Laptop-Price.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1af31ad7",
   "metadata": {},
   "source": [
    "## Download Dataset from Kaggle Using Opendatasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "676f52df",
   "metadata": {},
   "outputs": [],
   "source": [
    "import opendatasets as od\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cd1b3597",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skipping, found downloaded files in \".\\laptop-prices\" (use force=True to force download)\n"
     ]
    }
   ],
   "source": [
    "dataset_url = 'https://www.kaggle.com/ionaskel/laptop-prices'\n",
    "od.download(dataset_url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7d3f7d5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['laptops.csv']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_dir = './laptop-prices'\n",
    "os.listdir(data_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ce69c54d",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_csv = data_dir + '/laptops.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a91659db",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Libraries for data preprocessing and visualization\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b9ef9742",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Company</th>\n",
       "      <th>Product</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Inches</th>\n",
       "      <th>ScreenResolution</th>\n",
       "      <th>Cpu</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price_euros</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Apple</td>\n",
       "      <td>MacBook Pro</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 2.3GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>128GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 640</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37kg</td>\n",
       "      <td>1339.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Apple</td>\n",
       "      <td>Macbook Air</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>1440x900</td>\n",
       "      <td>Intel Core i5 1.8GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>128GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34kg</td>\n",
       "      <td>898.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>HP</td>\n",
       "      <td>250 G6</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>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>575.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Apple</td>\n",
       "      <td>MacBook Pro</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>15.4</td>\n",
       "      <td>IPS Panel Retina Display 2880x1800</td>\n",
       "      <td>Intel Core i7 2.7GHz</td>\n",
       "      <td>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>2537.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Apple</td>\n",
       "      <td>MacBook Pro</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 3.1GHz</td>\n",
       "      <td>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>1803.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 Company      Product   TypeName  Inches  \\\n",
       "0           1   Apple  MacBook Pro  Ultrabook    13.3   \n",
       "1           2   Apple  Macbook Air  Ultrabook    13.3   \n",
       "2           3      HP       250 G6   Notebook    15.6   \n",
       "3           4   Apple  MacBook Pro  Ultrabook    15.4   \n",
       "4           5   Apple  MacBook Pro  Ultrabook    13.3   \n",
       "\n",
       "                     ScreenResolution                         Cpu   Ram  \\\n",
       "0  IPS Panel Retina Display 2560x1600        Intel Core i5 2.3GHz   8GB   \n",
       "1                            1440x900        Intel Core i5 1.8GHz   8GB   \n",
       "2                   Full HD 1920x1080  Intel Core i5 7200U 2.5GHz   8GB   \n",
       "3  IPS Panel Retina Display 2880x1800        Intel Core i7 2.7GHz  16GB   \n",
       "4  IPS Panel Retina Display 2560x1600        Intel Core i5 3.1GHz   8GB   \n",
       "\n",
       "                Memory                           Gpu  OpSys  Weight  \\\n",
       "0            128GB SSD  Intel Iris Plus Graphics 640  macOS  1.37kg   \n",
       "1  128GB Flash Storage        Intel HD Graphics 6000  macOS  1.34kg   \n",
       "2            256GB SSD         Intel HD Graphics 620  No OS  1.86kg   \n",
       "3            512GB SSD            AMD Radeon Pro 455  macOS  1.83kg   \n",
       "4            256GB SSD  Intel Iris Plus Graphics 650  macOS  1.37kg   \n",
       "\n",
       "   Price_euros  \n",
       "0      1339.69  \n",
       "1       898.94  \n",
       "2       575.00  \n",
       "3      2537.45  \n",
       "4      1803.60  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the data through pandas \n",
    "df = pd.read_csv(train_csv, encoding='ISO-8859-1')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c387abee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1303, 13)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the shape of the dataframe\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2b46227d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1303 entries, 0 to 1302\n",
      "Data columns (total 13 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Unnamed: 0        1303 non-null   int64  \n",
      " 1   Company           1303 non-null   object \n",
      " 2   Product           1303 non-null   object \n",
      " 3   TypeName          1303 non-null   object \n",
      " 4   Inches            1303 non-null   float64\n",
      " 5   ScreenResolution  1303 non-null   object \n",
      " 6   Cpu               1303 non-null   object \n",
      " 7   Ram               1303 non-null   object \n",
      " 8   Memory            1303 non-null   object \n",
      " 9   Gpu               1303 non-null   object \n",
      " 10  OpSys             1303 non-null   object \n",
      " 11  Weight            1303 non-null   object \n",
      " 12  Price_euros       1303 non-null   float64\n",
      "dtypes: float64(2), int64(1), object(10)\n",
      "memory usage: 132.5+ KB\n"
     ]
    }
   ],
   "source": [
    "# Check the information about the dataframe\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1308d235",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0          0\n",
       "Company             0\n",
       "Product             0\n",
       "TypeName            0\n",
       "Inches              0\n",
       "ScreenResolution    0\n",
       "Cpu                 0\n",
       "Ram                 0\n",
       "Memory              0\n",
       "Gpu                 0\n",
       "OpSys               0\n",
       "Weight              0\n",
       "Price_euros         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check if the data has null values or not \n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3a0bb354",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <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",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <td>1303.0</td>\n",
       "      <td>660.155794</td>\n",
       "      <td>381.172104</td>\n",
       "      <td>1.0</td>\n",
       "      <td>331.5</td>\n",
       "      <td>659.0</td>\n",
       "      <td>990.50</td>\n",
       "      <td>1320.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inches</th>\n",
       "      <td>1303.0</td>\n",
       "      <td>15.017191</td>\n",
       "      <td>1.426304</td>\n",
       "      <td>10.1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15.6</td>\n",
       "      <td>15.60</td>\n",
       "      <td>18.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Price_euros</th>\n",
       "      <td>1303.0</td>\n",
       "      <td>1123.686992</td>\n",
       "      <td>699.009043</td>\n",
       "      <td>174.0</td>\n",
       "      <td>599.0</td>\n",
       "      <td>977.0</td>\n",
       "      <td>1487.88</td>\n",
       "      <td>6099.0</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "              count         mean         std    min    25%    50%      75%  \\\n",
       "Unnamed: 0   1303.0   660.155794  381.172104    1.0  331.5  659.0   990.50   \n",
       "Inches       1303.0    15.017191    1.426304   10.1   14.0   15.6    15.60   \n",
       "Price_euros  1303.0  1123.686992  699.009043  174.0  599.0  977.0  1487.88   \n",
       "\n",
       "                max  \n",
       "Unnamed: 0   1320.0  \n",
       "Inches         18.4  \n",
       "Price_euros  6099.0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the statistical measures\n",
    "df.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f1d6d8dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Inches</th>\n",
       "      <th>Price_euros</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.087796</td>\n",
       "      <td>0.067830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inches</th>\n",
       "      <td>-0.087796</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.068197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Price_euros</th>\n",
       "      <td>0.067830</td>\n",
       "      <td>0.068197</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "             Unnamed: 0    Inches  Price_euros\n",
       "Unnamed: 0     1.000000 -0.087796     0.067830\n",
       "Inches        -0.087796  1.000000     0.068197\n",
       "Price_euros    0.067830  0.068197     1.000000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the Correlation\n",
    "df.corr(method='pearson')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "91a0f55a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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frYyW/PMxH6Nnl87tdhwSwKBBg5gyZQonn3wynTp14tJLL1277lOf+hSjRlVukXHhhReuvQ3MIYccwqBBgwAYN24co0ePBmDIkCGceOKJbX8QKrUOm3fg4rPP458u+QqrV6/iE0efwG4f3Jnv//Rm9t5tD448+DBOOeYELrj2co79wqn06Nqday+4fO320575A/1692WHfv3b8Si0KYmIzYEbgKOBOcDUiLg3M6c36HYxcFdm3hgRewFjgQHr3W/TmUnNvPiL62jOamcCewpY7xdv+qld7wNd5q5o7xKkVrHZbttEe9fwq9/P2eCMc8L+2zd7HBHxUWB4Zh5bLH8DIDNHNOhzE/BCZn676H9tZh6yvtes9kkgO1XTT5IkSa2qPzC7wfIc4OAmfYYDv46IfwW6AC2OVlT9JJCI2BvYC+i0pi0zb6t2e0mSJL1bcZndsAZNI4vJtNU6E/hJZl5bjADeHhF7Z2azE3erCoARcSlwBJUAOBY4HngEMABKkiRtgIZ3TlmHV4EdGixvX7Q19I/AccW+fhcRnYBewHyaUe0s4FOAjwFzM/NzwL5Aj/VvIkmSpA00FdgtInaKiC2AM4B7m/R5hUpOIyL2pHK2dgHrUW0AfLMYRlwZEd2pJModWthGkiRJGyAzVwLnAuOA56jM9n02Ii6PiDW3Qvgq8IWIeBK4A/hstjDLt9prAKdFRE/gZuAJ4C/A72o/DEmSJNUiM8dSuQSvYdslDX6eDgyqZZ/VzgL+l+LH/4iIB4HumflULS8kSZKkjUMts4D7Ax9cs01EDM7MyfUqTJIkSfVR7SzgbwOnA9OBVUVzAgZASZKkTUy1I4AnA7tn5lstdZQkSdLGrdpZwC8AHetZiCRJktpGtSOAy4E/RMQEYO0oYGb+37pUJUmSpLqpNgDey7tvOihJkqRNULW3gbm13oVIkiRtarbd8ZVW2Mv2rbCP2lQ7C3gQMJx3bgMTQGbmzvUrTZIkSfVQ7SngW4CvUHkKyKoW+kqSJGkjVm0AXJyZD9S1EkmSJLWJagPgbyLiauAXNJ4F/Pu6VCVJkqS6qTYAHlx8P7BBWwJHtm45kiRJqrdqZwEPqXchkiRJahvrDYARcV6TpgQWAo9k5ot1q0qSJEl109Kj4Lo1+epO5TTwAxFxRp1rkyRJUh2sdwQwMy9bV3tEbA2MB+6sR1GSJEmqn5ZGANcpM1+ncjNoSZIkbWLeUwCMiCHAn1u5FkmSJLWBliaBPE1l4kdDWwOvAWfVqyhJkiTVT0u3gTmhyXICizJzWZ3qkSRJUp21NAnk5bYqRJIkSW3jPV0DKEmSpE2XAVCSJKlkDICSJEklYwCUJEkqGQOgJElSyRgAJUmSSsYAKEmSVDIGQEmSpJIxAEqSJJWMAVCSJKlkDICSJEklYwCUJEkqGQOgJElSyRgAJUmSSsYAKEmSVDIGQEmSpJIxAEqSJJWMAVCSJKlkDICSJEklYwCUJEkqGQOgJElSyXRoixd5c/BRbfEyUt1tNXl8e5cgbbiBg9u7Aul9Y+uXPrjhO+m14buolSOAkiRJJWMAlCRJKhkDoCRJUskYACVJkkrGAChJklQyBkBJkqSSMQBKkiSVjAFQkiRpIxYRx0XEjIiYFREXNtPntIiYHhHPRsSolvbZJjeCliRJUu0iYnPgBuBoYA4wNSLuzczpDfrsBnwDGJSZf46IPi3t1xFASZKkjdeHgVmZ+UJmrgDuBE5q0ucLwA2Z+WeAzJzf0k4NgJIkSRuv/sDsBstziraGBgIDI2JKRDwWEce1tFNPAUuSJLWjiBgGDGvQNDIzR9awiw7AbsARwPbA5IjYJzPfWN8GkiRJaidF2Gsu8L0K7NBgefuiraE5wH9n5tvAixExk0ognNrca3oKWJIkaeM1FdgtInaKiC2AM4B7m/S5h8roHxHRi8op4RfWt1MDoCRJ0kYqM1cC5wLjgOeAuzLz2Yi4PCJOLLqNAxZFxHTgN8DXMnPR+vbrKWBJkqSNWGaOBcY2abukwc8JnFd8VcURQEmSpJIxAEqSJJWMAVCSJKlkDICSJEklYwCUJEkqGQOgJElSyRgAJUmSSsYAKEmSVDIGQEmSpJIxAEqSJJWMAVCSJKlkDICSJEklYwCUJEkqGQOgJElSyRgAJUmSSsYAKEmSVDIGQEmSpJIxAEqSJJWMAVCSJKlkDICSJEkl06G9C5AkSdpUDeg+uRX2cmYr7KM2jgBKkiSVjAFQkiSpZAyAkiRJJWMAlCRJKpmqA2BEfCciukdEx4iYEBELIuLT9SxOkiRJra+WEcBjMnMJcALwErAr8LV6FCVJkqT6qSUArrllzN8BozNzcR3qkSRJUp3Vch/AX0XE88CbwD9HRG/gr/UpS5IkSfVS9QhgZl4IHAIcmJlvA8uBk+pVmCRJkuqjlkkgnYF/AW4smrYDDqxHUZIkSaqfWq4B/DGwgsooIMCrwJWtXpEkSZLqqpYAuEtmfgd4GyAzlwNRl6okSZJUN7VMAlkREVsBCRARuwBv1aUqNZKZXHPNNUyZMoVOnToxfPhw9thjj3f1e+655xg+fDhvvfUWgwYN4vzzzycimDlzJiNGjGD58uVst912XHHFFXTt2rUdjkRldvGdo5k0/Xm27tqVX379K+9an5mMGHMfk5+bwVZbdORbZ57KXtv3B+CeqU9w00MTAfji0Udy8kEHtGntUkMPP/EYV438HqtXr+KUYz7OF049q9H6FW+v4ILrrmD6rOfp2a0H111wBf37bst9vxnHj34xam2/GS/N4r+u/zF77jywrQ9BqmkE8FLgQWCHiPgZMAH4el2qUiNTpkxh9uzZjBkzhosuuogRI0ass9+IESO4+OKLGTNmDLNnz+bRRx8F4Morr+Tcc8/l5z//OUcccQS33357W5YvAXDyQQdw07DPN7v+4edm8PLChTzwzfMZfuonuPzuewB4Y9lybhw3gTu+dA53fvkcbhw3gcXLl7dR1VJjq1at4oobr2HkZddy3w9Hcf+k8cx65cVGfe7+9X306NKNcTeP5qyTTuean/wQgI8POZYxP7iVMT+4lW9/9RK277ut4U/tppZZwA8BnwA+C9xBZTbwb+tTlhqaNGkSQ4cOJSLYZ599WLp0KQsXLmzUZ+HChSxbtox99tmHiGDo0KH89re/BeDll19m//33B+Dggw9m4sSJbX0IEgfusjM9Om/V7PqJz0znxAP3JyLYd8COLH3zTRYsWcKUGTP56MBd6dmlMz06d+ajA3flkedntmHl0juemjmdHbfdnh369WeLjh0ZOvgoJj72cKM+Ex97mJM+djwAxx46hMeenEZmNupz/6SHGDr4qDarW2qq1mcBdwL+DCwB9oqIwa1fkppasGAB/fr1W7vct29f5s+f36jP/Pnz6du3b6M+CxYsAGCXXXZh0qRJAIwfP5558+a1QdVSbeYvWUK/nj3XLvft2YN5i5cwf/ES+n2gR6P2+YuXtEOFEsxftIB+vRv8X9urN/MWLWjUZ96iBWxb9OmweQe6de7CG0saPzvhgYfHM3Tw0fUvWGpG1dcARsS3gdOBZ4HVRXMCk+tQl1rRJZdcwtVXX81//ud/MnjwYDp27NjeJUlSaT0541k6bdmJgQN2ae9SVGK1TAI5Gdg9M6ua+BERw4BhANdffz2f+9znaq+uxO666y7uueceAPbaay/mzp27dt28efPo06dPo/59+vRpNLI3b948evfuDcCAAQO44YYbgMrp4EceeaTO1Uu169O9O3PfeGPt8rw3FtO3R3f69OjO1FkvNGo/aNed26FCCfps05u5Cxr8X7twAX236d2oT99tevOnBfPo16sPK1etZOnyZfTs/s4o9tjJ4/m7wx39U/uq5RTwC0DVQ0eZOTIzD8zMAw1/tTvttNMYNWoUo0aN4ogjjmDs2LFkJk8//TRdu3alV69ejfr36tWLLl268PTTT5OZjB07lsMPPxyA119/HYDVq1dzyy238MlPfrLNj0dqyZC99+Leab8nM3nypVfo2qkTvbt3Z9DuA3l05h9ZvHw5i5cv59GZf2TQ7l44r/axz8A9efm1OcyZ+xor3n6bsZPHM+TgQxv1GXLwYfxywgMAjHvkN3zkQwcQUblr2urVq3nw4Qle/6d21+IIYET8gMqp3uXAHyJiAg1u/5KZ/7d+5Qlg0KBBTJkyhZNPPplOnTpx6aWXrl33qU99ilGjKrcVuPDCC9feBuaQQw5h0KBBAIwbN47Ro0cDMGTIEE488cS2PwiV3vm338HUWS/wxrJlHHnZVZxz7NGsXL0KgNMP+QiD99ydyc89z/FXXU2njh258sxTAejZpTNnH30kp3+3Mor9z8d8jJ5dOrfbcajcOmzegYvPPo9/uuQrrF69ik8cfQK7fXBnvv/Tm9l7tz048uDDOOWYE7jg2ss59gun0qNrd6694PK120975g/0692XHfr1b8ejkCCazkx6V4eIz6xvfWbe2tKLLF26dP0vIm0itpo8vr1LkDbYZgOdv6f3h81226bdH0iRM+/Y4IwTA89s8+NocQRwTcCLiC7AXzNzVbG8ObBlfcuTJElSa6vlGsAJQMObeG0FOBwiSZK0iaklAHbKzL+sWSh+9kIcSZKkTUwtAXBZROy/ZiEiDgDebP2SJEmSVE+13Afwy8DoiHgNCKAflRtDS5IkaRNSdQDMzKkRsQewe9E0IzPfrk9ZkiRJqpdaRgABDgIGFNvtHxFk5m2tXpUkSZLqppZnAd8O7AL8AVhVNCdgAJQkSdqE1DICeCCwV7Z052hJkiRt1GqZBfwMlYkfkiRJaiMRcVxEzIiIWRFx4Xr6fTIiMiIObGmftYwA9gKmR8TjNH4WsA+WlSRJqoPiyWs3AEcDc4CpEXFvZk5v0q8b8CXgv6vZby0BcHgNfSVJkrThPgzMyswXACLiTuAkYHqTflcA3wa+Vs1Oa7kNzKRq+0qSJKlV9AdmN1ieAxzcsEPxoI4dMvP+iGidABgRS6nM9n3XKiAzs3s1LyRJkqR3i4hhwLAGTSMzc2SV224GXAd8tpbXbDEAZma3WnYoSZJUFjFw4Abvowh7zQW+V4EdGixvX7St0Q3YG/htREBlwu69EXFiZk5r7jVrmQUsSZKktjUV2C0idoqILYAzgHvXrMzMxZnZKzMHZOYA4DFgveEPDICSJEkbrcxcCZwLjAOeA+7KzGcj4vKIeM93Yqn1UXCSJElqQ5k5FhjbpO2SZvoeUc0+HQGUJEkqGQOgJElSyRgAJUmSSsYAKEmSVDIGQEmSpJIxAEqSJJWMAVCSJKlkDICSJEklYwCUJEkqGQOgJElSyRgAJUmSSsYAKEmSVDIGQEmSpJIxAEqSJJWMAVCSJKlkDICSJEklYwCUJEkqGQOgJElSyRgAJUmSSsYAKEmSVDIGQEmSpJIxAEqSJJWMAVCSJKlkDICSJEklYwCUJEkqGQOgJElSyRgAJUmSSsYAKEmSVDIGQEmSpJIxAEqSJJWMAVCSJKlkDICSJEkl06EtXqTL3BVt8TJS/Q0c3N4VSBts9czJ7V2C1Co22+3v27sEXpuxeIP3sd3urVBIjRwBlCRJKhkDoCRJUskYACVJkkrGAChJklQyBkBJkqSSMQBKkiSVjAFQkiSpZAyAkiRJJWMAlCRJKhkDoCRJUskYACVJkkrGAChJklQyBkBJkqSSMQBKkiSVjAFQkiSpZAyAkiRJJWMAlCRJKhkDoCRJUskYACVJkjZiEXFcRMyIiFkRceE61p8XEdMj4qmImBARH2xpnwZASZKkjVREbA7cABwP7AWcGRF7Nen2P8CBmfkh4G7gOy3t1wAoSZK08fowMCszX8jMFcCdwEkNO2TmbzJzebH4GLB9Szs1AEqSJG28+gOzGyzPKdqa84/AAy3ttMMGFiVJkqQNEBHDgGENmkZm5sj3sJ9PAwcCh7fU1wAoSZLUjoqw11zgexXYocHy9kVbIxFxFHARcHhmvtXSa3oKWJIkaeM1FdgtInaKiC2AM4B7G3aIiL8FbgJOzMz51ezUAChJkrSRysyVwLnAOOA54K7MfDYiLo+IE4tuVwNdgdER8YeIuLeZ3a3lKWBJkqSNWGaOBcY2abukwc9H1bpPRwAlSZJKpuoAGBGDIqJL8fOnI+K6au40LUmSpI1LLSOANwLLI2Jf4KvA/wK31aUqSZIk1U0tAXBlZiaVu0//e2beAHSrT1mSJEmql1omgSyNiG8A/wc4LCI2AzrWpyxJkiTVSy0jgKcDbwGfz8y5VG5EeHVdqpIkSVLdVB0Ai9D3M6BHRJwA/DUzvQZQkiRpE1PLLODTgMeBU4HTgP+OiFPqVZgkSZLqo5ZrAC8CDlrziJGI6A2MB+6uR2GSJEkbu36b7dveJbwntVwDuFmT58stqnF7SZIkbQRqGQF8MCLGAXcUy6fT5LEkkiRJ2vhVFQAjIoDvAwcBhxbNIzNzTL0KkyRJUn1UFQAzMyNibGbuA/yizjVJkiSpjmq5hu/3EXFQ3SqRJElSm6jlGsCDgX+IiJeBZUBQGRz8UF0qkyRJUl3UEgCPrVsVkiRJajO1BMCsWxWSJElqM7UEwPuphMAAOgE7ATOAv6lDXZIkSaqTqgNgMQN4rYjYH/iXVq9IkiRJdfWen+SRmb+nMjFEkiRJm5CqRwAj4rwGi5sB+wOvtXpFkiRJqqtargHs1uDnlVSuCfyv1i1HkiRJ9VbLNYCXAURE58xcXr+S1NTDTzzGVSO/x+rVqzjlmI/zhVPParR+xdsruOC6K5g+63l6duvBdRdcQf++23Lfb8bxo1+MWttvxkuz+K/rf8yeOw9s60OQAN/Len+4+M7RTJr+PFt37covv/6Vd63PTEaMuY/Jz81gqy068q0zT2Wv7fsDcM/UJ7jpoYkAfPHoIzn5oAPatHZpjaqvAYyIj0bEdOD5YnnfiPhh3SoTAKtWreKKG69h5GXXct8PR3H/pPHMeuXFRn3u/vV99OjSjXE3j+ask07nmp9U/lo+PuRYxvzgVsb84Fa+/dVL2L7vtv7CVLvxvaz3i5MPOoCbhn2+2fUPPzeDlxcu5IFvns/wUz/B5XffA8Aby5Zz47gJ3PGlc7jzy+dw47gJLF7ueIraRy2TQL5H5WbQiwAy80lgcB1qUgNPzZzOjttuzw79+rNFx44MHXwUEx97uFGfiY89zEkfOx6AYw8dwmNPTiOz8W0b75/0EEMHH9VmdUtN+V7W+8WBu+xMj85bNbt+4jPTOfHA/YkI9h2wI0vffJMFS5YwZcZMPjpwV3p26UyPzp356MBdeeT5mW1YufSOmmYBZ+bsJk2rWrEWrcP8RQvo17vv2uW+vXozb9GCRn3mLVrAtkWfDpt3oFvnLryxZHGjPg88PJ6hg4+uf8FSM3wvqyzmL1lCv5491y737dmDeYuXMH/xEvp9oEej9vmLl7RDhVJtk0BmR8QhQEZER+BLwHP1KUut6ckZz9Jpy04MHLBLe5cibRDfy5LUOmoZATwbOAfoD7wK7Fcsr1NEDIuIaRExbeSdt25QkWXWZ5vezF0wb+3yvIUL6LtN70Z9+m7Tmz8VfVauWsnS5cvo2f2dT5ljJ4/n7w53xETty/eyyqJP9+7MfeONtcvz3lhM3x7d6dOjO3P/vLhRe58e3duhQqmGAJiZCzPzHzKzb2b2ycxPZ+aiNesj4htN+o/MzAMz88BhZ3ymNWsulX0G7snLr81hztzXWPH224ydPJ4hBx/aqM+Qgw/jlxMeAGDcI7/hIx86gIgAYPXq1Tz48ASvmVK7872sshiy917cO+33ZCZPvvQKXTt1onf37gzafSCPzvwji5cvZ/Hy5Tw6848M2t3JTGoftZwCbsmpwIhW3J+oXAd18dnn8U+XfIXVq1fxiaNPYLcP7sz3f3oze++2B0cefBinHHMCF1x7Ocd+4VR6dO3OtRdcvnb7ac/8gX69+7JDv/7teBSS72W9f5x/+x1MnfUCbyxbxpGXXcU5xx7NytWVS+JPP+QjDN5zdyY/9zzHX3U1nTp25MozTwWgZ5fOnH30kZz+3RsA+OdjPkbPLp3b7ThUbtF0ht173lHE/2Tm365r3eo/LmqdF5EkbbDVMye3dwlSq+jwd38f7V1Da2SczXbbps2P4z0/C3gdDHmSJEmbgNYMgO2ewiVJktSy1gyAo1txX5IkSaqTWh4FNzAiJkTEM8XyhyLi4jXrM/OqehQoSZKk1lXLCODNwDeAtwEy8yngjHoUJUmSpPqpJQB2zszHm7StbM1iJEmSVH+1BMCFEbELxWzfiDgF+FNdqpIkSVLd1HIj6HOAkcAeEfEq8CLw6bpUJUmSpLqpOgBm5gvAURHRBdgsM5fWryxJkiTVSy2zgK+KiJ6ZuSwzl0bEByLiynoWJ0mSpNZXyzWAx2fmG2sWMvPPwNBWr0iSJEl1VUsA3DwitlyzEBFbAVuup78kSZI2QrVMAvkZMCEiflwsfw64tfVLkiRJUj3VMgnk2xHxFPCxoumKzBxXn7IkSZJUL7WMAJKZDwAP1KkWSZKkTcqyflts8D66tUIdtWoxAEbEI5l5aEQspbgJ9JpVQGZm97pVJ0mSpFbXYgDMzEOL7+0RUCVJktTKqpoFHBGbR8Tz9S5GkiRJ9VdVAMzMVcCMiNixzvVIkiSpzmqZBPIB4NmIeBxYtqYxM09s9aokSZJUN7UEwP9XtyokSZK0ThFxHHA9sDnwn5n5b03WbwncBhwALAJOz8yX1rfPamYBdwLOBnYFngZuycyV7+UAJEmSVL2I2By4ATgamANMjYh7M3N6g27/CPw5M3eNiDOAbwOnr2+/1VwDeCtwIJXwdzxw7XuoX5IkSbX7MDArM1/IzBXAncBJTfqcxDtPZ7sb+FhExPp2Ws0p4L0ycx+AiLgFeLymsiVJkvRe9QdmN1ieAxzcXJ/MXBkRi4FtgIXN7bSaEcC31/zgqV9JkqTWFRHDImJag69h9X7NakYA942IJcXPAWxVLPskEEmSpA2UmSOBkc2sfhXYocHy9kXbuvrMiYgOQA8qk0GaVc2TQDZvqY8kSZLqYiqwW0TsRCXonQF8qkmfe4HPAL8DTgEmZmayHrXcBkaSJEltqLim71xgHJXbwPwoM5+NiMuBaZl5L3ALcHtEzAJepxIS18sAKEmStBHLzLHA2CZtlzT4+a/AqbXss6pHwUmSJOn9wwAoSZJUMgZASZKkkjEASpIklYwBUJIkqWQMgJIkSSVjAJQkSSoZA6AkSVLJGAAlSZJKxgAoSZJUMgZASZKkkjEASpIklYwBUJIkqWQMgJIkSSVjAJQkSSoZA6AkSVLJGAAlSZJKxgAoSZJUMgZASZKkkjEASpIklYwBUJIkqWQiM9u7BrWCiBiWmSPbuw5pQ/le1vuF72VtzBwBfP8Y1t4FSK3E97LeL3wva6NlAJQkSSoZA6AkSVLJGADfP7zORO8Xvpf1fuF7WRstJ4FIkiSVjCOAkiRJJWMAbGURMSAinmnSNjwizm+vmqpRbY0R8Y2ImBURMyLi2LaoTZueiPjLe9zusxHx761djySpMQOgqhYRewFnAH8DHAf8MCI2b9+qJKk6EbEqIv4QEc9ExOiI6NxMv0fbujaprRkA21hE/DYivh0Rj0fEzIg4rGj/bET8IiIejIg/RsR3GmxzY0RMi4hnI+KyBu0vRcSI4j+0aRGxf0SMi4j/jYizG/T7WkRMjYinmmx/UVHDI8DuVZR/EnBnZr6VmS8Cs4APt8Ifi96nIuKI4j1/d0Q8HxE/i4go1h0UEY9GxJPFv4duxWbbNfPv4JiI+F1E/L745d21aP+3iJhevL+vaYfD1KbjzczcLzP3BlYAZzdcGREdADLzkPYorqmo8Pe06sI3VvvokJkfBr4MXNqgfT/gdGAf4PSI2KFovygzDwQ+BBweER9qsM0rmbkf8DDwE+AU4CPAZVD5pQnsRiWo7QccEBGDI+IAKqN5+wFDgYPW7DAizm4YIBvoD8xusDynaJPW52+pvNf3AnYGBkXEFsDPgS9l5r7AUcCbRf/9aPLvICJ6ARcDR2Xm/sA04LyI2Ab4e+BvMvNDwJVtdlTa1D0M7Fp8SHk4Iu4FpkPjSxgi4oKIeLr4oPJvRdsuxYeUJ4pt92juRSKid0T8V/EhfGpEDCraG112U4xKDii+ZkTEbcAzwA4RcXWx/umIOL3ov21ETG4wonlYPf6Q9P7Vob0LeB9qblp1w/ZfFN+fAAY0aJ+QmYsBImI68EEqgeu0iBhG5e9rWyq/SJ8qtrm3+P400DUzlwJLI+KtiOgJHFN8/U/RryuVQNgNGJOZy4vXW7MfMvM/ajheqSWPZ+YcgIj4A5X3/GLgT5k5FSAzlxTrYd3/DnpSed9PKfpsAfyu2M9fgVsi4lfAr9romLQJK0b6jgceLJr2B/Yuzmw07Hc8lTMfB2fm8ojYulg1Ejg7M/8YEQcDPwSObOblrge+m5mPRMSOwDhgzxZK3A34TGY+FhGfpPKhaF+gFzA1IiYDnwLGZea3onIpzjpPZ0vNMQC2vkXAB5q0bQ00/I/lreL7Khr/HbzV4OdVQIeI2Ak4HzgoM/8cET8BOq1jm9VNtl9d7DuAEZl5U8OCIuLLVR5PQ68COzRY3r5ok9bnXe/r99A/gIcy88ymnSPiw8DHqIx+n0vzv4ilrYoPIVAZAbwFOITKh5QX19H/KODHaz4oZ+brxaUHhwCjiw8jAFuu5zWPAvZq0Lf7mssX1uPlzHys+PlQ4I7MXAXMi4hJVM7YTAV+FBEdgXsy8w/r3pW0bp4CbmWZ+RfgTxFxJEDxifE44JH3uMvuwDJgcUT0pfKptRbjgM83uF6qf0T0ASYDJ0fEVsW1Vx+vYl/3AmdExJZFMN0NeLzGeiSAGcC2EXEQQER0W3P9VTMeo3LqeNeif5eIGFi8r3tk5ljgK1RGSaTmrLkGcL/M/NfMXFG0L6thH5sBbzTYz36Zub4Rvc2AjzTo27/4PbGSxr+DG36wb7GezJwMDKbyIfwnEXFWDccgGQDr5Czg/xWfNCcCl2Xm/76XHWXmk1RO3z4PjAKm1Lj9r4vtfhcRTwN3A90y8/dUrsF6EniAyqdJoPlrADPzWeAuKtfJPAicU3wqlWpS/OI9HfhBRDwJPETjX4BN+y8APgvcERFPUTn9uweVSxl+VbQ9ApxX59JVLg8Bn4titnBEbF1crvBiRJxatEVErO+Dx6+Bf12zEBH7FT++ROXUMxGxP7BTM9s/TOVa2M0jojeV0Pd4RHwQmJeZNwP/uWZfUrV8EogkqRQi4i+Z2bVJ2xHA+Zl5wrr6RcSFVD7UrwDGZuY3izMgN1K5JrsjlbsjXN7Ma/YCbqBy3V8HYHJmnh0RWwG/pDKR7r+Bj/LOGZ5fFTOVicq54+8U6xK4MjN/HhGfAb4GvA38BTirmdPY0joZACVJkkrGU8CSJEkl4yxgSZI2UERcBJzapHl0Zn6rPeqRWuIpYEmSpJLxFLAkSVLJGAAlSZJKxgAoSZJUMgZASZKkkjEASpIklcz/BzKmJrqMpEoOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Correlation Heatmap\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.title('Correlation Matrix')\n",
    "sns.heatmap(df.corr(method='pearson'), \n",
    "            annot=True, \n",
    "            cbar=True, \n",
    "            cmap='Pastel1_r', \n",
    "            fmt='.2f');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "89a7af17",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop the unnecessary colummns\n",
    "df.drop(columns=['Unnamed: 0', 'Product'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "dcf458a3",
   "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_euros</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 2.3GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>128GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 640</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37kg</td>\n",
       "      <td>1339.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>1440x900</td>\n",
       "      <td>Intel Core i5 1.8GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>128GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34kg</td>\n",
       "      <td>898.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86kg</td>\n",
       "      <td>575.00</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>2537.45</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>1803.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Inches                    ScreenResolution  \\\n",
       "0   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "1   Apple  Ultrabook    13.3                            1440x900   \n",
       "2      HP   Notebook    15.6                   Full HD 1920x1080   \n",
       "3   Apple  Ultrabook    15.4  IPS Panel Retina Display 2880x1800   \n",
       "4   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "\n",
       "                          Cpu   Ram               Memory  \\\n",
       "0        Intel Core i5 2.3GHz   8GB            128GB SSD   \n",
       "1        Intel Core i5 1.8GHz   8GB  128GB Flash Storage   \n",
       "2  Intel Core i5 7200U 2.5GHz   8GB            256GB SSD   \n",
       "3        Intel Core i7 2.7GHz  16GB            512GB SSD   \n",
       "4        Intel Core i5 3.1GHz   8GB            256GB SSD   \n",
       "\n",
       "                            Gpu  OpSys  Weight  Price_euros  \n",
       "0  Intel Iris Plus Graphics 640  macOS  1.37kg      1339.69  \n",
       "1        Intel HD Graphics 6000  macOS  1.34kg       898.94  \n",
       "2         Intel HD Graphics 620  No OS  1.86kg       575.00  \n",
       "3            AMD Radeon Pro 455  macOS  1.83kg      2537.45  \n",
       "4  Intel Iris Plus Graphics 650  macOS  1.37kg      1803.60  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checks the first 5rows\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9817cfaa",
   "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_euros</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>1339.69</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>898.94</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>575.00</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>2537.45</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>1803.60</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_euros  \n",
       "0  Intel Iris Plus Graphics 640  macOS   1.37      1339.69  \n",
       "1        Intel HD Graphics 6000  macOS   1.34       898.94  \n",
       "2         Intel HD Graphics 620  No OS   1.86       575.00  \n",
       "3            AMD Radeon Pro 455  macOS   1.83      2537.45  \n",
       "4  Intel Iris Plus Graphics 650  macOS   1.37      1803.60  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replace the string with empty string\n",
    "df['Ram'] = df['Ram'].str.replace('GB', '')\n",
    "df['Weight'] = df['Weight'].str.replace('kg', '')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7dc95c66",
   "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_euros       1303 non-null   float64\n",
      "dtypes: float32(1), float64(2), int32(1), object(7)\n",
      "memory usage: 101.9+ KB\n"
     ]
    }
   ],
   "source": [
    "# Set the datatype as int32\n",
    "df['Ram'] = df['Ram'].astype('int32')\n",
    "df['Weight'] = df['Weight'].astype('float32')\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "654c1111",
   "metadata": {},
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c8051513",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,6))\n",
    "sns.histplot(df['Price_euros'], \n",
    "             legend=False, \n",
    "             kde=True, \n",
    "             color='blue');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3d95123f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Company'].value_counts()\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.xticks(rotation=90)\n",
    "sns.countplot(data=df, x=df['Company'], \n",
    "              palette='pastel', \n",
    "              order=df['Company'].value_counts().index);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5720da4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sort_result = df.groupby([\"Company\"])['Price_euros'].median().reset_index().sort_values('Price_euros', ascending=False)\n",
    "df.sort_values('Price_euros')\n",
    "plt.figure(figsize=(12,6))\n",
    "sns.barplot(x='Company', y='Price_euros', \n",
    "            data=df, palette='Set3_r', \n",
    "            order=sort_result['Company'])\n",
    "plt.xticks(rotation=90)\n",
    "plt.xlabel('Company', size=15)\n",
    "plt.ylabel('Price', size=15)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6d018d58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['TypeName'].value_counts()\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.xticks(rotation=45)\n",
    "sns.countplot(data=df, x=df['TypeName'], \n",
    "              palette='cividis', \n",
    "              order=df['TypeName'].value_counts().index);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9388f34c",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "result = df.groupby([\"TypeName\"])['Price_euros'].median().reset_index().sort_values('Price_euros', ascending=False)\n",
    "plt.figure(figsize=(12,6))\n",
    "sns.barplot(x=df['TypeName'], \n",
    "            y=df['Price_euros'], \n",
    "            palette='Reds_r', \n",
    "            order=result['TypeName'])\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.xlabel('Laptop Type', size=15)\n",
    "plt.ylabel('Price', size=15)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "46a9431d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "sns.countplot(y=df['Inches'], \n",
    "              data=df, \n",
    "              palette='gist_earth', \n",
    "              order=df['Inches'].value_counts().index);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0d994454",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZXKnmJ5M9LWN81VhP65VUT7cnXEqSJEnaRYZvSZIkqRLDtyRJklSJfb4laYGZXLzXTs+SpNFh+JakBaa15tHDLqGqFVu+1dN8yyfva05unLyvp2WMjz2up/VK2r3Z7USSJEmqxPAtSZIkVWK3E0mSdhMrztwy53mWf69ce/x7kz3NP37u2JznkRYyw7ckSSNi8uGLd3qWtPAYviU9wKLFkzs9S6qj9YI1wy5B0oAZviU9wK+saQ27BEmSFiRPuJQkSZIqMXxLkiRJlRi+JUmSpEoM35IkSVIlhm9JkiSpEsO3JEmSVInhW5IkSarE8C1JkiRV4k12JElSR5MPXrzTs6RdY/iWJEkdtR7vLe+lfrLbiSRJklSJ4VuSJEmqxPAtSZIkVWL4liRJkioxfEuSJEmVVA3fEXF+RGyLiGvb2g6IiA0RcVN53r+0R0S8JyI2R8Q1EXFU2zyry/Q3RcTqmu9BkiRJ6lXtI98fBY6f0nYmsDEzDwM2ltcAJwCHlcdpwAegCevAW4EnAUcDb90R2CVJkqRRVjV8Z+aXgDumNK8CLijDFwDPb2u/MBuXA/tFxDLgOGBDZt6RmT8ANvDAQC9JkiSNnFHo831QZt5WhlvAQWX4YODWtum2lrZO7ZIkSdJIG4Xw/XOZmUD2a3kRcVpEbIqITdu3b+/XYiVJkqSejEL4vr10J6E8byvtE8AhbdMtL22d2h8gM8/LzJWZuXLJkiV9L1ySJEmai1EI3+uBHVcsWQ18pq39JeWqJ08Gfli6p1wKHBsR+5cTLY8tbZIkSdJI27PmyiLiE8AzgQMjYivNVUvOBT4VES8HbgFOLpNfApwIbAbuBl4KkJl3RMTbgSvKdGdn5tSTOCVJkqSRUzV8Z+aLOow6ZpppEzi9w3LOB87vY2mSJEnSwFUN35IkSf2wdu1aWq0WS5cuZd26dcMuR+qa4VuSJM07rVaLiYlpr7cgjbRROOFSkiRJ2i0YviVJkqRKDN+SJElSJYZvSZIkqRJPuJQkSUOz4pgtPc23nEkWAeMTkz0tY3zjWE/rlXaVR74lSZKkSgzfkiRJUiWGb0mSJKkSw7ckSZJUiSdcSpKkeWeSxTs9S/OF4VuSJM07LdYMuwSpJ3Y7kSRJkioxfEuSJEmV2O1EA7F4n8mdniVJkmT41oCsOa417BIkSZJGjt1OJEmSpEoM35IkSVIlhm9JkiSpEsO3JEmSVInhW5IkSarE8C1JkiRVYviWJEmSKjF8S5IkSZUYviVJkqRKDN+SJElSJYZvSZIkqRLDtyRJklSJ4VuSJEmqxPAtSZIkVWL4liRJkioxfEuSJEmVGL4lSZKkSvYcdgGSJA3D5OLFOz1LUg2Gb0nSbqm15jXDLkHSbshuJ5IkSVIlhm9JkiSpErudSJIkLVBr166l1WqxdOlS1q1bN+xyhOFbkiRpwWq1WkxMTAy7DLWx24kkSZJUieFbkiRJqsTwLUmSJFVi+JYkSZIqMXxLkiRJlRi+JUmSpEoM35IkSVIlIxO+I2JLRHwjIq6KiE2l7YCI2BARN5Xn/Ut7RMR7ImJzRFwTEUcNt3pJkiRpdqN2k51nZeb32l6fCWzMzHMj4szy+g3ACcBh5fEk4APlWZIkaZc8ecWWoaz38vGxoaxXdY3Mke8OVgEXlOELgOe3tV+YjcuB/SJi2RDqkyRJkro2Ske+E7gsIhL4YGaeBxyUmbeV8S3goDJ8MHBr27xbS9ttbW1ExGnAaQCHHnroAEuXJEkanFf3eDR++/JJWATbxyd7WsZ7PRrfd6MUvp+WmRMR8UhgQ0R8s31kZmYJ5l0rAf48gJUrV85pXkmSJKnfRqbbSWZOlOdtwMXA0cDtO7qTlOdtZfIJ4JC22ZeXNkmSJGlkjUT4joiHRcTDdwwDxwLXAuuB1WWy1cBnyvB64CXlqidPBn7Y1j1FkiRJGkmj0u3kIODiiICmpo9n5ucj4grgUxHxcuAW4OQy/SXAicBm4G7gpfVLliRJ0lytXbuWVqvF0qVLWbdu3bDLqW4kwndm3gz82jTt3weOmaY9gdMrlCZJkqQ+arVaTEzsvr2FR6LbiSRJkrQ7MHxLkiRJlRi+JUmSpEoM35IkSVIlhm9JkiSpEsO3JEmSVMlIXGpQkiRJ88vHV2zpab67lk/CIrhrfLKnZfzO+FhP6x0VHvmWJEmSKjF8S5IkSZXY7USSJGmB2mty8U7PGj7DtyRJ0gL16NaaYZegKQzfkiRJWjBu6vFE0F1x2BxOArXPtyRJklSJ4VuSJEmqxG4nkiRJqmafcvLnPrvpSaCGb0mSJFVzwm5+EqjdTiRJkqRKDN+SJElSJYZvSZIkqRLDtyRJklSJ4VuSJEmqxPAtSZIkVWL4liRJkioxfEuSJEmVGL4lSZKkSgzfkiRJUiWGb0mSJKkSw7ckSZJUieFbkiRJqsTwLUmSJFVi+JYkSZIqMXxLkiRJlRi+JUmSpEoM35IkSVIlhm9JkiSpEsO3JEmSVInhW5IkSarE8C1JkiRVYviWJEmSKjF8S5IkSZUYviVJkqRKDN+SJElSJYZvSZIkqRLDtyRJklSJ4VuSJEmqxPAtSZIkVTKvw3dEHB8RN0bE5og4c9j1SJIkSTOZt+E7IvYA3g+cABwOvCgiDh9uVZIkSVJn8zZ8A0cDmzPz5sy8F7gIWDXkmiRJkqSO5nP4Phi4te311tImSZIkjaTIzGHX0JOI+G3g+Mx8RXn9e8CTMvOMtmlOA04rLx8P3Nin1R8IfK9Py+oXa+reKNZlTd2xpu6NYl3W1B1r6t4o1mVN3VnoNT06M5dMN2LPPq1gGCaAQ9peLy9tP5eZ5wHn9XvFEbEpM1f2e7m7wpq6N4p1WVN3rKl7o1iXNXXHmro3inVZU3d255rmc7eTK4DDImJFRCwCTgHWD7kmSZIkqaN5e+Q7Mycj4gzgUmAP4PzMvG7IZUmSJEkdzdvwDZCZlwCXDGHVfe/K0gfW1L1RrMuaumNN3RvFuqypO9bUvVGsy5q6s9vWNG9PuJQkSZLmm/nc51uSJEmaVwzfbSLi/IjYFhHXtrUdGRGXR8RVEbEpIo6eYf59I2JrRLxvwDWdFRETpaarIuLEGebfIyK+HhGfG4WaIuL4iLgxIjZHxJn9qmmGut4eEdeUmi6LiEd1mPcdEXFtebxwwDX9WUR8s9R1cUTs12HeLRHxjR2fvQHX1O12+lnbPu7bCc4davpk27q2RMRV08y3d0R8NSKujojrIuJt/apphrq63VarI+Km8lg97Joi4tER8bUyzXUR8fsDrul/lfXcHxHTXj1gkPuv15+9UaypTFf1u7Nt3JqIyIg4sMO8NT/n3f6e2S8i/r5s1xsi4ikDrGnW76ky3UD2X4eaDoiIDWWfbIiI/TvM+/mIuDP6mA9mqGnW74MyXbV91zZuts94/7dTZvooD+AZwFHAtW1tlwEnlOETgX+ZYf7/B3wceN+AazoLeH2X8/9Rqelzw66J5sTYbwOPARYBVwOHD7iufduGXwP81TTzPRfYQHMOxMNorqSz7wBrOhbYswy/A3hHh3m3AAf2a/vs6nYq437c73o61TRl/LuAP56mPYB9yvBewFeAJw97WwEHADeX5/3L8P5DrmkR8OAyvE/5fD1qgDX9Cs39Ff4FWNlhvoHtv15/9ka0purfnaX9EJqLGtwy3XfRED7nZ9HF7z7gAuAVbZ/7/Qa5ndrGd/qeGtj+67Cd1gFnluEzp/tMlXHHAP+TPuaDGWqa9ftgGPtuts/4oLaTR77bZOaXgDumNgP7luFHAN+dbt6I+HXgIJqwPuiauhIRy2mC5YdHpKajgc2ZeXNm3gtcBKwaZF2Z+aO2lw+j2Z9THQ58KTMnM/MnwDXA8QOs6bLMnCwvL6e5Rn01u7Cdqta0Q0QEcDLwiWnmy8z8cXm5V3n0rfZd2FbHARsy847M/AHNH3eD/EzNWlNm3puZ95SXD6aP//nsUNMNmTnjjc0Guf96/dkbxZoYwndn8efAWjq//6qf825ExCNogtZHynLuzcw7B13TTN9TDHD/dahpFU2IpTw/v8O8G4G7+lHHbDV1830wpH0322d8INvJ8D271wJ/FhG3Au8E3jh1goh4EM1fvK+vWNcZ5d+U53f6lxLwFzQfqvtHpKaDgVvbXm8tbQMVEeeU/fdi4I+nmeRq4PiIeGj5t9Oz2PkGToP0MuCfOoxL4LKIuDKau7UOVBfbCWDvaLpfXR4Rzx90TcXTgdsz86bpRkbTteoqYBtNEPjKoAvqYltV/6x3s/8i4pCIuKbU9o7MnPZgQk3D2H9Fx5+9EaxpGJ+nVcBEZl49w2TD+E6f7ffMCmA78NfRdLn8cEQ8bMA1wczfU7W300GZeVsZbtEcGJwPqu67Lj/jA2H4nt0fAK/LzEOA11H+IpviVcAlmbm1Uk0fAB4LHAncRhP8dxIRJwHbMvPKUalpWDLzzWX/fQw4Y5rxl9FcsvI/aI5a/Cfws0HXFRFvBiZLXdN5WmYeBZwAnB4RzxhkPbNtp+LR2dz963eAv4iIxw6ypuJFTH80CYDM/FlmHklzxPDoiPjVQRfU5baqqpuaMvPWzDwC+CVgdUQM/ZfyMPbfbD97o1hTTRHxUOBNdP4jfFi6+T2zJ033gg9k5hOBn9B0vRi0Gb+nhiWbfhPz5bJ21fbdsD/jhu/ZrQY+XYb/jubfR1M9heav8S00R8dfEhHnDqqgzLy9/HK4H/hQh5qeCjyv1HQR8OyI+Nsh1zTBzkeUl5e2Wj4G/NZ0IzLznMw8MjOfQ9Pn81uDLCQiTgVOAl5cvhynq2miPG8DLmb6bToIM22nHTXdTNN374mDLCQi9gR+E/jkbNOWf09+kT7927tLnbbVMD/rHfffDuWI97U0R+tGQq39183P3gjWVPvz9Fiao5BXl98hy4GvRcTSYdbV5e+ZrcDWtv9W/D1NoBuYLr6nau+/2yNiWaltGc1/cOaDmvuu28/4QBi+Z/dd4DfK8LOBB/xLKTNfnJmHZuYYTdeTCzNzYH9p7/ihKl5A80t0ak1vzMzlpaZTgH/OzN8dZk00JzIeFhErImJRqatvV8zoUNdhbS9XAd+cZpo9ImJxGT4COII+992fsr7jaboDPS8z7+4wzcMi4uE7hmlOyppum/arpm620/4R8eAyfCDNH3jXD6qm4n8A3+z0X6WIWBLl6hAR8RDgOUxTez91s61oTuA5tmyz/Wn236XDrCkilpdtRKnpacCMfTAHrfb+6/Jnb+RqovJ3Z2Z+IzMfmZlj5XfIVuCozGxNmbT257yb330t4NaIeHxpOoYhf09R/3ffepoDh5TnzwxwXX1Tc9/N4TM+GNnHM1zn+4PmX0a3AfeVHfFyml9QV9L0C/4K8Otl2pXAh6dZxqn092on09X0N8A3aE4MXA8sK9M+iqb7y9RlPJP+Xu2k55porhjzLZozv99cYf/9A80X9DXAZ4GDp+4/YG+aH/DraU54OnLANW2m6f93VXn81dRtRXNW/NXlcV0/t9UubKf/Xvbx1eX55YOsqbR/FPj9KdO2b6cjgK+Xuq9lmisNDGNbldcvK/t6M/DSYddEEyKvKfvvGuC0Adf0gjJ8D3A7cGnN/bcLP3sjV1N5XfW7c8r4LZQrQQz5c97t75kjgU1lun+kf1dg6el7apD7r8N2WgxspDlY+AXggA777t9o+lj/tMx73ABrmvX7YBj7rsvPeN+3k3e4lCRJkiqx24kkSZJUieFbkiRJqsTwLUmSJFVi+JYkSZIqMXxLkiRJlRi+JWkOIuLUiLgyIu6KiB+U2yC/e9h19UNEjEVEljvk7uqyTi3L2qcftbUt95lluQO/66QkDYLhW5K6FBFvBD5McyOR3wReQnMDi+cNs64+uo3mjr1fHnYhkrRQ7TnsAiRpHjkD+GBmvqmt7bMR8bZdWWhEPCQzf7prpe26zLyH5kZTkqQB8ci3JHVvP+ABtx/OKXcri4iHRMS6iLglIu6JiPGI+NO28Vsi4l0R8ZaI2Ar8qLQ/KCLOjIjNZb5vRcTqKasjIlZFxKaI+K+IaJV17dU2/qyI+F5EPDEiLo+Iu0v3mKfP9Oam63ZSan1nRLwuIraWrjYX7bgFe7faln1yRHwwIn5Ylve2iHjQlGmPiIjPRsSdEfHjiPhqRDxnyiIPjIi/K+NvjohXTbPOp0fEv5b3//2I+FBEPLxt/H4R8eGI+G7Zlt+JiA/N5X1J0lwZviWpe18DXh0RqyNi8XQTRETQdEX5A+D9NLeVfitw4JRJfwf4DeBVwAtL23uB/wucBzwXuBg4f0oYPhn4NPBVmu4ubwNOA34e7ouHAhcAHwR+i+a2zp+OiIfO+V3DycAxZT1vAE4C/qSH5QCsA34M/Dbwt8Afl2EAIuKXgX8HlgG/T3Nb6ouBQ6Ys50PA1WX8vwDvj4ij25bzVJpba7fK8l9Lsy/+um0Z7waeBrwOOA54E+BtnyUN1q7en96HDx8+dpcHcARwM01Aux+4Djgb2LdtmuPK+OfNsJwtNP2r925r+6WyzNVTpr0QuKIMB3AL8NdTpnkZ8FNgcXl9Vqnh2W3THFnajp+hrrEyzUlTav02sGdb218ArVm21allWftMWfaFU6a7Crio7fUngK3AQzos95llOWe3te0FbAfObWv7N+CLU+Z9dpn3V8vra4FXD/tz5cOHj93r4ZFvSepSZl4D/ArNEee/pAnDbwE2tV3V49nAHZm5fpbFbczM/2p7fQxN+L44Ivbc8QA2AkdGxB7A44BDgU9Nmeafgb2B9iuA3EtzRHiH68vz8jm96cYXM3NyyrIe2d7VZQ4um/L6+ik1PRv4ZM7eB/7ny8nM+4CbdiynHN1/Cg/cTl8G7gN+vcx6FfB/IuJVEfG4Ht6LJM2Z4VuS5iAz78nMz2bmGZl5OPAK4DDg5WWSxTRHtWdz+5TXBwJ7AD+kCYg7Hh+lOTl+Gb/ounLJlGnGS3t714y7MvP+trrvLYN7d1HbVHdOeX0vzR8eD+7Tstpr6nb7zbSc/Wm25V+y83a6h+Yo+Y7tdAbwjzRdX26MiJsi4pQu1i1JPfNqJ5K0CzLzIxGxDvjl0vR9mqA866xTXt8BTAJPpTkCPtU2YMfJgqcBX59mmvFp2uabbrffTO6k2b5n0fyhMtV3ATLzTuA1wGsi4ghgLfCxiLgmM6+fZj5J2mWGb0nqUkQ8MjO3TWlbAjyCXxzJ3gisjYiTMvNzc1j8P9McrX1EZm7osP4bgQlgLDMX6lU5NgInR8Sbp3TL6Vpm/iQiLgcen5lndznPNRHxf4AX0/whZfiWNBCGb0nq3jci4jM0/Y23AY8GXg/cTXNlEYANNDfh+XhEnE1zhZRlwDMy85WdFpyZN0bEXwEXlSPpm2i6UTwBeFxmviIz74+INcDfRMS+wD/RdLd4DPB84Lcz8+5+v+nK3gZcAXwpIt5FcyT8icD3M/P8OSxnLbAxIu4H/h64i6a//HOBN2fmtyLiyzRXUrmW5kj5/wZ+QnMlGUkaCMO3JHXvbGAV8B7gAJrL2P0H8MLMHIfmmt8R8QLg7TSXt1tC083h410s/3TgWzQh8Gya639fD3xkxwSZ+cmI+BHNZfFeBvyM5gosn6MJ4vNa+SPkacC5NHcThWYbvKnzXNMu58sR8QyaMP83NP9VuAX4PL/4L8V/0lyVZYxmO34dOCEzt+7au5CkziLTS5pKkiRJNXi1E0mSJKkSw7ckSZJUieFbkiRJqsTwLUmSJFVi+JYkSZIqMXxLkiRJlRi+JUmSpEoM35IkSVIlhm9JkiSpkv8P3WL+FqMWehQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sort_bars = df.groupby(['Inches'])['Price_euros'].median().reset_index().sort_values('Price_euros', ascending=False)\n",
    "plt.figure(figsize=(12,6))\n",
    "sns.barplot(x=df['Inches'], \n",
    "            y=df['Price_euros'], \n",
    "            order=sort_bars['Inches'],\n",
    "            palette='gist_rainbow')\n",
    "plt.xlabel('Screen in Inches', size=15)\n",
    "plt.ylabel('Price', size=15)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d1c10797",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Full HD 1920x1080                                507\n",
       "1366x768                                         281\n",
       "IPS Panel Full HD 1920x1080                      230\n",
       "IPS Panel Full HD / Touchscreen 1920x1080         53\n",
       "Full HD / Touchscreen 1920x1080                   47\n",
       "1600x900                                          23\n",
       "Touchscreen 1366x768                              16\n",
       "Quad HD+ / Touchscreen 3200x1800                  15\n",
       "IPS Panel 4K Ultra HD 3840x2160                   12\n",
       "IPS Panel 4K Ultra HD / Touchscreen 3840x2160     11\n",
       "4K Ultra HD / Touchscreen 3840x2160               10\n",
       "4K Ultra HD 3840x2160                              7\n",
       "Touchscreen 2560x1440                              7\n",
       "IPS Panel 1366x768                                 7\n",
       "IPS Panel Quad HD+ / Touchscreen 3200x1800         6\n",
       "IPS Panel Retina Display 2560x1600                 6\n",
       "IPS Panel Retina Display 2304x1440                 6\n",
       "Touchscreen 2256x1504                              6\n",
       "IPS Panel Touchscreen 2560x1440                    5\n",
       "IPS Panel Retina Display 2880x1800                 4\n",
       "IPS Panel Touchscreen 1920x1200                    4\n",
       "1440x900                                           4\n",
       "IPS Panel 2560x1440                                4\n",
       "IPS Panel Quad HD+ 2560x1440                       3\n",
       "Quad HD+ 3200x1800                                 3\n",
       "1920x1080                                          3\n",
       "Touchscreen 2400x1600                              3\n",
       "2560x1440                                          3\n",
       "IPS Panel Touchscreen 1366x768                     3\n",
       "IPS Panel Touchscreen / 4K Ultra HD 3840x2160      2\n",
       "IPS Panel Full HD 2160x1440                        2\n",
       "IPS Panel Quad HD+ 3200x1800                       2\n",
       "IPS Panel Retina Display 2736x1824                 1\n",
       "IPS Panel Full HD 1920x1200                        1\n",
       "IPS Panel Full HD 2560x1440                        1\n",
       "IPS Panel Full HD 1366x768                         1\n",
       "Touchscreen / Full HD 1920x1080                    1\n",
       "Touchscreen / Quad HD+ 3200x1800                   1\n",
       "Touchscreen / 4K Ultra HD 3840x2160                1\n",
       "IPS Panel Touchscreen 2400x1600                    1\n",
       "Name: ScreenResolution, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the count of the screen resolution column\n",
    "df['ScreenResolution'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5432992b",
   "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_euros</th>\n",
       "      <th>Touchscreen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>549</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>IPS Panel Full HD 1920x1080</td>\n",
       "      <td>Intel Core i7 7500U 2.7GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.99</td>\n",
       "      <td>1729.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>467</th>\n",
       "      <td>Dell</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i5 8250U 1.6GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>2TB HDD</td>\n",
       "      <td>AMD Radeon 530</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.02</td>\n",
       "      <td>759.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</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>256GB SSD</td>\n",
       "      <td>AMD Radeon 530</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.20</td>\n",
       "      <td>870.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>741</th>\n",
       "      <td>Dell</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>Intel Core i3 6006U 2GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 520</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.00</td>\n",
       "      <td>665.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>931</th>\n",
       "      <td>HP</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>12.5</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i5 6200U 2.3GHz</td>\n",
       "      <td>4</td>\n",
       "      <td>500GB HDD</td>\n",
       "      <td>Intel HD Graphics 520</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.26</td>\n",
       "      <td>1579.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Company   TypeName  Inches             ScreenResolution  \\\n",
       "549  Lenovo   Notebook    15.6  IPS Panel Full HD 1920x1080   \n",
       "467    Dell   Notebook    15.6            Full HD 1920x1080   \n",
       "72     Dell   Notebook    15.6            Full HD 1920x1080   \n",
       "741    Dell   Notebook    15.6                     1366x768   \n",
       "931      HP  Ultrabook    12.5            Full HD 1920x1080   \n",
       "\n",
       "                            Cpu  Ram     Memory                    Gpu  \\\n",
       "549  Intel Core i7 7500U 2.7GHz    8  512GB SSD  Intel HD Graphics 620   \n",
       "467  Intel Core i5 8250U 1.6GHz    8    2TB HDD         AMD Radeon 530   \n",
       "72   Intel Core i7 8550U 1.8GHz    8  256GB SSD         AMD Radeon 530   \n",
       "741    Intel Core i3 6006U 2GHz    8  256GB SSD  Intel HD Graphics 520   \n",
       "931  Intel Core i5 6200U 2.3GHz    4  500GB HDD  Intel HD Graphics 520   \n",
       "\n",
       "          OpSys  Weight  Price_euros  Touchscreen  \n",
       "549  Windows 10    1.99       1729.0            0  \n",
       "467  Windows 10    2.02        759.0            0  \n",
       "72   Windows 10    2.20        870.0            0  \n",
       "741  Windows 10    2.00        665.0            0  \n",
       "931  Windows 10    1.26       1579.0            0  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a new column touchscreen\n",
    "df['Touchscreen'] = df['ScreenResolution'].apply(lambda x:1 if 'Touchscreen' in x else 0)\n",
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b21857f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Pd//x8m3d3Un6AM+3s6p2VdWuvXv3ruJMAQDgwC0ktKvq+VmK7I9296fH8Df3PRIyfj40xu9Pcvyyw7eOsT+nuy/p7u3dvX3z5s3zJg8AACuwaa0vWFWV5NIkd3f3f1q26dokZyd5//h5zbLxd1XVx5O8Lsljyx4xAeAQc8P/+eyipwBsAD/1gz+56Ck8qzUP7SSvT/ILSb5SVV8eY/8qS4F9dVWdm+TrSd42tl2X5C1Jdif5TpJz1nS2AABwENY8tLv7fyap/Ww+9Wn27yTnTZ0UAACsMr8ZEgAAJhDaAAAwgdAGAIAJhDYAAEwgtAEAYAKhDQAAEwhtAACYQGgDAMAEQhsAACYQ2gAAMIHQBgCACYQ2AABMILQBAGACoQ0AABMIbQAAmEBoAwDABEIbAAAmENoAADCB0AYAgAmENgAATCC0AQBgAqENAAATCG0AAJhAaAMAwARCGwAAJhDaAAAwgdAGAIAJhDYAAEwgtAEAYAKhDQAAEwhtAACYQGgDAMAEQhsAACYQ2gAAMIHQBgCACYQ2AABMILQBAGACoQ0AABMIbQAAmEBoAwDABEIbAAAmENoAADCB0AYAgAmENgAATCC0AQBgAqENAAATCG0AAJhgw4R2VZ1WVfdU1e6qumDR8wEAgGeyIUK7qg5L8htJTk9yUpK3V9VJi50VAADs34YI7SQnJ9nd3fd29+NJPp5kx4LnBAAA+7VRQntLkvuWre8ZYwAAsC5tWvQEVktV7Uyyc6x+q6ruWeR84Bkck+QPFz0J1pd656JnAOuefztZr/7K/jZslNC+P8nxy9a3jrHv6+5LklyylpOCg1FVu7p7+6LnAbCR+LeTjWijPDryxSQnVtUJVXV4krOSXLvgOQEAwH5tiDva3f1EVb0ryfVJDktyWXffueBpAQDAfm2I0E6S7r4uyXWLngesAo84ARw4/3ay4VR3L3oOAABwyNkoz2gDAMCGIrRhjVTVaVV1T1XtrqoLFj0fgPWuqi6rqoeq6o5FzwUOhtCGNVBVhyX5jSSnJzkpydur6qTFzgpg3bsiyWmLngQcLKENa+PkJLu7+97ufjzJx5PsWPCcANa17v58kkcWPQ84WEIb1saWJPctW98zxgCAQ5TQBgCACYQ2rI37kxy/bH3rGAMADlFCG9bGF5OcWFUnVNXhSc5Kcu2C5wQATCS0YQ109xNJ3pXk+iR3J7m6u+9c7KwA1req+liSLyT5oaraU1XnLnpOcCD8ZkgAAJjAHW0AAJhAaAMAwARCGwAAJhDaAAAwgdAGAIAJNi16AgA8qaqOTnLjWP2BJN9Lsnesn9zdjz+Hc3+ru1/8HKcIwAp5vR/AOlVVv5LkW9190Sqdb1VDu6oO6+7vrdb5AA41Hh0BWOeq6tSq+lJVfaWqLquqF4zxr1XVMWN5e1XdNJZfXFWXj/1vr6qfW3au91XV71XVzVV17Bg7s6ruGOOfH2OHVdVFY/z2qnr3smt+oKpuS3JmVb2pqr5QVbdV1Ser6sVjv9dW1eeq6taqur6qjhvjN43j/3dV/X5V/a21+5sEWFtCG2B9OyLJFUn+Xne/KkuP/P2TZznm3yR5rLtf1d0/kuQzY/xFSW7u7h9N8vkk/2iM/3KSN4/xnxljO5NsS/LXxzk+uuz8D3f3a5L8TpJ/neSNY31XkvOr6vlJPpTkjO5+bZLLkrxv2fGbuvvkJO9JcuFK/yIANhrPaAOsb4cl+YPu/v2xfmWS85L82jMc88YkZ+1b6e5Hx+LjSX57LN+a5KfG8u8muaKqrk7y6WXn+M3ufmKc45Fl5//E+HlKkpOS/G5VJcnhGb8uO8krk9wwxg9L8sCy4/dd49YsxTzAIUloA2xcT+TJ/zN5xAr2/9N+8os538v4b0B3/+Oqel2Sn05ya1W99lnO8+3xs5Lc0N1vX76xql6V5M7u/rH9HP9/nzoHgEORR0cA1rfvJdlWVa8Y67+Q5HNj+WtJ9kXxzy075oYs3fVOklTVUc90gar6we6+pbt/OUtvODl+nOMXq2rT2OdlT3PozUlev29uVfWiqvqrSe5JsrmqfmyMP7+q/toK/7wAhwyhDbC+fTfJOUk+WVVfSfJnSX5zbPu3SS6uql1ZCvJ9/n2So/Z9wTHJTz7LNf7D+OLkHUn+V5LfS/KRJN9Icvs4x99/6kHdvTfJO5J8rKpuz9JjIz88XkF4RpIPjGO/nOTHD/hPDrDBeb0fAABM4I42AABMILQBAGACoQ0AABMIbQAAmEBoAwDABEIbAAAmENoAADCB0AYAgAn+H84tCXscd7aqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Touchscreen'].value_counts()\n",
    "plt.figure(figsize=(12,6))\n",
    "sns.countplot(x=df['Touchscreen'], \n",
    "              data=df, \n",
    "              palette='GnBu_r');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "23e421bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sort_bar = df.groupby(['Touchscreen'])['Price_euros'].median().reset_index().sort_values(['Price_euros'], ascending=False)\n",
    "plt.figure(figsize=(12,6))\n",
    "sns.barplot(x=df['Touchscreen'], \n",
    "            y=df['Price_euros'],\n",
    "            data=df,\n",
    "            palette='RdYlGn', \n",
    "            order=sort_bar['Touchscreen'])\n",
    "plt.xlabel('Touchscreen', size=15)\n",
    "plt.ylabel('Price', size=15)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ca060dee",
   "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_euros</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>818</th>\n",
       "      <td>Dell</td>\n",
       "      <td>Gaming</td>\n",
       "      <td>17.3</td>\n",
       "      <td>IPS Panel Full HD 1920x1080</td>\n",
       "      <td>Intel Core i7 7700HQ 2.8GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>128GB SSD +  1TB HDD</td>\n",
       "      <td>Nvidia GeForce GTX 1060</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>4.36</td>\n",
       "      <td>2505.02</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Company TypeName  Inches             ScreenResolution  \\\n",
       "818    Dell   Gaming    17.3  IPS Panel Full HD 1920x1080   \n",
       "\n",
       "                             Cpu  Ram                Memory  \\\n",
       "818  Intel Core i7 7700HQ 2.8GHz   16  128GB SSD +  1TB HDD   \n",
       "\n",
       "                         Gpu       OpSys  Weight  Price_euros  Touchscreen  \\\n",
       "818  Nvidia GeForce GTX 1060  Windows 10    4.36      2505.02            0   \n",
       "\n",
       "     Ips  \n",
       "818    1  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a new column Ips\n",
    "df['Ips'] = df['ScreenResolution'].apply(lambda x:1 if 'IPS' in x else 0)\n",
    "df.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "16b8e80c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Ips'].value_counts()\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.xticks(rotation='horizontal')\n",
    "sns.countplot(x=df['Ips'], \n",
    "              data=df, \n",
    "              palette='Oranges_r');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d77c72e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sort_bar1 = df.groupby(['Ips'])['Price_euros'].median().reset_index().sort_values('Price_euros', ascending=False)\n",
    "plt.figure(figsize=(12,6))\n",
    "sns.barplot(x=df['Ips'], \n",
    "            y=df['Price_euros'], \n",
    "            data=df, \n",
    "            palette='BuPu', \n",
    "            order=sort_bar1['Ips'])\n",
    "plt.xlabel('IPS', size=15)\n",
    "plt.ylabel('Price', size=15)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "464b5e8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "new = df['ScreenResolution'].str.split('x',n=1,expand=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "83bea5b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new column x_res & y_res\n",
    "df['X_res'] = new[0]\n",
    "df['Y_res'] = new[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e085fa98",
   "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_euros</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>X_res</th>\n",
       "      <th>Y_res</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>751</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 6300U 2.4GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 520</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.84</td>\n",
       "      <td>1900.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Full HD 1920</td>\n",
       "      <td>1080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>795</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>2 in 1 Convertible</td>\n",
       "      <td>14.0</td>\n",
       "      <td>IPS Panel Touchscreen 2560x1440</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.42</td>\n",
       "      <td>2330.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>IPS Panel Touchscreen 2560</td>\n",
       "      <td>1440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>664</th>\n",
       "      <td>MSI</td>\n",
       "      <td>Gaming</td>\n",
       "      <td>17.3</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i7 7700HQ 2.8GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB SSD +  1TB HDD</td>\n",
       "      <td>Nvidia GeForce GTX 1060</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.70</td>\n",
       "      <td>1486.77</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Full HD 1920</td>\n",
       "      <td>1080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>14.0</td>\n",
       "      <td>IPS Panel 2560x1440</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>1.13</td>\n",
       "      <td>2282.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>IPS Panel 2560</td>\n",
       "      <td>1440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>598</th>\n",
       "      <td>Dell</td>\n",
       "      <td>2 in 1 Convertible</td>\n",
       "      <td>12.5</td>\n",
       "      <td>Full HD / Touchscreen 1920x1080</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.34</td>\n",
       "      <td>1670.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Full HD / Touchscreen 1920</td>\n",
       "      <td>1080</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Company            TypeName  Inches                 ScreenResolution  \\\n",
       "751      HP            Notebook    15.6                Full HD 1920x1080   \n",
       "795  Lenovo  2 in 1 Convertible    14.0  IPS Panel Touchscreen 2560x1440   \n",
       "664     MSI              Gaming    17.3                Full HD 1920x1080   \n",
       "385  Lenovo           Ultrabook    14.0              IPS Panel 2560x1440   \n",
       "598    Dell  2 in 1 Convertible    12.5  Full HD / Touchscreen 1920x1080   \n",
       "\n",
       "                             Cpu  Ram                Memory  \\\n",
       "751   Intel Core i5 6300U 2.4GHz    8             256GB SSD   \n",
       "795   Intel Core i5 7200U 2.5GHz    8             256GB SSD   \n",
       "664  Intel Core i7 7700HQ 2.8GHz    8  128GB SSD +  1TB HDD   \n",
       "385   Intel Core i7 7500U 2.7GHz    8             512GB SSD   \n",
       "598   Intel Core i5 7200U 2.5GHz    8             256GB SSD   \n",
       "\n",
       "                         Gpu       OpSys  Weight  Price_euros  Touchscreen  \\\n",
       "751    Intel HD Graphics 520  Windows 10    1.84      1900.00            0   \n",
       "795    Intel HD Graphics 620  Windows 10    1.42      2330.00            1   \n",
       "664  Nvidia GeForce GTX 1060  Windows 10    2.70      1486.77            0   \n",
       "385    Intel HD Graphics 620  Windows 10    1.13      2282.00            0   \n",
       "598    Intel HD Graphics 620  Windows 10    1.34      1670.00            1   \n",
       "\n",
       "     Ips                       X_res Y_res  \n",
       "751    0                Full HD 1920  1080  \n",
       "795    1  IPS Panel Touchscreen 2560  1440  \n",
       "664    0                Full HD 1920  1080  \n",
       "385    1              IPS Panel 2560  1440  \n",
       "598    0  Full HD / Touchscreen 1920  1080  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ffb5a471",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "        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_euros</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>X_res</th>\n",
       "      <th>Y_res</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>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>1339.69</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2560</td>\n",
       "      <td>1600</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>898.94</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1440</td>\n",
       "      <td>900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>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>575.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1920</td>\n",
       "      <td>1080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>15.4</td>\n",
       "      <td>IPS Panel Retina Display 2880x1800</td>\n",
       "      <td>Intel Core i7 2.7GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>2537.45</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2880</td>\n",
       "      <td>1800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 3.1GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>1803.60</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2560</td>\n",
       "      <td>1600</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_euros  Touchscreen  Ips  \\\n",
       "0  Intel Iris Plus Graphics 640  macOS    1.37      1339.69            0    1   \n",
       "1        Intel HD Graphics 6000  macOS    1.34       898.94            0    0   \n",
       "2         Intel HD Graphics 620  No OS    1.86       575.00            0    0   \n",
       "3            AMD Radeon Pro 455  macOS    1.83      2537.45            0    1   \n",
       "4  Intel Iris Plus Graphics 650  macOS    1.37      1803.60            0    1   \n",
       "\n",
       "  X_res Y_res  \n",
       "0  2560  1600  \n",
       "1  1440   900  \n",
       "2  1920  1080  \n",
       "3  2880  1800  \n",
       "4  2560  1600  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['X_res'] = df['X_res'].str.replace(',','').str.findall(r'(\\d+\\.?\\d+)').apply(lambda x:x[0])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "745cf15b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set the datatype as int32\n",
    "df['X_res'] = df['X_res'].astype('int32')\n",
    "df['Y_res'] = df['Y_res'].astype('int32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d8136444",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1303 entries, 0 to 1302\n",
      "Data columns (total 15 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Company           1303 non-null   object \n",
      " 1   TypeName          1303 non-null   object \n",
      " 2   Inches            1303 non-null   float64\n",
      " 3   ScreenResolution  1303 non-null   object \n",
      " 4   Cpu               1303 non-null   object \n",
      " 5   Ram               1303 non-null   int32  \n",
      " 6   Memory            1303 non-null   object \n",
      " 7   Gpu               1303 non-null   object \n",
      " 8   OpSys             1303 non-null   object \n",
      " 9   Weight            1303 non-null   float32\n",
      " 10  Price_euros       1303 non-null   float64\n",
      " 11  Touchscreen       1303 non-null   int64  \n",
      " 12  Ips               1303 non-null   int64  \n",
      " 13  X_res             1303 non-null   int32  \n",
      " 14  Y_res             1303 non-null   int32  \n",
      "dtypes: float32(1), float64(2), int32(3), int64(2), object(7)\n",
      "memory usage: 132.5+ KB\n"
     ]
    }
   ],
   "source": [
    "# Gives the information about the dataframe\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "6d1894e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Inches         0.068197\n",
       "Ram            0.743007\n",
       "Weight         0.210370\n",
       "Price_euros    1.000000\n",
       "Touchscreen    0.191226\n",
       "Ips            0.252208\n",
       "X_res          0.556529\n",
       "Y_res          0.552809\n",
       "Name: Price_euros, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checks the correlation related to price_euro column\n",
    "df.corr()['Price_euros']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "d1052227",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new column ppi\n",
    "df['ppi'] = (((df['X_res']**2) + (df['Y_res']**2))**0.5/df['Inches']).astype('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "ab1a058a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Inches         0.068197\n",
       "Ram            0.743007\n",
       "Weight         0.210370\n",
       "Price_euros    1.000000\n",
       "Touchscreen    0.191226\n",
       "Ips            0.252208\n",
       "X_res          0.556529\n",
       "Y_res          0.552809\n",
       "ppi            0.473487\n",
       "Name: Price_euros, dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checks the correlation related to price_euro column\n",
    "df.corr()['Price_euros']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ead43751",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop the screen resolution column\n",
    "df.drop(columns=['ScreenResolution'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "76b098a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Company</th>\n",
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       "      <th>Cpu</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
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       "      <th>Price_euros</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>X_res</th>\n",
       "      <th>Y_res</th>\n",
       "      <th>ppi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>Intel Core i5 2.3GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 640</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>1339.69</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2560</td>\n",
       "      <td>1600</td>\n",
       "      <td>226.983005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>Intel Core i5 1.8GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>898.94</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1440</td>\n",
       "      <td>900</td>\n",
       "      <td>127.677940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>575.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1920</td>\n",
       "      <td>1080</td>\n",
       "      <td>141.211998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>15.4</td>\n",
       "      <td>Intel Core i7 2.7GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>2537.45</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2880</td>\n",
       "      <td>1800</td>\n",
       "      <td>220.534624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>Intel Core i5 3.1GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>1803.60</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2560</td>\n",
       "      <td>1600</td>\n",
       "      <td>226.983005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Inches                         Cpu  Ram  \\\n",
       "0   Apple  Ultrabook    13.3        Intel Core i5 2.3GHz    8   \n",
       "1   Apple  Ultrabook    13.3        Intel Core i5 1.8GHz    8   \n",
       "2      HP   Notebook    15.6  Intel Core i5 7200U 2.5GHz    8   \n",
       "3   Apple  Ultrabook    15.4        Intel Core i7 2.7GHz   16   \n",
       "4   Apple  Ultrabook    13.3        Intel Core i5 3.1GHz    8   \n",
       "\n",
       "                Memory                           Gpu  OpSys  Weight  \\\n",
       "0            128GB SSD  Intel Iris Plus Graphics 640  macOS    1.37   \n",
       "1  128GB Flash Storage        Intel HD Graphics 6000  macOS    1.34   \n",
       "2            256GB SSD         Intel HD Graphics 620  No OS    1.86   \n",
       "3            512GB SSD            AMD Radeon Pro 455  macOS    1.83   \n",
       "4            256GB SSD  Intel Iris Plus Graphics 650  macOS    1.37   \n",
       "\n",
       "   Price_euros  Touchscreen  Ips  X_res  Y_res         ppi  \n",
       "0      1339.69            0    1   2560   1600  226.983005  \n",
       "1       898.94            0    0   1440    900  127.677940  \n",
       "2       575.00            0    0   1920   1080  141.211998  \n",
       "3      2537.45            0    1   2880   1800  220.534624  \n",
       "4      1803.60            0    1   2560   1600  226.983005  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checks the first 5 rows\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "355b1cef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop the inches, x_res & y_res column\n",
    "df.drop(columns=['Inches', 'X_res', 'Y_res'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "488ea43d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Cpu</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price_euros</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>ppi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>Intel Core i5 2.3GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 640</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>1339.69</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>Intel Core i5 1.8GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>898.94</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>127.677940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>575.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>Intel Core i7 2.7GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>2537.45</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>220.534624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>Intel Core i5 3.1GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>1803.60</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName                         Cpu  Ram               Memory  \\\n",
       "0   Apple  Ultrabook        Intel Core i5 2.3GHz    8            128GB SSD   \n",
       "1   Apple  Ultrabook        Intel Core i5 1.8GHz    8  128GB Flash Storage   \n",
       "2      HP   Notebook  Intel Core i5 7200U 2.5GHz    8            256GB SSD   \n",
       "3   Apple  Ultrabook        Intel Core i7 2.7GHz   16            512GB SSD   \n",
       "4   Apple  Ultrabook        Intel Core i5 3.1GHz    8            256GB SSD   \n",
       "\n",
       "                            Gpu  OpSys  Weight  Price_euros  Touchscreen  Ips  \\\n",
       "0  Intel Iris Plus Graphics 640  macOS    1.37      1339.69            0    1   \n",
       "1        Intel HD Graphics 6000  macOS    1.34       898.94            0    0   \n",
       "2         Intel HD Graphics 620  No OS    1.86       575.00            0    0   \n",
       "3            AMD Radeon Pro 455  macOS    1.83      2537.45            0    1   \n",
       "4  Intel Iris Plus Graphics 650  macOS    1.37      1803.60            0    1   \n",
       "\n",
       "          ppi  \n",
       "0  226.983005  \n",
       "1  127.677940  \n",
       "2  141.211998  \n",
       "3  220.534624  \n",
       "4  226.983005  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checks the first 5 rows\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "9e327c52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intel Core i5 7200U 2.5GHz       190\n",
       "Intel Core i7 7700HQ 2.8GHz      146\n",
       "Intel Core i7 7500U 2.7GHz       134\n",
       "Intel Core i7 8550U 1.8GHz        73\n",
       "Intel Core i5 8250U 1.6GHz        72\n",
       "                                ... \n",
       "Intel Core M M3-6Y30 0.9GHz        1\n",
       "AMD A9-Series 9420 2.9GHz          1\n",
       "Intel Core i3 6006U 2.2GHz         1\n",
       "AMD A6-Series 7310 2GHz            1\n",
       "Intel Xeon E3-1535M v6 3.1GHz      1\n",
       "Name: Cpu, Length: 118, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the count of the CPU column\n",
    "df['Cpu'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "8c192a71",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create new column as CPU Name & apply the lambda function \n",
    "df['Cpu Name'] = df['Cpu'].apply(lambda x:\" \".join(x.split()[0:3]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "91af11d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>Intel Iris Plus Graphics 640</td>\n",
       "      <td>macOS</td>\n",
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       "      <td>0</td>\n",
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       "      <td>Apple</td>\n",
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       "      <td>8</td>\n",
       "      <td>128GB Flash Storage</td>\n",
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       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
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       "      <td>127.677940</td>\n",
       "      <td>Intel Core i5</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>575.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>Intel Core i7 2.7GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>2537.45</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>220.534624</td>\n",
       "      <td>Intel Core i7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>Intel Core i5 3.1GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>1803.60</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName                         Cpu  Ram               Memory  \\\n",
       "0   Apple  Ultrabook        Intel Core i5 2.3GHz    8            128GB SSD   \n",
       "1   Apple  Ultrabook        Intel Core i5 1.8GHz    8  128GB Flash Storage   \n",
       "2      HP   Notebook  Intel Core i5 7200U 2.5GHz    8            256GB SSD   \n",
       "3   Apple  Ultrabook        Intel Core i7 2.7GHz   16            512GB SSD   \n",
       "4   Apple  Ultrabook        Intel Core i5 3.1GHz    8            256GB SSD   \n",
       "\n",
       "                            Gpu  OpSys  Weight  Price_euros  Touchscreen  Ips  \\\n",
       "0  Intel Iris Plus Graphics 640  macOS    1.37      1339.69            0    1   \n",
       "1        Intel HD Graphics 6000  macOS    1.34       898.94            0    0   \n",
       "2         Intel HD Graphics 620  No OS    1.86       575.00            0    0   \n",
       "3            AMD Radeon Pro 455  macOS    1.83      2537.45            0    1   \n",
       "4  Intel Iris Plus Graphics 650  macOS    1.37      1803.60            0    1   \n",
       "\n",
       "          ppi       Cpu Name  \n",
       "0  226.983005  Intel Core i5  \n",
       "1  127.677940  Intel Core i5  \n",
       "2  141.211998  Intel Core i5  \n",
       "3  220.534624  Intel Core i7  \n",
       "4  226.983005  Intel Core i5  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checks the first 5rows\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "91f63d2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to fetch the processor name\n",
    "def fetch_processor(text):\n",
    "    if text == 'Intel Core i7' or text == 'Intel Core i5' or text == 'Intel Core i3':\n",
    "        return text\n",
    "    else:\n",
    "        if text.split()[0] == 'Intel':\n",
    "            return 'Other Intel Processor'\n",
    "        else:\n",
    "            return 'AMD Processor'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "e7a252c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new column as CPU Brand\n",
    "df['Cpu brand'] = df['Cpu Name'].apply(fetch_processor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "2656b9b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
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       "      <td>8</td>\n",
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       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
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       "      <td>Intel Core i5</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>575.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>Intel Core i7 2.7GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>2537.45</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>220.534624</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>Intel Core i7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>Intel Core i5 3.1GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>1803.60</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName                         Cpu  Ram               Memory  \\\n",
       "0   Apple  Ultrabook        Intel Core i5 2.3GHz    8            128GB SSD   \n",
       "1   Apple  Ultrabook        Intel Core i5 1.8GHz    8  128GB Flash Storage   \n",
       "2      HP   Notebook  Intel Core i5 7200U 2.5GHz    8            256GB SSD   \n",
       "3   Apple  Ultrabook        Intel Core i7 2.7GHz   16            512GB SSD   \n",
       "4   Apple  Ultrabook        Intel Core i5 3.1GHz    8            256GB SSD   \n",
       "\n",
       "                            Gpu  OpSys  Weight  Price_euros  Touchscreen  Ips  \\\n",
       "0  Intel Iris Plus Graphics 640  macOS    1.37      1339.69            0    1   \n",
       "1        Intel HD Graphics 6000  macOS    1.34       898.94            0    0   \n",
       "2         Intel HD Graphics 620  No OS    1.86       575.00            0    0   \n",
       "3            AMD Radeon Pro 455  macOS    1.83      2537.45            0    1   \n",
       "4  Intel Iris Plus Graphics 650  macOS    1.37      1803.60            0    1   \n",
       "\n",
       "          ppi       Cpu Name      Cpu brand  \n",
       "0  226.983005  Intel Core i5  Intel Core i5  \n",
       "1  127.677940  Intel Core i5  Intel Core i5  \n",
       "2  141.211998  Intel Core i5  Intel Core i5  \n",
       "3  220.534624  Intel Core i7  Intel Core i7  \n",
       "4  226.983005  Intel Core i5  Intel Core i5  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checks the first 5 rows\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "e49515c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Cpu brand'].value_counts()\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.xticks(rotation=45)\n",
    "sns.countplot(x=df['Cpu brand'], \n",
    "              data=df, \n",
    "              palette='ocean', \n",
    "              order=df['Cpu brand'].value_counts().index);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "ebfe64c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sort_bar2 = df.groupby(['Cpu brand'])['Price_euros'].median().reset_index().sort_values('Price_euros', ascending=False)\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.xticks(rotation=45)\n",
    "sns.barplot(x=df['Cpu brand'], \n",
    "            y=df['Price_euros'], \n",
    "            data=df, \n",
    "            palette='BuGn', \n",
    "            order=sort_bar2['Cpu brand'])\n",
    "plt.xlabel('CPU Brands', size=15)\n",
    "plt.ylabel('Price', size=15)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "86602025",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop the CPU & CPU name column\n",
    "df.drop(columns=['Cpu', 'Cpu Name'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "51b2ce95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price_euros</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>ppi</th>\n",
       "      <th>Cpu brand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 640</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>1339.69</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>898.94</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>127.677940</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>575.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>2537.45</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>220.534624</td>\n",
       "      <td>Intel Core i7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>1803.60</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Ram               Memory                           Gpu  \\\n",
       "0   Apple  Ultrabook    8            128GB SSD  Intel Iris Plus Graphics 640   \n",
       "1   Apple  Ultrabook    8  128GB Flash Storage        Intel HD Graphics 6000   \n",
       "2      HP   Notebook    8            256GB SSD         Intel HD Graphics 620   \n",
       "3   Apple  Ultrabook   16            512GB SSD            AMD Radeon Pro 455   \n",
       "4   Apple  Ultrabook    8            256GB SSD  Intel Iris Plus Graphics 650   \n",
       "\n",
       "   OpSys  Weight  Price_euros  Touchscreen  Ips         ppi      Cpu brand  \n",
       "0  macOS    1.37      1339.69            0    1  226.983005  Intel Core i5  \n",
       "1  macOS    1.34       898.94            0    0  127.677940  Intel Core i5  \n",
       "2  No OS    1.86       575.00            0    0  141.211998  Intel Core i5  \n",
       "3  macOS    1.83      2537.45            0    1  220.534624  Intel Core i7  \n",
       "4  macOS    1.37      1803.60            0    1  226.983005  Intel Core i5  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checks the first 5rows\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "386e03ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Ram'].value_counts()\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.xticks(rotation='horizontal')\n",
    "sns.countplot(x=df['Ram'], \n",
    "              data=df, \n",
    "              palette='binary', \n",
    "              order=df['Ram'].value_counts().index);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "16f197f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sort_bar3 = df.groupby(['Ram'])['Price_euros'].median().reset_index().sort_values('Price_euros', ascending=False)\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.xticks(rotation='horizontal')\n",
    "sns.barplot(x=df['Ram'], \n",
    "            y=df['Price_euros'], \n",
    "            data=df, \n",
    "            palette='Accent_r',\n",
    "            order=sort_bar3['Ram'])\n",
    "plt.xlabel('RAM', size=15)\n",
    "plt.ylabel('Price', size=15)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "c60db34f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "256GB SSD                        412\n",
       "1TB HDD                          223\n",
       "500GB HDD                        132\n",
       "512GB SSD                        118\n",
       "128GB SSD +  1TB HDD              94\n",
       "128GB SSD                         76\n",
       "256GB SSD +  1TB HDD              73\n",
       "32GB Flash Storage                38\n",
       "2TB HDD                           16\n",
       "64GB Flash Storage                15\n",
       "512GB SSD +  1TB HDD              14\n",
       "1TB SSD                           14\n",
       "256GB SSD +  2TB HDD              10\n",
       "1.0TB Hybrid                       9\n",
       "256GB Flash Storage                8\n",
       "16GB Flash Storage                 7\n",
       "32GB SSD                           6\n",
       "180GB SSD                          5\n",
       "128GB Flash Storage                4\n",
       "512GB SSD +  2TB HDD               3\n",
       "16GB SSD                           3\n",
       "512GB Flash Storage                2\n",
       "1TB SSD +  1TB HDD                 2\n",
       "256GB SSD +  500GB HDD             2\n",
       "128GB SSD +  2TB HDD               2\n",
       "256GB SSD +  256GB SSD             2\n",
       "512GB SSD +  256GB SSD             1\n",
       "512GB SSD +  512GB SSD             1\n",
       "64GB Flash Storage +  1TB HDD      1\n",
       "1TB HDD +  1TB HDD                 1\n",
       "32GB HDD                           1\n",
       "64GB SSD                           1\n",
       "128GB HDD                          1\n",
       "240GB SSD                          1\n",
       "8GB SSD                            1\n",
       "508GB Hybrid                       1\n",
       "1.0TB HDD                          1\n",
       "512GB SSD +  1.0TB Hybrid          1\n",
       "256GB SSD +  1.0TB Hybrid          1\n",
       "Name: Memory, dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the count of the Memory column\n",
    "df['Memory'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "e8c0b642",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\yashg\\AppData\\Local\\Temp\\ipykernel_16288\\4075101850.py:16: FutureWarning: The default value of regex will change from True to False in a future version.\n",
      "  df['first'] = df['first'].str.replace(r'\\D', '')\n",
      "C:\\Users\\yashg\\AppData\\Local\\Temp\\ipykernel_16288\\4075101850.py:25: FutureWarning: The default value of regex will change from True to False in a future version.\n",
      "  df['second'] = df['second'].str.replace(r'\\D', '')\n"
     ]
    }
   ],
   "source": [
    "df['Memory'] = df['Memory'].astype(str).replace('\\.0', '', regex=True)\n",
    "df[\"Memory\"] = df[\"Memory\"].str.replace('GB', '')\n",
    "df[\"Memory\"] = df[\"Memory\"].str.replace('TB', '000')\n",
    "new = df[\"Memory\"].str.split(\"+\", n = 1, expand = True)\n",
    "\n",
    "df[\"first\"]= new[0]\n",
    "df[\"first\"]=df[\"first\"].str.strip()\n",
    "\n",
    "df[\"second\"]= new[1]\n",
    "\n",
    "df[\"Layer1HDD\"] = df[\"first\"].apply(lambda x: 1 if \"HDD\" in x else 0)\n",
    "df[\"Layer1SSD\"] = df[\"first\"].apply(lambda x: 1 if \"SSD\" in x else 0)\n",
    "df[\"Layer1Hybrid\"] = df[\"first\"].apply(lambda x: 1 if \"Hybrid\" in x else 0)\n",
    "df[\"Layer1Flash_Storage\"] = df[\"first\"].apply(lambda x: 1 if \"Flash Storage\" in x else 0)\n",
    "\n",
    "df['first'] = df['first'].str.replace(r'\\D', '')\n",
    "\n",
    "df[\"second\"].fillna(\"0\", inplace = True)\n",
    "\n",
    "df[\"Layer2HDD\"] = df[\"second\"].apply(lambda x: 1 if \"HDD\" in x else 0)\n",
    "df[\"Layer2SSD\"] = df[\"second\"].apply(lambda x: 1 if \"SSD\" in x else 0)\n",
    "df[\"Layer2Hybrid\"] = df[\"second\"].apply(lambda x: 1 if \"Hybrid\" in x else 0)\n",
    "df[\"Layer2Flash_Storage\"] = df[\"second\"].apply(lambda x: 1 if \"Flash Storage\" in x else 0)\n",
    "\n",
    "df['second'] = df['second'].str.replace(r'\\D', '')\n",
    "\n",
    "df[\"first\"] = df[\"first\"].astype(int)\n",
    "df[\"second\"] = df[\"second\"].astype(int)\n",
    "\n",
    "# Create a new column\n",
    "df[\"HDD\"]=(df[\"first\"]*df[\"Layer1HDD\"]+df[\"second\"]*df[\"Layer2HDD\"])\n",
    "df[\"SSD\"]=(df[\"first\"]*df[\"Layer1SSD\"]+df[\"second\"]*df[\"Layer2SSD\"])\n",
    "df[\"Hybrid\"]=(df[\"first\"]*df[\"Layer1Hybrid\"]+df[\"second\"]*df[\"Layer2Hybrid\"])\n",
    "df[\"Flash_Storage\"]=(df[\"first\"]*df[\"Layer1Flash_Storage\"]+df[\"second\"]*df[\"Layer2Flash_Storage\"])\n",
    "\n",
    "# Drop the unnecessary column\n",
    "df.drop(columns=['first', 'second', 'Layer1HDD', 'Layer1SSD', 'Layer1Hybrid',\n",
    "       'Layer1Flash_Storage', 'Layer2HDD', 'Layer2SSD', 'Layer2Hybrid',\n",
    "       'Layer2Flash_Storage'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "f0e85d3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price_euros</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>ppi</th>\n",
       "      <th>Cpu brand</th>\n",
       "      <th>HDD</th>\n",
       "      <th>SSD</th>\n",
       "      <th>Hybrid</th>\n",
       "      <th>Flash_Storage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1240</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>6</td>\n",
       "      <td>256 SSD</td>\n",
       "      <td>AMD Radeon 530</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.20</td>\n",
       "      <td>597.57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>AMD Processor</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>256 SSD</td>\n",
       "      <td>AMD Radeon 530</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.91</td>\n",
       "      <td>719.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>1000 HDD</td>\n",
       "      <td>Intel UHD Graphics 620</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.10</td>\n",
       "      <td>902.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>100.454670</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>Asus</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>4</td>\n",
       "      <td>32 Flash Storage</td>\n",
       "      <td>Intel HD Graphics 500</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.65</td>\n",
       "      <td>348.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111.141337</td>\n",
       "      <td>Other Intel Processor</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>4</td>\n",
       "      <td>32 Flash Storage</td>\n",
       "      <td>Intel HD Graphics 500</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.44</td>\n",
       "      <td>292.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111.935204</td>\n",
       "      <td>Other Intel Processor</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company  TypeName  Ram            Memory                     Gpu  \\\n",
       "1240  Lenovo  Notebook    6           256 SSD          AMD Radeon 530   \n",
       "100       HP  Notebook    8           256 SSD          AMD Radeon 530   \n",
       "323       HP  Notebook    8          1000 HDD  Intel UHD Graphics 620   \n",
       "882     Asus  Notebook    4  32 Flash Storage   Intel HD Graphics 500   \n",
       "325   Lenovo  Notebook    4  32 Flash Storage   Intel HD Graphics 500   \n",
       "\n",
       "           OpSys  Weight  Price_euros  Touchscreen  Ips         ppi  \\\n",
       "1240  Windows 10    2.20       597.57            0    0  141.211998   \n",
       "100   Windows 10    1.91       719.00            0    0  141.211998   \n",
       "323   Windows 10    2.10       902.00            0    1  100.454670   \n",
       "882   Windows 10    1.65       348.00            0    0  111.141337   \n",
       "325   Windows 10    1.44       292.00            0    0  111.935204   \n",
       "\n",
       "                  Cpu brand   HDD  SSD  Hybrid  Flash_Storage  \n",
       "1240          AMD Processor     0  256       0              0  \n",
       "100           Intel Core i7     0  256       0              0  \n",
       "323           Intel Core i7  1000    0       0              0  \n",
       "882   Other Intel Processor     0    0       0             32  \n",
       "325   Other Intel Processor     0    0       0             32  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "22126dad",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop the memory column\n",
    "df.drop(columns=['Memory'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "a47fcff4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price_euros</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>ppi</th>\n",
       "      <th>Cpu brand</th>\n",
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       "      <th>Flash_Storage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>Intel Iris Plus Graphics 640</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>1339.69</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>898.94</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>127.677940</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>575.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>16</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>2537.45</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>220.534624</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>512</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>1803.60</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Ram                           Gpu  OpSys  Weight  \\\n",
       "0   Apple  Ultrabook    8  Intel Iris Plus Graphics 640  macOS    1.37   \n",
       "1   Apple  Ultrabook    8        Intel HD Graphics 6000  macOS    1.34   \n",
       "2      HP   Notebook    8         Intel HD Graphics 620  No OS    1.86   \n",
       "3   Apple  Ultrabook   16            AMD Radeon Pro 455  macOS    1.83   \n",
       "4   Apple  Ultrabook    8  Intel Iris Plus Graphics 650  macOS    1.37   \n",
       "\n",
       "   Price_euros  Touchscreen  Ips         ppi      Cpu brand  HDD  SSD  Hybrid  \\\n",
       "0      1339.69            0    1  226.983005  Intel Core i5    0  128       0   \n",
       "1       898.94            0    0  127.677940  Intel Core i5    0    0       0   \n",
       "2       575.00            0    0  141.211998  Intel Core i5    0  256       0   \n",
       "3      2537.45            0    1  220.534624  Intel Core i7    0  512       0   \n",
       "4      1803.60            0    1  226.983005  Intel Core i5    0  256       0   \n",
       "\n",
       "   Flash_Storage  \n",
       "0              0  \n",
       "1            128  \n",
       "2              0  \n",
       "3              0  \n",
       "4              0  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checks the first 5rows\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "1086be74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ram              0.743007\n",
       "Weight           0.210370\n",
       "Price_euros      1.000000\n",
       "Touchscreen      0.191226\n",
       "Ips              0.252208\n",
       "ppi              0.473487\n",
       "HDD             -0.096441\n",
       "SSD              0.670799\n",
       "Hybrid           0.007989\n",
       "Flash_Storage   -0.040511\n",
       "Name: Price_euros, dtype: float64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the Correlation related to price column \n",
    "df.corr()['Price_euros']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "2e93cf85",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop the Hybrid & Flash_storage column\n",
    "df.drop(columns=['Hybrid', 'Flash_Storage'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "1f5eb257",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>Apple</td>\n",
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       "      <td>macOS</td>\n",
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       "      <td>Apple</td>\n",
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       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>898.94</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>127.677940</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>575.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>16</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>2537.45</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>220.534624</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>1803.60</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Ram                           Gpu  OpSys  Weight  \\\n",
       "0   Apple  Ultrabook    8  Intel Iris Plus Graphics 640  macOS    1.37   \n",
       "1   Apple  Ultrabook    8        Intel HD Graphics 6000  macOS    1.34   \n",
       "2      HP   Notebook    8         Intel HD Graphics 620  No OS    1.86   \n",
       "3   Apple  Ultrabook   16            AMD Radeon Pro 455  macOS    1.83   \n",
       "4   Apple  Ultrabook    8  Intel Iris Plus Graphics 650  macOS    1.37   \n",
       "\n",
       "   Price_euros  Touchscreen  Ips         ppi      Cpu brand  HDD  SSD  \n",
       "0      1339.69            0    1  226.983005  Intel Core i5    0  128  \n",
       "1       898.94            0    0  127.677940  Intel Core i5    0    0  \n",
       "2       575.00            0    0  141.211998  Intel Core i5    0  256  \n",
       "3      2537.45            0    1  220.534624  Intel Core i7    0  512  \n",
       "4      1803.60            0    1  226.983005  Intel Core i5    0  256  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the first 5rows \n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "bb449f76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intel HD Graphics 620      281\n",
       "Intel HD Graphics 520      185\n",
       "Intel UHD Graphics 620      68\n",
       "Nvidia GeForce GTX 1050     66\n",
       "Nvidia GeForce GTX 1060     48\n",
       "                          ... \n",
       "AMD Radeon R5 520            1\n",
       "AMD Radeon R7                1\n",
       "Intel HD Graphics 540        1\n",
       "AMD Radeon 540               1\n",
       "ARM Mali T860 MP4            1\n",
       "Name: Gpu, Length: 110, dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the count of the GPU column\n",
    "df['Gpu'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "e9db12f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new column as GPU Brand\n",
    "df['Gpu Brand'] = df['Gpu'].apply(lambda x:x.split()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "cebfefcd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>575.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>16</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>2537.45</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>220.534624</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>512</td>\n",
       "      <td>AMD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>1803.60</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Ram                           Gpu  OpSys  Weight  \\\n",
       "0   Apple  Ultrabook    8  Intel Iris Plus Graphics 640  macOS    1.37   \n",
       "1   Apple  Ultrabook    8        Intel HD Graphics 6000  macOS    1.34   \n",
       "2      HP   Notebook    8         Intel HD Graphics 620  No OS    1.86   \n",
       "3   Apple  Ultrabook   16            AMD Radeon Pro 455  macOS    1.83   \n",
       "4   Apple  Ultrabook    8  Intel Iris Plus Graphics 650  macOS    1.37   \n",
       "\n",
       "   Price_euros  Touchscreen  Ips         ppi      Cpu brand  HDD  SSD  \\\n",
       "0      1339.69            0    1  226.983005  Intel Core i5    0  128   \n",
       "1       898.94            0    0  127.677940  Intel Core i5    0    0   \n",
       "2       575.00            0    0  141.211998  Intel Core i5    0  256   \n",
       "3      2537.45            0    1  220.534624  Intel Core i7    0  512   \n",
       "4      1803.60            0    1  226.983005  Intel Core i5    0  256   \n",
       "\n",
       "  Gpu Brand  \n",
       "0     Intel  \n",
       "1     Intel  \n",
       "2     Intel  \n",
       "3       AMD  \n",
       "4     Intel  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the first 5 rows\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "743c83ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intel     722\n",
       "Nvidia    400\n",
       "AMD       180\n",
       "ARM         1\n",
       "Name: Gpu Brand, dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the count of the GPU Brand column\n",
    "df['Gpu Brand'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "8d15f28b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new column \n",
    "df = df[df['Gpu Brand'] != 'ARM']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "0d66758d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sort_bar4 = df.groupby(['Gpu Brand'])['Price_euros'].median().reset_index().sort_values('Price_euros', ascending=False)\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.xticks(rotation=45)\n",
    "sns.barplot(x=df['Gpu Brand'], \n",
    "            y=df['Price_euros'], \n",
    "            data=df, \n",
    "            palette='Greens_r', \n",
    "            order=sort_bar4['Gpu Brand'])\n",
    "plt.xlabel('GPU Brands', size=15)\n",
    "plt.ylabel('Price', size=15)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "79f206e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop the GPU column\n",
    "df.drop(columns=['Gpu'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "30461eb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Ram</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price_euros</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>ppi</th>\n",
       "      <th>Cpu brand</th>\n",
       "      <th>HDD</th>\n",
       "      <th>SSD</th>\n",
       "      <th>Gpu Brand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>1339.69</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>898.94</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>127.677940</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>575.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>16</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>2537.45</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>220.534624</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>512</td>\n",
       "      <td>AMD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>1803.60</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Ram  OpSys  Weight  Price_euros  Touchscreen  Ips  \\\n",
       "0   Apple  Ultrabook    8  macOS    1.37      1339.69            0    1   \n",
       "1   Apple  Ultrabook    8  macOS    1.34       898.94            0    0   \n",
       "2      HP   Notebook    8  No OS    1.86       575.00            0    0   \n",
       "3   Apple  Ultrabook   16  macOS    1.83      2537.45            0    1   \n",
       "4   Apple  Ultrabook    8  macOS    1.37      1803.60            0    1   \n",
       "\n",
       "          ppi      Cpu brand  HDD  SSD Gpu Brand  \n",
       "0  226.983005  Intel Core i5    0  128     Intel  \n",
       "1  127.677940  Intel Core i5    0    0     Intel  \n",
       "2  141.211998  Intel Core i5    0  256     Intel  \n",
       "3  220.534624  Intel Core i7    0  512       AMD  \n",
       "4  226.983005  Intel Core i5    0  256     Intel  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the first 5 rows\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "264fcd84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Windows 10      1072\n",
       "No OS             66\n",
       "Linux             62\n",
       "Windows 7         45\n",
       "Chrome OS         26\n",
       "macOS             13\n",
       "Mac OS X           8\n",
       "Windows 10 S       8\n",
       "Android            2\n",
       "Name: OpSys, dtype: int64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the Count of the Opsys column \n",
    "df['OpSys'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "38363011",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ga+DN4wG7JEmSNKqWt873OsCDgAuTnAhcwpITMKuqXresF6iqHyXZZELzzsC23f3PAD+graa5M3BUVRVwUpJ1kqzf7XtCVV0OkOQEWkD/+eX8fSRJkqTeLG/w/W/A9d39x0yyvWhB8/K6d1Vd3N0fA+7d3d8A+NPAfhd1bUtrv40k+9BGzdl4443vQNckSZKk6TGl4DvJXYCdgMNowfF3q+qSmehQVVWSaStnWFWHA4cDbLXVVpZJlCRJ0tBMZZGd+wLfBTYZaL4yybOr6jvT1I9LkqxfVRd3aSWXdu2LgI0G9tuwa1vE4jSV8fYfTFNfJEmSpBkxlQmXC4FbaGkmdwUeCJxBm3w5XY4DxiuWPB84dqB9z67qyTbAlV16yreB7ZOs20203L5rkyRJkkbWVNJOHgm8pqp+0j3+TZKXdLfrD+RqT0mSz9NGrddLchGtasm7gC8l2Rv4A7Bbt/vxtHSXc4FrgL0AquryJG8HTun2e9v45EtJkiRpVE0l+F4fOH9C23lAgPnAcgXfVbXHUjZtN8m+Bey3lNc5Ejhyed5bkiRJGqap1vl2oqIkSZK0gqZaavDbSSZb2fLEie1Vda8V75YkSZI0+0wl+H7rjPdCkiRJmgNuN/iuKoNvSZIkaRpMNedbkiRJ0goy+JYkSZJ6YvAtSZIk9cTgW5IkSeqJwbckSZLUE4NvSZIkqScG35IkSVJPDL4lSZKknhh8S5IkST0x+JYkSZJ6YvAtSZIk9cTgW5IkSeqJwbckSZLUE4NvSZIkqScG35IkSVJPVh12ByRNn7Xvvu4St5IkabQYfEuzyL89Z59hd0GSJC2DaSeSJElSTwy+JUmSpJ4YfEuSJEk9MfiWJEmSemLwLUmSJPXE4FuSJEnqicG3JEmS1BODb0mSJKknIxF8J7lfkjMGfq5KckCStyRZNNC+08BzDkpybpJzkjx5mP2XJEmSpmIkVrisqnOALQGSrAIsAr4G7AUcWlXvHdw/yRbA7sADgX8Cvptk86q6uc9+S5IkaXgWLFjA2NgY8+fPZ+HChcPuzpSMRPA9wXbAeVX1hyRL22dn4AtVdT1wQZJzga2Bn/XUR0mSJA3Z2NgYixYtGnY3lstIpJ1MsDvw+YHH+yc5M8mRSdbt2jYA/jSwz0Vd220k2SfJqUlOveyyy2amx5IkSdIUjFTwnWR14OnAl7umjwKb0lJSLgYOWd7XrKrDq2qrqtpq3rx509VVSZIkabmNVPAN7AicXlWXAFTVJVV1c1XdAnyClloCLSd8o4Hnbdi1SZIkSSNr1ILvPRhIOUmy/sC2ZwC/6u4fB+ye5M5J7gNsBpzcWy8lSZKkO2BkJlwmuRvwJOAlA80Lk2wJFHDh+Laq+nWSLwFnAzcB+1npRJIkSaNuZILvqroauOeEtuctY/+DgYNnul+SJEnSdBm1tBNJkiRp1jL4liRJknpi8C1JkiT1xOBbkiRJ6snITLiUJGlltWDBAsbGxpg/fz4LFy4cdnckjTCDb0mSVtDY2BiLFrnWm6TbZ9qJJEmS1BNHviVJUi9Mz5EMviVJUk9Mz5FMO5EkSZJ6Y/AtSZIk9cTgW5IkSeqJwbckSZLUE4NvSZIkqSdWO5EkzSqX/ubs3t/z5htuuPW27/e/1wO26PX9JK0YR74lSZKknhh8S5IkST0x+JYkSZJ6YvAtSZIk9cQJl5IkzUFnH3tM7+95w9VX33rb9/tvsfMuvb6ftDSOfEuSJEk9MfiWJEmSemLwLUmSJPXEnG9JkqQRs2DBAsbGxpg/fz4LFy4cdnc0jQy+JUmSRszY2BiLFi0adjeWyzH77dv7e1596aW33vb9/rt8+CN36HkG35IkraB597znEreStDQG35IkraDX77/fsLsgaSXhhEtJkiSpJwbfkiRJUk9GKvhOcmGSs5KckeTUru0eSU5I8vvudt2uPUk+mOTcJGcmedhwey9JkiQt20gF353HV9WWVbVV9/hA4MSq2gw4sXsMsCOwWfezD/DR3nsqSZKm7J5rrcW8ddbhnmutNeyuSEOzMky43BnYtrv/GeAHwOu69qOqqoCTkqyTZP2qungovZQkScu03zN2GXYXpKEbtZHvAr6T5LQk+3Rt9x4IqMeAe3f3NwD+NPDci7q2JSTZJ8mpSU697LLLZqrfkiRJ0u0atZHvR1fVoiT3Ak5I8tvBjVVVSWp5XrCqDgcOB9hqq62W67mSJEnSdBqpke+qWtTdXgp8DdgauCTJ+gDd7aXd7ouAjQaevmHXJkmSJI2kkQm+k9wtyVrj94HtgV8BxwHP73Z7PnBsd/84YM+u6sk2wJXme0uSJGmUjVLayb2BryWB1q/PVdW3kpwCfCnJ3sAfgN26/Y8HdgLOBa4B9uq/y5IkSdLUjUzwXVXnAw+ZpP2vwHaTtBfger6SJElaaYxM2okkSZI02xl8S5IkST0x+JYkSZJ6YvAtSZIk9WRkJlxKkiSNoh8fekjv73ntFX+79bbP93/Mq17T23vNVY58S5IkST0x+JYkSZJ6YvAtSZIk9cTgW5IkSeqJwbckSZLUE4NvSZIkqSeWGpQkSdJKaa3VVlvidmVg8C1JkqSV0i4bbzTsLiw3004kSZKknhh8S5IkST0x+JYkSZJ6YvAtSZIk9cTgW5IkSeqJwbckSZLUE4NvSZIkqScG35IkSVJPDL4lSZKknrjCpSRJ0ohZ5y53XeJWs4fBtyRJ0ojZc5uth90FzRDTTiRJkqSeGHxLkiRJPTH4liRJknpi8C1JkiT1xOBbkiRJ6slIBN9JNkry/SRnJ/l1kld27W9JsijJGd3PTgPPOSjJuUnOSfLk4fVekiRJmppRKTV4E/Caqjo9yVrAaUlO6LYdWlXvHdw5yRbA7sADgX8Cvptk86q6uddeS5IkScthJEa+q+riqjq9u/934DfABst4ys7AF6rq+qq6ADgXsCCmJEmSRtpIBN+DkmwCPBT4ede0f5IzkxyZZN2ubQPgTwNPu4ilBOtJ9klyapJTL7vsspnqtiRJknS7Rir4TrImcDRwQFVdBXwU2BTYErgYOGR5X7OqDq+qrapqq3nz5k1ndyVJkqTlMjLBd5LVaIH3Z6vqqwBVdUlV3VxVtwCfYHFqySJgo4Gnb9i1SZIkSSNrJILvJAGOAH5TVe8baF9/YLdnAL/q7h8H7J7kzknuA2wGnNxXfyVJkqQ7YlSqnTwKeB5wVpIzurbXA3sk2RIo4ELgJQBV9eskXwLOplVK2c9KJ5IkSRp1IxF8V9X/AZlk0/HLeM7BwMEz1ilJkiRpmo1E2okkSZI0Fxh8S5IkST0x+JYkSZJ6YvAtSZIk9cTgW5IkSeqJwbckSZLUE4NvSZIkqScG35IkSVJPDL4lSZKknhh8S5IkST0x+JYkSZJ6YvAtSZIk9cTgW5IkSeqJwbckSZLUE4NvSZIkqScG35IkSVJPDL4lSZKknhh8S5IkST0x+JYkSZJ6YvAtSZIk9cTgW5IkSeqJwbckSZLUE4NvSZIkqScG35IkSVJPDL4lSZKknhh8S5IkST0x+JYkSZJ6YvAtSZIk9WSlDr6T7JDknCTnJjlw2P2RJEmSlmWlDb6TrAJ8GNgR2ALYI8kWw+2VJEmStHQrbfANbA2cW1XnV9UNwBeAnYfcJ0mSJGmpUlXD7sMdkuSZwA5V9aLu8fOAR1TV/hP22wfYp3t4P+CcXjt6+9YD/jLsTqwEPE5T57GaGo/T1Hicps5jNTUep6nzWE3NKB6nf66qeZNtWLXvnvStqg4HDh92P5YmyalVtdWw+zHqPE5T57GaGo/T1Hicps5jNTUep6nzWE3NynacVua0k0XARgOPN+zaJEmSpJG0MgffpwCbJblPktWB3YHjhtwnSZIkaalW2rSTqropyf7At4FVgCOr6tdD7tYdMbIpMSPG4zR1Hqup8ThNjcdp6jxWU+NxmjqP1dSsVMdppZ1wKUmSJK1sVua0E0mSJGmlYvAtSZIk9cTgW7NaNxlXd5DHb0lJPGdKMyhJht2HlY3npZWP/2AjLMkqw+7DyizJU4APDrsfK6skTwIOTrLWsPsybEkelGTNqrpl2H2RZqMkGydZrZyINmVJHprkTlV1iwH4HZNk0kVwZpr/WCOqC3yem2SdYfdlZdQdv0OAPZPsMez+rGySPBl4P/ATIF1b5uKoVPd/cAEwlJP0yizJo5P8R5LHJrnvsPszLElWG3YfRlk3UHI4sM2w+7KySLIqcADwHQPwOybJjsDXk2ze93v7DzWCkuxEG7G9Erh2yN1Z6QwEjs8D/g14SJLV5mLgeEckuQ/wbmD/qjoGuKbbtM4cHZX6O7ABsP2wO7Iy6f4ffh64B7AX8Jbuw25OSbIrsF1333PQBN3fxH8B76qqH0/Y5vGaRBds31RVzwf+Cnw2SQzApy7J9sC7gLdX1e/6fn//kUZM9w3sHcCeVXUscEPXftehdmwl0aVI/Cvw0qo6BfgH8FTgQVVVnsyn5FrgpKr6fpJ7A69K8hXglCS7w9z4UEyyUZItqupm4EtAde2rdrez/hisoPvRPtgOAt5KW5Ph5bM9AB//uxgIgh4O7NTd9zN3QHeMng28sap+kGStLv3kGUnu2Z2zPWYTjKe/JXk+MEb7G/uiI+C3r7uAuybwcuAtVfX1JHdLco8kj0tyzz76sdIusjOL3R34c1WdkuQuwL8neSpwRZKTq+rDQ+7fSKuqvydZWFVXdyMBP07yReBtSZ5bVVcNu48rgWuBrZJ8jPbF5bvAD4FjgA8kOWslXdBqypL8E200bpsk3wCeCIwl+TbwR4DxqwDjH3hD6+zoWh14EnB4VV2Y5G9d+7OS/Lqq/jjEvs2YgatDj09yMvBbYONu281D69hoWgVYE1g3ybrAwcB9gH8GVk/y+Kr60zA7OEqS3LWqrunuPw14M7ADcDTt6tLRSXYdD8A9Ly2piwkK+EeSs4G7JLk/sC+wEfBo4AtJPl9VP53RvszNq8ijK8mdgSNpJ6UHAL8ALutunwMcVFVnDa+HK4+BUYDNgQOBd1fVOZ6Ulm7gmM2nBU4BvgJc241CHQF8tqq+N9SO9qD7v3gvWuD0JOBNwOnAybRUnNOBb1XV5UPr5IhJsspggJnkWOB3VfUf3eNNaQHW+6rq5CF1c0YMfLCT5O60L6sXAPcHHkHLz70WOBVYVFWXDaenw9flwKeqbkjyKOBzwE10X/Kr6rgkhwGrVdVLhtnXUZHkAbSrup/pVvjeCfjXqvrP7niuB3yVFi/sPEdTBJcpyepVNZ5N8ELgscAutM+4rwFnA28HflxVH5/JvjjyPQKSPIL2H+efgC/S8r0fAZwCfL6q/tzt9/ShdXIlNB5gV9Xv0krmHQw808B76brAe9WqGgP+e3BbkufR/i7fOpTO9WQggLyxG3X7E/CTJOvTAu8zgBfSjsXPAINvIMkTgMcmOayq/tI1Hwi8PskhVfWaqjovyRXAVrRjOWsMBN5b0/5mngTcQhtN+yLtS9xdgScArxhSN4euu5K7O7BBku8CJwKbApt2gyPjVb5+B9x7SN0cRWvQvtDdN8ktwJ+BvZMc343SXpzkJ7RBu38CFg2tpyOom4PynCT/AK6nXTU4HvhgVZ0+MPD0O2DD8fSxmfoS48j3kCXZGXgn7Rvro2n5W98AvlJV1w7s92/A64Bdq+qiYfR1FA2ONi1re5INaV9qXlxVf+2vh6MtyaOBRwI/By6qqvO79sFRvPsDTwNeADyrqs4eUndnVJK1qurvk7SvBtwMfBr4YVUd0bUv829vLun+jk4AjgPOBQ6tqr90x+4+wHtpV/NOpf0dbVdV5w6puzOiy7O9K21+wJnAYePn6iQLgS90H/Jrz9X0ty7wfjfwSmBtWgWhFwKfqqqPDey3O/Aa4AWzPcXt9kw4F69HG0S6hPZ/avvu9vXAOsCuwHOq6tLh9HY0dfNMPgr8B+2q5Z60Ac8FVXXaQJzwAuDVtM+5c2a0T352DE+SB9JO1M+pql92bS8BHgL8lFYpYD1a1Y49u/1+NaTujpwJJ6W9gTWWlhPfBQF3rqp/9NnHUdaNBHwS+B9gPi04+nxVfXPCfmsCLwa+MYxZ4X3oLuE+EfhwVZ23lH2eBuxYVfv22rmVQBd8P4R2te5ZtBSC9w2mViR5EW3S6s9m4xe48S9vSTahDaj8Djiyqv6Q5H+Ac6rq7XM17a27+vhp4IiqOrFruxOwLe1S/7uAbwEH0apUPXeuf95N9gU/yUNoX2AvBz5Gm2z5LNrI+MLxWEK3Tn6+K/Bl4KNV9b8D2w6mpZ1sD9wF2A14GS3OmvEvfAbfQ5TkX4FXVNXuSe5WVVd37fvSSlPtQcs5fR5w7Gz8wJoOSV5L+8b/4sH/NHP1Q26qkrwCuK6qDu8ChkcBzwU+NB6AD4wIzNpR3m407p20K0vfHPw9B/+Guv0WANv6d3VbXbrSTUkeQ7tScgtwyFzIbe5SB/eiBT/nJ9mYdqXtUuCNwObAn6rqwuH1cri64Ps7tC9lxw20rwLsR4tHPtCdi24uJ1reqvvi+mDaFbgP0f5vvYr29/XJqrpkNp+jV0RadaovA6/p/m+uUVXXddu+BFxQVa/r0sUWVVUv6TqWoxmu1YB7AlSrzrFqd/8jtJHIp3eXLd9j4L3YeC5Wd39dWg7lo4DLkuya5MNdIHDL4L66jdVpQRJdUPB14LO0ahTj1Rlq8Ha2SSsr9WrgRVV1PK3Cwl3TJgbemgPf3f868GwD78lV1U3d7Y9pqXN3AvZKcmCSt2QWlT+b5HcpWlnTfZPct1oll/+kldF7FvDzuRx4A3QT3U4A1hs/L3cB4820UdzHd+ftCw28F+sC7xcB3wOuAE4C7kwLwjehVURbfVj9G3XdeelmYO/u8XVZXLr5OLoSslV1cl+BNxh8925CMPgjYK0k74X2R5JWYQHgNNqs5Vs/1NQMpJo8sar+Rrus9G3gA8DWwJZ0y8rP1qDxjhqYzERVvRe4Jcl7usdX0k7sd6V9+ZsLVqF9kI11H2AHAscCRyX5SjfyfdN4sFVVFw+xryNvYJLSD4FP0Opbv4Z25W7WfGkZuBryyCQPolW++Qgt3eblaUtWz6Odl47rAk+1+QAvAh7T/d8aPz/fmTaKO+fP1xMGl1alLVJ1aFUdU1VvpU0UPBL4A/Ap4L+r6gY/625r4Evyh4H5SZ4DUF25Rtr/0bWTrNr3QJ3Bd48m5CjvSpvxvxewRZJDAarq+m7btnT1hNWM/0caCCDfnuRlwFNos8BfX1UH0iakxNGAJaVVo3hjN2ln3IG0k9IhAF2+8xW0v81Zr5uYdCytzNTvaJMDj6bNsRhfAITZFDiuqGV9SE0IAB5Du1T+uKr6xYx3rAcTAqP9gC/QJrt9g1Zd4qPAjcA3aYMBb65ZWs/8jqiqL9LmOb2DdpVg926+zgG0dLc5XQd9QoywL21y6nzald1xRwC/p5Vh/KmTK5du4Lx9evfzpCTvTfLA7vjuT5sYfVPfX17M+R6CJK+kfbg/Gzgf+BfapLcrgKuB+9JWuHTixCSSPLzaDOWH0kpWHTE+ETDJq2iTUf69rId+q1iN4lZJtgP+P1oe7ieBv9FG3jalTSodzwc8DPhJVX1+WH0dNROCg9ub5LwNcE1VndlnH2fKhHk5j6Wde/6rqv6ctiDVA4AdquraJA8G/lJdmdi5ZrL84ySrVdWN3f1daSkTjwX+Anxgrp+vM1Ajv/v7elNVPTFtwvtvaeeqQ4CdaaUqd6rFJT3FUv/uxuctrQ38P9qxu4G2uNN/1pCq6Rh896zLJT0c2L2qLpswoevBtNrrY3P1pD2ZCR/4DwTOos2O/xUtzeTUqvpi95/rg8B7a47Pkp8oVqMAbi3t+W5gIfBA2pyLq4BPDH74J3kmbQLmHrP1S8iKyO1Mcp5tk7/Sym0+FvgMi9Pc/gG8cDyXO8lHgMcBW48H6XPRhPP1ZsCVwN+q6sYkd66q6wf2vRMtDpnrI973o80ReCUtP/nDwIOAZ1TVBUk2Ao4Czuva9x5W0DiqJvzdbQrcsKy5AxlYcGcYDL5n2MQPobQJgkfRLlWePfBN91/8kF+2JHevqiuTfIp2heCntHzSNWkT5n6cCSvsabHM4WoUAGlLxn+FVmHo1K7tYbS0pXWB99FGRPYG/p02udIvcdzmg21d2sTcp9ByJh9Nq870yu7va1YF3gBdXvefaYu+/A1YixYgHUPLuf17t9/7aIt2XDicno6O7rL+C2mjthsBT6mqf4yfh4bbu9HRpTJtShuRXQN4OYtHaM+grfnxxyR3ow2a3LWb66RJJFlAS9u9M/B/tPKxl3bbBgc7h3qeMud7Bk34wFojrcTN32gF8h9C+8CnmwTw2iRrDa+3oy2ttunRXarJp2mjlj+hzfi+J/DStMmq5uYuRc2hahRLcTNtgYULur8Vqup02ipn84F53Un6TGAXA+/FBs5jc2aS82B+d/e3cAstMHoFLQB/FfBMYM+05eSpqlfP1cB78POr+4K/D+34vJAWRP40yV0MvBcbjxG6gbcfABsC76Fd1f0YLSVw1yQbV9XVVXW9gffSJdkeeHxV7URbZXYLusIVsOTcnWGfp1xefgYNfGC9graK4DVJPk4bYXsHsEOSq2mXM59Vk6yuN1dN8q30t7TJTEfSLs9dQMvrPjjJGHDW4OVMTW7gZP/DJH+mVaR4ILB9zY1JhTfQFlv663gOarX5A1fQ6ur/oqq+Mdwujo6BNJLxK0pv71IJngI8n1YX/YK0RYqeNuxLudNsFeCm8ZHaqroiyVHAM2hB+IdoE7b+G7ghySeH/YE+LN1l/pck+XJVnUKbv/SzqrqwO+e8MslngV1oi8eJJWKE19KuRp4G3A84jPa3Be1LzA1JPu5V3dt1M3B8kjeyuFxzJXlIjdgcutk+0jV0aTPid6WlmaxHK/Y+n7Zwzmdopd12cpRtsQlXDJ6bVonjRbQl0J9Nmyy3Fq2k132r6lvVY33OUTc4YjfRhOBg1lWjWJaquoRWnusb44F3FlfOuaj70YCBL2Rbdrf70ibKbVBVH+kC71fRFin6yGwJvNMqAp2b5B5dKs3qAFV1EvBV2sj/fsBfaQtTfWeuBt6du9OuDDwjyZa047J9kqcOHJdLaGtbaECXTrItLc3t1bQrKjfQ5qacRhsB/6qB95IGP+eyuLLZX2hXWx5Fu3p5Q5KXA+/pjvPIMPieZoOX7rtL2zfTlsrdtbu/gDZr+YlVdUJVfaqqLhhKZ0fUQOD9MtoCKOcBGwAfp9U8fSvwNlq5pbkwWjtlE7647N19+Vuas2mrNc7KyZWDxoPsqnoZ7ZLuKUn+hVZmcTfaqPd3htjFkTLhg+2BtOP1VlplpjsBD+22rU1LoXtuzaJqFdWqSLycliqxbvchvlq37ee06lTzaKP/v6+qPwyvt8OTZB24NX3rv2mfcf9OOy8/H/hUl9b2BlqAedJwejo6JhkcWYX2+faE7vG5wC+AJwPvrKqTqmqsxy6uFAY+5w4APpDkvd3o9rdo8zNe0g0MvBh4VY3YJGgnXM6QtKoK13Y/J9PyI59VrcLJd2nLxj8K+MccHzG5VZLNgXWq6uTu8XuBL1bVKd1Jfhda/emDTNFZtsyxahQTTfb7TZhs80FafuVdaJOcDhi1y5KjIHN8knOSHWkpAFtV1d/G02qSPBx4GHBMzZEJyxMleSJtYaFv0r6MXESrlvRS2mS3D9Cu8m5PGzT5TFX9Zji9HQ0TBke2oVU2u7BL23o5reb0N5L8O+2q5PvLymdLmHAefyCtgMVraAObVVVPSfIM4OG0c/sRo/h3Z/A9TSb8p9odeD9tYuB2tBPUA2gnqK1pkwDe3V0GF7deNlpIuyz5mao6OcmRwF2qao9uny1p+d7Pq6prh9bZETTh72/OVaMYNOFYPB24uqpO7B4PnrjXYfHSwlcOqbsjK22S8yHAfwBrA2+h1YB/PK0u/Ldpk+lm9ep6kwTg+9NKwj2+quZsqlJ3Pj6JliLxetoxeTdwf9okt3vRgkeXiuc256WX0SbuXkU7V/+Qtr7Hh2jrMWwHPHkUg8ZRkeTJtPPSWlV1ZNf2beDmahMub63wNcRuLpVpJ9Ngwn+qjWkf6I+qttriobQcrqfRcpEOAj5t4L1YFxDdUFUH0CYB757kvrRjdUM3Ag6tHNM6tNFKDRj4+5sz1SiWZuBYvJK2FPMfB7bdMnDZ98qqutLAu5nkcvjgJOc1WTzJ+dO01IIF1aovzPa/p2/SJr/9MMmrabnez5rLgTdAVZ1BG/2/nhZEbk9LN3k4bW7Oq4BXJ1l9WfNQ5oqB89LOtCILD6KN2N6PFmz/GNiGdp5+tIH3kiakwj2Xdm7aH3hmkn8FqKonA+t0QTiMcFqqI98raELg/Qra5Ju1aBVN/qeqrutG3z5M+6b7XVMmJpe2Yt4zaVcGfkoLHK/qbq+jpQnsOZtyS1dUJlSjSPIz2mW4o5ikGgVt9HtWTIpblixezOrZ1VbyvHXEu9s+q0f/l9eE89hzaUHV+cCxtC9yz6FVxXkM8K9Vdf6w+josSZ4C/C/wUFOUFkvy/wHfpZ1bPt3Nr3gILRg/dq4HkUnuPT7Y1s2R+BjwsKq6f9f2ONqXlUuBT83V+QNT1cUJD6YNrKxNK8ZwJ9rqxD/r9tlo1K+4OPK9ggY+sHah5SM/D/g6rUj+Nt1lj+No33B/aeA9uSSPpOW8PZU2cnIZbfnm66vqScCetMu8Bt4Dao5Wo5hokpG1y2k1vdfvvpiMp5rcH2b/6P/ymnA53EnOk6hWgnJNA+8lVSst+ETg0CT7VtXNVXV6Vb3LwDv3By5OcmiSF1XVVcB/AWcmOQygqn4IHE2rGGN8MMH4uX3gHP9o2oj3OlX1R1r1oQJ2S7I1wKgH3mDwPS2SbEC7VHRjVf0OeBNtxPbfgMd3AfiX5uJo0dJMEixdR1u4Yt1qlQbeSpuQ+qEkD6+qv1XVFT13c2RNuAQ356pRDJowant7i1kdkGTN4fV2tCTZfPwDq7Mp8NKq+ghtDsaXaSkma1bVWVX1mJqji8gAVNU1w+7DKBoIwA9L8sJh92eE/IN2FXcMeHY3cXlz2hyCvyd5P0A3J+X1VXX5sDo6irorluMDJesBVNVetJST7yS5c5f+dCytvOVKUznO4HsaVKsxfQCwY5I9quo6WvB4I61c0OrLePqcMyFYWrObbHkWbVTtMUnuVVV/pX3wXw1cOLTOjqiB43f3atVMPkNb6XM1WsmqtyV5TDfSsnfN0jryk6R9fQr4cBdQvo+WxvT+tMWt3kirJvCPoXV4hHT/7/YFnj8QgN+DNvJN92X3DFrFipGctKTRUVWn0a5a/mTYfRkV3byAk2lpXDvSJlM+D3gXLa3rsUkO6na/biidHGEDVyz3BY5M8vkkL6iq/WlVdk7tBltOBd5TK1HlIYPvaVJVX6XVkzyoC8BvopW+ebejJYtNCJZeS6sAcwwtd+toWoWOdyf5L2A34MAuENcEXTWKo5M8lFZZ54G0D74P0QLxl6bVmp+1aQIDf0v70VYedDGrKXCSs2ZCVf2iqs4Zdj9GwcDVyQNpaRHr0epPPww4HXg6bXDpaDAVbtCEK7vb0yY5HwB8H9g8yeur6pXAb4AfdLuuVCmVLi8/jarV57wFODzJTVX1ZVrusjoDwdITaPndL6Utn/tz2sqVv6FN1NmUVlnhvCF1deRMMklwsBrFf7K4GsXBScaAs6rq+iF0dcal1Rh+ZFW9vWtajVbXfC+WXMzqNVX1teH0cnQNjCjtDWxMm+S8Pm1y87tpi1b8L4snOXs5XFoOVVUDQeTvaWU7H05b8OWYJPehVVzy/9YEA3HCfWjnp+Oq6rwkf6TL+U5yz6raLck/DT5nZWG1kxmQ5EnAeeZ4L9blJa9bVf/Xze4+ADh9PHjqRtqeTltx8c8Tq1PMdVajaLoPs1WBXwIbAYdW1Zu69k1pEwR3q6q/xsWslqmb5PxRWkCwLm2uyi3Ah7oPunVpn2lXDK+X0sovyf1otbw/PDBgoAkmfM7tRRuQ+zJtQOA/usmpdAMDh1bV9yYZlFopmHYyA6otGz8rg587IsmGwGbA2UnuDpxJK6t0/y51gqp6La1c1QnplgLXYlajaKq5kbbwy1eBjZN8sDs+f6FdaVq/G9E9E3hSVf19ZTw5TzcnOUvD0aXiHAiskuSuw+7PqBr4nNuNNln+o1X1fVrp3BcleWGSZ9I++84ZfM7KxuBbMyqt3vJ+wNm03NFP0lb7XEArq/TMgQB8X1o5wZsd9W6sRrFYknsPPPwDbZ7AV4Frkry/CxTPB15H+6D7VLmYFeAkZ2kEnES7YqkJxgcGkozHpM/qfsbnmhwDfAXYBdgJ2KsrdLHSMu1EM6q73PYh2sTKz9KWIH4A8AngXODttBXSjqyqX62sl5BmQhcgLaTlM3+mqk5OciRwl6rao9tnS1q+9/Oq6tqhdXaGdTnen6flt38IWATsQUtV+iBtoYU/VtVbkqwKrG0uZTPJJOdH0yow/Tvtsu6zgFVox3QH2sJEzrWQplmSu1qAYemSzK+qse7+J2iphc8Y/2zrzu3UiC4Zvzwc+daMSHKfrgzeOcB7gENpixB9jJYO8BLaKO6bu6dcAivvJaTpZjWK27iUltu+N60izidoKRNn0UbB3wM8KMk7quomA+/FJpnkfCBtsu7Pu5+30arkrIKTnKUZY+C9pCQPS/KI7v7Lga8lOSzJA6rqxbQ0wi8nuRu0oHs2BN7gyLdmQJLNgS/ScrIOqKqxJC+iTQZcQCu79ExgW1q90zNMM5lcl7v8TFo1ip/SqlFc1d1ex+JqFLNyAZ1BSR5AKyv1EVqN1/fRJpkeWFUf7yb1Xl5VFw+vl6PDSc6SRlU3iv0yWirJ/wKPo63++ULaugKfq6qfJzmGtoDhs4bU1RlhqUHNhPOBX9P+M707yReBK4E/0crDHZPk27RFiC72A39yXTWKl7NkNYrdadUonjTXqlFU1W+S7AScCJxZVY9O8ii6+q7VFhsSS0xy/tFkk5yr6pdV9dpu8tcJSR5M+1IsSTMqycO7u9+nrQL6IuDYqjotyfm09NTnJFmtqnYZLyc4m5h2ommTZOMkm3eXhQ6g5ef+kZa39Qja8sPPSVuR6jzahDhHKTtWo7h91VbR2x74VJK9q+on1Za2VsdJzpJGVZIdaFW6HkCb3H0c7UrmXkkeWVV/o9VEvwF4ehcv/HloHZ4hBt+aFl1O1huBNyTZpQsWzwd+BXyJtuz3X2kpFB/onnbzMPo6iqxGMXVVdTKwHfCJrhaslrQq7WrJNrR8+J/RFh+6H20F0HvQlpR/ULe/C4FJmnFd+tuHgJdV1f9U1QXd59qHaemEr+8C8L/TBpsWVtV1Q+zyjDHnW9MmyXzgCbQKHQfTqpm8FHhHdznp3rTVLD/npK7FrEZxxyR5KHBNuZw1cOtqcJdX1ZVpC319gVaW6yzaRNUtaROeL6AF4e+sKgNvSb1I8mrg5qr6QJJVBydPJlkPeAbwPOC13SDLrGXwrWmX5GG0FalOoKVJ3B3Yu9qqeU7qWoquGsWbaF9Y9gGeRgu+16alWmxKKzn426F1UiPJSc6SRtX4AFOSDwFXVtV/DrSN3z4ACC1m+FZV/Wm4vZ5Zpp1o2lXV6bTL3H8FLgIeS8vduhNO6rpVkgcmeXR3/3G0yZUnVtVvq+rVtKXjTwZuqqpPAm8w8NZSjE9yfhRtkvNOLDnJ+VLg27QvxE5yltSbgRLCXwO26eYsVRcTjM912o52hfeI2R54gyPfmkFJVgPWoE2eOMT0gMW6ahRbAT+i5b7fiTYiuSYtz+2X3X4foVWNeTDtHGbQpFsl2RhYo6p+1122fTntb+kiWrWTR9MmPe9ZVddNvNQrSX3p5ob9B23Nhi92E+hJsjvwWmDXqvrjELvYG4NvqWddNYoX0Sah3kRL0TmUNnL5btoEuK8MBOD36kYupVt1H2Tvp80P+FpXwvP5tCo53wH+iTb/YkfgE1X1EleQlTRMSTagzUHZDjgVuJaWEvfMqvrVMPvWJ9NOpP5ZjUIrrKquplUY+jZwWJKXAX8GdgPu29U9fyFtFdmF3XMMvCUNTVUtoq1I/AZa6dM/AU+fS4E3OPIt9cZqFJopTnKWpJWHI99SD7pqFF8FPp5kflWdALwO2BdYi1ap4jRaPtzGtFJLBt6aEic5S9LKw+XlpX6MV6N4HK0axRdZshrFMUm+DdyI1Sh0B1TVRUmOoi1qtRpwvH9HkjR6TDuRZpDVKCRJ0iBHvqUZ0lWjeCOwepLxahTns2Q1igfQZnr/DXgJreygJEmapQy+pRlSVVcneSPwBFo1ivWBc2krWJ5bVacleSFtNcvPdc/xUpQkSbOYaSdSD6xGIUmSwGonUi+sRiFJksCRb6lXSVYD1gAOAQ6pqnOG3CVJktQjg29JkiSpJ6adSJIkST0x+JYkSZJ6YvAtSZIk9cTgW5IkSeqJwbckSZLUE4NvSZIkqScG35I0iTQvSPLzJP9IclWSHyZ5+rD7tjRJtk7ylkna35LkLz335UFJjklycZJrk1yQ5AtJHjSN77F697ttOV2vKUkzzeBbkib3EeCTwM+BZwDPBi4Ejk3yuiH2a1m2Bt48SfsngSf31Ykk/wKcBKwN7A88BXgXsB7w4Gl8q9Vpv++W0/iakjSjVh12ByRp1CTZBXgp8LKq+tjApm8mGQPekeSEqjq9h77cpaquXZHXqKqLgIumqUtTsRdwPbBjVV3ftX0P+HiS9NgPSRo5jnxL0m29EjgX+MQk294B/J02ogtAkh8k+UqSfZJc2KVZfCPJBoNPTLJGkoVJ/pTk+iS/TLLThH0uTHJIkjcmuQi4qmt/ZJLjujSOq5OckeS5A897AfCh7n51Pz/oHi+RdpJk2277tkm+3KXVnJ9k34m/bJL9u/5e3aWRbDf+3GUcv3WAKwYC71tVt6xykn27911zwvuN9+0h3eOnJzmte/+/dWlAj+t2/3t3+6mB33mT5TzW701yYHdcr+yOfZLslOTXSf7e/d7rLuP3laQpM/iWpAFJVgUeCfxvVd08cXtVXQl8H3jshE2PBF4OvBrYm5ZeccyEfb4CvIAWwD8NOAU4bpKc5ecAjwP2paW7APwz8JPutZ8GHE0LOvfotn8DOGSgL4/snr8snwB+SUur+QHw4SRbj29M8gxaQH9ct8+ZwBG385oApwP3TfKBJFssZZ/PAasAz5zQvhdwelX9MsmmtGP2Pdrv/Fzg68A9un2f0N3+F4t/54u7tqke691p6Tp7AQtp/37vA94OvJF2BeRxwDun8HtL0u2rKn/88ccff7ofYD5QwCuXsc/7gWsHHv8AuBHYeKDtUd3r7NA93q57/LgJr/Uj4MsDjy+kBZBrLOP9Q0sb/DjwvYH2/ekGlyfs/xbgLwOPt+368raBttWAy4B3DbSdAnxjwmt9pHvutsvo36rAF7v9Cvgr8N/AVhP2+x/ghwOP1wT+AezfPX4m8NdlvM+a3eu/YEL78hzrc4FVBtpOBm4C7jPQthC4ZNh/m/7448/s+HHkW5Kmx+lV9cfxB1X1E+BS2qgqwBOBMeAnSVYd/wFOBLaa8FonVtV1gw1J1k3ywSR/oAX6NwL7AJuvQJ+/M9DfG4HfAxt277cq8FDaqPegiY9vo6puqqpnAw+hjR6fBuwG/CzJUwZ2PQJ4TJL7do93owXun+senwXcPclnkmyf5G5T/L2W51j/oJa8wnEucGFVXTChbV6S1af4/pK0VE64lKQl/YU2WfCfl7HPPwOLJrRdOsl+lwLrd/fXo42q3zjJfhPTWy6ZZJ9PA9vQ0iHOpuWCvwzYeRn9vD1XTHh8A7BGd389WlrIZRP2mfh4qarqTFqqCl0u9o9oKSLf6Hb5AXA+LT3kTbTUj2Or6vLu+eck2Rk4EDgeuDHJ12hXJZbVj+U51ldMeHzDUtpCq65ywzLeV5Jul8G3JA2oqpuS/Ax4SpLXVtUtg9uTrE1L2/jahKfea5KXuxeLc5AvpwXsu0ylGxPecw3gqcB+NVB9JclMXr38Cy1QnTehfeLjKamqC5N8mYE89KqqJEcC+yT5H+DRwI4TnvcN4BtJ7k4rWfh+Wh767st4u+U51pLUK9NOJOm2PkBL53jRJNsOpNWvPmxC+8OSbDz+IMmjaMH3yV3TibTR2H9U1akTf26nP3emna9vrR6SZC1g4oI/N3Tb1mAFVdVNwC+47cj67S4ylGSyLyIAm3HbUf1P01JdjqAFzCcspT9XVtXnaF96xidxjo9CT/x9V+RYS9KMcuRbkiaoqmOSfIxW/WMLWoWNVWmVR14AHFS3rfF9GW2E9s20YPDdtDzwb3XbTwC+DZyQ5N3Ar2lB/Ja0yZUHLaM/VyY5BXhTkquAW2hfAq7sXmPcb7vbVyb5HnBVVZ1zR45B553A0UkOo+V6P4o2+kzXh6V5Y1cq8HPAb4C7AbvSqo68dsLv9uck3+pe952D+ddJXkKrYPIt4M+04P1ZwFHdc29IcgGwW5JfAdfR0lzu8LGWpJlm8C1Jk9uXtrrly4AX04LN04Gdq2qySYc/Bb5LS4uYR8tn3md8Y5disSvweuAAYGNaesQZdPW5b8dzaNVNjqJVDzkMuCsD9caBHwPvodUpfyctx3rbKbz2pKrqq0leAbwOeGH3O70W+BJd/fGl+CytEslrgA2Aa4DfAXtU1Rcm2f8YWvD9qQntZ9JG2t9HKy94Ma084psG9nkp8F7asb8zrUrJhSt4rCVpxqSqbn8vSdJSdYvZ/KWqJtasnnWS/CfwBuAetYIrbw685peA9avqMdPxepI0yhz5liRNKsk84CDaokLXAI+hjYIfMR2Bd5L/Ryv9tyvLnkApSbOGwbckaWluAO4P7AncnZb28QFa7e7p8L+0soAfqaqvTNNrStJIM+1EkiRJ6omlBiVJkqSeGHxLkiRJPTH4liRJknpi8C1JkiT1xOBbkiRJ6sn/D6Nvc1GtmOxkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sort_bar5 = df.groupby(['OpSys'])['Price_euros'].median().reset_index().sort_values('Price_euros', ascending=False)\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.xticks(rotation=45)\n",
    "sns.barplot(x=df['OpSys'], \n",
    "            y=df['Price_euros'], \n",
    "            data=df, \n",
    "            palette='vlag', \n",
    "            order=sort_bar5['OpSys'])\n",
    "plt.xlabel('Operating System', size=15)\n",
    "plt.ylabel('Price', size=15)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "4ff8eb76",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to fetch the operating system\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'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "100cd872",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new column as OS & apply cat_os function on it\n",
    "df['os'] = df['OpSys'].apply(cat_os)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "5a99638d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Ram</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price_euros</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>ppi</th>\n",
       "      <th>Cpu brand</th>\n",
       "      <th>HDD</th>\n",
       "      <th>SSD</th>\n",
       "      <th>Gpu Brand</th>\n",
       "      <th>os</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>1339.69</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Others/No OS/Linux</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>898.94</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>127.677940</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Others/No OS/Linux</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>575.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Others/No OS/Linux</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>16</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>2537.45</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>220.534624</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>512</td>\n",
       "      <td>AMD</td>\n",
       "      <td>Others/No OS/Linux</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>1803.60</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Others/No OS/Linux</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Ram  OpSys  Weight  Price_euros  Touchscreen  Ips  \\\n",
       "0   Apple  Ultrabook    8  macOS    1.37      1339.69            0    1   \n",
       "1   Apple  Ultrabook    8  macOS    1.34       898.94            0    0   \n",
       "2      HP   Notebook    8  No OS    1.86       575.00            0    0   \n",
       "3   Apple  Ultrabook   16  macOS    1.83      2537.45            0    1   \n",
       "4   Apple  Ultrabook    8  macOS    1.37      1803.60            0    1   \n",
       "\n",
       "          ppi      Cpu brand  HDD  SSD Gpu Brand                  os  \n",
       "0  226.983005  Intel Core i5    0  128     Intel  Others/No OS/Linux  \n",
       "1  127.677940  Intel Core i5    0    0     Intel  Others/No OS/Linux  \n",
       "2  141.211998  Intel Core i5    0  256     Intel  Others/No OS/Linux  \n",
       "3  220.534624  Intel Core i7    0  512       AMD  Others/No OS/Linux  \n",
       "4  226.983005  Intel Core i5    0  256     Intel  Others/No OS/Linux  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the first 5 rows\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "d2e95d0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop the opsys function\n",
    "df.drop(columns=['OpSys'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "cdeb762c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sort_bar6 = df.groupby(['os'])['Price_euros'].median().reset_index().sort_values('Price_euros', ascending=False)\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.xticks(rotation=45)\n",
    "sns.barplot(x=df['os'], \n",
    "            y=df['Price_euros'], \n",
    "            data=df, \n",
    "            palette='Oranges_r', \n",
    "            order=sort_bar6['os'])\n",
    "plt.xlabel('Operating System', size=15)\n",
    "plt.ylabel('Price', size=15)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "94afefbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.20    126\n",
       "2.10     58\n",
       "2.00     45\n",
       "2.40     44\n",
       "2.30     41\n",
       "       ... \n",
       "4.50      1\n",
       "1.14      1\n",
       "3.80      1\n",
       "2.26      1\n",
       "2.34      1\n",
       "Name: Weight, Length: 171, dtype: int64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the Count of the Weight column\n",
    "df['Weight'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "89de7e7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "sns.scatterplot(x=df['Weight'], \n",
    "                y=df['Price_euros'], \n",
    "                data=df, \n",
    "                color='magenta', \n",
    "                markers=['*']);\n",
    "plt.xlabel('Laptop Weight', size=15)\n",
    "plt.ylabel('Laptop Price', size=15)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "09b771dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "sns.histplot(df['Weight'], \n",
    "             kde=True, \n",
    "             fill=True, \n",
    "             color='r');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "b4d8b95b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ram            0.742905\n",
       "Weight         0.209867\n",
       "Price_euros    1.000000\n",
       "Touchscreen    0.192917\n",
       "Ips            0.253320\n",
       "ppi            0.475368\n",
       "HDD           -0.096891\n",
       "SSD            0.670660\n",
       "Name: Price_euros, dtype: float64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the correlation related to price column\n",
    "df.corr()['Price_euros']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "32b6d204",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create heatmap\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.title('Correlation Matrix')\n",
    "sns.heatmap(df.corr(), \n",
    "            annot=True, \n",
    "            cbar=True, \n",
    "            cmap='Pastel1_r', \n",
    "            fmt='.2f');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "60ec5d9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "log_price = np.log(df['Price_euros'])\n",
    "plt.figure(figsize=(12,6))\n",
    "sns.histplot(x=log_price, kde=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0db73b3",
   "metadata": {},
   "source": [
    "### Insight's From Analysis \n",
    "\n",
    "##### 1. Razer laptop is the most expensive followed by LG.\n",
    "##### 2. Workstation type laptop are very expensive followed by ultrabook & gaming laptop.\n",
    "##### 3. Laptop screen with 18.4 & 15.4inches are very expensive.\n",
    "##### 4. Touchscreen laptop's are very expensive as compare to non-touchscreen laptop's.\n",
    "##### 5. Laptop with IPS/ScreenResolution are very expensive.\n",
    "##### 6. Intel corei7 processor are very expensive followed by intel corei5.\n",
    "##### 7. 64 GB RAM Variant laptop's are very expensive followed by 32GB variant.\n",
    "##### 8. Nvidia GPU are very expensive.\n",
    "##### 9. Mac OperatingSystem laptop's are very expensive.\n",
    "##### 10. Heavy Laptop's are very expensive."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cbe3637",
   "metadata": {},
   "source": [
    "### Seprating Independent & Dependent Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "92b49379",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create X-varaible as independent and Y-variable as Dependent Feature\n",
    "X = df.drop(columns=['Price_euros'])\n",
    "Y = np.log(df['Price_euros'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "d8bdcf9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>ppi</th>\n",
       "      <th>Cpu brand</th>\n",
       "      <th>HDD</th>\n",
       "      <th>SSD</th>\n",
       "      <th>Gpu Brand</th>\n",
       "      <th>os</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>1.37</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Others/No OS/Linux</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>1.34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>127.677940</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Others/No OS/Linux</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>1.86</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Others/No OS/Linux</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>16</td>\n",
       "      <td>1.83</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>220.534624</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>512</td>\n",
       "      <td>AMD</td>\n",
       "      <td>Others/No OS/Linux</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>1.37</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Others/No OS/Linux</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Ram  Weight  Touchscreen  Ips         ppi  \\\n",
       "0   Apple  Ultrabook    8    1.37            0    1  226.983005   \n",
       "1   Apple  Ultrabook    8    1.34            0    0  127.677940   \n",
       "2      HP   Notebook    8    1.86            0    0  141.211998   \n",
       "3   Apple  Ultrabook   16    1.83            0    1  220.534624   \n",
       "4   Apple  Ultrabook    8    1.37            0    1  226.983005   \n",
       "\n",
       "       Cpu brand  HDD  SSD Gpu Brand                  os  \n",
       "0  Intel Core i5    0  128     Intel  Others/No OS/Linux  \n",
       "1  Intel Core i5    0    0     Intel  Others/No OS/Linux  \n",
       "2  Intel Core i5    0  256     Intel  Others/No OS/Linux  \n",
       "3  Intel Core i7    0  512       AMD  Others/No OS/Linux  \n",
       "4  Intel Core i5    0  256     Intel  Others/No OS/Linux  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checks the first 5rows\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "be2607b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    7.200194\n",
       "1    6.801216\n",
       "2    6.354370\n",
       "3    7.838915\n",
       "4    7.497540\n",
       "Name: Price_euros, dtype: float64"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checks the first 5rows\n",
    "Y.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "244e5c2c",
   "metadata": {},
   "source": [
    "## Train-Test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "7b0aa2ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "a14d6137",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train_Test_Split\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.15, random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "06215674",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X Shape :  (1302, 12)\n",
      "X_train_Shape :  (1106, 12)\n",
      "X_test_Shape :  (196, 12)\n"
     ]
    }
   ],
   "source": [
    "# Checks the X_train & X_test shape\n",
    "print('X Shape : ', X.shape)\n",
    "print('X_train_Shape : ', X_train.shape)\n",
    "print('X_test_Shape : ', X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "6e1dfbed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Y_Shape :  (1302,)\n",
      "Y_train_Shape :  (1106,)\n",
      "Y_test_Shape :  (196,)\n"
     ]
    }
   ],
   "source": [
    "# Checks the Y_train & Y_test shape\n",
    "print('Y_Shape : ', Y.shape)\n",
    "print('Y_train_Shape : ', Y_train.shape)\n",
    "print('Y_test_Shape : ', Y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "6aaeb28f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.metrics import r2_score, mean_absolute_error\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import OneHotEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "9bc9385d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression, Lasso, Ridge\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"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b85f8e1",
   "metadata": {},
   "source": [
    "### Model Building With Different Regression Techniques.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e985e09f",
   "metadata": {},
   "source": [
    "## Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "5ad92ab9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2_score : 0.8074747226284256\n",
      "MAE : 0.20953298603048753\n"
     ]
    }
   ],
   "source": [
    "step1 = ColumnTransformer(transformers=[\n",
    "    ('col_tnf', OneHotEncoder(sparse=False, drop='first'), [0,1,7,10,11])\n",
    "], remainder='passthrough')\n",
    "\n",
    "step2 = LinearRegression()\n",
    "\n",
    "pipe = Pipeline([\n",
    "    ('step1',step1),\n",
    "    ('step2', step2)\n",
    "])\n",
    "pipe.fit(X_train, Y_train)\n",
    "prediction = pipe.predict(X_test)\n",
    "\n",
    "print('r2_score :', r2_score(Y_test, prediction))\n",
    "print('MAE :', mean_absolute_error(Y_test, prediction))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e32d303b",
   "metadata": {},
   "source": [
    "## Lasso Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "5d248cd3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2_score : 0.8068842224407048\n",
      "MAE : 0.211260129546456\n"
     ]
    }
   ],
   "source": [
    "step1 = ColumnTransformer(transformers=[\n",
    "    ('col_tnf', OneHotEncoder(sparse=False, drop='first'), [0,1,7,10,11])\n",
    "], remainder='passthrough')\n",
    "\n",
    "step2 = Lasso(alpha=0.001)\n",
    "\n",
    "pipe = Pipeline([\n",
    "    ('step1',step1),\n",
    "    ('step2', step2)\n",
    "])\n",
    "pipe.fit(X_train, Y_train)\n",
    "prediction = pipe.predict(X_test)\n",
    "\n",
    "print('r2_score :', r2_score(Y_test, prediction))\n",
    "print('MAE :', mean_absolute_error(Y_test, prediction))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52e037e1",
   "metadata": {},
   "source": [
    "## Ridge Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "1a7eab14",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2_score : 0.8123566807797893\n",
      "MAE : 0.20989036127092292\n"
     ]
    }
   ],
   "source": [
    "step1 = ColumnTransformer(transformers=[\n",
    "    ('col_tnf', OneHotEncoder(sparse=False, drop='first'), [0,1,7,10,11])\n",
    "], remainder='passthrough')\n",
    "\n",
    "step2 = Ridge(alpha=10)\n",
    "\n",
    "pipe = Pipeline([\n",
    "    ('step1',step1),\n",
    "    ('step2', step2)\n",
    "])\n",
    "pipe.fit(X_train, Y_train)\n",
    "prediction = pipe.predict(X_test)\n",
    "\n",
    "print('r2_score :', r2_score(Y_test, prediction))\n",
    "print('MAE :', mean_absolute_error(Y_test, prediction))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d755a5f7",
   "metadata": {},
   "source": [
    "## K Nearest Neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "6bddc6ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2_score : 0.8021984604448553\n",
      "MAE : 0.193197167215211\n"
     ]
    }
   ],
   "source": [
    "step1 = ColumnTransformer(transformers=[\n",
    "    ('col_tnf', OneHotEncoder(sparse=False, drop='first'), [0,1,7,10,11])\n",
    "], remainder='passthrough')\n",
    "\n",
    "step2 = KNeighborsRegressor(n_neighbors=3)\n",
    "\n",
    "pipe = Pipeline([\n",
    "    ('step1',step1),\n",
    "    ('step2', step2)\n",
    "])\n",
    "pipe.fit(X_train, Y_train)\n",
    "prediction = pipe.predict(X_test)\n",
    "\n",
    "print('r2_score :', r2_score(Y_test, prediction))\n",
    "print('MAE :', mean_absolute_error(Y_test, prediction))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8667f5b9",
   "metadata": {},
   "source": [
    "## Decision Tree Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "44bcf8d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2_score : 0.8433007811643811\n",
      "MAE : 0.18075097903792622\n"
     ]
    }
   ],
   "source": [
    "step1 = ColumnTransformer(transformers=[\n",
    "    ('col_tnf', OneHotEncoder(sparse=False, drop='first'), [0,1,7,10,11])\n",
    "], remainder='passthrough')\n",
    "\n",
    "step2 = DecisionTreeRegressor(max_depth=8)\n",
    "\n",
    "pipe = Pipeline([\n",
    "    ('step1',step1),\n",
    "    ('step2', step2)\n",
    "])\n",
    "pipe.fit(X_train, Y_train)\n",
    "prediction = pipe.predict(X_test)\n",
    "\n",
    "print('r2_score :', r2_score(Y_test, prediction))\n",
    "print('MAE :', mean_absolute_error(Y_test, prediction))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e927a79c",
   "metadata": {},
   "source": [
    "## Support Vector Machine(SVM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "b523a64a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2_score : 0.8068761990813582\n",
      "MAE : 0.20267613811666124\n"
     ]
    }
   ],
   "source": [
    "step1 = ColumnTransformer(transformers=[\n",
    "    ('col_tnf', OneHotEncoder(sparse=False, drop='first'), [0,1,7,10,11])\n",
    "], remainder='passthrough')\n",
    "\n",
    "step2 = SVR(kernel='rbf', C=10000, epsilon=0.1)\n",
    "\n",
    "pipe = Pipeline([\n",
    "    ('step1',step1),\n",
    "    ('step2', step2)\n",
    "])\n",
    "pipe.fit(X_train, Y_train)\n",
    "prediction = pipe.predict(X_test)\n",
    "\n",
    "print('r2_score :', r2_score(Y_test, prediction))\n",
    "print('MAE :', mean_absolute_error(Y_test, prediction))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e808e46",
   "metadata": {},
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "2488231f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2_score : 0.8876986941462663\n",
      "MAE : 0.15894725646605792\n"
     ]
    }
   ],
   "source": [
    "step1 = ColumnTransformer(transformers=[\n",
    "    ('col_tnf', OneHotEncoder(sparse=False, drop='first'), [0,1,7,10,11])\n",
    "], remainder='passthrough')\n",
    "\n",
    "step2 = RandomForestRegressor(n_estimators=100,\n",
    "                             max_samples=0.5, \n",
    "                             random_state=2,\n",
    "                             max_features=0.75,\n",
    "                             max_depth=15)\n",
    "\n",
    "pipe = Pipeline([\n",
    "    ('step1',step1),\n",
    "    ('step2', step2)\n",
    "])\n",
    "pipe.fit(X_train, Y_train)\n",
    "prediction = pipe.predict(X_test)\n",
    "\n",
    "print('r2_score :', r2_score(Y_test, prediction))\n",
    "print('MAE :', mean_absolute_error(Y_test, prediction))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84e23fae",
   "metadata": {},
   "source": [
    "## Extra Trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "49ca6c3c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2_score : 0.8902130662434273\n",
      "MAE : 0.1577974295671131\n"
     ]
    }
   ],
   "source": [
    "step1 = ColumnTransformer(transformers=[\n",
    "    ('col_tnf', OneHotEncoder(sparse=False, drop='first'), [0,1,7,10,11])\n",
    "], remainder='passthrough')\n",
    "\n",
    "step2 = ExtraTreesRegressor(n_estimators=100,\n",
    "                           max_depth=15,\n",
    "                           random_state=3,\n",
    "                           max_samples=0.5,\n",
    "                           max_features=0.75,\n",
    "                           bootstrap=True)\n",
    "\n",
    "pipe = Pipeline([\n",
    "    ('step1',step1),\n",
    "    ('step2', step2)\n",
    "])\n",
    "pipe.fit(X_train, Y_train)\n",
    "prediction = pipe.predict(X_test)\n",
    "\n",
    "print('r2_score :', r2_score(Y_test, prediction))\n",
    "print('MAE :', mean_absolute_error(Y_test, prediction))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67a41348",
   "metadata": {},
   "source": [
    "## Ada Boost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "8b2a5789",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2_score : 0.7969504448688765\n",
      "MAE : 0.2296653051209314\n"
     ]
    }
   ],
   "source": [
    "step1 = ColumnTransformer(transformers=[\n",
    "    ('col_tnf', OneHotEncoder(sparse=False, drop='first'), [0,1,7,10,11])\n",
    "], remainder='passthrough')\n",
    "\n",
    "step2 = AdaBoostRegressor(n_estimators=100, learning_rate=0.1)\n",
    "\n",
    "pipe = Pipeline([\n",
    "    ('step1',step1),\n",
    "    ('step2', step2)\n",
    "])\n",
    "pipe.fit(X_train, Y_train)\n",
    "prediction = pipe.predict(X_test)\n",
    "\n",
    "print('r2_score :', r2_score(Y_test, prediction))\n",
    "print('MAE :', mean_absolute_error(Y_test, prediction))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44302de0",
   "metadata": {},
   "source": [
    "## Gradient Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "c72e319d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2_score : 0.8824973775166629\n",
      "MAE : 0.16350726213358605\n"
     ]
    }
   ],
   "source": [
    "step1 = ColumnTransformer(transformers=[\n",
    "    ('col_tnf', OneHotEncoder(sparse=False, drop='first'), [0,1,7,10,11])\n",
    "], remainder='passthrough')\n",
    "\n",
    "step2 = GradientBoostingRegressor(n_estimators=200)\n",
    "\n",
    "pipe = Pipeline([\n",
    "    ('step1',step1),\n",
    "    ('step2', step2)\n",
    "])\n",
    "pipe.fit(X_train, Y_train)\n",
    "prediction = pipe.predict(X_test)\n",
    "\n",
    "print('r2_score :', r2_score(Y_test, prediction))\n",
    "print('MAE :', mean_absolute_error(Y_test, prediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "ede804d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle.dump(df,open('df.pkl','wb'))\n",
    "pickle.dump(pipe,open('pipe.pkl','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cffab4b5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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