAAFzJVZeqSZMmKTIyUl5eXpo0adIlxzkcDkoVAAC441x1qTp06FCBPwMAAOA6rv67cOGC7r33Xu3atetm5AEAAHBJ11yqihYtqszMzJuRBQAAwGVd19V/UVFRGjt2rD7++GO5u1/3BYQAAFyXQ5M62/bZ2el/OP1sZ5bgvnNs+2zkd12NaMOGDUpISNCyZcsUEhKS79Y033zzjSXhAAAAXMV1lSp/f389/fTTVmcBAABwWddUqnJzczV+/Hjt3btXWVlZatGihYYPHy5vb++blQ8AAMAlXNOJ6u+++67efPNNFS9eXHfffbemTJmiqKiom5UNAADAZVxTqfrss8/0wQcfaOnSpVq0aJG+/fZbzZ07V7m5uTcrHwAAgEu4plJ15MgRtW7d2nweHh4uh8OhY8eOWR4MAADAlVxTqcrOzpaXl5fTsqJFi+rChQuWhgIAAHA113SiumEYeumll+Tp6Wkuy8zM1Kuvvuo0rQJTKgAAgDvNNZWqzp3zT3D2/PPPWxYGAADAVV1TqZo1a9bNygEAAODSrvnefwAAAMiPUgUAAGABShUAAIAFKFUAAAAWoFQBAABYgFIFAABggWuaUgEAgDtdSa8iBf4MFPo9Vb/99puef/55lS5dWt7e3goJCdHGjRvN9YZhaOjQoSpXrpy8vb0VHh6uffv2Ob3HyZMnFRkZKV9fX/n7+6tbt246c+aM05itW7eqadOm8vLyUoUKFTRu3Lh8Wb766itVr15dXl5eCgkJ0XfffXdzvjQAoNAa1Lisxj5STmMfKadBjcvaHQeFSKEuVadOnVLjxo1VtGhRff/999q5c6cmTJigkiVLmmPGjRunKVOmaMaMGVq3bp18fHwUERGhzMxMc0xkZKR27Nih+Ph4xcbGatWqVerRo4e5Pj09XS1btlTFihWVmJio8ePHa/jw4froo4/MMWvWrFHHjh3VrVs3bd68We3atVO7du20ffv2W7MxAABAoVaoD/+NHTtWFSpUcJrJPTg42PzZMAxNnjxZQ4YMUdu2bSVJn332mQICArRo0SJ16NBBu3btUlxcnDZs2KDQ0FBJ0vvvv6/WrVvrvffeU1BQkObOnausrCx9+umn8vDwUK1atZSUlKSJEyea5SsmJkatWrXSgAEDJElvv/224uPjNXXqVM2YMeNWbRIAAFBIFeo9VYsXL1ZoaKj+/ve/q2zZsnrggQc0c+ZMc/2hQ4eUnJys8PBwc5mfn58aNGigtWvXSpLWrl0rf39/s1BJUnh4uNzc3LRu3TpzTLNmzeTh4WGOiYiI0J49e3Tq1ClzzMWfkzcm73MKcv78eaWnpzs9AADA7alQl6qDBw9q+vTpqlq1qpYuXarXXntNvXr10pw5cyRJycnJkqSAgACn1wUEBJjrkpOTVbas8zFvd3d3lSpVymlMQe9x8Wdcakze+oKMHj1afn5+5qNChQrX9P0BAIDrKNSlKjc3Vw8++KBGjRqlBx54QD169FD37t1d5nDb4MGDlZaWZj6OHj1qdyQAAHCTFOpSVa5cOdWsWdNpWY0aNXTkyBFJUmBgoCQpJSXFaUxKSoq5LjAwUMePH3dan52drZMnTzqNKeg9Lv6MS43JW18QT09P+fr6Oj0AAMDtqVCXqsaNG2vPnj1Oy/bu3auKFStK+uuk9cDAQCUkJJjr09PTtW7dOoWFhUmSwsLClJqaqsTERHPM8uXLlZubqwYNGphjVq1apQsXLphj4uPjVa1aNfNKw7CwMKfPyRuT9zkAAODOVqhLVd++ffXLL79o1KhR2r9/v+bNm6ePPvpIUVFRkiSHw6E+ffronXfe0eLFi7Vt2za9+OKLCgoKUrt27ST9tWerVatW6t69u9avX6/Vq1crOjpaHTp0UFBQkCSpU6dO8vDwULdu3bRjxw4tWLBAMTEx6tevn5mld+/eiouL04QJE7R7924NHz5cGzduVHR09C3fLgAAoPAp1FMq/O1vf9PChQs1ePBgjRw5UsHBwZo8ebIiIyPNMQMHDlRGRoZ69Oih1NRUNWnSRHFxcfLy8jLHzJ07V9HR0XrkkUfk5uamp59+WlOmTDHX+/n5admyZYqKilK9evV01113aejQoU5zWTVq1Ejz5s3TkCFD9Oabb6pq1apatGiRateufWs2BgAAKNQchmEYdoe4U6Snp8vPz09paWmXPL/qmYDJtzZUIfV1Sh+7IwCW29hzoN0RCoXQ9/PfseJaHZrU2YIkri+475wbfo9fY1takMT1lX982SXXXc2/31IhP/wHAADgKihVAAAAFqBUAQAAWIBSBQAAYAFKFQAAgAUoVQAAABagVAEAAFiAUgUAAGABShUAAIAFKFUAAAAWoFQBAABYgFIFAABgAUoVAACABShVAAAAFqBUAQAAWIBSBQAAYAFKFQAAgAUoVQAAABagVAEAAFiAUgUAAGABShUAAIAFKFUAAAAWoFQBAABYgFIFAABgAUoVAACABdztDgAAl9O7d2+dOHFCklSmTBnFxMTYnAgACkapAlConThxQikpKXbHAIAr4vAfAACABShVAAAAFqBUAQAAWIBSBQAAYAFKFQAAgAUoVQAAABZgSgXgJmBuJQC481CqgJuAuZUA4M7D4T8AAAALUKoAAAAsQKkCAACwAKUKAADAApQqAAAAC7jU1X9jxozR4MGD1bt3b02ePFmSlJmZqf79+2v+/Pk6f/68IiIi9MEHHyggIMB83ZEjR/Taa69pxYoVKl68uDp37qzRo0fL3f3/vv7KlSvVr18/7dixQxUqVNCQIUP00ksvOX3+tGnTNH78eCUnJ+v+++/X+++/r/r169+Kr47rMOye92377NRK6VLR///zr+m2ZhlxpKdtnw0AdxKX2VO1YcMGffjhh6pTp47T8r59++rbb7/VV199pR9//FHHjh1T+/btzfU5OTlq06aNsrKytGbNGs2ZM0ezZ8/W0KFDzTGHDh1SmzZt1Lx5cyUlJalPnz56+eWXtXTpUnPMggUL1K9fPw0bNkybNm3S/fffr4iICB0/fvzmf3kAAFDouUSpOnPmjCIjIzVz5kyVLFnSXJ6WlqZPPvlEEydOVIsWLVSvXj3NmjVLa9as0S+//CJJWrZsmXbu3KnPP/9cdevW1WOPPaa3335b06ZNU1ZWliRpxowZCg4O1oQJE1SjRg1FR0frmWee0aRJk8zPmjhxorp3764uXbqoZs2amjFjhooVK6ZPP/301m4MAABQKLlEqYqKilKbNm0UHh7utDwxMVEXLlxwWl69enXdc889Wrt2rSRp7dq1CgkJcTocGBERofT0dO3YscMc87/vHRERYb5HVlaWEhMTnca4ubkpPDzcHFOQ8+fPKz093ekBAABuT4X+nKr58+dr06ZN2rBhQ751ycnJ8vDwkL+/v9PygIAAJScnm2MuLlR56/PWXW5Menq6zp07p1OnTiknJ6fAMbt3775k9tGjR2vEiBFX90UBAIBLK9R7qo4eParevXtr7ty58vLysjvONRs8eLDS0tLMx9GjR+2OhFvEM9tbnhf+/yPb2+44AIBboFDvqUpMTNTx48f14IMPmstycnK0atUqTZ06VUuXLlVWVpZSU1Od9lalpKQoMDBQkhQYGKj169c7vW/ePdkuHvO/92lLSUmRr6+vvL29VaRIERUpUqTAMXnvURBPT095enpe+xeHy6vzazO7IwAAbrFCvafqkUce0bZt25SUlGQ+QkNDFRkZaf5ctGhRJSQkmK/Zs2ePjhw5orCwMElSWFiYtm3b5nSVXnx8vHx9fVWzZk1zzMXvkTcm7z08PDxUr149pzG5ublKSEgwxwAAgDtbod5TVaJECdWuXdtpmY+Pj0qXLm0u79atm/r166dSpUrJ19dXPXv2VFhYmBo2bChJatmypWrWrKkXXnhB48aNU3JysoYMGaKoqChzL9Krr76qqVOnauDAgeratauWL1+uL7/8UkuWLDE/t1+/furcubNCQ0NVv359TZ48WRkZGerSpcst2hoAAKAwK9Sl6mpMmjRJbm5uevrpp50m/8xTpEgRxcbG6rXXXlNYWJh8fHzUuXNnjRw50hwTHBysJUuWqG/fvoqJiVH58uX18ccfKyIiwhzz3HPP6cSJExo6dKiSk5NVt25dxcXF5Tt5HQAA3JlcrlStXLnS6bmXl5emTZumadOmXfI1FStW1HfffXfZ93344Ye1efPmy46Jjo5WdHT0VWcFAAB3jkJ9ThUAAICroFQBAABYgFIFAABgAUoVAACABShVAAAAFqBUAQAAWIBSBQAAYAFKFQAAgAUoVQAAABZwuRnVAdx6C8Lfte2zM4y0//s5Jc3WLM/98E/bPhtA4ceeKgAAAAtQqgAAACxAqQIAALAApQoAAMAClCoAAAALUKoAAAAsQKkCAACwAKUKAADAApQqAAAAC1CqAAAALECpAgAAsAClCgAAwAKUKgAAAAtQqgAAACxAqQIAALAApQoAAMAClCoAAAALUKoAAAAsQKkCAACwAKUKAADAApQqAAAAC1CqAAAALECpAgAAsAClCgAAwAKUKgAAAAtQqgAAACxAqQIAALAApQoAAMAClCoAAAALuNsdAAAux0eeBf4MAIUNpQpAodbWUc/uCABwVQr14b/Ro0frb3/7m0qUKKGyZcuqXbt22rNnj9OYzMxMRUVFqXTp0ipevLiefvpppaSkOI05cuSI2rRpo2LFiqls2bIaMGCAsrOzncasXLlSDz74oDw9PVWlShXNnj07X55p06apUqVK8vLyUoMGDbR+/XrLvzMAAHBNhbpU/fjjj4qKitIvv/yi+Ph4XbhwQS1btlRGRoY5pm/fvvr222/11Vdf6ccff9SxY8fUvn17c31OTo7atGmjrKwsrVmzRnPmzNHs2bM1dOhQc8yhQ4fUpk0bNW/eXElJSerTp49efvllLV261ByzYMEC9evXT8OGDdOmTZt0//33KyIiQsePH781GwMAABRqhfrwX1xcnNPz2bNnq2zZskpMTFSzZs2UlpamTz75RPPmzVOLFi0kSbNmzVKNGjX0yy+/qGHDhlq2bJl27typH374QQEBAapbt67efvttvfHGGxo+fLg8PDw0Y8YMBQcHa8KECZKkGjVq6Oeff9akSZMUEREhSZo4caK6d++uLl26SJJmzJihJUuW6NNPP9WgQYNu4VYBAACFUaHeU/W/0tLSJEmlSpWSJCUmJurChQsKDw83x1SvXl333HOP1q5dK0lau3atQkJCFBAQYI6JiIhQenq6duzYYY65+D3yxuS9R1ZWlhITE53GuLm5KTw83BxTkPPnzys9Pd3pAQAAbk8uU6pyc3PVp08fNW7cWLVr15YkJScny8PDQ/7+/k5jAwIClJycbI65uFDlrc9bd7kx6enpOnfunP744w/l5OQUOCbvPQoyevRo+fn5mY8KFSpc+xcHAAAuwWVKVVRUlLZv36758+fbHeWqDR48WGlpaebj6NGjdkcCAAA3SaE+pypPdHS0YmNjtWrVKpUvX95cHhgYqKysLKWmpjrtrUpJSVFgYKA55n+v0su7OvDiMf97xWBKSop8fX3l7e2tIkWKqEiRIgWOyXuPgnh6esrTk3l1AAC4ExTqPVWGYSg6OloLFy7U8uXLFRwc7LS+Xr16Klq0qBISEsxle/bs0ZEjRxQWFiZJCgsL07Zt25yu0ouPj5evr69q1qxpjrn4PfLG5L2Hh4eH6tWr5zQmNzdXCQkJ5hgAAHBnK9R7qqKiojRv3jz95z//UYkSJczzl/z8/OTt7S0/Pz9169ZN/fr1U6lSpeTr66uePXsqLCxMDRs2lCS1bNlSNWvW1AsvvKBx48YpOTlZQ4YMUVRUlLkX6dVXX9XUqVM1cOBAde3aVcuXL9eXX36pJUuWmFn69eunzp07KzQ0VPXr19fkyZOVkZFhXg0IAADubIW6VE2fPl2S9PDDDzstnzVrll566SVJ0qRJk+Tm5qann35a58+fV0REhD744ANzbJEiRRQbG6vXXntNYWFh8vHxUefOnTVy5EhzTHBwsJYsWaK+ffsqJiZG5cuX18cff2xOpyBJzz33nE6cOKGhQ4cqOTlZdevWVVxcXL6T1wEAwJ2pUJcqwzCuOMbLy0vTpk3TtGnTLjmmYsWK+u677y77Pg8//LA2b9582THR0dGKjo6+YiYAAHDnKdTnVAEAALgKShUAAIAFKFUAAAAWoFQBAABYgFIFAABgAUoVAACABShVAAAAFqBUAQAAWIBSBQAAYAFKFQAAgAUoVQAAABagVAEAAFiAUgUAAGABShUAAIAFKFUAAAAWoFQBAABYgFIFAABgAUoVAACABShVAAAAFqBUAQAAWIBSBQAAYAFKFQAAgAUoVQAAABagVAEAAFiAUgUAAGABShUAAIAFKFUAAAAWoFQBAABYgFIFAABgAUoVAACABShVAAAAFqBUAQAAWIBSBQAAYAFKFQAAgAUoVQAAABagVAEAAFiAUgUAAGABShUAAIAFKFUAAAAWoFQBAABYgFJ1jaZNm6ZKlSrJy8tLDRo00Pr16+2OBAAACgFK1TVYsGCB+vXrp2HDhmnTpk26//77FRERoePHj9sdDQAA2IxSdQ0mTpyo7t27q0uXLqpZs6ZmzJihYsWK6dNPP7U7GgAAsJm73QFcRVZWlhITEzV48GBzmZubm8LDw7V27doCX3P+/HmdP3/efJ6WliZJSk9Pv+TnXMjNtCixa7vcNrpa53PPWZDE9VmxLc9m8+dSuvFteSbr/JUH3QGs+DN5OjPLgiSuz5JteTbbgiSu73LbMm+dYRiXfxMDV+W3334zJBlr1qxxWj5gwACjfv36Bb5m2LBhhiQePHjw4MGDx23wOHr06GW7AnuqbqLBgwerX79+5vPc3FydPHlSpUuXlsPhsDHZpaWnp6tChQo6evSofH197Y7j0tiW1mFbWoPtaB22pXVcYVsahqHTp08rKCjosuMoVVfprrvuUpEiRZSSkuK0PCUlRYGBgQW+xtPTU56enk7L/P39b1ZES/n6+hbaP9yuhm1pHbalNdiO1mFbWqewb0s/P78rjuFE9avk4eGhevXqKSEhwVyWm5urhIQEhYWF2ZgMAAAUBuypugb9+vVT586dFRoaqvr162vy5MnKyMhQly5d7I4GAABsRqm6Bs8995xOnDihoUOHKjk5WXXr1lVcXJwCAgLsjmYZT09PDRs2LN9hS1w7tqV12JbWYDtah21pndtpWzoM40rXBwIAAOBKOKcKAADAApQqAAAAC1CqAAAALECpAgAAsAClCgAAwAKUKgAArsJPP/2k559/XmFhYfrtt98kSf/617/0888/25wMhQWlCtq+ffsl1y1atOjWBbkNsC1vjXPnztkdwWWkp6cX+Dh9+rSysrLsjucy/v3vfysiIkLe3t7avHmzzp8/L0lKS0vTqFGjbE7nmrKysvTrr7/qyJEjTg9XRqmCIiIidOjQoXzL//3vfysyMtKGRK6LbWmdXr16Fbg8IyNDrVu3vsVpXJe/v79KliyZ7+Hv7y9vb29VrFhRw4YNU25urt1RC7V33nlHM2bM0MyZM1W0aFFzeePGjbVp0yYbk7meffv2qWnTpuafv+DgYAUHB6tSpUoKDg62O94NYUZ16OWXX1Z4eLhWr15t3hx6wYIF6tq1q2bPnm1vOBfDtrTOkiVLVLJkSY0YMcJclpGRoVatWtmYyvXMnj1b//znP/XSSy+pfv36kqT169drzpw5GjJkiE6cOKH33ntPnp6eevPNN21OW3jt2bNHzZo1y7fcz89Pqamptz6QC3vppZfk7u6u2NhYlStXTg6Hw+5I1jEAwzCio6ONWrVqGX/++acxd+5cw9vb2/j666/tjuWS2JbW2L9/v1GuXDlj0qRJhmEYRnp6uhEWFmY0bdrUOHPmjL3hXEiLFi2MBQsW5Fu+YMECo0WLFoZhGMZnn31mVKtW7VZHcynBwcFGfHy8YRiGUbx4cePAgQOGYRjGnDlzjBo1atgZzeUUK1bM2LVrl90xbgpKFUydOnUyqlatahQrVsxYtGiR3XFcGtvSGlu2bDFKlSplxMTEGA0bNjQeeughCtU18vLyMvbu3Ztv+d69ew1vb2/DMAzj4MGD5s8o2KhRo4yaNWsav/zyi1GiRAnjp59+Mj7//HOjTJkyxpQpU+yO51JCQ0ONn376ye4YNwWH/+5Qixcvzresffv2+umnn9SxY0c5HA5zzJNPPnmr47kUtuXNU6dOHcXGxurRRx9VgwYNFBsbK29vb7tjuZQKFSrok08+0ZgxY5yWf/LJJ6pQoYIk6c8//1TJkiXtiOcyBg0apNzcXD3yyCM6e/asmjVrJk9PT73++uvq2bOn3fFcytixYzVw4ECNGjVKISEhTueoSZKvr69NyW4cN1S+Q7m5Xd01Cg6HQzk5OTc5jWtjW1rngQceKPD8isOHD6ts2bJOhYqTg6/O4sWL9fe//13Vq1fX3/72N0nSxo0btXv3bn399dd6/PHHNX36dO3bt08TJ060OW3hl5WVpf379+vMmTOqWbOmihcvbnckl5P3d+b//r9uGIbL/z1JqQJQaFx8UvqVDBs27CYmub0cOnRIH374ofbu3StJqlatml555RVVqlTJ3mC4I/3444+XXf/QQw/doiTWo1QBAFCA9u3bX/XYb7755iYmgavgnCpI+us3h/fee0+7du2SJNWsWVMDBgxQ06ZNbU7metiW1kpMTDS3Za1atfTAAw/YnMj1pKamav369Tp+/Hi++ahefPFFm1IVfn5+fnZHuG2lpqbqk08+cfp/u2vXri6/zdlTBX3++efq0qWL2rdvr8aNG0uSVq9erYULF2r27Nnq1KmTzQldB9vSOsePH1eHDh20cuVK+fv7S/rrL+LmzZtr/vz5KlOmjL0BXcS3336ryMhInTlzRr6+vk7nsTgcDp08edLGdLgTbdy40ZydPm/utA0bNujcuXNatmyZHnzwQZsTXj9KFVSjRg316NFDffv2dVo+ceJEzZw50/xNAlfGtrTOc889p4MHD+qzzz5TjRo1JEk7d+5U586dVaVKFX3xxRc2J3QN9913n1q3bq1Ro0apWLFidsdxecePH9eePXsk/XVuWtmyZW1O5HqaNm2qKlWqaObMmXJ3/+uAWXZ2tl5++WUdPHhQq1atsjnh9aNUQZ6entqxY4eqVKnitHz//v2qXbu2MjMzbUrmetiW1vHz89MPP/xgXrGWZ/369WrZsiWzWF8lHx8fbdu2TZUrV7Y7iktLT09XVFSU5s+fb16dVqRIET333HOaNm2ayx+2upXy7p9YvXp1p+U7d+5UaGiozp49a1OyG8e9/6AKFSooISEh3/IffvjBnMcGV4dtaZ3c3Nx889dIUtGiRblP3TWIiIjQxo0b7Y7h8rp3765169YpNjZWqampSk1NVWxsrDZu3KhXXnnF7nguxdfXt8AbJx89elQlSpSwIZF1OFEd6t+/v3r16qWkpCQ1atRI0l/nAc2ePVsxMTE2p3MtbEvrtGjRQr1799YXX3yhoKAgSdJvv/2mvn376pFHHrE5neto06aNBgwYoJ07dxY40SIT0l6d2NhYLV26VE2aNDGXRUREaObMmdyP8ho999xz6tatm9577z2nvycHDBigjh072pzuBtkyjzsKnW+++cZo3LixUapUKaNUqVJG48aNub3KdWJbWuPIkSNG3bp1jaJFixqVK1c2KleubBQtWtR44IEHjKNHj9odz2U4HI5LPtzc3OyO5zIqVKhgbN26Nd/yLVu2GHfffbcNiVzX+fPnjV69ehkeHh6Gm5ub4ebmZnh6ehp9+vQxMjMz7Y53QzinCkChZRiGfvjhB+3evVvSXxcChIeH25wKd6KPPvpIX331lf71r38pMDBQkpScnKzOnTurffv2HAK8DmfPntWBAwckSffee+9tcSEFpQom5gOyDtsScH3/e9ukffv26fz587rnnnskSUeOHJGnp6eqVq3KbZMgiXOqIOYDshLb0lpMpHp9pkyZoh49esjLy0tTpky57NhevXrdolSup127dnZHuG20b99es2fPlq+v7xVnqnfl2enZUwXmA7IQ29I6BU2k+vPPP2vRokVMpHoFwcHB2rhxo0qXLq3g4OBLjnM4HDp48OAtTIY7VZcuXTRlyhSVKFFCL730UoE3Ts8za9asW5jMWpQqMB+QhdiW1mEiVQCuhnmqwHxAFmJbWufgwYN64okn8i1/8skndejQIRsS4U5TqlQp/fHHH5KkkiVLqlSpUpd84Oq1aNGiwF8w09PT1aJFi1sfyEKcUwXmA7IQ29I6eROp/u/s9Eykem0Mw9DXX3+tFStWFHhDZVc+f+VmmzRpkjkZ5aRJky57yApXb+XKlcrKysq3PDMzUz/99JMNiaxDqYKmTp2qJ598UpUqVTL/sTp69Khq166tzz//3OZ0roVtaR0mUrVGnz599OGHH6p58+YKCAigGFyDzp07mz+/9NJL9gW5TWzdutX8eefOnUpOTjaf5+TkKC4uTnfffbcd0SzDOVWQxHxAVmJbWmfhwoWaMGGCef5UjRo1NGDAALVt29bmZK6jVKlS+vzzz9W6dWu7o7i0IkWK6Pfff893A+U///xTZcuWNe8HiEtzc3MzS31B1cPb21vvv/++unbtequjWYZSBQC3seDgYH3//ff5bl6La+Pm5qbk5OR8perYsWO69957de7cOZuSuY7Dhw/LMAxVrlxZ69evd5pixsPDQ2XLllWRIkVsTHjjOPwHSVJCQoImTZrktEegT58+7GG5DmxLa23cuNFpnqp69erZnMi1DB8+XCNGjNCnn34qb29vu+O4nLx5vhwOhz7++GMVL17cXJeTk6NVq1ZRWK9SxYoVJem2vmiHPVXQBx98oN69e+uZZ55RWFiYJOmXX37R119/rUmTJikqKsrmhK6DbWmdX3/9VR07dtTq1audJlJt1KiR5s+fr/Lly9sb0EWcO3dOTz31lFavXq1KlSrluzqVmcAvL2+er8OHD6t8+fJOe1I8PDxUqVIljRw5Ug0aNLArosvauXOnjhw5ku+kdVe+yTelCipfvrwGDRqk6Ohop+XTpk3TqFGj9Ntvv9mUzPWwLa3TqlUrpaamas6cOapWrZokac+ePerSpYt8fX0VFxdnc0LX8Oyzz2rFihV65plnCjxRfdiwYTYlcy3NmzfXwoULzYKP63fw4EE99dRT2rZtmxwOh3l+Vd6fTVc+P41SBRUvXlxJSUn5Ll3ft2+fHnjgAZ05c8amZK6HbWkdb29vrVmzJt99ExMTE9W0aVOdPXvWpmSuxcfHR0uXLlWTJk3sjuKyLly4oOrVqys2Nta8UwKu3xNPPKEiRYro448/VnBwsNavX68///xT/fv313vvvefSt6Fi8k/oySef1MKFC/Mt/89//qPHH3/chkSui21pnQoVKujChQv5lufk5JhzgOHKKlSoIF9fX7tjuLSiRYsqMzPT7hi3jbVr12rkyJG666675ObmJjc3NzVp0kSjR492+XtRcqL6Herim6zWrFlT7777rlauXOl0HtDq1avVv39/uyK6DLblzTF+/Hj17NlT06ZNU2hoqKS/Tlrv3bu33nvvPZvTuY4JEyZo4MCBmjFjhipVqmR3HJcVFRWlsWPH6uOPP5a7O/903oicnBxzUtW77rpLx44dU7Vq1VSxYkXt2bPH5nQ3hsN/d6jL3WT1Ytxw9crYltYpWbKk0zk/GRkZys7ONv8Ry/vZx8dHJ0+etCumSylZsqTOnj2r7OxsFStWLN+J6mzHq/PUU08pISFBxYsXV0hIiHx8fJzWMzP91WvatKn69++vdu3aqVOnTjp16pSGDBmijz76SImJidq+fbvdEa8bdfsOxb3TrMO2tM7kyZPtjnDbYZtaw9/fX08//bTdMW4LQ4YMUUZGhiRp5MiRevzxx9W0aVOVLl1a8+fPtzndjWFPFXATZGVl6dChQ7r33ns5VAAAV3Dy5Ml8e6pdEaUKkv6aE2jx4sUFzhkyceJEm1K5nrNnz6pnz56aM2eOJGnv3r2qXLmyevbsqbvvvluDBg2yOaHr4LYg1svMzMz3/zcnsV+97OxsrVy5UgcOHFCnTp1UokQJHTt2TL6+vk6TguLyunbtqpiYGPO8qjwZGRnq2bOnPv30U5uS3Tiu/oMSEhJUrVo1TZ8+XRMmTNCKFSs0a9Ysffrpp0pKSrI7nksZPHiwtmzZopUrV8rLy8tcHh4ergULFtiYzPVc6ve98+fPy8PD4xancV0ZGRmKjo5W2bJl5ePjo5IlSzo9cHUOHz6skJAQtW3bVlFRUTpx4oQkaezYsXr99ddtTuda5syZU+Btfc6dO6fPPvvMhkTW4bgENHjwYL3++usaMWKESpQooX//+98qW7asIiMj1apVK7vjuZRFixZpwYIFatiwodNu7Fq1aunAgQM2JnMd3BbEWgMHDtSKFSs0ffp0vfDCC5o2bZp+++03ffjhhxozZozd8VxG7969FRoaqi1btqh06dLm8qeeekrdu3e3MZnrSE9Pl2EYMgxDp0+fdvrFMycnR999912+PdOuhlIF7dq1S1988YUkyd3dXefOnVPx4sU1cuRItW3bVq+99prNCV3HiRMnCvxLISMjw+XPFbhVJk2aJOmvPVUzZswo8LYgM2bMsCuey/n222/12Wef6eGHH1aXLl3UtGlTValSRRUrVtTcuXMVGRlpd0SX8NNPP2nNmjX59pJWqlSJOyVcJX9/fzkcDjkcDt1333351jscDo0YMcKGZNahVEE+Pj7meRblypXTgQMHVKtWLUnSH3/8YWc0lxMaGqolS5aoZ8+ekv7vtgsff/yxOW8VLi/vasrmzZvrm2++4RDVDTp58qQqV64s6a/zp/KmUGjSpAm/MF2D3NzcAs/j+/XXX/OdG4SCrVixQoZhqEWLFvr3v/+tUqVKmes8PDxUsWJFl5/Yl1IFNWzYUD///LNq1Kih1q1bq3///tq2bZu++eYbNWzY0O54LmXUqFF67LHHtHPnTmVnZysmJkY7d+7UmjVr9OOPP9odz6WsWLFCEldS3qjKlSvr0KFDuueee1S9enV9+eWXql+/vr799lvuY3cNWrZsqcmTJ+ujjz6S9NcvTGfOnNGwYcPUunVrm9O5hoceekiSzD+Pt+Pee05UhyZOnGjeYX3EiBF65JFHtGDBAlWqVEmffPKJzelcS5MmTZSUlKTs7GyFhIRo2bJlKlu2rNauXat69erZHc+lnDt3Tt26dVOxYsVUq1YtHTlyRJLUs2dPzgW6Bl26dNGWLVskSYMGDdK0adPk5eWlvn37asCAATancx0TJkzQ6tWrVbNmTWVmZqpTp07mob+xY8faHc+l7Nq1S6tXrzafT5s2TXXr1jUnAnVlTKlwh8vJydHq1atVp04dfmtFodK7d2+tXr1akydPVqtWrbR161ZVrlxZ//nPfzR8+HBt3rzZ7ogu6fDhw0pMTFSVKlVUp04du+O4lOzsbM2fP19bt27VmTNn9OCDDyoyMlLe3t52R3MpISEhGjt2rFq3bq1t27YpNDRU/fv314oVK1S9enXNmjXL7ojXjVIFeXl5adeuXVd9uxVcGnMrWadixYrmlZQlSpTQli1bVLlyZe3fv18PPvig0tPT7Y5YqJ07d04JCQnmjbwHDx6s8+fPm+vd3d01cuRIpyuwcGmZmZlsK4sUL15c27dvV6VKlTR8+HBt375dX3/9tTZt2qTWrVsrOTnZ7ojXjcN/UO3atbknnUWYW8k6XEl5Y+bMmaMPP/zQfD516lStWbNGmzdv1ubNm/Wvf/1L06dPtzGhaylbtqw6d+6s+Ph45ebm2h3HpXl4eOjs2bOSpB9++EEtW7aUJJUqVcrlf1nirE/onXfe0euvv663335b9erVy3ejUGZcvjLmVrIeV1LemLlz52rgwIFOy+bNm2deCfj5559r2rRp6tu3rx3xXM6cOXM0b948tW3bVn5+fnruuef0/PPPKzQ01O5oLqdJkybq16+fGjdurPXr15sTI+/du1fly5e3Od2N4fAf5Ob2fzssL94DYBiGHA4Hh6yuQt6h08OHD6t8+fIFzq00cuRI84IAXNnPP/+sxx57TM8//7xmz56tV155xelKSk78v7xy5cpp7dq1qlSpkiSpTJky2rBhg/l87969+tvf/qa0tDT7Qrqg06dP6+uvv9YXX3yh5cuXq3Llynr++ec1dOhQu6O5jCNHjugf//iHjh49ql69eqlbt26SpL59+yonJ8f8JdUVUapwxUv98y6DxZUxt5K1Dhw4oDFjxmjLli3micFvvPGGQkJC7I5W6Hl7eyspKUnVqlUrcP3u3btVt25dZWZm3uJkt4+dO3cqMjJSW7du5ZdPi5w7d86lT/zn8B8UHBysChUq5DtPxTAMHT161KZUrilvbiXp/86v4vyf63fvvfdq5syZdsdwSeXLl9f27dsvWaq2bt3q8oda7JCZmanFixdr3rx5iouLU0BAAFNTXKNevXoVuDcqIyNDjz/+uNPfo66GE9Wh4OBg8+agFzt58iRXBF6Hzz77TCEhIfL29pa3t7fq1Kmjf/3rX3bHchlubm4qUqTIZR9MAnplrVu31tChQwvcE3Xu3DmNGDFCbdq0sSGZa1q6dKk6d+6sgIAAvfbaawoICNCyZct0+PBh5k27RkuWLNGwYcOclmVkZKhVq1bKzs62KZU1+JsJ5rlT/+vMmTNcQnyNJk6cqLfeekvR0dFq3LixpL/ODXr11Vf1xx9/cFLwVVi4cOEl161du1ZTpkzh6qur8Oabb+rLL79UtWrVFB0dbd5rbc+ePZo6daqys7P15ptv2pzSdTz11FN6/PHH9dlnn6l169YqWrSo3ZFc1rJly9S0aVOVLFlSffr00enTpxURESF3d3d9//33dse7IZxTdQfr16+fJCkmJkbdu3dXsWLFzHU5OTlat26dihQp4jTzLS4vODhYI0aM0Isvvui0fM6cORo+fLh5Xztcmz179mjQoEH69ttvFRkZqZEjR6pixYp2xyr0Dh06pNdee03x8fFOh6MfffRRffDBB+aVgLiy06dPc48/C23dulXNmzfXsGHD9MUXX8jT01NLlizJd/W5q2FP1R0sb0ZqwzC0bds2p3mUPDw8dP/99+v111+3K55L+v3339WoUaN8yxs1aqTff//dhkSu7dixYxo2bJjmzJmjiIgIJSUlqXbt2nbHchnBwcGKi4vTyZMntX//fklSlSpVnG5ki6tTokQJ5ebmav/+/Tp+/Hi+vaXNmjWzKZlrqlOnjmJjY/Xoo4+qQYMGio2NdekT1PNQqu5geScDdunSRTExMcxHZYEqVaroyy+/zHdYZcGCBapatapNqVxPWlqaRo0apffff19169ZVQkKCmjZtancsl1WqVCnVr1/f7hgu7ZdfflGnTp10+PDhfJP8MvXMlT3wwAMFnmbi6empY8eOmadLSNKmTZtuZTRLUarg0vdZKmxGjBih5557TqtWrTL/kli9erUSEhL05Zdf2pzONYwbN05jx45VYGCgvvjiC7Vt29buSIBeffVVc0LacuXKcVXvNWrXrp3dEW4Jzqm6Q7Vv3/6qx37zzTc3McntJzExURMnTtTu3bslSTVq1FD//v31wAMP2JzMNbi5ucnb21vh4eFOk6j+L/5c4lby8fHRli1bVKVKFbujuLScnBytXr1aderUkb+/v91xLMeeqjuUn5+f3RFuW/Xq1dPcuXPtjuGyXnzxRfYCoNBp0KCB9u/fT6m6QUWKFFHLli21a9cuShVuHxzys5abm9sVi4DD4XD5OVhuhdmzZ9sdAcinZ8+e6t+/v5KTkxUSEpJvSoU6derYlMz11K5dWwcPHrwt50Hk8N8drGTJkgUWAT8/P9133316/fXX9eijj9qQzPX85z//ueS6i+dW4pYggGu6+B6peRwOB/dIvQ5xcXEaPHiw3n77bdWrVy/fNAqufNEUpeoONmfOnAKXp6amKjExUQsWLNDXX3+tJ5544hYnuz0wtxJw+zh8+PBl1/P/9tW7uKBe/Iv97VBQOfx3B+vcufNl19etW1ejR4+mVF0j5lYCbj+UJuu48r39roQ9VbikvXv3qmHDhjp58qTdUVzC/86tNHbsWOZWAm4jBw4c0OTJk7Vr1y5JUs2aNdW7d2/de++9NidDYcGeKlzS+fPnnWZZx6UxtxJwe1u6dKmefPJJ1a1b12kOulq1aunbb7/l/NNrlJqaqk8++cQsqLVq1VLXrl1d/sp09lThkvr06aPdu3crLi7O7iiFHnMrAbe3Bx54QBERERozZozT8kGDBmnZsmUuPQv4rbZx40ZFRETI29vbnOl/w4YNOnfunJYtW6YHH3zQ5oTXj1J1B8u7ofL/SktL06ZNm7R3716tWrVK9erVu8XJXM9LL710VXMrMZUF4Jq8vLy0bdu2fLeb2rt3r+rUqcOVvdegadOmqlKlimbOnCl3978OmGVnZ+vll1/WwYMHtWrVKpsTXj8O/93B8m6o/L98fX316KOP6ptvvrkt5xG5GZhbCbi9lSlTRklJSflKVVJSksqWLWtTKte0ceNGp0IlSe7u7ho4cKBCQ0NtTHbjKFV3sNv5CgwAsFL37t3Vo0cPHTx4UI0aNZL01zlVY8eOveRefxTM19dXR44cUfXq1Z2WHz16VCVKlLAplTU4/AcAwBUYhqHJkydrwoQJOnbsmCQpKChIAwYMUK9evbi10jXo1auXFi5cqPfee8+poA4YMEBPP/20Jk+ebG/AG0CpAgDgGpw+fVqSXH6vyq126NAhBQcHKysrSwMGDNCMGTOUnZ0twzDk4eGh1157TWPGjJGnp6fdUa8bpQoAgEs4d+6c4uPj1bx583wlKj09XStXrlRERIRLF4Fbxc3NTRUrVlTz5s3VvHlzPfzww0pNTZUk3XvvvSpWrJi9AS1AqQIA4BJiYmK0ePFiJSQkFLg+PDxcTz31lKKiom5xMtezcuVK87Fu3TplZWWpcuXKatGihVq0aKGHH35YAQEBdse8IZQqAAAuoX79+nrrrbcuebuu2NhYjRw5UuvXr7/FyVxbZmam1qxZY5as9evX68KFC6pevbp27Nhhd7zrRqkCAOASSpYsqS1btuiee+4pcP2RI0d0//3369SpU7c42e0hKytLq1ev1vfff68PP/xQZ86ccekbKrtdeQgAAHem7OxsnThx4pLrT5w4oezs7FuYyLVlZWVp1apVGjFihJo3by5/f3+9+uqrOnXqlKZOnapDhw7ZHfGGME8VAACXUKtWLf3www+XvLPEsmXLVKtWrVucyjW1aNFC69atU3BwsB566CG98sormjdvnsqVK2d3NMuwpwoAgEvo2rWr3n77bcXGxuZb9+233+rdd99V165dbUjmen766SeVLl1aLVq00COPPKJHH330tipUEudUAQBwWc8//7zmzZun6tWrq1q1apKk3bt3a+/evXr22Wf1xRdf2JzQNWRkZOinn37SypUrtWLFCiUlJem+++7TQw89pIcfflgPPfSQypQpY3fMG0KpAgDgCr788kvNmzdP+/btk2EYuu+++9SpUyc9++yzdkdzWadPn9bPP/+sFStWaOXKldqyZYuqVq2q7du32x3tulGqAADALZebm6sNGzZoxYoVWrFihX7++WdlZma69NV/lCoAAHDT5ebmauPGjebhv9WrVysjI0N33323Oct68+bNVbFiRbujXjdKFQAAuOl8fX2VkZGhwMBAp1vV3HvvvXZHswylCgAA3HQffvihmjdvrvvuu8/uKDcNpQoAAMACzFMFAABgAUoVAADX6cCBA2rRooXdMVBIUKoAALhOZ86c0Y8//mh3DBQS3PsPAIBLmDJlymXX//bbb7coCVwBJ6oDAHAJbm5uKleunDw8PApcn5WVpeTkZJeesBLWYU8VAACXULFiRY0dO/aSt6NJSkpSvXr1bnEqFFacUwUAwCXUq1dPiYmJl1zvcDjEAR/k4fAfAACXsHPnTp09e1ahoaEFrr9w4YKOHTvm0rdWgXUoVQAAABbg8B8AAIAFKFUAAAAWoFQBAABYgFIFAABgAUoVAACABZj8EwCAq5CQkKCEhAQdP35cubm5Tus+/fRTm1KhMKFUAQBwBSNGjNDIkSMVGhqqcuXKyeFw2B0JhRDzVAEAcAXlypXTuHHj9MILL9gdBYUY51QBAHAFWVlZatSokd0xUMhRqgAAuIKXX35Z8+bNszsGCjnOqQIA4AoyMzP10Ucf6YcfflCdOnVUtGhRp/UTJ060KRkKE86pAgDgCpo3b37JdQ6HQ8uXL7+FaVBYUaoAAAAswDlVAAAAFuCcKgAACtC+fXvNnj1bvr6+at++/WXHfvPNN7coFQozShUAAAXw8/MzJ/n08/OzOQ1cAedUAQAAWIBzqgAAACxAqQIAALAApQoAAMAClCoAAAALUKoAAAAsQKkCAOAyzp07p59//lk7d+7Mty4zM1OfffaZDalQGDGlAgAAl7B37161bNlSR44ckcPhUJMmTTR//nyVK1dOkpSSkqKgoCDl5OTYnBSFAXuqAAC4hDfeeEO1a9fW8ePHtWfPHpUoUUKNGzfWkSNH7I6GQog9VQAAXEJAQIB++OEHhYSESJIMw9A//vEPfffdd1qxYoV8fHzYUwUTe6oAALiEc+fOyd39/+7o5nA4NH36dD3xxBN66KGHtHfvXhvTobDh3n8AAFxC9erVtXHjRtWoUcNp+dSpUyVJTz75pB2xUEixpwoAgEt46qmn9MUXXxS4burUqerYsaM4iwZ5OKcKAADAAuypAgAAsAClCgAAwAKUKgAAAAtQqgAAACxAqQIAALAApQqAS3I4HJd9DB8+/KZneOmll+RwODRmzBin5YsWLZLD4bjpnw+gcKFUAXBJv//+u/mYPHmyfH19nZa9/vrrtySHl5eXxo4dq1OnTt2SzwNQeFGqALikwMBA8+Hn5yeHw6HAwECVKFFC9913n+Li4pzGL1q0SD4+Pjp9+rT++9//yuFwaP78+WrUqJG8vLxUu3Zt/fjjj06v2b59ux577DEVL15cAQEBeuGFF/THH384jQkPD1dgYKBGjx59yax//vmnOnbsqLvvvlvFihVTSEhIvgklH374YfXs2VN9+vRRyZIlFRAQoJkzZyojI0NdunRRiRIlVKVKFX3//ffXnBHArUGpAnBb8fHxUYcOHTRr1iyn5bNmzdIzzzyjEiVKmMsGDBig/v37a/PmzQoLC9MTTzyhP//8U5KUmpqqFi1a6IEHHtDGjRsVFxenlJQUPfvss07vW6RIEY0aNUrvv/++fv311wIzZWZmql69elqyZIm2b9+uHj166IUXXtD69eudxs2ZM0d33XWX1q9fr549e+q1117T3//+dzVq1EibNm1Sy5Yt9cILL+js2bPXlBHALWIAgIubNWuW4efnZz5ft26dUaRIEePYsWOGYRhGSkqK4e7ubqxcudIwDMM4dOiQIckYM2aM+ZoLFy4Y5cuXN8aOHWsYhmG8/fbbRsuWLZ0+5+jRo4YkY8+ePYZhGEbnzp2Ntm3bGoZhGA0bNjS6du1qGIZhLFy40LjSX69t2rQx+vfvbz5/6KGHjCZNmpjPs7OzDR8fH+OFF14wl/3++++GJGPt2rVXnRHArcOeKgC3nfr166tWrVqaM2eOJOnzzz9XxYoV1axZM6dxYWFh5s/u7u4KDQ3Vrl27JElbtmzRihUrVLx4cfNRvXp1SdKBAwfyfebYsWM1Z84c8/UXy8nJ0dtvv62QkBCVKlVKxYsX19KlS3XkyBGncXXq1DF/LlKkiEqXLq2QkBBzWUBAgCTp+PHj15URwM3lbncAALgZXn75ZU2bNk2DBg3SrFmz1KVLl2u6Iu/MmTN64oknNHbs2HzrypUrl29Zs2bNFBERocGDB+ull15yWjd+/HjFxMRo8uTJCgkJkY+Pj/r06aOsrCyncUWLFnV67nA4nJbl5c/Nzb2ujABuLkoVgNvS888/r4EDB2rKlCnauXOnOnfunG/ML7/8Yu69ys7OVmJioqKjoyVJDz74oP7973+rUqVKcne/ur8qx4wZo7p166patWpOy1evXq22bdvq+eefl/RXKdq7d69q1qx5I1/xujICuHk4/AfgtlSyZEm1b99eAwYMUMuWLVW+fPl8Y6ZNm6aFCxdq9+7dioqK0qlTp9S1a1dJUlRUlE6ePKmOHTtqw4YNOnDggJYuXaouXbooJyenwM8MCQlRZGSkpkyZ4rS8atWqio+P15o1a7Rr1y698sorSklJueHveD0ZAdw8lCoAt61u3bopKyvLLEr/a8yYMRozZozuv/9+/fzzz1q8eLHuuusuSVJQUJBWr16tnJwctWzZUiEhIerTp4/8/f3l5nbpvzpHjhxpHp7LM2TIED344IOKiIjQww8/rMDAQLVr1+6Gv9/1ZgRwczgMwzDsDgEAN8O//vUv9e3bV8eOHZOHh4e5/L///a+Cg4O1efNm1a1b176AAG4rHIQHcNs5e/asfv/9d40ZM0avvPKKU6ECgJuF/cMAbjvjxo1T9erVFRgYqMGDB9sdB8AdgsN/AAAAFmBPFQAAgAUoVQAAABagVAEAAFiAUgUAAGABShUAAIAFKFUAAAAWoFQBAABYgFIFAABgAUoVAACABf4fU1brK/QjfcwAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#type of laptop\n",
    "sns.barplot(x=df['TypeName'], y=df['Price'],palette='plasma')\n",
    "plt.xticks(rotation=\"vertical\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5bd9d23e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Inches'>"
      ]
     },
     "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": [
    "df['Inches'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "421fb9c7-c18b-46d4-a390-95502df55b31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Inches', 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": [
    "#price vary with laptop size in inches?\n",
    "sns.scatterplot(x=df['Inches'],y=df['Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2266e2ed-fe5f-4925-8031-66a670d96fb1",
   "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",
       "4K Ultra HD 3840x2160                              7\n",
       "Touchscreen 2560x1440                              7\n",
       "IPS Panel 1366x768                                 7\n",
       "IPS Panel Quad HD+ / Touchscreen 3200x1800         6\n",
       "IPS Panel Retina Display 2560x1600                 6\n",
       "IPS Panel Retina Display 2304x1440                 6\n",
       "Touchscreen 2256x1504                              6\n",
       "IPS Panel Touchscreen 2560x1440                    5\n",
       "IPS Panel Retina Display 2880x1800                 4\n",
       "IPS Panel Touchscreen 1920x1200                    4\n",
       "1440x900                                           4\n",
       "IPS Panel 2560x1440                                4\n",
       "IPS Panel Quad HD+ 2560x1440                       3\n",
       "Quad HD+ 3200x1800                                 3\n",
       "1920x1080                                          3\n",
       "Touchscreen 2400x1600                              3\n",
       "2560x1440                                          3\n",
       "IPS Panel Touchscreen 1366x768                     3\n",
       "IPS Panel Touchscreen / 4K Ultra HD 3840x2160      2\n",
       "IPS Panel Full HD 2160x1440                        2\n",
       "IPS Panel Quad HD+ 3200x1800                       2\n",
       "IPS Panel Retina Display 2736x1824                 1\n",
       "IPS Panel Full HD 1920x1200                        1\n",
       "IPS Panel Full HD 2560x1440                        1\n",
       "IPS Panel Full HD 1366x768                         1\n",
       "Touchscreen / Full HD 1920x1080                    1\n",
       "Touchscreen / Quad HD+ 3200x1800                   1\n",
       "Touchscreen / 4K Ultra HD 3840x2160                1\n",
       "IPS Panel Touchscreen 2400x1600                    1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Extract Touch screen information\n",
    "df['ScreenResolution'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3eaf5964-287c-460e-a9bf-cc0876029587",
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>HP</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>IPS Panel Full HD 1920x1080</td>\n",
       "      <td>Intel Core i7 8550U 1.8GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Nvidia GeForce GTX 940MX</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.83</td>\n",
       "      <td>37242.7200</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>454</th>\n",
       "      <td>Dell</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i7 8550U 1.8GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB SSD +  1TB HDD</td>\n",
       "      <td>AMD Radeon 530</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.36</td>\n",
       "      <td>52161.1200</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>Dell</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i7 8550U 1.8GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB SSD +  2TB HDD</td>\n",
       "      <td>AMD Radeon 530</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.02</td>\n",
       "      <td>51729.5520</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1115</th>\n",
       "      <td>Dell</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i7 7560U 2.4GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 640</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.23</td>\n",
       "      <td>73473.1200</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1251</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>AMD A6-Series 9220 2.5GHz</td>\n",
       "      <td>4</td>\n",
       "      <td>500GB HDD</td>\n",
       "      <td>AMD Radeon R4 Graphics</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.86</td>\n",
       "      <td>21258.7200</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>Dell</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>Intel Core i3 6006U 2GHz</td>\n",
       "      <td>4</td>\n",
       "      <td>500GB HDD</td>\n",
       "      <td>Intel HD Graphics 520</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.65</td>\n",
       "      <td>45234.7200</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>17.3</td>\n",
       "      <td>1600x900</td>\n",
       "      <td>AMD A4-Series 7210 2.2GHz</td>\n",
       "      <td>4</td>\n",
       "      <td>500GB HDD</td>\n",
       "      <td>AMD Radeon R3</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.80</td>\n",
       "      <td>20193.1200</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1154</th>\n",
       "      <td>Dell</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>IPS Panel Touchscreen / 4K Ultra HD 3840x2160</td>\n",
       "      <td>Intel Core i5 6300HQ 2.3GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Nvidia GeForce 960M</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.04</td>\n",
       "      <td>119916.2304</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>874</th>\n",
       "      <td>Samsung</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>15.0</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i7 7500U 2.7GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Nvidia GeForce 940MX</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.23</td>\n",
       "      <td>98514.7200</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>575</th>\n",
       "      <td>Mediacom</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Atom x5-Z8350 1.44GHz</td>\n",
       "      <td>4</td>\n",
       "      <td>32GB SSD</td>\n",
       "      <td>Intel HD Graphics</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.40</td>\n",
       "      <td>13266.7200</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Company   TypeName  Inches  \\\n",
       "86          HP  Ultrabook    15.6   \n",
       "454       Dell   Notebook    15.6   \n",
       "355       Dell   Notebook    15.6   \n",
       "1115      Dell  Ultrabook    13.3   \n",
       "1251        HP   Notebook    15.6   \n",
       "764       Dell   Notebook    13.3   \n",
       "889     Lenovo   Notebook    17.3   \n",
       "1154      Dell   Notebook    15.6   \n",
       "874    Samsung  Ultrabook    15.0   \n",
       "575   Mediacom   Notebook    14.0   \n",
       "\n",
       "                                   ScreenResolution  \\\n",
       "86                      IPS Panel Full HD 1920x1080   \n",
       "454                               Full HD 1920x1080   \n",
       "355                               Full HD 1920x1080   \n",
       "1115                              Full HD 1920x1080   \n",
       "1251                                       1366x768   \n",
       "764                                        1366x768   \n",
       "889                                        1600x900   \n",
       "1154  IPS Panel Touchscreen / 4K Ultra HD 3840x2160   \n",
       "874                               Full HD 1920x1080   \n",
       "575                               Full HD 1920x1080   \n",
       "\n",
       "                              Cpu  Ram                Memory  \\\n",
       "86     Intel Core i7 8550U 1.8GHz    8             256GB SSD   \n",
       "454    Intel Core i7 8550U 1.8GHz    8  128GB SSD +  1TB HDD   \n",
       "355    Intel Core i7 8550U 1.8GHz    8  128GB SSD +  2TB HDD   \n",
       "1115   Intel Core i7 7560U 2.4GHz    8             256GB SSD   \n",
       "1251    AMD A6-Series 9220 2.5GHz    4             500GB HDD   \n",
       "764      Intel Core i3 6006U 2GHz    4             500GB HDD   \n",
       "889     AMD A4-Series 7210 2.2GHz    4             500GB HDD   \n",
       "1154  Intel Core i5 6300HQ 2.3GHz    8             256GB SSD   \n",
       "874    Intel Core i7 7500U 2.7GHz   16             256GB SSD   \n",
       "575   Intel Atom x5-Z8350 1.44GHz    4              32GB SSD   \n",
       "\n",
       "                               Gpu       OpSys  Weight        Price  \\\n",
       "86        Nvidia GeForce GTX 940MX  Windows 10    1.83   37242.7200   \n",
       "454                 AMD Radeon 530  Windows 10    2.36   52161.1200   \n",
       "355                 AMD Radeon 530  Windows 10    2.02   51729.5520   \n",
       "1115  Intel Iris Plus Graphics 640  Windows 10    1.23   73473.1200   \n",
       "1251        AMD Radeon R4 Graphics  Windows 10    1.86   21258.7200   \n",
       "764          Intel HD Graphics 520  Windows 10    1.65   45234.7200   \n",
       "889                  AMD Radeon R3  Windows 10    2.80   20193.1200   \n",
       "1154           Nvidia GeForce 960M  Windows 10    2.04  119916.2304   \n",
       "874           Nvidia GeForce 940MX  Windows 10    1.23   98514.7200   \n",
       "575              Intel HD Graphics  Windows 10    1.40   13266.7200   \n",
       "\n",
       "     Touchscreen  \n",
       "86            No  \n",
       "454           No  \n",
       "355           No  \n",
       "1115          No  \n",
       "1251          No  \n",
       "764           No  \n",
       "889           No  \n",
       "1154         Yes  \n",
       "874           No  \n",
       "575           No  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Touchscreen'] = df['ScreenResolution'].apply(lambda element:'Yes' if 'Touchscreen' in element else 'No')\n",
    "df.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "de0858d1-d3cd-4648-acc1-f97a02f49bd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Touchscreen'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hQ4bYTz3t3r1beXl5Gjly5EG9AwkAABzewh4wp556qpYvX67Jkydr2rRpSk5O1ty5c5WdnW2vufXWW9XQ0KAxY8aovr5eAwYMUHFxsdq2bWuvWbZsmfLy8jRo0CDFxMQoKytLRUVF4R4XAAAYKOyfAxMt+BwYHCn4HBgAh5OIfQ4MAABASyNgAACAcQgYAABgHAIGAAAYh4ABAADGIWAAAIBxCBgAAGAcAgYAABiHgAEAAMYhYAAAgHEIGAAAYBwCBgAAGIeAAQAAxiFgAACAcQgYAABgHAIGAAAYh4ABAADGIWAAAIBxCBgAAGAcAgYAABiHgAEAAMYhYAAAgHEIGAAAYBwCBgAAGIeAAQAAxiFgAACAcQgYAABgHAIGAAAYh4ABAADGIWAAAIBxCBgAAGAcAgYAABiHgAEAAMYhYAAAgHEIGAAAYBwCBgAAGIeAAQAAxiFgAACAcQgYAABgHAIGAAAYh4ABAADGIWAAAIBxCBgAAGAcAgYAABiHgAEAAMYhYAAAgHEIGAAAYBwCBgAAGIeAAQAAxiFgAACAcQgYAABgHAIGAAAYp8UD5p577pHD4dC4cePsbTt37lRubq46d+6sDh06KCsrSzU1NSG327Ztm4YNG6Z27dopMTFRt9xyi/bs2dPS4wIAAAO0aMCsX79eDz/8sHr16hWyffz48XrhhRf0zDPPaPXq1dq+fbsuuugie39TU5OGDRumXbt26c0339SSJUu0ePFi5efnt+S4AADAEC0WMDt27FB2drYWLVqkjh072tsDgYAeeeQRzZ49WwMHDlTfvn316KOP6s0339Rbb70lSVq1apU+/PBDPfbYY+rTp4/OO+88TZ8+XfPmzdOuXbtaamQAAGCIFguY3NxcDRs2TBkZGSHbN2zYoN27d4ds79Gjh4499liVlZVJksrKypSamiqPx2OvyczMVDAYVGVlZUuNDAAADBHbEgd98skntXHjRq1fv36ffX6/X23atFFCQkLIdo/HI7/fb6/5Ybzs3b933/40NjaqsbHRvh4MBn/LQwAAAFEs7Gdgqqur9Ze//EXLli1T27Ztw334n1RQUCC3221fkpKSDtl9AwCAQyvsAbNhwwbV1tbqlFNOUWxsrGJjY7V69WoVFRUpNjZWHo9Hu3btUn19fcjtampq5PV6JUler3efdyXtvb53zY9NnjxZgUDAvlRXV4f7oQEAgCgR9oAZNGiQ3n//fVVUVNiXfv36KTs72/5z69atVVpaat+mqqpK27ZtU3p6uiQpPT1d77//vmpra+01JSUlcrlcSklJ2e/9Op1OuVyukAsAADg8hf01MPHx8TrppJNCtrVv316dO3e2t+fk5GjChAnq1KmTXC6XbrzxRqWnp+v000+XJA0ePFgpKSm66qqrVFhYKL/fr9tvv125ublyOp3hHhkAABimRV7EeyBz5sxRTEyMsrKy1NjYqMzMTD300EP2/latWmnlypW64YYblJ6ervbt22vUqFGaNm1aJMYFAABRxmFZlhXpIVpCMBiU2+1WIBA44p5OOm7Si5EeAYfQZ/cMi/QIABA2B/v7m+9CAgAAxiFgAACAcQgYAABgHAIGAAAYh4ABAADGIWAAAIBxCBgAAGAcAgYAABiHgAEAAMYhYAAAgHEIGAAAYBwCBgAAGIeAAQAAxiFgAACAcQgYAABgHAIGAAAYh4ABAADGIWAAAIBxCBgAAGAcAgYAABiHgAEAAMYhYAAAgHEIGAAAYBwCBgAAGIeAAQAAxiFgAACAcQgYAABgHAIGAAAYh4ABAADGIWAAAIBxCBgAAGAcAgYAABiHgAEAAMYhYAAAgHEIGAAAYBwCBgAAGIeAAQAAxiFgAACAcQgYAABgHAIGAAAYh4ABAADGIWAAAIBxCBgAAGAcAgYAABiHgAEAAMYhYAAAgHEIGAAAYBwCBgAAGIeAAQAAxiFgAACAcQgYAABgHAIGAAAYh4ABAADGCXvAFBQU6NRTT1V8fLwSExM1YsQIVVVVhazZuXOncnNz1blzZ3Xo0EFZWVmqqakJWbNt2zYNGzZM7dq1U2Jiom655Rbt2bMn3OMCAAADhT1gVq9erdzcXL311lsqKSnR7t27NXjwYDU0NNhrxo8frxdeeEHPPPOMVq9ere3bt+uiiy6y9zc1NWnYsGHatWuX3nzzTS1ZskSLFy9Wfn5+uMcFAAAGcliWZbXkHXz55ZdKTEzU6tWrddZZZykQCOjoo4/W448/rosvvliS9NFHH6lnz54qKyvT6aefrpdeeknnn3++tm/fLo/HI0lasGCBbrvtNn355Zdq06bNAe83GAzK7XYrEAjI5XK15EOMOsdNejHSI+AQ+uyeYZEeAQDC5mB/f7f4a2ACgYAkqVOnTpKkDRs2aPfu3crIyLDX9OjRQ8cee6zKysokSWVlZUpNTbXjRZIyMzMVDAZVWVm53/tpbGxUMBgMuQAAgMNTiwZMc3Ozxo0bp/79++ukk06SJPn9frVp00YJCQkhaz0ej/x+v73mh/Gyd//efftTUFAgt9ttX5KSksL8aAAAQLRo0YDJzc3VBx98oCeffLIl70aSNHnyZAUCAftSXV3d4vcJAAAiI7alDpyXl6eVK1dqzZo1OuaYY+ztXq9Xu3btUn19fchZmJqaGnm9XnvNunXrQo63911Ke9f8mNPplNPpDPOjAAAA0SjsAWNZlm688UYtX75cr7/+upKTk0P29+3bV61bt1ZpaamysrIkSVVVVdq2bZvS09MlSenp6ZoxY4Zqa2uVmJgoSSopKZHL5VJKSkq4RwYAY/Ai/SMLL9L/aWEPmNzcXD3++ON67rnnFB8fb79mxe12Ky4uTm63Wzk5OZowYYI6deokl8ulG2+8Uenp6Tr99NMlSYMHD1ZKSoquuuoqFRYWyu/36/bbb1dubi5nWQAAQPgDZv78+ZKkc845J2T7o48+qj//+c+SpDlz5igmJkZZWVlqbGxUZmamHnroIXttq1attHLlSt1www1KT09X+/btNWrUKE2bNi3c4wIAAAO1yFNIB9K2bVvNmzdP8+bN+8k1Xbt21b/+9a9wjgYAAA4TfBcSAAAwDgEDAACMQ8AAAADjEDAAAMA4BAwAADAOAQMAAIxDwAAAAOMQMAAAwDgEDAAAMA4BAwAAjEPAAAAA4xAwAADAOAQMAAAwDgEDAACMQ8AAAADjEDAAAMA4BAwAADAOAQMAAIxDwAAAAOMQMAAAwDgEDAAAMA4BAwAAjEPAAAAA4xAwAADAOAQMAAAwDgEDAACMQ8AAAADjEDAAAMA4BAwAADAOAQMAAIxDwAAAAOMQMAAAwDgEDAAAMA4BAwAAjEPAAAAA4xAwAADAOAQMAAAwDgEDAACMQ8AAAADjEDAAAMA4BAwAADAOAQMAAIxDwAAAAOMQMAAAwDgEDAAAMA4BAwAAjEPAAAAA4xAwAADAOAQMAAAwDgEDAACMQ8AAAADjEDAAAMA4UR0w8+bN03HHHae2bdsqLS1N69ati/RIAAAgCkRtwDz11FOaMGGC7rjjDm3cuFG9e/dWZmamamtrIz0aAACIsKgNmNmzZ2v06NG65pprlJKSogULFqhdu3b629/+FunRAABAhEVlwOzatUsbNmxQRkaGvS0mJkYZGRkqKyuL4GQAACAaxEZ6gP356quv1NTUJI/HE7Ld4/Hoo48+2u9tGhsb1djYaF8PBAKSpGAw2HKDRqnmxm8jPQIOoSPxv/EjGT/fR5Yj8ed772O2LOtn10VlwPwaBQUFuvPOO/fZnpSUFIFpgEPHPTfSEwBoKUfyz/c333wjt9v9k/ujMmCOOuootWrVSjU1NSHba2pq5PV693ubyZMna8KECfb15uZm1dXVqXPnznI4HC06LyIvGAwqKSlJ1dXVcrlckR4HQBjx831ksSxL33zzjXw+38+ui8qAadOmjfr27avS0lKNGDFC0vdBUlpaqry8vP3exul0yul0hmxLSEho4UkRbVwuF/+DAw5T/HwfOX7uzMteURkwkjRhwgSNGjVK/fr102mnnaa5c+eqoaFB11xzTaRHAwAAERa1AXPZZZfpyy+/VH5+vvx+v/r06aPi4uJ9XtgLAACOPFEbMJKUl5f3k08ZAT/kdDp1xx137PM0IgDz8fON/XFYB3qfEgAAQJSJyg+yAwAA+DkEDAAAMA4BAwAAjEPAAACiynfffadvv/3/X5mwdetWzZ07V6tWrYrgVIg2BAwAIKpceOGFWrp0qSSpvr5eaWlpmjVrli688ELNnz8/wtMhWhAwOCxYlnXAL/4CYIaNGzfqzDPPlCQ9++yz8ng82rp1q5YuXaqioqIIT4doQcDAaEuXLlVqaqri4uIUFxenXr166e9//3ukxwLwG3z77beKj4+XJK1atUoXXXSRYmJidPrpp2vr1q0Rng7RgoCBsWbPnq0bbrhBQ4cO1dNPP62nn35aQ4YM0fXXX685c+ZEejwAv1K3bt20YsUKVVdX6+WXX9bgwYMlSbW1tXwXEmx8kB2MlZycrDvvvFNXX311yPYlS5Zo6tSp2rJlS4QmA/BbPPvss7riiivU1NSkgQMHqqSkRJJUUFCgNWvW6KWXXorwhIgGBAyM1bZtW33wwQfq1q1byPbNmzcrNTVVO3fujNBkAH4rv9+vL774Qr1791ZMzPdPFqxbt04ul0s9evSI8HSIBjyFBGN169ZNTz/99D7bn3rqKXXv3j0CEwEIF6/Xq/j4eJWUlOi7776TJJ166qnEC2xR/WWOwM+58847ddlll2nNmjXq37+/JGnt2rUqLS3db9gAMMP//vc/XXrppXrttdfkcDi0efNmHX/88crJyVHHjh01a9asSI+IKMAZGBgrKytL5eXl6ty5s1asWKEVK1boqKOO0rp16/SnP/0p0uMB+JXGjx+v1q1ba9u2bWrXrp29/bLLLlNxcXEEJ0M04TUwAICo4vV69fLLL6t3796Kj4/Xu+++q+OPP16ffvqpevXqpR07dkR6REQBnkKCcWJiYuRwOH52jcPh0J49ew7RRADCqaGhIeTMy151dXVyOp0RmAjRiICBcZYvX/6T+8rKylRUVKTm5uZDOBGAcNi+fbt8Pp/OPPNMLV26VNOnT5f0/T9ImpubVVhYqHPPPTfCUyJa8BQSDgtVVVWaNGmSXnjhBWVnZ2vatGnq2rVrpMcC8At07NhR8+bNU+/evTVw4ECdcsopevXVV3XBBReosrJSdXV1Wrt2rU444YRIj4oowIt4YbTt27dr9OjRSk1N1Z49e1RRUaElS5YQL4CBZsyYoeuuu05Tp07Vhx9+qAEDBujCCy9UQ0ODLrroIr3zzjvEC2ycgYGRAoGA7r77bj3wwAPq06ePZs6caX/5GwBzbdmyRTk5Ofrwww+1cOFCXXDBBZEeCVGKgIFxCgsLNXPmTHm9Xt1999268MILIz0SgDB78MEHNX78ePXs2VOxsaEv19y4cWOEpkI0IWBgnJiYGMXFxSkjI0OtWrX6yXX//Oc/D+FUAMJl69atuuaaa/TBBx/ouuuu2ydg7rjjjghNhmjCu5BgnKuvvvqAb6MGYKZFixZp4sSJysjIUGVlpY4++uhIj4QoxRkYAEBUGDJkiNatW6e5c+fu8y3zwI9xBgYAEBWampr03nvv6Zhjjon0KDAAZ2AAAIBx+BwYAABgHAIGAAAYh4ABAADGIWAAAIBxCBgAAGAcAgZA1DjnnHM0bty4SI8BwAAEDACbw+H42cvUqVMjPSIASOKD7AD8wBdffGH/+amnnlJ+fr6qqqrsbR06dIjEWC3Csiw1NTXt8z07AMzAGRgANq/Xa1/cbrccDod9PTExUbNnz9Yxxxwjp9OpPn36qLi42L7t66+/LofDofr6entbRUWFHA6HPvvsM3vb2rVrdc4556hdu3bq2LGjMjMz9fXXX9v7m5ubdeutt6pTp07yer0hZ30sy9LUqVN17LHHyul0yufz6aabbrL3NzY26rbbblNSUpKcTqe6deumRx55JGS+l156SX379pXT6dQbb7yh5uZmFRQUKDk5WXFxcerdu7eeffbZkL+XDz74QOedd546dOggj8ejq666Sl999ZW9/5xzztFNN930k3MDCD8CBsBBuf/++zVr1izdd999eu+995SZmakLLrhAmzdvPuhjVFRUaNCgQUpJSVFZWZneeOMNDR8+XE1NTfaaJUuWqH379iovL1dhYaGmTZumkpISSdI//vEPzZkzRw8//LA2b96sFStWKDU11b7t1VdfrSeeeEJFRUXatGmTHn744X3OGk2aNEn33HOPNm3apF69eqmgoEBLly7VggULVFlZqfHjx+vKK6/U6tWrJUn19fUaOHCgTj75ZL399tsqLi5WTU2NLr300pDj/tzcAFqABQD78eijj1put9u+7vP5rBkzZoSsOfXUU62xY8dalmVZr732miXJ+vrrr+3977zzjiXJ2rJli2VZlnX55Zdb/fv3/8n7PPvss60BAwbscx+33XabZVmWNWvWLOvEE0+0du3atc9tq6qqLElWSUnJfo+9d74VK1bY23bu3Gm1a9fOevPNN0PW5uTkWJdffrllWZY1ffp0a/DgwSH7q6urLUlWVVXVQc0NIPw4AwPggILBoLZv367+/fuHbO/fv782bdp00MfZewbm5/Tq1SvkepcuXVRbWytJuuSSS/Tdd9/p+OOP1+jRo7V8+XLt2bPHPnarVq109tln/+zx+/XrZ//5k08+0bfffqs//vGP6tChg31ZunSp/vOf/0iS3n33Xb322msh+3v06CFJ9poDzQ0g/Hj1GoCwiIn5/t9D1g++H3b37t0ha+Li4g54nNatW4dcdzgcam5uliQlJSWpqqpKr7zyikpKSjR27Fjde++9Wr169UEdW5Lat29v/3nHjh2SpBdffFG/+93vQtY5nU57zfDhwzVz5sx9jtWlS5eDmhtA+HEGBsABuVwu+Xw+rV27NmT72rVrlZKSIkk6+uijJYW+k6mioiJkfa9evVRaWvqbZomLi9Pw4cNVVFSk119/XWVlZXr//feVmpqq5uZm+7UrByMlJUVOp1Pbtm1Tt27dQi5JSUmSpFNOOUWVlZU67rjj9lnzwxgCcGhxBgbAQbnlllt0xx136IQTTlCfPn306KOPqqKiQsuWLZMk+5f+1KlTNWPGDH388ceaNWtWyDEmT56s1NRUjR07Vtdff73atGmj1157TZdccomOOuqoA86wePFiNTU1KS0tTe3atdNjjz2muLg4de3aVZ07d9aoUaN07bXXqqioSL1799bWrVtVW1u7zwtu94qPj9fNN9+s8ePHq7m5WQMGDFAgENDatWvlcrk0atQo5ebmatGiRbr88svtdxl98sknevLJJ/XXv/5VrVq1+u1/uQB+Mc7AADgoN910kyZMmKCJEycqNTVVxcXFev7559W9e3dJ3z+F8sQTT+ijjz5Sr169NHPmTN11110hxzjxxBO1atUqvfvuuzrttNOUnp6u55577qA/iyUhIUGLFi1S//791atXL73yyit64YUX1LlzZ0nS/PnzdfHFF2vs2LHq0aOHRo8erYaGhp895vTp0zVlyhQVFBSoZ8+eGjJkiF588UUlJydLkn3mqampSYMHD1ZqaqrGjRunhIQE+2kzAIeew/rhE9YAAAAG4J8PAADAOAQMAAAwDgEDAACMQ8AAAADjEDAAAMA4BAwAADAOAQMAAIxDwAAAAOMQMAAAwDgEDAAAMA4BAwAAjEPAAAAA4/w/pjpRxKJDmGMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Countplot for Touchscreen laptops\n",
    "df['Touchscreen'].value_counts().plot(kind='bar')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "dd24c651-8034-45a2-b3e5-74a861ade540",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Touchscreen', ylabel='Price'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QINXU1Gj16tXG/vv06aPY2FhlZ2f/5GdxuVyy2Wyqrq6W1Wr9WccFwMVp3/SHvd0C0Oxc8eTic7r+6fz+9uqZprffflu9e/fWPffco+DgYF177bV6+eWXjfn9+/fL6XQqISHBGLPZbIqPj1dRUZEkqaioSEFBQUZgkqSEhAT5+vpqy5YtRs1NN91kBCZJcjgcKi0t1TfffGPUnLifxprG/fxQbW2tXC6XxwYAAC5cXg1N//rXv7Ro0SJdccUVys/P16hRo/Too49q6dKlkiSn0ylJCgkJ8XhfSEiIMed0OhUcHOwx36pVK7Vv396j5mRrnLiPH6tpnP+hrKws2Ww2YwsPDz/tzw8AAFoOr4amhoYG9erVS88884yuvfZaPfTQQxoxYoSpr8O8LSMjQ9XV1cZ24MABb7cEAADOoVbe3HmnTp0UHR3tMRYVFaU333xTkhQaGipJKi8vV6dOnYya8vJyxcbGGjUVFRUeaxw/flyHDx823h8aGqry8nKPmsbXP1XTOP9DFotFFovF9GfFheOxxx5TZWWlJKljx46aO3eulzsCAJwPXj3T1LdvX5WWlnqMffbZZ+rataskKSIiQqGhoSosLDTmXS6XtmzZIrvdLkmy2+2qqqpScXGxUbNu3To1NDQoPj7eqNm4caOOHTtm1BQUFOiqq64y7tSz2+0e+2msadwP0KiyslLl5eUqLy83whMA4MLn1dA0duxYffjhh3rmmWf0+eefa9myZXrppZeUmpoqSfLx8VFaWpqeeuopvf3229q5c6fuv/9+hYWFacCAAZK+PzN12223acSIEdq6das2bdqk0aNHa/DgwQoLC5Mk3XvvvfL391dKSop2796t5cuXa+7cuUpPTzd6eeyxx5SXl6dZs2Zp7969mjp1qrZv367Ro0ef9+MCAACaH69+PXfdddfprbfeUkZGhqZNm6aIiAi98MILGjp0qFEzYcIE1dTU6KGHHlJVVZVuuOEG5eXlKSAgwKh5/fXXNXr0aPXr10++vr4aOHCg5s2bZ8zbbDatXbtWqampiouL02WXXabMzEyPZzn96le/0rJlyzR58mT96U9/0hVXXKFVq1bpmmuuOT8HAwAANGtefU7ThYTnNF087r33XuP6t5CQEC1btszLHeFCwXOagKZ4ThMAAEALQ2gCAAAwgdAEAABgAqEJAADABEITAACACYQmAAAAE7z6nCacvuejZnu7hYueK9hl/Mlx/Y+L/ybNwLg96T9dBAA/E2eaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEzgieDAaQqoDzjpzwCACxuhCThNff7zK2+3AADwAr6eAwAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmeDU0TZ06VT4+Ph5bZGSkMX/06FGlpqaqQ4cOateunQYOHKjy8nKPNcrKypSUlKQ2bdooODhY48eP1/Hjxz1q1q9fr169eslisah79+7Kyclp0suCBQvUrVs3BQQEKD4+Xlu3bj0nnxkAALRMXj/TdPXVV+vrr782tg8++MCYGzt2rN555x2tXLlSGzZs0MGDB3XXXXcZ8/X19UpKSlJdXZ02b96spUuXKicnR5mZmUbN/v37lZSUpFtuuUUlJSVKS0vTgw8+qPz8fKNm+fLlSk9P15QpU/TRRx+pZ8+ecjgcqqioOD8HAQAANHteD02tWrVSaGiosV122WWSpOrqar366quaPXu2br31VsXFxWnJkiXavHmzPvzwQ0nS2rVr9emnn+q1115TbGys+vfvr+nTp2vBggWqq6uTJGVnZysiIkKzZs1SVFSURo8erbvvvltz5swxepg9e7ZGjBihYcOGKTo6WtnZ2WrTpo3+/Oc//2jftbW1crlcHhsAALhweT007du3T2FhYfrlL3+poUOHqqysTJJUXFysY8eOKSEhwaiNjIxUly5dVFRUJEkqKipSTEyMQkJCjBqHwyGXy6Xdu3cbNSeu0VjTuEZdXZ2Ki4s9anx9fZWQkGDUnExWVpZsNpuxhYeH/8wjAQAAmjOvhqb4+Hjl5OQoLy9PixYt0v79+3XjjTfq22+/ldPplL+/v4KCgjzeExISIqfTKUlyOp0egalxvnHuVDUul0vfffedDh06pPr6+pPWNK5xMhkZGaqurja2AwcOnNExAAAALUMrb+68f//+xs89evRQfHy8unbtqhUrVigwMNCLnf00i8Uii8Xi7TYAAMB54vWv504UFBSkK6+8Up9//rlCQ0NVV1enqqoqj5ry8nKFhoZKkkJDQ5vcTdf4+qdqrFarAgMDddlll8nPz++kNY1rAAAANKvQdOTIEX3xxRfq1KmT4uLi1Lp1axUWFhrzpaWlKisrk91ulyTZ7Xbt3LnT4y63goICWa1WRUdHGzUnrtFY07iGv7+/4uLiPGoaGhpUWFho1AAAAHg1NI0bN04bNmzQl19+qc2bN+v3v/+9/Pz8NGTIENlsNqWkpCg9PV3vv/++iouLNWzYMNntdvXp00eSlJiYqOjoaN13333asWOH8vPzNXnyZKWmphpfnY0cOVL/+te/NGHCBO3du1cLFy7UihUrNHbsWKOP9PR0vfzyy1q6dKn27NmjUaNGqaamRsOGDfPKcQEAAM2PV69p+uqrrzRkyBD95z//UceOHXXDDTfoww8/VMeOHSVJc+bMka+vrwYOHKja2lo5HA4tXLjQeL+fn59Wr16tUaNGyW63q23btkpOTta0adOMmoiICOXm5mrs2LGaO3euOnfurFdeeUUOh8OoGTRokCorK5WZmSmn06nY2Fjl5eU1uTgcAABcvHzcbrfb201cCFwul2w2m6qrq2W1Ws/Zfp6Pmn3O1gZaqnF70r3dwlmxb/rD3m4BaHaueHLxOV3/dH5/N6trmgAAAJorQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACY8LNCU11dnUpLS3X8+PGz1Q8AAECzdEah6b///a9SUlLUpk0bXX311SorK5MkjRkzRs8+++xZbRAAAKA5OKPQlJGRoR07dmj9+vUKCAgwxhMSErR8+fKz1hwAAEBzcUb/jMqqVau0fPly9enTRz4+Psb41VdfrS+++OKsNQcAANBcnNGZpsrKSgUHBzcZr6mp8QhRAAAAF4ozCk29e/dWbm6u8boxKL3yyiuy2+1npzMAAIBm5Iy+nnvmmWfUv39/ffrppzp+/Ljmzp2rTz/9VJs3b9aGDRvOdo8AAABed0Znmm644QaVlJTo+PHjiomJ0dq1axUcHKyioiLFxcWd7R4BAAC87ozONEnS5Zdfrpdffvls9gIAANBsndGZpnfffVf5+flNxvPz87VmzZqf3RQAAEBzc0ahadKkSaqvr28y7na7NWnSpJ/dFAAAQHNzRqFp3759io6ObjIeGRmpzz///Gc3BQAA0NycUWiy2Wz617/+1WT8888/V9u2bX92UwAAAM3NGYWm3/3ud0pLS/N4+vfnn3+uxx9/XL/97W/PWnMAAADNxRmFppkzZ6pt27aKjIxURESEIiIiFBUVpQ4dOuj5558/2z0CAAB43Rk9csBms2nz5s0qKCjQjh07FBgYqB49euimm2462/0BAAA0C2f8nCYfHx8lJiYqMTHxbPYDAADQLJkOTfPmzdNDDz2kgIAAzZs375S1jz766M9uDAAAoDkxHZrmzJmjoUOHKiAgQHPmzPnROh8fH0ITAAC44JgOTfv37z/pzwAAABeD07577tixY7r88su1Z8+ec9EPAABAs3Taoal169Y6evTouegFAACg2Tqj5zSlpqZqxowZOn78+NnuBwAAoFk6o0cObNu2TYWFhVq7dq1iYmKa/NMpf//7389KcwAAAM3FGYWmoKAgDRw48Gz3AgAA0Gyd1tdzDQ0NmjFjhj777DPt2rVLwcHBWrhwoZYsWeKxnYlnn31WPj4+SktLM8aOHj2q1NRUdejQQe3atdPAgQNVXl7u8b6ysjIlJSWpTZs2Cg4O1vjx45t8bbh+/Xr16tVLFotF3bt3V05OTpP9L1iwQN26dVNAQIDi4+O1devWM/ocAADgwnRaoenpp5/Wn/70J7Vr106/+MUvNG/ePKWmpv7sJrZt26bFixerR48eHuNjx47VO++8o5UrV2rDhg06ePCg7rrrLmO+vr5eSUlJqqur0+bNm7V06VLl5OQoMzPTqNm/f7+SkpJ0yy23qKSkRGlpaXrwwQeVn59v1Cxfvlzp6emaMmWKPvroI/Xs2VMOh0MVFRU/+7MBAIALw2mFpr/85S9auHCh8vPztWrVKr3zzjt6/fXX1dDQcMYNHDlyREOHDtXLL7+sSy+91Bivrq7Wq6++qtmzZ+vWW29VXFyclixZos2bN+vDDz+UJK1du1affvqpXnvtNcXGxqp///6aPn26FixYoLq6OklSdna2IiIiNGvWLEVFRWn06NG6++67PR7QOXv2bI0YMULDhg1TdHS0srOz1aZNG/35z3/+0b5ra2vlcrk8NgAAcOE6rdBUVlam22+/3XidkJAgHx8fHTx48IwbSE1NVVJSkhISEjzGi4uLdezYMY/xyMhIdenSRUVFRZKkoqIixcTEKCQkxKhxOBxyuVzavXu3UfPDtR0Oh7FGXV2diouLPWp8fX2VkJBg1JxMVlaWbDabsYWHh5/hEQAAAC3BaYWm48ePKyAgwGOsdevWOnbs2Bnt/I033tBHH32krKysJnNOp1P+/v4KCgryGA8JCZHT6TRqTgxMjfONc6eqcblc+u6773To0CHV19eftKZxjZPJyMhQdXW1sR04cMDchwYAAC3Sad0953a79cADD8hisRhjR48e1ciRIz0eO2DmkQMHDhzQY489poKCgiZBrCWwWCwexwEAAFzYTis0JScnNxn74x//eEY7Li4uVkVFhXr16mWM1dfXa+PGjZo/f77y8/NVV1enqqoqj7NN5eXlCg0NlSSFhoY2ucut8e66E2t+eMddeXm5rFarAgMD5efnJz8/v5PWNK4BAABwWqHpTB8ncDL9+vXTzp07PcaGDRumyMhITZw4UeHh4WrdurUKCwuNZ0KVlpaqrKxMdrtdkmS32/X000+roqJCwcHBkqSCggJZrVZFR0cbNe+++67HfgoKCow1/P39FRcXp8LCQg0YMEDS949WKCws1OjRo8/a5wUAAC3bGT3c8my45JJLdM0113iMtW3bVh06dDDGU1JSlJ6ervbt28tqtWrMmDGy2+3q06ePJCkxMVHR0dG67777NHPmTDmdTk2ePFmpqanGV2cjR47U/PnzNWHCBA0fPlzr1q3TihUrlJuba+w3PT1dycnJ6t27t66//nq98MILqqmp0bBhw87T0QAAAM2d10KTGXPmzJGvr68GDhyo2tpaORwOLVy40Jj38/PT6tWrNWrUKNntdrVt21bJycmaNm2aURMREaHc3FyNHTtWc+fOVefOnfXKK6/I4XAYNYMGDVJlZaUyMzPldDoVGxurvLy8JheHAwCAi5eP2+12e7uJC4HL5ZLNZlN1dbWsVus528/zUbPP2dpASzVuT7q3Wzgr9k1/2NstAM3OFU8uPqfrn87v79N65AAAAMDFitAEAABgAqEJAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATPBqaFq0aJF69Oghq9Uqq9Uqu92uNWvWGPNHjx5VamqqOnTooHbt2mngwIEqLy/3WKOsrExJSUlq06aNgoODNX78eB0/ftyjZv369erVq5csFou6d++unJycJr0sWLBA3bp1U0BAgOLj47V169Zz8pkBAEDL5NXQ1LlzZz377LMqLi7W9u3bdeutt+p3v/uddu/eLUkaO3as3nnnHa1cuVIbNmzQwYMHdddddxnvr6+vV1JSkurq6rR582YtXbpUOTk5yszMNGr279+vpKQk3XLLLSopKVFaWpoefPBB5efnGzXLly9Xenq6pkyZoo8++kg9e/aUw+FQRUXF+TsYAACgWfNxu91ubzdxovbt2+u5557T3XffrY4dO2rZsmW6++67JUl79+5VVFSUioqK1KdPH61Zs0Z33HGHDh48qJCQEElSdna2Jk6cqMrKSvn7+2vixInKzc3Vrl27jH0MHjxYVVVVysvLkyTFx8fruuuu0/z58yVJDQ0NCg8P15gxYzRp0iRTfbtcLtlsNlVXV8tqtZ7NQ+Lh+ajZ52xtoKUatyfd2y2cFfumP+ztFoBm54onF5/T9U/n93ezuaapvr5eb7zxhmpqamS321VcXKxjx44pISHBqImMjFSXLl1UVFQkSSoqKlJMTIwRmCTJ4XDI5XIZZ6uKioo81misaVyjrq5OxcXFHjW+vr5KSEgwak6mtrZWLpfLYwMAABcur4emnTt3ql27drJYLBo5cqTeeustRUdHy+l0yt/fX0FBQR71ISEhcjqdkiSn0+kRmBrnG+dOVeNyufTdd9/p0KFDqq+vP2lN4xonk5WVJZvNZmzh4eFn9PkBAEDL4PXQdNVVV6mkpERbtmzRqFGjlJycrE8//dTbbf2kjIwMVVdXG9uBAwe83RIAADiHWnm7AX9/f3Xv3l2SFBcXp23btmnu3LkaNGiQ6urqVFVV5XG2qby8XKGhoZKk0NDQJne5Nd5dd2LND++4Ky8vl9VqVWBgoPz8/OTn53fSmsY1TsZischisZzZhwYAAC2O1880/VBDQ4Nqa2sVFxen1q1bq7Cw0JgrLS1VWVmZ7Ha7JMlut2vnzp0ed7kVFBTIarUqOjraqDlxjcaaxjX8/f0VFxfnUdPQ0KDCwkKjBgAAwKtnmjIyMtS/f3916dJF3377rZYtW6b169crPz9fNptNKSkpSk9PV/v27WW1WjVmzBjZ7Xb16dNHkpSYmKjo6Gjdd999mjlzppxOpyZPnqzU1FTjLNDIkSM1f/58TZgwQcOHD9e6deu0YsUK5ebmGn2kp6crOTlZvXv31vXXX68XXnhBNTU1GjZsmFeOCwAAaH68GpoqKip0//336+uvv5bNZlOPHj2Un5+v3/zmN5KkOXPmyNfXVwMHDlRtba0cDocWLlxovN/Pz0+rV6/WqFGjZLfb1bZtWyUnJ2vatGlGTUREhHJzczV27FjNnTtXnTt31iuvvCKHw2HUDBo0SJWVlcrMzJTT6VRsbKzy8vKaXBwOAAAuXs3uOU0tFc9pAryH5zQBFy6e0wQAANDCEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACV4NTVlZWbruuut0ySWXKDg4WAMGDFBpaalHzdGjR5WamqoOHTqoXbt2GjhwoMrLyz1qysrKlJSUpDZt2ig4OFjjx4/X8ePHPWrWr1+vXr16yWKxqHv37srJyWnSz4IFC9StWzcFBAQoPj5eW7duPeufGQAAtExeDU0bNmxQamqqPvzwQxUUFOjYsWNKTExUTU2NUTN27Fi98847WrlypTZs2KCDBw/qrrvuMubr6+uVlJSkuro6bd68WUuXLlVOTo4yMzONmv379yspKUm33HKLSkpKlJaWpgcffFD5+flGzfLly5Wenq4pU6boo48+Us+ePeVwOFRRUXF+DgYAAGjWfNxut9vbTTSqrKxUcHCwNmzYoJtuuknV1dXq2LGjli1bprvvvluStHfvXkVFRamoqEh9+vTRmjVrdMcdd+jgwYMKCQmRJGVnZ2vixImqrKyUv7+/Jk6cqNzcXO3atcvY1+DBg1VVVaW8vDxJUnx8vK677jrNnz9fktTQ0KDw8HCNGTNGkyZN+sneXS6XbDabqqurZbVaz/ahMTwfNfucrQ20VOP2pHu7hbNi3/SHvd0C0Oxc8eTic7r+6fz+blbXNFVXV0uS2rdvL0kqLi7WsWPHlJCQYNRERkaqS5cuKioqkiQVFRUpJibGCEyS5HA45HK5tHv3bqPmxDUaaxrXqKurU3FxsUeNr6+vEhISjJofqq2tlcvl8tgAAMCFq9mEpoaGBqWlpalv37665pprJElOp1P+/v4KCgryqA0JCZHT6TRqTgxMjfONc6eqcblc+u6773To0CHV19eftKZxjR/KysqSzWYztvDw8DP74AAAoEVoNqEpNTVVu3bt0htvvOHtVkzJyMhQdXW1sR04cMDbLQEAgHOolbcbkKTRo0dr9erV2rhxozp37myMh4aGqq6uTlVVVR5nm8rLyxUaGmrU/PAut8a7606s+eEdd+Xl5bJarQoMDJSfn5/8/PxOWtO4xg9ZLBZZLJYz+8AAAKDF8eqZJrfbrdGjR+utt97SunXrFBER4TEfFxen1q1bq7Cw0BgrLS1VWVmZ7Ha7JMlut2vnzp0ed7kVFBTIarUqOjraqDlxjcaaxjX8/f0VFxfnUdPQ0KDCwkKjBgAAXNy8eqYpNTVVy5Yt0z/+8Q9dcsklxvVDNptNgYGBstlsSklJUXp6utq3by+r1aoxY8bIbrerT58+kqTExERFR0frvvvu08yZM+V0OjV58mSlpqYaZ4JGjhyp+fPna8KECRo+fLjWrVunFStWKDc31+glPT1dycnJ6t27t66//nq98MILqqmp0bBhw87/gQEAAM2OV0PTokWLJEk333yzx/iSJUv0wAMPSJLmzJkjX19fDRw4ULW1tXI4HFq4cKFR6+fnp9WrV2vUqFGy2+1q27atkpOTNW3aNKMmIiJCubm5Gjt2rObOnavOnTvrlVdekcPhMGoGDRqkyspKZWZmyul0KjY2Vnl5eU0uDgcAABenZvWcppaM5zQB3sNzmoALF89pAgAAaGEITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJXg1NGzdu1J133qmwsDD5+Pho1apVHvNut1uZmZnq1KmTAgMDlZCQoH379nnUHD58WEOHDpXValVQUJBSUlJ05MgRj5pPPvlEN954owICAhQeHq6ZM2c26WXlypWKjIxUQECAYmJi9O677571zwsAAFour4ammpoa9ezZUwsWLDjp/MyZMzVv3jxlZ2dry5Ytatu2rRwOh44ePWrUDB06VLt371ZBQYFWr16tjRs36qGHHjLmXS6XEhMT1bVrVxUXF+u5557T1KlT9dJLLxk1mzdv1pAhQ5SSkqKPP/5YAwYM0IABA7Rr165z9+EBAECL4uN2u93ebkKSfHx89NZbb2nAgAGSvj/LFBYWpscff1zjxo2TJFVXVyskJEQ5OTkaPHiw9uzZo+joaG3btk29e/eWJOXl5en222/XV199pbCwMC1atEhPPPGEnE6n/P39JUmTJk3SqlWrtHfvXknSoEGDVFNTo9WrVxv99OnTR7GxscrOzj5pv7W1taqtrTVeu1wuhYeHq7q6Wlar9awfn0bPR80+Z2sDLdW4PenebuGs2Df9YW+3ADQ7Vzy5+Jyu73K5ZLPZTP3+brbXNO3fv19Op1MJCQnGmM1mU3x8vIqKiiRJRUVFCgoKMgKTJCUkJMjX11dbtmwxam666SYjMEmSw+FQaWmpvvnmG6PmxP001jTu52SysrJks9mMLTw8/Od/aAAA0Gw129DkdDolSSEhIR7jISEhxpzT6VRwcLDHfKtWrdS+fXuPmpOtceI+fqymcf5kMjIyVF1dbWwHDhw43Y8IAABakFbebqClslgsslgs3m4DAACcJ832TFNoaKgkqby83GO8vLzcmAsNDVVFRYXH/PHjx3X48GGPmpOtceI+fqymcR4AAKDZhqaIiAiFhoaqsLDQGHO5XNqyZYvsdrskyW63q6qqSsXFxUbNunXr1NDQoPj4eKNm48aNOnbsmFFTUFCgq666SpdeeqlRc+J+Gmsa9wMAAODV0HTkyBGVlJSopKRE0vcXf5eUlKisrEw+Pj5KS0vTU089pbfffls7d+7U/fffr7CwMOMOu6ioKN12220aMWKEtm7dqk2bNmn06NEaPHiwwsLCJEn33nuv/P39lZKSot27d2v58uWaO3eu0tP/726bxx57THl5eZo1a5b27t2rqVOnavv27Ro9evT5PiQAAKCZ8uo1Tdu3b9ctt9xivG4MMsnJycrJydGECRNUU1Ojhx56SFVVVbrhhhuUl5engIAA4z2vv/66Ro8erX79+snX11cDBw7UvHnzjHmbzaa1a9cqNTVVcXFxuuyyy5SZmenxLKdf/epXWrZsmSZPnqw//elPuuKKK7Rq1Spdc8015+EoAACAlqDZPKeppTud5zz8HDynCWiK5zQBFy6e0wQAANDCEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACYSmH1iwYIG6deumgIAAxcfHa+vWrd5uCQAANAOEphMsX75c6enpmjJlij766CP17NlTDodDFRUV3m4NAAB4GaHpBLNnz9aIESM0bNgwRUdHKzs7W23atNGf//xnb7cGAAC8rJW3G2gu6urqVFxcrIyMDGPM19dXCQkJKioqalJfW1ur2tpa43V1dbUkyeVyndM+j9YfPafrAy3Ruf5zd74cOVrn7RaAZudc//luXN/tdv9kLaHpfx06dEj19fUKCQnxGA8JCdHevXub1GdlZen//b//12Q8PDz8nPUI4OSetD3h7RYAnCvP5JyX3Xz77bey2WynrCE0naGMjAylp6cbrxsaGnT48GF16NBBPj4+XuwM54PL5VJ4eLgOHDggq9Xq7XYAnEX8+b64uN1uffvttwoLC/vJWkLT/7rsssvk5+en8vJyj/Hy8nKFhoY2qbdYLLJYLB5jQUFB57JFNENWq5W/VIELFH++Lx4/dYapEReC/y9/f3/FxcWpsLDQGGtoaFBhYaHsdrsXOwMAAM0BZ5pOkJ6eruTkZPXu3VvXX3+9XnjhBdXU1GjYsGHebg0AAHgZoekEgwYNUmVlpTIzM+V0OhUbG6u8vLwmF4cDFotFU6ZMafIVLYCWjz/f+DE+bjP32AEAAFzkuKYJAADABEITAACACYQmAAAAEwhNAAAAJhCagB/xwAMPyMfHR88++6zH+KpVq3jqO9ACud1uJSQkyOFwNJlbuHChgoKC9NVXX3mhM7QUhCbgFAICAjRjxgx988033m4FwM/k4+OjJUuWaMuWLVq8eLExvn//fk2YMEEvvviiOnfu7MUO0dwRmoBTSEhIUGhoqLKysn605s0339TVV18ti8Wibt26adasWeexQwCnIzw8XHPnztW4ceO0f/9+ud1upaSkKDExUddee6369++vdu3aKSQkRPfdd58OHTpkvPdvf/ubYmJiFBgYqA4dOighIUE1NTVe/DQ43whNwCn4+fnpmWee0YsvvnjS0/bFxcX6wx/+oMGDB2vnzp2aOnWqnnzySeXk5Jz/ZgGYkpycrH79+mn48OGaP3++du3apcWLF+vWW2/Vtddeq+3btysvL0/l5eX6wx/+IEn6+uuvNWTIEA0fPlx79uzR+vXrddddd4lHHV5ceLgl8CMeeOABVVVVadWqVbLb7YqOjtarr76qVatW6fe//73cbreGDh2qyspKrV271njfhAkTlJubq927d3uxewCnUlFRoauvvlqHDx/Wm2++qV27dumf//yn8vPzjZqvvvpK4eHhKi0t1ZEjRxQXF6cvv/xSXbt29WLn8CbONAEmzJgxQ0uXLtWePXs8xvfs2aO+fft6jPXt21f79u1TfX39+WwRwGkIDg7Www8/rKioKA0YMEA7duzQ+++/r3bt2hlbZGSkJOmLL75Qz5491a9fP8XExOiee+7Ryy+/zLWOFyFCE2DCTTfdJIfDoYyMDG+3AuAsadWqlVq1+v6fYD1y5IjuvPNOlZSUeGz79u3TTTfdJD8/PxUUFGjNmjWKjo7Wiy++qKuuukr79+/38qfA+cQ/2AuY9Oyzzyo2NlZXXXWVMRYVFaVNmzZ51G3atElXXnml/Pz8zneLAM5Qr1699Oabb6pbt25GkPohHx8f9e3bV3379lVmZqa6du2qt956S+np6ee5W3gLZ5oAk2JiYjR06FDNmzfPGHv88cdVWFio6dOn67PPPtPSpUs1f/58jRs3zoudAjhdqampOnz4sIYMGaJt27bpiy++UH5+voYNG6b6+npt2bJFzzzzjLZv366ysjL9/e9/V2VlpaKiorzdOs4jQhNwGqZNm6aGhgbjda9evbRixQq98cYbuuaaa5SZmalp06bpgQce8F6TAE5bWFiYNm3apPr6eiUmJiomJkZpaWkKCgqSr6+vrFarNm7cqNtvv11XXnmlJk+erFmzZql///7ebh3nEXfPAQAAmMCZJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAF62bb75ZaWlp3m4DQAtBaALgNT4+Pqfcpk6d6u0WAcBw8n/KGQDOg6+//tr4efny5crMzFRpaakx1q5dO2+0dU643W7V19erVSv+2gVaKs40AfCa0NBQY7PZbPLx8TFeBwcHa/bs2ercubMsFotiY2OVl5dnvHf9+vXy8fFRVVWVMVZSUiIfHx99+eWXxtimTZt08803q02bNrr00kvlcDj0zTffGPMNDQ2aMGGC2rdvr9DQUI+zW263W1OnTlWXLl1ksVgUFhamRx991Jivra3VxIkTFR4eLovFou7du+vVV1/16G/NmjWKi4uTxWLRBx98oIaGBmVlZSkiIkKBgYHq2bOn/va3v3kcl127dql///5q166dQkJCdN999+nQoUPG/M0336xHH330R/sGcG4QmgA0S3PnztWsWbP0/PPP65NPPpHD4dBvf/tb7du3z/QaJSUl6tevn6Kjo1VUVKQPPvhAd955p+rr642apUuXqm3bttqyZYtmzpypadOmqaCgQJL05ptvas6cOVq8eLH27dunVatWKSYmxnjv/fffr7/+9a+aN2+e9uzZo8WLFzc5OzZp0iQ9++yz2rNnj3r06KGsrCz95S9/UXZ2tnbv3q2xY8fqj3/8ozZs2CBJqqqq0q233qprr71W27dvV15ensrLy/WHP/zBY91T9Q3gHHEDQDOwZMkSt81mM16HhYW5n376aY+a6667zv3II4+43W63+/3333dLcn/zzTfG/Mcff+yW5N6/f7/b7Xa7hwwZ4u7bt++P7vPXv/61+4Ybbmiyj4kTJ7rdbrd71qxZ7iuvvNJdV1fX5L2lpaVuSe6CgoKTrt3Y36pVq4yxo0ePutu0aePevHmzR21KSop7yJAhbrfb7Z4+fbo7MTHRY/7AgQNuSe7S0lJTfQM4NzjTBKDZcblcOnjwoPr27esx3rdvX+3Zs8f0Oo1nmk6lR48eHq87deqkiooKSdI999yj7777Tr/85S81YsQIvfXWWzp+/Lixtp+fn37961+fcv3evXsbP3/++ef673//q9/85jdq166dsf3lL3/RF198IUnasWOH3n//fY/5yMhISTJqfqpvAOcGVyQCaJF8fb//fz63222MHTt2zKMmMDDwJ9dp3bq1x2sfHx81NDRIksLDw1VaWqr33ntPBQUFeuSRR/Tcc89pw4YNptaWpLZt2xo/HzlyRJKUm5urX/ziFx51FovFqLnzzjs1Y8aMJmt16tTJVN8Azg3ONAFodqxWq8LCwrRp0yaP8U2bNik6OlqS1LFjR0med+CVlJR41Pfo0UOFhYU/q5fAwEDdeeedmjdvntavX6+ioiLt3LlTMTExamhoMK5FMiM6OloWi0VlZWXq3r27xxYeHi5J6tWrl3bv3q1u3bo1qTkxgAE4/zjTBKBZGj9+vKZMmaLLL79csbGxWrJkiUpKSvT6669LkhE0pk6dqqefflqfffaZZs2a5bFGRkaGYmJi9Mgjj2jkyJHy9/fX+++/r3vuuUeXXXbZT/aQk5Oj+vp6xcfHq02bNnrttdcUGBiorl27qkOHDkpOTtbw4cM1b9489ezZU//+979VUVHR5KLtRpdcconGjRunsWPHqqGhQTfccIOqq6u1adMmWa1WJScnKzU1VS+//LKGDBli3B33+eef64033tArr7wiPz+/n39wAZwRzjQBaJYeffRRpaen6/HHH1dMTIzy8vL09ttv64orrpD0/ddTf/3rX7V371716NFDM2bM0FNPPeWxxpVXXqm1a9dqx44duv7662W32/WPf/zD9LOSgoKC9PLLL6tv377q0aOH3nvvPb3zzjvq0KGDJGnRokW6++679cgjjygyMlIjRoxQTU3NKdecPn26nnzySWVlZSkqKkq33XabcnNzFRERIUnGGbb6+nolJiYqJiZGaWlpCgoKMr6SBOAdPu4TLwgAAADASfG/LQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACY8P8B7SV8NBhwE4EAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Barplot - Price variation on Touch screen laptops\n",
    "sns.barplot(x=df['Touchscreen'],y=df['Price'],palette='plasma')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ad82a5db-571f-4346-aa42-9250e28bff84",
   "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>100</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i7 7500U 2.7GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>AMD Radeon 530</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.91</td>\n",
       "      <td>38308.32</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>775</th>\n",
       "      <td>Asus</td>\n",
       "      <td>2 in 1 Convertible</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD / Touchscreen 1920x1080</td>\n",
       "      <td>Intel Core i7 7500U 2.7GHz</td>\n",
       "      <td>12</td>\n",
       "      <td>2TB HDD</td>\n",
       "      <td>Nvidia GeForce 940MX</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.30</td>\n",
       "      <td>44701.92</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</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 8550U 1.8GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>Intel UHD Graphics 620</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.63</td>\n",
       "      <td>57755.52</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>Dell</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>Intel Celeron Dual Core N3060 1.60GHz</td>\n",
       "      <td>4</td>\n",
       "      <td>500GB HDD</td>\n",
       "      <td>Intel HD Graphics 400</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.80</td>\n",
       "      <td>16463.52</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>617</th>\n",
       "      <td>HP</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i7 7500U 2.7GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.84</td>\n",
       "      <td>74005.92</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Company            TypeName  Inches                 ScreenResolution  \\\n",
       "100      HP            Notebook    15.6                Full HD 1920x1080   \n",
       "775    Asus  2 in 1 Convertible    15.6  Full HD / Touchscreen 1920x1080   \n",
       "116      HP            Notebook    14.0                Full HD 1920x1080   \n",
       "377    Dell            Notebook    15.6                         1366x768   \n",
       "617      HP           Ultrabook    15.6                Full HD 1920x1080   \n",
       "\n",
       "                                       Cpu  Ram     Memory  \\\n",
       "100             Intel Core i7 7500U 2.7GHz    8  256GB SSD   \n",
       "775             Intel Core i7 7500U 2.7GHz   12    2TB HDD   \n",
       "116             Intel Core i7 8550U 1.8GHz    8  512GB SSD   \n",
       "377  Intel Celeron Dual Core N3060 1.60GHz    4  500GB HDD   \n",
       "617             Intel Core i7 7500U 2.7GHz    8  512GB SSD   \n",
       "\n",
       "                        Gpu       OpSys  Weight     Price Touchscreen IPS  \n",
       "100          AMD Radeon 530  Windows 10    1.91  38308.32          No  No  \n",
       "775    Nvidia GeForce 940MX  Windows 10    2.30  44701.92         Yes  No  \n",
       "116  Intel UHD Graphics 620  Windows 10    1.63  57755.52          No  No  \n",
       "377   Intel HD Graphics 400  Windows 10    1.80  16463.52          No  No  \n",
       "617   Intel HD Graphics 620  Windows 10    1.84  74005.92          No  No  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Extract IPS Channel presence information\n",
    "df['IPS'] = df['ScreenResolution'].apply(lambda element:'Yes' if \"IPS\" in element else 'No')\n",
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "90886585-4222-4b40-a915-e954a86de1a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='IPS'>"
      ]
     },
     "execution_count": 28,
     "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": [
    "#Countplot for IPS laptops\n",
    "df['IPS'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "4da8144b-b491-469e-be5d-abc316c6ddb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='IPS', ylabel='Price'>"
      ]
     },
     "execution_count": 29,
     "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": [
    "#Barplot - Price variation on IPS laptops\n",
    "sns.barplot(x=df['IPS'],y=df['Price'],palette='plasma')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bc29509-71b5-4815-a9bb-a31ace5df51f",
   "metadata": {},
   "source": [
    "## Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "574a521c",
   "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>No</td>\n",
       "      <td>Yes</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>No</td>\n",
       "      <td>No</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>No</td>\n",
       "      <td>No</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>No</td>\n",
       "      <td>Yes</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>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1298</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>2 in 1 Convertible</td>\n",
       "      <td>14.0</td>\n",
       "      <td>IPS Panel Full HD / Touchscreen 1920x1080</td>\n",
       "      <td>Intel Core i7 6500U 2.5GHz</td>\n",
       "      <td>4</td>\n",
       "      <td>128GB SSD</td>\n",
       "      <td>Intel HD Graphics 520</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.80</td>\n",
       "      <td>33992.6400</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1299</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>2 in 1 Convertible</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Quad HD+ / Touchscreen 3200x1800</td>\n",
       "      <td>Intel Core i7 6500U 2.5GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>Intel HD Graphics 520</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.30</td>\n",
       "      <td>79866.7200</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1300</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>Intel Celeron Dual Core N3050 1.6GHz</td>\n",
       "      <td>2</td>\n",
       "      <td>64GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.50</td>\n",
       "      <td>12201.1200</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1301</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>Intel Core i7 6500U 2.5GHz</td>\n",
       "      <td>6</td>\n",
       "      <td>1TB HDD</td>\n",
       "      <td>AMD Radeon R5 M330</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.19</td>\n",
       "      <td>40705.9200</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1302</th>\n",
       "      <td>Asus</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>Intel Celeron Dual Core N3050 1.6GHz</td>\n",
       "      <td>4</td>\n",
       "      <td>500GB HDD</td>\n",
       "      <td>Intel HD Graphics</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.20</td>\n",
       "      <td>19660.3200</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1303 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company            TypeName  Inches  \\\n",
       "0      Apple           Ultrabook    13.3   \n",
       "1      Apple           Ultrabook    13.3   \n",
       "2         HP            Notebook    15.6   \n",
       "3      Apple           Ultrabook    15.4   \n",
       "4      Apple           Ultrabook    13.3   \n",
       "...      ...                 ...     ...   \n",
       "1298  Lenovo  2 in 1 Convertible    14.0   \n",
       "1299  Lenovo  2 in 1 Convertible    13.3   \n",
       "1300  Lenovo            Notebook    14.0   \n",
       "1301      HP            Notebook    15.6   \n",
       "1302    Asus            Notebook    15.6   \n",
       "\n",
       "                                ScreenResolution  \\\n",
       "0             IPS Panel Retina Display 2560x1600   \n",
       "1                                       1440x900   \n",
       "2                              Full HD 1920x1080   \n",
       "3             IPS Panel Retina Display 2880x1800   \n",
       "4             IPS Panel Retina Display 2560x1600   \n",
       "...                                          ...   \n",
       "1298   IPS Panel Full HD / Touchscreen 1920x1080   \n",
       "1299  IPS Panel Quad HD+ / Touchscreen 3200x1800   \n",
       "1300                                    1366x768   \n",
       "1301                                    1366x768   \n",
       "1302                                    1366x768   \n",
       "\n",
       "                                       Cpu  Ram               Memory  \\\n",
       "0                     Intel Core i5 2.3GHz    8            128GB SSD   \n",
       "1                     Intel Core i5 1.8GHz    8  128GB Flash Storage   \n",
       "2               Intel Core i5 7200U 2.5GHz    8            256GB SSD   \n",
       "3                     Intel Core i7 2.7GHz   16            512GB SSD   \n",
       "4                     Intel Core i5 3.1GHz    8            256GB SSD   \n",
       "...                                    ...  ...                  ...   \n",
       "1298            Intel Core i7 6500U 2.5GHz    4            128GB SSD   \n",
       "1299            Intel Core i7 6500U 2.5GHz   16            512GB SSD   \n",
       "1300  Intel Celeron Dual Core N3050 1.6GHz    2   64GB Flash Storage   \n",
       "1301            Intel Core i7 6500U 2.5GHz    6              1TB HDD   \n",
       "1302  Intel Celeron Dual Core N3050 1.6GHz    4            500GB HDD   \n",
       "\n",
       "                               Gpu       OpSys  Weight        Price  \\\n",
       "0     Intel Iris Plus Graphics 640       macOS    1.37   71378.6832   \n",
       "1           Intel HD Graphics 6000       macOS    1.34   47895.5232   \n",
       "2            Intel HD Graphics 620       No OS    1.86   30636.0000   \n",
       "3               AMD Radeon Pro 455       macOS    1.83  135195.3360   \n",
       "4     Intel Iris Plus Graphics 650       macOS    1.37   96095.8080   \n",
       "...                            ...         ...     ...          ...   \n",
       "1298         Intel HD Graphics 520  Windows 10    1.80   33992.6400   \n",
       "1299         Intel HD Graphics 520  Windows 10    1.30   79866.7200   \n",
       "1300             Intel HD Graphics  Windows 10    1.50   12201.1200   \n",
       "1301            AMD Radeon R5 M330  Windows 10    2.19   40705.9200   \n",
       "1302             Intel HD Graphics  Windows 10    2.20   19660.3200   \n",
       "\n",
       "     Touchscreen  IPS  \n",
       "0             No  Yes  \n",
       "1             No   No  \n",
       "2             No   No  \n",
       "3             No  Yes  \n",
       "4             No  Yes  \n",
       "...          ...  ...  \n",
       "1298         Yes  Yes  \n",
       "1299         Yes  Yes  \n",
       "1300          No   No  \n",
       "1301          No   No  \n",
       "1302          No   No  \n",
       "\n",
       "[1303 rows x 13 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9792961d-ebef-4889-9f26-95162f7d018d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Price     1.000000\n",
       "Ram       0.743007\n",
       "Weight    0.210370\n",
       "Inches    0.068197\n",
       "Name: Price, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr(numeric_only=True)['Price'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3f46366e-504d-4088-b028-7252f5f2e89c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#first we will extract Name of CPU which is first 3 words from Cpu column and then we will check which processor it is\n",
    "def fetch_processor(x):\n",
    "    cpu_name = \" \".join(x.split()[0:3])\n",
    "    if cpu_name == 'Intel Core i7' or cpu_name == 'Intel Core i5' or cpu_name == 'Intel Core i3':\n",
    "        return cpu_name\n",
    "    elif cpu_name.split()[0] == 'Intel':\n",
    "        return 'Other Intel Processor'\n",
    "    else:\n",
    "        return 'AMD Processor'\n",
    "df['Cpu_brand'] = df['Cpu'].apply(lambda x: fetch_processor(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "57911752-5a05-4cdd-9842-18b7b0a6c484",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Inches</th>\n",
       "      <th>ScreenResolution</th>\n",
       "      <th>Cpu</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>IPS</th>\n",
       "      <th>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>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>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>1440x900</td>\n",
       "      <td>Intel Core i5 1.8GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>47895.5232</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>30636.0000</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>15.4</td>\n",
       "      <td>IPS Panel Retina Display 2880x1800</td>\n",
       "      <td>Intel Core i7 2.7GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>135195.3360</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Intel Core i7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 3.1GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>96095.8080</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Intel Core i5</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          No  Yes   \n",
       "1        Intel HD Graphics 6000  macOS    1.34   47895.5232          No   No   \n",
       "2         Intel HD Graphics 620  No OS    1.86   30636.0000          No   No   \n",
       "3            AMD Radeon Pro 455  macOS    1.83  135195.3360          No  Yes   \n",
       "4  Intel Iris Plus Graphics 650  macOS    1.37   96095.8080          No  Yes   \n",
       "\n",
       "       Cpu_brand  \n",
       "0  Intel Core i5  \n",
       "1  Intel Core i5  \n",
       "2  Intel Core i5  \n",
       "3  Intel Core i7  \n",
       "4  Intel Core i5  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "5ddc428e-8966-455a-9238-900f2e040249",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Cpu_brand'>"
      ]
     },
     "execution_count": 34,
     "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": [
    "#Countplot for Cpu brand of laptops\n",
    "df['Cpu_brand'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ddcccda8-458f-439d-a474-1613bc3c66b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Cpu_brand', ylabel='Price'>"
      ]
     },
     "execution_count": 35,
     "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": [
    "#Barplot - Price variation on Cpu brand of laptops\n",
    "sns.barplot(x=df['Cpu_brand'],y=df['Price'],palette='plasma')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e6b3e76c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Inches</th>\n",
       "      <th>ScreenResolution</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>IPS</th>\n",
       "      <th>Cpu_brand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>813</th>\n",
       "      <td>Dell</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>8</td>\n",
       "      <td>1TB HDD</td>\n",
       "      <td>Nvidia GeForce GT 940MX</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.98</td>\n",
       "      <td>51202.08</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Intel Core i7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Gaming</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>Nvidia GeForce GTX 1060</td>\n",
       "      <td>No OS</td>\n",
       "      <td>2.40</td>\n",
       "      <td>74485.44</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Intel Core i7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>774</th>\n",
       "      <td>HP</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>12.5</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>4</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 520</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.26</td>\n",
       "      <td>66280.32</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>702</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>8</td>\n",
       "      <td>1TB HDD</td>\n",
       "      <td>AMD Radeon R7</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.20</td>\n",
       "      <td>22857.12</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>AMD Processor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1053</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>4</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.04</td>\n",
       "      <td>52161.12</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company   TypeName  Inches   ScreenResolution  Ram     Memory  \\\n",
       "813     Dell   Notebook    15.6  Full HD 1920x1080    8    1TB HDD   \n",
       "205   Lenovo     Gaming    15.6  Full HD 1920x1080   16  512GB SSD   \n",
       "774       HP  Ultrabook    12.5           1366x768    4  256GB SSD   \n",
       "702   Lenovo   Notebook    15.6           1366x768    8    1TB HDD   \n",
       "1053      HP   Notebook    15.6           1366x768    4  256GB SSD   \n",
       "\n",
       "                          Gpu       OpSys  Weight     Price Touchscreen IPS  \\\n",
       "813   Nvidia GeForce GT 940MX  Windows 10    1.98  51202.08          No  No   \n",
       "205   Nvidia GeForce GTX 1060       No OS    2.40  74485.44          No  No   \n",
       "774     Intel HD Graphics 520  Windows 10    1.26  66280.32          No  No   \n",
       "702             AMD Radeon R7  Windows 10    2.20  22857.12          No  No   \n",
       "1053    Intel HD Graphics 620  Windows 10    2.04  52161.12          No  No   \n",
       "\n",
       "          Cpu_brand  \n",
       "813   Intel Core i7  \n",
       "205   Intel Core i7  \n",
       "774   Intel Core i5  \n",
       "702   AMD Processor  \n",
       "1053  Intel Core i5  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(['Cpu'],axis=1,inplace=True)\n",
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "0179957d-f52d-4594-be65-149015ea603e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Ram'>"
      ]
     },
     "execution_count": 37,
     "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": [
    "#Countplot for RAM of laptops\n",
    "df['Ram'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "2cf638c8-347f-48ac-8d41-d5a082c7f29f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Ram', ylabel='Price'>"
      ]
     },
     "execution_count": 38,
     "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": [
    "#Barplot - Price variation on RAM of laptops\n",
    "sns.barplot(x=df['Ram'],y=df['Price'],palette='plasma')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "06a04649-df2d-4111-b8b7-cfe87c05632e",
   "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                38\n",
       "2TB HDD                           16\n",
       "64GB Flash Storage                15\n",
       "512GB SSD +  1TB HDD              14\n",
       "1TB SSD                           14\n",
       "256GB SSD +  2TB HDD              10\n",
       "1.0TB Hybrid                       9\n",
       "256GB Flash Storage                8\n",
       "16GB Flash Storage                 7\n",
       "32GB SSD                           6\n",
       "180GB SSD                          5\n",
       "128GB Flash Storage                4\n",
       "512GB SSD +  2TB HDD               3\n",
       "16GB SSD                           3\n",
       "512GB Flash Storage                2\n",
       "1TB SSD +  1TB HDD                 2\n",
       "256GB SSD +  500GB HDD             2\n",
       "128GB SSD +  2TB HDD               2\n",
       "256GB SSD +  256GB SSD             2\n",
       "512GB SSD +  256GB SSD             1\n",
       "512GB SSD +  512GB SSD             1\n",
       "64GB Flash Storage +  1TB HDD      1\n",
       "1TB HDD +  1TB HDD                 1\n",
       "32GB HDD                           1\n",
       "64GB SSD                           1\n",
       "128GB HDD                          1\n",
       "240GB SSD                          1\n",
       "8GB SSD                            1\n",
       "508GB Hybrid                       1\n",
       "1.0TB HDD                          1\n",
       "512GB SSD +  1.0TB Hybrid          1\n",
       "256GB SSD +  1.0TB Hybrid          1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Memory'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9b1bfcf-01c7-45d5-a09d-6aa3fd341e4e",
   "metadata": {},
   "source": [
    "In here, we will do as follows,\n",
    "\n",
    "*Four most common variants observed: HDD, SSD, Flash, Hybrid\n",
    "\n",
    "*We will remove decimal space for example 1.0 TB will become 1TB\n",
    "\n",
    "*We will replace the word GB with \"\"\n",
    "\n",
    "*We will replace the word TB with \"000\"\n",
    "\n",
    "*We will split the word across the \"+\" character"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "a0a03cf8-a242-4afa-b486-c523ef9e5d84",
   "metadata": {},
   "outputs": [],
   "source": [
    "#preprocessing\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')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2b3e0115",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "227                 256 SSD\n",
       "808                 512 SSD\n",
       "458                 256 SSD\n",
       "1253                256 SSD\n",
       "750                 128 SSD\n",
       "994                 256 SSD\n",
       "1047    256 SSD +  1000 HDD\n",
       "549                 512 SSD\n",
       "677        64 Flash Storage\n",
       "1085                  0 HDD\n",
       "Name: Memory, dtype: object"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Memory\"].sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "8ba24beb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>523</th>\n",
       "      <td>128 SSD</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>467</th>\n",
       "      <td>2000 HDD</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1258</th>\n",
       "      <td>1000 Hybrid</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1018</th>\n",
       "      <td>256 SSD</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>683</th>\n",
       "      <td>256 SSD</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1300</th>\n",
       "      <td>64 Flash Storage</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>256 SSD</td>\n",
       "      <td>1000 HDD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>925</th>\n",
       "      <td>0 HDD</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>1000 HDD</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>2000 HDD</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     0           1\n",
       "523            128 SSD        None\n",
       "467           2000 HDD        None\n",
       "1258       1000 Hybrid        None\n",
       "1018           256 SSD        None\n",
       "683            256 SSD        None\n",
       "1300  64 Flash Storage        None\n",
       "404           256 SSD     1000 HDD\n",
       "925              0 HDD        None\n",
       "202           1000 HDD        None\n",
       "358           2000 HDD        None"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new = df[\"Memory\"].str.split(\"+\", expand = True)\n",
    "new.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "7d95cea3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "first\n",
       "256 SSD              500\n",
       "1000 HDD             225\n",
       "128 SSD              172\n",
       "512 SSD              138\n",
       "0 HDD                132\n",
       "32 Flash Storage      38\n",
       "64 Flash Storage      16\n",
       "2000 HDD              16\n",
       "1000 SSD              16\n",
       "1000 Hybrid            9\n",
       "256 Flash Storage      8\n",
       "16 Flash Storage       7\n",
       "32 SSD                 6\n",
       "1 SSD                  5\n",
       "128 Flash Storage      4\n",
       "16 SSD                 3\n",
       "512 Flash Storage      2\n",
       "2 SSD                  1\n",
       "8 SSD                  1\n",
       "64 SSD                 1\n",
       "128 HDD                1\n",
       "32 HDD                 1\n",
       "8 Hybrid               1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"first\"]= new[0]\n",
    "df[\"first\"]=df[\"first\"].str.strip()\n",
    "df[\"first\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "580a2098",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "second\n",
       "1000 HDD       185\n",
       "2000 HDD        15\n",
       "256 SSD          3\n",
       "0 HDD            2\n",
       "1000 Hybrid      2\n",
       "512 SSD          1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"second\"]= new[1]\n",
    "df[\"second\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "c639de94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Layer1HDD\n",
       "0    928\n",
       "1    375\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Layer1HDD\"] = df[\"first\"].apply(lambda x: 1 if \"HDD\" in x else 0)\n",
    "df[\"Layer1HDD\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "ce016362",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Layer1SSD\n",
       "1    843\n",
       "0    460\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Layer1SSD\"] = df[\"first\"].apply(lambda x: 1 if \"SSD\" in x else 0)\n",
    "df[\"Layer1SSD\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "83dfe951",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        128\n",
       "1        128\n",
       "2        256\n",
       "3        512\n",
       "4        256\n",
       "        ... \n",
       "1298     128\n",
       "1299     512\n",
       "1300      64\n",
       "1301    1000\n",
       "1302       0\n",
       "Name: first, Length: 1303, dtype: object"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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",
    "df['first'] = df['first'].str.replace(r'\\D', '',regex=True)\n",
    "df[\"second\"].fillna(\"0\", inplace = True)\n",
    "df['first']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "4740337f-b155-4221-8c1b-b1ecf0c8c0cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "#binary encoding\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "86f8744f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "second\n",
       "0       1097\n",
       "1000     187\n",
       "2000      15\n",
       "256        3\n",
       "512        1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Clean 'second' column to retain only digits\n",
    "df['second'] = df['second'].str.replace(r'\\D', '',regex=True)\n",
    "df['second'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "ec297fd0-f8c7-4a2b-91e7-d7d49ab09694",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert 'first' column to integers directly\n",
    "df[\"first\"] = pd.to_numeric(df[\"first\"]).astype(int)\n",
    "\n",
    "# Convert 'second' column to integers\n",
    "df[\"second\"] = pd.to_numeric(df[\"second\"]).astype(int)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "932f56bf-25b5-4534-93ab-b4f0e05939cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#finalize the columns by keeping value\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "c1d41f51-cfb0-4689-847d-ccf9d065d359",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Drop the un required columns\n",
    "df.drop(columns=['first', 'second', 'Layer1HDD', 'Layer1SSD', 'Layer1Hybrid',\n",
    "       'Layer1Flash_Storage', 'Layer2HDD', 'Layer2SSD', 'Layer2Hybrid',\n",
    "       'Layer2Flash_Storage'],inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "f05aae14-d994-4014-ae87-1d9aab033fcf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Inches</th>\n",
       "      <th>ScreenResolution</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>IPS</th>\n",
       "      <th>Cpu_brand</th>\n",
       "      <th>HDD</th>\n",
       "      <th>SSD</th>\n",
       "      <th>Hybrid</th>\n",
       "      <th>Flash_Storage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>851</th>\n",
       "      <td>Asus</td>\n",
       "      <td>Gaming</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>16</td>\n",
       "      <td>128 SSD +  1000 HDD</td>\n",
       "      <td>Nvidia GeForce GTX 1060</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.10</td>\n",
       "      <td>101178.7200</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>1000</td>\n",
       "      <td>128</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1266</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>6</td>\n",
       "      <td>1000 Hybrid</td>\n",
       "      <td>AMD Radeon R7 M440</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.04</td>\n",
       "      <td>29303.4672</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>AMD Processor</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>Dell</td>\n",
       "      <td>Gaming</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>8</td>\n",
       "      <td>1000 Hybrid</td>\n",
       "      <td>Nvidia GeForce GTX 1050</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.62</td>\n",
       "      <td>47898.7200</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>2 in 1 Convertible</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Touchscreen 2560x1440</td>\n",
       "      <td>16</td>\n",
       "      <td>1000 SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.42</td>\n",
       "      <td>150462.7200</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>HP</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>8</td>\n",
       "      <td>256 SSD</td>\n",
       "      <td>Intel HD Graphics 520</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.43</td>\n",
       "      <td>79920.0000</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company            TypeName  Inches       ScreenResolution  Ram  \\\n",
       "851     Asus              Gaming    15.6      Full HD 1920x1080   16   \n",
       "1266      HP            Notebook    15.6      Full HD 1920x1080    6   \n",
       "151     Dell              Gaming    15.6      Full HD 1920x1080    8   \n",
       "190   Lenovo  2 in 1 Convertible    14.0  Touchscreen 2560x1440   16   \n",
       "417       HP           Ultrabook    14.0      Full HD 1920x1080    8   \n",
       "\n",
       "                   Memory                      Gpu       OpSys  Weight  \\\n",
       "851   128 SSD +  1000 HDD  Nvidia GeForce GTX 1060  Windows 10    2.10   \n",
       "1266          1000 Hybrid       AMD Radeon R7 M440  Windows 10    2.04   \n",
       "151           1000 Hybrid  Nvidia GeForce GTX 1050  Windows 10    2.62   \n",
       "190              1000 SSD    Intel HD Graphics 620  Windows 10    1.42   \n",
       "417               256 SSD    Intel HD Graphics 520  Windows 10    1.43   \n",
       "\n",
       "            Price Touchscreen IPS      Cpu_brand   HDD   SSD  Hybrid  \\\n",
       "851   101178.7200          No  No  Intel Core i7  1000   128       0   \n",
       "1266   29303.4672          No  No  AMD Processor     0     0    1000   \n",
       "151    47898.7200          No  No  Intel Core i7     0     0    1000   \n",
       "190   150462.7200         Yes  No  Intel Core i7     0  1000       0   \n",
       "417    79920.0000          No  No  Intel Core i7     0   256       0   \n",
       "\n",
       "      Flash_Storage  \n",
       "851               0  \n",
       "1266              0  \n",
       "151               0  \n",
       "190               0  \n",
       "417               0  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "1db79bbc-253c-4c55-905e-2035777b2cbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['Memory'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "8ff018a6-3282-4246-ba5f-249f1fdbb2d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Inches</th>\n",
       "      <th>ScreenResolution</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>IPS</th>\n",
       "      <th>Cpu_brand</th>\n",
       "      <th>HDD</th>\n",
       "      <th>SSD</th>\n",
       "      <th>Hybrid</th>\n",
       "      <th>Flash_Storage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>8</td>\n",
       "      <td>Intel Iris Plus Graphics 640</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>71378.6832</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>1440x900</td>\n",
       "      <td>8</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>47895.5232</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>8</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>30636.0000</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>15.4</td>\n",
       "      <td>IPS Panel Retina Display 2880x1800</td>\n",
       "      <td>16</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>135195.3360</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>512</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>8</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>96095.8080</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Inches                    ScreenResolution  Ram  \\\n",
       "0   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600    8   \n",
       "1   Apple  Ultrabook    13.3                            1440x900    8   \n",
       "2      HP   Notebook    15.6                   Full HD 1920x1080    8   \n",
       "3   Apple  Ultrabook    15.4  IPS Panel Retina Display 2880x1800   16   \n",
       "4   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600    8   \n",
       "\n",
       "                            Gpu  OpSys  Weight        Price Touchscreen  IPS  \\\n",
       "0  Intel Iris Plus Graphics 640  macOS    1.37   71378.6832          No  Yes   \n",
       "1        Intel HD Graphics 6000  macOS    1.34   47895.5232          No   No   \n",
       "2         Intel HD Graphics 620  No OS    1.86   30636.0000          No   No   \n",
       "3            AMD Radeon Pro 455  macOS    1.83  135195.3360          No  Yes   \n",
       "4  Intel Iris Plus Graphics 650  macOS    1.37   96095.8080          No  Yes   \n",
       "\n",
       "       Cpu_brand  HDD  SSD  Hybrid  Flash_Storage  \n",
       "0  Intel Core i5    0  128       0              0  \n",
       "1  Intel Core i5    0    0       0            128  \n",
       "2  Intel Core i5    0  256       0              0  \n",
       "3  Intel Core i7    0  512       0              0  \n",
       "4  Intel Core i5    0  256       0              0  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "12170a87-7e33-4faa-81b3-563564a5f205",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Inches           0.068197\n",
       "Ram              0.743007\n",
       "Weight           0.210370\n",
       "Price            1.000000\n",
       "HDD             -0.026830\n",
       "SSD              0.666734\n",
       "Hybrid           0.008811\n",
       "Flash_Storage   -0.040511\n",
       "Name: Price, dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df.corr(numeric_only=True)['Price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "5170d0e5-84f4-4e08-8750-39ad76736dc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Inches</th>\n",
       "      <th>ScreenResolution</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>IPS</th>\n",
       "      <th>Cpu_brand</th>\n",
       "      <th>HDD</th>\n",
       "      <th>SSD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>8</td>\n",
       "      <td>Intel Iris Plus Graphics 640</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>71378.6832</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>1440x900</td>\n",
       "      <td>8</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>47895.5232</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>8</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>30636.0000</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>15.4</td>\n",
       "      <td>IPS Panel Retina Display 2880x1800</td>\n",
       "      <td>16</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>135195.3360</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>8</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>96095.8080</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Inches                    ScreenResolution  Ram  \\\n",
       "0   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600    8   \n",
       "1   Apple  Ultrabook    13.3                            1440x900    8   \n",
       "2      HP   Notebook    15.6                   Full HD 1920x1080    8   \n",
       "3   Apple  Ultrabook    15.4  IPS Panel Retina Display 2880x1800   16   \n",
       "4   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600    8   \n",
       "\n",
       "                            Gpu  OpSys  Weight        Price Touchscreen  IPS  \\\n",
       "0  Intel Iris Plus Graphics 640  macOS    1.37   71378.6832          No  Yes   \n",
       "1        Intel HD Graphics 6000  macOS    1.34   47895.5232          No   No   \n",
       "2         Intel HD Graphics 620  No OS    1.86   30636.0000          No   No   \n",
       "3            AMD Radeon Pro 455  macOS    1.83  135195.3360          No  Yes   \n",
       "4  Intel Iris Plus Graphics 650  macOS    1.37   96095.8080          No  Yes   \n",
       "\n",
       "       Cpu_brand  HDD  SSD  \n",
       "0  Intel Core i5    0  128  \n",
       "1  Intel Core i5    0    0  \n",
       "2  Intel Core i5    0  256  \n",
       "3  Intel Core i7    0  512  \n",
       "4  Intel Core i5    0  256  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(columns = ['Hybrid','Flash_Storage'],inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "0047cc76-abd8-4d88-bf98-f28d9175c34d",
   "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",
       "AMD Radeon R5 520            1\n",
       "AMD Radeon R7                1\n",
       "Intel HD Graphics 540        1\n",
       "AMD Radeon 540               1\n",
       "ARM Mali T860 MP4            1\n",
       "Name: count, Length: 110, dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Analysis on GPU\n",
    "df['Gpu'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "7f25bf19-ab10-4e29-9ecc-b67db27b8f17",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intel\n"
     ]
    }
   ],
   "source": [
    "#Extracting the GPU brands\n",
    "a = df['Gpu'].iloc[1]\n",
    "print(a.split()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "183d4020-8240-4997-9091-79bd3b357cb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Gpu_brand\n",
       "Intel     722\n",
       "Nvidia    400\n",
       "AMD       180\n",
       "ARM         1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,7))\n",
    "df['Gpu_brand'] = df['Gpu'].apply(lambda x:x.split()[0])\n",
    "sns.countplot(y=df['Gpu_brand'],palette='plasma')\n",
    "plt.show()\n",
    "df['Gpu_brand'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "b1a22a6d-36df-4471-868f-cc66c0814642",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,7))\n",
    "df = df[df['Gpu_brand']!='ARM']\n",
    "sns.countplot(y=df['Gpu_brand'],palette='plasma')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "6f5fae3a-22e7-42d6-93c5-a5827a8f3d99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AQCHiHAAAAAAUIs4BAAAAQCHiHAAAAAAUIs4BAAAAQCHiHAAAAAAUIs4BAAAAQCHiHAAAAAAUIs4BAAAAQCHiHAAAAAAUIs4BAAAAQCHiHAAAAAAUIs4BAAAAQCHiHAAAAAAUIs4BAAAAQCHiHAAAAAAUIs4BAAAAQCHiHAAAAAAUIs4BAAAAQCHiHAAAAAAUIs4BAAAAQCHiHAAAAAAUIs4BAAAAQCHiHAAAAAAUIs4BAAAAQCHiHAAAAAAUIs4BAAAAQCHiHAAAAAAUIs4BAAAAQCHiHAAAAAAUIs4BAAAAQCHiHAAAAAAUIs4BAAAAQCHiHAAAAAAUIs4BAAAAQCHiHAAAAAAUIs4BAAAAQCHiHAAAAAAUIs4BAAAAQCHiHAAAAAAUIs4BAAAAQCHiHAAAAAAUIs4BAAAAQCHiHAAAAAAU0rr0AsDO4cwzz8yyZcuSJHvuuWcmT55ceCMAAADY/olzwBaxbNmyLF26tPQaAAAAsEPxtlYAAAAAKEScAwAAAIBCxDkAAAAAKEScAwAAAIBCxDkAAAAAKEScAwAAAIBCxDkAAAAAKEScAwAAAIBCxDkAAAAAKEScAwAAAIBCxDkAAAAAKKRonLvwwgtTVVXV4nbggQdWzq9atSpjxoxJly5dsttuu2X48OFZunRpi/tYsmRJhg0blo4dO6Zr164566yzsm7duhYzDzzwQA4++OC0a9cu+++/f6ZNm/amXa6++ursu+++ad++fQYPHpxHHnlkqzxnAAAAANig+JVzH/zgB/PSSy9Vbj/96U8r58aPH5977rknd9xxRx588MG8+OKLOfrooyvn169fn2HDhmXNmjWZN29epk+fnmnTpmXixImVmeeffz7Dhg3LYYcdloULF2bcuHE5+eSTM3v27MrM7bffngkTJuSCCy7I448/ngEDBqS+vj4vv/zytnkRAAAAANglFY9zrVu3Tvfu3Su3PfbYI0myYsWK3Hjjjbn88svz8Y9/PIMGDcrNN9+cefPm5ec//3mS5L777svTTz+d73znOxk4cGCOPPLIXHzxxbn66quzZs2aJMnUqVPTu3fvXHbZZenTp0/Gjh2bY445JldccUVlh8svvzynnHJKTjzxxPTt2zdTp05Nx44dc9NNN237FwQAAACAXUbxOPfss8+mR48e2W+//TJy5MgsWbIkSbJgwYKsXbs2Q4cOrcweeOCB6dWrV+bPn58kmT9/fvr3759u3bpVZurr69PY2JinnnqqMvP6+9gws+E+1qxZkwULFrSYqa6uztChQyszG7N69eo0Nja2uAEAAADApiga5wYPHpxp06Zl1qxZufbaa/P888/n7/7u7/LKK6+koaEhbdu2TefOnVv8Trdu3dLQ0JAkaWhoaBHmNpzfcO7tZhobG/Paa6/lD3/4Q9avX7/RmQ33sTGXXHJJamtrK7eePXtu1msAAAAAwK6rdckHP/LIIys/H3TQQRk8eHD22WeffO9730uHDh0KbvbXnXfeeZkwYULlz42NjQIdAAAAAJuk+NtaX69z5875m7/5mzz33HPp3r171qxZk+XLl7eYWbp0abp3754k6d69+5u+vXXDn//aTE1NTTp06JA99tgjrVq12ujMhvvYmHbt2qWmpqbFDQAAAAA2xXYV51599dX8+te/zl577ZVBgwalTZs2mTt3buX84sWLs2TJktTV1SVJ6urq8uSTT7b4VtU5c+akpqYmffv2rcy8/j42zGy4j7Zt22bQoEEtZpqamjJ37tzKDAAAAABsDUXj3Fe+8pU8+OCD+e1vf5t58+blc5/7XFq1apXjjjsutbW1GT16dCZMmJCf/OQnWbBgQU488cTU1dVlyJAhSZIjjjgiffv2zQknnJAnnngis2fPzvnnn58xY8akXbt2SZLTTjstv/nNb3L22Wdn0aJFueaaa/K9730v48ePr+wxYcKEXH/99Zk+fXqeeeaZnH766Vm5cmVOPPHEIq8LAAAAALuGop8595//+Z857rjj8sc//jF77rlnPvrRj+bnP/959txzzyTJFVdckerq6gwfPjyrV69OfX19rrnmmsrvt2rVKvfee29OP/301NXVpVOnThk1alQuuuiiykzv3r0zc+bMjB8/PpMnT87ee++dG264IfX19ZWZY489NsuWLcvEiRPT0NCQgQMHZtasWW/6kggAAAAA2JKqmpubm0svsTNobGxMbW1tVqxY4fPn2CUdf/zxlc9u7NatW2bMmFF4IwAANmbJ1BGlVwDYonqddlvpFTbqnbai7eoz5wAAAABgVyLOAQAAAEAh4hwAAAAAFCLOAQAAAEAh4hwAAAAAFCLOAQAAAEAh4hwAAAAAFCLOAQAAAEAhrUsvwLZx1l5TSq/ATu7P+7+StP3vn194xT9zbHWXvjS29AoAAADvmivnAAAAAKAQcQ4AAAAAChHnAAAAAKAQcQ4AAAAAChHnAAAAAKAQcQ4AAAAAChHnAAAAAKAQcQ4AAAAAChHnAAAAAKAQcQ4AAAAAChHnAAAAAKAQcQ4AAAAAChHnAAAAAKAQcQ4AAAAAChHnAAAAAKAQcQ4AAAAAChHnAAAAAKAQcQ4AAAAAChHnAAAAAKAQcQ4AAAAAChHnAAAAAKAQcQ4AAAAAChHnAAAAAKAQcQ4AAAAAChHnAAAAAKAQcQ4AAAAAChHnAAAAAKCQ1qUXAHYObda13+jPAAAAwFsT54At4gO//R+lVwAAAIAdjre1AgAAAEAh4hwAAAAAFCLOAQAAAEAh4hwAAAAAFCLOAQAAAEAh4hwAAAAAFCLOAQAAAEAh4hwAAAAAFCLOAQAAAEAh4hwAAAAAFCLOAQAAAEAh4hwAAAAAFCLOAQAAAEAh4hwAAAAAFCLOAQAAAEAh4hwAAAAAFCLOAQAAAEAh4hwAAAAAFCLOAQAAAEAh4hwAAAAAFCLOAQAAAEAh4hwAAAAAFCLOAQAAAEAh4hwAAAAAFLLdxLlvfvObqaqqyrhx4yrHVq1alTFjxqRLly7ZbbfdMnz48CxdurTF7y1ZsiTDhg1Lx44d07Vr15x11llZt25di5kHHnggBx98cNq1a5f9998/06ZNe9PjX3311dl3333Tvn37DB48OI888sjWeJoAAAAAULFdxLlHH300/+f//J8cdNBBLY6PHz8+99xzT+644448+OCDefHFF3P00UdXzq9fvz7Dhg3LmjVrMm/evEyfPj3Tpk3LxIkTKzPPP/98hg0blsMOOywLFy7MuHHjcvLJJ2f27NmVmdtvvz0TJkzIBRdckMcffzwDBgxIfX19Xn755a3/5AEAAADYZRWPc6+++mpGjhyZ66+/Pu9973srx1esWJEbb7wxl19+eT7+8Y9n0KBBufnmmzNv3rz8/Oc/T5Lcd999efrpp/Od73wnAwcOzJFHHpmLL744V199ddasWZMkmTp1anr37p3LLrssffr0ydixY3PMMcfkiiuuqDzW5ZdfnlNOOSUnnnhi+vbtm6lTp6Zjx4656aabtu2LAQAAAMAupXicGzNmTIYNG5ahQ4e2OL5gwYKsXbu2xfEDDzwwvXr1yvz585Mk8+fPT//+/dOtW7fKTH19fRobG/PUU09VZt543/X19ZX7WLNmTRYsWNBiprq6OkOHDq3MbMzq1avT2NjY4gYAAAAAm6J1yQe/7bbb8vjjj+fRRx9907mGhoa0bds2nTt3bnG8W7duaWhoqMy8PsxtOL/h3NvNNDY25rXXXsuf//znrF+/fqMzixYtesvdL7nkknz9619/Z08UAGAXcOaZZ2bZsmVJkj333DOTJ08uvBEAwPav2JVzL7zwQs4888x897vfTfv27UutsdnOO++8rFixonJ74YUXSq8EAFDUsmXLsnTp0ixdurQS6QAAeHvF4tyCBQvy8ssv5+CDD07r1q3TunXrPPjgg7nqqqvSunXrdOvWLWvWrMny5ctb/N7SpUvTvXv3JEn37t3f9O2tG/7812ZqamrSoUOH7LHHHmnVqtVGZzbcx8a0a9cuNTU1LW4AAAAAsCmKxbnDDz88Tz75ZBYuXFi5HXLIIRk5cmTl5zZt2mTu3LmV31m8eHGWLFmSurq6JEldXV2efPLJFt+qOmfOnNTU1KRv376Vmdffx4aZDffRtm3bDBo0qMVMU1NT5s6dW5kBAAAAgK2h2GfOvec970m/fv1aHOvUqVO6dOlSOT569OhMmDAhu+++e2pqanLGGWekrq4uQ4YMSZIcccQR6du3b0444YRMmjQpDQ0NOf/88zNmzJi0a9cuSXLaaadlypQpOfvss3PSSSfl/vvvz/e+973MnDmz8rgTJkzIqFGjcsghh+TDH/5wrrzyyqxcuTInnnjiNno1AAAAANgVFf1CiL/miiuuSHV1dYYPH57Vq1envr4+11xzTeV8q1atcu+99+b0009PXV1dOnXqlFGjRuWiiy6qzPTu3TszZ87M+PHjM3ny5Oy999654YYbUl9fX5k59thjs2zZskycODENDQ0ZOHBgZs2a9aYviQAAAACALamqubm5eXN/ec2aNXn++efz/ve/P61bb9edb6trbGxMbW1tVqxYsV1+/txZe00pvQLAFnXpS2NLrwC8wfHHH1/5HN9u3bplxowZhTcCNmbJ1BGlVwDYonqddlvpFTbqnbaizfrMub/85S8ZPXp0OnbsmA9+8INZsmRJkuSMM87IN7/5zc3bGAAAAAB2MZsV584777w88cQTeeCBB9K+ffvK8aFDh+b222/fYssBAAAAwM5ss96Letddd+X222/PkCFDUlVVVTn+wQ9+ML/+9a+32HIAAAAAsDPbrCvnli1blq5du77p+MqVK1vEOgAAAADgrW1WnDvkkEMyc+bMyp83BLkbbrghdXV1W2YzAAAAANjJbdbbWr/xjW/kyCOPzNNPP51169Zl8uTJefrppzNv3rw8+OCDW3pHAAAAANgpbdaVcx/96EezcOHCrFu3Lv379899992Xrl27Zv78+Rk0aNCW3hEAAAAAdkqbdeVckrz//e/P9ddfvyV3AQAAAIBdymZdOffDH/4ws2fPftPx2bNn50c/+tG7XgoAAAAAdgWbFefOPffcrF+//k3Hm5ubc+65577rpQAAAABgV7BZce7ZZ59N375933T8wAMPzHPPPfeulwIAAACAXcFmxbna2tr85je/edPx5557Lp06dXrXSwEAAADArmCz4txnP/vZjBs3Lr/+9a8rx5577rn84z/+Yz7zmc9sseUAAAAAYGe2WXFu0qRJ6dSpUw488MD07t07vXv3Tp8+fdKlS5f867/+65beEQAAAAB2Sq0355dqa2szb968zJkzJ0888UQ6dOiQgw46KB/72Me29H4AAAAAsNParDiXJFVVVTniiCNyxBFHbMl9AAAAAGCX8Y7j3FVXXZVTTz017du3z1VXXfW2s1/+8pff9WIAAAAAsLN7x3HuiiuuyMiRI9O+fftcccUVbzlXVVUlzgEAAADAO/CO49zzzz+/0Z8BAAAAgM2zyd/Wunbt2rz//e/PM888szX2AQAAAIBdxibHuTZt2mTVqlVbYxcAAAAA2KVscpxLkjFjxuRb3/pW1q1bt6X3AQAAAIBdxjv+zLnXe/TRRzN37tzcd9996d+/fzp16tTi/J133rlFlgMAtpwfH/e10iuwk1v1h+X//8/Llvtnjq1u6K0Xl14BAN61zYpznTt3zvDhw7f0LgAAAACwS9mkONfU1JRLL700v/rVr7JmzZp8/OMfz4UXXpgOHTpsrf0AAAAAYKe1SZ859y//8i/56le/mt122y3ve9/7ctVVV2XMmDFbazcAAAAA2KltUpy75ZZbcs0112T27Nm56667cs899+S73/1umpqattZ+AAAAALDT2qQ4t2TJknzyk5+s/Hno0KGpqqrKiy++uMUXAwAAAICd3SbFuXXr1qV9+/YtjrVp0yZr167doksBAAAAwK5gk74Qorm5OV/60pfSrl27yrFVq1bltNNOS6dOnSrH7rzzzi23IQAAAADspDYpzo0aNepNx77whS9ssWUAAAAAYFeySXHu5ptv3lp7AAAAAMAuZ5M+cw4AAAAA2HLEOQAAAAAoRJwDAAAAgELEOQAAAAAoRJwDAAAAgELEOQAAAAAoRJwDAAAAgELEOQAAAAAoRJwDAAAAgEJal14AAICdw3tatd3ozwAAvDVxDgCALeKE9/YvvQIAwA7H21oBAAAAoBBxDgAAAAAKEecAAAAAoBBxDgAAAAAKEecAAAAAoBBxDgAAAAAKEecAAAAAoBBxDgAAAAAKEecAAAAAoBBxDgAAAAAKEecAAAAAoBBxDgAAAAAKEecAAAAAoBBxDgAAAAAKEecAAAAAoBBxDgAAAAAKEecAAAAAoBBxDgAAAAAKKRrnrr322hx00EGpqalJTU1N6urq8qMf/ahyftWqVRkzZky6dOmS3XbbLcOHD8/SpUtb3MeSJUsybNiwdOzYMV27ds1ZZ52VdevWtZh54IEHcvDBB6ddu3bZf//9M23atDftcvXVV2ffffdN+/btM3jw4DzyyCNb5TkDAAAAwAZF49zee++db37zm1mwYEEee+yxfPzjH89nP/vZPPXUU0mS8ePH55577skdd9yRBx98MC+++GKOPvroyu+vX78+w4YNy5o1azJv3rxMnz4906ZNy8SJEyszzz//fIYNG5bDDjssCxcuzLhx43LyySdn9uzZlZnbb789EyZMyAUXXJDHH388AwYMSH19fV5++eVt92IAAAAAsMupam5ubi69xOvtvvvuufTSS3PMMcdkzz33zIwZM3LMMcckSRYtWpQ+ffpk/vz5GTJkSH70ox/lU5/6VF588cV069YtSTJ16tScc845WbZsWdq2bZtzzjknM2fOzC9/+cvKY4wYMSLLly/PrFmzkiSDBw/OoYcemilTpiRJmpqa0rNnz5xxxhk599xz39HejY2Nqa2tzYoVK1JTU7MlX5It4qy9ppReAWCLuvSlsaVX2OH8+LivlV4BYIsaeuvFpVfYIS2ZOqL0CgBbVK/Tbiu9wka901a03Xzm3Pr163Pbbbdl5cqVqaury4IFC7J27doMHTq0MnPggQemV69emT9/fpJk/vz56d+/fyXMJUl9fX0aGxsrV9/Nnz+/xX1smNlwH2vWrMmCBQtazFRXV2fo0KGVmY1ZvXp1GhsbW9wAAAAAYFMUj3NPPvlkdtttt7Rr1y6nnXZafvCDH6Rv375paGhI27Zt07lz5xbz3bp1S0NDQ5KkoaGhRZjbcH7DubebaWxszGuvvZY//OEPWb9+/UZnNtzHxlxyySWpra2t3Hr27LlZzx8AAACAXVfxOPeBD3wgCxcuzMMPP5zTTz89o0aNytNPP116rb/qvPPOy4oVKyq3F154ofRKAAAAAOxgWpdeoG3bttl///2TJIMGDcqjjz6ayZMn59hjj82aNWuyfPnyFlfPLV26NN27d0+SdO/e/U3fqrrh21xfP/PGb3hdunRpampq0qFDh7Rq1SqtWrXa6MyG+9iYdu3apV27dpv3pAEAAAAg28GVc2/U1NSU1atXZ9CgQWnTpk3mzp1bObd48eIsWbIkdXV1SZK6uro8+eSTLb5Vdc6cOampqUnfvn0rM6+/jw0zG+6jbdu2GTRoUIuZpqamzJ07tzIDAAAAAFtD0SvnzjvvvBx55JHp1atXXnnllcyYMSMPPPBAZs+endra2owePToTJkzI7rvvnpqampxxxhmpq6vLkCFDkiRHHHFE+vbtmxNOOCGTJk1KQ0NDzj///IwZM6ZyVdtpp52WKVOm5Oyzz85JJ52U+++/P9/73vcyc+bMyh4TJkzIqFGjcsghh+TDH/5wrrzyyqxcuTInnnhikdcFAAAAgF1D0Tj38ssv54tf/GJeeuml1NbW5qCDDsrs2bPz93//90mSK664ItXV1Rk+fHhWr16d+vr6XHPNNZXfb9WqVe69996cfvrpqaurS6dOnTJq1KhcdNFFlZnevXtn5syZGT9+fCZPnpy99947N9xwQ+rr6yszxx57bJYtW5aJEyemoaEhAwcOzKxZs970JREAAAAAsCVVNTc3N5deYmfQ2NiY2trarFixIjU1NaXXeZOz9ppSegWALerSl8aWXmGH8+PjvlZ6BYAtauitF5deYYe0ZOqI0isAbFG9Trut9Aob9U5b0Xb3mXMAAAAAsKsQ5wAAAACgEHEOAAAAAAoR5wAAAACgEHEOAAAAAAoR5wAAAACgEHEOAAAAAAoR5wAAAACgEHEOAAAAAAoR5wAAAACgEHEOAAAAAAoR5wAAAACgEHEOAAAAAAoR5wAAAACgEHEOAAAAAAoR5wAAAACgEHEOAAAAAAoR5wAAAACgEHEOAAAAAAoR5wAAAACgEHEOAAAAAAoR5wAAAACgEHEOAAAAAAoR5wAAAACgEHEOAAAAAAoR5wAAAACgEHEOAAAAAAoR5wAAAACgEHEOAAAAAAoR5wAAAACgEHEOAAAAAAoR5wAAAACgEHEOAAAAAAoR5wAAAACgEHEOAAAAAAoR5wAAAACgEHEOAAAAAAoR5wAAAACgEHEOAAAAAAoR5wAAAACgEHEOAAAAAAoR5wAAAACgEHEOAAAAAAoR5wAAAACgEHEOAAAAAAoR5wAAAACgEHEOAAAAAAoR5wAAAACgEHEOAAAAAAoR5wAAAACgEHEOAAAAAAoR5wAAAACgEHEOAAAAAAoR5wAAAACgEHEOAAAAAAoR5wAAAACgEHEOAAAAAAoR5wAAAACgEHEOAAAAAAoR5wAAAACgEHEOAAAAAAoR5wAAAACgkKJx7pJLLsmhhx6a97znPenatWuOOuqoLF68uMXMqlWrMmbMmHTp0iW77bZbhg8fnqVLl7aYWbJkSYYNG5aOHTuma9euOeuss7Ju3boWMw888EAOPvjgtGvXLvvvv3+mTZv2pn2uvvrq7Lvvvmnfvn0GDx6cRx55ZIs/ZwAAAADYoGice/DBBzNmzJj8/Oc/z5w5c7J27docccQRWblyZWVm/Pjxueeee3LHHXfkwQcfzIsvvpijjz66cn79+vUZNmxY1qxZk3nz5mX69OmZNm1aJk6cWJl5/vnnM2zYsBx22GFZuHBhxo0bl5NPPjmzZ8+uzNx+++2ZMGFCLrjggjz++OMZMGBA6uvr8/LLL2+bFwMAAACAXU5Vc3Nzc+klNli2bFm6du2aBx98MB/72MeyYsWK7LnnnpkxY0aOOeaYJMmiRYvSp0+fzJ8/P0OGDMmPfvSjfOpTn8qLL76Ybt26JUmmTp2ac845J8uWLUvbtm1zzjnnZObMmfnlL39ZeawRI0Zk+fLlmTVrVpJk8ODBOfTQQzNlypQkSVNTU3r27Jkzzjgj55577l/dvbGxMbW1tVmxYkVqamq29Evzrp2115TSKwBsUZe+NLb0CjucHx/3tdIrAGxRQ2+9uPQKO6QlU0eUXgFgi+p12m2lV9iod9qKtqvPnFuxYkWSZPfdd0+SLFiwIGvXrs3QoUMrMwceeGB69eqV+fPnJ0nmz5+f/v37V8JcktTX16exsTFPPfVUZeb197FhZsN9rFmzJgsWLGgxU11dnaFDh1Zm3mj16tVpbGxscQMAAACATbHdxLmmpqaMGzcuH/nIR9KvX78kSUNDQ9q2bZvOnTu3mO3WrVsaGhoqM68PcxvObzj3djONjY157bXX8oc//CHr16/f6MyG+3ijSy65JLW1tZVbz549N++JAwAAALDL2m7i3JgxY/LLX/4yt922fV6K+EbnnXdeVqxYUbm98MILpVcCAAAAYAfTuvQCSTJ27Njce++9eeihh7L33ntXjnfv3j1r1qzJ8uXLW1w9t3Tp0nTv3r0y88ZvVd3wba6vn3njN7wuXbo0NTU16dChQ1q1apVWrVptdGbDfbxRu3bt0q5du817wgAAAACQwlfONTc3Z+zYsfnBD36Q+++/P717925xftCgQWnTpk3mzp1bObZ48eIsWbIkdXV1SZK6uro8+eSTLb5Vdc6cOampqUnfvn0rM6+/jw0zG+6jbdu2GTRoUIuZpqamzJ07tzIDAAAAAFta0SvnxowZkxkzZuT//b//l/e85z2Vz3erra1Nhw4dUltbm9GjR2fChAnZfffdU1NTkzPOOCN1dXUZMmRIkuSII45I3759c8IJJ2TSpElpaGjI+eefnzFjxlSubDvttNMyZcqUnH322TnppJNy//3353vf+15mzpxZ2WXChAkZNWpUDjnkkHz4wx/OlVdemZUrV+bEE0/c9i8MAAAAALuEonHu2muvTZL8z//5P1scv/nmm/OlL30pSXLFFVekuro6w4cPz+rVq1NfX59rrrmmMtuqVavce++9Of3001NXV5dOnTpl1KhRueiiiyozvXv3zsyZMzN+/PhMnjw5e++9d2644YbU19dXZo499tgsW7YsEydOTENDQwYOHJhZs2a96UsiAAAAAGBLqWpubm4uvcTOoLGxMbW1tVmxYkVqampKr/MmZ+01pfQKAFvUpS+NLb3CDufHx32t9AoAW9TQWy8uvcIOacnUEaVXANiiep22fX656DttRdvNt7UCAAAAwK5GnAMAAACAQsQ5AAAAAChEnAMAAACAQsQ5AAAAAChEnAMAAACAQsQ5AAAAAChEnAMAAACAQsQ5AAAAAChEnAMAAACAQsQ5AAAAAChEnAMAAACAQsQ5AAAAAChEnAMAAACAQsQ5AAAAAChEnAMAAACAQsQ5AAAAAChEnAMAAACAQsQ5AAAAAChEnAMAAACAQsQ5AAAAAChEnAMAAACAQsQ5AAAAAChEnAMAAACAQsQ5AAAAAChEnAMAAACAQsQ5AAAAAChEnAMAAACAQsQ5AAAAAChEnAMAAACAQsQ5AAAAAChEnAMAAACAQsQ5AAAAAChEnAMAAACAQsQ5AAAAAChEnAMAAACAQsQ5AAAAAChEnAMAAACAQsQ5AAAAAChEnAMAAACAQsQ5AAAAAChEnAMAAACAQsQ5AAAAAChEnAMAAACAQsQ5AAAAAChEnAMAAACAQsQ5AAAAAChEnAMAAACAQsQ5AAAAAChEnAMAAACAQsQ5AAAAAChEnAMAAACAQsQ5AAAAAChEnAMAAACAQsQ5AAAAAChEnAMAAACAQsQ5AAAAAChEnAMAAACAQsQ5AAAAAChEnAMAAACAQsQ5AAAAACikaJx76KGH8ulPfzo9evRIVVVV7rrrrhbnm5ubM3HixOy1117p0KFDhg4dmmeffbbFzJ/+9KeMHDkyNTU16dy5c0aPHp1XX321xcwvfvGL/N3f/V3at2+fnj17ZtKkSW/a5Y477siBBx6Y9u3bp3///vnhD3+4xZ8vAAAAALxe0Ti3cuXKDBgwIFdfffVGz0+aNClXXXVVpk6dmocffjidOnVKfX19Vq1aVZkZOXJknnrqqcyZMyf33ntvHnrooZx66qmV842NjTniiCOyzz77ZMGCBbn00ktz4YUX5rrrrqvMzJs3L8cdd1xGjx6d//iP/8hRRx2Vo446Kr/85S+33pMHAAAAYJfXuuSDH3nkkTnyyCM3eq65uTlXXnllzj///Hz2s59Nktxyyy3p1q1b7rrrrowYMSLPPPNMZs2alUcffTSHHHJIkuTb3/52PvnJT+Zf//Vf06NHj3z3u9/NmjVrctNNN6Vt27b54Ac/mIULF+byyy+vRLzJkyfnE5/4RM4666wkycUXX5w5c+ZkypQpmTp16jZ4JQAAAADYFW23nzn3/PPPp6GhIUOHDq0cq62tzeDBgzN//vwkyfz589O5c+dKmEuSoUOHprq6Og8//HBl5mMf+1jatm1bmamvr8/ixYvz5z//uTLz+sfZMLPhcTZm9erVaWxsbHEDAAAAgE2x3ca5hoaGJEm3bt1aHO/WrVvlXENDQ7p27drifOvWrbP77ru3mNnYfbz+Md5qZsP5jbnkkktSW1tbufXs2XNTnyIAAAAAu7jtNs5t784777ysWLGicnvhhRdKrwQAAADADma7jXPdu3dPkixdurTF8aVLl1bOde/ePS+//HKL8+vWrcuf/vSnFjMbu4/XP8ZbzWw4vzHt2rVLTU1NixsAAAAAbIrtNs717t073bt3z9y5cyvHGhsb8/DDD6euri5JUldXl+XLl2fBggWVmfvvvz9NTU0ZPHhwZeahhx7K2rVrKzNz5szJBz7wgbz3ve+tzLz+cTbMbHgcAAAAANgaisa5V199NQsXLszChQuT/NeXQCxcuDBLlixJVVVVxo0bl3/+53/O3XffnSeffDJf/OIX06NHjxx11FFJkj59+uQTn/hETjnllDzyyCP52c9+lrFjx2bEiBHp0aNHkuT4449P27ZtM3r06Dz11FO5/fbbM3ny5EyYMKGyx5lnnplZs2blsssuy6JFi3LhhRfmsccey9ixY7f1SwIAAADALqR1yQd/7LHHcthhh1X+vCGYjRo1KtOmTcvZZ5+dlStX5tRTT83y5cvz0Y9+NLNmzUr79u0rv/Pd7343Y8eOzeGHH57q6uoMHz48V111VeV8bW1t7rvvvowZMyaDBg3KHnvskYkTJ+bUU0+tzPzt3/5tZsyYkfPPPz9f/epXc8ABB+Suu+5Kv379tsGrAAAAAMCuqqq5ubm59BI7g8bGxtTW1mbFihXb5efPnbXXlNIrAGxRl77k6uZN9ePjvlZ6BYAtauitF5deYYe0ZOqI0isAbFG9Trut9Aob9U5b0Xb7mXMAAAAAsLMT5wAAAACgEHEOAAAAAAoR5wAAAACgEHEOAAAAAAoR5wAAAACgEHEOAAAAAAoR5wAAAACgEHEOAAAAAAoR5wAAAACgEHEOAAAAAAoR5wAAAACgEHEOAAAAAAoR5wAAAACgEHEOAAAAAAoR5wAAAACgEHEOAAAAAAoR5wAAAACgEHEOAAAAAAoR5wAAAACgEHEOAAAAAAoR5wAAAACgEHEOAAAAAAoR5wAAAACgEHEOAAAAAAoR5wAAAACgEHEOAAAAAAoR5wAAAACgEHEOAAAAAAoR5wAAAACgEHEOAAAAAAoR5wAAAACgEHEOAAAAAAoR5wAAAACgEHEOAAAAAAoR5wAAAACgEHEOAAAAAAoR5wAAAACgEHEOAAAAAAoR5wAAAACgEHEOAAAAAAoR5wAAAACgEHEOAAAAAAoR5wAAAACgEHEOAAAAAAoR5wAAAACgEHEOAAAAAAoR5wAAAACgEHEOAAAAAAoR5wAAAACgEHEOAAAAAAoR5wAAAACgEHEOAAAAAAoR5wAAAACgEHEOAAAAAAoR5wAAAACgEHEOAAAAAAoR5wAAAACgEHEOAAAAAAoR5wAAAACgEHEOAAAAAAoR597g6quvzr777pv27dtn8ODBeeSRR0qvBAAAAMBOSpx7ndtvvz0TJkzIBRdckMcffzwDBgxIfX19Xn755dKrAQAAALATEude5/LLL88pp5ySE088MX379s3UqVPTsWPH3HTTTaVXAwAAAGAnJM79tzVr1mTBggUZOnRo5Vh1dXWGDh2a+fPnF9wMAAAAgJ1V69ILbC/+8Ic/ZP369enWrVuL4926dcuiRYveNL969eqsXr268ucVK1YkSRobG7fuoptpddNrpVcA2KK21/++3Z6tXLv6rw8B7ED8XbB5XnltbekVALao7fXvgw17NTc3v+2cOLeZLrnkknz9619/0/GePXsW2AZg1/Pt2rNLrwBAad+/tPQGAGwPJtxZeoO39corr6S2tvYtz4tz/22PPfZIq1atsnTp0hbHly5dmu7du79p/rzzzsuECRMqf25qasqf/vSndOnSJVVVVVt9X9geNTY2pmfPnnnhhRdSU1NTeh0ACvB3AQCJvw8g+a8r5l555ZX06NHjbefEuf/Wtm3bDBo0KHPnzs1RRx2V5L+C29y5czN27Ng3zbdr1y7t2rVrcaxz587bYFPY/tXU1PgLGGAX5+8CABJ/H8DbXTG3gTj3OhMmTMioUaNyyCGH5MMf/nCuvPLKrFy5MieeeGLp1QAAAADYCYlzr3Psscdm2bJlmThxYhoaGjJw4MDMmjXrTV8SAQAAAABbgjj3BmPHjt3o21iBv65du3a54IIL3vSWbwB2Hf4uACDx9wFsiqrmv/Z9rgAAAADAVlFdegEAAAAA2FWJcwAAAABQiDgHAAAAAIWIcwAAAABQiDgHAAAAAIW0Lr0AAAA7tubm5ixYsCC//e1vU1VVld69e+dDH/pQqqqqSq8GALDdE+eATXb33Xe/49nPfOYzW3ETAEr7yU9+ktGjR+d3v/tdmpubk6QS6G666aZ87GMfK7whAMD2rap5w/+KAniHqqvf2Tviq6qqsn79+q28DQClPPfccxkwYEAGDx6cM888MwceeGCam5vz9NNP56qrrspjjz2WX/ziF9lvv/1KrwrANvKf//mfufvuu7NkyZKsWbOmxbnLL7+80FawfRPnAADYLGPHjs0zzzyTuXPnvulcc3Nzhg4dmr59++bb3/52ge0A2Nbmzp2bz3zmM9lvv/2yaNGi9OvXL7/97W/T3Nycgw8+OPfff3/pFWG75AshgC1m1apVpVcAYBt64IEHMm7cuI2eq6qqyrhx4/KTn/xk2y4FQDHnnXdevvKVr+TJJ59M+/bt8/3vfz8vvPBC/sf/+B/5/Oc/X3o92G6Jc8C7sn79+lx88cV53/vel9122y2/+c1vkiRf+9rXcuONNxbeDoCtacmSJenfv/9bnu/Xr19+97vfbcONACjpmWeeyRe/+MUkSevWrfPaa69lt912y0UXXZRvfetbhbeD7Zc4B7wr//Iv/5Jp06Zl0qRJadu2beV4v379csMNNxTcDICt7dVXX03Hjh3f8nzHjh3zl7/8ZRtuBEBJnTp1qnzO3F577ZVf//rXlXN/+MMfSq0F2z3f1gq8K7fcckuuu+66HH744TnttNMqxwcMGJBFixYV3AyAbeHpp59OQ0PDRs/5FzGAXcuQIUPy05/+NH369MknP/nJ/OM//mOefPLJ3HnnnRkyZEjp9WC7Jc4B78rvf//77L///m863tTUlLVr1xbYCIBt6fDDD8/Gvl+sqqoqzc3NqaqqKrAVACVcfvnlefXVV5MkX//61/Pqq6/m9ttvzwEHHOCbWuFtiHPAu9K3b9/8+7//e/bZZ58Wx//t3/4tH/rQhwptBcC28Pzzz5deAYDtyH777Vf5uVOnTpk6dWrBbWDHIc4B78rEiRMzatSo/P73v09TU1PuvPPOLF68OLfcckvuvffe0usBsBW98f+YAQBg01U1b+x9CACb4N///d9z0UUX5Yknnsirr76agw8+OBMnTswRRxxRejUAtqIlS5a8o7levXpt5U0AKGX33XfPr371q+yxxx5573vf+7YfZ/CnP/1pG24GOw5xDgCAzdKqVavKzxv+J+Xr/6Vsw2fOrV+/fpvvBsC2MX369IwYMSLt2rXL9OnT33Z21KhR22gr2LGIc8C7st9+++XRRx9Nly5dWhxfvnx5Dj744PzmN78ptBkAW1vr1q2z995750tf+lI+/elPp3XrjX9iyoABA7bxZgAAOw5xDnhXqqur09DQkK5du7Y4vnTp0vTq1SurV68utBkAW1tDQ0OmT5+em2++OcuXL88XvvCFjB49On369Cm9GgDbSGNj4zueramp2YqbwI5LnAM2y913350kOeqoozJ9+vTU1tZWzq1fvz5z587NnDlzsnjx4lIrArAN/fSnP83NN9+cO+64I3379s3o0aMzevToVFdXl14NgK2ourr6bT9n7vV8zAFsnDgHbJYN/7JVVVWVN/7XSJs2bbLvvvvmsssuy6c+9akS6wFQyNKlS3PcccflwQcfzLJly7L77ruXXgmArejBBx+s/Pzb3/425557br70pS+lrq4uSTJ//vxMnz49l1xyic+cg7cgzgHvSu/evfPoo49mjz32KL0KAAXNmzcvN910U+6444584AMfyEknnZRTTz3VlXMAu5DDDz88J598co477rgWx2fMmJHrrrsuDzzwQJnFYDsnzgEAsFleeuml3HLLLbn55pvz5z//OSNHjsxJJ52Ufv36lV4NgAI6duyYJ554IgcccECL47/61a8ycODA/OUvfym0GWzfNv6VWgCbYO7cuZk7d25efvnlNDU1tTh30003FdoKgK2tV69eed/73pdRo0blM5/5TNq0aZOmpqb84he/aDF30EEHFdoQgG2pZ8+euf766zNp0qQWx2+44Yb07Nmz0Faw/XPlHPCufP3rX89FF12UQw45JHvttdebPgz2Bz/4QaHNANjaXv+W1Q3//f/G/2lZVVXlA8ABdhE//OEPM3z48Oy///4ZPHhwkuSRRx7Js88+m+9///v55Cc/WXhD2D6Jc8C7stdee2XSpEk54YQTSq8CwDb2u9/97q/OvPLKK97mCrALeeGFF3Lttddm0aJFSZI+ffrktNNOc+UcvA1xDnhXunTpkkceeSTvf//7S68CwHbilVdeya233pobb7wxjz32mCvnAADehjgHvCvnnHNOdtttt3zta18rvQoAhT300EO58cYb8/3vfz89evTI0UcfneHDh+fQQw8tvRoAW8kvfvGL9OvXL9XV1W/6zNE38hmksHG+EAJ4V1atWpXrrrsuP/7xj3PQQQelTZs2Lc5ffvnlhTYDYFtoaGjItGnTcuONN6axsTH/8A//kNWrV+euu+5K3759S68HwFY2cODANDQ0pGvXrhk4cGCqqqre9Pmjic8ghbfjyjngXTnssMPe9vxPfvKTbbQJANvapz/96Tz00EMZNmxYRo4cmU984hNp1apV2rRpkyeeeEKcA9gF/O53v0uvXr1SVVX1Vz+LdJ999tlGW8GORZwDAGCztG7dOl/+8pdz+umn54ADDqgcF+cAdk2rVq1K+/btS68BOxxvawU2y9FHH/1XZ6qqqvL9739/G2wDQAk//elPc+ONN2bQoEHp06dPTjjhhIwYMaL0WgAU0rVr13zuc5/LF77whRx++OGprq4uvRLsEPwnBdgstbW1f/VWU1NTek0AtqIhQ4bk+uuvz0svvZT/9b/+V2677bb06NEjTU1NmTNnTl555ZXSKwKwDU2fPj1/+ctf8tnPfjbve9/7Mm7cuDz22GOl14Ltnre1AgCwxSxevDg33nhj/u///b9Zvnx5/v7v/z5333136bUA2IZeeeWV/Nu//VtuvfXW3H///dlvv/3yhS98IRMnTiy9GmyXxDkAALa49evX55577slNN90kzgHswp5++umMHDkyv/jFL3xbK7wFcQ4AAADYYlatWpW77747M2bMyKxZs9KtW7ccd9xx+eY3v1l6Ndgu+UIIAAAA4F2bPXt2ZsyYkbvuuiutW7fOMccck/vuuy8f+9jHSq8G2zVXzgEAAADvWseOHfOpT30qI0eOzCc/+cm0adOm9EqwQxDnAAAAgHftlVdeyXve857Sa8AOx9taAQAAgM1WXV2dqqqqt52pqqrKunXrttFGsGMR5wAAAIDN9oMf/OAtz82fPz9XXXVVmpqatuFGsGPxtlYAAABgi1q8eHHOPffc3HPPPRk5cmQuuuii7LPPPqXXgu1SdekFAAAAgJ3Diy++mFNOOSX9+/fPunXrsnDhwkyfPl2Yg7chzgEAAADvyooVK3LOOedk//33z1NPPZW5c+fmnnvuSb9+/UqvBts9nzkHAAAAbLZJkyblW9/6Vrp3755bb701n/3sZ0uvBDsUnzkHAAAAbLbq6up06NAhQ4cOTatWrd5y7s4779yGW8GOw5VzAAAAwGb74he/mKqqqtJrwA7LlXMAAAAAUIgvhAAAAACAQsQ5AAAAAChEnAMAAACAQsQ5AAC2iGnTpqVz586l12hhe9wJAOD1xDkAgJ1AQ0NDzjzzzOy///5p3759unXrlo985CO59tpr85e//KX0egAAvIXWpRcAAODd+c1vfpOPfOQj6dy5c77xjW+kf//+adeuXZ588slcd911ed/73pfPfOYzpdfcqDVr1qRt27al1wAAKMaVcwAAO7j//b//d1q3bp3HHnss//AP/5A+ffpkv/32y2c/+9nMnDkzn/70p5MkVVVVufbaa3PkkUemQ4cO2W+//fJv//Zvlft54IEHUlVVleXLl1eOLVy4MFVVVfntb3/7jve56667csABB6R9+/apr6/PCy+8UDl34YUXZuDAgbnhhhvSu3fvtG/fPkkya9asfPSjH03nzp3TpUuXfOpTn8qvf/3ryu/99re/TVVVVe68884cdthh6dixYwYMGJD58+e3eOxp06alV69e6dixYz73uc/lj3/846a8lAAA25w4BwCwA/vjH/+Y++67L2PGjEmnTp02OlNVVVX5+Wtf+1qGDx+eJ554IiNHjsyIESPyzDPPbLF9/vKXv+Rf/uVfcsstt+RnP/tZli9fnhEjRrSYee655/L9738/d955ZxYuXJgkWblyZSZMmJDHHnssc+fOTXV1dT73uc+lqampxe/+0z/9U77yla9k4cKF+Zu/+Zscd9xxWbduXZLk4YcfzujRozN27NgsXLgwhx12WP75n/95iz03AICtwdtaAQB2YM8991yam5vzgQ98oMXxPfbYI6tWrUqSjBkzJt/61reSJJ///Odz8sknJ0kuvvjizJkzJ9/+9rdzzTXXbJF91q5dmylTpmTw4MFJkunTp6dPnz555JFH8uEPfzjJf72V9ZZbbsmee+5Z+b3hw4e3uJ+bbrope+65Z55++un069evcvwrX/lKhg0bliT5+te/ng9+8IN57rnncuCBB2by5Mn5xCc+kbPPPjtJ8jd/8zeZN29eZs2atUWeGwDA1uDKOQCAndAjjzyShQsX5oMf/GBWr15dOV5XV9dirq6uboteOde6desceuihlT8feOCB6dy5c4vH2GeffVqEuSR59tlnc9xxx2W//fZLTU1N9t133yTJkiVLWswddNBBlZ/32muvJMnLL7+cJHnmmWcqUXCDNz5fAIDtjSvnAAB2YPvvv3+qqqqyePHiFsf322+/JEmHDh3e8X1VV//X/2/b3NxcObZ27dotsGVLG3v77ac//enss88+uf7669OjR480NTWlX79+WbNmTYu5Nm3aVH7e8HbdN771FQBgR+LKOQCAHViXLl3y93//95kyZUpWrlz5V+d//vOfv+nPffr0SZLK1WwvvfRS5fyGz4R7p9atW5fHHnus8ufFixdn+fLllcfYmD/+8Y9ZvHhxzj///Bx++OHp06dP/vznP2/S4yZJnz598vDDD7c49sbnCwCwvRHnAAB2cNdcc03WrVuXQw45JLfffnueeeaZLF68ON/5zneyaNGitGrVqjJ7xx135KabbsqvfvWrXHDBBXnkkUcyduzYJP91FV7Pnj1z4YUX5tlnn83MmTNz2WWXbdIubdq0yRlnnJGHH344CxYsyJe+9KUMGTKk8nlzG/Pe9743Xbp0yXXXXZfnnnsu999/fyZMmLDJr8OXv/zlzJo1K//6r/+aZ599NlOmTPF5cwDAdk+cAwDYwb3//e/Pf/zHf2To0KE577zzMmDAgBxyyCH59re/na985Su5+OKLK7Nf//rXc9ttt+Wggw7KLbfckltvvTV9+/ZN8l9h7dZbb82iRYty0EEH5Vvf+tYmf9tpx44dc8455+T444/PRz7ykey22265/fbb3/Z3qqurc9ttt2XBggXp169fxo8fn0svvXSTX4chQ4bk+uuvz+TJkzNgwIDcd999Of/88zf5fgAAtqWq5td/qAgAADutqqqq/OAHP8hRRx1VehUAAP6bK+cAAAAAoBBxDgCAd+TII4/MbrvtttHbN77xjdLrAQDskLytFQCAd+T3v/99XnvttY2e23333bP77rtv440AAHZ84hwAAAAAFOJtrQAAAABQiDgHAAAAAIWIcwAAAABQiDgHAAAAAIWIcwAAAABQiDgHAAAAAIWIcwAAAABQiDgHAAAAAIX8fzXWKo1jHS9RAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1500x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,7))\n",
    "sns.barplot(x=df['Gpu_brand'],y=df['Price'],estimator=np.median,palette='plasma')\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "fcd7a27a-cabb-4b6e-adec-970c90474659",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Inches</th>\n",
       "      <th>ScreenResolution</th>\n",
       "      <th>Ram</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>IPS</th>\n",
       "      <th>Cpu_brand</th>\n",
       "      <th>HDD</th>\n",
       "      <th>SSD</th>\n",
       "      <th>Gpu_brand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>8</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>71378.6832</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>1440x900</td>\n",
       "      <td>8</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>47895.5232</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>8</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>30636.0000</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>15.4</td>\n",
       "      <td>IPS Panel Retina Display 2880x1800</td>\n",
       "      <td>16</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>135195.3360</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>512</td>\n",
       "      <td>AMD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>8</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>96095.8080</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Inches                    ScreenResolution  Ram  OpSys  \\\n",
       "0   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600    8  macOS   \n",
       "1   Apple  Ultrabook    13.3                            1440x900    8  macOS   \n",
       "2      HP   Notebook    15.6                   Full HD 1920x1080    8  No OS   \n",
       "3   Apple  Ultrabook    15.4  IPS Panel Retina Display 2880x1800   16  macOS   \n",
       "4   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600    8  macOS   \n",
       "\n",
       "   Weight        Price Touchscreen  IPS      Cpu_brand  HDD  SSD Gpu_brand  \n",
       "0    1.37   71378.6832          No  Yes  Intel Core i5    0  128     Intel  \n",
       "1    1.34   47895.5232          No   No  Intel Core i5    0    0     Intel  \n",
       "2    1.86   30636.0000          No   No  Intel Core i5    0  256     Intel  \n",
       "3    1.83  135195.3360          No  Yes  Intel Core i7    0  512       AMD  \n",
       "4    1.37   96095.8080          No  Yes  Intel Core i5    0  256     Intel  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(columns=['Gpu'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "9e30aaa5-bb76-48e4-9902-298b7074a871",
   "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": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Operating System Column\n",
    "df['OpSys'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "532fd3b0-e5de-4394-ac2f-3547aa2af28c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,7))\n",
    "sns.barplot(x=df['OpSys'],y=df['Price'],palette='plasma')\n",
    "plt.xticks(rotation = 'vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "5bcf9ff7-ecfa-42dc-8b7b-2f633624256b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['macOS', 'No OS', 'Windows 10', 'Mac OS X', 'Linux', 'Android',\n",
       "       'Windows 10 S', 'Chrome OS', 'Windows 7'], dtype=object)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['OpSys'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "297ddbea-3666-4495-b492-dc7c7b72746e",
   "metadata": {},
   "source": [
    "We can see that there are many versions of one type of OS, espicially for Windows and macOS. So we can club them together like,\n",
    "\n",
    "club {Windows 10,Windows 7,Windows 7 S}-->Windows\n",
    "\n",
    "club {macOS,mac OS X}--> mac\n",
    "\n",
    "others --> others"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "5f4d7386-a9bd-42fe-a75c-7b65403d4b1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Get which OP sys\n",
    "def cat_os(inp):\n",
    "    if inp == 'Windows 10' or inp == 'Windows 7' or inp == 'Windows 10 S':\n",
    "        return 'Windows'\n",
    "    elif inp == 'macOS' or inp == 'Mac OS X':\n",
    "        return 'Mac'\n",
    "    else:\n",
    "        return 'Others/No OS/Linux'\n",
    "df['os'] = df['OpSys'].apply(cat_os)\n",
    "df.drop(columns=['OpSys'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "670c5656-991f-4830-83eb-f8714e19fd1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=df['os'],y=df['Price'],palette='plasma')\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "03463d8a-e715-4f3c-92f5-15489c781b32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Weight analysis\n",
    "plt.figure(figsize=(15,7))\n",
    "sns.distplot(df['Weight'])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "299b9c10-ce38-454c-ba2a-c274930ecdef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Scatterplot - Price variation on weight\n",
    "plt.figure(figsize=(15,7))\n",
    "sns.scatterplot(x=df['Weight'],y=df['Price'])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "a0d2628c-9f72-4ab6-8183-34164fcb1db7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Price analysis\n",
    "plt.figure(figsize=(15,7))\n",
    "sns.distplot(df['Price'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "b16e1232-49be-43f9-849c-cd4a54199cca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#apply np.log to get guassian distribution of price column\n",
    "plt.figure(figsize=(15,7))\n",
    "sns.distplot(np.log(df['Price']))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "b59fcbec-6c2e-4cf2-b762-a2f0b3bc3b00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Inches    0.067329\n",
       "Ram       0.742905\n",
       "Weight    0.209867\n",
       "Price     1.000000\n",
       "HDD      -0.027189\n",
       "SSD       0.666593\n",
       "Name: Price, dtype: float64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr(numeric_only=True)['Price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "c2187be0-55bb-41cd-aad8-3c4749e0f7c4",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Heatmap - Correlation for each features\n",
    "plt.figure(figsize=(15,7))\n",
    "sns.heatmap(df.corr(numeric_only=True),annot=True,cmap='plasma')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "9d4d17fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ppi(df):\n",
    "    splitdf = df['ScreenResolution'].str.split('x',n = 1,expand=True)\n",
    "    df['X_res'] = splitdf[0]\n",
    "    df['Y_res'] = splitdf[1]\n",
    "    df['X_res'] = df['X_res'].str.replace(',','').str.findall(r'(\\d+.?\\d+)').apply(lambda x:x[0])\n",
    "    df['X_res'] = df['X_res'].astype('int')\n",
    "    df['Y_res'] = df['Y_res'].astype('int')\n",
    "    df['ppi'] = (((df['X_res']**2) + (df['Y_res']**2))**0.5/(df['Inches']+0.00000001)).astype('float')\n",
    "    df.drop(columns = ['ScreenResolution', 'Inches','X_res','Y_res'], inplace=True)\n",
    "ppi(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "a44f3b4a",
   "metadata": {},
   "outputs": [
    {
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       "      <td>Lenovo</td>\n",
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       "      <td>Asus</td>\n",
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       "     Company            TypeName  Ram  Weight        Price Touchscreen  IPS  \\\n",
       "0      Apple           Ultrabook    8    1.37   71378.6832          No  Yes   \n",
       "1      Apple           Ultrabook    8    1.34   47895.5232          No   No   \n",
       "2         HP            Notebook    8    1.86   30636.0000          No   No   \n",
       "3      Apple           Ultrabook   16    1.83  135195.3360          No  Yes   \n",
       "4      Apple           Ultrabook    8    1.37   96095.8080          No  Yes   \n",
       "...      ...                 ...  ...     ...          ...         ...  ...   \n",
       "1298  Lenovo  2 in 1 Convertible    4    1.80   33992.6400         Yes  Yes   \n",
       "1299  Lenovo  2 in 1 Convertible   16    1.30   79866.7200         Yes  Yes   \n",
       "1300  Lenovo            Notebook    2    1.50   12201.1200          No   No   \n",
       "1301      HP            Notebook    6    2.19   40705.9200          No   No   \n",
       "1302    Asus            Notebook    4    2.20   19660.3200          No   No   \n",
       "\n",
       "                  Cpu_brand   HDD  SSD Gpu_brand                  os  \\\n",
       "0             Intel Core i5     0  128     Intel                 Mac   \n",
       "1             Intel Core i5     0    0     Intel                 Mac   \n",
       "2             Intel Core i5     0  256     Intel  Others/No OS/Linux   \n",
       "3             Intel Core i7     0  512       AMD                 Mac   \n",
       "4             Intel Core i5     0  256     Intel                 Mac   \n",
       "...                     ...   ...  ...       ...                 ...   \n",
       "1298          Intel Core i7     0  128     Intel             Windows   \n",
       "1299          Intel Core i7     0  512     Intel             Windows   \n",
       "1300  Other Intel Processor     0    0     Intel             Windows   \n",
       "1301          Intel Core i7  1000    0       AMD             Windows   \n",
       "1302  Other Intel Processor     0    0     Intel             Windows   \n",
       "\n",
       "             ppi  \n",
       "0     226.983005  \n",
       "1     127.677940  \n",
       "2     141.211998  \n",
       "3     220.534624  \n",
       "4     226.983005  \n",
       "...          ...  \n",
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       "1300  111.935203  \n",
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       "1302  100.454670  \n",
       "\n",
       "[1302 rows x 13 columns]"
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     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cccee13-72cc-4843-849c-1422c2184fc1",
   "metadata": {},
   "source": [
    "## Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "39f7e596-2210-43df-9b49-97ce6064f8b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(columns=['Price'])\n",
    "y = np.log(df['Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "21dcb706",
   "metadata": {},
   "outputs": [
    {
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       "      <td>Asus</td>\n",
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       "<p>1302 rows × 12 columns</p>\n",
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      ],
      "text/plain": [
       "     Company            TypeName  Ram  Weight Touchscreen  IPS  \\\n",
       "0      Apple           Ultrabook    8    1.37          No  Yes   \n",
       "1      Apple           Ultrabook    8    1.34          No   No   \n",
       "2         HP            Notebook    8    1.86          No   No   \n",
       "3      Apple           Ultrabook   16    1.83          No  Yes   \n",
       "4      Apple           Ultrabook    8    1.37          No  Yes   \n",
       "...      ...                 ...  ...     ...         ...  ...   \n",
       "1298  Lenovo  2 in 1 Convertible    4    1.80         Yes  Yes   \n",
       "1299  Lenovo  2 in 1 Convertible   16    1.30         Yes  Yes   \n",
       "1300  Lenovo            Notebook    2    1.50          No   No   \n",
       "1301      HP            Notebook    6    2.19          No   No   \n",
       "1302    Asus            Notebook    4    2.20          No   No   \n",
       "\n",
       "                  Cpu_brand   HDD  SSD Gpu_brand                  os  \\\n",
       "0             Intel Core i5     0  128     Intel                 Mac   \n",
       "1             Intel Core i5     0    0     Intel                 Mac   \n",
       "2             Intel Core i5     0  256     Intel  Others/No OS/Linux   \n",
       "3             Intel Core i7     0  512       AMD                 Mac   \n",
       "4             Intel Core i5     0  256     Intel                 Mac   \n",
       "...                     ...   ...  ...       ...                 ...   \n",
       "1298          Intel Core i7     0  128     Intel             Windows   \n",
       "1299          Intel Core i7     0  512     Intel             Windows   \n",
       "1300  Other Intel Processor     0    0     Intel             Windows   \n",
       "1301          Intel Core i7  1000    0       AMD             Windows   \n",
       "1302  Other Intel Processor     0    0     Intel             Windows   \n",
       "\n",
       "             ppi  \n",
       "0     226.983005  \n",
       "1     127.677940  \n",
       "2     141.211998  \n",
       "3     220.534624  \n",
       "4     226.983005  \n",
       "...          ...  \n",
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       "1299  276.053530  \n",
       "1300  111.935203  \n",
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       "1302  100.454670  \n",
       "\n",
       "[1302 rows x 12 columns]"
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     "execution_count": 78,
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    "X"
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   "id": "99d482f6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       11.175755\n",
       "1       10.776777\n",
       "2       10.329931\n",
       "3       11.814476\n",
       "4       11.473101\n",
       "          ...    \n",
       "1298    10.433899\n",
       "1299    11.288115\n",
       "1300     9.409283\n",
       "1301    10.614129\n",
       "1302     9.886358\n",
       "Name: Price, Length: 1302, dtype: float64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "21ea4491",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split,GridSearchCV\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": 81,
   "id": "0ee202fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>IPS</th>\n",
       "      <th>Cpu_brand</th>\n",
       "      <th>HDD</th>\n",
       "      <th>SSD</th>\n",
       "      <th>Gpu_brand</th>\n",
       "      <th>os</th>\n",
       "      <th>ppi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>Toshiba</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>2.00</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Windows</td>\n",
       "      <td>100.454670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1141</th>\n",
       "      <td>MSI</td>\n",
       "      <td>Gaming</td>\n",
       "      <td>8</td>\n",
       "      <td>2.40</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>1000</td>\n",
       "      <td>128</td>\n",
       "      <td>Nvidia</td>\n",
       "      <td>Windows</td>\n",
       "      <td>141.211998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1049</th>\n",
       "      <td>Asus</td>\n",
       "      <td>Netbook</td>\n",
       "      <td>4</td>\n",
       "      <td>1.20</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Other Intel Processor</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Others/No OS/Linux</td>\n",
       "      <td>135.094211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1020</th>\n",
       "      <td>Dell</td>\n",
       "      <td>2 in 1 Convertible</td>\n",
       "      <td>4</td>\n",
       "      <td>2.08</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Intel Core i3</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Windows</td>\n",
       "      <td>141.211998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>878</th>\n",
       "      <td>Dell</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>4</td>\n",
       "      <td>2.18</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>1000</td>\n",
       "      <td>128</td>\n",
       "      <td>Nvidia</td>\n",
       "      <td>Windows</td>\n",
       "      <td>141.211998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466</th>\n",
       "      <td>Acer</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>4</td>\n",
       "      <td>2.20</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Intel Core i3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Nvidia</td>\n",
       "      <td>Windows</td>\n",
       "      <td>100.454670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>Asus</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>16</td>\n",
       "      <td>1.63</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>512</td>\n",
       "      <td>Nvidia</td>\n",
       "      <td>Windows</td>\n",
       "      <td>141.211998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>Acer</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>2.20</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>AMD Processor</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>AMD</td>\n",
       "      <td>Windows</td>\n",
       "      <td>100.454670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>527</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>2.20</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Intel Core i3</td>\n",
       "      <td>2000</td>\n",
       "      <td>0</td>\n",
       "      <td>Nvidia</td>\n",
       "      <td>Others/No OS/Linux</td>\n",
       "      <td>100.454670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1193</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>0.92</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Other Intel Processor</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Mac</td>\n",
       "      <td>226.415547</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1106 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Company            TypeName  Ram  Weight Touchscreen  IPS  \\\n",
       "183   Toshiba            Notebook    8    2.00          No   No   \n",
       "1141      MSI              Gaming    8    2.40          No   No   \n",
       "1049     Asus             Netbook    4    1.20          No   No   \n",
       "1020     Dell  2 in 1 Convertible    4    2.08         Yes  Yes   \n",
       "878      Dell            Notebook    4    2.18          No   No   \n",
       "...       ...                 ...  ...     ...         ...  ...   \n",
       "466      Acer            Notebook    4    2.20          No   No   \n",
       "299      Asus           Ultrabook   16    1.63          No   No   \n",
       "493      Acer            Notebook    8    2.20          No   No   \n",
       "527    Lenovo            Notebook    8    2.20          No   No   \n",
       "1193    Apple           Ultrabook    8    0.92          No  Yes   \n",
       "\n",
       "                  Cpu_brand   HDD  SSD Gpu_brand                  os  \\\n",
       "183           Intel Core i5     0  128     Intel             Windows   \n",
       "1141          Intel Core i7  1000  128    Nvidia             Windows   \n",
       "1049  Other Intel Processor     0    0     Intel  Others/No OS/Linux   \n",
       "1020          Intel Core i3  1000    0     Intel             Windows   \n",
       "878           Intel Core i5  1000  128    Nvidia             Windows   \n",
       "...                     ...   ...  ...       ...                 ...   \n",
       "466           Intel Core i3     0    0    Nvidia             Windows   \n",
       "299           Intel Core i7     0  512    Nvidia             Windows   \n",
       "493           AMD Processor  1000    0       AMD             Windows   \n",
       "527           Intel Core i3  2000    0    Nvidia  Others/No OS/Linux   \n",
       "1193  Other Intel Processor     0    0     Intel                 Mac   \n",
       "\n",
       "             ppi  \n",
       "183   100.454670  \n",
       "1141  141.211998  \n",
       "1049  135.094211  \n",
       "1020  141.211998  \n",
       "878   141.211998  \n",
       "...          ...  \n",
       "466   100.454670  \n",
       "299   141.211998  \n",
       "493   100.454670  \n",
       "527   100.454670  \n",
       "1193  226.415547  \n",
       "\n",
       "[1106 rows x 12 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "cda451a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import OneHotEncoder,OrdinalEncoder\n",
    "from sklearn.metrics import r2_score,mean_absolute_error\n",
    "from sklearn.linear_model import LinearRegression,Ridge,Lasso\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor,GradientBoostingRegressor,AdaBoostRegressor,ExtraTreesRegressor\n",
    "from sklearn.svm import SVR\n",
    "from xgboost import XGBRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "a9e370eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "step1 = ColumnTransformer(transformers=[\n",
    "    ('lb_tnf',OrdinalEncoder(),['Touchscreen', 'IPS']),\n",
    "    ('col_tnf',OneHotEncoder(sparse_output=False,drop='first'),['Company', 'TypeName','Cpu_brand','Gpu_brand',\n",
    "       'os'])\n",
    "],remainder='passthrough')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfe87e38",
   "metadata": {},
   "source": [
    "# Linear regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "2ba7069d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.801022031686908\n",
      "MAE 0.21201335143271546\n"
     ]
    }
   ],
   "source": [
    "step2 = LinearRegression(fit_intercept=False)\n",
    "\n",
    "pipe = Pipeline([\n",
    "    ('step1',step1),\n",
    "    ('step2',step2)\n",
    "])\n",
    "\n",
    "pipe.fit(X_train,y_train)\n",
    "\n",
    "y_pred = pipe.predict(X_test)\n",
    "\n",
    "print('R2 score',r2_score(y_test,y_pred))\n",
    "print('MAE',mean_absolute_error(y_test,y_pred))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e017ebc",
   "metadata": {},
   "source": [
    "# Ridge Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "8f50a3de",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.8062995194195492\n",
      "MAE 0.21101950512771336\n"
     ]
    }
   ],
   "source": [
    "step2 = Ridge(alpha=10)\n",
    "\n",
    "pipe = Pipeline([\n",
    "    ('step1',step1),\n",
    "    ('step2',step2)\n",
    "])\n",
    "\n",
    "pipe.fit(X_train,y_train)\n",
    "\n",
    "y_pred = pipe.predict(X_test)\n",
    "\n",
    "print('R2 score',r2_score(y_test,y_pred))\n",
    "print('MAE',mean_absolute_error(y_test,y_pred))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3798cd0e",
   "metadata": {},
   "source": [
    "# Lasso Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "551b0157",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.8007793192541209\n",
      "MAE 0.21283349276959748\n"
     ]
    }
   ],
   "source": [
    "step2 = Lasso(alpha=0.001)\n",
    "\n",
    "pipe = Pipeline([\n",
    "    ('step1',step1),\n",
    "    ('step2',step2)\n",
    "])\n",
    "\n",
    "pipe.fit(X_train,y_train)\n",
    "\n",
    "y_pred = pipe.predict(X_test)\n",
    "\n",
    "print('R2 score',r2_score(y_test,y_pred))\n",
    "print('MAE',mean_absolute_error(y_test,y_pred))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12f66a70",
   "metadata": {},
   "source": [
    "# KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "3952279d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.8097824661161942\n",
      "MAE 0.20566090140346502\n"
     ]
    }
   ],
   "source": [
    "step2 = KNeighborsRegressor(n_neighbors=7)\n",
    "\n",
    "pipe = Pipeline([\n",
    "    ('step1',step1),\n",
    "    ('step2',step2)\n",
    "])\n",
    "\n",
    "pipe.fit(X_train,y_train)\n",
    "\n",
    "y_pred = pipe.predict(X_test)\n",
    "\n",
    "print('R2 score',r2_score(y_test,y_pred))\n",
    "print('MAE',mean_absolute_error(y_test,y_pred))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51deb457",
   "metadata": {},
   "source": [
    "# Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "7da7cb82",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.83660247809498\n",
      "MAE 0.184252834239309\n"
     ]
    }
   ],
   "source": [
    "step2 = DecisionTreeRegressor(max_depth=8)\n",
    "\n",
    "pipe = Pipeline([\n",
    "    ('step1',step1),\n",
    "    ('step2',step2)\n",
    "])\n",
    "\n",
    "pipe.fit(X_train,y_train)\n",
    "\n",
    "y_pred = pipe.predict(X_test)\n",
    "\n",
    "print('R2 score',r2_score(y_test,y_pred))\n",
    "print('MAE',mean_absolute_error(y_test,y_pred))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd01b138",
   "metadata": {},
   "source": [
    "# SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "733b1791",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.8146850283124546\n",
      "MAE 0.20509146420669763\n"
     ]
    }
   ],
   "source": [
    "step2 = SVR(kernel='rbf',C=10000,epsilon=0.1)\n",
    "\n",
    "pipe = Pipeline([\n",
    "    ('step1',step1),\n",
    "    ('step2',step2)\n",
    "])\n",
    "\n",
    "pipe.fit(X_train,y_train)\n",
    "\n",
    "y_pred = pipe.predict(X_test)\n",
    "\n",
    "print('R2 score',r2_score(y_test,y_pred))\n",
    "print('MAE',mean_absolute_error(y_test,y_pred))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6c2912c",
   "metadata": {},
   "source": [
    "# Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "d6a12154",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.8858148738620861\n",
      "MAE 0.15623196287967459\n"
     ]
    }
   ],
   "source": [
    "step2 = RandomForestRegressor(n_estimators=100,\n",
    "                              random_state=3,\n",
    "                              max_samples=0.8,\n",
    "                              max_features=0.75,\n",
    "                              max_depth=15)\n",
    "\n",
    "pipe = Pipeline([\n",
    "    ('step1',step1),\n",
    "    ('step2',step2)\n",
    "])\n",
    "\n",
    "pipe.fit(X_train,y_train)\n",
    "\n",
    "y_pred = pipe.predict(X_test)\n",
    "\n",
    "print('R2 score',r2_score(y_test,y_pred))\n",
    "print('MAE',mean_absolute_error(y_test,y_pred))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f34815d",
   "metadata": {},
   "source": [
    "# ExtraTrees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "d5c422bf",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.8822180951391274\n",
      "MAE 0.15756549269906067\n"
     ]
    }
   ],
   "source": [
    "step2 = ExtraTreesRegressor(n_estimators=1000,\n",
    "                              random_state=3,\n",
    "                            bootstrap=True,\n",
    "                              max_samples=0.8,\n",
    "                              max_features=0.75,\n",
    "                              max_depth=15)\n",
    "\n",
    "pipe = Pipeline([\n",
    "    ('step1',step1),\n",
    "    ('step2',step2)\n",
    "])\n",
    "\n",
    "pipe.fit(X_train,y_train)\n",
    "\n",
    "y_pred = pipe.predict(X_test)\n",
    "\n",
    "print('R2 score',r2_score(y_test,y_pred))\n",
    "print('MAE',mean_absolute_error(y_test,y_pred))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe6e47c8",
   "metadata": {},
   "source": [
    "# AdaBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "54602308",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.7916093868897554\n",
      "MAE 0.22738267602778792\n"
     ]
    }
   ],
   "source": [
    "step2 = AdaBoostRegressor(n_estimators=17,learning_rate=1.0)\n",
    "\n",
    "pipe = Pipeline([\n",
    "    ('step1',step1),\n",
    "    ('step2',step2)\n",
    "])\n",
    "\n",
    "pipe.fit(X_train,y_train)\n",
    "\n",
    "y_pred = pipe.predict(X_test)\n",
    "\n",
    "print('R2 score',r2_score(y_test,y_pred))\n",
    "print('MAE',mean_absolute_error(y_test,y_pred))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4103281c",
   "metadata": {},
   "source": [
    "# Gradient Boost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "3d23f695",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.8766998270948109\n",
      "MAE 0.15889976015665577\n"
     ]
    }
   ],
   "source": [
    "step2 = GradientBoostingRegressor(n_estimators=1000)\n",
    "\n",
    "pipe = Pipeline([\n",
    "    ('step1',step1),\n",
    "    ('step2',step2)\n",
    "])\n",
    "\n",
    "pipe.fit(X_train,y_train)\n",
    "\n",
    "y_pred = pipe.predict(X_test)\n",
    "\n",
    "print('R2 score',r2_score(y_test,y_pred))\n",
    "print('MAE',mean_absolute_error(y_test,y_pred))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4355f146",
   "metadata": {},
   "source": [
    "# XgBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "01d1f490",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.8800890444088412\n",
      "MAE 0.1619878994873955\n"
     ]
    }
   ],
   "source": [
    "step2 = XGBRegressor(n_estimators=45,max_depth=5,learning_rate=0.5)\n",
    "\n",
    "pipe = Pipeline([\n",
    "    ('step1',step1),\n",
    "    ('step2',step2)\n",
    "])\n",
    "\n",
    "pipe.fit(X_train,y_train)\n",
    "\n",
    "y_pred = pipe.predict(X_test)\n",
    "\n",
    "print('R2 score',r2_score(y_test,y_pred))\n",
    "print('MAE',mean_absolute_error(y_test,y_pred))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4d0a4c9",
   "metadata": {},
   "source": [
    "# Voting Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "67eb72cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.8929517030606153\n",
      "MAE 0.1521524876196368\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import VotingRegressor,StackingRegressor\n",
    "\n",
    "rf =  RandomForestRegressor(n_estimators=100,\n",
    "                              random_state=3,\n",
    "                              max_samples=0.8,\n",
    "                              max_features=0.75,\n",
    "                              max_depth=15)\n",
    "gbdt = GradientBoostingRegressor(n_estimators=1000)\n",
    "xgb = XGBRegressor(n_estimators=45,max_depth=5,learning_rate=0.5)\n",
    "et = ExtraTreesRegressor(n_estimators=1000,\n",
    "                              random_state=3,\n",
    "                            bootstrap=True,\n",
    "                              max_samples=0.8,\n",
    "                              max_features=0.75,\n",
    "                              max_depth=15)\n",
    "estimators=[('rf', rf), ('gbdt', gbdt), ('xgb',xgb), ('et',et)]\n",
    "step2 = VotingRegressor(estimators=estimators,weights=[1,1,5,4])\n",
    "\n",
    "pipe = Pipeline([\n",
    "    ('step1',step1),\n",
    "    ('step2',step2)\n",
    "])\n",
    "\n",
    "pipe.fit(X_train,y_train)\n",
    "\n",
    "y_pred = pipe.predict(X_test)\n",
    "\n",
    "print('R2 score',r2_score(y_test,y_pred))\n",
    "print('MAE',mean_absolute_error(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "5cd6c913",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.8844406405464337\n",
      "MAE 0.1542361571089348\n"
     ]
    }
   ],
   "source": [
    "step2 = StackingRegressor(estimators=estimators, final_estimator=ExtraTreesRegressor(n_estimators=1000,random_state=3,bootstrap=True,max_samples=0.8,max_features=0.75,max_depth=15))\n",
    "\n",
    "pipe = Pipeline([\n",
    "    ('step1',step1),\n",
    "    ('step2',step2)\n",
    "])\n",
    "\n",
    "pipe.fit(X_train,y_train)\n",
    "\n",
    "y_pred = pipe.predict(X_test)\n",
    "\n",
    "print('R2 score',r2_score(y_test,y_pred))\n",
    "print('MAE',mean_absolute_error(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "537f7d10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;step1&#x27;,\n",
       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;lb_tnf&#x27;, OrdinalEncoder(),\n",
       "                                                  [&#x27;Touchscreen&#x27;, &#x27;IPS&#x27;]),\n",
       "                                                 (&#x27;col_tnf&#x27;,\n",
       "                                                  OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                sparse_output=False),\n",
       "                                                  [&#x27;Company&#x27;, &#x27;TypeName&#x27;,\n",
       "                                                   &#x27;Cpu_brand&#x27;, &#x27;Gpu_brand&#x27;,\n",
       "                                                   &#x27;os&#x27;])])),\n",
       "                (&#x27;step2&#x27;,\n",
       "                 VotingRegressor(estimators=[(&#x27;rf&#x27;,\n",
       "                                              RandomForestRegressor(max_depth=15,\n",
       "                                                                    max_features=0.75,\n",
       "                                                                    max_samp...\n",
       "                                                           max_cat_to_onehot=None,\n",
       "                                                           max_delta_step=None,\n",
       "                                                           max_depth=5,\n",
       "                                                           max_leaves=None,\n",
       "                                                           min_child_weight=None,\n",
       "                                                           missing=nan,\n",
       "                                                           monotone_constraints=None,\n",
       "                                                           multi_strategy=None,\n",
       "                                                           n_estimators=45,\n",
       "                                                           n_jobs=None,\n",
       "                                                           num_parallel_tree=None,\n",
       "                                                           random_state=None, ...)),\n",
       "                                             (&#x27;et&#x27;,\n",
       "                                              ExtraTreesRegressor(bootstrap=True,\n",
       "                                                                  max_depth=15,\n",
       "                                                                  max_features=0.75,\n",
       "                                                                  max_samples=0.8,\n",
       "                                                                  n_estimators=1000,\n",
       "                                                                  random_state=3))],\n",
       "                                 weights=[1, 1, 5, 4]))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;step1&#x27;,\n",
       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;lb_tnf&#x27;, OrdinalEncoder(),\n",
       "                                                  [&#x27;Touchscreen&#x27;, &#x27;IPS&#x27;]),\n",
       "                                                 (&#x27;col_tnf&#x27;,\n",
       "                                                  OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                sparse_output=False),\n",
       "                                                  [&#x27;Company&#x27;, &#x27;TypeName&#x27;,\n",
       "                                                   &#x27;Cpu_brand&#x27;, &#x27;Gpu_brand&#x27;,\n",
       "                                                   &#x27;os&#x27;])])),\n",
       "                (&#x27;step2&#x27;,\n",
       "                 VotingRegressor(estimators=[(&#x27;rf&#x27;,\n",
       "                                              RandomForestRegressor(max_depth=15,\n",
       "                                                                    max_features=0.75,\n",
       "                                                                    max_samp...\n",
       "                                                           max_cat_to_onehot=None,\n",
       "                                                           max_delta_step=None,\n",
       "                                                           max_depth=5,\n",
       "                                                           max_leaves=None,\n",
       "                                                           min_child_weight=None,\n",
       "                                                           missing=nan,\n",
       "                                                           monotone_constraints=None,\n",
       "                                                           multi_strategy=None,\n",
       "                                                           n_estimators=45,\n",
       "                                                           n_jobs=None,\n",
       "                                                           num_parallel_tree=None,\n",
       "                                                           random_state=None, ...)),\n",
       "                                             (&#x27;et&#x27;,\n",
       "                                              ExtraTreesRegressor(bootstrap=True,\n",
       "                                                                  max_depth=15,\n",
       "                                                                  max_features=0.75,\n",
       "                                                                  max_samples=0.8,\n",
       "                                                                  n_estimators=1000,\n",
       "                                                                  random_state=3))],\n",
       "                                 weights=[1, 1, 5, 4]))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;step1: ColumnTransformer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for step1: ColumnTransformer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                  transformers=[(&#x27;lb_tnf&#x27;, OrdinalEncoder(),\n",
       "                                 [&#x27;Touchscreen&#x27;, &#x27;IPS&#x27;]),\n",
       "                                (&#x27;col_tnf&#x27;,\n",
       "                                 OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                               sparse_output=False),\n",
       "                                 [&#x27;Company&#x27;, &#x27;TypeName&#x27;, &#x27;Cpu_brand&#x27;,\n",
       "                                  &#x27;Gpu_brand&#x27;, &#x27;os&#x27;])])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">lb_tnf</label><div class=\"sk-toggleable__content fitted\"><pre>[&#x27;Touchscreen&#x27;, &#x27;IPS&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;OrdinalEncoder<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.OrdinalEncoder.html\">?<span>Documentation for OrdinalEncoder</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>OrdinalEncoder()</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">col_tnf</label><div class=\"sk-toggleable__content fitted\"><pre>[&#x27;Company&#x27;, &#x27;TypeName&#x27;, &#x27;Cpu_brand&#x27;, &#x27;Gpu_brand&#x27;, &#x27;os&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;OneHotEncoder<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>OneHotEncoder(drop=&#x27;first&#x27;, sparse_output=False)</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">remainder</label><div class=\"sk-toggleable__content fitted\"><pre>[&#x27;Ram&#x27;, &#x27;Weight&#x27;, &#x27;HDD&#x27;, &#x27;SSD&#x27;, &#x27;ppi&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" ><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">passthrough</label><div class=\"sk-toggleable__content fitted\"><pre>passthrough</pre></div> </div></div></div></div></div></div></div><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-23\" type=\"checkbox\" ><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;step2: VotingRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.VotingRegressor.html\">?<span>Documentation for step2: VotingRegressor</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>VotingRegressor(estimators=[(&#x27;rf&#x27;,\n",
       "                             RandomForestRegressor(max_depth=15,\n",
       "                                                   max_features=0.75,\n",
       "                                                   max_samples=0.8,\n",
       "                                                   random_state=3)),\n",
       "                            (&#x27;gbdt&#x27;,\n",
       "                             GradientBoostingRegressor(n_estimators=1000)),\n",
       "                            (&#x27;xgb&#x27;,\n",
       "                             XGBRegressor(base_score=None, booster=None,\n",
       "                                          callbacks=None,\n",
       "                                          colsample_bylevel=None,\n",
       "                                          colsample_bynode=None,\n",
       "                                          colsample_bytree=None, device=None,\n",
       "                                          early_stopping_rounds=None,\n",
       "                                          enable...\n",
       "                                          max_cat_to_onehot=None,\n",
       "                                          max_delta_step=None, max_depth=5,\n",
       "                                          max_leaves=None,\n",
       "                                          min_child_weight=None, missing=nan,\n",
       "                                          monotone_constraints=None,\n",
       "                                          multi_strategy=None, n_estimators=45,\n",
       "                                          n_jobs=None, num_parallel_tree=None,\n",
       "                                          random_state=None, ...)),\n",
       "                            (&#x27;et&#x27;,\n",
       "                             ExtraTreesRegressor(bootstrap=True, max_depth=15,\n",
       "                                                 max_features=0.75,\n",
       "                                                 max_samples=0.8,\n",
       "                                                 n_estimators=1000,\n",
       "                                                 random_state=3))],\n",
       "                weights=[1, 1, 5, 4])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>rf</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-24\" type=\"checkbox\" ><label for=\"sk-estimator-id-24\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;RandomForestRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestRegressor(max_depth=15, max_features=0.75, max_samples=0.8,\n",
       "                      random_state=3)</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>gbdt</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-25\" type=\"checkbox\" ><label for=\"sk-estimator-id-25\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;GradientBoostingRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html\">?<span>Documentation for GradientBoostingRegressor</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>GradientBoostingRegressor(n_estimators=1000)</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>xgb</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-26\" type=\"checkbox\" ><label for=\"sk-estimator-id-26\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">XGBRegressor</label><div class=\"sk-toggleable__content fitted\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=0.5, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=5, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=45, n_jobs=None,\n",
       "             num_parallel_tree=None, random_state=None, ...)</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>et</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-27\" type=\"checkbox\" ><label for=\"sk-estimator-id-27\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;ExtraTreesRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.ExtraTreesRegressor.html\">?<span>Documentation for ExtraTreesRegressor</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>ExtraTreesRegressor(bootstrap=True, max_depth=15, max_features=0.75,\n",
       "                    max_samples=0.8, n_estimators=1000, random_state=3)</pre></div> </div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('step1',\n",
       "                 ColumnTransformer(remainder='passthrough',\n",
       "                                   transformers=[('lb_tnf', OrdinalEncoder(),\n",
       "                                                  ['Touchscreen', 'IPS']),\n",
       "                                                 ('col_tnf',\n",
       "                                                  OneHotEncoder(drop='first',\n",
       "                                                                sparse_output=False),\n",
       "                                                  ['Company', 'TypeName',\n",
       "                                                   'Cpu_brand', 'Gpu_brand',\n",
       "                                                   'os'])])),\n",
       "                ('step2',\n",
       "                 VotingRegressor(estimators=[('rf',\n",
       "                                              RandomForestRegressor(max_depth=15,\n",
       "                                                                    max_features=0.75,\n",
       "                                                                    max_samp...\n",
       "                                                           max_cat_to_onehot=None,\n",
       "                                                           max_delta_step=None,\n",
       "                                                           max_depth=5,\n",
       "                                                           max_leaves=None,\n",
       "                                                           min_child_weight=None,\n",
       "                                                           missing=nan,\n",
       "                                                           monotone_constraints=None,\n",
       "                                                           multi_strategy=None,\n",
       "                                                           n_estimators=45,\n",
       "                                                           n_jobs=None,\n",
       "                                                           num_parallel_tree=None,\n",
       "                                                           random_state=None, ...)),\n",
       "                                             ('et',\n",
       "                                              ExtraTreesRegressor(bootstrap=True,\n",
       "                                                                  max_depth=15,\n",
       "                                                                  max_features=0.75,\n",
       "                                                                  max_samples=0.8,\n",
       "                                                                  n_estimators=1000,\n",
       "                                                                  random_state=3))],\n",
       "                                 weights=[1, 1, 5, 4]))])"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "5495514c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "pickle.dump(df,open('df.pkl','wb'))\n",
    "pickle.dump(pipe,open('pipe.pkl','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "85445a03",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "name": "python",
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